From d2f061ee5fb5fd615a569ed5b4fb6c7c580a1df6 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Wed, 22 May 2024 16:16:41 -0500 Subject: [PATCH 01/71] ADD INDEX_VCF process --- main.nf | 43 +++++++++++++++++++++++++++++++++++++++++++ nextflow.config | 5 +++++ 2 files changed, 48 insertions(+) create mode 100644 main.nf create mode 100644 nextflow.config diff --git a/main.nf b/main.nf new file mode 100644 index 0000000..f41552a --- /dev/null +++ b/main.nf @@ -0,0 +1,43 @@ +nextflow.enable.dsl = 2 + +// Process to handle the VCF file +process INDEX_VCF { + input: + path vcf + + output: + path "*.tbi" + path "*.gz" + + script: + """ + # Determine the file type + INPUT_VCF_TYPE="\$(file -b $vcf)" + + if echo "\${INPUT_VCF_TYPE}" | grep -q 'symbolic link to'; then + SYM_LINK="\$(readlink -f $vcf)" + INPUT_VCF_TYPE="\$(file -b \${SYM_LINK})" + fi + + + if echo "\${INPUT_VCF_TYPE}" | grep -q 'BGZF'; then + echo "The file is in BGZF format, ready for tabix." + cp $vcf input.vcf.gz + elif echo "\${INPUT_VCF_TYPE}" | grep -q 'gzip compressed data'; then + echo "GZIP format detected, converting to BGZF." + gunzip -c $vcf | bgzip > input.vcf.gz + elif echo "\${INPUT_VCF_TYPE}" | grep -q 'ASCII text'; then + echo "Plain VCF file detected, compressing and indexing." + bgzip -c $vcf > input.vcf.gz + else + echo "The file $vcf does not exist or is not a recognized format." + exit 1 + fi + + tabix -p vcf input.vcf.gz + """ +} + +workflow { + INDEX_VCF(params.input_vcf) +} \ No newline at end of file diff --git a/nextflow.config b/nextflow.config new file mode 100644 index 0000000..fe7fd2b --- /dev/null +++ b/nextflow.config @@ -0,0 +1,5 @@ +params { + input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" + input_hpo = null + outdir = "/home/hwan/AI_MARRVEL/test_output/" +} \ No newline at end of file From b9ee91353477e24d1428b9bdb7a4c8d3217d4065 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Wed, 22 May 2024 16:34:54 -0500 Subject: [PATCH 02/71] added pre-processing process --- main.nf | 23 +++++++++++++++++++++-- nextflow.config | 5 +++++ 2 files changed, 26 insertions(+), 2 deletions(-) diff --git a/main.nf b/main.nf index f41552a..ca43db3 100644 --- a/main.nf +++ b/main.nf @@ -6,8 +6,8 @@ process INDEX_VCF { path vcf output: - path "*.tbi" - path "*.gz" + path "input.vcf.gz" + path "input.vcf.gz.tbi" script: """ @@ -38,6 +38,25 @@ process INDEX_VCF { """ } +process VCF_PRE_PROCESS { + input: + path vcf + path tbi + + output: + path "*-add-id.vcf.gz" + + script: + """ + # Annotate with new chromosomes and preserve original coordinates in ID + bcftools annotate --rename-chrs ${params.chrmap} -x ID $vcf -Oz -o ${params.run_id}-annot + + # Annotate with new IDs based on CHROM, POS, REF, ALT + bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o ${params.run_id}-add-id.vcf.gz + """ +} + workflow { INDEX_VCF(params.input_vcf) + VCF_PRE_PROCESS(INDEX_VCF.out) } \ No newline at end of file diff --git a/nextflow.config b/nextflow.config index fe7fd2b..4828b9e 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,5 +1,10 @@ params { + run_id = null input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" input_hpo = null outdir = "/home/hwan/AI_MARRVEL/test_output/" + + + // for data dependency + chrmap = "/home/hwan/AI_MARRVEL/test_ref/chrmap.txt" } \ No newline at end of file From 4a4bd412b0c120b9b097d20eb65ae9774cdb3252 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 11:42:37 -0500 Subject: [PATCH 03/71] change dirname run -> script. --- scripts/.gitignore | 43 + scripts/README.md | 1 + scripts/VarTierDiseaseDBFalse.R | 381 + scripts/add_c_nc.py | 74 + scripts/annotation/main.py | 487 + scripts/annotation/marrvel_score_recalc.py | 266 + scripts/annotation/utils_1.py | 536 + .../annotation/utils_for_marrvel_flatfile.py | 1060 + .../utils_for_marrvel_flatfile_module_2.py | 186 + scripts/annotation/utils_for_symMatch.py | 143 + scripts/extraModel/__init__.py | 0 scripts/extraModel/confidence.py | 50 + scripts/extraModel/generate_bivar_data.py | 179 + scripts/extraModel/integrate_output.py | 150 + scripts/extraModel/main.py | 120 + scripts/fillna_tier.py | 682 + scripts/generate_new_matrix_2.py | 66 + scripts/keepna_tier.py | 443 + scripts/labelling.py | 62 + scripts/merge_rm.py | 25 + scripts/mod5_diffusion.py | 147 + scripts/phenoSim.R | 133 + scripts/phrank/src/location_to_gene.py | 88 + scripts/phrank/src/run_phrank.py | 34 + scripts/predict_new/.py | 14 + scripts/predict_new/causal_distr.py | 14 + scripts/predict_new/causal_distr.txt | 74 + scripts/predict_new/confidence.py | 50 + scripts/predict_new/features.csv | 1 + scripts/predict_new/main.py | 162 + scripts/predict_new/run_final.py | 21 + scripts/predict_new/test.csv | 17460 ++++++++++++++++ scripts/predict_new/utilities.py | 266 + scripts/proc.sh | 284 + scripts/simple_repeat_anno.py | 29 + scripts/split_chunks.py | 44 + 36 files changed, 23775 insertions(+) create mode 100644 scripts/.gitignore create mode 100644 scripts/README.md create mode 100644 scripts/VarTierDiseaseDBFalse.R create mode 100755 scripts/add_c_nc.py create mode 100644 scripts/annotation/main.py create mode 100755 scripts/annotation/marrvel_score_recalc.py create mode 100755 scripts/annotation/utils_1.py create mode 100755 scripts/annotation/utils_for_marrvel_flatfile.py create mode 100755 scripts/annotation/utils_for_marrvel_flatfile_module_2.py create mode 100755 scripts/annotation/utils_for_symMatch.py create mode 100755 scripts/extraModel/__init__.py create mode 100755 scripts/extraModel/confidence.py create mode 100755 scripts/extraModel/generate_bivar_data.py create mode 100755 scripts/extraModel/integrate_output.py create mode 100755 scripts/extraModel/main.py create mode 100755 scripts/fillna_tier.py create mode 100755 scripts/generate_new_matrix_2.py create mode 100755 scripts/keepna_tier.py create mode 100755 scripts/labelling.py create mode 100755 scripts/merge_rm.py create mode 100755 scripts/mod5_diffusion.py create mode 100755 scripts/phenoSim.R create mode 100755 scripts/phrank/src/location_to_gene.py create mode 100755 scripts/phrank/src/run_phrank.py create mode 100755 scripts/predict_new/.py create mode 100755 scripts/predict_new/causal_distr.py create mode 100755 scripts/predict_new/causal_distr.txt create mode 100755 scripts/predict_new/confidence.py create mode 100755 scripts/predict_new/features.csv create mode 100755 scripts/predict_new/main.py create mode 100755 scripts/predict_new/run_final.py create mode 100755 scripts/predict_new/test.csv create mode 100755 scripts/predict_new/utilities.py create mode 100755 scripts/proc.sh create mode 100755 scripts/simple_repeat_anno.py create mode 100644 scripts/split_chunks.py diff --git a/scripts/.gitignore b/scripts/.gitignore new file mode 100644 index 0000000..801baac --- /dev/null +++ b/scripts/.gitignore @@ -0,0 +1,43 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + + +# Mac/OSX +.DS_Store + +# Windows +Thumbs.db + +# Data dependencies +predict_new/*.job +data_dependencies/ +model_inputs/ + +# Misc. +old_bak/ + diff --git a/scripts/README.md b/scripts/README.md new file mode 100644 index 0000000..826d7c2 --- /dev/null +++ b/scripts/README.md @@ -0,0 +1 @@ +# aim_one_pipeline diff --git a/scripts/VarTierDiseaseDBFalse.R b/scripts/VarTierDiseaseDBFalse.R new file mode 100644 index 0000000..e53439c --- /dev/null +++ b/scripts/VarTierDiseaseDBFalse.R @@ -0,0 +1,381 @@ +# Run Tier + +# Updates history #### +# Update on 2021.Sep13 +#' Updates: +#' 1. corrected chromosome number 23->X, 24-> Y +#' 2. add interaction term of phenotype and Tier. +#' 3. for non-coding var, if ClinVarClinSig == conflict -> push the Tier higher (if IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict, Tier = Tier + 1) +#' 4. Keep all variant that in-trans (sudo) with a strong Var in the same gene, adjust the Tier to 3.5? +# Updates on 2022.Apr5 +#' 1. remove the pre-processing of OMIM inheritance to VarTier_preprocessOMIM.R +#' 2. Add the number of variants (H/M/L impact) per gene: "No.Var.HM","No.Var.H","No.Var.M","No.Var.L" +#' 3. Add the option if IncludeClinVarHGMD +#' +#' Updates on 2022.Sep29 +#' ClinVar clinvarSignDesc is in dif format after optimizing the pipeline, update the code to be compatible for that +#' gnomadAF, gnomadAFg is "-" not "NA", update the code for that +#' add IncludeClinVarHGMD for line 101 + + +#' change genemap2.Inh into 2 features, AD, AR, save to tsv file +#' keep GeneID == "-" assinge Tier value +#' input output /out/explain/ "ID" + "_Tier" +#' + +# parameter settings #### + +# set adj.k value to adjust Tier +adj.k <- 1.05 + +# set if diseases DB will be included +IncludeClinVarHGMD <- FALSE + +# load database: OMIM inheritance #### +# genemap2.Inh.F <- readRDS(file.path("/run/genemap2.Inh.F.rds")) +#genemap2.Inh.F <- read.delim2(file.path("/run/data_dependencies/var_tier/hg19/genemap2.Inh.F.txt")) +# genemap2.Inh.F <- read.delim2("/houston_10t/dongxue/MARRVEL_AI/resource/OMIM/out/genemap2.Inh.F.txt") +args = commandArgs(trailingOnly=TRUE) +refg <- args[2] +# load database: OMIM inheritance #### +# genemap2.Inh.F <- readRDS(file.path("/run/genemap2.Inh.F.rds")) +genemap2.Inh.F <- read.delim2(file.path(paste0('/run/data_dependencies/var_tier/',refg,'/genemap2.Inh.F.txt'))) + +# set the input output file / directory #### + + + +# in.f.path <- "/out/input/" +in.f.path <- "/out/rami-test/" +out.f.path <- "/out/tier-test-false/" + +# When test the code without docker +#in.f.path <- out.f.path <- "/out/" + +# in.f <- paste0(args[1],"_Tier") +# in.f <- paste0(args[1],"_scores.csv") +in.f <- paste0(args[1],"_scores.csv") +out.f <- paste0(args[1],"_Tier.v2.tsv") + +# load libraries #### +library("data.table") +library("dplyr") + + + +# load the input #### +anno.columns <- c('varId_dash','zyg','geneSymbol','geneEnsId','gnomadAF','gnomadAFg','omimSymptomSimScore','hgmdSymptomSimScore', + 'IMPACT','Consequence','hgmdVarFound','clinvarSignDesc','spliceAImax') + +anno.orig <- read.table(file.path(in.f.path,in.f), stringsAsFactors = F, sep = ",", header = T) +# To test: +# score.path <- "/houston_20t/chaozhong/MARRVEL_AI_2022/explain/906246_raw.csv" +# score.path <- "/houston_20t/chaozhong/MARRVEL_AI_2022/opt_09302022/output/rami-test/906246_scores.csv" +# score.path <- "/houston_30t/chaozhong/NDV_model/fake_VCF_test/out/rami-test/NDVG_causal_scores.csv" +# anno.orig <- read.csv(score.path) +anno <- anno.orig[,anno.columns] + +# rename the col +colnames(anno) <- c("Uploaded_variation","GT","SYMBOL","Gene","gnomadAF","gnomadAFg", + "omimSymptomSimScore", "hgmdSymptomSimScore","IMPACT", + "Consequence","hgmdVarFound","clinvarSignDesc","spliceAImax") + +if ("-" %in% anno$Gene) { + # for rows without gene ID -> Assign Tier now: + anno_noGeneID <- anno[anno$Gene == "-",c("Uploaded_variation","Gene","IMPACT")] + # convert IMPACT to numeric value + anno_noGeneID$IMPACT.max <- ifelse(anno_noGeneID$IMPACT == "MODIFIER", 1, ifelse(anno_noGeneID$IMPACT == "LOW", 2, ifelse(anno_noGeneID$IMPACT == "MODERATE",3,4)) ) + anno_noGeneID$IMPACT <- NULL + # "TierAD","TierAR","TierAR.adj" + anno_noGeneID$TierAD <- 5 - anno_noGeneID$IMPACT.max + anno_noGeneID[,c("TierAR","TierAR.adj")] <- ifelse(anno_noGeneID$IMPACT.max >= 3, 3,4) + anno_noGeneID$No.Var.HM <- as.numeric(anno_noGeneID$IMPACT.max >= 3) + anno_noGeneID$No.Var.H <- as.numeric(anno_noGeneID$IMPACT.max == 4) + anno_noGeneID$No.Var.M <- as.numeric(anno_noGeneID$IMPACT.max == 3) + anno_noGeneID$No.Var.L <- as.numeric(anno_noGeneID$IMPACT.max <=2) +} + + + +# For rows with Gene ID -> Tier analysis +anno <- anno[anno$Gene != "-",] +anno <- unique(anno) + + +# Tier #### + + +# STEP 0. Adjust variant IMPACT for ClinVar, HGMD, SpliceAImax >= 0.8 #### + +if (IncludeClinVarHGMD){ + anno$IMPACT <- ifelse(grepl("Pathogenic|Likely_pathogenic",anno$clinvarSignDesc) | anno$hgmdVarFound == 1 , "HIGH",anno$IMPACT) +} + +# label Splice AI > 0.8 as HIGH +anno$IMPACT <- ifelse(anno$spliceAImax >=0.8, "HIGH",anno$IMPACT) +# convert IMPACT to numeric value +anno$IMPACT.no <- ifelse(anno$IMPACT == "MODIFIER", 1, ifelse(anno$IMPACT == "LOW", 2, ifelse(anno$IMPACT == "MODERATE",3,4)) ) + + +# Implement the rule based Tier classification #### + +# take IMPACT HIGH, MODERATE, LOW, or MODIFIER as H,M,L,L +# Tier at Gene level + +# STEP 1. classify T4: genes with only L variants, nonCodingVar2keep #### + +# STEP 1.1 Keep Genes with IMPACT H or M variants +Gene.all <- unique(anno$Gene) +Gene.IMPACT <- unique(anno[,c("Gene","IMPACT")]) +Gene.IMPACT.HM <- Gene.IMPACT[Gene.IMPACT$IMPACT %in% c("HIGH","MODERATE"),] + +# GeneTier4 will be removed for remaining Tier analysis +GeneTier4 <- Gene.all[!(Gene.all %in% Gene.IMPACT.HM$Gene)] +anno$TierAD[anno$Gene %in% GeneTier4] = anno$TierAR[anno$Gene %in% GeneTier4] <- 4 + +# STEP 1.2 Keep Var with max IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict +# note: many variants with “Conflicting” are non-coding and coding for different transcripts, only keep the var with max impact as MODIFIER + +# find the non-Coding Var to keep +nonCodingVar2keep <- c() + +if(IncludeClinVarHGMD){ + nonCodingModifier.idx <- which(grepl("Conflicting_interpretations_of_pathogenicity", anno$clinvarSignDesc) & (anno$IMPACT %in% c("MODIFIER"))) + nonCodingModifier <- anno$Uploaded_variation[nonCodingModifier.idx] + nonCodingModifier.df <- anno[anno$Uploaded_variation %in% nonCodingModifier,] + + for(var in unique(nonCodingModifier.df$Uploaded_variation)){ + var.df <- nonCodingModifier.df[nonCodingModifier.df$Uploaded_variation == var,] + var.impact <- unique(var.df$IMPACT) + if(length(var.impact) == 1 ){ + if(var.impact == "MODIFIER"){ + nonCodingVar2keep <- c(nonCodingVar2keep,var) + } + } + } +} + + +# split var to two groups: #### + +# Run Full Tier analysis: +# non - GeneTier4 and nonCodingVar2keep +idx <- is.na(anno$TierAD) | (anno$Uploaded_variation %in% nonCodingVar2keep) +Var.RunTier <- unique(anno$Uploaded_variation[idx]) + +# Only Count No.Var.L： +# Others: +# check if there are variants without Tier +ParseVar.NoTier <- F +if(F %in% idx){ + ParseVar.NoTier <- T + Var.NoTier <- unique(anno$Uploaded_variation[!idx]) +} + +anno.f1 <- anno[idx,] + +# set nonCodingVar2keep IMPACT as 3 +anno.f1$IMPACT.no[anno.f1$Uploaded_variation %in% nonCodingVar2keep] <- 3 + + +# STEP 2: Get the most severe IMPACT for each variant #### + +VEP.f1 <- anno.f1 + +# for Gene-Var pair with multiple IMPACT, take the MAX +VEP.f1.maxIMAPCT <- data.frame(matrix(ncol = ncol(VEP.f1) + 1, nrow = 0)) +colnames(VEP.f1.maxIMAPCT) <- c(colnames(VEP.f1),"IMPACT.max") +for (var in Var.RunTier){ + var.df <- VEP.f1[VEP.f1$Uploaded_variation == var,] + # check no. of gene for this variant + if(length(unique(var.df$Gene)) == 1){ + var.df$IMPACT.max <- max(var.df$IMPACT.no) + VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT,var.df) + } else { + for (gene in unique(var.df$Gene)){ + var.g.df <- var.df[var.df$Gene == gene,] + var.g.df$IMPACT.max <- max(var.g.df$IMPACT.no) + VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT,var.g.df) + } + } +} + +# find all intronic Var with AF == 0, will adjust the Tier if in trans with a high impact variant. #### + +rareVar <- VEP.f1.maxIMAPCT$gnomadAF == "-" & VEP.f1.maxIMAPCT$gnomadAFg == "-" +intronicVar <- grepl("intron_variant",VEP.f1.maxIMAPCT$Consequence) & VEP.f1.maxIMAPCT$IMPACT.max == 1 +rareIntronicVar <- VEP.f1.maxIMAPCT[rareVar & intronicVar, c("Uploaded_variation","Gene")] +rareIntronicVar <- unique(rareIntronicVar) + +# for each Variant keep the most severe IMPACT only +VEP.f2 <- unique(VEP.f1.maxIMAPCT[,c("Uploaded_variation", "SYMBOL","Gene","GT","IMPACT.max","omimSymptomSimScore","hgmdSymptomSimScore")]) + +# STEP 3: assign Tiers #### + +# VEP.f2$GT <- ifelse(VEP.f2$GT=="HET", "0/1","1/1") + +# though Tier classification is Gene based, Tier should be assigned to each Variant + +VEP.Tier.part1 <- data.frame(matrix(ncol = ncol(VEP.f2) + 6, nrow = 0)) +colnames(VEP.Tier.part1) <- c(colnames(VEP.f2),"TierAR","TierAD","No.Var.HM","No.Var.H","No.Var.M","No.Var.L") + +for (g in unique(VEP.f2$Gene)){ + + g.df <- VEP.f2[VEP.f2$Gene == g,] + + # X linked, seudo autosomal region -> treat as autosomal + # if (sum(grepl("X",g.df$Uploaded_variation)) >0) { + # g.df$TierAD = g.df$TierAR = ifelse(g.df$IMPACT.max <3, 4, 5-g.df$IMPACT.max) + # } + + # AD regardless of the GT + g.df$TierAD <- ifelse(g.df$IMPACT.max <3, 4, 5-g.df$IMPACT.max) + + # AR + g.df2 <- g.df + + # duplicate rows with HOM var + if ("HOM" %in% g.df$GT){ + g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1",]) + } + IMPACT.max <- g.df2$IMPACT.max + if(sum(IMPACT.max==4) >=2){ + # >=2 strong alleles + g.df$TierAR <- 5-g.df$IMPACT.max + # update 202221005: H+H: M-> change Tie from 2 to 1.5 + g.df$TierAR[g.df$IMPACT.max ==3] <- 1.5 + } else if (sum(IMPACT.max==4) ==1 & sum(IMPACT.max==3) >=1) { + # H + M + g.df$TierAR[g.df$IMPACT.max >=3] <- 1.5 # (need more thought, 2 or 1.5 ???) + g.df$TierAR[g.df$IMPACT.max <3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max <3] + } else if (sum(IMPACT.max==4) ==1 ) { + # H only + g.df$TierAR[g.df$IMPACT.max ==4] <- 3 # (need more thought, 2 or 3 ???) + g.df$TierAR[g.df$IMPACT.max <=3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max <=3] + } else if (sum(IMPACT.max==3) >=2 ) { + # M + M + g.df$TierAR[g.df$IMPACT.max ==3] <- 2 # (need more thought, 2 or 3 ???) + g.df$TierAR[g.df$IMPACT.max < 3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max < 3] + } else if (sum(IMPACT.max==3) == 1 ) { + # M only + g.df$TierAR[g.df$IMPACT.max ==3] <- 3 # (need more thought, 2 or 3 ???) + g.df$TierAR[g.df$IMPACT.max < 3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max < 3] + } + + # count the number of high IMPACT var per gene. + No.Var.HM <- sum(IMPACT.max >=3) + No.Var.H <- sum(IMPACT.max == 4) + No.Var.M <- sum(IMPACT.max ==3) + No.Var.L <- sum(IMPACT.max == 2) + g.df$No.Var.HM <- No.Var.HM + g.df$No.Var.H <- No.Var.H + g.df$No.Var.M <- No.Var.M + g.df$No.Var.L <- No.Var.L + + VEP.Tier.part1 <- rbind(VEP.Tier.part1,g.df) +} + +# STEP 4: Find all genes with No.Var.H == 1, adjust the Tier for rareIntronicVar #### + +if (IncludeClinVarHGMD){ + gene.w1HighVar <- VEP.Tier.part1$Gene[VEP.Tier.part1$No.Var.H == 1] + rareIntronicVar.2adj <- rareIntronicVar[rareIntronicVar$Gene %in% gene.w1HighVar,] + + # adjust the Tier for rareIntronicVar based on similarity + phenoSimi <- unique(VEP.f1.maxIMAPCT[,c("Gene","omimSymptomSimScore","hgmdSymptomSimScore")]) + + # check for non numeric values and convert to 0 + omim_NA_idx <- which(is.na(as.numeric(phenoSimi$omimSymptomSimScore))) + hgmd_NA_idx <- which(is.na(as.numeric(phenoSimi$hgmdSymptomSimScore))) + # print(hgmd_NA_idx) + phenoSimi$omimSymptomSimScore[omim_NA_idx] <- 0 + phenoSimi$hgmdSymptomSimScore[hgmd_NA_idx] <- 0 + + phenoSimi$omimSymptomSimScore <- as.numeric(phenoSimi$omimSymptomSimScore) + phenoSimi$hgmdSymptomSimScore <- as.numeric(phenoSimi$hgmdSymptomSimScore) + + phenoSimi <- phenoSimi[phenoSimi$omimSymptomSimScore != 0 | phenoSimi$hgmdSymptomSimScore != 0,] + + rareIntronicVar.2adj.wPhenoSimi <- merge(rareIntronicVar.2adj, phenoSimi, by.x = "Gene", by.y = "Gene") + rareIntronicVar.2adj.wPhenoSimi$simi <- apply(rareIntronicVar.2adj.wPhenoSimi[, 3:4], 1, max) + rareIntronicVar.2adj.wPhenoSimi$TierAR.adj <- 5-(5-4)*exp(adj.k*rareIntronicVar.2adj.wPhenoSimi$simi) + + rareIntronicVar.df <- rareIntronicVar.2adj.wPhenoSimi[,c("Uploaded_variation","Gene","TierAR.adj")] + VEP.Tier.part1 <- merge(VEP.Tier.part1,rareIntronicVar.df, + by.x <- c("Uploaded_variation","Gene"), + by.y = c("Uploaded_variation","Gene"), + all.x = T) +} else { + VEP.Tier.part1$TierAR.adj <- VEP.Tier.part1$TierAR +} + +# add the TierAR.adj to the VEP.Tier df + +VEP.Tier.part1 <- VEP.Tier.part1[,c("Uploaded_variation","Gene","GT","IMPACT.max","TierAD","TierAR","TierAR.adj","No.Var.HM","No.Var.H","No.Var.M","No.Var.L")] + +# STEP 5: add the No.Var for Var.NoTier #### +if (ParseVar.NoTier){ + VEP.Tier.part2 <- anno[anno$Uploaded_variation %in% Var.NoTier,c("Uploaded_variation","Gene","GT")] + VEP.Tier.part2$IMPACT.max <- 1 + VEP.Tier.part2[,c("TierAD","TierAR","TierAR.adj")] <- 4 + VEP.Tier.part2[,c("No.Var.HM","No.Var.H","No.Var.M","No.Var.L")] <- NA + + VEP.Tier <- rbind(VEP.Tier.part1, VEP.Tier.part2) +} else { + VEP.Tier <- VEP.Tier.part1 +} + +VEP.Tier.wGene <- data.frame(matrix(ncol = ncol(VEP.Tier), nrow = 0)) +for (g in unique(VEP.Tier$Gene)){ + + g.df <- VEP.Tier[VEP.Tier$Gene == g,] + # AR + g.df2 <- g.df + + # duplicate rows with HOM var + if ("HOM" %in% g.df$GT){ + g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1",]) + } + IMPACT.max <- g.df2$IMPACT.max + + No.Var.HM <- sum(IMPACT.max >=3) + No.Var.H <- sum(IMPACT.max == 4) + No.Var.M <- sum(IMPACT.max ==3) + No.Var.L <- sum(IMPACT.max == 2) + g.df$No.Var.HM <- No.Var.HM + g.df$No.Var.H <- No.Var.H + g.df$No.Var.M <- No.Var.M + g.df$No.Var.L <- No.Var.L + + VEP.Tier.wGene <- rbind(VEP.Tier.wGene,g.df) +} +# remove the GT column +VEP.Tier.wGene$GT <- NULL + + +# STEP 6: add the Tiers from anno_noGeneID +if ("-" %in% anno$Gene) { + VEP.Tier.final <- rbind(VEP.Tier.wGene, anno_noGeneID) +} +VEP.Tier.final <- VEP.Tier.wGene + +colnames(VEP.Tier.final)[colnames(VEP.Tier.final) == "IMPACT.max"] <- "IMPACT.from.Tier" +VEP.Tier.final$TierAR.adj[is.na(VEP.Tier.final$TierAR.adj)] <- VEP.Tier.final$TierAR[is.na(VEP.Tier.final$TierAR.adj)] + + +# add inheritance information +colnames(genemap2.Inh.F)[1] <- "Gene" +VEP.Tier.wInh <- merge(VEP.Tier.final, genemap2.Inh.F, by.x = "Gene", by.y = "Gene", all.x = T) +VEP.Tier.wInh$dominant[is.na(VEP.Tier.wInh$dominant)] <- 0 +VEP.Tier.wInh$recessive[is.na(VEP.Tier.wInh$recessive)] <- 0 +VEP.Tier.wInh$AD.matched <- ifelse(VEP.Tier.wInh$TierAD <=2 & (VEP.Tier.wInh$dominant == 1),1,0) +VEP.Tier.wInh$AR.matched <- ifelse(VEP.Tier.wInh$TierAR <=2 & (VEP.Tier.wInh$recessive == 1),1,0) + +write.table(VEP.Tier.wInh,file.path(out.f.path,out.f), sep = "\t", quote = F, col.names = T, row.names = F) + +# write.table(VEP.Tier.wInh,"/houston_20t/dongxue/MARRVEL_AI/V2/FeatureEng/TestOut/906246_Tier.tsv", sep = "\t", quote = F, col.names = T, row.names = F) +# write.table(VEP.Tier.wInh,"/houston_30t/dongxue/AI/UDN_Sample_2023/ModifiedAIMCode/NDVG_causal_Tier.v2.tsv", sep = "\t", quote = F, col.names = T, row.names = F) +### Plot for Tier adjustment #### + + + diff --git a/scripts/add_c_nc.py b/scripts/add_c_nc.py new file mode 100755 index 0000000..dc43bd8 --- /dev/null +++ b/scripts/add_c_nc.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python +# coding: utf-8 +import pandas as pd +import numpy as np + + + +def add_c_nc(score, ref): + + #read data files + if ref == 'hg38': + clin_c=pd.read_csv("/run/data_dependencies/merge_expand/hg38/clin_c.tsv.gz", sep="\t") + clin_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg38/clin_nc.tsv.gz", sep="\t") + hgmd_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t") + hgmd_c=pd.read_csv("/run/data_dependencies/merge_expand/hg38/hgmd_c.tsv.gz", sep="\t") + else: + clin_c=pd.read_csv("/run/data_dependencies/merge_expand/hg19/clin_c.tsv.gz", sep="\t") + clin_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg19/clin_nc.tsv.gz", sep="\t") + hgmd_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t") + hgmd_c=pd.read_csv("/run/data_dependencies/merge_expand/hg19/hgmd_c.tsv.gz", sep="\t") + + + a = score.pos.values + ac = score.chrom.values + temp=score[["varId"]] + + bh = clin_nc.new_stop.values + bl = clin_nc.new_start.values + bc= clin_nc.new_chr.values + + i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) &(ac[:, None]==bc)) + + cln=pd.concat([ + temp.loc[i, :].reset_index(drop=True), + clin_nc.loc[j, :].reset_index(drop=True) + ], axis=1) + + # Take into account When HGMD data base is empty + if hgmd_nc.shape[0] == 0: + pass + + else: + #merge by region + a = score.pos.values + ac = score.chrom.values + temp=score[["varId"]] + bh = hgmd_nc.new_stop.values + bl = hgmd_nc.new_start.values + bc= hgmd_nc.new_chr.values + i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) &(ac[:, None]==bc)) + + hgmd=pd.concat([ + temp.loc[i, :].reset_index(drop=True), + hgmd_nc.loc[j, :].reset_index(drop=True) + ], axis=1) + + + merged=score.merge(cln, how="left", on="varId") + if hgmd_nc.shape[0] == 0: + merged['nc_HGMD_Exp'] = np.NaN + merged['nc_RANKSCORE'] = np.NaN + else: + merged=merged.merge(hgmd, how="left", on="varId") + + if hgmd_c.shape[0] == 0: + merged['c_HGMD_Exp'] = np.NaN + merged['c_RANKSCORE'] = np.NaN + merged['CLASS'] = np.NaN + else: + merged=merged.merge(hgmd_c, how="left", left_on=["chrom", "pos", "ref", "alt"], right_on=["new_chr", "new_pos", "ref", "alt"]) + merged=merged.merge(clin_c, how="left", left_on=["chrom", "pos", "ref", "alt"], right_on=["new_chr", "new_pos", "ref", "alt"]) + + return merged + diff --git a/scripts/annotation/main.py b/scripts/annotation/main.py new file mode 100644 index 0000000..c167909 --- /dev/null +++ b/scripts/annotation/main.py @@ -0,0 +1,487 @@ +## UPDATE +# Changes made by Chaozhong noted as #CL +# 1. added clinvar feature and changed database to clinvar.vcf.gz in VEP annotation +# 2. chang the HGMD database file to VEP annotation and similarity screo files +# hg19 +import time +import math +import os +import argparse +import re +import pandas as pd +import numpy as np +import json +import sys +import requests +import subprocess +#local methods +from utils_1 import * +#from utils_for_marrvel_annotate_script import * +from utils_for_symMatch import * +from utils_for_marrvel_flatfile import * +#from utils_for_marrvel_flatfile_module_3 import * +import multiprocessing +from functools import partial +from marrvel_score_recalc import * + + +############################################################# +# Database list: +# (common for all) gnomad.v2.1.1.lof_metrics.by_gene.txt +# (hg38 pos needed) gene_clinvar.csv +# (common for all) gene_omim.json +# (common for all) omim_alleric_variants.json +# (hg38 ver needed) dgv.csv (/houston_30t/chaozhong/aimarrvel_pipeline/database/dgv/dgv.38.csv) +# (hg38 pos needed) decipher.csv +# +############################################################# + + + + +def checkUserArgs(inArgs): + #check the user arguments and if anything necessary is missing + pass + + +def main(): + #example of running the script: + #python3 main.py -outPrefix ./test/r1 \ + #-patientID UDN630665 -patientHPOsimiOMIM /houston_10t/dongxue/MARRVEL_AI/BG/HPO2Gene/out_May12021/UDN/HPOsimi_UDN630665_simi_0.tsv \ + #-patientHPOsimiHGMD /six_tera/chaozhong/module_1/out/UDN/HPOsimi_UDNUDN630665_simi_0.tsv \ + #-varFile /houston_20t/rami/vep_PassVarQual_filt_UDN_Apr2521/540200-UDN630665-P_HGYTFBCXY-2-ID01-vep_gnomADg_gnomADe_splice_clin_hgmd_filt.txt \ + #-inFileType vepAnnotTab -patientFile testPatientFile.txt -patientFileType one -genomeRef hg19 -diseaseInh AD -modules curate,conserve + + + parser = argparse.ArgumentParser() + parser.add_argument("-varFile", "--varFile", help="Proivde input variant file") + parser.add_argument("-inFileType", "--inFileType", help="Proivde type of input file:vcf, vepAnnotTab") + parser.add_argument("-patientFile", "--patientFile", help="Proivde HPO IDs")#currently not used + parser.add_argument("-patientFileType", "--patientFileType", help="Proivde type of file:one, two")#currently not used + parser.add_argument("-patientID", "--patientID", help="Proivde patientID") + parser.add_argument("-patientHPOsimiOMIM", "--patientHPOsimiOMIM", help="Proivde patient HPO similarity file-OMIM") + parser.add_argument("-patientHPOsimiHGMD", "--patientHPOsimiHGMD", help="Proivde patient HPO similarity file-HGMD") + parser.add_argument("-diseaseInh", "--diseaseInh", help="Proivde disease Inheritance:AD, AR, XD, XR") + parser.add_argument("-modules", "--modules", help="Select modules to run:all,curate,conserve,effectOnGene") + parser.add_argument("-genomeRef", "--genomeRef", help="Proivde genome ref: hg19, hg38") + parser.add_argument("-outPrefix", "--outPrefix", help="Proivde prefix for output files") + args = parser.parse_args() + #check the user args + checkUserArgs(args) + print('input file:', args.varFile) + print('type of input file:', args.inFileType) + print('outPrefix:', args.outPrefix) + print('modules:', args.modules) + moduleList=args.modules.split(',') + print('modules list:', moduleList) + + #start time + start_time = time.time() + + #need to write user options to a log file + + #read the gnomad gene metrics file + fileName='/run/data_dependencies/annotate/anno_hg19/gnomad.v2.1.1.lof_metrics.by_gene.txt' + #fileName='/database_test/gnomad.v2.1.1.lof_metrics.by_gene.txt' + gnomadMetricsGeneDf=pd.read_csv(fileName, sep='\t') + #print('gnomadMetricsGeneDf shape:', gnomadMetricsGeneDf.shape) + + #set up flags + #debug flag; if 1 then can check/test the code + debugFlag=0 + #print('debugFlag:', debugFlag) + #use flat files to read the marrvel databases instead of using API + flatFileFlag=1 + #use marrvel annotation script and API to get marrvel info. this is slower + annotateFlag=0 + + #the directory for marrvel database flat file + #flatFilesDir='/home/dongxue/hdd_10t/MARRVEL_AI/resource/MARRVEL/' + + #initialization + dgvDf=[] + decipherDf=[] + hgmdDf=[] + omimHPOScoreDf=[] + hgmdHPOScoreDf=[] + clinvarGeneDf=[] + clinvarAlleleDf=[] + omimAlleleList=[] + + + ## CL: It's no longer needed to extract info from HGMD_allmut.txt + """ + #read HGMD files + if 'curate' in moduleList: + #hgmd dir + fileName='/run/annotation/HGMD_allmut.txt' + hgmdDf=pd.read_csv(fileName, sep='\t') + print('hgmdDf shape:', hgmdDf.shape) + hgmdDf=hgmdDf.fillna(0) + hgmdDf['startCoord']=hgmdDf['startCoord'].astype(int) + hgmdDf['endCoord']=hgmdDf['endCoord'].astype(int) + hgmdDf['chromosome']=hgmdDf['chromosome'].replace('X',23 ) + hgmdDf['chromosome']=hgmdDf['chromosome'].replace('Y',24 ) + hgmdDf['chromosome']=hgmdDf['chromosome'].replace('MT',25 ) + hgmdDf['chromosome']=hgmdDf['chromosome'].replace('GL.*','26',regex=True ) + #make chr as int + hgmdDf['chromosome']=hgmdDf['chromosome'].astype(int) + #sort by gene name + hgmdDf.sort_values("gene", inplace=True) + #setting gene as index column + hgmdDf.set_index("gene", inplace = True,drop=False) + + #test/check HGMD + if debugFlag==1: + print('in HGMD') + #print('\tvar:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol) + geneSymbol='SAMD11' + pos=865595 + chrom='1' + print('\tchrom:', chrom, 'pos:',pos,'geneSymbol:', geneSymbol) + HGMDDict={} + hgmdGeneFound=0 + hgmdVarFound=0 + hgmdVarPhenIdList=[] + hgmdVarHPOIdList=[] + hgmdVarHPOStrList=[] + #check variant + print('\ttype pos:', type(hgmdVarDf['pos'][0]), 'type chrom:', type(hgmdVarDf['chrom'][0])) + vals=hgmdVarDf[(hgmdVarDf['pos']==pos) & (hgmdVarDf['chrom'] == int(chrom))] + numRows=len(vals.index) + print('\tvar numRows:', numRows) + if numRows>0: + hgmdVarFound=1 + hgmdVarPhenIdList.extend(vals['phen_id'].tolist()) + hgmdVarHPOIdList.extend(vals['hpo_id'].tolist()) + hgmdVarHPOStrList.extend(vals['hpo_str'].tolist()) + + #check gene + vals=hgmdGeneDf[(hgmdGeneDf['gene']==geneSymbol)] + numRows=len(vals.index) + print('\tgene numRows:', numRows) + if numRows>0: + hgmdGeneFound=1 + print('\thgmdVarFound:',hgmdVarFound,'hgmdGeneFound:',hgmdGeneFound, + 'hgmdVarPhenIdList:',hgmdVarPhenIdList,'hgmdVarHPOIdList:',hgmdVarHPOIdList, + 'hgmdVarHPOStrList:',hgmdVarHPOStrList) + + """ + + #read HPO files + if 'curate' in moduleList: + fileName=args.patientHPOsimiOMIM + omimHPOScoreDf = pd.read_csv(fileName, sep='\t') + print('patientHPOsimi-OMIM dimension:',omimHPOScoreDf.shape) + + fileName=args.patientHPOsimiHGMD + hgmdHPOScoreDf = pd.read_csv(fileName, sep='\t') + print('patientHPOsimi-HGMD dimension:',hgmdHPOScoreDf.shape) + + #read the clinvar genes file + if 'curate' in moduleList: + + # Q: Why we use the same for hg19 and hg38? + # A: + if args.genomeRef == 'hg38': + fileName='/run/data_dependencies/annotate/anno_hg19/gene_clinvar.csv' + else: + fileName='/run/data_dependencies/annotate/anno_hg19/gene_clinvar.csv' + #fileName='/database_test/gene_clinvar.csv' + #print('\nfileName:', fileName) + clinvarGeneDf=pd.read_csv(fileName, sep=',') + #sort by gene name + clinvarGeneDf.sort_values("symbol", inplace=True) + clinvarGeneDf.set_index(['symbol'], inplace = True,drop=False) + #print('clinvarGenes dim:', clinvarGeneDf.shape)#41754 x 10,, entrezId,symbol,chr,hg19Start,hg19Stop,totalClinvarVars,P,LP,LB,B + #print(clinvarGeneDf.head()) + + ### CL: this part was substituted by VEP custom annotation from clinvar.vcf.gz + """ + #read the clinvar variant file + fileName='/run/annotation/clinvar.csv' + print('\nfileName:', fileName) + clinvarAlleleDf=pd.read_csv(fileName, sep=',') + print('clinvarAllele dim:', clinvarAlleleDf.shape)# (507196, 5) title chr start stop significance.description + + #convert float to int + clinvarAlleleDf = clinvarAlleleDf.fillna(0) + clinvarAlleleDf['start'] = clinvarAlleleDf['start'].astype(int) + clinvarAlleleDf['stop'] = clinvarAlleleDf['stop'].astype(int) + #in chr column change X and Y to numbers + clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('X',23) + clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('Y',24) + clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('MT',25) + clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('GL.*','26',regex=True ) + #make chr as int + clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].astype(int) + #make index + clinvarAlleleDf['id1']=clinvarAlleleDf['chr'].astype(str)+ '_' +clinvarAlleleDf['start'].astype(str)+'_'+ clinvarAlleleDf['stop'].astype(str) + # setting first name as index column + clinvarAlleleDf.set_index("id1", inplace = True,drop=False) + #make sure start and stop are int + print('type chr:', type(clinvarAlleleDf['chr'][0]), 'start:', type(clinvarAlleleDf['start'][0]), 'stop:', type(clinvarAlleleDf['stop'][0])) + """ + + #read OMIM + if 'curate' in moduleList: + #read the OMIM gene file + fileName='/run/data_dependencies/annotate/anno_hg19/gene_omim.json' + #fileName='/database_test/gene_omim.json' + #print('\nfileName:', fileName) + with open(fileName) as f: + omimGeneList=json.load(f) + #print('type omimGeneList:', type(omimGeneList), 'len:', len(omimGeneList)) + #check the type of data and keys + if debugFlag==1: + for omimGeneDict in omimGeneList: + print('type of omimGeneDict:', type(omimGeneDict)) + print('keys:', omimGeneDict.keys()) + for keyVal in omimGeneDict.keys(): + print('keyVal:', keyVal) + print('\tsubkeys type:', type(omimGeneDict[keyVal]) ) + if isinstance(omimGeneDict[keyVal], list): + print('\n\t\tfound list') + print('\t\t type:',type(omimGeneDict[keyVal])) + #print('\t\t type:',omimGeneDict[keyVal]) + #for row in omimGeneDict[keyVal]: + # print('row keys:', row.keys()) + + break + + #read the OMIM allele file + fileName='/run/data_dependencies/annotate/anno_hg19/omim_alleric_variants.json' + #fileName='/database_test/omim_alleric_variants.json' + #print('\nfileName:', fileName) + with open(fileName) as f: + omimAlleleList=json.load(f) + if debugFlag==1: + print('type of omim allelic :', type(omimAlleleList), 'len:', len(omimAlleleList)) + for omimAlleleDict in omimAlleleList: + print('type of omimAlleleDict:', type(omimAlleleDict)) + #check the keys + print('keys:', omimAlleleDict.keys()) + for keyVal in omimAlleleDict.keys(): + print('keyVal:', keyVal) + print('\tsubkeys type:', type(omimAlleleDict[keyVal]) ) + break + + #read DGV database + if 'conserve' in moduleList: + print('reading DGV flat file') + if args.genomeRef == 'hg38': + fileName='/run/data_dependencies/annotate/anno_hg38/dgv.csv' + else: + fileName='/run/data_dependencies/annotate/anno_hg19/dgv.csv' + #fileName='/database_test/dgv.csv' + dgvDf=pd.read_csv(fileName, sep=',') + #print('dgvDf shape:', dgvDf.shape) + #print('dgvHead:', dgvDf.head()) + #hg19Chr hg19Start hg19Stop + dgvDf=dgvDf.fillna(0) + + #if args.genomeRef == 'hg19': + dgvDf.columns = ['Chr', 'Start', 'Stop'] + dgvDf.columns.tolist()[3:] + + dgvDf['Start']=dgvDf['Start'].astype(int) + dgvDf['Stop']=dgvDf['Stop'].astype(int) + dgvDf['Chr']=dgvDf['Chr'].replace('X',23 ) + dgvDf['Chr']=dgvDf['Chr'].replace('Y',24 ) + dgvDf['Chr']=dgvDf['Chr'].replace('MT',25 ) + dgvDf['Chr']=dgvDf['Chr'].replace('GL.*',26,regex=True ) + + #make chr as int + #dgvDf = dgvDf.loc[dgvDf['Chr'].isin(list(range(27))),:].copy() + dgvDf['Chr']=dgvDf['Chr'].astype(int) + + ''' CL: change column name to be compatible with hg38 + dgvDf['hg19Start']=dgvDf['hg19Start'].astype(int) + dgvDf['hg19Stop']=dgvDf['hg19Stop'].astype(int) + dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('X',23 ) + dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('Y',24 ) + dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('MT',25 ) + dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('GL.*','26',regex=True ) + #make chr as int + dgvDf['hg19Chr']=dgvDf['hg19Chr'].astype(int) + ''' + print('finsihed reading DGV') + + #read DECIPHER database + if 'conserve' in moduleList: + print('reading Decipher flat file') + if args.genomeRef == 'hg38': + fileName='/run/data_dependencies/annotate/anno_hg38/decipher.csv' + else: + fileName='/run/data_dependencies/annotate/anno_hg19/decipher.csv' + #fileName='/database_test/decipher.csv' + decipherDf=pd.read_csv(fileName, sep=',') + #print('decipherDf shape:', decipherDf.shape) + #hg19Chr,hg19Start,hg19Stop + decipherDf=decipherDf.fillna(0) + + #if args.genomeRef == 'hg19': + decipherDf.columns = ['Chr', 'Start', 'Stop'] + decipherDf.columns.tolist()[3:] + + decipherDf['Start']=decipherDf['Start'].astype(int) + decipherDf['Stop']=decipherDf['Stop'].astype(int) + decipherDf['Chr']=decipherDf['Chr'].replace('X',23 ) + decipherDf['Chr']=decipherDf['Chr'].replace('Y',24 ) + decipherDf['Chr']=decipherDf['Chr'].replace('MT',25 ) + decipherDf['Chr']=decipherDf['Chr'].replace('GL.*','26',regex=True ) + #make chr as int + decipherDf['Chr']=decipherDf['Chr'].astype(int) + + ''' CL: change column name to be compatible with hg38 + decipherDf['hg19Start']=decipherDf['hg19Start'].astype(int) + decipherDf['hg19Stop']=decipherDf['hg19Stop'].astype(int) + decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('X',23 ) + decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('Y',24 ) + decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('MT',25 ) + decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('GL.*','26',regex=True ) + #make chr as int + decipherDf['hg19Chr']=decipherDf['hg19Chr'].astype(int) + ''' + print('finsihed reading DECIPHER') + + #read the input variant file + if args.inFileType=='vepAnnotTab' and flatFileFlag==1: + #numHeaderSkip=354#this depends on the type of annotation used + numHeaderSkip=0 + with open(args.varFile,'r') as F: + for line in F: + if line.startswith('##'): + numHeaderSkip += 1 + else: + break + print('input annoatated varFile:', args.varFile) + t1 = time.time() + varDf = pd.read_csv(args.varFile,sep='\t', skiprows=numHeaderSkip,error_bad_lines=False) + #varDf=varDf[0:10]#do this if need to have a small test + print('shape:', varDf.shape) + t2 = time.time() + inputReadTime = t2 - t1 + inputNumRows=len(varDf.index) + + #print('head:', varDf.head) + #print('column names:', varDf.columns) + #update the GERp column names since cant use ++ in the varaible name + if 'GERP++_RS' in varDf.columns: + print('found GERP++RS') + varDf['GERPpp_RS'] = varDf['GERP++_RS'] + if 'GERP++_NR' in varDf.columns: + print('found GERP++NR') + varDf['GERPpp_NR'] = varDf['GERP++_NR'] + #update column names which have - in it + if 'fathmm-MKL_coding_score' in varDf.columns: + varDf['fathmm_MKL_coding_score']=varDf['fathmm-MKL_coding_score'] + if 'M-CAP_score' in varDf.columns: + varDf['M_CAP_score']=varDf['M-CAP_score'] + + + print('start making variant objects') + numCores=8 + p1 = time.time() + #store rows into list for parallel function + inputList =[] + for index, rows in varDf.iterrows(): + inputList.append(rows) + p2 = time.time() + makeListTime = p2 - p1 + print('makeListTime :', makeListTime) + print('type inputList:', type(inputList[0]), 'len inputList:', len(inputList)) + #group or chunks for parallel function + groupSize=min((len(inputList) // numCores) + (len(inputList) %numCores), 1000) + print('groupSize:', groupSize) + groupList=list( getVarGroups(inputList, groupSize) ) + print('type groupList:', type(groupList), 'len:', len(groupList)) + + #call parallel function, pass it the main function "getAnnotateInfo_2" from utils_for_marrvel_flatfile.py and any needed files. + #this function will get the marrvel annoatations from marrvel flat files + parallelFlag=1 + if parallelFlag==1: + pool = multiprocessing.Pool(processes=numCores) + # CL: hgmdDf changed to hgmdHPOScoreDf + func = partial(getAnnotateInfo_2, genomeRef=args.genomeRef,clinvarGeneDf=clinvarGeneDf, clinvarAlleleDf=clinvarAlleleDf, + omimGeneList=omimGeneList, omimAlleleList=omimAlleleList,hgmdDf=hgmdHPOScoreDf, moduleList=moduleList, + dgvDf=dgvDf, decipherDf=decipherDf, gnomadMetricsGeneDf=gnomadMetricsGeneDf) + varDictList=pool.map(func,groupList) + pool.close() + else: + pass + + print('finsihed getAnnotateInfo_2') + #make a var obj list after paralle processing for each group, need a better more efficient way for doing this !! + t1 = time.time() + varObjList = getVarObjListFromDict(varDictList, len(varDf.index)) + t2 = time.time() + makeVarObjListTime = t2 - t1 + #print('len varObjList:', len(varObjList)) + print('makeVarObjListTime 1:', makeVarObjListTime) + + #we now have the variant object list "varObjList" with annotations; loop thru it and get the scores + for varObj in varObjList: + #print('var:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol, 'var-gene-pLI:', varObj.gnomadGenePLI) + #the curate score is under the utils_1.py file + if 'curate' in moduleList: + #print('finding symptom matching') + omimSymMatch(varObj, omimHPOScoreDf, args.inFileType) + hgmdSymMatch(varObj, hgmdHPOScoreDf) + clinVarSymMatch(varObj,args.inFileType) + #OMIM and clinvar info + retList=getCurationScore(varObj)#retList=[omimScore, hgmdScore, clinVarScore, totalScore] + varObj.curationScoreOMIM=retList[0] + varObj.curationScoreHGMD=retList[1] + varObj.curationScoreClinVar=retList[2] + varObj.curationScoreTotal=retList[3] + #print('curation score total:', varObj.curationScoreTotal) + #conserve score is under + if 'conserve' in moduleList: + retList=getConservationScore(varObj, args.diseaseInh) + #retList=[conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] + varObj.conservationScoreGnomad=retList[0] + varObj.conservationScoreDGV=retList[1] + varObj.conservationScoreOELof=retList[2] + #print('conservation score GERP++:',varObj.conservationScoreGerpPP,'DGV:',varObj.conservationScoreDGV,) + #print('conserve score DGV:',varObj.conservationScoreDGV,'conserve score gnomad:', varObj.conservationScoreGnomad) + + + #write the objects info scores to a file + print('writing results') + df = pd.DataFrame([t.__dict__ for t in varObjList ]) + print('shape of output file:',df.shape) + #write + fileName=args.outPrefix+'_'+args.patientID+'_scores.txt' + print('out file name:', fileName) + df.to_csv(fileName, sep='\t', index=False) + + end_time = time.time() + process_time = end_time - start_time + print('pipeline time:', process_time) + #log file for times + fileName=args.outPrefix+'_'+args.patientID+'_log.txt' + f = open(fileName, "w") + f.write("Process time:"+str(process_time)+ ' seconds and in mins:'+ str(process_time/60)+"\n") + f.close() + print('log file name:', fileName) + print('input read time:', inputReadTime) + print('input num rows:', inputNumRows) + print('makeListTime from pd:', makeListTime) + print('makeVarObjListTime 1:', makeVarObjListTime) + print('m:', moduleList) + + print("Score re-calculation:") + score = load_raw_matrix(df) + score = omimCurate(score) + score = hgmdCurate(score) + score = clinvarCurate(score) + score = conservationCurate(score) + + score.to_csv('/out/rami-test/%s_scores.csv'%(args.patientID), index=False) + + + exit() + + + +main() + diff --git a/scripts/annotation/marrvel_score_recalc.py b/scripts/annotation/marrvel_score_recalc.py new file mode 100755 index 0000000..2e413bf --- /dev/null +++ b/scripts/annotation/marrvel_score_recalc.py @@ -0,0 +1,266 @@ +#!/usr/bin/env python +# coding: utf-8 +import pandas as pd +import numpy as np + + + + +def load_raw_matrix(score): + #score_file = '/out/rami-test/r1_%s_scores.txt'%(ID) #'/out/rami-test/r1_%s_scores.txt'%(ID) + #score = pd.read_csv(score_file, sep='\t') + + raw_features = ["chrom", "pos", "ref", + "alt",'varId','varId_dash','zyg','geneSymbol','geneEnsId','gnomadAF','gnomadAFg','CADD_phred', + 'CADD_PHRED','GERPpp_RS','GERPpp_NR','DANN_score','FATHMM_pred','FATHMM_score', + 'Polyphen2_HDIV_score','Polyphen2_HVAR_score','REVEL_score','SIFT_score', + 'fathmm_MKL_coding_score','LRT_score','LRT_Omega','phyloP100way_vertebrate', + 'M_CAP_score','MutationAssessor_score','MutationTaster_score','ESP6500_AA_AF', + 'ESP6500_EA_AF','symptomName','omimSymptomSimScore','omimSymMatchFlag','hgmdSymptomScore', + 'hgmdSymptomSimScore','hgmdSymMatchFlag','clinVarSymMatchFlag','gnomadGeneZscore', + 'gnomadGenePLI','gnomadGeneOELof','gnomadGeneOELofUpper','IMPACT','Consequence', + 'omimGeneFound','omimVarFound','hgmdGeneFound','hgmdVarFound', + 'clinVarVarFound','clinVarGeneFound','clinvarTotalNumVars', + 'clinvarNumP','clinvarNumLP','clinvarNumLB','clinvarNumB','clinvarSignDesc', + 'clinvarCondition','dgvVarFound','decipherVarFound','curationScoreHGMD','curationScoreOMIM', + 'curationScoreClinVar','conservationScoreDGV','conservationScoreGnomad','conservationScoreOELof', + 'hom','hgmd_rs','clin_dict','clin_PLP','clin_PLP_perc','spliceAImax','clin_code','hgmd_id', + 'hgmd_CLASS','rsId', 'HGVSc', 'HGVSp', 'phenoList', 'phenoInhList'] + + return score.loc[:,raw_features].copy() + + + + +def omimCurate(vardf): + ''' + When related value changed, + + change the following value: + omimSymptomSimScore + omimSymMatchFlag + curationScoreOMIM + + according to: + omimGeneFound + omimVarFound + + ''' + print('\nIn OMIM curation now...') + + # change omimSymptomSimScore according to wether gene found in OMIM database to avoid info leaking + zero_score_bool = vardf['omimGeneFound']==0 + vardf.loc[zero_score_bool, 'omimVarFound'] = 0 + vardf.loc[zero_score_bool, 'omimSymptomSimScore'] = 0.0 + + # change omimSymMatchFlag accordingly + flag_0_bool = vardf['omimSymptomSimScore'] < 0.2 + vardf.loc[flag_0_bool, 'omimSymMatchFlag'] = 0 + vardf.loc[~flag_0_bool, 'omimSymMatchFlag'] = 1 + + # change final curation score + high_bool = (vardf['omimVarFound']==1) & (vardf['omimSymMatchFlag']==1) + medi_bool = ((vardf['omimVarFound']==1) & (vardf['omimSymMatchFlag']==0)) | ((vardf['omimVarFound']==0) & (vardf['omimGeneFound']==1) & (vardf['omimSymMatchFlag']==1)) + + vardf['curationScoreOMIM'] = 'Low' + vardf.loc[high_bool, 'curationScoreOMIM'] = 'High' + vardf.loc[medi_bool, 'curationScoreOMIM'] = 'Medium' + + return vardf + + + + +def hgmdCurate(vardf): + ''' + When related value changed, + + change the following value: + hgmdSymptomScore + hgmdSymptomSimScore + hgmdSymMatchFlag + curationScoreHGMD + + according to: + hgmdGeneFound + hgmdVarFound + + ''' + print('\nIn HGMD curation now...') + + # change hgmdSymptomSimScore according to wether gene found in HGMD database to avoid info leaking + zero_score_bool = vardf['hgmdGeneFound']==0 + vardf.loc[zero_score_bool, 'hgmdVarFound'] = 0 + vardf.loc[zero_score_bool, 'hgmdSymptomSimScore'] = 0.0 + + # change hgmdSymptomScore according to wether variant found in HGMD database to avoid info leaking + zero_score_bool = vardf['hgmdVarFound']==0 + vardf.loc[zero_score_bool, 'hgmdSymptomScore'] = 0.0 + + # change hgmdSymMatchFlag accordingly + score_numeric = vardf['hgmdSymptomSimScore'].copy() + score_numeric[score_numeric=='-'] = np.NaN + score_numeric = score_numeric.astype(float) + flag_0_bool = score_numeric < 0.2 + vardf.loc[flag_0_bool, 'hgmdSymMatchFlag'] = 0 + vardf.loc[~flag_0_bool, 'hgmdSymMatchFlag'] = 1 + + # change final curation score + high_bool = (vardf['hgmdVarFound']==1) & (vardf['hgmdSymMatchFlag']==1) + medi_bool = ((vardf['hgmdVarFound']==1) & (vardf['hgmdSymMatchFlag']==0)) | ((vardf['hgmdVarFound']==0) & (vardf['hgmdGeneFound']==1) & (vardf['hgmdSymMatchFlag']==1)) + + vardf['curationScoreHGMD'] = 'Low' + vardf.loc[high_bool, 'curationScoreHGMD'] = 'High' + vardf.loc[medi_bool, 'curationScoreHGMD'] = 'Medium' + + return vardf + + + + + +def clinvarCurate(vardf): + ''' + When related value changed, + + change the following value: + clinVarSymMatchFlag + curationScoreClinVar + + according to: + clinVarVarFound + clinVarGeneFound + clinvarSignDesc + + ''' + print('\nIn ClinVar curation now...') + + # change clinVarSymMatchFlag + vardf.loc[vardf['clinVarGeneFound']==0, 'clinVarVarFound'] = 0 + one_bool = (vardf['clinVarSymMatchFlag']==1) & ((vardf['clinVarVarFound']==1) | (vardf['clinVarGeneFound']==1)) + vardf.loc[~one_bool, 'clinVarSymMatchFlag'] = 0 + + # change final curation score + + pathList=['Pathogenic','Likely pathogenic','Pathogenic, Affects','Pathogenic/Likely pathogenic, other', + 'Pathogenic/Likely pathogenic','Pathogenic/Likely pathogenic, drug response', + 'Pathogenic/Likely pathogenic, risk factor','Likely pathogenic, drug response','Likely pathogenic, risk factor','Likely pathogenic, association', + 'Likely pathogenic, other','Pathogenic, association, protective','Pathogenic, Affects','Pathogenic, association','Pathogenic, other', + 'Pathogenic, protective','Pathogenic, protective, risk factor','Pathogenic, risk factor', + 'Pathogenic/Likely pathogenic, other','Pathogenic/Likely pathogenic, risk factor'] + + #pathList=['Pathogenic','Likely pathogenic', 'Pathogenic/Likely pathogenic'] + benignList=['Benign', 'Likely benign', 'Benign/Likely benign', 'Benign, association', 'Benign, drug response', + 'Benign, other', 'Benign, protective', ' Benign/Likely benign, Affects', 'Benign/Likely benign, association', + 'Benign/Likely benign, drug response', 'Benign/Likely benign, drug response, risk factor', + 'Benign/Likely benign, other', 'Benign/Likely benign, protective', 'Benign/Likely benign, protective, risk factor', + 'Benign/Likely benign, risk factor', 'Likely benign, drug response, other','Likely benign, other', + 'Likely benign, other, risk factor', 'Likely benign, risk factor'] + + #right logic + #path_bool = vardf['clinvarSignDesc'].str.contains('Pathogenic', case=False) & (~vardf['clinvarSignDesc'].str.contains('Conflicting', case=False)) + #bngn_bool = vardf['clinvarSignDesc'].str.contains('Benign', case=False) & (~vardf['clinvarSignDesc'].str.contains('Conflicting', case=False)) + # wrong but current logic + path_bool = vardf['clinvarSignDesc'].isin(pathList) + bngn_bool = vardf['clinvarSignDesc'].isin(benignList) + + high_bool = (vardf['hgmdVarFound']==1) & path_bool & (vardf['hgmdSymMatchFlag']==1) + medi_bool = ((vardf['hgmdVarFound']==1) & path_bool & (vardf['hgmdSymMatchFlag']==0)) | \ + ((vardf['hgmdVarFound']==1) & (~path_bool) & (~bngn_bool) & (vardf['hgmdSymMatchFlag']==1) & (vardf['hgmdSymMatchFlag']==1)) | \ + ((vardf['hgmdVarFound']==0) & (vardf['hgmdGeneFound']==1) & (vardf['clinVarSymMatchFlag']==1)) + + + vardf['curationScoreHGMD'] = 'Low' + vardf.loc[high_bool, 'curationScoreHGMD'] = 'High' + vardf.loc[medi_bool, 'curationScoreHGMD'] = 'Medium' + + return vardf + + + + +def conservationCurate(vardf): + ''' + When related value changed, + + change the following value: + conservationScoreDGV + conservationScoreGnomad + conservationScoreOELof + + according to: + gnomadAF + gnomadAFg + dgvVarFound + gnomadGeneOELofUpper + + ''' + + # gnomad + gnomadAFVal = vardf['gnomadAF'].copy() + gnomadAFVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFVal ]) + #gnomadAFVal[gnomadAFVal=='-'] = np.NaN + gnomadAFVal = gnomadAFVal.astype(float) + + gnomadAFgVal = vardf['gnomadAFg'].copy() + gnomadAFgVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFgVal ]) + #gnomadAFgVal[gnomadAFgVal=='-'] = np.NaN + gnomadAFgVal = gnomadAFgVal.astype(float) + + low_bool = (gnomadAFVal>=0.01) | (gnomadAFgVal>=0.01) + vardf.loc[low_bool, 'conservationScoreGnomad'] = 'Low' + vardf.loc[~low_bool, 'conservationScoreGnomad'] = 'High' + + + # conservationScoreDGV + low_bool = (vardf['conservationScoreDGV'] == 'Low') & (vardf['dgvVarFound']==1) + vardf['conservationScoreDGV'] = 'High' + vardf.loc[low_bool,'conservationScoreDGV'] = 'Low' + + # conservationScoreOELof + gnomadGeneOELofUpperVal = vardf['gnomadGeneOELofUpper'].copy() + gnomadGeneOELofUpperVal[gnomadGeneOELofUpperVal=='-'] = np.NaN + gnomadGeneOELofUpperVal = gnomadGeneOELofUpperVal.astype(float) + + high_bool = gnomadGeneOELofUpperVal < 0.35 + vardf['conservationScoreOELof'] = 'Low' + vardf.loc[high_bool, 'conservationScoreOELof'] = 'High' + + return vardf + +def getValFromStr(valStr: str, select: str = 'min'): + """ + Function to convert string to float, + and takes care of situation when multiple values exist + + Some test example: + >> a = '-' + >> b = '-,0.012' + >> c = '0.0003' + >> d = '6.38284e-05' + >> e = '6.38284e-05,3.19142e-05' + >> f = '-,-' + >> getValFromStr(a) + '-' + >> getValFromStr(b) + '-' + >> getValFromStr(c) + 0.0003 + >> getValFromStr(d) + 6.38284e-05 + >> getValFromStr(e) + 3.19142e-05 + >> getValFromStr(f) + '-' + """ + select_method = {'min':min, 'max':max} + vals = valStr.split(',') + if '-' in vals: + return np.NaN + else: + vals = [float(i) for i in vals] + return select_method[select](vals) + + + + diff --git a/scripts/annotation/utils_1.py b/scripts/annotation/utils_1.py new file mode 100755 index 0000000..8dc67a4 --- /dev/null +++ b/scripts/annotation/utils_1.py @@ -0,0 +1,536 @@ +## UPDATE +# Changes made by Chaozhong noted as #CL +#from liftover import get_lifter +import pandas as pd +import numpy as np +import pprint +import json +import requests + +#class for variant +class Variant(): + """Create a class to create an object for each variant + Parameters: + A class name for initiation call + Output: + An object of class Variant + """ + def __init__(self): + self.chrom='-' + self.pos='-' + self.start='-' + self.stop='-' + self.ref='-' + self.alt='-' + self.hg19Chrom='-' + self.hg19Pos='-' + self.hg38Chrom='-' + self.hg38Pos='-' + self.varId='-' + self.varId_dash='-'#varinat ID separated by dash like '6-99365567-T-C' + + self.patientID = '-' + self.zyg='-' + self.geneSymbol='-' + self.geneEnsId='-' + self.rsId='-' + self.featureType='-' + self.gnomadAF='-' + self.gnomadAFg='-'#genome gnomad score + #dbnsfp attributes + self.CLIN_SIG='-' + self.CADD_phred='-'#phred score from dbnsfp + self.CADD_PHRED='-' + self.GERPpp_RS='-'#GERP plus plus + self.GERPpp_NR='-' + self.DANN_score='-' + self.FATHMM_pred='-' + self.FATHMM_score='-' + self.GTEx_V8_gene='-' + self.GTEx_V8_tissue='-' + self.Polyphen2_HDIV_score='-' + self.Polyphen2_HVAR_score='-' + self.REVEL_score='-' + self.SIFT_score='-' + + self.clinvar_AlleleID = '-' #CL added + self.clinvar_clnsig='-' + self.clinvar_CLNREVSTAT = '-' #CL added + self.clinvar_CLNSIGCONF = '-' #CL added + + self.fathmm_MKL_coding_score='-' + self.HGVSc='-' + self.HGVSp='-' + #LRT + self.LRT_score='-' + self.LRT_Omega='-' + #Phylo + self.phyloP100way_vertebrate='-' + self.M_CAP_score='-' + self.MutationAssessor_score='-' + self.MutationTaster_score='-' + self.ESP6500_AA_AC='-' + self.ESP6500_AA_AF='-' + self.ESP6500_EA_AC='-' + self.ESP6500_EA_AF='-' + + + #symtom info + self.SymptomMatched = {'omim':0, 'clinvar':0, 'hgmd':0} #list containing omimMatched, clinvarMatched, hgmdMatched + self.symptomScore = {}#for OMIM + self.symptomName = {}#for OMIM + self.omimSymptomSimScore='-' + self.omimSymMatchFlag= '-' + self.hgmdSymptomScore='-' + self.hgmdSymptomSimScore='-' + self.hgmdSymMatchFlag='-' + self.clinVarSymMatchFlag='-' + + #gnomad gene metrics from flat file + self.gnomadGeneZscore='-' + self.gnomadGenePLI='-' + self.gnomadGeneOELof='-'#O/E lof + self.gnomadGeneOELofUpper='-'#O/E lof upper + #conseqeunce and impact + self.IMPACT='-' + self.Consequence='-' + #omim + self.omimDict={} + self.omimGeneFound='-' + self.omimVarFound='-' + self.omimList=[] + self.omimGeneDict={} + self.omimAlleleDict={} + self.phenoList=[] + self.phenoInhList=[] + self.phenoMimList=[] + #HGMD + self.HGMDDict={} + self.hgmdGeneFound='-' + self.hgmdVarFound='-' + self.hgmdVarPhenIdList=[] + self.hgmdVarHPOIdList=[] + self.hgmdVarHPOStrList=[] + #clinvar + self.clinVarVarDict={} + self.clinVarGeneDict={} + self.clinVarVarFound='-' + self.clinVarGeneFound='-' + self.clinVarList=[] + self.clinvarTotalNumVars='-' + self.clinvarNumP='-'#number of clinvar variants pathogenic + self.clinvarNumLP='-'#number of clinvar variants likely pathogenic + self.clinvarNumLB='-' + self.clinvarNumB='-' + self.clinvarTitle='-'#title from the flat file + self.clinvarSignDesc='-'#significance description from the flat file + self.clinvarCondition='-' + #dgv + self.DGVDict={} + self.dgvDictList=[] + self.dgvTypeList=[] + self.dgvSubtypeList=[] + self.dgvVarFound='-' + #Decipher + self.DecipherDict={} + self.decipherDictList=[] + self.decipherDeletionObsList=[] + self.decipherStudyList=[] + self.decipherVarFound='-' + #the module scores. The scores will be broken down so they can be added as features + #curation score + self.curationScoreTotal='-' + self.curationScoreHGMD='-' + self.curationScoreOMIM='-' + self.curationScoreClinVar='-' + #conservation scores + self.conservationScoreTotal='-' + self.conservationScorePhylop='-' + self.conservationScoreLRT='-' + self.conservationScoreGerpPP='-' + self.conservationScoreCNV='-' + self.conservationScoreDGV='-' + self.conservationScoreDecipher='-' + self.conservationScoreGnomad='-' + self.conservationScoreGeneConstZ='-' + self.conservationScoreGeneConstPLi='-' + self.conservationScoreDomino='-' + self.conservationScoreOELof='-' + #module 3 scores + self.effectOnGeneScore='-' + self.geneDiseaseAssocScore='-' + self.modelOrganismScore='-' + + +#function to get var objects from dataframe read from a VEP annoataed file +def getVarObjFromVEPAnnotDf(df, genomeRef): + #create the objects + varObjList = [ Variant() for i in range(len(df.index)) ] + #create a list of var objects + i=0 + for index, rows in df.iterrows(): + print('rows:', rows.Uploaded_variation, rows.Location, rows.Gene, rows.Feature_type) + s=rows.Uploaded_variation.split('_') + transcriptId=rows.Feature + chrom=s[0] + pos=s[1] + #if it is hg38 get its hg19 coordinates + if genomeRef=='hg38': + pass + varObjList[i].hg38Chrom=chrom + varObjList[i].hg38pos=pos + retList=gethg19LocFromHg38(chr, pos) + retList=[newChrom, newPos] + varObjList[i].hg19Chrom=retList[0] + varObjList[i].hg19pos=retList[1] + chrom=retList[0] + pos=retList[1] + else: + varObjList[i].hg19Chrom=chrom + varObjList[i].hg19pos=pos + + tmp=s[2] + s=tmp.split('/') + refAllele=s[0] + altAllele=s[1] + print(chr,pos, tmp, refAllele, altAllele) + + varObjList[i].chrom=chrom + varObjList[i].pos=pos + varObjList[i].ref=refAllele + varObjList[i].alt=altAllele + #cerate a unique ID since we have transcripts + varId='_'.join([chrom,pos,refAllele,altAllele,transcriptId]) + varObjList[i].varId=varId + varObjList[i].varId_dash='-'.join([chrom,pos,refAllele,altAllele]) + varObjList[i].zyg=rows.ZYG + varObjList[i].geneSymbol=rows.SYMBOL + varObjList[i].geneEnsId=rows.Gene + varObjList[i].rsId=rows.Existing_variation + varObjList[i].GERPpp_RS=rows.GERPpp_RS + varObjList[i].featureType=rows.Feature_type + varObjList[i].gnomadAF=rows.gnomAD_AF + varObjList[i].CLIN_SIG=rows.CLIN_SIG + varObjList[i].HGVSc=rows.HGVSc + varObjList[i].HGVSp=rows.HGVSp + + #increment + i=i+1 + + #print('in def len of objects:', len(varObjList)) + return varObjList + +#function to get OMIM info using MArrvel API +def getOMIMUsingMarrvelAPI(varObj): + print('getOMIM:',varObj.varId_dash, varObj.geneSymbol, varObj.rsId) + url='http://marrvel.org/data/omim/gene/symbol/{}/variant/{}'.format(varObj.geneSymbol,varObj.varId_dash) + #Example: 6:99365567 T>C / FBXL4 + #url='http://marrvel.org/data/omim/gene/symbol/{}/variant/{}'.format('FBXL4','6-99365567-T-C') + #print('url:', url) + + req=requests.get(url) + #print('reqText:', req) + #print('resqStatusCode:', req.status_code) + varFound=0 + geneFound=0 + omimDict={} + if req.status_code!=200: + #print('OMIM query not found') + pass + else: + #the entry for the gene exists but need to check if the variants exists also using the rs ID + geneFound=1 + #print('reqText:', req.text) + dat=json.loads(req.text) + #search var in the list of dict + retVal=next((item for item in dat['allelicVariants'] if item["dbSnps"] == varObj.rsId), None) + if retVal is not None: + varFound=1 + omimDict=data + #print('varFoundFlag:',varFoundFlag) + + + retList=[varFound, geneFound, omimDict] + return retList + +def getClinVarUsingMarrvelAPI(varObj): + print('getClinvar:',varObj.varId_dash, varObj.geneSymbol, varObj.rsId) + #the APIs + #/clinvar/gene/symbol/:geneSymbol + #/clinvar/gene/variant/:variant + + #check if varaint annotated + url='http://marrvel.org/data/clinvar/variant/{}'.format(varObj.varId_dash) + req=requests.get(url) + #print('reqText:', req) + #print('reqText:', req.text) + varFound=0 + varDict={} + if req.text: + varFound=1 + varDict=json.loads(req.text) + else: + varFound=0 + + + #check if gene annotated + url='http://marrvel.org/data/clinvar/symbol/{}'.format(varObj.geneSymbol) + req=requests.get(url) + #print('reqText:', req) + #print('reqText:', req.text) + geneFound=0 + geneDict={} + if req.text: + geneFound=1 + geneDict=json.loads(req.text) + else: + geneFound=0 + + #return + retList=[varFound,varDict, geneFound, geneDict] + return retList + + +def getCurationScore(varObj): + #print('in curation score') + #get the curation score per each DB + #omim score + omimScore='' + if varObj.omimVarFound==1: + if varObj.omimSymMatchFlag==1: + omimScore='High' + else: + omimScore='Medium' + elif varObj.omimGeneFound==1: + if varObj.omimSymMatchFlag==1: + omimScore='Medium' + else: + omimScore='Low' + else: + omimScore='Low' + + #hgmd score + hgmdScore='' + if varObj.hgmdVarFound==1: + if varObj.hgmdSymMatchFlag==1: + hgmdScore='High' + else: + hgmdScore='Medium' + elif varObj.hgmdGeneFound==1: + if varObj.hgmdSymMatchFlag==1: + hgmdScore='Medium' + else: + hgmdScore='Low' + else: + hgmdScore='Low' + + + #clinvar score + #if anything is not in the path/likely path list then it should have a low score + clinVarScore='' + pathList=['Pathogenic','Likely pathogenic','Pathogenic, Affects','Pathogenic/Likely pathogenic, other', + 'Pathogenic/Likely pathogenic','Pathogenic/Likely pathogenic, drug response', + 'Pathogenic/Likely pathogenic, risk factor','Likely pathogenic, drug response','Likely pathogenic, risk factor','Likely pathogenic, association', + 'Likely pathogenic, other','Pathogenic, association, protective','Pathogenic, Affects','Pathogenic, association','Pathogenic, other', + 'Pathogenic, protective','Pathogenic, protective, risk factor','Pathogenic, risk factor', + 'Pathogenic/Likely pathogenic, other','Pathogenic/Likely pathogenic, risk factor'] + + #pathList=['Pathogenic','Likely pathogenic', 'Pathogenic/Likely pathogenic'] + benignList=['Benign', 'Likely benign', 'Benign/Likely benign', 'Benign, association', 'Benign, drug response', + 'Benign, other', 'Benign, protective', ' Benign/Likely benign, Affects', 'Benign/Likely benign, association', + 'Benign/Likely benign, drug response', 'Benign/Likely benign, drug response, risk factor', + 'Benign/Likely benign, other', 'Benign/Likely benign, protective', 'Benign/Likely benign, protective, risk factor', + 'Benign/Likely benign, risk factor', 'Likely benign, drug response, other','Likely benign, other', + 'Likely benign, other, risk factor', 'Likely benign, risk factor'] + + clinVarScore='' + if varObj.clinVarVarFound==1: + if varObj.clinvarSignDesc in pathList: + if varObj.clinVarSymMatchFlag==1: + clinVarScore='High' + else: + clinVarScore='Medium' + elif varObj.clinvarSignDesc in benignList: + clinVarScore='Low' + else: + if varObj.clinVarSymMatchFlag==1: + clinVarScore='Medium' + else: + clinVarScore='Low' + elif varObj.clinVarGeneFound==1: + if varObj.clinVarSymMatchFlag==1: + clinVarScore='Medium' + else: + clinVarScore='Low' + else: + clinVarScore='Low' + + + + #OLD + #pathList=['Pathogenic','Likely pathogenic'] + #if varObj.clinvarSignDesc not in pathList: + # clinVarScore='Low' + #elif varObj.clinVarVarFound==1: + # if varObj.clinVarSymMatchFlag==1: + # clinVarScore='High' + # else: + # clinVarScore='Medium' + #elif varObj.clinVarGeneFound==1: + # if varObj.clinVarSymMatchFlag==1: + # clinVarScore='Medium' + # else: + # clinVarScore='Low' + #else: + # clinVarScore='Low' + + #print('\tomimScore:', omimScore,'hgmdScore:',hgmdScore,'clinVarScore:',clinVarScore) + + #total score + totalScore='-' + #need more work on this total score + if 0: + #if omim, clinvar, HGMD avaiable then use omim > hgmd > clinvar for the final score + varFound=0 + geneFound=0 + if varObj.omimVarFound==1: + varFound=1 + geneFound=varObj.omimGeneFound + elif varObj.hgmdVarFound==1: + varFound=1 + geneFound=varObj.hgmdGeneFound + elif varObj.clinVarVarFound==1: + varFound=1 + geneFound=varObj.clinVarGeneFound + else: + totalScore='Low' + retList=[omimScore, hgmdScore, clinVarScore, totalScore] + return retList + + #print('\tgetCuration func varFound:', varFound,'gene found:', geneFound) + #continue total score and follow the flowchart + if varFound==1: + if symMatchFlag==1: + totalScore='High' + elif symMatchFlag==0: + totalScore='Low' + elif geneFound==1: + if symMatchFlag==1: + totalScore='High' + elif symMatchFlag==0: + totalScore='Low' + else: + totalScore='Low' + + #print('\t curation total score:', totalScore) + retList=[omimScore, hgmdScore, clinVarScore, totalScore] + return retList + +def gethg19LocFromHg38(chrom, pos): + converter = get_lifter('hg38', 'hg19') + pos = int(pos) + converter[chrom][pos] + + retVal=converter.query(chrom, pos) + #print('retVal:', retVal) + newChrom=retVal[0][0] + newChrom=newChrom.replace('chr', '') + newPos=retVal[0][1] + #print('newChr:',newChrom,'newPos:', newPos) + retList=[newChrom, newPos] + return retList + + +def getDGVUsingMarrvelAPI(varObj): + #varObj=varObjList[0] + print('v:',varObj.geneSymbol) + #DGV returns a list not a dict + url='http://marrvel.org/data/dgv/variant/{}'.format(varObj.varId_dash) + req=requests.get(url) + #print('req:', req.text) + dgvDictList=[] + #geneList=[] + typeList=[] + subtypeList=[] + if req.status_code!=200: + #print('OMIM query not found') + pass + else: + dat=json.loads(req.text) + dgvDictList=dat + for row in dat: + dgvType=row['type'] + typeList.append(dgvType) + dgvSubtype=row['subType'] + subtypeList.append(dgvSubtype) + #dgvGeneList=[d['symbol'] for d in row['genes']] + #geneList.append(dgvGeneList) + + retList=[dgvDictList,typeList,subtypeList] + return retList + + +def getDecipherUsingMarrvelAPI(varObj): + print('v:',varObj.geneSymbol) + url='http://marrvel.org/data/decipher/variant/{}'.format(varObj.varId_dash)#varId_dash + #decipher/variant/:variant + #decipher/genomloc/:hg19Chr/:hg19Start/:hg19Stop + req=requests.get(url) + #print('req:', req.text) + decipherDictList=[] + decipherDeletionObsList=[] + decipherStudyList=[] + if req.status_code!=200: + #print('Decipher query not found') + pass + else: + dat=json.loads(req.text) + decipherDictList=dat + for row in dat: + #print('\n row:', row) + deleteObs=row['deletion']['obs'] + decipherDeletionObsList.append(deleteObs) + deleteStudy=row['study'] + decipherStudyList.append(deleteObs) + #print('deleteObs:', deleteObs, 'study:',deleteStudy) + + retList=[decipherDictList,decipherDeletionObsList,decipherStudyList] + return retList + +def checkVarGeneFile(inFile, fileType): + #check if input files are in right format + pass + +def checkPatientFile(inFile, fileType): + #check if input files are in right format + pass + + +def changeVariantFormat(inVar): + #change the varaint format from 6:99365567 T>C to 6-99365567-T-C + #replace : > and space with - + inVar=inVar.replace(' ', '-') + inVar=inVar.replace(':', '-') + inVar=inVar.replace('>', '-') + return inVar + +def readVarGeneFile(inFile, fileType): + #read the var/gene file and return a data frame; if it is varList only then need to call + if fileType=='varList': + df=pd.read_csv(inFile, sep='\t') + print('df head:', df.head(), '\n') + #change variant format + df=df.apply(np.vectorize(changeVariantFormat)) + print('df head:', df.head(), '\n') + return df + +def checkVarGnomad(varId): + url='http://marrvel.org/data/gnomad/variant/{}'.format(varId) + req=requests.get(url) + pass + + +def getVarGroups(varList, groupSize): + #get a list of variant groups for parallel processing + for i in range(0, len(varList), groupSize): + yield varList[i:i + groupSize] diff --git a/scripts/annotation/utils_for_marrvel_flatfile.py b/scripts/annotation/utils_for_marrvel_flatfile.py new file mode 100755 index 0000000..f1e3f94 --- /dev/null +++ b/scripts/annotation/utils_for_marrvel_flatfile.py @@ -0,0 +1,1060 @@ +## UPDATE +# Changes made by Chaozhong noted as #CL + +from utils_1 import * +from utils_for_marrvel_flatfile_module_2 import * +import numpy as np +import time +import re + +def getOMIMUsingMarrvelFlatFile(varObj, omimGeneList, omimAlleleList): + ''' + function to get OMIM info using marrrvel flat file + Params:a varaint object read from VEP annotation + + Returns: + List of OMIM annotations + ''' + #print('In OMIM call:') + #print('\tvar:', varObj.varId_dash, 'rsId:', varObj.rsId) + #check if rsId has a comma in it + inputSnpList=[] + if ',' in varObj.rsId: + inputSnpList = varObj.rsId.split(',') + else: + inputSnpList=varObj.rsId + #print('\tinputSnpList:', inputSnpList) + varFound=0 + geneFound=0 + omimDict={} + omimGeneDict={} + omimAlleleDict={} + phenoList=[] + phenoInhList=[] + phenoMimList=[] + #check gene + #keys: dict_keys(['phenotypes', 'allelicVariants', 'mimNumber', 'status', 'title', 'description', 'geneEntrezId', 'geneSymbol']) + if any(d['geneSymbol'] == varObj.geneSymbol for d in omimGeneList): + #print('\tgene:', varObj.geneSymbol, 'found') + geneFound=1 + omimGeneDict = next((sub for sub in omimGeneList if sub['geneSymbol'] == varObj.geneSymbol), None) + #print('\tgeneSymbol from dict:', omimGeneDict['geneSymbol']) + #print('\talleleic:', type(omimGeneDict['allelicVariants']), ' len:', len(omimGeneDict['allelicVariants']) ) + #check if snp found + snpList=[] + for a in omimGeneDict['allelicVariants']: + #print('a:', a) + #print('type:', type(a)) + if 'dbSnps' in a: + snpList.append(a['dbSnps']) + #print('\tsnpList:', snpList) + #print('\tlen snpList:', len(snpList)) + #check if input snpID matches the OMIM one + set1 = set(inputSnpList) + set2 = set(snpList) + if set1.intersection(set2): + varFound=1 + else: + varFound=0 + + #get disease info from OMIM + #print('\tphenotypes:', type(omimGeneDict['phenotypes']), ' len:', len(omimGeneDict['phenotypes']) ) + for a in omimGeneDict['phenotypes']: + #print('type:', type(a)) + pheno=a['phenotype'] + if 'phenotypeMimNumber' in a: + phenoMim=a['phenotypeMimNumber'] + else: + phenoMim='-' + if 'phenotypeInheritance' in a: + phenoInh=a['phenotypeInheritance'] + else: + phenoInh='-' + phenoList.append(pheno) + phenoInhList.append(phenoInh) + phenoMimList.append(str(phenoMim)) + #print('phenotype:', pheno,phenoMim,phenoInh) + + #print('\tvarFound:', varFound) + #print('\tphenoList:', phenoList) + #print('\tphenoInhList:', phenoInhList) + #print('\tphenoMimList:', phenoMimList) + + retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict, phenoList, phenoInhList, phenoMimList] + return retList + +def getClinVarUsingMarrvelFlatFile(varObj,clinvarAlleleDf,clinvarGeneDf): + ''' + function to get clinvar info using marrrvel flat file + Params:a varaint object read from VEP annotation + + Returns: + List of clinvar annotations + ''' + #print('in clinvar using flatfile') + #print('\tvar:',varObj.varId_dash) + varFound=0 + varDict={} + geneFound=0 + geneDict={} + clinvarTotalNumVars=0 + clinvarNumP=0 + clinvarNumLP=0 + clinvarNumLB=0 + clinvarNumB=0 + ## CL: no use but kept + clinvarTitle='' + clinvarSignDesc='' + clinvarCondition='' + + #print('in function type clinavrDf:', type(clinvarAlleleDf) ) + chromVal=int(varObj.chrom) + posVal=int(varObj.pos) + + """ #CL: Old implementation + #using inetger column not index + if 1: + vals=clinvarAlleleDf.loc[(clinvarAlleleDf['chr']==chromVal ) & + (clinvarAlleleDf['start'] == posVal ) & + (clinvarAlleleDf['stop'] == posVal )] + numRows=len(vals.index) + print('columns numRows:', numRows) + #using index + if 0: + idVal=str(chromVal)+'_'+str(posVal)+'_'+str(posVal) + #vals=clinvarAlleleDf.loc[(clinvarAlleleDf['id1']==idVal)] + try: + vals=clinvarAlleleDf.loc[idVal] + numRows=len(vals.index) + print('index numRows:', numRows) + except: + numRows=0 + + print('\tcheck var numRows:', numRows) + if numRows > 0: + varFound=1 + print('\tclinvar var found') + print('\t clinvar vals:', vals) + clinvarTitle=vals.iloc[0]['title'] + clinvarSignDesc=vals.iloc[0]['significance.description'] + clinvarCondition=vals.iloc[0]['condition'] + print('\ttitle:', clinvarTitle, 'signDes:', clinvarSignDesc, 'condition:', clinvarCondition) + else: + varFound=0 + """ + + #CL: check if var annotated in clinvar using VEP clinvar.vcf.gz custom annotation + if varObj.clinvar_AlleleID != '-': + varFound = 1 + #print('\tclinvar var found') + else: + varFound = 0 + + + + #check if gene annotated in clinvar using clinvarGeneDf + geneSymbol=varObj.geneSymbol + #print('\tgene symbol:', geneSymbol) + try: + vals=clinvarGeneDf.loc[geneSymbol] + numRows=len(vals.index) + except: + numRows=0 + #print('\tcheck gene numRows:', numRows) + if numRows>0: + #print('\tclinvar gene found') + geneFound=1 + clinvarTotalNumVars=vals['totalClinvarVars'] + clinvarNumP=vals['P'] + clinvarNumLP=vals['LP'] + clinvarNumLB=vals['LB'] + clinvarNumB=vals['B'] + #return + retList=[varFound,varDict, geneFound, geneDict, clinvarTotalNumVars,clinvarNumP,clinvarNumLP,clinvarNumLB, + clinvarNumB, clinvarTitle, clinvarSignDesc,clinvarCondition] + return retList + +def getHGMDUsingFlatFile(varObj,hgmdDf): + ''' + function to get HGMD from local flat file + Params: + varObj:a varaint object read from VEP annotation + hgmdDf: HGMD data frame read from local file (CL: now it refers to hgmdHPOScoreDf in main.py) + + Returns: + List of HGMD annotations + + Update by CL: + ''' + #print('\nin HGMD') + #print('\tvar:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol) + HGMDDict={} + hgmdGeneFound=0 + hgmdVarFound=0 + hgmdVarPhenIdList=[] + hgmdVarHPOIdList=[] + hgmdVarHPOStrList=[] + chromVal=varObj.chrom + posVal=int(varObj.pos) + startVal=int(varObj.start) + stopVal=int(varObj.stop) + #print('\tpos type:',type(varObj.start),'chrom:', type(chromVal) ) + + """ + #using int columns + if 1: + vals=hgmdDf[ ( hgmdDf['chromosome'] == chromVal ) & ( hgmdDf['startCoord']==startVal ) & (hgmdDf['endCoord']==stopVal) ] + numRows=len(vals.index) + #using index + if 0: + idVal=str(chromVal)+'_'+str(startVal)+'_'+str(stopVal) + try: + vals=hgmdVarDf.loc[idVal] + numRows=len(vals.index) + print('index numRows:', numRows) + except: + numRows=0 + + print('\tvar numRows:', numRows) + + + if numRows>0: + hgmdVarFound=1 + print('\tnumrows:',numRows) + print('\tvals:', vals) + if 'phen_id' in vals: + hgmdVarPhenIdList.extend(vals['phen_id'].tolist()) + if 'hpo_id' in vals: + hgmdVarHPOIdList.extend(vals['hpo_id'].tolist()) + if 'hpo_str' in vals: + hgmdVarHPOStrList.extend(vals['hpo_str'].tolist()) + print('\tvals:', vals) + """ + # CL: check VarFound + if varObj.hgmd_id != '-': + hgmdVarFound = 1 + else: + hgmdVarFound = 0 + + """ + #check gene + #vals=hgmdGeneDf[(hgmdGeneDf['gene']==varObj.geneSymbol)] + print('\t1 HGMD geneSymbol:', varObj.geneSymbol) + try: + print('\t2 HGMD geneSymbol:', varObj.geneSymbol) + vals=hgmdDf.loc[varObj.geneSymbol] + #vals=hgmdDf[ ( hgmdDf['gene'] == varObj.geneSymbol ) ] + numRows=len(vals.index) + except: + numRows=0 + + print('\tHGMD gene found numRows:', numRows) + if numRows>0: + hgmdGeneFound=1 + """ + # CL: check geneFound + if np.any(hgmdDf['gene_sym'].isin([varObj.geneSymbol])): + hgmdGeneFound = 1 + else: + hgmdGeneFound = 0 + + + #print('\thgmdVarFound:',hgmdVarFound,'hgmdGeneFound:',hgmdGeneFound, + # 'hgmdVarPhenIdList:',hgmdVarPhenIdList,'hgmdVarHPOIdList:',hgmdVarHPOIdList, + # 'hgmdVarHPOStrList:',hgmdVarHPOStrList) + #return + retList=[hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList] + return retList + +def getVarObjFromMarrvelFlatFile(varDf,genomeRef,clinvarGeneDf,clinvarAlleleDf, omimGeneList, omimAlleleList,hgmdDf): + ''' + Function to get the marrvel annotaion if not done in parallel. Use parallel option better + ''' + #dict_keys(['chr', 'pos', 'ref', 'alt', 'omim', 'clinvar', 'gtex', 'dgv', + #'decipher', 'decipherDisease', 'gnomad', 'dbnsfp']) + numVars=len(varDf.index) + print('type:', type(varDf), 'len/numVars:', numVars) + varObjList = [ Variant() for i in range(numVars) ] + + debugFlag=0 + + i=0 + for index, row in varDf.iterrows(): + print('type of row:', type(row)) + transcriptId=row.Feature + #s=row.Uploaded_variation.split('_') '1_10204_-/T' + s=row[0].split('_') + #print('s:', s) + chrom=s[0] + pos=int(s[1]) + tmp=s[2] + s=tmp.split('/') + ref=s[0] + alt=s[1] + #get the start and stop from second column like '1:10203-10204' + if '-' in row[1]: + s=row[1].split(':') + tmp=s[1] + s=tmp.split('-') + print('s:',s) + start=int(s[0]) + stop=int(s[1]) + else: + #start and stop the same + s=row[1].split(':') + start=int(s[1]) + stop=int(s[1]) + + + print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) + #change chrom X and Y and MT to numbers + if chrom=='X': + chrom=23 + elif chrom=='Y': + chrom=24 + elif chrom=='MT': + chrom=25 + chrom=int(chrom) + #if it is hg38 get its hg19 coordinates + if genomeRef=='hg38': + varObjList[i].hg38Chrom=chrom + varObjList[i].hg38Pos=pos + retList=gethg19LocFromHg38(chrom, pos) + retList=[newChrom, newPos] + varObjList[i].hg19Chrom=retList[0] + varObjList[i].hg19Pos=retList[1] + chrom=retList[0] + pos=retList[1] + #get the start + retList=gethg19LocFromHg38(chrom, start) + varObjList[i].start=int(retList[1]) + #get the stop + retList=gethg19LocFromHg38(chrom, stop) + varObjList[i].stop=int(retList[1]) + else: + varObjList[i].hg19Chrom=chrom + varObjList[i].hg19Pos=pos + varObjList[i].chrom=chrom + varObjList[i].pos=pos + varObjList[i].start=start + varObjList[i].stop=stop + + + geneSymbol=row.SYMBOL + print('gene:', geneSymbol) + varObjList[i].geneSymbol=geneSymbol + varObjList[i].CADD_phred=row.CADD_phred + varObjList[i].CADD_PHRED=row.CADD_PHRED + #assign + varObjList[i].ref=ref + varObjList[i].alt=alt + varObjList[i].varId_dash='-'.join([str(chrom),str(pos),ref,alt]) + varId='_'.join([str(chrom),str(pos),ref,alt,transcriptId]) + varObjList[i].varId=varId + varObjList[i].zyg=row.ZYG + varObjList[i].geneEnsId=row.Gene + varObjList[i].rsId=row.Existing_variation + varObjList[i].GERPpp_RS=row.GERPpp_RS + varObjList[i].featureType=row.Feature_type + varObjList[i].gnomadAF=row.gnomAD_AF + varObjList[i].gnomadAFg=row.gnomADg_AF + varObjList[i].CLIN_SIG=row.CLIN_SIG + varObjList[i].HGVSc=row.HGVSc + varObjList[i].HGVSp=row.HGVSp + #dbnsfp attributes + varObjList[i].GERPpp_NR=row.GERPpp_NR + varObjList[i].DANN_score=row.DANN_score + varObjList[i].FATHMM_pred=row.FATHMM_pred + varObjList[i].FATHMM_score=row.FATHMM_score + varObjList[i].GTEx_V8_gene=row.GTEx_V8_gene + varObjList[i].GTEx_V8_tissue=row.GTEx_V8_tissue + varObjList[i].Polyphen2_HDIV_score=row.Polyphen2_HDIV_score + varObjList[i].Polyphen2_HVAR_score=row.Polyphen2_HVAR_score + varObjList[i].REVEL_score=row.REVEL_score + varObjList[i].SIFT_score=row.SIFT_score + varObjList[i].clinvar_clnsig=row.clinvar_clnsig + varObjList[i].fathmm_MKL_coding_score=row.fathmm_MKL_coding_score + varObjList[i].LRT_score=row.LRT_score + varObjList[i].LRT_Omega=row.LRT_Omega + varObjList[i].phyloP100way_vertebrate=row.phyloP100way_vertebrate + varObjList[i].M_CAP_score=row.M_CAP_score + varObjList[i].MutationAssessor_score=row.MutationAssessor_score + varObjList[i].MutationTaster_score=row.MutationTaster_score + varObjList[i].ESP6500_AA_AC=row.ESP6500_AA_AC + varObjList[i].ESP6500_AA_AF=row.ESP6500_AA_AF + varObjList[i].ESP6500_EA_AC=row.ESP6500_EA_AC + varObjList[i].ESP6500_EA_AF=row.ESP6500_EA_AF + #dbnsfp + + #for module one will need OMIM, HGMD, clinvar + #get OMIM + if 1: + print('\nReading OMIM') + #varObjList[i].omimList=jsonDict['omim'] + #retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] + omimRet=getOMIMUsingMarrvelFlatFile(varObjList[i], omimGeneList, omimAlleleList) + varObjList[i].omimVarFound=omimRet[0] + varObjList[i].omimGeneFound=omimRet[1] + varObjList[i].omimDict=omimRet[2] + varObjList[i].omimGeneDict=omimRet[3] + varObjList[i].omimAlleleDict=omimRet[4] + varObjList[i].phenoList=omimRet[5] + varObjList[i].phenoInhList=omimRet[6] + varObjList[i].phenoMimList=omimRet[7] + print('OMIM res:') + print('\tgeneFound:',varObjList[i].omimGeneFound,'varFound:',varObjList[i].omimVarFound ) + + #get clinvar + if 1: + print('\nReading clinVar') + #clinVarList=jsonDict['clinvar'] + #varObjList[i].clinVarList=clinVarList + #retList=[varFound,varDict, geneFound, geneDict, clinvarTotalNumVars,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB] + clinVarRet=getClinVarUsingMarrvelFlatFile(varObjList[i], clinvarAlleleDf, clinvarGeneDf) + varObjList[i].clinVarVarFound=clinVarRet[0] + varObjList[i].clinVarVarDict=clinVarRet[1] + varObjList[i].clinVarGeneFound=clinVarRet[2] + varObjList[i].clinVarGeneDict=clinVarRet[3] + varObjList[i].clinvarTotalNumVars=clinVarRet[4] + varObjList[i].clinvarNumP=clinVarRet[5] + varObjList[i].clinvarNumLP=clinVarRet[6] + varObjList[i].clinvarNumLB=clinVarRet[7] + varObjList[i].clinvarNumB=clinVarRet[8] + varObjList[i].clinvarTitle= clinVarRet[9] + varObjList[i].clinvarSignDesc=clinVarRet[10] + varObjList[i].clinvarCondition=clinVarRet[11] + print('clinVar res:') + if debugFlag==1: + print('\tgeneFound::',varObjList[i].clinVarGeneFound,'varFound:',varObjList[i].clinVarVarFound) + print('\tnumVars:',varObjList[i].clinvarTotalNumVars,'numPathologic:',varObjList[i].clinvarNumP,'numBenign:',varObjList[i].clinvarNumB) + print('\tsignDesc:', varObjList[i].clinvarSignDesc) + + #get HGMD annotation + if 1: + print('\nReading HGMD') + hgmdRet=getHGMDUsingFlatFile(varObjList[i],hgmdDf) + #hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList + varObjList[i].hgmdVarFound=hgmdRet[0] + varObjList[i].hgmdGeneFound=hgmdRet[1] + varObjList[i].hgmdVarPhenIdList=hgmdRet[2] + varObjList[i].hgmdVarHPOIdList=hgmdRet[3] + varObjList[i].hgmdVarHPOStrList=hgmdRet[4] + print('HGMD results:') + print('\thgmdVarFound:',varObjList[i].hgmdVarFound,'hgmdGeneFound:',varObjList[i].hgmdGeneFound, + 'hgmdVarPhenIdList:',varObjList[i].hgmdVarPhenIdList,'hgmdVarHPOIdList:', + varObjList[i].hgmdVarHPOIdList, + 'hgmdVarHPOStrList:',varObjList[i].hgmdVarHPOStrList) + + #get DGV annotation + if 1: + print('Reading DGV') + dgvList=jsonDict['dgv'] + #print('dgvList:', dgvList) + dgvRet=getDGVUsingMarrvelAnnotScript(varObjList[i],dgvList) + varObjList[i].dgvDictList=dgvRet[0] + varObjList[i].dgvTypeList=dgvRet[1] + varObjList[i].dgvSubtypeList=dgvRet[2] + #get DECIPHER annotation + if 1: + print('Reading DECIPHER') + decipherList=jsonDict['decipher'] + decipherRet=getDecipherUsingMarrvelAnnotScript(varObjList[i], decipherList) + # + varObjList[i].decipherDictList=decipherRet[0] + varObjList[i].decipherDeletionObsList=decipherRet[1] + varObjList[i].decipherStudyList=decipherRet[2] + + #break for debug + i=i+1 + if debugFlag==1: + if i==2: + break + + + return varObjList + +def getVarObjListFromDict(varDictList, numVars): + ''' + Make a var obj list after paralle processing + the list has three levels since from pool get a first level list which is equal to the number of cores used + ''' + numProc=len(varDictList) + print('numProc:', numProc) + #initialize + varObjList = [ Variant() for i in range(numVars) ] + i=0 + for procList in varDictList: + for varDict in procList: + #print('i:', i) + #for varDict in dictList: + varObjList[i].chrom=varDict['chrom'] + varObjList[i].pos=varDict['pos'] + varObjList[i].start=varDict['start'] + varObjList[i].stop=varDict['stop'] + varObjList[i].ref=varDict['ref'] + varObjList[i].alt=varDict['alt'] + varObjList[i].hg19Chrom=varDict['hg19Chrom'] + varObjList[i].hg19Pos=varDict['hg19Pos'] + varObjList[i].varId=varDict['varId'] + varObjList[i].varId_dash=varDict['varId_dash'] + + #varObjList[i].patientID=varDict['patientID'] + varObjList[i].zyg=varDict['ZYG'] + varObjList[i].geneSymbol=varDict['geneSymbol'] + varObjList[i].CADD_phred=varDict['CADD_phred'] + varObjList[i].CADD_PHRED=varDict['CADD_PHRED'] + varObjList[i].geneEnsId=varDict['Gene'] + varObjList[i].rsId=varDict['Existing_variation'] + varObjList[i].GERPpp_RS=varDict['GERPpp_RS'] + varObjList[i].featureType=varDict['Feature_type'] + varObjList[i].gnomadAF=varDict['gnomadAF'] + varObjList[i].gnomadAFg=varDict['gnomadAFg'] + varObjList[i].CLIN_SIG=varDict['CLIN_SIG'] + varObjList[i].HGVSc=varDict['HGVSc'] + varObjList[i].HGVSp=varDict['HGVSp'] + + varObjList[i].VARIANT_CLASS=varDict['VARIANT_CLASS'] + varObjList[i].Feature=varDict['Feature'] + varObjList[i].hom=varDict['hom'] + varObjList[i].hgmd_rs=varDict['hgmd_rs'] + + varObjList[i].clin_dict=varDict['clin_dict'] + varObjList[i].clin_PLP=varDict['clin_PLP'] + varObjList[i].clin_PLP_perc=varDict['clin_PLP_perc'] + varObjList[i].spliceAI=varDict['spliceAI'] + varObjList[i].spliceAImax=varDict['spliceAImax'] + + + #dbnsfp attributes + varObjList[i].GERPpp_NR=varDict['GERPpp_NR'] + varObjList[i].DANN_score=varDict['DANN_score'] + varObjList[i].FATHMM_pred=varDict['FATHMM_pred'] + varObjList[i].FATHMM_score=varDict['FATHMM_score'] + varObjList[i].GTEx_V8_gene=varDict['GTEx_V8_gene'] + varObjList[i].GTEx_V8_tissue=varDict['GTEx_V8_tissue'] + varObjList[i].Polyphen2_HDIV_score=varDict['Polyphen2_HDIV_score'] + varObjList[i].Polyphen2_HVAR_score=varDict['Polyphen2_HVAR_score'] + varObjList[i].REVEL_score=varDict['REVEL_score'] + varObjList[i].SIFT_score=varDict['SIFT_score'] + #varObjList[i].clinvar_clnsig=varDict['clinvar_clnsig'] + + varObjList[i].clinvar_AlleleID = varDict['clinvar_AlleleID'] # Clinvar allele ID from clinvar.vcf.gz + varObjList[i].clinvar_clnsig = varDict['clinvar_clnsig'] #CL: Clinvar SIG from clinvar.vcf.gz + varObjList[i].clinvar_CLNREVSTAT = varDict['clinvar_CLNREVSTAT'] #CL: Clinvar STAT from clinvar.vcf.gz, for interface only + varObjList[i].clinvar_CLNSIGCONF = varDict['clinvar_CLNSIGCONF'] #CL: Clinvar STAT from clinvar.vcf.gz + varObjList[i].clin_code = varDict['clin_code'] #CL: feature for ai + + + varObjList[i].fathmm_MKL_coding_score=varDict['fathmm_MKL_coding_score'] + varObjList[i].LRT_score=varDict['LRT_score'] + varObjList[i].LRT_Omega=varDict['LRT_Omega'] + varObjList[i].phyloP100way_vertebrate=varDict['phyloP100way_vertebrate'] + varObjList[i].M_CAP_score=varDict['M_CAP_score'] + varObjList[i].MutationAssessor_score=varDict['MutationAssessor_score'] + varObjList[i].MutationTaster_score=varDict['MutationTaster_score'] + varObjList[i].ESP6500_AA_AC=varDict['ESP6500_AA_AC'] + varObjList[i].ESP6500_AA_AF=varDict['ESP6500_AA_AF'] + varObjList[i].ESP6500_EA_AC=varDict['ESP6500_EA_AC'] + varObjList[i].ESP6500_EA_AF=varDict['ESP6500_EA_AF'] + #conservation + varObjList[i].LRT_Omega=varDict['LRT_Omega'] + varObjList[i].LRT_score=varDict['LRT_score'] + varObjList[i].phyloP100way_vertebrate=varDict['phyloP100way_vertebrate'] + varObjList[i].IMPACT=varDict['IMPACT'] + varObjList[i].Consequence=varDict['Consequence'] + #DGV + varObjList[i].dgvDictList=varDict['dgvDictList'] + varObjList[i].dgvTypeList=varDict['dgvTypeList'] + varObjList[i].dgvSubtypeList=varDict['dgvSubtypeList'] + varObjList[i].dgvVarFound=varDict['dgvVarFound'] + #Decipher + varObjList[i].decipherDictList=varDict['decipherDictList'] + varObjList[i].decipherDeletionObsList=varDict['decipherDeletionObsList'] + varObjList[i].decipherStudyList=varDict['decipherStudyList'] + varObjList[i].decipherVarFound=varDict['decipherVarFound'] + #gnomadG + varObjList[i].gnomadGeneZscore=varDict['gnomadGeneZscore'] + varObjList[i].gnomadGenePLI=varDict['gnomadGenePLI'] + varObjList[i].gnomadGeneOELof=varDict['gnomadGeneOELof'] + varObjList[i].gnomadGeneOELofUpper=varDict['gnomadGeneOELofUpper'] + + varObjList[i].omimVarFound=varDict['omimVarFound'] + varObjList[i].omimGeneFound=varDict['omimGeneFound'] + varObjList[i].omimDict=varDict['omimDict'] + varObjList[i].omimGeneDict=varDict['omimGeneDict'] + varObjList[i].omimAlleleDict=varDict['omimAlleleDict'] + varObjList[i].phenoList=varDict['phenoList'] + varObjList[i].phenoInhList=varDict['phenoInhList'] + varObjList[i].phenoMimList=varDict['phenoMimList'] + + varObjList[i].clinVarVarFound=varDict['clinVarVarFound'] + varObjList[i].clinVarVarDict=varDict['clinVarVarDict'] + varObjList[i].clinVarGeneFound=varDict['clinVarGeneFound'] + varObjList[i].clinVarGeneDict=varDict['clinVarGeneDict'] + varObjList[i].clinvarTotalNumVars=varDict['clinvarTotalNumVars'] + varObjList[i].clinvarNumP=varDict['clinvarNumP'] + varObjList[i].clinvarNumLP=varDict['clinvarNumLP'] + varObjList[i].clinvarNumLB=varDict['clinvarNumLB'] + varObjList[i].clinvarNumB=varDict['clinvarNumB'] + varObjList[i].clinvarTitle= varDict['clinvarTitle'] + varObjList[i].clinvarSignDesc=varDict['clinvarSignDesc'] + varObjList[i].clinvarCondition=varDict['clinvarCondition'] + + varObjList[i].hgmdVarFound=varDict['hgmdVarFound'] + varObjList[i].hgmdGeneFound=varDict['hgmdGeneFound'] + varObjList[i].hgmdVarPhenIdList=varDict['hgmdVarPhenIdList'] + varObjList[i].hgmdVarHPOIdList=varDict['hgmdVarHPOIdList'] + varObjList[i].hgmdVarHPOStrList=varDict['hgmdVarHPOStrList'] + + varObjList[i].hgmd_id = varDict['hgmd_id'] # CL added + varObjList[i].hgmd_symbol = varDict['hgmd_symbol'] # CL added + varObjList[i].hgmd_PHEN = varDict['hgmd_PHEN'] # CL added + varObjList[i].hgmd_CLASS = varDict['hgmd_CLASS'] # CL added + + #symptom + varObjList[i].SymptomMatched=varDict['SymptomMatched'] + varObjList[i].symptomScore=varDict['symptomScore'] + varObjList[i].symptomName=varDict['symptomName'] + varObjList[i].omimSymptomSimScore=varDict['omimSymptomSimScore'] + varObjList[i].omimSymMatchFlag=varDict['omimSymMatchFlag'] + varObjList[i].hgmdSymptomScore=varDict['hgmdSymptomScore'] + varObjList[i].hgmdSymptomSimScore=varDict['hgmdSymptomSimScore'] + varObjList[i].hgmdSymMatchFlag=varDict['hgmdSymMatchFlag'] + varObjList[i].clinVarSymMatchFlag=varDict['clinVarSymMatchFlag'] + #increment + i=i+1 + #print('i index:', i) + #return + return varObjList + +def getAnnotateInfo_2(rowList,genomeRef,clinvarGeneDf,clinvarAlleleDf, omimGeneList, omimAlleleList,hgmdDf, moduleList, dgvDf, decipherDf, gnomadMetricsGeneDf): + ''' + Function to read the input annoatated varaint file and call the marrvel flat files to get annotations such as OMIM, DGV, Decipher + Parameters: + rowList: the input variants as a lits + other inputs are the flat file objects read locally instaed of an API + + Returns: + A dict of annoatated varaints + ''' + debugFlag=0 + #print('\nlen numVars:', len(rowList), 'typerow00:', type(rowList[0][0]), 'typerow[0]:', + # type(rowList[0]) ) + #print('len(row[0][0]):', len(rowList[0][0])) + #print('moduleList:', moduleList) + varObjList=[] + varDictList=[] + i=0 + #loop thru the rows + for row in rowList: + # CL 03-14-2023: commented all printing lines + #print('type of row:', type(row)) + varObj=Variant() + transcriptId=row.Feature + #s=row.Uploaded_variation.split('_') '1_10204_-/T' 1_1588250_T_A + ####row[0]: 21_11039079_C/A + ####s: ['21', '11039079', 'C/A'] + #print('row[0]:', row[0]) + + #two ways of input of first column either 1_1588250_T_A OR 21_11039079_C/A, so use the option flag + optFlag=0 + if(row[0].find('/')!=-1): + optFlag=1 + + if optFlag==0: + s=row[0].split('_') + #print('s:', s) + chrom=s[0] + pos=int(s[1]) + ref=s[2] + alt=s[3] + elif optFlag==1: + s=row[0].split('_') + #print('s:', s) + chrom=s[0] + pos=int(s[1]) + s=s[2].split('/') + ref=s[0] + alt=s[1] + + #get the start and stop from second column like '1:10203-10204' + if '-' in row[1]: + s=row[1].split(':') + tmp=s[1] + s=tmp.split('-') + #print('s:',s) + start=int(s[0]) + stop=int(s[1]) + else: + #start and stop the same + s=row[1].split(':') + start=int(s[1]) + stop=int(s[1]) + + #print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) + #change chrom X and Y and MT to numbers + if chrom=='X': + chrom=23 + elif chrom=='Y': + chrom=24 + elif chrom=='MT': + chrom=25 + elif re.search(r'GL', chrom): + chrom=26 + chrom=int(chrom) + + #if it is hg38 get its hg19 coordinates + # CL 03-14-2023: we have separate database for hg19 and hg38, + # we don't need to use LiftOver which is inaccurate + # related codes commented and modified + if genomeRef=='hg38': + varObj.hg38Chrom=chrom + varObj.hg38Pos=pos + varObj.chrom=chrom + varObj.pos=pos + varObj.start=start + varObj.stop=stop + + ''' + retList=gethg19LocFromHg38(chrom, pos)#called from the utils_1.py + # retList=[newChrom, newPos] + varObj.hg19Chrom=retList[0] + varObj.hg19Pos=retList[1] + varObj.chrom=retList[0] + varObj.pos=retList[1] + #get the start + retList=gethg19LocFromHg38(chrom, start) + varObj.start=int(retList[1]) + #get the stop + retList=gethg19LocFromHg38(chrom, stop) + varObj.stop=int(retList[1]) + ''' + else: + varObj.hg19Chrom=chrom + varObj.hg19Pos=pos + varObj.chrom=chrom + varObj.pos=pos + varObj.start=start + varObj.stop=stop + + + geneSymbol=row.SYMBOL + #print('gene:', geneSymbol) + varObj.geneSymbol=geneSymbol + varObj.CADD_phred=row.CADD_phred + varObj.CADD_PHRED=row.CADD_PHRED + + #assign + varObj.ref=ref + varObj.alt=alt + varObj.varId_dash='-'.join([str(chrom),str(start),ref,alt]) + #print('varId dash:', varObj.varId_dash) + varId='_'.join([str(chrom),str(pos),ref,alt,transcriptId]) + varObj.varId=varId + if 'ZYG' in row: + varObj.zyg=row.ZYG + varObj.geneEnsId=row.Gene + varObj.rsId=row.Existing_variation + varObj.GERPpp_RS=row.GERPpp_RS + varObj.featureType=row.Feature_type + varObj.gnomadAF=row.gnomAD_AF + varObj.gnomadAFg=row.gnomADg_AF + varObj.CLIN_SIG=row.CLIN_SIG #CL: useless but kept for now + varObj.LRT_Omega=row.LRT_Omega + varObj.LRT_score=row.LRT_score + varObj.phyloP100way_vertebrate=row.phyloP100way_vertebrate + varObj.IMPACT=row.IMPACT + varObj.Consequence=row.Consequence + varObj.HGVSc=row.HGVSc + varObj.HGVSp=row.HGVSp + #dbnsfp attributes + varObj.GERPpp_NR=row.GERPpp_NR + varObj.DANN_score=row.DANN_score + varObj.FATHMM_pred=row.FATHMM_pred + varObj.FATHMM_score=row.FATHMM_score + varObj.GTEx_V8_gene=row.GTEx_V8_gene + varObj.GTEx_V8_tissue=row.GTEx_V8_tissue + varObj.Polyphen2_HDIV_score=row.Polyphen2_HDIV_score + varObj.Polyphen2_HVAR_score=row.Polyphen2_HVAR_score + varObj.REVEL_score=row.REVEL_score + varObj.SIFT_score=row.SIFT_score + + varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz + varObj.clinvar_clnsig= row.clinvar_CLNSIG #CL: Clinvar SIG from clinvar.vcf.gz + #varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted + varObj.clinvar_CLNREVSTAT= row.clinvar_CLNREVSTAT #CL: Clinvar STAT from clinvar.vcf.gz, for interface only + varObj.clinvar_CLNSIGCONF= row.clinvar_CLNSIGCONF #CL: Clinvar SIGCONF from clinvar.vcf.gz + varObj.clin_code = row.clinvar_CLNSIG #CL: feature name for ai + + varObj.fathmm_MKL_coding_score=row.fathmm_MKL_coding_score + varObj.LRT_score=row.LRT_score + varObj.LRT_Omega=row.LRT_Omega + varObj.phyloP100way_vertebrate=row.phyloP100way_vertebrate + varObj.M_CAP_score=row.M_CAP_score + varObj.MutationAssessor_score=row.MutationAssessor_score + varObj.MutationTaster_score=row.MutationTaster_score + varObj.ESP6500_AA_AC=row.ESP6500_AA_AC + varObj.ESP6500_AA_AF=row.ESP6500_AA_AF + varObj.ESP6500_EA_AC=row.ESP6500_EA_AC + varObj.ESP6500_EA_AF=row.ESP6500_EA_AF + + varObj.VARIANT_CLASS=row.VARIANT_CLASS + varObj.Feature=row.Feature + varObj.hom=row.gnomADg_controls_nhomalt + varObj.hgmd_id=row.hgmd # CL added + varObj.hgmd_symbol=row.hgmd_GENE # CL added + varObj.hgmd_rs=row.hgmd_RANKSCORE + varObj.hgmd_PHEN=row.hgmd_PHEN # CL added + varObj.hgmd_CLASS=row.hgmd_CLASS # CL added + + if row.clinvar_CLNSIGCONF !="-": + clin_dict=dict() + for ro in row.clinvar_CLNSIGCONF.split("|_"): + temp=ro.split("(") + clin_dict[temp[0]]=int(temp[1][0]) + PLP_sum=clin_dict.get("Pathogenic", 0)+clin_dict.get("Likely_pathogenic", 0) + varObj.clin_dict=clin_dict + varObj.clin_PLP=PLP_sum + varObj.clin_PLP_perc=PLP_sum/sum(clin_dict.values()) + else: + if "benign" in row.clinvar_clnsig.lower(): + varObj.clin_PLP_perc=0 + elif "pathogenic" in row.clinvar_clnsig.lower(): + varObj.clin_PLP_perc=1 + else: + varObj.clin_PLP_perc="-" + varObj.clin_PLP="-" + varObj.clin_dict="-" + + + if row.SpliceAI_pred !="-": + varObj.spliceAI=row.SpliceAI_pred + temp=row.SpliceAI_pred.split("|") + varObj.spliceAImax=max(float(temp[1]),float(temp[2]),float(temp[3]),float(temp[4])) + else: + varObj.spliceAI="-" + varObj.spliceAImax="-" + + #get dgv + if 'conserve' in moduleList: + #print('\nGetting DGV') + dgvRet=getDGVUsingMarrvelFlatFile(varObj, dgvDf) + varObj.dgvDictList=dgvRet[0] + varObj.dgvTypeList=dgvRet[1] + varObj.dgvSubtypeList=dgvRet[2] + varObj.dgvVarFound=dgvRet[3] + + #get decipher + if 'conserve' in moduleList: + #print('\nGetting DECIPHER') + decipherRet=getDecipherUsingMarrvelFlatFile(varObj, decipherDf) + #[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] + varObj.decipherDictList=decipherRet[0] + varObj.decipherDeletionObsList=decipherRet[1] + varObj.decipherStudyList=decipherRet[2] + varObj.decipherVarFound=decipherRet[3] + + #get gnomad gene metrics from gnomad file + if 'conserve' in moduleList: + #print('\nGetting gnomad gene metrics') + gnomadGeneMetricRet=getGnomadGeneMetricFlatFile(varObj, gnomadMetricsGeneDf) + #[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] + varObj.gnomadGeneZscore=gnomadGeneMetricRet[0] + varObj.gnomadGenePLI=gnomadGeneMetricRet[1] + varObj.gnomadGeneOELof=gnomadGeneMetricRet[2]#O/E lof + varObj.gnomadGeneOELofUpper=gnomadGeneMetricRet[3]#O/E lof upper + + #for module one will need OMIM, HGMD, clinvar + #get OMIM + if 'curate' in moduleList: + #print('\nGetting OMIM') + #varObj.omimList=jsonDict['omim'] + #retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] + omimRet=getOMIMUsingMarrvelFlatFile(varObj, omimGeneList, omimAlleleList) + varObj.omimVarFound=omimRet[0] + varObj.omimGeneFound=omimRet[1] + varObj.omimDict=omimRet[2] + varObj.omimGeneDict=omimRet[3] + varObj.omimAlleleDict=omimRet[4] + varObj.phenoList=omimRet[5] + varObj.phenoInhList=omimRet[6] + varObj.phenoMimList=omimRet[7] + #print('OMIM res:') + #print('\tgeneFound:',varObj.omimGeneFound,'varFound:',varObj.omimVarFound ) + + #get clinvar + if 'curate' in moduleList: + #print('\nReading clinVar') + clinVarRet=getClinVarUsingMarrvelFlatFile(varObj, clinvarAlleleDf, clinvarGeneDf) + varObj.clinVarVarFound=clinVarRet[0] + varObj.clinVarVarDict=clinVarRet[1] + varObj.clinVarGeneFound=clinVarRet[2] + varObj.clinVarGeneDict=clinVarRet[3] + varObj.clinvarTotalNumVars=clinVarRet[4] + varObj.clinvarNumP=clinVarRet[5] + varObj.clinvarNumLP=clinVarRet[6] + varObj.clinvarNumLB=clinVarRet[7] + varObj.clinvarNumB=clinVarRet[8] + varObj.clinvarTitle= clinVarRet[9] + varObj.clinvarSignDesc= row.clinvar_CLNSIG #clinVarRet[10] #CL: changed to clinvar.vcf.gz annotation + varObj.clinvarCondition=clinVarRet[11] + #print('clinVar res:') + ''' + if debugFlag==1: + print('\tgeneFound::',varObj.clinVarGeneFound,'varFound:',varObj.clinVarVarFound) + print('\tnumVars:',varObj.clinvarTotalNumVars,'numPathologic:',varObj.clinvarNumP,'numBenign:',varObj.clinvarNumB) + print('\tsignDesc:', varObj.clinvarSignDesc) + ''' + + #get HGMD + if 'curate' in moduleList: + #print('\nReading HGMD') + hgmdRet=getHGMDUsingFlatFile(varObj,hgmdDf) + #hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList + varObj.hgmdVarFound=hgmdRet[0] + varObj.hgmdGeneFound=hgmdRet[1] + varObj.hgmdVarPhenIdList=hgmdRet[2] + varObj.hgmdVarHPOIdList=hgmdRet[3] + varObj.hgmdVarHPOStrList=hgmdRet[4] + #print('HGMD results:') + #print('\thgmdVarFound:',varObj.hgmdVarFound,'hgmdGeneFound:',varObj.hgmdGeneFound, + # 'hgmdVarPhenIdList:',varObj.hgmdVarPhenIdList,'hgmdVarHPOIdList:', + # varObj.hgmdVarHPOIdList, + # 'hgmdVarHPOStrList:',varObj.hgmdVarHPOStrList) + + #make a dict from the varObj. the varObj has one element for each row of the input VEP annotated file + varDict={ + 'hg19Chrom':varObj.hg19Chrom, + 'hg19Pos':varObj.hg19Pos, + 'chrom':varObj.chrom, + 'pos':varObj.pos, + 'start':varObj.start, + 'stop':varObj.stop, + 'geneSymbol':varObj.geneSymbol, + 'CADD_phred':varObj.CADD_phred, + 'CADD_PHRED':varObj.CADD_PHRED, + 'ref':varObj.ref, + 'alt':varObj.alt, + 'varId':varObj.varId, + 'ZYG':varObj.zyg, + 'HGVSc':varObj.HGVSc, + 'HGVSp':varObj.HGVSp, + 'Gene':varObj.geneEnsId, + 'Existing_variation':varObj.rsId, + 'GERPpp_RS':varObj.GERPpp_RS, + 'Feature_type':varObj.featureType, + 'gnomadAF':varObj.gnomadAF, + 'gnomadAFg':varObj.gnomadAFg, + 'CLIN_SIG':varObj.CLIN_SIG, + 'LRT_Omega':varObj.LRT_Omega, + 'LRT_score':varObj.LRT_score, + 'phyloP100way_vertebrate':varObj.phyloP100way_vertebrate, + #dbnsfp attributes + 'GERPpp_NR':varObj.GERPpp_NR, + 'DANN_score':varObj.DANN_score, + 'FATHMM_pred':varObj.FATHMM_pred, + 'FATHMM_score':varObj.FATHMM_score, + 'GTEx_V8_gene':varObj.GTEx_V8_gene, + 'GTEx_V8_tissue':varObj.GTEx_V8_tissue, + 'Polyphen2_HDIV_score':varObj.Polyphen2_HDIV_score, + 'Polyphen2_HVAR_score':varObj.Polyphen2_HVAR_score, + 'REVEL_score':varObj.REVEL_score, + 'SIFT_score':varObj.SIFT_score, + + 'clinvar_AlleleID': varObj.clinvar_AlleleID, # Clinvar allele ID from clinvar.vcf.gz + 'clinvar_clnsig': varObj.clinvar_clnsig, #CL: Clinvar SIG from clinvar.vcf.gz + 'clinvar_CLNREVSTAT': varObj.clinvar_CLNREVSTAT, #CL: Clinvar STAT from clinvar.vcf.gz, for interface only + 'clinvar_CLNSIGCONF': varObj.clinvar_CLNSIGCONF, #CL: Clinvar SIGCONF from clinvar.vcf.gz + 'clin_code': varObj.clin_code, #CL: feature for ai + + 'fathmm_MKL_coding_score':varObj.fathmm_MKL_coding_score, + 'LRT_score':varObj.LRT_score, + 'LRT_Omega':varObj.LRT_Omega, + 'phyloP100way_vertebrate':varObj.phyloP100way_vertebrate, + 'M_CAP_score':varObj.M_CAP_score, + 'MutationAssessor_score':varObj.MutationAssessor_score, + 'MutationTaster_score':varObj.MutationTaster_score, + 'ESP6500_AA_AC':varObj.ESP6500_AA_AC, + 'ESP6500_AA_AF':varObj.ESP6500_AA_AF, + 'ESP6500_EA_AC':varObj.ESP6500_EA_AC, + 'ESP6500_EA_AF':varObj.ESP6500_EA_AF, + #dbnsfp + 'gnomadGeneZscore':varObj.gnomadGeneZscore, + 'gnomadGenePLI':varObj.gnomadGenePLI, + 'gnomadGeneOELof':varObj.gnomadGeneOELof,#O/E lof + 'gnomadGeneOELofUpper':varObj.gnomadGeneOELofUpper,#O/E lof upper, + 'IMPACT':varObj.IMPACT, + 'Consequence':varObj.Consequence, + 'omimVarFound':varObj.omimVarFound, + 'omimGeneFound':varObj.omimGeneFound, + 'omimDict':varObj.omimDict, + 'omimGeneDict':varObj.omimGeneDict, + 'omimAlleleDict':varObj.omimAlleleDict, + 'phenoList':varObj.phenoList, + 'phenoInhList':varObj.phenoInhList, + 'phenoMimList':varObj.phenoMimList, + 'clinVarVarFound':varObj.clinVarVarFound, + 'clinVarVarDict':varObj.clinVarVarDict, + 'clinVarGeneFound':varObj.clinVarGeneFound, + 'clinVarGeneDict':varObj.clinVarGeneDict, + 'clinvarTotalNumVars':varObj.clinvarTotalNumVars, + 'clinvarNumP':varObj.clinvarNumP, + 'clinvarNumLP':varObj.clinvarNumLP, + 'clinvarNumLB':varObj.clinvarNumLB, + 'clinvarNumB':varObj.clinvarNumB, + 'clinvarTitle':varObj.clinvarTitle, + 'clinvarSignDesc':varObj.clinvarSignDesc, + 'clinvarCondition':varObj.clinvarCondition, + 'hgmdVarFound':varObj.hgmdVarFound, + 'hgmdGeneFound':varObj.hgmdGeneFound, + 'hgmdVarPhenIdList':varObj.hgmdVarPhenIdList, + 'hgmdVarHPOIdList':varObj.hgmdVarHPOIdList, + 'hgmdVarHPOStrList':varObj.hgmdVarHPOStrList, + 'varId_dash':varObj.varId_dash, + 'dgvDictList':varObj.dgvDictList, + 'dgvTypeList':varObj.dgvTypeList, + 'dgvSubtypeList':varObj.dgvSubtypeList, + 'dgvVarFound':varObj.dgvVarFound, + 'decipherDictList':varObj.decipherDictList, + 'decipherDeletionObsList':varObj.decipherDeletionObsList, + 'decipherStudyList':varObj.decipherStudyList, + 'decipherVarFound':varObj.decipherVarFound, + 'gnomadGeneZscore':varObj.gnomadGeneZscore, + 'gnomadGenePLI':varObj.gnomadGenePLI, + 'gnomadGeneOELof':varObj.gnomadGeneOELof, + 'gnomadGeneOELofUpper':varObj.gnomadGeneOELofUpper, + #symptom + 'SymptomMatched':varObj.SymptomMatched, + 'symptomScore':varObj.symptomScore, + 'symptomName':varObj.symptomName, + 'omimSymptomSimScore':varObj.omimSymptomSimScore, + 'omimSymMatchFlag':varObj.omimSymMatchFlag, + 'hgmdSymptomScore':varObj.hgmdSymptomScore, + 'hgmdSymptomSimScore':varObj.hgmdSymptomSimScore, + 'hgmdSymMatchFlag':varObj.hgmdSymMatchFlag, + 'clinVarSymMatchFlag':varObj.clinVarSymMatchFlag, + 'VARIANT_CLASS':varObj.VARIANT_CLASS, + 'Feature':varObj.Feature, + 'hom':varObj.hom, + 'hgmd_rs':varObj.hgmd_rs, + + 'hgmd_id':varObj.hgmd_id, # CL added + 'hgmd_symbol':varObj.hgmd_symbol, # CL added + 'hgmd_PHEN':varObj.hgmd_PHEN, # CL added + 'hgmd_CLASS':varObj.hgmd_CLASS, # CL added + + 'clin_dict':varObj.clin_dict, + 'clin_PLP':varObj.clin_PLP, + 'clin_PLP_perc':varObj.clin_PLP_perc, + 'spliceAI':varObj.spliceAI, + 'spliceAImax':varObj.spliceAImax + + } + varDictList.append(varDict) + #print('finsihed making dict') + #increment index + i=i+1 + + #return object + return varDictList + diff --git a/scripts/annotation/utils_for_marrvel_flatfile_module_2.py b/scripts/annotation/utils_for_marrvel_flatfile_module_2.py new file mode 100755 index 0000000..bd3179a --- /dev/null +++ b/scripts/annotation/utils_for_marrvel_flatfile_module_2.py @@ -0,0 +1,186 @@ +## UPDATE +# Changes made by Chaozhong noted as #CL + +from utils_1 import * +import numpy as np +import time +import re + +def getDecipherUsingMarrvelFlatFile(varObj, decipherDf): + ''' + Get Decipher annotation using marrvel flat file + Params: + varObj:variant object + decipherDf:dataframe of input flat file + Return: + Decipher list + ''' + #print('in DECIPHER call') + decipherDictList=[] + decipherDeletionObsList=[] + decipherStudyList=[] + decipherVarFound=0 + deletionObs='-' + #get the varaint object info from varObj + chromVal=int(varObj.chrom) + posVal=int(varObj.pos) + startVal=int(varObj.start) + stopVal=int(varObj.stop) + + # CL 03-14-2023: changed column names to be compatible with hg38 + #vals=decipherDf[ ( decipherDf['hg19Chr'] == chromVal ) & ( decipherDf['hg19Start']==startVal ) & (decipherDf['hg19Stop']==stopVal) ] + vals=decipherDf[ ( decipherDf['Chr'] == chromVal ) & ( decipherDf['Start']==startVal ) & (decipherDf['Stop']==stopVal) ] + numRows=len(vals.index) + + if numRows>0: + decipherVarFound=1 + deletionObs=vals.iloc[0]['deletion.obs'] + decipherDeletionObsList.append(deletionObs) + + #print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) + #print('\tdecipherVarFound:',decipherVarFound,'decipherDeletionObs:', deletionObs) + retList=[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] + return retList + +def getDGVUsingMarrvelFlatFile(varObj, dgvDf): + ''' + Get DGV annotation using marrvel flat file + Params: + varObj:variant object + dgvDf:dataframe of input flat file + Return: + DGV list + ''' + #print('in DGV call') + dgvDictList=[] + typeList=[] + subtypeList=[] + dgvVarFound=0 + dgvType='-' + dgvSubtype='-' + + chromVal=int(varObj.chrom) + posVal=int(varObj.pos) + startVal=int(varObj.start) + stopVal=int(varObj.stop) + + #CL 03-14-2023: changed column names to be compatible with hg38 + #vals=dgvDf[ ( dgvDf['hg19Chr'] == chromVal ) & ( dgvDf['hg19Start']<=startVal ) & (dgvDf['hg19Stop']>=stopVal) ] + vals=dgvDf[ ( dgvDf['Chr'] == chromVal ) & ( dgvDf['Start']<=startVal ) & (dgvDf['Stop']>=stopVal) ] + numRows=len(vals.index) + + if numRows>0: + dgvVarFound=1 + #print('\tnumrows:',numRows) + #print('\t type of vals:', type(vals)) + #print('\tvals:', vals) + dgvType=vals.iloc[0]['type'] + dgvSubtype=vals.iloc[0]['subType'] + + #print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) + #print('\tdgvVarFound:',dgvVarFound,'dgvType:', dgvType, 'dgvsubtype:', dgvSubtype) + typeList.append(dgvType) + subtypeList.append(dgvSubtype) + retList=[dgvDictList,typeList,subtypeList,dgvVarFound] + return retList + +def getGnomadGeneMetricFlatFile(varObj, gnomadMetricsGeneDf): + ''' + Get GnomadGeneMetric from flat file + Params: + varObj:variant object + gnomadMetricsGeneDf:dataframe of input flat file + Return: + gnomadMetricsGeneDf list + ''' + #print('in gnomadGeneMetricFlatFile call') + #check if gene in file + vals=gnomadMetricsGeneDf[gnomadMetricsGeneDf['gene'] == varObj.geneSymbol] + numRows=len(vals.index) + + if numRows>0:#pLI, oe_lof, oe_lof_upper,mis_z + gnomadGeneZscore=vals.iloc[0]['mis_z'] + gnomadGenePLI=vals.iloc[0]['pLI'] + gnomadGeneOELof=vals.iloc[0]['oe_lof'] + gnomadGeneOELofUpper=vals.iloc[0]['oe_lof_upper'] + else: + #get the values + gnomadGeneZscore='-' + gnomadGenePLI='-' + gnomadGeneOELof='-' + gnomadGeneOELofUpper='-' + + retList=[gnomadGeneZscore, gnomadGenePLI, gnomadGeneOELof, gnomadGeneOELofUpper] + #print('\t gene symbol:', varObj.geneSymbol, 'zScore:', gnomadGeneZscore, 'gnomadGenePLI:',gnomadGenePLI,'gnomadGeneOELof:', gnomadGeneOELof,'gnomadGeneOELofUpper:', gnomadGeneOELofUpper) + #print('\t gene symbol:', varObj.geneSymbol, 'zScore:', gnomadGeneZscore) + return retList + +def getConservationScore(varObj, diseaseInh): + ''' + Get conservation score + Params: + varObj:variant object + diseaseInh: the disease inheritance + Return: + return three conservation scores: conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof + ''' + #print('in conservation score call') + #create conservation scores + #set up thresholds + gnomadGFreqCut=0.01#genome g + gnomadEFreqCut=0.01#exome e + # GERP ranges + gerpPPCut=4 + #LRT cut + #phylo cut + + #gnomad + conservationScoreGnomad='-' + gnomadAFVal = getValFromStr(str(varObj.gnomadAF), 'min') + gnomadAFgVal = getValFromStr(str(varObj.gnomadAFg), 'min') + if gnomadAFVal!='-' and gnomadAFgVal!='-': + #gnomadAFVal=float(varObj.gnomadAF) + #gnomadAFgVal=float(varObj.gnomadAFg) + if gnomadAFVal<0.01 and gnomadAFgVal<0.01: + conservationScoreGnomad='High' + else: + conservationScoreGnomad='Low' + else: + conservationScoreGnomad='-' + + #DGV + conservationScoreDGV='-' + if 'deletion' in varObj.dgvSubtypeList or 'loss' in varObj.dgvSubtypeList: + conservationScoreDGV='Low' + else: + conservationScoreDGV='High' + + #gene O/E score + conservationScoreOELof='-' + if varObj.gnomadGeneOELofUpper!='-': + gnomadGeneOELofUpperVal=float(varObj.gnomadGeneOELofUpper) + if gnomadGeneOELofUpperVal<0.35: + conservationScoreOELof='High' + else: + conservationScoreOELof='Low' + + #return + retList=[conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] + return retList + +def getValFromStr(valStr: str, select: str = 'min'): + """ + Function to convert string to float, + and takes care of situation when multiple values exist + """ + select_method = {'min':min, 'max':max} + vals = valStr.split(',') + if '-' in vals: + return '-' + else: + vals = [float(i) for i in vals] + return select_method[select](vals) + + + + diff --git a/scripts/annotation/utils_for_symMatch.py b/scripts/annotation/utils_for_symMatch.py new file mode 100755 index 0000000..9cbc296 --- /dev/null +++ b/scripts/annotation/utils_for_symMatch.py @@ -0,0 +1,143 @@ +## UPDATE +# Changes made by Chaozhong noted as #CL + +from utils_1 import * + + +def omimSymMatch(varObj, omimHPOScoreDf, inFileType): + ''' + Find OMIM symptom match score + Param: + omimHPOScoreDf: this is read from user input OMIM symptom mathc file. Foe example:/houston_10t/dongxue/MARRVEL_AI/BG/HPO2Gene/out_May12021/UDN/HPOsimi_UDN630665_simi_0.tsv + inFileType:the type of input file. Will be used later. + Return: + Calculate the variant symptom score + ''' + #print('\nin omimSymMatch') + #print('type varObj:', type(varObj)) + #print('\tvar:', varObj.varId_dash) + simScore=0 + if 'phenotypes' in varObj.omimGeneDict.keys(): + #print('\t phenotypes key found') + for pheno in varObj.omimGeneDict['phenotypes']: + if 'phenotypeMimNumber' in pheno.keys(): + PminNum = pheno['phenotypeMimNumber'] + #print('\tPminNum:', PminNum) + #print('\ttmp1:', omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum]) + valDf=omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum] + numRows=len(valDf.index) + if numRows!=0: + simScore = omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum].iloc[0]['Similarity_Score'] + else: + simScore=0 + #print('\tsimScore:', simScore) + else: + simScore=0 + if simScore >= 0.2: #change the threshold to a user arg later + varObj.symptomScore[PminNum] = simScore + symptomList = omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum].iloc[0]['Disease_Name'].split(';') + symptomList = [i.strip().upper() for i in symptomList] #remove space and tranfer all words to capital + varObj.symptomName[PminNum] = symptomList + varObj.omimSymMatchFlag = 1 + else: + continue + #store the sim score we get + varObj.omimSymptomSimScore=simScore + #print('omimSymMatch results:') + #print('\tsymtomSimScore:', varObj.omimSymptomSimScore) + #print('\tsymptomScore:', varObj.symptomScore) + #print('\tsymptomName:', varObj.symptomName) + #print('\tomimSymMatchFlag:', varObj.omimSymMatchFlag) + +def hgmdSymMatch(varObj, hgmdHPOScoreDf): + ''' + Find HGMD symptom match score + Param: + omimHPOScoreDf: this is read from user input OMIM symptom mathc file. For example:/six_tera/chaozhong/module_1/out/UDN/HPOsimi_UDNUDN630665_simi_0.tsv + inFileType:the type of input file. Will be used later. + Return: + Calculate the variant symptom score + ''' + + + #print('\nin HGMDSymMatch') + #print('\tvar:', varObj.varId_dash) + hgmdSymptomSimScore='-' + """ old version + if varObj.hgmdVarFound: + var_tmp = varObj.varId_dash.split('-') + var_tmp = 'chr%s:%s %s>%s'%(var_tmp[0],var_tmp[1],var_tmp[2],var_tmp[3]) + print('\tvar_tmp:', var_tmp) + if var_tmp in hgmdHPOScoreDf['hgvs'].tolist(): + varDf = hgmdHPOScoreDf[hgmdHPOScoreDf['hgvs'] == var_tmp] + varScore = max(varDf['Similarity_Score'].tolist()) + varObj.hgmdSymptomScore = varScore + if varScore >= 0.2: + varObj.hgmdSymMatchFlag = 1 + hgmdSymptomSimScore=varScore + else: + pass + elif varObj.hgmdGeneFound: + if varObj.geneSymbol in hgmdHPOScoreDf['Gene'].tolist(): + geneDf = hgmdHPOScoreDf[hgmdHPOScoreDf['Gene'] == varObj.geneSymbol] + geneScore = max(geneDf['Similarity_Score'].tolist()) + if geneScore >= 0.2: + varObj.hgmdSymMatchFlag = 1 + hgmdSymptomSimScore=geneScore + #store + varObj.hgmdSymptomSimScore=hgmdSymptomSimScore + print('hgmdSymMatch results:') + print('\thgmdSymMatchFlag:', varObj.hgmdSymMatchFlag) + print('\thgmdSymptomSimScore:', varObj.hgmdSymptomSimScore) + """ + if np.any(hgmdHPOScoreDf['acc_num'].isin([varObj.hgmd_id])): + varDf = hgmdHPOScoreDf[hgmdHPOScoreDf['acc_num'] == varObj.hgmd_id] + varScore = max(varDf['Similarity_Score'].tolist()) + varObj.hgmdSymptomScore = varScore + if varScore >= 0.2: + varObj.hgmdSymMatchFlag = 1 + hgmdSymptomSimScore=varScore + + elif varObj.hgmdGeneFound: + geneDf = hgmdHPOScoreDf[hgmdHPOScoreDf['gene_sym'] == varObj.geneSymbol] + geneScore = max(geneDf['Similarity_Score'].tolist()) + if geneScore >= 0.2: + varObj.hgmdSymMatchFlag = 1 + hgmdSymptomSimScore=geneScore + + varObj.hgmdSymptomSimScore=hgmdSymptomSimScore + #print('hgmdSymMatch results:') + #print('\thgmdSymMatchFlag:', varObj.hgmdSymMatchFlag) + #print('\thgmdSymptomSimScore:', varObj.hgmdSymptomSimScore) + + + + +def clinVarSymMatch(varObj, inFileType): + ''' + Find clinvar symptom match score + Param: + Return: + Calculate the variant symptom score + ''' + #print('\nin clinvarSymMatch') + if varObj.clinVarVarFound: + #pheno = varObj.clinVarVarDict['condition'].strip().upper() + #print('clinvar condition:', varObj.clinvarCondition) + if type(varObj.clinvarCondition) is str: + pheno=varObj.clinvarCondition.strip().upper() + for PminNum in varObj.symptomName.keys(): + if pheno in varObj.symptomName[PminNum] or '#%s %s'%(PminNum,pheno) in varObj.symptomName[PminNum]: + varObj.clinVarSymMatchFlag = 1 + else: + varObj.clinVarSymMatchFlag = 0 + + #Need more checking on this one per gene condition + if varObj.clinVarGeneFound: + for var in varObj.clinVarGeneDict: + pheno = var['condition'].strip().upper() + for PminNum in varObj.symptomName.keys(): + if pheno in varObj.symptomName[PminNum] or '#%s %s'%(PminNum,pheno) in varObj.symptomName[PminNum]: + varObj.clinVarGeneSymMatchFlag = 1 + + #print('\tvarObj.clinVarVarSymMatchFlag:', varObj.clinVarSymMatchFlag) diff --git a/scripts/extraModel/__init__.py b/scripts/extraModel/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/scripts/extraModel/confidence.py b/scripts/extraModel/confidence.py new file mode 100755 index 0000000..08505d6 --- /dev/null +++ b/scripts/extraModel/confidence.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python +"""Script to estimate the confidence of whether a case is solved or not and assign +a confidence score to each prediction. +""" +__author__ = "Linhua Wang" +__email__ = "linhuaw@bcm.edu" +import pandas as pd +import os +import numpy as np +import scipy + +def get_causal_distribution(pred_fd, label_col='is_strong'): + # pred_fd: folder for prediction files only + fns = os.listdir(pred_fd) + # get top20 predictions + top20_preds = [] + for fn in fns: + top20_preds.append(pd.read_csv(f'{pred_fd}/{fn}', index_col=0).iloc[:20,:]) + top20_preds = pd.concat(top20_preds) + # get scores of causal variants + top20_causals = np.ravel(top20_preds.loc[top20_preds[label_col]==1, 'predict'].to_numpy()) + return top20_causals + +def assign_confidence_score(causal_distr, pred_df): + # pred_fd: folder for prediction files only + # pred_df: prediction data frame of the sample of interest + + prediction_scores = pred_df.predict.to_numpy() + + # assign confidence score using percentile + conf_scores = [scipy.stats.percentileofscore(causal_distr, i) for i in prediction_scores] + # assign confidence category by discretizing + conf_categories = [] + for conf_score in conf_scores: + if conf_score < 25: + conf_categories.append("Unsolved") + elif conf_score < 50: + conf_categories.append("Solved (Low)") + elif conf_score < 75: + conf_categories.append("Solved (Medium)") + else: + conf_categories.append("Solved (High)") + pred_df['confidence'] = conf_scores + pred_df['confidence level'] = conf_categories + pred_df.sort_values("confidence", ascending=False) + return pred_df + +# def determine_case_confidence(causal_distr, pred_df, label_col='is_strong'): +# pred_df = assign_confidence_score(causal_distr, pred_df, label_col) +# return pred_df['confidence level'].tolist()[0] \ No newline at end of file diff --git a/scripts/extraModel/generate_bivar_data.py b/scripts/extraModel/generate_bivar_data.py new file mode 100755 index 0000000..87268a6 --- /dev/null +++ b/scripts/extraModel/generate_bivar_data.py @@ -0,0 +1,179 @@ +import pandas as pd +import os +import numpy as np +from glob import glob +from tqdm import tqdm, trange +import logging +from multiprocessing import Pool +from os.path import exists +import shutil + +def process_sample(data_folder, sample_id, + default_pred, labeling=False, n_thread = 10): + + # recessive_folder = f'{data_folder}/recessive_matrix' + # if not os.path.exists(recessive_folder): + # os.mkdir(recessive_folder) + + recessive_folder = f'{data_folder}/recessive_matrix/' + if not os.path.exists(recessive_folder): + os.mkdir(recessive_folder) + + tmp_folder = f'{data_folder}/recessive_matrix/tmp/' + if not os.path.exists(tmp_folder): + os.mkdir(tmp_folder) + + ## read feature matrix for single var + feature_fn = f"/out/final_matrix/{sample_id}.csv" + #feature_df = [] + #for feature_fn in feature_fns: + if 'csv' in feature_fn: + feature_df = pd.read_csv(feature_fn, index_col=0, sep=",") + else: + feature_df = pd.read_csv(feature_fn, index_col=0, sep="\t") + #feature_df.append(df) + #feature_df = pd.concat(feature_df) + feature_df = feature_df.loc[~feature_df.index.duplicated(keep='first')] + + ## Group variants by Ens IDs + expanded_fn = f"/out/final_matrix_expanded/{sample_id}.expanded.csv.gz" + gene_dict = {} + #for expanded_fn in expanded_fns: + if 'csv' in expanded_fn: + df = pd.read_csv(expanded_fn, sep=",", index_col=0, compression='infer') + else: + df = pd.read_csv(expanded_fn, sep="\t", index_col=0, compression='infer') + + if df.shape[0] > 100000: + df = df.loc[df['IMPACT.from.Tier'] > 1] + + for varId, gene in zip(df.index.tolist(), df.geneEnsId.tolist()): + if gene.startswith('ENSG'): + if gene not in gene_dict: + gene_dict[gene] = {varId} + else: + gene_dict[gene].add(varId) + + ## create parameters and run bivar feature generation in parallel + params = [{'feature_df': feature_df, + 'default_pred': default_pred, + 'labeling': labeling, + 'out_folder': f'{tmp_folder}', + 'gene': gene, + 'varIDs': list(gene_dict[gene])} for gene in gene_dict + ] + print(f"Now starting to generate recessive feature matrix for {len(gene_dict)} genes, {feature_df.shape[0]} variants using {n_thread} threads.") + p = Pool(processes=n_thread) + + with tqdm(total=len(params)) as pbar: + for result in p.imap_unordered(process_gene, params): + pbar.update() + p.close() + p.join() + print("Recessive features for each gene finished, now putting together...") + + bivar_feature_mats = [] + fns = glob(tmp_folder + "/*.csv") + for fn in tqdm(fns, total=len(fns)): + df = pd.read_csv(fn, index_col=0) + bivar_feature_mats.append(df) + if bivar_feature_mats: + bivar_feature_mats = pd.concat(bivar_feature_mats) + bivar_feature_mats.to_csv(f"{recessive_folder}/{sample_id}.csv") + print("Recessive features saved, now removing tmp files...") + print("Recessive features generation finished...") + else: + print("No recessive variant pair found. End and remove temp files") + + shutil.rmtree(f'{tmp_folder}') + +def process_gene(param): + + feature_names = 'diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat' + feature_names = feature_names.split(",") + nFeatures = len(feature_names) + bivar_feature_mats = [] + + gene = param['gene'] + varIDs, labeling = param['varIDs'], param['labeling'] + + + feature_df, out_folder = param['feature_df'], param['out_folder'] + + seen_pairs = set() + + allVars = feature_df.index.tolist() + varIDs = [v for v in varIDs if v in allVars] + + if len(varIDs) == 0: + return + + if len(varIDs) > 6: + defaultPred = param['default_pred'].copy() + defaultPred = defaultPred.loc[varIDs,:].sort_values("predict", ascending=False) + varIDs = defaultPred.index.tolist()[:6] + + gene_feats = feature_df.loc[varIDs, feature_names].copy() + gene_feats = gene_feats.values + assert gene_feats.shape[0] == len(varIDs) + # print(gene_feats, varIDs) + + def generate_feature_vector(i, j): + fmat = np.zeros((2, nFeatures * 2 + 1)) + dist = np.absolute(int(varIDs[i].split("-")[1])-int(varIDs[j].split("-")[1])) + + fmat[0] = np.append(np.append(gene_feats[i], gene_feats[j]), [dist]) + fmat[1] = np.append(np.append(gene_feats[j], gene_feats[i]), [dist]) + + f1_features = [f'{f}_1' for f in feature_names] + f2_features = [f'{f}_2' for f in feature_names] + + return pd.DataFrame(data=fmat, columns=f1_features + f2_features + ['var_dist'], + index=[f'{varIDs[i]}_{varIDs[j]}', f'{varIDs[j]}_{varIDs[i]}']) + + + for i in range(len(varIDs)): + varii_feature_matrix = generate_feature_vector(i,i) + var1 = varIDs[i] + + if f'{var1}_{var1}' not in seen_pairs: + if feature_df.loc[var1, 'zyg'] == 2: + + if labeling: + if feature_df.loc[var1, 'is_strong'] == 1: + is_causal_wo_OMIM = 1 + else: + is_causal_wo_OMIM = 0 + varii_feature_matrix['is_causal'] = is_causal_wo_OMIM + + bivar_feature_mats.append(varii_feature_matrix) + seen_pairs.add(f'{var1}_{var1}') + + if i < len(varIDs) - 1: + for j in range(i+1, len(varIDs)): + var2 = varIDs[j] + if (f'{var1}_{var2}' not in seen_pairs) and (f'{var2}_{var1}' not in seen_pairs) and (var1 != var2): + varij_feature_matrix = generate_feature_vector(i, j) + + if labeling: + is_causal_wo_OMIM = 0 + + if (feature_df.loc[[var1, var2], 'is_strong'] == 1).all(): + if (feature_df.loc[[var1, var2], 'zyg'] == 1).all(): + is_causal_wo_OMIM = 1 + + varij_feature_matrix['is_causal'] = is_causal_wo_OMIM + + bivar_feature_mats.append(varij_feature_matrix) + + seen_pairs.add(f'{var1}_{var2}') + seen_pairs.add(f'{var2}_{var1}') + else: + pass + + if len(bivar_feature_mats) > 0: + bivar_feature_mats = pd.concat(bivar_feature_mats, axis=0) + bivar_feature_mats = bivar_feature_mats.drop_duplicates() + bivar_feature_mats.to_csv(f"{out_folder}/{gene}.csv") + + return \ No newline at end of file diff --git a/scripts/extraModel/integrate_output.py b/scripts/extraModel/integrate_output.py new file mode 100755 index 0000000..3c3c6cc --- /dev/null +++ b/scripts/extraModel/integrate_output.py @@ -0,0 +1,150 @@ +import pandas as pd +from glob import glob +import os + +def process_recessive_matrix(df): + df['varPair'] = df.index.copy() + df['var1'] = df.varPair.apply(lambda x: x.split("_")[0]) + df['var2'] = df.varPair.apply(lambda x: x.split("_")[1]) + var_grps = df.groupby("var1") + + result = [] + for _, var_grp in var_grps: + var_grp = var_grp.sort_values("predict", ascending=False) + result.append(var_grp.iloc[[0],:]) + result = pd.concat(result) + result.set_index("var1", inplace=True) + return result + +def integrate_output(prj_folder, data_folder, sample_id): + ## Group variants by Ens IDs + expanded_fn = f"/out/final_matrix_expanded/{sample_id}.expanded.csv.gz" + #expanded_df = [] + # for expanded_fn in expanded_fns: + if 'csv' in expanded_fn: + expanded_df = pd.read_csv(expanded_fn, sep=",", index_col=0, compression='infer') + else: + expanded_df = pd.read_csv(expanded_fn, sep="\t", index_col=0, compression='infer') + #expanded_df.append(df) + #expanded_df = pd.concat(expanded_df) + # expanded_df = expanded_df.loc[~expanded_df.index.duplicated(keep='first')] + ## read predictions for default model + default_fn = f"{data_folder}/{sample_id}_default_predictions.csv" + default_pred = pd.read_csv(default_fn, index_col=0) + default_raw_pred_dict = dict(zip(default_pred.index.tolist(), default_pred.predict.tolist())) + default_conf_dict = dict(zip(default_pred.index.tolist(), default_pred['confidence'].tolist())) + default_conf_lvl_dict = dict(zip(default_pred.index.tolist(), default_pred['confidence level'].tolist())) + default_rank_dict = dict(zip(default_pred.index.tolist(), default_pred.ranking.tolist())) + + expanded_df['predict (default)'] = expanded_df.index.map(default_raw_pred_dict) + expanded_df['confidence (default)'] = expanded_df.index.map(default_conf_dict) + expanded_df['confidence level (default)'] = expanded_df.index.map(default_conf_lvl_dict) + expanded_df['ranking (default)'] = expanded_df.index.map(default_rank_dict) + + ## read predictions for novel disease model + nd_fn = f"{data_folder}/{sample_id}_nd_predictions.csv" + nd_pred = pd.read_csv(nd_fn, index_col=0) + nd_raw_pred_dict = dict(zip(nd_pred.index.tolist(), nd_pred.predict.tolist())) + nd_conf_dict = dict(zip(nd_pred.index.tolist(), nd_pred['confidence'].tolist())) + nd_rank_dict = dict(zip(nd_pred.index.tolist(), nd_pred.ranking.tolist())) + nd_conf_lvl_dict = dict(zip(nd_pred.index.tolist(), nd_pred['confidence level'].tolist())) + + expanded_df['predict (nd)'] = expanded_df.index.map(nd_raw_pred_dict) + expanded_df['confidence (nd)'] = expanded_df.index.map(nd_conf_dict) + expanded_df['ranking (nd)'] = expanded_df.index.map(nd_rank_dict) + expanded_df['confidence level (nd)'] = expanded_df.index.map(nd_conf_lvl_dict) + + ## recessive predictions for default model + recessive_fn = f"{data_folder}/{sample_id}_recessive_predictions.csv" + if os.path.exists(recessive_fn): + recessive_pred = pd.read_csv(recessive_fn, index_col=0) + recessive_pred = process_recessive_matrix(recessive_pred) + + recessive_pred_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred.predict.tolist())) + recessive_conf_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred['confidence'].tolist())) + recessive_rank_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred.ranking.tolist())) + recessive_other_var = dict(zip(recessive_pred.index.tolist(), recessive_pred.var2.tolist())) + recessive_conf_lvl_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred['confidence level'].tolist())) + + rec_preds, rec_confs, rec_ranks, rec_var2s = [], [], [], [] + rec_conf_lvls = [] + for var in expanded_df.index.tolist(): + if var in recessive_pred_dict: + rec_preds.append(recessive_pred_dict[var]) + rec_confs.append(recessive_conf_dict[var]) + rec_ranks.append(recessive_rank_dict[var]) + rec_var2s.append(recessive_other_var[var]) + rec_conf_lvls.append(recessive_conf_lvl_dict[var]) + else: + rec_preds.append(-1) + rec_confs.append(-1) + rec_ranks.append(99999) + rec_var2s.append('NA') + rec_conf_lvls.append('Unsolved') + + expanded_df['predict (recessive)'] = rec_preds + expanded_df['confidence (recessive)'] = rec_confs + expanded_df['ranking (recessive)'] = rec_ranks + expanded_df['recessive var2'] = rec_var2s + expanded_df['confidence level (recessive)'] = rec_conf_lvls + else: + expanded_df['predict (recessive)'] = -1 + expanded_df['confidence (recessive)'] = -1 + expanded_df['ranking (recessive)'] = 99999 + expanded_df['recessive var2'] = 'NA' + expanded_df['confidence level (recessive)'] = 'Unsolved' + + ## novel disease recessive + nd_recessive_fn = f"{data_folder}/{sample_id}_nd_recessive_predictions.csv" + if os.path.exists(nd_recessive_fn): + nd_recessive_pred = pd.read_csv(nd_recessive_fn, index_col=0) + nd_recessive_pred = process_recessive_matrix(nd_recessive_pred) + + nd_recessive_pred_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.predict.tolist())) + nd_recessive_conf_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred['confidence'].tolist())) + nd_recessive_rank_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.ranking.tolist())) + nd_recessive_conf_lvl_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred['confidence level'].tolist())) + nd_recessive_other_var = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.var2.tolist())) + + nd_rec_preds, nd_rec_confs, nd_rec_ranks, nd_rec_var2s = [], [], [], [] + nd_rec_lvls = [] + for var in expanded_df.index.tolist(): + if var in nd_recessive_pred_dict: + nd_rec_preds.append(nd_recessive_pred_dict[var]) + nd_rec_confs.append(nd_recessive_conf_dict[var]) + nd_rec_ranks.append(nd_recessive_rank_dict[var]) + nd_rec_var2s.append(nd_recessive_other_var[var]) + nd_rec_lvls.append(nd_recessive_conf_lvl_dict[var]) + else: + nd_rec_preds.append(-1) + nd_rec_confs.append(-1) + nd_rec_ranks.append(99999) + nd_rec_var2s.append('NA') + nd_rec_lvls.append('Unsolved') + + expanded_df['predict (nd recessive)'] = nd_rec_preds + expanded_df['confidence (nd recessive)'] = nd_rec_confs + expanded_df['confidence level (nd recessive)'] = nd_rec_lvls + expanded_df['ranking (nd recessive)'] = nd_rec_ranks + expanded_df['nd recessive var2'] = nd_rec_var2s + else: + expanded_df['predict (nd recessive)'] = -1 + expanded_df['confidence (nd recessive)'] = -1 + expanded_df['confidence level (nd recessive)'] = 99999 + expanded_df['ranking (nd recessive)'] = 'NA' + expanded_df['nd recessive var2'] = 'Unsolved' + + return expanded_df + +if __name__ == "__main__": + from tqdm import tqdm + import os + prj_folder = 'projects/Texome/' + data_folder = 'data/Texome/' + sample_ids = os.listdir(data_folder) + for sample_id in tqdm(sample_ids): + integrated_df = integrate_output(prj_folder, data_folder, sample_id) + if not os.path.exists(f"{prj_folder}/AIM_integrated"): + os.mkdir(f"{prj_folder}/AIM_integrated") + integrated_df.to_csv(f'{prj_folder}/AIM_integrated/{sample_id}_integrated.csv') + diff --git a/scripts/extraModel/main.py b/scripts/extraModel/main.py new file mode 100755 index 0000000..258aa73 --- /dev/null +++ b/scripts/extraModel/main.py @@ -0,0 +1,120 @@ +import argparse +import os +import pandas as pd +from confidence import * +import joblib +from time import time +import generate_bivar_data +from datetime import datetime +from multiprocessing import Pool +from tqdm import tqdm +from scipy.stats import rankdata +from integrate_output import * + +parser = argparse.ArgumentParser() + +parser.add_argument('-id', metavar='I', type=str, + help = 'sample ID') + +parser.add_argument('-n_cpu', type=int, default=10, + help = 'folders containing all extended final matrices') + +args = parser.parse_args() + +#st_time = time() +#prj_name = args.project +sample_id = args.id +n_cpu = args.n_cpu + +out_folder = '/out/conf_4Model' + +if not os.path.exists(out_folder): + os.mkdir(out_folder) + +print(f"### Results will be saved at: {out_folder}.") +print(f"### Process started at: {datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}.") + +model_names = ['default', 'recessive', 'nd', 'nd_recessive'] +model_dict = {} + +for mn in model_names: + model = joblib.load(f"/run/data_dependencies/model_inputs/{mn}/final_model.job") + ref_panel = joblib.load(f"/run/data_dependencies/model_inputs/{mn}/reference_panel.job") + features = open(f"/run/data_dependencies/model_inputs/{mn}/features.csv", 'r').readline().split(",") + model_dict[mn] = {'model': model, 'ref': ref_panel, 'features': features} + #if not os.path.exists(f"./{out_folder}/{mn}"): + # os.mkdir(f"./{out_folder}/{mn}") + +print(f"### All models, features, reference panels loaded.") + +print(f"### Start generating recessive matrix and running predictions.") + +def assign_ranking(df): + pred_df = df.copy() + pred_df = pred_df.sort_values("predict", ascending=False) + min_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='min').astype(int) + max_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='max').astype(int) + pred_df['min_ranking'] = min_rankings + pred_df['max_ranking'] = max_rankings + pred_df['ranking'] = max_rankings + return pred_df + + +def AIM(data_folder, sample_id, n_thread): + feature_fn = f'/out/final_matrix/{sample_id}.csv' + + if not os.path.exists(feature_fn): + print(f"{feature_fn} does not exist.") + return + + df = pd.read_csv(feature_fn, index_col=0) + + for mn in ['default', 'nd']: + df_pred = df.copy() + if 'predict' in df_pred.columns: + df_pred = df_pred.drop(columns=['predict']) + predict = model_dict[mn]['model'].predict_proba(df_pred.loc[:, model_dict[mn]['features']])[:, 1] + df_pred.insert(loc=df_pred.shape[1]-1,column='predict',value=predict) + df_pred = assign_confidence_score(model_dict[mn]['ref'], df_pred) + df_pred = df_pred.sort_values('confidence', ascending=False) + df_pred = assign_ranking(df_pred) + df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") + + print(f"### Start processing recessive data and make predictions.") + + + default_pred = pd.read_csv(f"{out_folder}/{sample_id}_default_predictions.csv", index_col=0) + + generate_bivar_data.process_sample( data_folder = out_folder, + sample_id = sample_id, + default_pred = default_pred, + labeling=False, n_thread = n_thread) + + recessive_feature_file = f"{out_folder}/recessive_matrix/{sample_id}.csv" + if os.path.exists(recessive_feature_file): + # AIM found recessive variant pairs and generated the matrix + df = pd.read_csv(recessive_feature_file, index_col=0) + for mn in ['recessive', 'nd_recessive']: + df_pred = df.copy() + if 'predict' in df_pred.columns: + df_pred = df_pred.drop(columns=['predict']) + predict = model_dict[mn]['model'].predict_proba(df_pred.loc[:, model_dict[mn]['features']])[:, 1] + df_pred.insert(loc=df_pred.shape[1]-1,column='predict',value=predict) + df_pred = assign_confidence_score(model_dict[mn]['ref'], df_pred) + df_pred = df_pred.sort_values('confidence', ascending=False) + df_pred = assign_ranking(df_pred) + df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") + else: + # AIM found no recessive variant pair + pass + + print(f"Integrating all information for sample {sample_id}...") + ######### Construction Paused here ######### + integrated_df = integrate_output(out_folder, data_folder, sample_id) + if not os.path.exists(f"{out_folder}/integrated"): + os.mkdir(f"{out_folder}/integrated") + integrated_df.to_csv(f'{out_folder}/integrated/{sample_id}_integrated.csv') + return + +#for sample_id in tqdm(sample_folders): +AIM(out_folder, sample_id, n_thread = n_cpu) diff --git a/scripts/fillna_tier.py b/scripts/fillna_tier.py new file mode 100755 index 0000000..d0e2148 --- /dev/null +++ b/scripts/fillna_tier.py @@ -0,0 +1,682 @@ +from random import sample +import numpy as np +import pandas as pd + +__author__ = "Chaozhong Liu" +__email__ = "chaozhol@bcm.edu" + +###Feature engineering and incorporation of tier module outputs. +###fills NA w/ 0 + +def getValFromStr(valStr: str, select: str = 'min'): + select_method = {'min':min, 'max':max} + vals = valStr.split(',') + if '-' in vals or '.' in vals: + return np.NaN + else: + vals = [float(i) for i in vals] + return select_method[select](vals) + +def feature_engineering(score_file, tier_file): + variable_name = ['varId_dash', 'hgmdSymptomScore', + 'omimSymMatchFlag', 'hgmdSymMatchFlag', 'clinVarSymMatchFlag', + 'omimGeneFound', 'omimVarFound', 'hgmdGeneFound', 'hgmdVarFound', + 'clinVarVarFound', 'clinVarGeneFound', + 'clinvarTotalNumVars','clinvarNumP', 'clinvarNumLP', 'clinvarNumLB', 'clinvarNumB', + 'dgvVarFound','decipherVarFound', + 'curationScoreHGMD', 'curationScoreOMIM', 'curationScoreClinVar', + 'conservationScoreDGV', 'omimSymptomSimScore', 'hgmdSymptomSimScore', 'clin_code', + 'GERPpp_RS', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'phyloP100way_vertebrate', + 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELof', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_phred', + 'CADD_PHRED', 'DANN_score', 'REVEL_score', 'fathmm_MKL_coding_score', 'conservationScoreGnomad', + 'conservationScoreOELof', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'SIFT_score', 'zyg', + 'FATHMM_score', 'M_CAP_score', 'MutationAssessor_score', 'ESP6500_AA_AF', + 'ESP6500_EA_AF', 'hom', 'hgmd_rs', 'spliceAImax', 'clin_PLP_perc', 'Consequence', + 'nc_ClinVar_Exp', 'c_ClinVar_Exp', 'c_HGMD_Exp', 'nc_HGMD_Exp', + 'nc_isPLP', 'nc_isBLB', 'c_isPLP', 'c_isBLB', + 'nc_CLNREVSTAT', 'c_CLNREVSTAT', + 'nc_RANKSCORE', 'c_RANKSCORE', 'CLASS', "phrank"] + + + + #sample_name = score_file.split('/')[-1].split('_')[1] + #patient = pd.read_csv(score_file,sep='\t') + feature_stat = pd.read_csv('/run/data_dependencies/annotate/feature_stats.csv', index_col=0) + + patient = score_file.copy() + + patient = patient[variable_name] + patient = patient.fillna('-') + indel_index = [len(i.split('-')[-1]) != 1 or len(i.split('-')[-2]) != 1 for i in patient['varId_dash'].to_list()] + + patient.loc[patient['phrank'] == '-','phrank'] = 0 + patient['phrank'] = patient['phrank'].astype('float64') + + patient.loc[patient['hgmdSymptomScore'] == '-','hgmdSymptomScore'] = 0 + patient['hgmdSymptomScore'] = patient['hgmdSymptomScore'].astype('float64') + + patient.loc[patient['omimSymMatchFlag'] == '-','omimSymMatchFlag'] = 0 + patient['omimSymMatchFlag'] = patient['omimSymMatchFlag'].astype('float64') + + patient.loc[patient['hgmdSymMatchFlag'] == '-','hgmdSymMatchFlag'] = 0 + patient['hgmdSymMatchFlag'] = patient['hgmdSymMatchFlag'].astype('float64') + + patient.loc[patient['clinVarSymMatchFlag'] == '-','clinVarSymMatchFlag'] = 0 + patient['clinVarSymMatchFlag'] = patient['clinVarSymMatchFlag'].astype('float64') + + + patient['clinvarNumLP'] = (patient['clinvarNumLP']+patient['clinvarNumP']) / patient['clinvarTotalNumVars'] + patient['clinvarNumLP'] = patient['clinvarNumLP'].fillna(0.0) + patient['clinvarNumP'] = patient['clinvarNumP'] / patient['clinvarTotalNumVars'] + patient['clinvarNumP'] = patient['clinvarNumP'].fillna(0.0) + patient['clinvarNumLB'] = (patient['clinvarNumLB']+patient['clinvarNumB']) / patient['clinvarTotalNumVars'] + patient['clinvarNumLB'] = patient['clinvarNumLB'].fillna(1.0) + patient['clinvarNumB'] = patient['clinvarNumB'] / patient['clinvarTotalNumVars'] + patient['clinvarNumB'] = patient['clinvarNumB'].fillna(1.0) + patient = patient.drop(columns=['clinvarTotalNumVars']) + variable_name.remove('clinvarTotalNumVars') + + + patient.loc[patient['curationScoreHGMD'] == 'Low','curationScoreHGMD'] = 1 + patient.loc[patient['curationScoreHGMD'] == 'Medium','curationScoreHGMD'] = 2 + patient.loc[patient['curationScoreHGMD'] == 'High','curationScoreHGMD'] = 3 + patient['curationScoreHGMD'] = patient['curationScoreHGMD'].astype('float64') + + patient.loc[patient['curationScoreOMIM'] == 'Low','curationScoreOMIM'] = 1 + patient.loc[patient['curationScoreOMIM'] == 'Medium','curationScoreOMIM'] = 2 + patient.loc[patient['curationScoreOMIM'] == 'High','curationScoreOMIM'] = 3 + patient['curationScoreOMIM'] = patient['curationScoreOMIM'].astype('float64') + + patient.loc[patient['curationScoreClinVar'] == 'Low','curationScoreClinVar'] = 1 + patient.loc[patient['curationScoreClinVar'] == 'Medium','curationScoreClinVar'] = 2 + patient.loc[patient['curationScoreClinVar'] == 'High','curationScoreClinVar'] = 3 + patient['curationScoreClinVar'] = patient['curationScoreClinVar'].astype('float64') + + patient.loc[patient['conservationScoreDGV'] == 'Low','conservationScoreDGV'] = 1 + patient.loc[patient['conservationScoreDGV'] == 'Medium','conservationScoreDGV'] = 2 + patient.loc[patient['conservationScoreDGV'] == 'High','conservationScoreDGV'] = 3 + patient['conservationScoreDGV'] = patient['conservationScoreDGV'].astype('float64') + + + patient.loc[patient['omimSymptomSimScore'] == '-','omimSymptomSimScore'] = 0.0 + patient['omimSymptomSimScore'] = patient['omimSymptomSimScore'].astype('float64') + + patient.loc[patient['hgmdSymptomSimScore'] == '-','hgmdSymptomSimScore'] = 0.0 + patient['hgmdSymptomSimScore'] = patient['hgmdSymptomSimScore'].astype('float64') + + + patient['isB/LB'] = 0 + patient['isP/LP'] = 0 + patient.loc[patient['clin_code'].str.contains('Benign'),'isB/LB'] = 1 + patient.loc[patient['clin_code'].str.contains('Likely_benign'),'isB/LB'] = 1 + patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isB/LB'] = 0 + + patient.loc[patient['clin_code'].str.contains('Likely_pathogenic'),'isP/LP'] = 1 + patient.loc[patient['clin_code'].str.contains('Pathogenic'),'isP/LP'] = 1 + patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isP/LP'] = 0 + + patient.loc[patient['clin_PLP_perc']!='-','isP/LP'] = patient.loc[patient['clin_PLP_perc']!='-','clin_PLP_perc'].astype('float64') + + variable_name.append('isB/LB') + variable_name.append('isP/LP') + variable_name.remove('clin_PLP_perc') + variable_name.remove('clin_code') + + + patient.loc[patient['GERPpp_RS'] == '-', 'GERPpp_RS'] = np.NaN + if np.all(pd.isna(patient['GERPpp_RS'])): + # Single variant query + patient.loc[(pd.isna(patient['GERPpp_RS'])) & np.array(indel_index), 'GERPpp_RS'] = feature_stat.loc['GERPpp_RS', 'mean'] + patient.loc[pd.isna(patient['GERPpp_RS']), 'GERPpp_RS'] = feature_stat.loc['GERPpp_RS', 'mean'] + patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') + else: + # Patient whole VCF + patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') + patient.loc[(pd.isna(patient['GERPpp_RS'])) & np.array(indel_index), 'GERPpp_RS'] = patient['GERPpp_RS'].describe()['mean'] + patient.loc[pd.isna(patient['GERPpp_RS']), 'GERPpp_RS'] = patient['GERPpp_RS'].describe()['mean'] + + + gnomadAFVal = patient['gnomadAF'].copy() + gnomadAFVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFVal ]) + gnomadAFVal = gnomadAFVal.astype(float) + patient['gnomadAF'] = gnomadAFVal + + gnomadAFgVal = patient['gnomadAFg'].copy() + gnomadAFgVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFgVal ]) + gnomadAFgVal = gnomadAFgVal.astype(float) + patient['gnomadAFg'] = gnomadAFgVal + patient.loc[patient['gnomadAFg'].isna(), 'gnomadAFg'] = patient.loc[patient['gnomadAFg'].isna(), 'gnomadAF'] + patient.loc[patient['gnomadAF'].isna(), 'gnomadAF'] = patient.loc[patient['gnomadAF'].isna(), 'gnomadAFg'] + patient['gnomadAF'] = patient['gnomadAF'].fillna(0.0) + patient['gnomadAFg'] = patient['gnomadAFg'].fillna(0.0) + + #patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '-', 'gnomadAF'] #0.0 # + #patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '.', 'gnomadAF'] # + #patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '-', 'gnomadAFg'] #0.0 # + #patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '.', 'gnomadAFg'] + #patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = 0.0 + #patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = 0.0 + #patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = 0.0 + #patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = 0.0 + #patient['gnomadAFg'] = patient['gnomadAFg'].astype('float64') + #patient['gnomadAF'] = patient['gnomadAF'].astype('float64') + #patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] + + + patient.loc[patient['LRT_score'] == '-', 'LRT_score'] = np.NaN + if np.all(pd.isna(patient['LRT_score'])): + # Single variant query + patient.loc[(pd.isna(patient['LRT_score'])) & np.array(indel_index), 'LRT_score'] = feature_stat.loc['LRT_score', 'mean'] + patient.loc[pd.isna(patient['LRT_score']), 'LRT_score'] = feature_stat.loc['LRT_score', 'mean'] + patient['LRT_score'] = patient['LRT_score'].astype('float64') + else: + # Patient whole VCF + patient['LRT_score'] = patient['LRT_score'].astype('float64') + patient.loc[(pd.isna(patient['LRT_score'])) & np.array(indel_index), 'LRT_score'] = patient['LRT_score'].describe()['mean'] + patient.loc[pd.isna(patient['LRT_score']), 'LRT_score'] = patient['LRT_score'].describe()['mean'] + + + patient.loc[patient['LRT_Omega'] == '-', 'LRT_Omega'] = np.NaN + if np.all(pd.isna(patient['LRT_Omega'])): + # Single variant query + patient.loc[(pd.isna(patient['LRT_Omega'])) & np.array(indel_index), 'LRT_Omega'] = feature_stat.loc['LRT_Omega', 'mean'] + patient.loc[pd.isna(patient['LRT_Omega']), 'LRT_Omega'] = feature_stat.loc['LRT_Omega', 'mean'] + patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') + else: + # Patient whole VCF + patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') + patient.loc[(pd.isna(patient['LRT_Omega'])) & np.array(indel_index), 'LRT_Omega'] = patient['LRT_Omega'].describe()['mean'] + patient.loc[pd.isna(patient['LRT_Omega']), 'LRT_Omega'] = patient['LRT_Omega'].describe()['mean'] + + + patient.loc[patient['phyloP100way_vertebrate'] == '-', 'phyloP100way_vertebrate'] = np.NaN + if np.all(pd.isna(patient['phyloP100way_vertebrate'])): + # Single variant query + patient.loc[(pd.isna(patient['phyloP100way_vertebrate'])) & np.array(indel_index), 'phyloP100way_vertebrate'] = feature_stat.loc['phyloP100way_vertebrate', 'mean'] + patient.loc[pd.isna(patient['phyloP100way_vertebrate']), 'phyloP100way_vertebrate'] = feature_stat.loc['phyloP100way_vertebrate', 'mean'] + patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') + else: + # Patient whole VCF + patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') + patient.loc[(pd.isna(patient['phyloP100way_vertebrate'])) & np.array(indel_index), 'phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].describe()['mean'] + patient.loc[pd.isna(patient['phyloP100way_vertebrate']), 'phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].describe()['mean'] + + + patient.loc[patient['gnomadGeneZscore'] == '-', 'gnomadGeneZscore'] = np.NaN + if np.all(pd.isna(patient['gnomadGeneZscore'])): + # Single variant query + patient.loc[pd.isna(patient['gnomadGeneZscore']), 'gnomadGeneZscore'] = feature_stat.loc['gnomadGeneZscore', 'mean'] + patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') + else: + # Patient whole VCF + patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') + patient.loc[pd.isna(patient['gnomadGeneZscore']), 'gnomadGeneZscore'] = patient['gnomadGeneZscore'].describe()['mean'] + + + patient.loc[patient['gnomadGenePLI'] == '-', 'gnomadGenePLI'] = np.NaN + if np.all(pd.isna(patient['gnomadGenePLI'])): + # Single variant query + patient.loc[pd.isna(patient['gnomadGenePLI']), 'gnomadGenePLI'] = feature_stat.loc['gnomadGenePLI', 'mean'] + patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') + else: + # Patient whole VCF + patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') + patient.loc[pd.isna(patient['gnomadGenePLI']), 'gnomadGenePLI'] = patient['gnomadGenePLI'].describe()['mean'] + + + patient.loc[patient['gnomadGeneOELof'] == '-', 'gnomadGeneOELof'] = np.NaN + if np.all(pd.isna(patient['gnomadGeneOELof'])): + # Single variant query + patient.loc[pd.isna(patient['gnomadGeneOELof']), 'gnomadGeneOELof'] = feature_stat.loc['gnomadGeneOELof', 'mean'] + patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') + else: + # Patient whole VCF + patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') + patient.loc[pd.isna(patient['gnomadGeneOELof']), 'gnomadGeneOELof'] = patient['gnomadGeneOELof'].describe()['mean'] + + patient.loc[patient['gnomadGeneOELofUpper'] == '-', 'gnomadGeneOELofUpper'] = np.NaN + if np.all(pd.isna(patient['gnomadGeneOELofUpper'])): + # Single variant query + patient.loc[pd.isna(patient['gnomadGeneOELofUpper']), 'gnomadGeneOELofUpper'] = feature_stat.loc['gnomadGeneOELofUpper', 'mean'] + patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') + else: + # Patient whole VCF + patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') + patient.loc[pd.isna(patient['gnomadGeneOELofUpper']), 'gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].describe()['mean'] + + + patient.loc[patient['IMPACT'] == '-','IMPACT'] = 0 + patient.loc[patient['IMPACT'] == 'MODIFIER','IMPACT'] = 1 + patient.loc[patient['IMPACT'] == 'LOW','IMPACT'] = 2 + patient.loc[patient['IMPACT'] == 'MODERATE','IMPACT'] = 3 + patient.loc[patient['IMPACT'] == 'HIGH','IMPACT'] = 4 + patient['IMPACT'] = patient['IMPACT'].astype('float64') + + + #"CADD_phred" + patient.loc[patient['CADD_phred'] == '-', 'CADD_phred'] = np.NaN + if np.all(pd.isna(patient['CADD_phred'])): + # Single variant query + patient.loc[(pd.isna(patient['CADD_phred'])) & np.array(indel_index), 'CADD_phred'] = feature_stat.loc['CADD_phred', 'mean'] + patient.loc[pd.isna(patient['CADD_phred']), 'CADD_phred'] = feature_stat.loc['CADD_phred', 'mean'] + patient['CADD_phred'] = patient['CADD_phred'].astype('float64') + else: + # Patient whole VCF + patient['CADD_phred'] = patient['CADD_phred'].astype('float64') + patient.loc[(pd.isna(patient['CADD_phred'])) & np.array(indel_index), 'CADD_phred'] = patient['CADD_phred'].describe()['mean'] + patient.loc[pd.isna(patient['CADD_phred']), 'CADD_phred'] = patient['CADD_phred'].describe()['mean'] + + + patient.loc[patient['CADD_PHRED'] == '-', 'CADD_PHRED'] = np.NaN + if np.all(pd.isna(patient['CADD_PHRED'])): + # Single variant query + patient.loc[(pd.isna(patient['CADD_PHRED'])) & np.array(indel_index), 'CADD_PHRED'] = feature_stat.loc['CADD_PHRED', 'mean'] + patient.loc[pd.isna(patient['CADD_PHRED']), 'CADD_PHRED'] = feature_stat.loc['CADD_PHRED', 'mean'] + patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') + else: + # Patient whole VCF + patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') + patient.loc[(pd.isna(patient['CADD_PHRED'])) & np.array(indel_index), 'CADD_PHRED'] = patient['CADD_PHRED'].describe()['mean'] + patient.loc[pd.isna(patient['CADD_PHRED']), 'CADD_PHRED'] = patient['CADD_PHRED'].describe()['mean'] + + + #DANN_score + patient.loc[patient['DANN_score'] == '-', 'DANN_score'] = np.NaN + if np.all(pd.isna(patient['DANN_score'])): + # Single variant query + patient.loc[(pd.isna(patient['DANN_score'])) & np.array(indel_index), 'DANN_score'] = feature_stat.loc['DANN_score', 'mean'] + patient.loc[pd.isna(patient['DANN_score']), 'DANN_score'] = feature_stat.loc['DANN_score', 'mean'] + patient['DANN_score'] = patient['DANN_score'].astype('float64') + else: + # Patient whole VCF + patient['DANN_score'] = patient['DANN_score'].astype('float64') + patient.loc[(pd.isna(patient['DANN_score'])) & np.array(indel_index), 'DANN_score'] = patient['DANN_score'].describe()['mean'] + patient.loc[pd.isna(patient['DANN_score']), 'DANN_score'] = patient['DANN_score'].describe()['mean'] + + + #REVEL_score + #patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') + patient.loc[patient['REVEL_score'] == '-', 'REVEL_score'] = np.NaN + for i in patient[~patient['REVEL_score'].isna()].index: + score_list = patient.loc[i, 'REVEL_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'REVEL_score'] = np.NaN + else: + patient.loc[i, 'REVEL_score'] = max(score_list) + + if np.all(pd.isna(patient['REVEL_score'])): + # Single variant query + patient.loc[(pd.isna(patient['REVEL_score'])) & np.array(indel_index), 'REVEL_score'] = feature_stat.loc['REVEL_score', 'mean'] + patient.loc[pd.isna(patient['REVEL_score']), 'REVEL_score'] = feature_stat.loc['REVEL_score', 'mean'] + patient['REVEL_score'] = patient['REVEL_score'].astype('float64') + else: + # Patient whole VCF + patient['REVEL_score'] = patient['REVEL_score'].astype('float64') + patient.loc[(patient['REVEL_score'].isna()) & np.array(indel_index), 'REVEL_score'] = patient['REVEL_score'].describe()['mean'] + patient.loc[pd.isna(patient['REVEL_score']), 'REVEL_score'] = patient['REVEL_score'].describe()['mean'] + + + + #fathmm_MKL_coding_score + patient.loc[patient['fathmm_MKL_coding_score'] == '-', 'fathmm_MKL_coding_score'] = np.NaN + if np.all(pd.isna(patient['fathmm_MKL_coding_score'])): + # Single variant query + patient.loc[(pd.isna(patient['fathmm_MKL_coding_score'])) & np.array(indel_index), 'fathmm_MKL_coding_score'] = feature_stat.loc['fathmm_MKL_coding_score', 'mean'] + patient.loc[pd.isna(patient['fathmm_MKL_coding_score']), 'fathmm_MKL_coding_score'] = feature_stat.loc['fathmm_MKL_coding_score', 'mean'] + patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') + else: + # Patient whole VCF + patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') + patient.loc[(pd.isna(patient['fathmm_MKL_coding_score'])) & np.array(indel_index), 'fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].describe()['mean'] + patient.loc[pd.isna(patient['fathmm_MKL_coding_score']), 'fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].describe()['mean'] + + + #conservationScoreGnomad + patient.loc[patient['conservationScoreGnomad'] == '-','conservationScoreGnomad'] = 1 + patient.loc[patient['conservationScoreGnomad'] == 'Low','conservationScoreGnomad'] = 1 + patient.loc[patient['conservationScoreGnomad'] == 'High','conservationScoreGnomad'] = 2 + patient['conservationScoreGnomad'] = patient['conservationScoreGnomad'].astype('float64') + + #conservationScoreOELof + patient.loc[patient['conservationScoreOELof'] == '-','conservationScoreOELof'] = 1 + patient.loc[patient['conservationScoreOELof'] == 'Low','conservationScoreOELof'] = 1 + patient.loc[patient['conservationScoreOELof'] == 'High','conservationScoreOELof'] = 2 + patient['conservationScoreOELof'] = patient['conservationScoreOELof'].astype('float64') + + + #Polyphen2_HDIV_score + patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') + patient.loc[patient['Polyphen2_HDIV_score'] == '-','Polyphen2_HDIV_score'] = np.NaN + for i in patient[~patient['Polyphen2_HDIV_score'].isna()].index: + score_list = patient.loc[i, 'Polyphen2_HDIV_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'Polyphen2_HDIV_score'] = np.NaN + else: + patient.loc[i, 'Polyphen2_HDIV_score'] = max(score_list) + + if np.all(pd.isna(patient['Polyphen2_HDIV_score'])): + # Single variant query + patient.loc[(pd.isna(patient['Polyphen2_HDIV_score'])) & np.array(indel_index), 'Polyphen2_HDIV_score'] = feature_stat.loc['Polyphen2_HDIV_score', '50%'] + patient.loc[pd.isna(patient['Polyphen2_HDIV_score']), 'Polyphen2_HDIV_score'] = feature_stat.loc['Polyphen2_HDIV_score', '50%'] + patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') + else: + # Patient whole VCF + patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') + patient.loc[(pd.isna(patient['Polyphen2_HDIV_score'])) & np.array(indel_index), 'Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].describe()['50%'] + patient.loc[pd.isna(patient['Polyphen2_HDIV_score']), 'Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].describe()['50%'] + + + #Polyphen2_HVAR_score + patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'] #.str.split(',') + patient.loc[patient['Polyphen2_HVAR_score'] == '-','Polyphen2_HVAR_score'] = np.NaN + for i in patient[~patient['Polyphen2_HVAR_score'].isna()].index: + score_list = patient.loc[i, 'Polyphen2_HVAR_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'Polyphen2_HVAR_score'] = np.NaN + else: + patient.loc[i, 'Polyphen2_HVAR_score'] = max(score_list) + + if np.all(pd.isna(patient['Polyphen2_HVAR_score'])): + # Single variant query + patient.loc[(pd.isna(patient['Polyphen2_HVAR_score'])) & np.array(indel_index), 'Polyphen2_HVAR_score'] = feature_stat.loc['Polyphen2_HVAR_score', '50%'] + patient.loc[pd.isna(patient['Polyphen2_HVAR_score']), 'Polyphen2_HVAR_score'] = feature_stat.loc['Polyphen2_HVAR_score', '50%'] + patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') + else: + # Patient whole VCF + patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') + patient.loc[(pd.isna(patient['Polyphen2_HVAR_score'])) & np.array(indel_index), 'Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].describe()['50%'] + patient.loc[pd.isna(patient['Polyphen2_HVAR_score']), 'Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].describe()['50%'] + + + #SIFT_score + patient['SIFT_score'] = patient['SIFT_score'] #.str.split(',') + patient.loc[patient['SIFT_score'] == '-','SIFT_score'] = np.NaN + for i in patient[~patient['SIFT_score'].isna()].index: + score_list = patient.loc[i, 'SIFT_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'SIFT_score'] = np.NaN + else: + patient.loc[i, 'SIFT_score'] = min(score_list) + + if np.all(pd.isna(patient['SIFT_score'])): + # Single variant query + patient.loc[(pd.isna(patient['SIFT_score'])) & np.array(indel_index), 'SIFT_score'] = feature_stat.loc['SIFT_score', '50%'] + patient.loc[pd.isna(patient['SIFT_score']), 'SIFT_score'] = feature_stat.loc['SIFT_score', '50%'] + patient['SIFT_score'] = patient['SIFT_score'].astype('float64') + else: + # Patient whole VCF + patient['SIFT_score'] = patient['SIFT_score'].astype('float64') + patient.loc[(pd.isna(patient['SIFT_score'])) & np.array(indel_index), 'SIFT_score'] = patient['SIFT_score'].describe()['50%'] + patient.loc[pd.isna(patient['SIFT_score']), 'SIFT_score'] = patient['SIFT_score'].describe()['50%'] + + + patient.loc[patient['zyg'] == 'HET','zyg'] = 1 + patient.loc[patient['zyg'] == 'HOM','zyg'] = 2 + patient.loc[patient['zyg'] == '-','zyg'] = 0 + + patient['zyg'] = patient['zyg'].astype('float64') + + + #patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN + #patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') + #patient.loc[(pd.isna(patient['GERPpp_NR'])) & np.array(indel_index), 'GERPpp_NR'] = patient.loc[patient['GERPpp_NR'] != -100, 'GERPpp_NR'].describe()['mean'] + #patient.loc[pd.isna(patient['GERPpp_NR']), 'GERPpp_NR'] = patient['GERPpp_NR'].describe()['min'] + + + #FATHMM_score + patient['FATHMM_score'] = patient['FATHMM_score'] #.str.split(',') + patient.loc[patient['FATHMM_score'] == '-','FATHMM_score'] = np.NaN + for i in patient[~patient['FATHMM_score'].isna()].index: + score_list = patient.loc[i, 'FATHMM_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'FATHMM_score'] = np.NaN + else: + patient.loc[i, 'FATHMM_score'] = min(score_list) + + if np.all(pd.isna(patient['FATHMM_score'])): + # Single variant query + patient.loc[(pd.isna(patient['FATHMM_score'])) & np.array(indel_index), 'FATHMM_score'] = feature_stat.loc['FATHMM_score', '50%'] + patient.loc[pd.isna(patient['FATHMM_score']), 'FATHMM_score'] = feature_stat.loc['FATHMM_score', '50%'] + patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') + else: + # Patient whole VCF + patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') + patient.loc[(pd.isna(patient['FATHMM_score'])) & np.array(indel_index), 'FATHMM_score'] = patient['FATHMM_score'].describe()['50%'] + patient.loc[pd.isna(patient['FATHMM_score']), 'FATHMM_score'] = patient['FATHMM_score'].describe()['50%'] + + + #M_CAP_score + patient.loc[patient['M_CAP_score'] == '-', 'M_CAP_score'] = np.NaN + if np.all(pd.isna(patient['M_CAP_score'])): + # Single variant query + patient.loc[(pd.isna(patient['M_CAP_score'])) & np.array(indel_index), 'M_CAP_score'] = feature_stat.loc['M_CAP_score', 'mean'] + patient.loc[pd.isna(patient['M_CAP_score']), 'M_CAP_score'] = feature_stat.loc['M_CAP_score', 'mean'] + patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') + else: + # Patient whole VCF + patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') + patient.loc[(pd.isna(patient['M_CAP_score'])) & np.array(indel_index), 'M_CAP_score'] = patient['M_CAP_score'].describe()['mean'] + patient.loc[pd.isna(patient['M_CAP_score']), 'M_CAP_score'] = patient['M_CAP_score'].describe()['mean'] + + + #MutationAssessor_score + patient['MutationAssessor_score'] = patient['MutationAssessor_score'] #.str.split(',') + patient.loc[patient['MutationAssessor_score'] == '-','MutationAssessor_score'] = np.NaN + for i in patient[~patient['MutationAssessor_score'].isna()].index: + score_list = patient.loc[i, 'MutationAssessor_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'MutationAssessor_score'] = np.NaN + else: + patient.loc[i, 'MutationAssessor_score'] = max(score_list) + + if np.all(pd.isna(patient['MutationAssessor_score'])): + # Single variant query + patient.loc[(pd.isna(patient['MutationAssessor_score'])) & np.array(indel_index), 'MutationAssessor_score'] = feature_stat.loc['MutationAssessor_score', '50%'] + patient.loc[pd.isna(patient['MutationAssessor_score']), 'MutationAssessor_score'] = feature_stat.loc['MutationAssessor_score', '50%'] + patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') + else: + # Patient whole VCF + patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') + patient.loc[(pd.isna(patient['MutationAssessor_score'])) & np.array(indel_index), 'MutationAssessor_score'] = patient['MutationAssessor_score'].describe()['50%'] + patient.loc[pd.isna(patient['MutationAssessor_score']), 'MutationAssessor_score'] = patient['MutationAssessor_score'].describe()['50%'] + + + #ESP6500_AA_AF + patient.loc[patient['ESP6500_AA_AF'] == '-', 'ESP6500_AA_AF'] = 0.0 + patient['ESP6500_AA_AF'] = patient['ESP6500_AA_AF'].astype('float64') + + + #ESP6500_AA_AF + patient.loc[patient['ESP6500_EA_AF'] == '-', 'ESP6500_EA_AF'] = 0.0 + patient['ESP6500_EA_AF'] = patient['ESP6500_EA_AF'].astype('float64') + + + #hom + patient.loc[patient['hom'] == '-','hom'] = np.NaN + for i in patient[~patient['hom'].isna()].index: + score_list = patient.loc[i, 'hom'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'hom'] = np.NaN + else: + patient.loc[i, 'hom'] = max(score_list) + patient['hom'] = patient['hom'].fillna(0) + patient['hom'] = patient['hom'].astype('float64') + + patient['hgmd_rs'] = patient['hgmd_rs'].apply(lambda x: x.split(",")[0] if type(x) == str else str(x)) + + patient.loc[patient['hgmd_rs'] == '-','hgmd_rs'] = 0 + + patient['hgmd_rs'] = patient['hgmd_rs'].astype('float64') + + + #spliceAImax + patient.loc[patient['spliceAImax'] == '-', 'spliceAImax'] = np.NaN + if np.all(pd.isna(patient['spliceAImax'])): + # Single variant query + patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = feature_stat.loc['spliceAImax', 'mean'] + patient['spliceAImax'] = patient['spliceAImax'].astype('float64') + else: + # Patient whole VCF + patient['spliceAImax'] = patient['spliceAImax'].astype('float64') + patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = patient.loc[~pd.isna(patient['spliceAImax']), 'spliceAImax'].describe()['mean'] + + + #Consequence + consequence = ['transcript_ablation', 'splice_acceptor_variant', 'splice_donor_variant', 'stop_gained', + 'frameshift_variant', 'stop_lost', 'start_lost', 'transcript_amplification', 'inframe_insertion', + 'inframe_deletion', 'missense_variant', 'protein_altering_variant', 'splice_region_variant', + 'splice_donor_5th_base_variant', 'splice_donor_region_variant'] + + for cons in consequence: + patient['cons_%s'%cons] = patient['Consequence'].str.contains(cons).astype('int') + variable_name.append('cons_%s'%cons) + + variable_name.remove('Consequence') + + + #nc_isPLP + patient.loc[patient['nc_isPLP']!=True, 'nc_isPLP'] = 0 + patient.loc[patient['nc_isPLP']==True, 'nc_isPLP'] = 1 + patient['nc_isPLP'] = patient['nc_isPLP'].astype('float64') + + #nc_isBLB + patient.loc[patient['nc_isBLB']!=True, 'nc_isBLB'] = 0 + patient.loc[patient['nc_isBLB']==True, 'nc_isBLB'] = 1 + patient['nc_isBLB'] = patient['nc_isBLB'].astype('float64') + + #c_isPLP + patient.loc[patient['c_isPLP']!=True, 'c_isPLP'] = 0 + patient.loc[patient['c_isPLP']==True, 'c_isPLP'] = 1 + patient['c_isPLP'] = patient['c_isPLP'].astype('float64') + + #c_isBLB + patient.loc[patient['c_isBLB']!=True, 'c_isBLB'] = 0 + patient.loc[patient['c_isBLB']==True, 'c_isBLB'] = 1 + patient['c_isBLB'] = patient['c_isBLB'].astype('float64') + + + #nc_ClinVar_Exp + patient.loc[patient['nc_ClinVar_Exp']!='nonCoding','nc_ClinVar_Exp'] = 0 + patient.loc[patient['nc_ClinVar_Exp']=='nonCoding','nc_ClinVar_Exp'] = 1 + patient['nc_ClinVar_Exp'] = patient['nc_ClinVar_Exp'].astype('float64') + + #nc_HGMD_Exp + patient.loc[patient['nc_HGMD_Exp']!='nonCoding','nc_HGMD_Exp'] = 0 + patient.loc[patient['nc_HGMD_Exp']=='nonCoding','nc_HGMD_Exp'] = 1 + patient['nc_HGMD_Exp'] = patient['nc_HGMD_Exp'].astype('float64') + + #c_ClinVar_Exp + c_clin_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange'] + + for exp in c_clin_exp: + + patient['c_ClinVar_Exp_%s'%exp] = patient['c_ClinVar_Exp'].str.contains(exp).astype('int') + variable_name.append('c_ClinVar_Exp_%s'%exp) + + variable_name.remove('c_ClinVar_Exp') + + #c_HGMD_Exp + c_hgmd_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange', 'Stop_Loss', 'Start_Loss'] + + for exp in c_hgmd_exp: + + patient['c_HGMD_Exp_%s'%exp] = patient['c_HGMD_Exp'].str.contains(exp).astype('int') + variable_name.append('c_HGMD_Exp_%s'%exp) + + variable_name.remove('c_HGMD_Exp') + + + #nc_CLNREVSTAT + CLN_STAT = ['-', 'no_assertion_provided', 'no_assertion_criteria_provided','no_assertion_for_the_individual_variant', + 'criteria_provided,_single_submitter', 'criteria_provided,_conflicting_interpretations', + 'criteria_provided,_multiple_submitters,_no_conflicts', 'reviewed_by_expert_panel', 'practice_guideline'] + + CLN_STAT_SCORE = [0,0,0,0,1,1,2,3,4] + + for i in range(len(CLN_STAT)): + patient.loc[patient['nc_CLNREVSTAT'] == CLN_STAT[i], 'nc_CLNREVSTAT'] = CLN_STAT_SCORE[i] + patient.loc[patient['c_CLNREVSTAT'] == CLN_STAT[i], 'c_CLNREVSTAT'] = CLN_STAT_SCORE[i] + + patient['nc_CLNREVSTAT'] = patient['nc_CLNREVSTAT'].astype('float64') + patient['c_CLNREVSTAT'] = patient['c_CLNREVSTAT'].astype('float64') + + + #nc_RANKSCORE + patient.loc[patient['nc_RANKSCORE'] == '-', 'nc_RANKSCORE'] = 0 + patient['nc_RANKSCORE'] = patient['nc_RANKSCORE'].astype('float64') + + #nc_RANKSCORE + patient.loc[patient['c_RANKSCORE'] == '-', 'c_RANKSCORE'] = 0 + patient['c_RANKSCORE'] = patient['c_RANKSCORE'].astype('float64') + + #CLASS + patient.loc[patient['CLASS'] == '-', 'CLASS'] = 0 + patient.loc[patient['CLASS'] == 'DM?', 'CLASS'] = 1 + patient.loc[patient['CLASS'] == 'DM', 'CLASS'] = 2 + patient['CLASS'] = patient['CLASS'].astype('float64') + + + + patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', + 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] + patient = patient.groupby(['varId_dash'], sort=False)[variable_name[1:]].max() + patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', + 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] + + + #adding in tier features + #tier = pd.read_csv(tier_file, sep='\t') + tier=tier_file.copy() + tier_vars = ['IMPACT.from.Tier','TierAD','TierAR','TierAR.adj', + 'No.Var.HM','No.Var.H','No.Var.M','No.Var.L', + 'AD.matched','AR.matched', + 'recessive', 'dominant'] #, + #'OMIM.Inheritance.both', 'OMIM.Inheritance.others'] + """ CL: deleted + # recessive and dominant is enough + tier['OMIM.Inheritance.recessive'] = 0 + tier['OMIM.Inheritance.dominant'] = 0 + tier['OMIM.Inheritance.both'] = 0 + tier['OMIM.Inheritance.others'] = 0 + tier.loc[tier['OMIM.Inheritance'] == 'recessive', 'OMIM.Inheritance.recessive'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'dominant', 'OMIM.Inheritance.dominant'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'both', 'OMIM.Inheritance.both'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'others', 'OMIM.Inheritance.others'] = 1 + """ + + tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] + tier = tier.groupby(['Uploaded_variation'], sort=False)[tier_vars].max() + tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] + + patient = pd.concat([patient, tier], axis=1) + patient.loc[patient['IMPACT.from.Tier'].isna(), 'IMPACT.from.Tier'] = 1 + patient.loc[patient['TierAD'].isna(), 'TierAD'] = 4 + patient.loc[patient['TierAR'].isna(), 'TierAR'] = 4 + patient.loc[patient['TierAR.adj'].isna(), 'TierAR.adj'] = 4 + + for var in tier_vars[4:8]: + patient.loc[patient[var].isna(), var] = 0 + + patient.loc[patient['AD.matched'].isna(), 'AD.matched'] = 0 + patient.loc[patient['AR.matched'].isna(), 'AR.matched'] = 0 + + patient.loc[patient['recessive'].isna(), 'recessive'] = 0 + patient.loc[patient['dominant'].isna(), 'dominant'] = 0 + #patient.loc[patient['OMIM.Inheritance.both'].isna(), 'OMIM.Inheritance.both'] = 0 ##CL: deleted + #patient.loc[patient['OMIM.Inheritance.others'].isna(), 'OMIM.Inheritance.others'] = 0 ##CL: deleted + + return patient + + + + + + + diff --git a/scripts/generate_new_matrix_2.py b/scripts/generate_new_matrix_2.py new file mode 100755 index 0000000..10b61e0 --- /dev/null +++ b/scripts/generate_new_matrix_2.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python +# coding: utf-8 + +import os +import pandas as pd +import numpy as np +import sys + +#from marrvel_score_recalc import * +from add_c_nc import add_c_nc +from fillna_tier import feature_engineering as fillna +from mod5_diffusion import diffusion, diffuseSample +#from labelling import add_label +from simple_repeat_anno import simple_repeat_anno + +### read score file ### +score=pd.read_csv('/out/rami-test/%s_scores.csv'%(sys.argv[1])) + +### add more clin and hgmd features ### +merged = add_c_nc(score, sys.argv[2]) + +### run diffusion module 5 ### +if os.path.getsize(f"/out/phrank/{sys.argv[1]}.txt") == 0: + phrank_empty = True +else: + phrank_empty = False + mod5 = diffuseSample(sys.argv[1], merged, "/out/phrank/") + +### get original coordinates ### +merged["varId"]=merged["varId"].apply(lambda x: x.split("_E")[0]) + +### read tier ### +tier_f=pd.read_csv("/out/tier-test-false/"+sys.argv[1]+"_Tier.v2.tsv", sep="\t") + +### add phrank calculated elsewhere for final combination ### +if phrank_empty: + merged['phrank'] = 0 +else: + phr=pd.read_csv("/out/phrank/"+sys.argv[1]+".txt", sep="\t", names=["ENSG", "phrank"]) + merged=merged.merge(phr,left_on="geneEnsId", right_on="ENSG", how="left") + +### save intermediate file for merging ### +merged.to_csv("/out/scores/"+sys.argv[1]+".txt.gz", compression="gzip", sep="\t") + +### run Chaozhong's feature engineering code ### +iff=fillna(merged,tier_f) + +### add diffusion module data ### +if phrank_empty: + iff.insert(loc=0, column='diffuse_Phrank_STRING', value=0) +else: + iff.insert(loc=0, column='diffuse_Phrank_STRING', value=mod5['diffuse_Phrank_STRING']) + +### remove chr 26 ### +iff= iff.loc[~iff.index.str.startswith('26')] +#iff.to_csv(sys.argv[1]+".out.txt", sep="\t") + +### run chaozhong's simple repeat annotation ### +iff=simple_repeat_anno(sys.argv[1], iff, f"/run/data_dependencies/merge_expand/{sys.argv[2]}/simpleRepeats.{sys.argv[2]}.bed") + +### delete intermediate files ### +os.remove("/out/"+sys.argv[1]+".bed") +os.remove("/out/"+sys.argv[1]+".sp.bed") + +### save final file for prediction ### +iff.to_csv("/out/"+sys.argv[1]+".matrix.txt", sep="\t") diff --git a/scripts/keepna_tier.py b/scripts/keepna_tier.py new file mode 100755 index 0000000..e67d65c --- /dev/null +++ b/scripts/keepna_tier.py @@ -0,0 +1,443 @@ +from random import sample +import numpy as np +import pandas as pd + +__author__ = "Chaozhong Liu" +__email__ = "chaozhol@bcm.edu" + +###feature engineering and incorporation of tier features +###keeps NA fields + +def feature_engineering(score_file, tier_file): + variable_name = ['varId_dash', 'hgmdSymptomScore', + 'omimSymMatchFlag', 'hgmdSymMatchFlag', 'clinVarSymMatchFlag', + 'omimGeneFound', 'omimVarFound', 'hgmdGeneFound', 'hgmdVarFound', + 'clinVarVarFound', 'clinVarGeneFound', + 'clinvarTotalNumVars','clinvarNumP', 'clinvarNumLP', 'clinvarNumLB', 'clinvarNumB', + 'dgvVarFound','decipherVarFound', + 'curationScoreHGMD', 'curationScoreOMIM', 'curationScoreClinVar', + 'conservationScoreDGV', 'omimSymptomSimScore', 'hgmdSymptomSimScore', 'clin_code', + 'GERPpp_RS', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'phyloP100way_vertebrate', + 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELof', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_phred', + 'CADD_PHRED', 'DANN_score', 'REVEL_score', 'fathmm_MKL_coding_score', 'conservationScoreGnomad', + 'conservationScoreOELof', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'SIFT_score', 'zyg', + 'FATHMM_score', 'M_CAP_score', 'MutationAssessor_score', 'ESP6500_AA_AF', + 'ESP6500_EA_AF', 'hom', 'hgmd_rs', 'spliceAImax', 'clin_PLP_perc', 'Consequence', + 'nc_ClinVar_Exp', 'c_ClinVar_Exp', 'c_HGMD_Exp', 'nc_HGMD_Exp', + 'nc_isPLP', 'nc_isBLB', 'c_isPLP', 'c_isBLB', + 'nc_CLNREVSTAT', 'c_CLNREVSTAT', + 'nc_RANKSCORE', 'c_RANKSCORE', 'CLASS', "phrank"] + + + + #sample_name = score_file.split('/')[-1].split('_')[1] + #patient = pd.read_csv(score_file,sep='\t') + patient = score_file + + patient = patient[variable_name] + patient = patient.fillna('-') + indel_index = [len(i.split('-')[-1]) != 1 or len(i.split('-')[-2]) != 1 for i in patient['varId_dash'].to_list()] + + patient.loc[patient['phrank'] == '-','phrank'] = np.NaN + patient['phrank'] = patient['phrank'].astype('float64') + + patient.loc[patient['hgmdSymptomScore'] == '-','hgmdSymptomScore'] = np.NaN + patient['hgmdSymptomScore'] = patient['hgmdSymptomScore'].astype('float64') + + patient.loc[patient['omimSymMatchFlag'] == '-','omimSymMatchFlag'] = np.NaN + patient['omimSymMatchFlag'] = patient['omimSymMatchFlag'].astype('float64') + + patient.loc[patient['hgmdSymMatchFlag'] == '-','hgmdSymMatchFlag'] = np.NaN + patient['hgmdSymMatchFlag'] = patient['hgmdSymMatchFlag'].astype('float64') + + patient.loc[patient['clinVarSymMatchFlag'] == '-','clinVarSymMatchFlag'] = np.NaN + patient['clinVarSymMatchFlag'] = patient['clinVarSymMatchFlag'].astype('float64') + + + patient['clinvarNumLP'] = (patient['clinvarNumLP']+patient['clinvarNumP']) / patient['clinvarTotalNumVars'] + patient['clinvarNumP'] = patient['clinvarNumP'] / patient['clinvarTotalNumVars'] + patient['clinvarNumLB'] = (patient['clinvarNumLB']+patient['clinvarNumB']) / patient['clinvarTotalNumVars'] + patient['clinvarNumB'] = patient['clinvarNumB'] / patient['clinvarTotalNumVars'] + patient = patient.drop(columns=['clinvarTotalNumVars']) + variable_name.remove('clinvarTotalNumVars') + + + patient.loc[patient['curationScoreHGMD'] == 'Low','curationScoreHGMD'] = 1 + patient.loc[patient['curationScoreHGMD'] == 'Medium','curationScoreHGMD'] = 2 + patient.loc[patient['curationScoreHGMD'] == 'High','curationScoreHGMD'] = 3 + patient['curationScoreHGMD'] = patient['curationScoreHGMD'].astype('float64') + + patient.loc[patient['curationScoreOMIM'] == 'Low','curationScoreOMIM'] = 1 + patient.loc[patient['curationScoreOMIM'] == 'Medium','curationScoreOMIM'] = 2 + patient.loc[patient['curationScoreOMIM'] == 'High','curationScoreOMIM'] = 3 + patient['curationScoreOMIM'] = patient['curationScoreOMIM'].astype('float64') + + patient.loc[patient['curationScoreClinVar'] == 'Low','curationScoreClinVar'] = 1 + patient.loc[patient['curationScoreClinVar'] == 'Medium','curationScoreClinVar'] = 2 + patient.loc[patient['curationScoreClinVar'] == 'High','curationScoreClinVar'] = 3 + patient['curationScoreClinVar'] = patient['curationScoreClinVar'].astype('float64') + + patient.loc[patient['conservationScoreDGV'] == 'Low','conservationScoreDGV'] = 1 + patient.loc[patient['conservationScoreDGV'] == 'Medium','conservationScoreDGV'] = 2 + patient.loc[patient['conservationScoreDGV'] == 'High','conservationScoreDGV'] = 3 + patient['conservationScoreDGV'] = patient['conservationScoreDGV'].astype('float64') + + + patient.loc[patient['omimSymptomSimScore'] == '-','omimSymptomSimScore'] = np.NaN + patient['omimSymptomSimScore'] = patient['omimSymptomSimScore'].astype('float64') + + patient.loc[patient['hgmdSymptomSimScore'] == '-','hgmdSymptomSimScore'] = np.NaN + patient['hgmdSymptomSimScore'] = patient['hgmdSymptomSimScore'].astype('float64') + + + patient['isB/LB'] = 0 + patient['isP/LP'] = 0 + patient.loc[patient['clin_code'].str.contains('Benign'),'isB/LB'] = 1 + patient.loc[patient['clin_code'].str.contains('Likely_benign'),'isB/LB'] = 1 + patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isB/LB'] = 0 + + patient.loc[patient['clin_code'].str.contains('Likely_pathogenic'),'isP/LP'] = 1 + patient.loc[patient['clin_code'].str.contains('Pathogenic'),'isP/LP'] = 1 + patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isP/LP'] = 0 + + patient.loc[patient['clin_PLP_perc']!='-','isP/LP'] = patient.loc[patient['clin_PLP_perc']!='-','clin_PLP_perc'].astype('float64') + + variable_name.append('isB/LB') + variable_name.append('isP/LP') + variable_name.remove('clin_PLP_perc') + variable_name.remove('clin_code') + + + patient.loc[patient['GERPpp_RS'] == '-', 'GERPpp_RS'] = np.NaN + patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') + + + patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '-', 'gnomadAF'] #0.0 # + patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '.', 'gnomadAF'] # + patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '-', 'gnomadAFg'] #0.0 # + patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '.', 'gnomadAFg'] + patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = np.NaN + patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = np.NaN + patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = np.NaN + patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = np.NaN + patient['gnomadAFg'] = patient['gnomadAFg'].astype('float64') + patient['gnomadAF'] = patient['gnomadAF'].astype('float64') + #patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] + + + patient.loc[patient['LRT_score'] == '-', 'LRT_score'] = np.NaN + patient['LRT_score'] = patient['LRT_score'].astype('float64') + + + patient.loc[patient['LRT_Omega'] == '-', 'LRT_Omega'] = np.NaN + patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') + + + patient.loc[patient['phyloP100way_vertebrate'] == '-', 'phyloP100way_vertebrate'] = np.NaN + patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') + + + patient.loc[patient['gnomadGeneZscore'] == '-', 'gnomadGeneZscore'] = np.NaN + patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') + + patient.loc[patient['gnomadGenePLI'] == '-', 'gnomadGenePLI'] = np.NaN + patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') + + patient.loc[patient['gnomadGeneOELof'] == '-', 'gnomadGeneOELof'] = np.NaN + patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') + + patient.loc[patient['gnomadGeneOELofUpper'] == '-', 'gnomadGeneOELofUpper'] = np.NaN + patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') + + + patient.loc[patient['IMPACT'] == '-','IMPACT'] = np.NaN + patient.loc[patient['IMPACT'] == 'MODIFIER','IMPACT'] = 1 + patient.loc[patient['IMPACT'] == 'LOW','IMPACT'] = 2 + patient.loc[patient['IMPACT'] == 'MODERATE','IMPACT'] = 3 + patient.loc[patient['IMPACT'] == 'HIGH','IMPACT'] = 4 + patient['IMPACT'] = patient['IMPACT'].astype('float64') + + + #"CADD_phred" + patient.loc[patient['CADD_phred'] == '-', 'CADD_phred'] = np.NaN + patient['CADD_phred'] = patient['CADD_phred'].astype('float64') + + patient.loc[patient['CADD_PHRED'] == '-', 'CADD_PHRED'] = np.NaN + patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') + + + #DANN_score + patient.loc[patient['DANN_score'] == '-', 'DANN_score'] = np.NaN + patient['DANN_score'] = patient['DANN_score'].astype('float64') + + + #REVEL_score + patient.loc[patient['REVEL_score'] == '-', 'REVEL_score'] = np.NaN + patient['REVEL_score'] = patient['REVEL_score'].astype('float64') + + + #fathmm_MKL_coding_score + patient.loc[patient['fathmm_MKL_coding_score'] == '-', 'fathmm_MKL_coding_score'] = np.NaN + patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') + + + #conservationScoreGnomad + patient.loc[patient['conservationScoreGnomad'] == '-','conservationScoreGnomad'] = np.NaN + patient.loc[patient['conservationScoreGnomad'] == 'Low','conservationScoreGnomad'] = 1 + patient.loc[patient['conservationScoreGnomad'] == 'High','conservationScoreGnomad'] = 2 + patient['conservationScoreGnomad'] = patient['conservationScoreGnomad'].astype('float64') + + #conservationScoreOELof + patient.loc[patient['conservationScoreOELof'] == '-','conservationScoreOELof'] = np.NaN + patient.loc[patient['conservationScoreOELof'] == 'Low','conservationScoreOELof'] = 1 + patient.loc[patient['conservationScoreOELof'] == 'High','conservationScoreOELof'] = 2 + patient['conservationScoreOELof'] = patient['conservationScoreOELof'].astype('float64') + + + #Polyphen2_HDIV_score + patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') + patient.loc[patient['Polyphen2_HDIV_score'] == '-','Polyphen2_HDIV_score'] = -100.0 + for i in patient[patient['Polyphen2_HDIV_score'] != -100.0].index: + score_list = patient.loc[i, 'Polyphen2_HDIV_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'Polyphen2_HDIV_score'] = np.NaN + else: + patient.loc[i, 'Polyphen2_HDIV_score'] = max(score_list) + patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') + patient.loc[patient['Polyphen2_HVAR_score'] == -100, 'Polyphen2_HVAR_score'] = np.NaN + + #Polyphen2_HVAR_score + patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'] #.str.split(',') + patient.loc[patient['Polyphen2_HVAR_score'] == '-','Polyphen2_HVAR_score'] = -100.0 + for i in patient[patient['Polyphen2_HVAR_score'] != -100.0].index: + score_list = patient.loc[i, 'Polyphen2_HVAR_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'Polyphen2_HVAR_score'] = np.NaN + else: + patient.loc[i, 'Polyphen2_HVAR_score'] = max(score_list) + patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') + patient.loc[patient['Polyphen2_HVAR_score'] == -100, 'Polyphen2_HVAR_score'] = np.NaN + + + #SIFT_score + patient['SIFT_score'] = patient['SIFT_score'] #.str.split(',') + patient.loc[patient['SIFT_score'] == '-','SIFT_score'] = -100.0 + for i in patient[patient['SIFT_score'] != -100.0].index: + score_list = patient.loc[i, 'SIFT_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'SIFT_score'] = np.NaN + else: + patient.loc[i, 'SIFT_score'] = min(score_list) + patient['SIFT_score'] = patient['SIFT_score'].astype('float64') + patient.loc[patient['SIFT_score'] == -100, 'SIFT_score'] = np.NaN + + + patient.loc[patient['zyg'] == 'HET','zyg'] = 1 + patient.loc[patient['zyg'] == 'HOM','zyg'] = 2 + patient.loc[patient['zyg'] == '-','zyg'] = np.NaN + + patient['zyg'] = patient['zyg'].astype('float64') + + + #patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN + #patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') + + + #FATHMM_score + patient['FATHMM_score'] = patient['FATHMM_score'] #.str.split(',') + patient.loc[patient['FATHMM_score'] == '-','FATHMM_score'] = -100.0 + for i in patient[patient['FATHMM_score'] != -100.0].index: + score_list = patient.loc[i, 'FATHMM_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'FATHMM_score'] = np.NaN + else: + patient.loc[i, 'FATHMM_score'] = min(score_list) + patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') + patient.loc[patient['FATHMM_score'] == -100, 'FATHMM_score'] = np.NaN + + + #M_CAP_score + patient.loc[patient['M_CAP_score'] == '-', 'M_CAP_score'] = np.NaN + patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') + + + #MutationAssessor_score + patient['MutationAssessor_score'] = patient['MutationAssessor_score'] #.str.split(',') + patient.loc[patient['MutationAssessor_score'] == '-','MutationAssessor_score'] = -100.0 + for i in patient[patient['MutationAssessor_score'] != -100.0].index: + score_list = patient.loc[i, 'MutationAssessor_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'MutationAssessor_score'] = np.NaN + else: + patient.loc[i, 'MutationAssessor_score'] = max(score_list) + patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') + patient.loc[patient['MutationAssessor_score'] == -100, 'MutationAssessor_score'] = np.NaN + + + + #ESP6500_AA_AF + patient.loc[patient['ESP6500_AA_AF'] == '-', 'ESP6500_AA_AF'] = np.NaN + patient['ESP6500_AA_AF'] = patient['ESP6500_AA_AF'].astype('float64') + + + #ESP6500_AA_AF + patient.loc[patient['ESP6500_EA_AF'] == '-', 'ESP6500_EA_AF'] = np.NaN + patient['ESP6500_EA_AF'] = patient['ESP6500_EA_AF'].astype('float64') + + + #hom + patient.loc[patient['hom'] == '-', 'hom'] = np.NaN + patient['hom'] = patient['hom'].astype('float64') + + patient['hgmd_rs'] = patient['hgmd_rs'].apply(lambda x: x.split(",")[0]) + patient.loc[patient['hgmd_rs'] == '-','hgmd_rs'] = np.NaN + patient['hgmd_rs'] = patient['hgmd_rs'].astype('float64') + + + #spliceAImax + patient.loc[patient['spliceAImax'] == '-', 'spliceAImax'] = np.NaN + patient['spliceAImax'] = patient['spliceAImax'].astype('float64') + patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = patient.loc[~pd.isna(patient['spliceAImax']), 'spliceAImax'].describe()['min'] + + + #Consequence + consequence = ['transcript_ablation', 'splice_acceptor_variant', 'splice_donor_variant', 'stop_gained', + 'frameshift_variant', 'stop_lost', 'start_lost', 'transcript_amplification', 'inframe_insertion', + 'inframe_deletion', 'missense_variant', 'protein_altering_variant', 'splice_region_variant', + 'splice_donor_5th_base_variant', 'splice_donor_region_variant'] + + for cons in consequence: + patient['cons_%s'%cons] = patient['Consequence'].str.contains(cons).astype('int') + variable_name.append('cons_%s'%cons) + + variable_name.remove('Consequence') + + + #nc_isPLP + patient.loc[patient['nc_isPLP']!=True, 'nc_isPLP'] = 0 + patient.loc[patient['nc_isPLP']==True, 'nc_isPLP'] = 1 + patient['nc_isPLP'] = patient['nc_isPLP'].astype('float64') + + #nc_isBLB + patient.loc[patient['nc_isBLB']!=True, 'nc_isBLB'] = 0 + patient.loc[patient['nc_isBLB']==True, 'nc_isBLB'] = 1 + patient['nc_isBLB'] = patient['nc_isBLB'].astype('float64') + + #c_isPLP + patient.loc[patient['c_isPLP']!=True, 'c_isPLP'] = 0 + patient.loc[patient['c_isPLP']==True, 'c_isPLP'] = 1 + patient['c_isPLP'] = patient['c_isPLP'].astype('float64') + + #c_isBLB + patient.loc[patient['c_isBLB']!=True, 'c_isBLB'] = 0 + patient.loc[patient['c_isBLB']==True, 'c_isBLB'] = 1 + patient['c_isBLB'] = patient['c_isBLB'].astype('float64') + + + #nc_ClinVar_Exp + patient.loc[patient['nc_ClinVar_Exp']!='nonCoding','nc_ClinVar_Exp'] = 0 + patient.loc[patient['nc_ClinVar_Exp']=='nonCoding','nc_ClinVar_Exp'] = 1 + patient['nc_ClinVar_Exp'] = patient['nc_ClinVar_Exp'].astype('float64') + + #nc_HGMD_Exp + patient.loc[patient['nc_HGMD_Exp']!='nonCoding','nc_HGMD_Exp'] = 0 + patient.loc[patient['nc_HGMD_Exp']=='nonCoding','nc_HGMD_Exp'] = 1 + patient['nc_HGMD_Exp'] = patient['nc_HGMD_Exp'].astype('float64') + + #c_ClinVar_Exp + c_clin_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange'] + + for exp in c_clin_exp: + + patient['c_ClinVar_Exp_%s'%exp] = patient['c_ClinVar_Exp'].str.contains(exp).astype('int') + variable_name.append('c_ClinVar_Exp_%s'%exp) + + variable_name.remove('c_ClinVar_Exp') + + #c_HGMD_Exp + c_hgmd_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange', 'Stop_Loss', 'Start_Loss'] + + for exp in c_hgmd_exp: + + patient['c_HGMD_Exp_%s'%exp] = patient['c_HGMD_Exp'].str.contains(exp).astype('int') + variable_name.append('c_HGMD_Exp_%s'%exp) + + variable_name.remove('c_HGMD_Exp') + + + #nc_CLNREVSTAT + CLN_STAT = ['no_assertion_provided', 'no_assertion_criteria_provided','no_assertion_for_the_individual_variant', + 'criteria_provided,_single_submitter', 'criteria_provided,_conflicting_interpretations', + 'criteria_provided,_multiple_submitters,_no_conflicts', 'reviewed_by_expert_panel', 'practice_guideline'] + + CLN_STAT_SCORE = [0,0,0,1,1,2,3,4] + patient.loc[patient['nc_CLNREVSTAT'] == '-', 'nc_CLNREVSTAT'] = np.NaN + patient.loc[patient['c_CLNREVSTAT'] == '-', 'c_CLNREVSTAT'] = np.NaN + + for i in range(len(CLN_STAT)): + patient.loc[patient['nc_CLNREVSTAT'] == CLN_STAT[i], 'nc_CLNREVSTAT'] = CLN_STAT_SCORE[i] + patient.loc[patient['c_CLNREVSTAT'] == CLN_STAT[i], 'c_CLNREVSTAT'] = CLN_STAT_SCORE[i] + + patient['nc_CLNREVSTAT'] = patient['nc_CLNREVSTAT'].astype('float64') + patient['c_CLNREVSTAT'] = patient['c_CLNREVSTAT'].astype('float64') + + + #nc_RANKSCORE + patient.loc[patient['nc_RANKSCORE'] == '-', 'nc_RANKSCORE'] = np.NaN + patient['nc_RANKSCORE'] = patient['nc_RANKSCORE'].astype('float64') + + #nc_RANKSCORE + patient.loc[patient['c_RANKSCORE'] == '-', 'c_RANKSCORE'] = np.NaN + patient['c_RANKSCORE'] = patient['c_RANKSCORE'].astype('float64') + + #CLASS + patient.loc[patient['CLASS'] == '-', 'CLASS'] = np.NaN + patient.loc[patient['CLASS'] == 'DM?', 'CLASS'] = 1 + patient.loc[patient['CLASS'] == 'DM', 'CLASS'] = 2 + patient['CLASS'] = patient['CLASS'].astype('float64') + + + patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', + 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] + patient = patient.groupby(['varId_dash'], sort=False)[variable_name[1:]].max() + patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', + 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] + + + #adding in tier features + #tier = pd.read_csv(tier_file, sep='\t') + tier=tier_file + tier_vars = ['IMPACT.from.Tier','TierAD','TierAR','TierAR.adj', + 'No.Var.HM','No.Var.H','No.Var.M','No.Var.L', + 'AD.matched','AR.matched', + 'OMIM.Inheritance.recessive', 'OMIM.Inheritance.dominant', + 'OMIM.Inheritance.both', 'OMIM.Inheritance.others'] + + tier['OMIM.Inheritance.recessive'] = 0 + tier['OMIM.Inheritance.dominant'] = 0 + tier['OMIM.Inheritance.both'] = 0 + tier['OMIM.Inheritance.others'] = 0 + tier.loc[tier['OMIM.Inheritance'] == 'recessive', 'OMIM.Inheritance.recessive'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'dominant', 'OMIM.Inheritance.dominant'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'both', 'OMIM.Inheritance.both'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'others', 'OMIM.Inheritance.others'] = 1 + + tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] + tier = tier.groupby(['Uploaded_variation'], sort=False)[tier_vars].max() + tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] + + patient = pd.concat([patient, tier], axis=1) + + + return patient + + + + + + diff --git a/scripts/labelling.py b/scripts/labelling.py new file mode 100755 index 0000000..eb372f4 --- /dev/null +++ b/scripts/labelling.py @@ -0,0 +1,62 @@ +import numpy as np +import pandas as pd + +__author__ = "Chaozhong Liu" +__email__ = "chaozhol@bcm.edu" + +###adds known strong/weak labels to matrix (not used in production pipeline) + +def add_label(feature_mtx, label_file, sample_id): + """ + feature_mtx: file path of matrix after feature engineering + + label_file: path of file containing all diagnosed causal variants with columns "BG_ID", "var" and "Confidence" + + sample_id: id of sample to be labeled + """ + + patient = feature_mtx + # is_causal - including strong and weak + label_df = pd.read_csv(label_file) + label_df = label_df[label_df['BG_ID'] == sample_id] + label = label_df['var'].values.tolist() + label = list(set(label)) + + if len(label) == 0: + print('No causal variant found in label file: %s'%(sample_id)) + return + + if sum(patient.index.isin(label)) == 0: + print('True label not found in matrix: %s.'%(sample_id)) + return + + + patient['is_causal'] = 0 + patient.loc[patient.index.isin(label),'is_causal'] = 1 + + + # is_strong - including strong only + label_df = pd.read_csv(label_file) + label_df = label_df.loc[label_df['Confidence']=='Strong',:] + label_df = label_df[label_df['BG_ID'] == sample_id] + label = label_df['var'].values.tolist() + label = list(set(label)) + + patient['is_strong'] = 0 + + if len(label) == 0: + print('No strong causal variant found in label file: %s'%(sample_id)) + return patient + + + if sum(patient.index.isin(label)) == 0: + print('Strong label not found in matrix: %s.'%(sample_id)) + return patient + + + patient.loc[patient.index.isin(label),'is_strong'] = 1 + + + return patient + + diff --git a/scripts/merge_rm.py b/scripts/merge_rm.py new file mode 100755 index 0000000..fb06026 --- /dev/null +++ b/scripts/merge_rm.py @@ -0,0 +1,25 @@ +import pandas as pd +import sys + +###merge score files with final rank matrix + +#read rank matrix and score file +rm=pd.read_csv("/out/final_matrix/"+sys.argv[1]+".csv") +annot=pd.read_csv("/out/scores/"+sys.argv[1]+".txt.gz", sep="\t", compression="gzip") + +#annot=pd.read_csv("/local/LucianLi/rami-test/r1_"+sys.argv[1]+"_scores.txt", sep="\t", compression="gzip") + +#get columns from score file +annot=annot[["varId","varId_dash", "geneSymbol", "geneEnsId", "rsId", "HGVSc", "HGVSp", "IMPACT", "Consequence", "phenoList", "phenoInhList", "clin_code", "clinvarCondition"]] + +#get original coordinates +annot["varId"]=annot["varId"].apply(lambda x: x.split("_E")[0]) + +#rename for final output +annot=annot.rename(columns={"varId":"origId", "IMPACT": "IMPACT_text", "clin_code": "clinvarSignDesc"}) + +#merge +test=rm.merge(annot, right_on="varId_dash", left_on="Unnamed: 0", how="left") + +test.to_csv("/out/final_matrix_expanded/"+sys.argv[1]+".expanded.csv.gz", index=False, compression="gzip") + diff --git a/scripts/mod5_diffusion.py b/scripts/mod5_diffusion.py new file mode 100755 index 0000000..9cfbec1 --- /dev/null +++ b/scripts/mod5_diffusion.py @@ -0,0 +1,147 @@ +from os.path import exists +from scipy import stats +from os import mkdir +import numpy as np +import pandas as pd + +__author__ = "Dongxue Mao; Linhua Wang" +__email__ = "dmao@bcm.edu; linhuaw@bcm.edu" + + +#runs diffusion module + +def diffusion(nn, y, alpha = 0.5, max_iter = 100): + """Helper function that runs the network diffusion. + + Parameters: + ---------- + nn: 2D-numpy array, normalized version of the adjacency matrix of the network of interest + y: list of float, initial heat of each node + alpha: float, parameter for diffusion, representing the percentage of heat diffused out at each iteration + max_iter: integer, max number of iterations of the diffusion + + Return: + ------- + F: 2D-numpy array, diffused network + """ + alpha = 0.5 + max_iter = 100 + y = y.to_numpy() + F = y + ## initialize an empty array for the heat + Fs = np.empty((nn.shape[0], 0), float) + ## restart + fY = (1-alpha) * y + for i in range(0, max_iter): + Fs = np.append(Fs, F, axis=1) + F = alpha * nn @ Fs[:,[i]] + fY + return(F) + +def diffuseSample(ID, Anno_df, Phrank_folder): + """Method that runs diffusion network for a specific sample. + + Parameters: + ---------- + ID: str, sample ID. + Anno_folder: str, path to the folder that contains the annotation file for module 1 & 2. + Phrank_folder: str, path to the folder that contains Phrank gene ranking score for diffusion. + + Return: + ------- + Pandas Data Frame, index indicates variant ID, + columnns contatins diffused phrank scores normalized as percentile. + """ + + ## Get the PPI network for diffusion + cor_path = "/run/data_dependencies/mod5_diffusion/combined_score.hdf5" + cor_df = pd.read_hdf(cor_path, mode='r') + cor_df = cor_df[(cor_df.T != 0).sum() > 1] + cor_df = cor_df.loc[cor_df.index, cor_df.index] + cor = cor_df.values + cor_GeneID = pd.DataFrame({"ID":cor_df.columns.tolist()}) + + Phrank_path = Phrank_folder + ID.split('.')[0] + ".txt" + ## Validate Phrank file's existence + if not exists(Phrank_path): + print("File %s not exists, skipping..." %Phrank_path) + return + + Phrank = pd.read_csv(Phrank_path, sep = "\t", header = None) + Phrank = Phrank.rename({0:"Ensembl_Gene_ID",1:"Score"}, axis = "columns") + ## remove rows without Ensembl_Gene_ID + Phrank["Ensembl_Gene_ID"].replace('', np.nan, inplace=True) + Phrank.dropna(subset=["Ensembl_Gene_ID"], inplace= True) + ## remove duplicated genes and keep the highest similarity score + Phrank = Phrank.sort_values('Score', ascending=False).drop_duplicates('Ensembl_Gene_ID').sort_index() + simi = Phrank.rename({0:"Ensembl_Gene_ID","Score":"Similarity_Score"}, axis = "columns") + + + ## load module 1 & 2 file as reference + #geneFiltFn = "%s/%s.txt.gz" %(Anno_folder, ID) + + ## Validate annotation file's existence + #if not exists(geneFiltFn): + #print("Module 1-2 file not exists for %s, skipping..." %ID) + #return + + #m12_df = pd.read_csv(geneFiltFn, sep="\t", compression="gzip") + m12_df=Anno_df + m12_genes = [g for g in m12_df.loc[:, "geneEnsId"].tolist() if "ENSG" in g] + m12_gc = len(set(m12_genes)) + + ## Filter Genes + ## Keep all genes in filtered GeneID in module 1 and 2 and Phrank, but not in cor_GeneID + m12_wSimi = list(set(Phrank["Ensembl_Gene_ID"]) & set(m12_genes)) + m12_wSimi_woCor = list(set(m12_wSimi) - (set(cor_GeneID["ID"]))) + m12_wSimi_woCor = simi[simi["Ensembl_Gene_ID"].isin(m12_wSimi_woCor)] + ## normalized the cor matrix + net = abs(cor) + D = 1/np.sqrt(net.sum(axis = 1)) + D2 = np.diag(D) + net_norm = D2 @ net @ D2 + + ## Set Y as similarity score + Y = cor_GeneID.merge(simi, left_on = "ID", right_on = "Ensembl_Gene_ID", how = "left") + Y = Y[["ID","Similarity_Score"]].fillna(0) + Y.set_index("ID", inplace = True) + + ## Start diffusion + diff_res = diffusion(net_norm,Y,0.5,100) + + ## Combine diffusion result with GeneID + diff_wGeneID = pd.DataFrame(diff_res, columns=["Final_Heat"]) + diff_wGeneID["GeneID"] = cor_GeneID + + ## remove the genes with zero heat + diff_wGeneID = diff_wGeneID[diff_wGeneID["Final_Heat"] != 0] + diff_wGeneID = diff_wGeneID[["GeneID","Final_Heat"]] + + ## combine diffusion result with similarity scores + FinalHeat_wSimi = pd.concat([m12_wSimi_woCor,diff_wGeneID],ignore_index=True, sort=True) + + ## remove diffusion result with non-vep genes + diff_wGeneID_in_m12 = diff_wGeneID[diff_wGeneID['GeneID'].isin(m12_genes)] + m12_wSimi_woCor = m12_wSimi_woCor.rename(columns = {"Similarity_Score":"Final_Heat"}) + diff_wGeneID_in_m12 = diff_wGeneID_in_m12.rename(columns = {"GeneID":"Ensembl_Gene_ID"}) + FinalHeat_wSimi = pd.concat([m12_wSimi_woCor,diff_wGeneID_in_m12],ignore_index=True, sort=True) + FinalHeat_wSimi = FinalHeat_wSimi.sort_values(by = 'Final_Heat', ascending = False) + + ## Get the diffusion scores for genes in the annotation data, 0 if not existed + score_ordered = [] + gene_ordered = m12_df.loc[:, "geneEnsId"].tolist() + for gene in gene_ordered: + if gene in FinalHeat_wSimi.Ensembl_Gene_ID.tolist(): + score_ordered.append(FinalHeat_wSimi.loc[FinalHeat_wSimi.Ensembl_Gene_ID == gene, 'Final_Heat'].to_numpy()[0]) + else: + score_ordered.append(0) + + ## Normalize to have a z-score (max 1, min 0) + scores = stats.rankdata(score_ordered, 'max')/len(score_ordered) + + m12_df['diffuse_Phrank_STRING'] = scores + m12_df = m12_df.drop_duplicates(subset = ["varId_dash"]) + m12_df = m12_df[["varId_dash", "diffuse_Phrank_STRING"]] + m12_df = m12_df.set_index("varId_dash", drop=True) + return m12_df + + diff --git a/scripts/phenoSim.R b/scripts/phenoSim.R new file mode 100755 index 0000000..e721259 --- /dev/null +++ b/scripts/phenoSim.R @@ -0,0 +1,133 @@ +rm(list=ls()) +library(ontologyIndex) +library(ontologySimilarity) +args = commandArgs(trailingOnly=TRUE) + +id=args[1] +code=args[2] +refg=args[3] +#dat <- read.csv('/run/data_dependencies/omim_annotate/HGMD_feature_20190510.csv') +#dat <- dat[!is.na(dat$hpo_id),] +dat <- read.csv(paste0('/run/data_dependencies/omim_annotate/',refg,'/HGMD_phen.tsv'), + sep='\t') + +library(dplyr) +get_HPO_list <- function(df1){ + df2 <- df1 %>% + dplyr::group_by(acc_num, phen_id, gene_sym) %>% + dplyr::summarise(HPO = paste0(hpo_id, collapse = " ")) %>% + dplyr::ungroup() %>% as.data.frame() + df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) + #df2$Gene <- lapply(df2$Gene, function(i) unique(strsplit(i, ",")[[1]])) + #rownames(df2) <- ifelse(is.na(df2$OMIM_ID), df2$Disease_Name, df2$OMIM_ID) + return(df2) +} + + +# Load HPO_obo +HPO_obo <- get_OBO("/run/data_dependencies/omim_annotate/hp.obo",propagate_relationships = c('is_a','part_of'), extract_tags = "minimal") + +# set simi_thresh +simi_thresh <- 0 +# Set pwd: +out_pwd <- "./" + + +# In public release, there might be empty HGMD phenotype file +if (dim(dat)[1] == 0) { + col_names <- c('acc_num', 'phen_id', 'gene_sym', 'HPO', 'HPO_list', 'Similarity_Score') + dat2 <- data.frame(matrix(nrow = 0, ncol = length(col_names))) + colnames(dat2)<- col_names + +} else { + dat <- dat[!is.na(dat$hpo_id),] + dat2_ori <- get_HPO_list(dat) + #dat2_ori <- dat2_ori[with(dat2_ori,order(hgvs)),] + #dat2_ori$Gene <- unlist(lapply(dat2_ori$Gene, function(x) x[[1]])) + dat2 <- dat2_ori + + # Load patient HPO + HPO.file <- file.path(id) + HPO.orig <- read.table(HPO.file, sep = "\t", fill=T, header=F, stringsAsFactors=F) + HPO <- HPO.orig$V1 + + # remove terms without a HPO ID + HPO <- HPO[grepl("HP:", HPO)] + HPO <- list(HPO) + + sim_mat <- get_asym_sim_grid(HPO, dat2$HPO_list, ontology=HPO_obo) + dat2$Similarity_Score <- as.vector(sim_mat) + dat2$HPO_list <- unlist(lapply(dat2$HPO_list, function(x) paste0(unlist(x), collapse = "|"))) + dat2 <- dat2[order(dat2$Similarity_Score, decreasing = T),] +} + +output_file_name2 <- paste0("/out/",code,"-cz") +write.table(dat2, output_file_name2, sep = "\t", quote = F, row.names = F) + + + + +# Load OMIM gene-disease data +genemap2 <-readRDS("/run/data_dependencies/omim_annotate/hg19/genemap2_v2022.rds") + +## ---- Load OMIM Phenotype ---- +HPO_orig <- read.table("/run/data_dependencies/omim_annotate/hg19/HPO_OMIM.tsv", sep="\t", header= T, stringsAsFactors = FALSE, comment.char = "", fill = TRUE, quote = "\"") +OMIM_HPO <- HPO_orig[,c('OMIM_ID', 'DiseaseName', 'HPO_ID')] +# rename colnames +colnames(OMIM_HPO) = c("OMIM_ID", "Disease_Name", "HPO_ID") +OMIM_HPO_cl <- unique(OMIM_HPO) + + +# Function to get_HPO_list +get_HPO_list <- function(df1){ + df2 <- df1 %>% + dplyr::group_by(OMIM_ID, Disease_Name) %>% + dplyr::summarise(HPO = paste0(HPO_ID, collapse = " ")) %>% + dplyr::ungroup() %>% as.data.frame() + df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) + #rownames(df2) <- ifelse(is.na(df2$OMIM_ID), df2$Disease_Name, df2$OMIM_ID) + return(df2) +} +# Prepare OMIM HPO list for ontology similarity comparision +OMIM_HPO_all <- get_HPO_list(OMIM_HPO_cl[, c("OMIM_ID", "Disease_Name","HPO_ID")]) + +HPO.file <- file.path(id) +#HPO.file <- '/mnt/atlas_local/sasidhar/data/1127895/P-031-101_HPOTerms.txt' +HPO.orig <- read.table(HPO.file, sep = "\t", fill=T, header=F, stringsAsFactors=F) +HPO <- HPO.orig$V1 +# remove terms without a HPO ID +HPO <- HPO[grepl("HP:", HPO)] +HPO <- list(HPO) + + +# Generate HPO list +#Unknown_Disease <- data.frame(Disease_Name = "Unknown", +# HPO_ID = HPO, +# OMIM_ID = 'NA') + +#Unknown_Disease_all <- get_HPO_list(Unknown_Disease) + +# compare disease similarity +#sim_mat <- get_asym_sim_grid(Unknown_Disease_all$HPO_list, OMIM_HPO_all$HPO_list, ontology=HPO_obo) +sim_mat <- get_asym_sim_grid(HPO, OMIM_HPO_all$HPO_list, ontology=HPO_obo) + + +# OMIM_HPO_all$HPO_term <- HPO_obo$name[OMIM_HPO_all$HPO] +OMIM_HPO_all$Similarity_Score <- as.vector(sim_mat) +# convert HPO ID to HPO term +OMIM_HPO_all$HPO_term <- unlist(lapply(OMIM_HPO_all$HPO_list, function(x) paste0(HPO_obo$name[unlist(x)], collapse = "|"))) + +## Add gene - disease relationship +OMIM_HPO_all_wGene <- merge(unique(genemap2[,c("Pheno_ID","Approved_Gene_Symbol","Ensembl_Gene_ID","Entrez_Gene_ID")]), + OMIM_HPO_all[,c("OMIM_ID","Disease_Name","Similarity_Score","HPO_term")], by.y = "OMIM_ID", by.x = "Pheno_ID") +colnames(OMIM_HPO_all_wGene)[2] <- 'Gene_Symbol' +OMIM_HPO_all_order <-OMIM_HPO_all_wGene[order(OMIM_HPO_all_wGene$Similarity_Score, decreasing = T),] +# write.table(OMIM_HPO_all_order, paste0(unknown_disease_path,"HPOsimi_all_",output_file_name), sep = "\t", quote = F, row.names = F) + +# OMIM_HPO_all_filt <- head(OMIM_HPO_all_order, n = No_candidate) +OMIM_HPO_all_filt <- OMIM_HPO_all_order[OMIM_HPO_all_order$Similarity_Score >= simi_thresh,] +output_file_name2 <- paste0("/out/",code,"-dx") +write.table(OMIM_HPO_all_filt, output_file_name2, sep = "\t", quote = F, row.names = F) + + + diff --git a/scripts/phrank/src/location_to_gene.py b/scripts/phrank/src/location_to_gene.py new file mode 100755 index 0000000..dc6a461 --- /dev/null +++ b/scripts/phrank/src/location_to_gene.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python2.7 +import sys +import os +from collections import defaultdict + + +GENE_LOCS = sys.argv[2] + + +def load_loc_maps(locmap): + by_start = defaultdict(list) + by_end = defaultdict(list) + by_symbol = defaultdict(lambda: defaultdict(lambda: None)) + for line in open(locmap): + lineData = line.strip().split("\t") + symbol = lineData[0] + if "." in symbol: continue + chrom = lineData[1] + start = int(lineData[2]) + end = int(lineData[3]) + if not by_symbol[chrom][symbol]: by_symbol[chrom][symbol] = [start, end] + else: + if by_symbol[chrom][symbol][0] > start: by_symbol[chrom][symbol][0] = start + if by_symbol[chrom][symbol][1] < end: by_symbol[chrom][symbol][1] = end + by_start[chrom].append((start, end, symbol)) + by_end[chrom].append((end, start, symbol)) + for chrom in by_start.keys(): + by_start[chrom].sort() + by_end[chrom].sort() + return by_start, by_end, by_symbol + +def binary_search(loc_to_symbol, start, end): + minimum = 0 #Inclusive + maximum = len(loc_to_symbol) #Exclusive + returnSet = set() + index = None + while minimum < maximum: + index = (minimum + maximum) // 2 + location = loc_to_symbol[index] + if location[0] < start: + minimum = index + 1 + continue + if location[0] > end: + maximum = index + continue + break + if index is None: return returnSet + returnSet.add(loc_to_symbol[index][2]) + i = index - 1 + while i >= 0: + location = loc_to_symbol[i] + i -= 1 + if location[0] < start: break + returnSet.add(location[2]) + i = index + 1 + while i < len(loc_to_symbol): + location = loc_to_symbol[i] + i += 1 + if location[0] > end: break + returnSet.add(location[2]) + return returnSet + +def find_overlapping_genes(by_symbol, by_start, by_end): + gene_overlaps = defaultdict(set) + for chrom in by_symbol.keys(): + for symbol in by_symbol[chrom].keys(): + start, end = by_symbol[chrom][symbol] + start_overlaps = binary_search(by_start[chrom], start, end) + end_overlaps = binary_search(by_end[chrom], start, end) + gene_overlaps[symbol] = (start_overlaps | end_overlaps) - set([symbol]) + return gene_overlaps + +def main(variants): + by_start, by_end, by_symbol = load_loc_maps(GENE_LOCS) + #atxn1start = by_symbol["6"]["ENSG00000124788"][0] + #atxn1end = by_symbol["6"]["ENSG00000124788"][1] + for line in open(variants): + lineData = line.strip().split(":") + chrom = lineData[0] + pos = int(lineData[1]) + start_overlaps = binary_search(by_start[chrom], pos, pos) + end_overlaps = binary_search(by_end[chrom], pos, pos) + genes = start_overlaps | end_overlaps + for gene in genes: print(line.strip() + "\t" + gene) + +if __name__ == "__main__": + main(sys.argv[1]) + diff --git a/scripts/phrank/src/run_phrank.py b/scripts/phrank/src/run_phrank.py new file mode 100755 index 0000000..d356ae4 --- /dev/null +++ b/scripts/phrank/src/run_phrank.py @@ -0,0 +1,34 @@ +import sys +import os +from collections import defaultdict +from phrank import * + +DAGFILE = f"/run/data_dependencies/phrank/{sys.argv[3]}/child_to_parent.txt" +DISEASEANNOTATIONS = f"/run/data_dependencies/phrank/{sys.argv[3]}/disease_to_pheno.txt" +GENEANNOTATIONS = f"/run/data_dependencies/phrank/{sys.argv[3]}/gene_to_phenotype.txt" +DISEASEGENE = f"/run/data_dependencies/phrank/{sys.argv[3]}/disease_to_gene.txt" + +#/houston_10t/cole/MARRVEL/udn/phrank/data/knowledgebase/gene_to_symbol.txt +#/houston_10t/cole/MARRVEL/udn/phrank/data/knowledgebase/hpo_to_name.txt + +def load_list(listfile): + returnList = [] + for line in open(listfile): + returnList.append(line.strip()) + return returnList + +def load_set(listfile): + returnSet = set() + for line in open(listfile): + returnSet.add(line.strip().split("\t")[0]) + return returnSet + +def main(genelist, phenotypelist): + p = Phrank(DAGFILE, DISEASEANNOTATIONS, DISEASEGENE, GENEANNOTATIONS) + genes = load_set(genelist) + phenos = load_set(phenotypelist) + ranking = p.rank_genes(genes, phenos) + for item in ranking: print(str(item[1]) + "\t" + str(item[0])) + +if __name__ == "__main__": main(sys.argv[1], sys.argv[2]) + diff --git a/scripts/predict_new/.py b/scripts/predict_new/.py new file mode 100755 index 0000000..1c1b44b --- /dev/null +++ b/scripts/predict_new/.py @@ -0,0 +1,14 @@ +from utilities import * +import warnings +import json +import os +from glob import glob +from datetime import datetime +import joblib +from confidence import * + +model=joblib.load("/home/lul0614/pipeline/predict_new/final_model_wo_bg_val.job") +features=pd.read_csv("features.csv").columns +out=rank_patient(model, "/local/LucianLi/final-all/989068_fillna_tierFalse.txt", features, train_causals=None) + +out.to_csv("test.csv") \ No newline at end of file diff --git a/scripts/predict_new/causal_distr.py b/scripts/predict_new/causal_distr.py new file mode 100755 index 0000000..80ba2aa --- /dev/null +++ b/scripts/predict_new/causal_distr.py @@ -0,0 +1,14 @@ +from utilities import * +import warnings +import json +import os +from glob import glob +from datetime import datetime +import joblib +from confidence import * + + +causal_distr = get_causal_distribution('/houston_30t/alexw/MARRVEL_ML/results/rf_fillNA_tierNDB_is_causal_version1_tuneFalse_05-31-2022/BG_pred_raw_rank', + label_col='is_strong') + +pd.DataFrame(causal_distr).to_csv('causal_distr.txt',index=False,header=False) \ No newline at end of file diff --git a/scripts/predict_new/causal_distr.txt b/scripts/predict_new/causal_distr.txt new file mode 100755 index 0000000..bfb814a --- /dev/null +++ b/scripts/predict_new/causal_distr.txt @@ -0,0 +1,74 @@ +0.7766666666666666 +0.07333333333333332 +0.01 +0.06666666666666668 +0.03666666666666667 +0.6866666666666666 +0.016666666666666666 +0.0033333333333333327 +0.07333333333333332 +0.21666666666666667 +0.06333333333333334 +0.0033333333333333327 +0.57 +0.12333333333333335 +0.04 +0.7366666666666667 +0.11666666666666667 +0.13333333333333333 +0.4033333333333333 +0.5966666666666667 +0.13 +0.05 +0.2433333333333333 +0.11333333333333333 +0.24 +0.36666666666666653 +0.2433333333333333 +0.3933333333333333 +0.7933333333333333 +0.08 +0.06666666666666668 +0.28 +0.15333333333333332 +0.12666666666666668 +0.7266666666666667 +0.06333333333333334 +0.5633333333333334 +0.67 +0.016666666666666666 +0.36 +0.21666666666666667 +0.6166666666666667 +0.02333333333333333 +0.6233333333333333 +0.09333333333333334 +0.4133333333333333 +0.35 +0.2933333333333333 +0.006666666666666667 +0.7233333333333334 +0.7233333333333334 +0.06 +0.5133333333333333 +0.4033333333333333 +0.2533333333333333 +0.14333333333333334 +0.06 +0.17 +0.02 +0.26 +0.5233333333333333 +0.016666666666666666 +0.07 +0.15 +0.15 +0.01 +0.49 +0.57 +0.013333333333333334 +0.11333333333333333 +0.5433333333333333 +0.45 +0.2933333333333333 +0.68 diff --git a/scripts/predict_new/confidence.py b/scripts/predict_new/confidence.py new file mode 100755 index 0000000..3ebf265 --- /dev/null +++ b/scripts/predict_new/confidence.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python +"""Script to estimate the confidence of whether a case is solved or not and assign +a confidence score to each prediction. +""" +__author__ = "Linhua Wang" +__email__ = "linhuaw@bcm.edu" +import pandas as pd +import os +import numpy as np +import scipy + +def get_causal_distribution(pred_fd, label_col='is_strong'): + # pred_fd: folder for prediction files only + fns = os.listdir(pred_fd) + # get top20 predictions + top20_preds = [] + for fn in fns: + top20_preds.append(pd.read_csv(f'{pred_fd}/{fn}', index_col=0).iloc[:20,:]) + top20_preds = pd.concat(top20_preds) + # get scores of causal variants + top20_causals = np.ravel(top20_preds.loc[top20_preds[label_col]==1, 'predict'].to_numpy()) + return top20_causals + +def assign_confidence_score(causal_distr, pred_df, label_col='is_strong'): + # pred_fd: folder for prediction files only + # pred_df: prediction data frame of the sample of interest + + prediction_scores = pred_df.predict.to_numpy() + + # assign confidence score using percentile + conf_scores = [scipy.stats.percentileofscore(causal_distr, i) for i in prediction_scores] + # assign confidence category by discretizing + conf_categories = [] + for conf_score in conf_scores: + if conf_score < 25: + conf_categories.append("Unsolved") + elif conf_score < 50: + conf_categories.append("Solved (Low)") + elif conf_score < 75: + conf_categories.append("Solved (Medium)") + else: + conf_categories.append("Solved (High)") + pred_df['confidence'] = conf_scores + pred_df['confidence level'] = conf_categories + pred_df.sort_values("confidence", ascending=False) + return pred_df + +# def determine_case_confidence(causal_distr, pred_df, label_col='is_strong'): +# pred_df = assign_confidence_score(causal_distr, pred_df, label_col) +# return pred_df['confidence level'].tolist()[0] \ No newline at end of file diff --git a/scripts/predict_new/features.csv b/scripts/predict_new/features.csv new file mode 100755 index 0000000..b3fe29e --- /dev/null +++ b/scripts/predict_new/features.csv @@ -0,0 +1 @@ +diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat \ No newline at end of file diff --git a/scripts/predict_new/main.py b/scripts/predict_new/main.py new file mode 100755 index 0000000..7edc4c8 --- /dev/null +++ b/scripts/predict_new/main.py @@ -0,0 +1,162 @@ +from utilities import * +import warnings +import json +import os +from glob import glob +from datetime import datetime +import joblib +from confidence import * + +def test(final_model, val_fns, features, test_label, out_folder, mode, train_causals, train_pred_folder): + if train_pred_folder is not None: + causal_distr = get_causal_distribution(train_pred_folder, label_col=test_label) + else: + causal_distr = None + + causal_rank_raw = rank_test_patients(final_model, val_fns, features, test_label, causal_distr = causal_distr, + out_folder=f"{out_folder}/{mode}_pred_raw_rank", train_causals=None) + + f_raw, eval_df_raw = plot_topK_performance(causal_rank_raw) + f_raw.savefig(f"{out_folder}/{mode}_test_perf_raw.png", dpi=100) + eval_df_raw.to_csv(f"{out_folder}/{mode}_test_perf_raw.csv") + + ## evaluate only on non-seen variants +# causal_rank_exclude = rank_test_patients(final_model, val_fns, features, test_label,train_pred_folder, +# out_folder=f"{out_folder}/{mode}_pred_exclude_rank", +# train_causals=train_causals) + +# f_exclude, eval_df_exclude = plot_topK_performance(causal_rank_exclude) +# f_exclude.savefig(f"{out_folder}/{mode}_test_perf_exclude.png", dpi=100) +# eval_df_exclude.to_csv(f"{out_folder}/{mode}_test_perf_exclude.csv") + +def main(feature_fillna=None, feature_tier_with_clinvar=None, + train_label=None, test_label=None, out_folder=None, + tune=True, train_test=None): + """ + feature_fillna: bool, fill na feature or not + feature_tier_with_clinvar: bool, tier feature use clinvar/hgmd info or not + train_label: str, label to be used in training (is_causal, is_strong) + test_label: str, label to be used in training (is_causal, is_strong) + out_folder: if not specified, will be created automatically + tune: bool, whether or not tuning is needed + train_test: train/test split strategy, + if str, must be one of ['BG_val', 'version1', 'leave_one_out'] + if int/float, represent number/fraction of test samples to be randomly used. + """ + ### -> load parameters + f = open("meta.json") + meta = json.load(f) + data_folder = meta["data_folder"] + classifier = meta['classifier'] + if train_test is None: + train_test = meta['train_test'] + if feature_fillna is None: + feature_fillna = meta["feature_fillna"] + if feature_tier_with_clinvar is None: + feature_tier_with_clinvar = meta["feature_tier_with_clinvar"] + if train_label is None: + train_label = meta["train_label"] + if test_label is None: + test_label = meta["test_label"] + if out_folder is None: + fillna = "fillNA" if feature_fillna else "withNA" + tier = 'tierDB' if feature_tier_with_clinvar else 'tierNDB' + dateid = datetime.now().strftime('%m-%d-%Y') + fd = '_'.join([classifier, fillna, tier, train_label, train_test, f'tune{tune}', dateid]) + out_folder = f"./results/{fd}" + + if not os.path.exists("./results/"): + os.mkdir("./results/") + if not os.path.exists(out_folder): + os.mkdir(out_folder) + + print(f"### Classifier: {classifier}.") + print(f"### DataHub: {data_folder}.") + print(f"### TrainTestMode: {train_test}.") + print(f"### Features' NA filled: {feature_fillna}.") + print(f"### Tier used disease DB: {feature_tier_with_clinvar}.") + print(f"### Results will be saved at: {out_folder}.") + print(f"### Process started at: {datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}.") + + ### <- load parameters + if feature_fillna and feature_tier_with_clinvar: + ext = "fillna_tierTrue.txt" + fns = glob(f"{data_folder}/*_{ext}") + elif feature_fillna and (not feature_tier_with_clinvar): + ext = "fillna_tierFalse.txt" + fns = glob(f"{data_folder}/*_{ext}") + elif (not feature_fillna) and feature_tier_with_clinvar: + ext = "keepna_tierTrue.txt" + fns = glob(f"{data_folder}/*_{ext}") + else: + ext = "keepna_tierFalse.txt" + fns = glob(f"{data_folder}/*_{ext}") + + ### [Start] ---> Get training and testing files + ## Files BG provided for additional validation are taken out of training + fns = [os.path.abspath(fn) for fn in fns] # take absolute paths + sample_meta = pd.read_csv(meta["sample_meta_fn"], sep=" ") + bg_samples = sample_meta.loc[sample_meta.val==1,'BG_ID'].tolist() + print(len(bg_samples)) + ## Testing files + bg_val_fns = [os.path.abspath(f"{data_folder}/{vid}_{ext}") for vid in bg_samples] + udn_val_fns = [fn for fn in fns if 'UDN' in fn] + ddd_val_fns = [fn for fn in fns if 'DDD' in fn] + print(f'#fns={len(fns)}, #BG validation={len(bg_val_fns)}, #UDN validation={len(udn_val_fns)}, #DDD validation={len(ddd_val_fns)}') + ## Training files + train_fns = list(set(fns) - set(udn_val_fns) - set(bg_val_fns) - set(ddd_val_fns)) + ### <- Get training and testing files [End] + + # if isinstance(train_sample_num, int) and (len(train_fns) > train_sample_num): + # train_fns = np.random.choice(train_fns, train_sample_num, replace=False) + if tune: + validation_performances = tune_params(train_fns, classifier=classifier) + validation_performances.to_csv(f"{out_folder}/param_tuning_perfs.csv") + val_best_params = validation_performances.iloc[0, :].tolist() + if classifier == 'xgb': + n_est, md, classifier = int(val_best_params[0]), int(val_best_params[1]), float(val_best_params[2]) + else: + n_est, md = int(val_best_params[0]), int(val_best_params[1]) + else: + ### Fix parameter tuned, TO BE REMOVED --> + if classifier == 'xgb': + n_est, lr, md = 300, 0.1, None + print(f"n_est={n_est}, lr={lr}, md={md}") + elif classifier == 'rf': + n_est, md = 300, None + ### <-- Fix parameter tuned, TO BE REMOVED + + if classifier == 'xgb': + trainX, trainY, neg_pos_ratio = get_training_data(train_fns, train_label) + features = trainX.columns.tolist() + print(f"[Final XGB] Training with {len(train_fns)} samples.") + final_model = train_XGBoost(trainX, trainY, n_est, lr, md, neg_pos_ratio) + causal_idx = np.where(trainY == 1)[0] + train_causals = trainX.iloc[causal_idx,:] + train_causals.to_csv(f"{out_folder}/train_causal_df.csv") + train_causals = train_causals.index.tolist() + elif classifier == 'rf': + final_model, features, train_causals = train_minibatch_RF(train_fns, n_estimator=n_est, max_depth=md, + label_col=train_label, batch=2000) + print(f"[Final RF] Trained, #trees = {final_model.get_params('n_estimators')}") + + joblib.dump(final_model, f"{out_folder}/final_model_wo_bg_val.job") + + with open(f"{out_folder}/features.csv", "w") as feature_f: + feature_f.write(",".join(features)) + + # ### predict on training samples + # test(final_model, train_fns, features, test_label, out_folder, 'Training', train_causals, train_pred_folder=None) + ### BG validation + test(final_model, bg_val_fns, features, test_label, out_folder, 'BG', train_causals, train_pred_folder=None) + ### UDN validation + test(final_model, udn_val_fns, features, test_label, out_folder, 'UDN', train_causals, train_pred_folder=f"{out_folder}/BG_pred_raw_rank") + ### DDD validation + test(final_model, ddd_val_fns, features, test_label, out_folder, 'DDD', train_causals, train_pred_folder=f"{out_folder}/BG_pred_raw_rank") + + + +if __name__ == "__main__": + warnings.filterwarnings('ignore') + main(tune=False) + \ No newline at end of file diff --git a/scripts/predict_new/run_final.py b/scripts/predict_new/run_final.py new file mode 100755 index 0000000..9baf945 --- /dev/null +++ b/scripts/predict_new/run_final.py @@ -0,0 +1,21 @@ +from utilities import * +import warnings +import json +import os +from glob import glob +from datetime import datetime +import joblib +from confidence import * +import sys + +#read in precalulated model +model=joblib.load("/run/data_dependencies/predict_new/hg19/final_model_wo_bg_val.job") + +#read in features to use +features=list(pd.read_csv("/run/data_dependencies/predict_new/hg19/features.csv")) + +#predict with Linhua's code +out=rank_patient(model, "/out/"+sys.argv[1]+".matrix.txt", features, train_causals=None) + +#write out +out.to_csv("/out/final_matrix/"+sys.argv[1]+".csv") \ No newline at end of file diff --git a/scripts/predict_new/test.csv b/scripts/predict_new/test.csv new file mode 100755 index 0000000..4955d87 --- /dev/null +++ b/scripts/predict_new/test.csv @@ -0,0 +1,17460 @@ +Unnamed: 0,diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,OMIM.Inheritance.recessive,OMIM.Inheritance.dominant,OMIM.Inheritance.both,OMIM.Inheritance.others,is_causal,is_strong,simple_repeat,predict,min_ranking,max_ranking,ranking,identifier 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RandomForestClassifier as RF +import itertools +from confidence import * + +def get_training_data(fns, label_col): + """Read in training data, drop features with specified mode in the meta['feature_mode'] json. + Allowed modes are ['all', 'raw', 'var_disase_db_off', 'gene_disase_db_off'] + """ + np.random.seed(0) + ### READ PARAMETERS + f = open("meta.json") + meta = json.load(f) + feature_mode = meta["feature_mode"] + feature_meta = pd.read_csv(meta["feature_meta_fn"], sep=',') + ### READ training samples + t0 = time() + print("[TrainVal-Train] Start reading training data.") + dfs = [] + for i in trange(len(fns)): + fn = fns[i] + df = pd.read_csv(fn, index_col=0, sep="\t") + dfs.append(df) + dfs = pd.concat(dfs) + + t1 = time() + print(f"[TrainVal-Train] Completed reading training data in {(t1-t0):.2f} seconds.") + train_Y = dfs[label_col].to_numpy().astype(int) + neg_pos_ratios = [(train_Y == 0).sum()/(train_Y == 1).sum()] + + ## Drop features based on feature mode + if feature_mode == "all": + features = feature_meta.Feature.tolist() + elif feature_mode == "raw": + features = feature_meta.loc[feature_meta.Engineered != 1,:].Feature.tolist() + elif feature_mode == "var_disase_db_off": + features = feature_meta.loc[feature_meta.DiseaseDB_VariantLevel != 1,:].Feature.tolist() + elif feature_mode == "gene_disase_db_off": + features = feature_meta.loc[feature_meta.DiseaseDB_GeneLevel != 1,:].Feature.tolist() + else: + print(f"Wrong feature mode entered, should be one of\ + ['all', 'raw', 'var_disease_db_off', 'gene_disease_db_off'],\ + your input is {feature_mode}." + ) + sys.exit(0) + train_X = dfs[features] + print("[TrainVal-Train] Training data obtained.") + return train_X, train_Y, np.mean(neg_pos_ratios) + +def tune_params(fns, classifier='xgb'): + ### READ PARAMETERS + f = open("meta.json") + meta = json.load(f) + train_label = meta["train_label"] + test_label = meta["test_label"] + train_val_frac = meta["train_val_frac"] + sep2 = "\t" + + train_fns_sampled = list(np.random.choice(fns, int(train_val_frac * len(fns)))) + val_fns_sampled = list(set(fns) - set(train_fns_sampled)) + + print(f"[TrainVal-Train] train {len(train_fns_sampled)} samples with label {train_label},\ + validate {len(val_fns_sampled)} samples with label {test_label}.") + + para_train_dfs = [pd.read_csv(train_fn, usecols=[train_label], sep=sep2) for train_fn in train_fns_sampled] + para_train_dfs = pd.concat(para_train_dfs) + if classifier == 'xgb': + ## tune parameters + n_estimators = [200, 300] + learning_rates = list(np.logspace(0.001, 0.2, num=10, endpoint=True)) + max_depths = [None] + params = list(itertools.product(n_estimators, learning_rates, max_depths)) + elif classifier == 'rf': + n_estimators = [100, 200, 300, 400] + max_depths = [None] + params = list(itertools.product(n_estimators, max_depths)) + + train_X, train_Y, neg_pos_ratio = get_training_data(train_fns_sampled, train_label) + perf_dfs = [] + for param in params: + t0 = time() + if classifier == 'xgb': + n_est, lr, md = param + print(f"[TrainVal-Train] n_trees: {n_est}, lr: {lr}, max_depth: {md}, n/p ratio: {neg_pos_ratio}.") + clf = train_XGBoost(train_X, train_Y, n_est, lr, md, neg_pos_ratio) + + elif classifier == 'rf': + n_est, md = param + print(f"[TrainVal-Train] n_trees: {n_est}, max_depth: {md}, n/p ratio: {neg_pos_ratio}.") + clf, _, _ = train_minibatch_RF(train_X,train_Y, n_est, md, neg_pos_ratio) + t1 = time() + print(f"[TrainVal-Train] done in {(t1-t0):.2f} seconds.") + rank_dfs = rank_test_patients(clf, val_fns_sampled, train_X.columns.tolist(), test_label) + obj_df = objective(rank_dfs) + if classifier == 'xgb': + perf_df = pd.DataFrame(data=[[n_est, md,lr, obj_df['objective'], + obj_df['var_top1_acc'], obj_df['var_top5_acc'], obj_df['var_top10_acc'] ]], + columns=['n_estimator', 'max_depth', 'learning_rate', + 'objective', 'var_top1', 'var_top5', 'var_top10']) + else: + perf_df = pd.DataFrame(data=[[n_est, md, obj_df['objective'], + obj_df['var_top1_acc'], obj_df['var_top5_acc'], obj_df['var_top10_acc'] ]], + columns=['n_estimator', 'max_depth', + 'objective', 'var_top1', 'var_top5', 'var_top10']) + perf_dfs.append(perf_df) + t2 = time() + print(f"[TrainVal-Validate] done in {(t2-t1):.2f} seconds, objective = {obj_df['objective']}.") + perf_dfs = pd.concat(perf_dfs).sort_values(["objective", "var_top1", "var_top5", "var_top10"], ascending=False) + return perf_dfs + +def quantify_perf(test_perfs, topK=5, mode='variant_level'): + if mode == 'sample_level': + sample_perfs = test_perfs.groupby('identifier')[['ranking']].min() + else: + sample_perfs = test_perfs.copy() + + topK_acc = (sample_perfs['ranking'] <= topK).sum() / sample_perfs.shape[0] + return topK_acc + +def plot_topK_performance(rank_dfs): + f = plt.figure(figsize=(10,4)) + topks = range(1, 101) + var_accs = [] + sample_accs = [] + + for i in topks: + var_accs.append(quantify_perf(rank_dfs, topK=i)) + sample_accs.append(quantify_perf(rank_dfs, topK=i, mode='sample_level')) + df = pd.DataFrame({"top_k": topks, "var_accuracy": var_accs, "sample_accuracy": sample_accs}) + ax1 = plt.subplot(1,2,1) + ax1.plot(topks, var_accs, linestyle='--', marker='o', color='blue') + ax1.set_xlabel("Top K", fontsize=14) + ax1.set_ylabel("Variant Accuracy", fontsize=14) + ax2 = plt.subplot(1,2,2) + ax2.plot(topks, sample_accs, linestyle='--', marker='o', color='green') + ax2.set_xlabel("Top K", fontsize=14) + ax2.set_ylabel("Sample Accuracy", fontsize=14) + plt.subplots_adjust(wspace=0.3) + return f, df + +def objective(rank_dfs): + ### READ PARAMETERS + f = open("meta.json") + meta = json.load(f) + alpha = meta["objective_params"]["alpha"] + beta = meta["objective_params"]["beta"] + gamma = meta["objective_params"]["gamma"] + f.close() + ### Get accuracy and calculate objective + vl1 = quantify_perf(rank_dfs, topK=1) + vl5 = quantify_perf(rank_dfs, topK=5) + vl10 = quantify_perf(rank_dfs, topK=10) + sl1 = quantify_perf(rank_dfs, topK=1, mode='sample_level') + sl5 = quantify_perf(rank_dfs, topK=5, mode='sample_level') + sl10 = quantify_perf(rank_dfs, topK=10, mode='sample_level') + obj_score = alpha * vl1 + beta * (vl5 - vl1) + gamma * (vl10 - vl5) + result = {"var_top1_acc": vl1, + "var_top5_acc": vl5, + "var_top10_acc": vl10, + "sample_top1_acc": sl1, + "sample_top5_acc": sl5, + "sample_top10_acc": sl10, + "objective": obj_score} + return result + +def train_XGBoost(X, Y, n_estimator, lr, max_depth, ratio): + xgb.config_context(verbosity=0) + bst = xgb.XGBClassifier(max_depth = max_depth, + n_estimators=n_estimator, + learning_rate= lr, + scale_pos_weight = ratio, + verbosity=0, n_jobs=-1) + bst.fit(X, Y) + return bst + +def train_RF(X, Y, n_estimator=None, max_depth=None, ratio=None, model=None): + if model is None: + model = RF(class_weight = {0:1, 1:ratio}, random_state=2021, n_jobs=-1, + n_estimators=n_estimator, max_depth=max_depth, warm_start=True) + model.fit(X, Y) + return model + +def train_minibatch_RF(fns, n_estimator, max_depth, label_col, batch=500): + k = ceil(len(fns) / batch) + model = RF(random_state=2021, n_jobs=-1, + n_estimators=n_estimator, + max_depth=max_depth, warm_start=True) + train_causals = [] + for i in range(k): + if i < k-1: + batch_fns = fns[i*batch:(i+1)*batch] + else: + batch_fns = fns[i*batch:] + trainX, trainY, neg_pos_ratio = get_training_data(batch_fns, label_col=label_col) + features = trainX.columns.tolist() + model.set_params(**{'class_weight': {0:1, 1:neg_pos_ratio}, + 'n_estimators': n_estimator}) + model.fit(trainX, trainY) + n_estimator += 10 + train_causals += trainX.index[np.where(trainY==1)[0]].tolist() + return model, features, train_causals + + +def rank_patient(model, fn, features, train_causals=None): + df = pd.read_csv(fn, sep="\t", index_col=0) ## File name format can be a parameter in the future + identifier = fn.split("/")[-1].split(".")[0] + patient_features = df.loc[:, features] + patient_preds = model.predict_proba(patient_features)[:, 1] + pred_df = df.copy() + pred_df["predict"] = patient_preds + + if train_causals is not None: + pred_df = pred_df.loc[~pred_df.index.isin(train_causals),:] + pred_df = pred_df.sort_values("predict", ascending=False) + min_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='min').astype(int) + max_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='max').astype(int) + pred_df['min_ranking'] = min_rankings + pred_df['max_ranking'] = max_rankings + pred_df['ranking'] = max_rankings + pred_df['identifier'] = identifier + return pred_df + +def rank_test_patients(model, fns, features, label_col, causal_distr = None, + out_folder=None, train_causals=None): + print(out_folder) + causal_rank_dfs = [] + for i in trange(len(fns)): + fn = fns[i] + + if not os.path.exists(fn): + continue + + identifier = fn.split("/")[-1].split("_")[0] + rank_df = rank_patient(model, fn, features, train_causals) + + if causal_distr is not None: + rank_df = assign_confidence_score(causal_distr, rank_df, label_col='is_strong') + + if out_folder is not None: + if not os.path.exists(out_folder): + os.mkdir(out_folder) + + rank_df.to_csv(f"{out_folder}/{identifier}_variant_rank.csv") + + causal_rank = get_causal_rank(rank_df, label_col) + + if causal_rank is not None: + causal_rank_dfs.append(causal_rank) + + causal_rank_dfs = pd.concat(causal_rank_dfs) + return causal_rank_dfs + +def get_causal_rank(pred_df, label_col): + if (pred_df[label_col] == 1).sum() == 0: + return None + return pred_df.loc[pred_df[label_col] == 1,:] \ No newline at end of file diff --git a/scripts/proc.sh b/scripts/proc.sh new file mode 100755 index 0000000..4e535ac --- /dev/null +++ b/scripts/proc.sh @@ -0,0 +1,284 @@ +#!/bin/bash +#$1 submission id +#$2 ref genome +#$3 memory limitation + +#make output subdirectories +echo $(date +"%T") > /out/whole_log.txt +mkdir -m777 /out/phrank +mkdir -m777 /out/input +mkdir -m777 /out/tier-test-false +mkdir -m777 /out/rami-test +mkdir -m777 /out/scores +mkdir -m777 /out/final_matrix +mkdir -m777 /out/final_matrix_expanded + +REF_DIR=$2 +CHUNK_RAM=$3 +KEEP_INTERMEDIATE=${4:-True} + +if [ "$REF_DIR" = "hg19" ] +then + REF_GEN="GRCh37" +else + REF_GEN="GRCh38" +fi + +#Check if hpo.txt is empty or has no valid HPO ID +if [[ -z $(egrep 'HP:[0-9]{7}' /input/hpo.txt) ]] ; then + #Replace it with HP:0000001 + echo "HP:0000001" > /input/hpo.txt +fi + + +cp /input/vcf.gz /out/vcf.gz ### New Step: Copying file to output directory + + +# Initial path of the VCF file +INPUT_VCF_PATH="/out/vcf.gz" ### Changed: Adjust path to new output location + +#Check if the file exists +if [ -e "$INPUT_VCF_PATH" ]; then + #Use file command to determine the file type + INPUT_VCF_TYPE=$(file -b "$INPUT_VCF_PATH") + + # Determine if the file is gzip or bgzip format + if echo "$INPUT_VCF_TYPE" | grep -q 'gzip compressed data'; then + # Check if it's bgzip format for tabix compatibility + if echo "$INPUT_VCF_TYPE" | grep -q 'BGZF'; then + echo "The file is in BGZF format, ready for tabix." + tabix -p vcf "$INPUT_VCF_PATH" ### Adjusted Step: Only index if BGZF + else + echo "GZIP format detected, converting to BGZF." + gunzip -c "$INPUT_VCF_PATH" | bgzip > "${INPUT_VCF_PATH%.gz}" + mv "${INPUT_VCF_PATH%.gz}" "$INPUT_VCF_PATH" ### Ensuring the correct .gz extension + tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file + fi + elif echo "$INPUT_VCF_TYPE" | grep -q 'ASCII text'; then + echo "Plain VCF file detected, compressing and indexing." + bgzip -c "$INPUT_VCF_PATH" > "${INPUT_VCF_PATH}.gz" + mv "${INPUT_VCF_PATH}.gz" "$INPUT_VCF_PATH" ### Replace uncompressed with compressed + tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file + fi +else + echo "The file $INPUT_VCF_PATH does not exist." +fi + + + +echo "VCF pre-processing" +#annotate with new chromosomes and preserve original coordinates in ID +bcftools annotate --rename-chrs /run/data_dependencies/bcf_annotate/chrmap.txt -x ID /out/vcf.gz -Oz -o /out/$1-annot.txt +bcftools annotate --set-id +'%CHROM\_%POS\_%REF\_%FIRST_ALT' /out/$1-annot.txt -Oz -o /out/$1-add-id.vcf.gz + + +echo "VCF quality filtering" +#run quality filters #removed 27th line +bcftools filter /out/$1-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o /out/$1.filt.vcf.gz + +#check number of variants left +variant_count=$(bcftools view -H /out/$1.filt.vcf.gz | wc -l) +if [ "$variant_count" -gt 0 ]; then + echo "Quality filtering completed successfully. Variants passing the filters: $variant_count" +else + echo "The VCF file doesn't have FILTER annotation, or all variants filtered." + echo "Pipeline will proceed with unfiltered VCF file." + cp /out/$1-add-id.vcf.gz /out/$1.filt.vcf.gz +fi + +echo "Remove mitochondrial and unknown chromosome variants" +#gunzip /out/$1.filt.vcf.gz +#grep -vE "chrM" /out/$1.filt.vcf > /out/$1.filt.rmMT.vcf +tabix -p vcf /out/$1.filt.vcf.gz +bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y /out/$1.filt.vcf.gz -o /out/$1.filt.rmMT.vcf + +#Phrank annotation +echo "Phrank scoring" +zcat /out/vcf.gz | awk 'substr($0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > /out/$1-var.txt +cat /out/$1-var.txt | sed 's/chr//g' | sort -u > /out/$1-var-filt.txt + + +#annotate with ENSG +#echo "ensg annotates" +python2.7 /run/phrank/src/location_to_gene.py /out/$1-var-filt.txt ${REF_DIR} | sed 's/:/\t/g' | sed 's/X\t/23\t/g' | sed 's/Y\t/24\t/g' | sed 's/MT\t/25\t/g' > /out/$1-ensmbl.txt + + +#echo "gene-sym" +cat /out/$1-ensmbl.txt | sort -k5,5 | join -1 5 -2 1 - /run/data_dependencies/phrank/hg19/ensembl_to_symbol.txt | sed 's/ /\t/g' | cut -f2- > /out/$1-genesym.txt +cat /out/$1-genesym.txt | cut -f5 | sort -u | join -t$'\t' -1 1 -2 2 - /run/data_dependencies/phrank/hg19/gene_to_symbol_sorted.txt | cut -f2 | sort -u > /out/$1-gene.txt + + +# #run phrank scoring +#echo "phrank scoring" +python3.8 /run/phrank/src/run_phrank.py /out/$1-gene.txt /input/hpo.txt ${REF_DIR} > /out/phrank/$1.txt + + +# #annotate with OMIM and HPO path +echo "Calculating phenotype similarities" +Rscript /run/phenoSim.R /input/hpo.txt $1 ${REF_DIR} > /dev/null + + +#Filter proband vcf with blacklist +echo "Filtering VCF with blacklist" +bgzip /out/$1.filt.rmMT.vcf + +tabix /out/$1.filt.rmMT.vcf.gz +mkdir -m777 /out/isec_tmp1 +bcftools isec -p /out/isec_tmp1 -w 1 -Oz \ +/out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.genomes.vcf.gz + +tabix /out/isec_tmp1/0000.vcf.gz +mkdir -m777 /out/isec_tmp2 +bcftools isec -p /out/isec_tmp2 -w 1 -Oz \ +/out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.exomes.vcf.gz + +tabix /out/isec_tmp2/0000.vcf.gz +mkdir -m777 /out/isec_tmp3 +bcftools isec -p /out/isec_tmp3 -Ov \ +/out/isec_tmp1/0000.vcf.gz /out/isec_tmp2/0000.vcf.gz + +mv /out/isec_tmp3/0002.vcf /out/$1.filt.rmBL.vcf +rm -rf /out/isec_tmp1 +rm -rf /out/isec_tmp2 +rm -rf /out/isec_tmp3 + + +#annotate with vep +echo "VEP annotation" + +if [ "$REF_DIR" = "hg38" ] +then + + /opt/vep/src/ensembl-vep/vep \ + --dir_cache /run/data_dependencies/vep/hg38/ \ + --dir_plugins /run/data_dependencies/vep/hg38/Plugins/ \ + --fork 10 --everything --format vcf \ + --cache --offline --tab --force_overwrite \ + --species homo_sapiens --assembly GRCh38 \ + --custom /run/data_dependencies/vep/hg38/gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ + --custom /run/data_dependencies/vep/hg38/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ + --custom /run/data_dependencies/vep/hg38/HGMD_Pro_2022.2_hg38.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ + --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg38/new_tabbed_revel_grch38.tsv.gz,ALL \ + --plugin SpliceAI,snv=/run/data_dependencies/vep/hg38/spliceai_scores.masked.snv.hg38.vcf.gz,indel=/run/data_dependencies/vep/hg38/spliceai_scores.masked.indel.hg38.vcf.gz,cutoff=0.5 \ + --plugin CADD,/run/data_dependencies/vep/hg38/hg38_whole_genome_SNV.tsv.gz,ALL \ + --plugin dbNSFP,/run/data_dependencies/vep/hg38/dbNSFP4.1a_grch38.gz,ALL \ + --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf +else + + /opt/vep/src/ensembl-vep/vep \ + --dir_cache /run/data_dependencies/vep/hg19/ \ + --dir_plugins /run/data_dependencies/vep/hg19/Plugins/ \ + --fork 10 --everything --format vcf \ + --cache --offline --tab --force_overwrite \ + --species homo_sapiens --assembly GRCh37 \ + --custom /run/data_dependencies/vep/hg19/gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ + --custom /run/data_dependencies/vep/hg19/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ + --custom /run/data_dependencies/vep/hg19/HGMD_Pro_2022.2_hg19.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ + --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg19/new_tabbed_revel.tsv.gz,ALL \ + --plugin SpliceAI,snv=/run/data_dependencies/vep/hg19/spliceai_scores.masked.snv.hg19.vcf.gz,indel=/run/data_dependencies/vep/hg19/spliceai_scores.masked.indel.hg19.vcf.gz,cutoff=0.5 \ + --plugin CADD,/run/data_dependencies/vep/hg19/hg19_whole_genome_SNVs.tsv.gz,ALL \ + --plugin dbNSFP,/run/data_dependencies/vep/hg19/dbNSFP4.3a_grch37.gz,ALL \ + --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf +fi + +echo "Feature engineering" +#annotate w/ marrvel flatfiles +AIM_FREE_RAM=$(free -g | awk 'NR==2{printf $7}') + +if (( AIM_FREE_RAM > CHUNK_RAM )); then + RAM_MAX=${CHUNK_RAM} +else + RAM_MAX=${AIM_FREE_RAM} +fi + +python3.8 /run/split_chunks.py $1 $RAM_MAX + +while read -r INDEX LINEH LINEA LINEB +do + sed -n -e "${LINEH}p" -e "${LINEA},${LINEB}p" /out/$1-vep.txt > /out/$1-vep-${INDEX}.txt + + python3.8 /run/annotation/main.py \ + -outPrefix /out/rami-test/r1 \ + -patientID $1 \ + -patientHPOsimiOMIM /out/$1-dx \ + -patientHPOsimiHGMD /out/$1-cz \ + -varFile /out/$1-vep-$INDEX.txt \ + -inFileType vepAnnotTab \ + -patientFileType one \ + -genomeRef ${REF_DIR} \ + -diseaseInh AD \ + -modules curate,conserve + + if [ $INDEX -gt 1 ]; then + sed -n "2,\$p" /out/rami-test/$1_scores.csv > /out/rami-test/$1_scores_$INDEX.csv + rm /out/rami-test/$1_scores.csv + sed -n "2,\$p" /out/rami-test/r1_$1_scores.txt > /out/rami-test/r1_$1_scores_$INDEX.txt + rm /out/rami-test/r1_$1_scores.txt + else + mv /out/rami-test/$1_scores.csv /out/rami-test/$1_scores_$INDEX.csv + mv /out/rami-test/r1_$1_scores.txt /out/rami-test/r1_$1_scores_$INDEX.txt + fi +done < /out/vep_split.txt + + +# Combine all $1_scores_$INDEX.csv +for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) +do + cat /out/rami-test/$1_scores_$INDEX.csv +done > /out/rami-test/$1_scores.csv + +for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) +do + cat /out/rami-test/r1_$1_scores_$INDEX.txt +done > /out/rami-test/r1_$1_scores.txt + +rm /out/$1-vep-*.txt +rm /out/rami-test/r1_$1_scores_*.txt +rm /out/rami-test/$1_scores_*.csv + +Rscript /run/VarTierDiseaseDBFalse.R $1 ${REF_DIR} + +python3.8 /run/generate_new_matrix_2.py $1 ${REF_DIR} + +echo "Making prediction" +python3.8 /run/predict_new/run_final.py $1 + +python3.8 /run/merge_rm.py $1 + +python3.8 /run/extraModel/main.py -id $1 + + +echo $(date +"%T") >> /out/whole_log.txt + +# delete intermediate files +if [ "$KEEP_INTERMEDIATE" = "False" ]; then + rm /out/$1-add-id.vcf.gz + rm /out/$1-annot.txt + rm /out/$1-cz + rm /out/$1-dx + rm /out/$1-ensmbl.txt + rm /out/$1.filt.rmBL.vcf + rm /out/$1.filt.rmMT.vcf.gz + rm /out/$1.filt.rmMT.vcf.gz.tbi + rm /out/$1.filt.vcf + rm /out/$1-genesym.txt + rm /out/$1-gene.txt + rm /out/$1.matrix.txt + rm /out/$1-var-filt.txt + rm /out/$1-var.txt + rm /out/$1-vep.txt + rm /out/$1-vep.txt* + rm /out/vep_split.txt + rm -r /out/final_matrix + rm -r /out/final_matrix_expanded + rm -r /out/input + rm -r /out/phrank + rm -r /out/rami-test + rm -r /out/scores + rm -r /out/tier-test-false +fi + +mv /out/conf_4Model/*.csv /out/ +mv /out/conf_4Model/integrated/*.csv /out/ +rm -r /out/conf_4Model + diff --git a/scripts/simple_repeat_anno.py b/scripts/simple_repeat_anno.py new file mode 100755 index 0000000..39d9d17 --- /dev/null +++ b/scripts/simple_repeat_anno.py @@ -0,0 +1,29 @@ +import pandas as pd +import numpy as np +import os + +#annotates a feature matrix with a simple repeat column + +def simple_repeat_anno(sample_id, feature_file, sp_bed_file): + sample = feature_file + sample_bed = pd.DataFrame({'chr':sample.index.str.split('-').str[0], + 'start':sample.index.str.split('-').str[1], + 'end':sample.index.str.split('-').str[1], + 'ref':sample.index.str.split('-').str[2], + 'alt':sample.index.str.split('-').str[3]}) + sample_bed.to_csv('/out/%s.bed'%(sample_id),index=False,header=False,sep='\t') + + # bedtools needed + os.system('/run/bedtools intersect -a /out/%s.bed -b %s -wa > /out/%s.sp.bed'%(sample_id, sp_bed_file, sample_id)) + + if os.stat('/out/%s.sp.bed'%(sample_id)).st_size == 0: + sample['simple_repeat'] = 0 + else: + sample_sp = pd.read_csv('/out/%s.sp.bed'%(sample_id),sep='\t',header=None) + sample_sp['var_dash'] = sample_sp[0].astype(str) + '-' + sample_sp[2].astype(str) + '-' + sample_sp[3].astype(str) + '-' + sample_sp[4].astype(str) + sample['simple_repeat'] = sample.index.isin(sample_sp['var_dash'].to_numpy()).astype(int) + + return sample + + + diff --git a/scripts/split_chunks.py b/scripts/split_chunks.py new file mode 100644 index 0000000..0837bb8 --- /dev/null +++ b/scripts/split_chunks.py @@ -0,0 +1,44 @@ +import math +import os +import sys +import subprocess + +patientID = sys.argv[1] +RAMlimit = float(sys.argv[2]) + +#numHeaderSkip=354#this depends on the type of annotation used +numHeaderSkip=0 +with open(f"/out/{patientID}-vep.txt",'r') as F: + for line in F: + if line.startswith('##'): + numHeaderSkip += 1 + else: + break + +# 06/30/2023: Process the vep annotation result chunk by chunk +command = f'wc -l /out/{patientID}-vep.txt | cut -d" " -f1' +nlines = int(subprocess.check_output(command, shell=True, text=True).strip()) - numHeaderSkip - 1 + +command = f"du /out/{patientID}-vep.txt" +filesize = int(subprocess.check_output(command, shell=True, text=True).strip().split('\t')[0]) + +num_chunk = math.ceil( (filesize/1024) / (RAMlimit * 1024 / 23) ) + +chunk_lines = math.ceil(nlines/num_chunk) + +lines = [] +if num_chunk > 1: + for i in range(num_chunk-1): + lines.append(f"{i+1}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + i*chunk_lines)}\t{int(numHeaderSkip+1 + (i+1)*chunk_lines)}") + + +lines.append(f"{num_chunk}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + (num_chunk-1)*chunk_lines)}\t{int(nlines+numHeaderSkip+1)}") + +vep_split = open("/out/vep_split.txt", 'w') +vep_split.write('\n'.join(lines)) +vep_split.write('\n') +vep_split.close() + + + + From dc6728b03d2941a269ed9a4058277f0bd8edc1fc Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 11:43:08 -0500 Subject: [PATCH 04/71] added ANNOT PHRANK/ENSMBL --- main.nf | 62 +- run/.gitignore | 43 - run/README.md | 1 - run/VarTierDiseaseDBFalse.R | 381 - run/add_c_nc.py | 74 - run/annotation/main.py | 487 - run/annotation/marrvel_score_recalc.py | 266 - run/annotation/utils_1.py | 536 - run/annotation/utils_for_marrvel_flatfile.py | 1060 - .../utils_for_marrvel_flatfile_module_2.py | 186 - run/annotation/utils_for_symMatch.py | 143 - run/extraModel/__init__.py | 0 run/extraModel/confidence.py | 50 - run/extraModel/generate_bivar_data.py | 179 - run/extraModel/integrate_output.py | 150 - run/extraModel/main.py | 120 - run/fillna_tier.py | 682 - run/generate_new_matrix_2.py | 66 - run/keepna_tier.py | 443 - run/labelling.py | 62 - run/merge_rm.py | 25 - run/mod5_diffusion.py | 147 - run/phenoSim.R | 133 - run/phrank/src/location_to_gene.py | 89 - run/phrank/src/run_phrank.py | 34 - run/predict_new/.py | 14 - run/predict_new/causal_distr.py | 14 - run/predict_new/causal_distr.txt | 74 - run/predict_new/confidence.py | 50 - run/predict_new/features.csv | 1 - run/predict_new/main.py | 162 - run/predict_new/run_final.py | 21 - run/predict_new/test.csv | 17460 ---------------- run/predict_new/utilities.py | 266 - run/proc.sh | 284 - run/simple_repeat_anno.py | 29 - run/split_chunks.py | 44 - 37 files changed, 57 insertions(+), 23781 deletions(-) delete mode 100644 run/.gitignore delete mode 100644 run/README.md delete mode 100644 run/VarTierDiseaseDBFalse.R delete mode 100755 run/add_c_nc.py delete mode 100644 run/annotation/main.py delete mode 100755 run/annotation/marrvel_score_recalc.py delete mode 100755 run/annotation/utils_1.py delete mode 100755 run/annotation/utils_for_marrvel_flatfile.py delete mode 100755 run/annotation/utils_for_marrvel_flatfile_module_2.py delete mode 100755 run/annotation/utils_for_symMatch.py delete mode 100755 run/extraModel/__init__.py delete mode 100755 run/extraModel/confidence.py delete mode 100755 run/extraModel/generate_bivar_data.py delete mode 100755 run/extraModel/integrate_output.py delete mode 100755 run/extraModel/main.py delete mode 100755 run/fillna_tier.py delete mode 100755 run/generate_new_matrix_2.py delete mode 100755 run/keepna_tier.py delete mode 100755 run/labelling.py delete mode 100755 run/merge_rm.py delete mode 100755 run/mod5_diffusion.py delete mode 100755 run/phenoSim.R delete mode 100755 run/phrank/src/location_to_gene.py delete mode 100755 run/phrank/src/run_phrank.py delete mode 100755 run/predict_new/.py delete mode 100755 run/predict_new/causal_distr.py delete mode 100755 run/predict_new/causal_distr.txt delete mode 100755 run/predict_new/confidence.py delete mode 100755 run/predict_new/features.csv delete mode 100755 run/predict_new/main.py delete mode 100755 run/predict_new/run_final.py delete mode 100755 run/predict_new/test.csv delete mode 100755 run/predict_new/utilities.py delete mode 100755 run/proc.sh delete mode 100755 run/simple_repeat_anno.py delete mode 100644 run/split_chunks.py diff --git a/main.nf b/main.nf index ca43db3..6218f4a 100644 --- a/main.nf +++ b/main.nf @@ -42,21 +42,73 @@ process VCF_PRE_PROCESS { input: path vcf path tbi + path chrmap output: - path "*-add-id.vcf.gz" + path "${params.run_id}.filt.vcf.gz" script: """ # Annotate with new chromosomes and preserve original coordinates in ID - bcftools annotate --rename-chrs ${params.chrmap} -x ID $vcf -Oz -o ${params.run_id}-annot + bcftools annotate --rename-chrs $chrmap -x ID $vcf -Oz -o ${params.run_id}-annot # Annotate with new IDs based on CHROM, POS, REF, ALT - bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o ${params.run_id}-add-id.vcf.gz + bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o ${params.run_id}.filt.vcf.gz """ } -workflow { + +process REMOVE_MITO_AND_UNKOWN_CHR { + input: + path vcf + + output: + path "${params.run_id}.filt.rmMT.vcf" + + + script: + """ + tabix -p vcf $vcf + bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y $vcf -o ${params.run_id}.filt.rmMT.vcf + """ +} + +process ANNOT_PHRANK { + input: + path vcf + + output: + path "*-var-filt.txt" + + script: + """ + zcat $vcf | awk 'substr(\$0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > var.txt + cat var.txt | sed 's/chr//g' | sort -u > ${params.run_id}-var-filt.txt + """ +} + +process ANNOT_ENSMBLE { + input: + path ref + path vcf + + output: + path "*-ensmbl.txt" + + script: + """ + python2.7 ${workflow.projectDir}/scripts/phrank/src/location_to_gene.py $vcf $ref | \\ + sed 's/:/\\t/g' | sed 's/X\\t/23\\t/g' | sed 's/Y\\t/24\\t/g' | \\ + sed 's/MT\\t/25\\t/g' > ${params.run_id}-ensmbl.txt + """ + +} + + +workflow { INDEX_VCF(params.input_vcf) - VCF_PRE_PROCESS(INDEX_VCF.out) + VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) + REMOVE_MITO_AND_UNKOWN_CHR(VCF_PRE_PROCESS.out) + ANNOT_PHRANK(VCF_PRE_PROCESS.out) + ANNOT_ENSMBLE(params.ref_dir, ANNOT_PHRANK.out) } \ No newline at end of file diff --git a/run/.gitignore b/run/.gitignore deleted file mode 100644 index 801baac..0000000 --- a/run/.gitignore +++ /dev/null @@ -1,43 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - - -# Mac/OSX -.DS_Store - -# Windows -Thumbs.db - -# Data dependencies -predict_new/*.job -data_dependencies/ -model_inputs/ - -# Misc. -old_bak/ - diff --git a/run/README.md b/run/README.md deleted file mode 100644 index 826d7c2..0000000 --- a/run/README.md +++ /dev/null @@ -1 +0,0 @@ -# aim_one_pipeline diff --git a/run/VarTierDiseaseDBFalse.R b/run/VarTierDiseaseDBFalse.R deleted file mode 100644 index e53439c..0000000 --- a/run/VarTierDiseaseDBFalse.R +++ /dev/null @@ -1,381 +0,0 @@ -# Run Tier - -# Updates history #### -# Update on 2021.Sep13 -#' Updates: -#' 1. corrected chromosome number 23->X, 24-> Y -#' 2. add interaction term of phenotype and Tier. -#' 3. for non-coding var, if ClinVarClinSig == conflict -> push the Tier higher (if IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict, Tier = Tier + 1) -#' 4. Keep all variant that in-trans (sudo) with a strong Var in the same gene, adjust the Tier to 3.5? -# Updates on 2022.Apr5 -#' 1. remove the pre-processing of OMIM inheritance to VarTier_preprocessOMIM.R -#' 2. Add the number of variants (H/M/L impact) per gene: "No.Var.HM","No.Var.H","No.Var.M","No.Var.L" -#' 3. Add the option if IncludeClinVarHGMD -#' -#' Updates on 2022.Sep29 -#' ClinVar clinvarSignDesc is in dif format after optimizing the pipeline, update the code to be compatible for that -#' gnomadAF, gnomadAFg is "-" not "NA", update the code for that -#' add IncludeClinVarHGMD for line 101 - - -#' change genemap2.Inh into 2 features, AD, AR, save to tsv file -#' keep GeneID == "-" assinge Tier value -#' input output /out/explain/ "ID" + "_Tier" -#' - -# parameter settings #### - -# set adj.k value to adjust Tier -adj.k <- 1.05 - -# set if diseases DB will be included -IncludeClinVarHGMD <- FALSE - -# load database: OMIM inheritance #### -# genemap2.Inh.F <- readRDS(file.path("/run/genemap2.Inh.F.rds")) -#genemap2.Inh.F <- read.delim2(file.path("/run/data_dependencies/var_tier/hg19/genemap2.Inh.F.txt")) -# genemap2.Inh.F <- read.delim2("/houston_10t/dongxue/MARRVEL_AI/resource/OMIM/out/genemap2.Inh.F.txt") -args = commandArgs(trailingOnly=TRUE) -refg <- args[2] -# load database: OMIM inheritance #### -# genemap2.Inh.F <- readRDS(file.path("/run/genemap2.Inh.F.rds")) -genemap2.Inh.F <- read.delim2(file.path(paste0('/run/data_dependencies/var_tier/',refg,'/genemap2.Inh.F.txt'))) - -# set the input output file / directory #### - - - -# in.f.path <- "/out/input/" -in.f.path <- "/out/rami-test/" -out.f.path <- "/out/tier-test-false/" - -# When test the code without docker -#in.f.path <- out.f.path <- "/out/" - -# in.f <- paste0(args[1],"_Tier") -# in.f <- paste0(args[1],"_scores.csv") -in.f <- paste0(args[1],"_scores.csv") -out.f <- paste0(args[1],"_Tier.v2.tsv") - -# load libraries #### -library("data.table") -library("dplyr") - - - -# load the input #### -anno.columns <- c('varId_dash','zyg','geneSymbol','geneEnsId','gnomadAF','gnomadAFg','omimSymptomSimScore','hgmdSymptomSimScore', - 'IMPACT','Consequence','hgmdVarFound','clinvarSignDesc','spliceAImax') - -anno.orig <- read.table(file.path(in.f.path,in.f), stringsAsFactors = F, sep = ",", header = T) -# To test: -# score.path <- "/houston_20t/chaozhong/MARRVEL_AI_2022/explain/906246_raw.csv" -# score.path <- "/houston_20t/chaozhong/MARRVEL_AI_2022/opt_09302022/output/rami-test/906246_scores.csv" -# score.path <- "/houston_30t/chaozhong/NDV_model/fake_VCF_test/out/rami-test/NDVG_causal_scores.csv" -# anno.orig <- read.csv(score.path) -anno <- anno.orig[,anno.columns] - -# rename the col -colnames(anno) <- c("Uploaded_variation","GT","SYMBOL","Gene","gnomadAF","gnomadAFg", - "omimSymptomSimScore", "hgmdSymptomSimScore","IMPACT", - "Consequence","hgmdVarFound","clinvarSignDesc","spliceAImax") - -if ("-" %in% anno$Gene) { - # for rows without gene ID -> Assign Tier now: - anno_noGeneID <- anno[anno$Gene == "-",c("Uploaded_variation","Gene","IMPACT")] - # convert IMPACT to numeric value - anno_noGeneID$IMPACT.max <- ifelse(anno_noGeneID$IMPACT == "MODIFIER", 1, ifelse(anno_noGeneID$IMPACT == "LOW", 2, ifelse(anno_noGeneID$IMPACT == "MODERATE",3,4)) ) - anno_noGeneID$IMPACT <- NULL - # "TierAD","TierAR","TierAR.adj" - anno_noGeneID$TierAD <- 5 - anno_noGeneID$IMPACT.max - anno_noGeneID[,c("TierAR","TierAR.adj")] <- ifelse(anno_noGeneID$IMPACT.max >= 3, 3,4) - anno_noGeneID$No.Var.HM <- as.numeric(anno_noGeneID$IMPACT.max >= 3) - anno_noGeneID$No.Var.H <- as.numeric(anno_noGeneID$IMPACT.max == 4) - anno_noGeneID$No.Var.M <- as.numeric(anno_noGeneID$IMPACT.max == 3) - anno_noGeneID$No.Var.L <- as.numeric(anno_noGeneID$IMPACT.max <=2) -} - - - -# For rows with Gene ID -> Tier analysis -anno <- anno[anno$Gene != "-",] -anno <- unique(anno) - - -# Tier #### - - -# STEP 0. Adjust variant IMPACT for ClinVar, HGMD, SpliceAImax >= 0.8 #### - -if (IncludeClinVarHGMD){ - anno$IMPACT <- ifelse(grepl("Pathogenic|Likely_pathogenic",anno$clinvarSignDesc) | anno$hgmdVarFound == 1 , "HIGH",anno$IMPACT) -} - -# label Splice AI > 0.8 as HIGH -anno$IMPACT <- ifelse(anno$spliceAImax >=0.8, "HIGH",anno$IMPACT) -# convert IMPACT to numeric value -anno$IMPACT.no <- ifelse(anno$IMPACT == "MODIFIER", 1, ifelse(anno$IMPACT == "LOW", 2, ifelse(anno$IMPACT == "MODERATE",3,4)) ) - - -# Implement the rule based Tier classification #### - -# take IMPACT HIGH, MODERATE, LOW, or MODIFIER as H,M,L,L -# Tier at Gene level - -# STEP 1. classify T4: genes with only L variants, nonCodingVar2keep #### - -# STEP 1.1 Keep Genes with IMPACT H or M variants -Gene.all <- unique(anno$Gene) -Gene.IMPACT <- unique(anno[,c("Gene","IMPACT")]) -Gene.IMPACT.HM <- Gene.IMPACT[Gene.IMPACT$IMPACT %in% c("HIGH","MODERATE"),] - -# GeneTier4 will be removed for remaining Tier analysis -GeneTier4 <- Gene.all[!(Gene.all %in% Gene.IMPACT.HM$Gene)] -anno$TierAD[anno$Gene %in% GeneTier4] = anno$TierAR[anno$Gene %in% GeneTier4] <- 4 - -# STEP 1.2 Keep Var with max IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict -# note: many variants with “Conflicting” are non-coding and coding for different transcripts, only keep the var with max impact as MODIFIER - -# find the non-Coding Var to keep -nonCodingVar2keep <- c() - -if(IncludeClinVarHGMD){ - nonCodingModifier.idx <- which(grepl("Conflicting_interpretations_of_pathogenicity", anno$clinvarSignDesc) & (anno$IMPACT %in% c("MODIFIER"))) - nonCodingModifier <- anno$Uploaded_variation[nonCodingModifier.idx] - nonCodingModifier.df <- anno[anno$Uploaded_variation %in% nonCodingModifier,] - - for(var in unique(nonCodingModifier.df$Uploaded_variation)){ - var.df <- nonCodingModifier.df[nonCodingModifier.df$Uploaded_variation == var,] - var.impact <- unique(var.df$IMPACT) - if(length(var.impact) == 1 ){ - if(var.impact == "MODIFIER"){ - nonCodingVar2keep <- c(nonCodingVar2keep,var) - } - } - } -} - - -# split var to two groups: #### - -# Run Full Tier analysis: -# non - GeneTier4 and nonCodingVar2keep -idx <- is.na(anno$TierAD) | (anno$Uploaded_variation %in% nonCodingVar2keep) -Var.RunTier <- unique(anno$Uploaded_variation[idx]) - -# Only Count No.Var.L： -# Others: -# check if there are variants without Tier -ParseVar.NoTier <- F -if(F %in% idx){ - ParseVar.NoTier <- T - Var.NoTier <- unique(anno$Uploaded_variation[!idx]) -} - -anno.f1 <- anno[idx,] - -# set nonCodingVar2keep IMPACT as 3 -anno.f1$IMPACT.no[anno.f1$Uploaded_variation %in% nonCodingVar2keep] <- 3 - - -# STEP 2: Get the most severe IMPACT for each variant #### - -VEP.f1 <- anno.f1 - -# for Gene-Var pair with multiple IMPACT, take the MAX -VEP.f1.maxIMAPCT <- data.frame(matrix(ncol = ncol(VEP.f1) + 1, nrow = 0)) -colnames(VEP.f1.maxIMAPCT) <- c(colnames(VEP.f1),"IMPACT.max") -for (var in Var.RunTier){ - var.df <- VEP.f1[VEP.f1$Uploaded_variation == var,] - # check no. of gene for this variant - if(length(unique(var.df$Gene)) == 1){ - var.df$IMPACT.max <- max(var.df$IMPACT.no) - VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT,var.df) - } else { - for (gene in unique(var.df$Gene)){ - var.g.df <- var.df[var.df$Gene == gene,] - var.g.df$IMPACT.max <- max(var.g.df$IMPACT.no) - VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT,var.g.df) - } - } -} - -# find all intronic Var with AF == 0, will adjust the Tier if in trans with a high impact variant. #### - -rareVar <- VEP.f1.maxIMAPCT$gnomadAF == "-" & VEP.f1.maxIMAPCT$gnomadAFg == "-" -intronicVar <- grepl("intron_variant",VEP.f1.maxIMAPCT$Consequence) & VEP.f1.maxIMAPCT$IMPACT.max == 1 -rareIntronicVar <- VEP.f1.maxIMAPCT[rareVar & intronicVar, c("Uploaded_variation","Gene")] -rareIntronicVar <- unique(rareIntronicVar) - -# for each Variant keep the most severe IMPACT only -VEP.f2 <- unique(VEP.f1.maxIMAPCT[,c("Uploaded_variation", "SYMBOL","Gene","GT","IMPACT.max","omimSymptomSimScore","hgmdSymptomSimScore")]) - -# STEP 3: assign Tiers #### - -# VEP.f2$GT <- ifelse(VEP.f2$GT=="HET", "0/1","1/1") - -# though Tier classification is Gene based, Tier should be assigned to each Variant - -VEP.Tier.part1 <- data.frame(matrix(ncol = ncol(VEP.f2) + 6, nrow = 0)) -colnames(VEP.Tier.part1) <- c(colnames(VEP.f2),"TierAR","TierAD","No.Var.HM","No.Var.H","No.Var.M","No.Var.L") - -for (g in unique(VEP.f2$Gene)){ - - g.df <- VEP.f2[VEP.f2$Gene == g,] - - # X linked, seudo autosomal region -> treat as autosomal - # if (sum(grepl("X",g.df$Uploaded_variation)) >0) { - # g.df$TierAD = g.df$TierAR = ifelse(g.df$IMPACT.max <3, 4, 5-g.df$IMPACT.max) - # } - - # AD regardless of the GT - g.df$TierAD <- ifelse(g.df$IMPACT.max <3, 4, 5-g.df$IMPACT.max) - - # AR - g.df2 <- g.df - - # duplicate rows with HOM var - if ("HOM" %in% g.df$GT){ - g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1",]) - } - IMPACT.max <- g.df2$IMPACT.max - if(sum(IMPACT.max==4) >=2){ - # >=2 strong alleles - g.df$TierAR <- 5-g.df$IMPACT.max - # update 202221005: H+H: M-> change Tie from 2 to 1.5 - g.df$TierAR[g.df$IMPACT.max ==3] <- 1.5 - } else if (sum(IMPACT.max==4) ==1 & sum(IMPACT.max==3) >=1) { - # H + M - g.df$TierAR[g.df$IMPACT.max >=3] <- 1.5 # (need more thought, 2 or 1.5 ???) - g.df$TierAR[g.df$IMPACT.max <3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max <3] - } else if (sum(IMPACT.max==4) ==1 ) { - # H only - g.df$TierAR[g.df$IMPACT.max ==4] <- 3 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max <=3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max <=3] - } else if (sum(IMPACT.max==3) >=2 ) { - # M + M - g.df$TierAR[g.df$IMPACT.max ==3] <- 2 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max < 3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max < 3] - } else if (sum(IMPACT.max==3) == 1 ) { - # M only - g.df$TierAR[g.df$IMPACT.max ==3] <- 3 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max < 3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max < 3] - } - - # count the number of high IMPACT var per gene. - No.Var.HM <- sum(IMPACT.max >=3) - No.Var.H <- sum(IMPACT.max == 4) - No.Var.M <- sum(IMPACT.max ==3) - No.Var.L <- sum(IMPACT.max == 2) - g.df$No.Var.HM <- No.Var.HM - g.df$No.Var.H <- No.Var.H - g.df$No.Var.M <- No.Var.M - g.df$No.Var.L <- No.Var.L - - VEP.Tier.part1 <- rbind(VEP.Tier.part1,g.df) -} - -# STEP 4: Find all genes with No.Var.H == 1, adjust the Tier for rareIntronicVar #### - -if (IncludeClinVarHGMD){ - gene.w1HighVar <- VEP.Tier.part1$Gene[VEP.Tier.part1$No.Var.H == 1] - rareIntronicVar.2adj <- rareIntronicVar[rareIntronicVar$Gene %in% gene.w1HighVar,] - - # adjust the Tier for rareIntronicVar based on similarity - phenoSimi <- unique(VEP.f1.maxIMAPCT[,c("Gene","omimSymptomSimScore","hgmdSymptomSimScore")]) - - # check for non numeric values and convert to 0 - omim_NA_idx <- which(is.na(as.numeric(phenoSimi$omimSymptomSimScore))) - hgmd_NA_idx <- which(is.na(as.numeric(phenoSimi$hgmdSymptomSimScore))) - # print(hgmd_NA_idx) - phenoSimi$omimSymptomSimScore[omim_NA_idx] <- 0 - phenoSimi$hgmdSymptomSimScore[hgmd_NA_idx] <- 0 - - phenoSimi$omimSymptomSimScore <- as.numeric(phenoSimi$omimSymptomSimScore) - phenoSimi$hgmdSymptomSimScore <- as.numeric(phenoSimi$hgmdSymptomSimScore) - - phenoSimi <- phenoSimi[phenoSimi$omimSymptomSimScore != 0 | phenoSimi$hgmdSymptomSimScore != 0,] - - rareIntronicVar.2adj.wPhenoSimi <- merge(rareIntronicVar.2adj, phenoSimi, by.x = "Gene", by.y = "Gene") - rareIntronicVar.2adj.wPhenoSimi$simi <- apply(rareIntronicVar.2adj.wPhenoSimi[, 3:4], 1, max) - rareIntronicVar.2adj.wPhenoSimi$TierAR.adj <- 5-(5-4)*exp(adj.k*rareIntronicVar.2adj.wPhenoSimi$simi) - - rareIntronicVar.df <- rareIntronicVar.2adj.wPhenoSimi[,c("Uploaded_variation","Gene","TierAR.adj")] - VEP.Tier.part1 <- merge(VEP.Tier.part1,rareIntronicVar.df, - by.x <- c("Uploaded_variation","Gene"), - by.y = c("Uploaded_variation","Gene"), - all.x = T) -} else { - VEP.Tier.part1$TierAR.adj <- VEP.Tier.part1$TierAR -} - -# add the TierAR.adj to the VEP.Tier df - -VEP.Tier.part1 <- VEP.Tier.part1[,c("Uploaded_variation","Gene","GT","IMPACT.max","TierAD","TierAR","TierAR.adj","No.Var.HM","No.Var.H","No.Var.M","No.Var.L")] - -# STEP 5: add the No.Var for Var.NoTier #### -if (ParseVar.NoTier){ - VEP.Tier.part2 <- anno[anno$Uploaded_variation %in% Var.NoTier,c("Uploaded_variation","Gene","GT")] - VEP.Tier.part2$IMPACT.max <- 1 - VEP.Tier.part2[,c("TierAD","TierAR","TierAR.adj")] <- 4 - VEP.Tier.part2[,c("No.Var.HM","No.Var.H","No.Var.M","No.Var.L")] <- NA - - VEP.Tier <- rbind(VEP.Tier.part1, VEP.Tier.part2) -} else { - VEP.Tier <- VEP.Tier.part1 -} - -VEP.Tier.wGene <- data.frame(matrix(ncol = ncol(VEP.Tier), nrow = 0)) -for (g in unique(VEP.Tier$Gene)){ - - g.df <- VEP.Tier[VEP.Tier$Gene == g,] - # AR - g.df2 <- g.df - - # duplicate rows with HOM var - if ("HOM" %in% g.df$GT){ - g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1",]) - } - IMPACT.max <- g.df2$IMPACT.max - - No.Var.HM <- sum(IMPACT.max >=3) - No.Var.H <- sum(IMPACT.max == 4) - No.Var.M <- sum(IMPACT.max ==3) - No.Var.L <- sum(IMPACT.max == 2) - g.df$No.Var.HM <- No.Var.HM - g.df$No.Var.H <- No.Var.H - g.df$No.Var.M <- No.Var.M - g.df$No.Var.L <- No.Var.L - - VEP.Tier.wGene <- rbind(VEP.Tier.wGene,g.df) -} -# remove the GT column -VEP.Tier.wGene$GT <- NULL - - -# STEP 6: add the Tiers from anno_noGeneID -if ("-" %in% anno$Gene) { - VEP.Tier.final <- rbind(VEP.Tier.wGene, anno_noGeneID) -} -VEP.Tier.final <- VEP.Tier.wGene - -colnames(VEP.Tier.final)[colnames(VEP.Tier.final) == "IMPACT.max"] <- "IMPACT.from.Tier" -VEP.Tier.final$TierAR.adj[is.na(VEP.Tier.final$TierAR.adj)] <- VEP.Tier.final$TierAR[is.na(VEP.Tier.final$TierAR.adj)] - - -# add inheritance information -colnames(genemap2.Inh.F)[1] <- "Gene" -VEP.Tier.wInh <- merge(VEP.Tier.final, genemap2.Inh.F, by.x = "Gene", by.y = "Gene", all.x = T) -VEP.Tier.wInh$dominant[is.na(VEP.Tier.wInh$dominant)] <- 0 -VEP.Tier.wInh$recessive[is.na(VEP.Tier.wInh$recessive)] <- 0 -VEP.Tier.wInh$AD.matched <- ifelse(VEP.Tier.wInh$TierAD <=2 & (VEP.Tier.wInh$dominant == 1),1,0) -VEP.Tier.wInh$AR.matched <- ifelse(VEP.Tier.wInh$TierAR <=2 & (VEP.Tier.wInh$recessive == 1),1,0) - -write.table(VEP.Tier.wInh,file.path(out.f.path,out.f), sep = "\t", quote = F, col.names = T, row.names = F) - -# write.table(VEP.Tier.wInh,"/houston_20t/dongxue/MARRVEL_AI/V2/FeatureEng/TestOut/906246_Tier.tsv", sep = "\t", quote = F, col.names = T, row.names = F) -# write.table(VEP.Tier.wInh,"/houston_30t/dongxue/AI/UDN_Sample_2023/ModifiedAIMCode/NDVG_causal_Tier.v2.tsv", sep = "\t", quote = F, col.names = T, row.names = F) -### Plot for Tier adjustment #### - - - diff --git a/run/add_c_nc.py b/run/add_c_nc.py deleted file mode 100755 index dc43bd8..0000000 --- a/run/add_c_nc.py +++ /dev/null @@ -1,74 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 -import pandas as pd -import numpy as np - - - -def add_c_nc(score, ref): - - #read data files - if ref == 'hg38': - clin_c=pd.read_csv("/run/data_dependencies/merge_expand/hg38/clin_c.tsv.gz", sep="\t") - clin_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg38/clin_nc.tsv.gz", sep="\t") - hgmd_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t") - hgmd_c=pd.read_csv("/run/data_dependencies/merge_expand/hg38/hgmd_c.tsv.gz", sep="\t") - else: - clin_c=pd.read_csv("/run/data_dependencies/merge_expand/hg19/clin_c.tsv.gz", sep="\t") - clin_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg19/clin_nc.tsv.gz", sep="\t") - hgmd_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t") - hgmd_c=pd.read_csv("/run/data_dependencies/merge_expand/hg19/hgmd_c.tsv.gz", sep="\t") - - - a = score.pos.values - ac = score.chrom.values - temp=score[["varId"]] - - bh = clin_nc.new_stop.values - bl = clin_nc.new_start.values - bc= clin_nc.new_chr.values - - i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) &(ac[:, None]==bc)) - - cln=pd.concat([ - temp.loc[i, :].reset_index(drop=True), - clin_nc.loc[j, :].reset_index(drop=True) - ], axis=1) - - # Take into account When HGMD data base is empty - if hgmd_nc.shape[0] == 0: - pass - - else: - #merge by region - a = score.pos.values - ac = score.chrom.values - temp=score[["varId"]] - bh = hgmd_nc.new_stop.values - bl = hgmd_nc.new_start.values - bc= hgmd_nc.new_chr.values - i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) &(ac[:, None]==bc)) - - hgmd=pd.concat([ - temp.loc[i, :].reset_index(drop=True), - hgmd_nc.loc[j, :].reset_index(drop=True) - ], axis=1) - - - merged=score.merge(cln, how="left", on="varId") - if hgmd_nc.shape[0] == 0: - merged['nc_HGMD_Exp'] = np.NaN - merged['nc_RANKSCORE'] = np.NaN - else: - merged=merged.merge(hgmd, how="left", on="varId") - - if hgmd_c.shape[0] == 0: - merged['c_HGMD_Exp'] = np.NaN - merged['c_RANKSCORE'] = np.NaN - merged['CLASS'] = np.NaN - else: - merged=merged.merge(hgmd_c, how="left", left_on=["chrom", "pos", "ref", "alt"], right_on=["new_chr", "new_pos", "ref", "alt"]) - merged=merged.merge(clin_c, how="left", left_on=["chrom", "pos", "ref", "alt"], right_on=["new_chr", "new_pos", "ref", "alt"]) - - return merged - diff --git a/run/annotation/main.py b/run/annotation/main.py deleted file mode 100644 index c167909..0000000 --- a/run/annotation/main.py +++ /dev/null @@ -1,487 +0,0 @@ -## UPDATE -# Changes made by Chaozhong noted as #CL -# 1. added clinvar feature and changed database to clinvar.vcf.gz in VEP annotation -# 2. chang the HGMD database file to VEP annotation and similarity screo files -# hg19 -import time -import math -import os -import argparse -import re -import pandas as pd -import numpy as np -import json -import sys -import requests -import subprocess -#local methods -from utils_1 import * -#from utils_for_marrvel_annotate_script import * -from utils_for_symMatch import * -from utils_for_marrvel_flatfile import * -#from utils_for_marrvel_flatfile_module_3 import * -import multiprocessing -from functools import partial -from marrvel_score_recalc import * - - -############################################################# -# Database list: -# (common for all) gnomad.v2.1.1.lof_metrics.by_gene.txt -# (hg38 pos needed) gene_clinvar.csv -# (common for all) gene_omim.json -# (common for all) omim_alleric_variants.json -# (hg38 ver needed) dgv.csv (/houston_30t/chaozhong/aimarrvel_pipeline/database/dgv/dgv.38.csv) -# (hg38 pos needed) decipher.csv -# -############################################################# - - - - -def checkUserArgs(inArgs): - #check the user arguments and if anything necessary is missing - pass - - -def main(): - #example of running the script: - #python3 main.py -outPrefix ./test/r1 \ - #-patientID UDN630665 -patientHPOsimiOMIM /houston_10t/dongxue/MARRVEL_AI/BG/HPO2Gene/out_May12021/UDN/HPOsimi_UDN630665_simi_0.tsv \ - #-patientHPOsimiHGMD /six_tera/chaozhong/module_1/out/UDN/HPOsimi_UDNUDN630665_simi_0.tsv \ - #-varFile /houston_20t/rami/vep_PassVarQual_filt_UDN_Apr2521/540200-UDN630665-P_HGYTFBCXY-2-ID01-vep_gnomADg_gnomADe_splice_clin_hgmd_filt.txt \ - #-inFileType vepAnnotTab -patientFile testPatientFile.txt -patientFileType one -genomeRef hg19 -diseaseInh AD -modules curate,conserve - - - parser = argparse.ArgumentParser() - parser.add_argument("-varFile", "--varFile", help="Proivde input variant file") - parser.add_argument("-inFileType", "--inFileType", help="Proivde type of input file:vcf, vepAnnotTab") - parser.add_argument("-patientFile", "--patientFile", help="Proivde HPO IDs")#currently not used - parser.add_argument("-patientFileType", "--patientFileType", help="Proivde type of file:one, two")#currently not used - parser.add_argument("-patientID", "--patientID", help="Proivde patientID") - parser.add_argument("-patientHPOsimiOMIM", "--patientHPOsimiOMIM", help="Proivde patient HPO similarity file-OMIM") - parser.add_argument("-patientHPOsimiHGMD", "--patientHPOsimiHGMD", help="Proivde patient HPO similarity file-HGMD") - parser.add_argument("-diseaseInh", "--diseaseInh", help="Proivde disease Inheritance:AD, AR, XD, XR") - parser.add_argument("-modules", "--modules", help="Select modules to run:all,curate,conserve,effectOnGene") - parser.add_argument("-genomeRef", "--genomeRef", help="Proivde genome ref: hg19, hg38") - parser.add_argument("-outPrefix", "--outPrefix", help="Proivde prefix for output files") - args = parser.parse_args() - #check the user args - checkUserArgs(args) - print('input file:', args.varFile) - print('type of input file:', args.inFileType) - print('outPrefix:', args.outPrefix) - print('modules:', args.modules) - moduleList=args.modules.split(',') - print('modules list:', moduleList) - - #start time - start_time = time.time() - - #need to write user options to a log file - - #read the gnomad gene metrics file - fileName='/run/data_dependencies/annotate/anno_hg19/gnomad.v2.1.1.lof_metrics.by_gene.txt' - #fileName='/database_test/gnomad.v2.1.1.lof_metrics.by_gene.txt' - gnomadMetricsGeneDf=pd.read_csv(fileName, sep='\t') - #print('gnomadMetricsGeneDf shape:', gnomadMetricsGeneDf.shape) - - #set up flags - #debug flag; if 1 then can check/test the code - debugFlag=0 - #print('debugFlag:', debugFlag) - #use flat files to read the marrvel databases instead of using API - flatFileFlag=1 - #use marrvel annotation script and API to get marrvel info. this is slower - annotateFlag=0 - - #the directory for marrvel database flat file - #flatFilesDir='/home/dongxue/hdd_10t/MARRVEL_AI/resource/MARRVEL/' - - #initialization - dgvDf=[] - decipherDf=[] - hgmdDf=[] - omimHPOScoreDf=[] - hgmdHPOScoreDf=[] - clinvarGeneDf=[] - clinvarAlleleDf=[] - omimAlleleList=[] - - - ## CL: It's no longer needed to extract info from HGMD_allmut.txt - """ - #read HGMD files - if 'curate' in moduleList: - #hgmd dir - fileName='/run/annotation/HGMD_allmut.txt' - hgmdDf=pd.read_csv(fileName, sep='\t') - print('hgmdDf shape:', hgmdDf.shape) - hgmdDf=hgmdDf.fillna(0) - hgmdDf['startCoord']=hgmdDf['startCoord'].astype(int) - hgmdDf['endCoord']=hgmdDf['endCoord'].astype(int) - hgmdDf['chromosome']=hgmdDf['chromosome'].replace('X',23 ) - hgmdDf['chromosome']=hgmdDf['chromosome'].replace('Y',24 ) - hgmdDf['chromosome']=hgmdDf['chromosome'].replace('MT',25 ) - hgmdDf['chromosome']=hgmdDf['chromosome'].replace('GL.*','26',regex=True ) - #make chr as int - hgmdDf['chromosome']=hgmdDf['chromosome'].astype(int) - #sort by gene name - hgmdDf.sort_values("gene", inplace=True) - #setting gene as index column - hgmdDf.set_index("gene", inplace = True,drop=False) - - #test/check HGMD - if debugFlag==1: - print('in HGMD') - #print('\tvar:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol) - geneSymbol='SAMD11' - pos=865595 - chrom='1' - print('\tchrom:', chrom, 'pos:',pos,'geneSymbol:', geneSymbol) - HGMDDict={} - hgmdGeneFound=0 - hgmdVarFound=0 - hgmdVarPhenIdList=[] - hgmdVarHPOIdList=[] - hgmdVarHPOStrList=[] - #check variant - print('\ttype pos:', type(hgmdVarDf['pos'][0]), 'type chrom:', type(hgmdVarDf['chrom'][0])) - vals=hgmdVarDf[(hgmdVarDf['pos']==pos) & (hgmdVarDf['chrom'] == int(chrom))] - numRows=len(vals.index) - print('\tvar numRows:', numRows) - if numRows>0: - hgmdVarFound=1 - hgmdVarPhenIdList.extend(vals['phen_id'].tolist()) - hgmdVarHPOIdList.extend(vals['hpo_id'].tolist()) - hgmdVarHPOStrList.extend(vals['hpo_str'].tolist()) - - #check gene - vals=hgmdGeneDf[(hgmdGeneDf['gene']==geneSymbol)] - numRows=len(vals.index) - print('\tgene numRows:', numRows) - if numRows>0: - hgmdGeneFound=1 - print('\thgmdVarFound:',hgmdVarFound,'hgmdGeneFound:',hgmdGeneFound, - 'hgmdVarPhenIdList:',hgmdVarPhenIdList,'hgmdVarHPOIdList:',hgmdVarHPOIdList, - 'hgmdVarHPOStrList:',hgmdVarHPOStrList) - - """ - - #read HPO files - if 'curate' in moduleList: - fileName=args.patientHPOsimiOMIM - omimHPOScoreDf = pd.read_csv(fileName, sep='\t') - print('patientHPOsimi-OMIM dimension:',omimHPOScoreDf.shape) - - fileName=args.patientHPOsimiHGMD - hgmdHPOScoreDf = pd.read_csv(fileName, sep='\t') - print('patientHPOsimi-HGMD dimension:',hgmdHPOScoreDf.shape) - - #read the clinvar genes file - if 'curate' in moduleList: - - # Q: Why we use the same for hg19 and hg38? - # A: - if args.genomeRef == 'hg38': - fileName='/run/data_dependencies/annotate/anno_hg19/gene_clinvar.csv' - else: - fileName='/run/data_dependencies/annotate/anno_hg19/gene_clinvar.csv' - #fileName='/database_test/gene_clinvar.csv' - #print('\nfileName:', fileName) - clinvarGeneDf=pd.read_csv(fileName, sep=',') - #sort by gene name - clinvarGeneDf.sort_values("symbol", inplace=True) - clinvarGeneDf.set_index(['symbol'], inplace = True,drop=False) - #print('clinvarGenes dim:', clinvarGeneDf.shape)#41754 x 10,, entrezId,symbol,chr,hg19Start,hg19Stop,totalClinvarVars,P,LP,LB,B - #print(clinvarGeneDf.head()) - - ### CL: this part was substituted by VEP custom annotation from clinvar.vcf.gz - """ - #read the clinvar variant file - fileName='/run/annotation/clinvar.csv' - print('\nfileName:', fileName) - clinvarAlleleDf=pd.read_csv(fileName, sep=',') - print('clinvarAllele dim:', clinvarAlleleDf.shape)# (507196, 5) title chr start stop significance.description - - #convert float to int - clinvarAlleleDf = clinvarAlleleDf.fillna(0) - clinvarAlleleDf['start'] = clinvarAlleleDf['start'].astype(int) - clinvarAlleleDf['stop'] = clinvarAlleleDf['stop'].astype(int) - #in chr column change X and Y to numbers - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('X',23) - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('Y',24) - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('MT',25) - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('GL.*','26',regex=True ) - #make chr as int - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].astype(int) - #make index - clinvarAlleleDf['id1']=clinvarAlleleDf['chr'].astype(str)+ '_' +clinvarAlleleDf['start'].astype(str)+'_'+ clinvarAlleleDf['stop'].astype(str) - # setting first name as index column - clinvarAlleleDf.set_index("id1", inplace = True,drop=False) - #make sure start and stop are int - print('type chr:', type(clinvarAlleleDf['chr'][0]), 'start:', type(clinvarAlleleDf['start'][0]), 'stop:', type(clinvarAlleleDf['stop'][0])) - """ - - #read OMIM - if 'curate' in moduleList: - #read the OMIM gene file - fileName='/run/data_dependencies/annotate/anno_hg19/gene_omim.json' - #fileName='/database_test/gene_omim.json' - #print('\nfileName:', fileName) - with open(fileName) as f: - omimGeneList=json.load(f) - #print('type omimGeneList:', type(omimGeneList), 'len:', len(omimGeneList)) - #check the type of data and keys - if debugFlag==1: - for omimGeneDict in omimGeneList: - print('type of omimGeneDict:', type(omimGeneDict)) - print('keys:', omimGeneDict.keys()) - for keyVal in omimGeneDict.keys(): - print('keyVal:', keyVal) - print('\tsubkeys type:', type(omimGeneDict[keyVal]) ) - if isinstance(omimGeneDict[keyVal], list): - print('\n\t\tfound list') - print('\t\t type:',type(omimGeneDict[keyVal])) - #print('\t\t type:',omimGeneDict[keyVal]) - #for row in omimGeneDict[keyVal]: - # print('row keys:', row.keys()) - - break - - #read the OMIM allele file - fileName='/run/data_dependencies/annotate/anno_hg19/omim_alleric_variants.json' - #fileName='/database_test/omim_alleric_variants.json' - #print('\nfileName:', fileName) - with open(fileName) as f: - omimAlleleList=json.load(f) - if debugFlag==1: - print('type of omim allelic :', type(omimAlleleList), 'len:', len(omimAlleleList)) - for omimAlleleDict in omimAlleleList: - print('type of omimAlleleDict:', type(omimAlleleDict)) - #check the keys - print('keys:', omimAlleleDict.keys()) - for keyVal in omimAlleleDict.keys(): - print('keyVal:', keyVal) - print('\tsubkeys type:', type(omimAlleleDict[keyVal]) ) - break - - #read DGV database - if 'conserve' in moduleList: - print('reading DGV flat file') - if args.genomeRef == 'hg38': - fileName='/run/data_dependencies/annotate/anno_hg38/dgv.csv' - else: - fileName='/run/data_dependencies/annotate/anno_hg19/dgv.csv' - #fileName='/database_test/dgv.csv' - dgvDf=pd.read_csv(fileName, sep=',') - #print('dgvDf shape:', dgvDf.shape) - #print('dgvHead:', dgvDf.head()) - #hg19Chr hg19Start hg19Stop - dgvDf=dgvDf.fillna(0) - - #if args.genomeRef == 'hg19': - dgvDf.columns = ['Chr', 'Start', 'Stop'] + dgvDf.columns.tolist()[3:] - - dgvDf['Start']=dgvDf['Start'].astype(int) - dgvDf['Stop']=dgvDf['Stop'].astype(int) - dgvDf['Chr']=dgvDf['Chr'].replace('X',23 ) - dgvDf['Chr']=dgvDf['Chr'].replace('Y',24 ) - dgvDf['Chr']=dgvDf['Chr'].replace('MT',25 ) - dgvDf['Chr']=dgvDf['Chr'].replace('GL.*',26,regex=True ) - - #make chr as int - #dgvDf = dgvDf.loc[dgvDf['Chr'].isin(list(range(27))),:].copy() - dgvDf['Chr']=dgvDf['Chr'].astype(int) - - ''' CL: change column name to be compatible with hg38 - dgvDf['hg19Start']=dgvDf['hg19Start'].astype(int) - dgvDf['hg19Stop']=dgvDf['hg19Stop'].astype(int) - dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('X',23 ) - dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('Y',24 ) - dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('MT',25 ) - dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('GL.*','26',regex=True ) - #make chr as int - dgvDf['hg19Chr']=dgvDf['hg19Chr'].astype(int) - ''' - print('finsihed reading DGV') - - #read DECIPHER database - if 'conserve' in moduleList: - print('reading Decipher flat file') - if args.genomeRef == 'hg38': - fileName='/run/data_dependencies/annotate/anno_hg38/decipher.csv' - else: - fileName='/run/data_dependencies/annotate/anno_hg19/decipher.csv' - #fileName='/database_test/decipher.csv' - decipherDf=pd.read_csv(fileName, sep=',') - #print('decipherDf shape:', decipherDf.shape) - #hg19Chr,hg19Start,hg19Stop - decipherDf=decipherDf.fillna(0) - - #if args.genomeRef == 'hg19': - decipherDf.columns = ['Chr', 'Start', 'Stop'] + decipherDf.columns.tolist()[3:] - - decipherDf['Start']=decipherDf['Start'].astype(int) - decipherDf['Stop']=decipherDf['Stop'].astype(int) - decipherDf['Chr']=decipherDf['Chr'].replace('X',23 ) - decipherDf['Chr']=decipherDf['Chr'].replace('Y',24 ) - decipherDf['Chr']=decipherDf['Chr'].replace('MT',25 ) - decipherDf['Chr']=decipherDf['Chr'].replace('GL.*','26',regex=True ) - #make chr as int - decipherDf['Chr']=decipherDf['Chr'].astype(int) - - ''' CL: change column name to be compatible with hg38 - decipherDf['hg19Start']=decipherDf['hg19Start'].astype(int) - decipherDf['hg19Stop']=decipherDf['hg19Stop'].astype(int) - decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('X',23 ) - decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('Y',24 ) - decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('MT',25 ) - decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('GL.*','26',regex=True ) - #make chr as int - decipherDf['hg19Chr']=decipherDf['hg19Chr'].astype(int) - ''' - print('finsihed reading DECIPHER') - - #read the input variant file - if args.inFileType=='vepAnnotTab' and flatFileFlag==1: - #numHeaderSkip=354#this depends on the type of annotation used - numHeaderSkip=0 - with open(args.varFile,'r') as F: - for line in F: - if line.startswith('##'): - numHeaderSkip += 1 - else: - break - print('input annoatated varFile:', args.varFile) - t1 = time.time() - varDf = pd.read_csv(args.varFile,sep='\t', skiprows=numHeaderSkip,error_bad_lines=False) - #varDf=varDf[0:10]#do this if need to have a small test - print('shape:', varDf.shape) - t2 = time.time() - inputReadTime = t2 - t1 - inputNumRows=len(varDf.index) - - #print('head:', varDf.head) - #print('column names:', varDf.columns) - #update the GERp column names since cant use ++ in the varaible name - if 'GERP++_RS' in varDf.columns: - print('found GERP++RS') - varDf['GERPpp_RS'] = varDf['GERP++_RS'] - if 'GERP++_NR' in varDf.columns: - print('found GERP++NR') - varDf['GERPpp_NR'] = varDf['GERP++_NR'] - #update column names which have - in it - if 'fathmm-MKL_coding_score' in varDf.columns: - varDf['fathmm_MKL_coding_score']=varDf['fathmm-MKL_coding_score'] - if 'M-CAP_score' in varDf.columns: - varDf['M_CAP_score']=varDf['M-CAP_score'] - - - print('start making variant objects') - numCores=8 - p1 = time.time() - #store rows into list for parallel function - inputList =[] - for index, rows in varDf.iterrows(): - inputList.append(rows) - p2 = time.time() - makeListTime = p2 - p1 - print('makeListTime :', makeListTime) - print('type inputList:', type(inputList[0]), 'len inputList:', len(inputList)) - #group or chunks for parallel function - groupSize=min((len(inputList) // numCores) + (len(inputList) %numCores), 1000) - print('groupSize:', groupSize) - groupList=list( getVarGroups(inputList, groupSize) ) - print('type groupList:', type(groupList), 'len:', len(groupList)) - - #call parallel function, pass it the main function "getAnnotateInfo_2" from utils_for_marrvel_flatfile.py and any needed files. - #this function will get the marrvel annoatations from marrvel flat files - parallelFlag=1 - if parallelFlag==1: - pool = multiprocessing.Pool(processes=numCores) - # CL: hgmdDf changed to hgmdHPOScoreDf - func = partial(getAnnotateInfo_2, genomeRef=args.genomeRef,clinvarGeneDf=clinvarGeneDf, clinvarAlleleDf=clinvarAlleleDf, - omimGeneList=omimGeneList, omimAlleleList=omimAlleleList,hgmdDf=hgmdHPOScoreDf, moduleList=moduleList, - dgvDf=dgvDf, decipherDf=decipherDf, gnomadMetricsGeneDf=gnomadMetricsGeneDf) - varDictList=pool.map(func,groupList) - pool.close() - else: - pass - - print('finsihed getAnnotateInfo_2') - #make a var obj list after paralle processing for each group, need a better more efficient way for doing this !! - t1 = time.time() - varObjList = getVarObjListFromDict(varDictList, len(varDf.index)) - t2 = time.time() - makeVarObjListTime = t2 - t1 - #print('len varObjList:', len(varObjList)) - print('makeVarObjListTime 1:', makeVarObjListTime) - - #we now have the variant object list "varObjList" with annotations; loop thru it and get the scores - for varObj in varObjList: - #print('var:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol, 'var-gene-pLI:', varObj.gnomadGenePLI) - #the curate score is under the utils_1.py file - if 'curate' in moduleList: - #print('finding symptom matching') - omimSymMatch(varObj, omimHPOScoreDf, args.inFileType) - hgmdSymMatch(varObj, hgmdHPOScoreDf) - clinVarSymMatch(varObj,args.inFileType) - #OMIM and clinvar info - retList=getCurationScore(varObj)#retList=[omimScore, hgmdScore, clinVarScore, totalScore] - varObj.curationScoreOMIM=retList[0] - varObj.curationScoreHGMD=retList[1] - varObj.curationScoreClinVar=retList[2] - varObj.curationScoreTotal=retList[3] - #print('curation score total:', varObj.curationScoreTotal) - #conserve score is under - if 'conserve' in moduleList: - retList=getConservationScore(varObj, args.diseaseInh) - #retList=[conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] - varObj.conservationScoreGnomad=retList[0] - varObj.conservationScoreDGV=retList[1] - varObj.conservationScoreOELof=retList[2] - #print('conservation score GERP++:',varObj.conservationScoreGerpPP,'DGV:',varObj.conservationScoreDGV,) - #print('conserve score DGV:',varObj.conservationScoreDGV,'conserve score gnomad:', varObj.conservationScoreGnomad) - - - #write the objects info scores to a file - print('writing results') - df = pd.DataFrame([t.__dict__ for t in varObjList ]) - print('shape of output file:',df.shape) - #write - fileName=args.outPrefix+'_'+args.patientID+'_scores.txt' - print('out file name:', fileName) - df.to_csv(fileName, sep='\t', index=False) - - end_time = time.time() - process_time = end_time - start_time - print('pipeline time:', process_time) - #log file for times - fileName=args.outPrefix+'_'+args.patientID+'_log.txt' - f = open(fileName, "w") - f.write("Process time:"+str(process_time)+ ' seconds and in mins:'+ str(process_time/60)+"\n") - f.close() - print('log file name:', fileName) - print('input read time:', inputReadTime) - print('input num rows:', inputNumRows) - print('makeListTime from pd:', makeListTime) - print('makeVarObjListTime 1:', makeVarObjListTime) - print('m:', moduleList) - - print("Score re-calculation:") - score = load_raw_matrix(df) - score = omimCurate(score) - score = hgmdCurate(score) - score = clinvarCurate(score) - score = conservationCurate(score) - - score.to_csv('/out/rami-test/%s_scores.csv'%(args.patientID), index=False) - - - exit() - - - -main() - diff --git a/run/annotation/marrvel_score_recalc.py b/run/annotation/marrvel_score_recalc.py deleted file mode 100755 index 2e413bf..0000000 --- a/run/annotation/marrvel_score_recalc.py +++ /dev/null @@ -1,266 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 -import pandas as pd -import numpy as np - - - - -def load_raw_matrix(score): - #score_file = '/out/rami-test/r1_%s_scores.txt'%(ID) #'/out/rami-test/r1_%s_scores.txt'%(ID) - #score = pd.read_csv(score_file, sep='\t') - - raw_features = ["chrom", "pos", "ref", - "alt",'varId','varId_dash','zyg','geneSymbol','geneEnsId','gnomadAF','gnomadAFg','CADD_phred', - 'CADD_PHRED','GERPpp_RS','GERPpp_NR','DANN_score','FATHMM_pred','FATHMM_score', - 'Polyphen2_HDIV_score','Polyphen2_HVAR_score','REVEL_score','SIFT_score', - 'fathmm_MKL_coding_score','LRT_score','LRT_Omega','phyloP100way_vertebrate', - 'M_CAP_score','MutationAssessor_score','MutationTaster_score','ESP6500_AA_AF', - 'ESP6500_EA_AF','symptomName','omimSymptomSimScore','omimSymMatchFlag','hgmdSymptomScore', - 'hgmdSymptomSimScore','hgmdSymMatchFlag','clinVarSymMatchFlag','gnomadGeneZscore', - 'gnomadGenePLI','gnomadGeneOELof','gnomadGeneOELofUpper','IMPACT','Consequence', - 'omimGeneFound','omimVarFound','hgmdGeneFound','hgmdVarFound', - 'clinVarVarFound','clinVarGeneFound','clinvarTotalNumVars', - 'clinvarNumP','clinvarNumLP','clinvarNumLB','clinvarNumB','clinvarSignDesc', - 'clinvarCondition','dgvVarFound','decipherVarFound','curationScoreHGMD','curationScoreOMIM', - 'curationScoreClinVar','conservationScoreDGV','conservationScoreGnomad','conservationScoreOELof', - 'hom','hgmd_rs','clin_dict','clin_PLP','clin_PLP_perc','spliceAImax','clin_code','hgmd_id', - 'hgmd_CLASS','rsId', 'HGVSc', 'HGVSp', 'phenoList', 'phenoInhList'] - - return score.loc[:,raw_features].copy() - - - - -def omimCurate(vardf): - ''' - When related value changed, - - change the following value: - omimSymptomSimScore - omimSymMatchFlag - curationScoreOMIM - - according to: - omimGeneFound - omimVarFound - - ''' - print('\nIn OMIM curation now...') - - # change omimSymptomSimScore according to wether gene found in OMIM database to avoid info leaking - zero_score_bool = vardf['omimGeneFound']==0 - vardf.loc[zero_score_bool, 'omimVarFound'] = 0 - vardf.loc[zero_score_bool, 'omimSymptomSimScore'] = 0.0 - - # change omimSymMatchFlag accordingly - flag_0_bool = vardf['omimSymptomSimScore'] < 0.2 - vardf.loc[flag_0_bool, 'omimSymMatchFlag'] = 0 - vardf.loc[~flag_0_bool, 'omimSymMatchFlag'] = 1 - - # change final curation score - high_bool = (vardf['omimVarFound']==1) & (vardf['omimSymMatchFlag']==1) - medi_bool = ((vardf['omimVarFound']==1) & (vardf['omimSymMatchFlag']==0)) | ((vardf['omimVarFound']==0) & (vardf['omimGeneFound']==1) & (vardf['omimSymMatchFlag']==1)) - - vardf['curationScoreOMIM'] = 'Low' - vardf.loc[high_bool, 'curationScoreOMIM'] = 'High' - vardf.loc[medi_bool, 'curationScoreOMIM'] = 'Medium' - - return vardf - - - - -def hgmdCurate(vardf): - ''' - When related value changed, - - change the following value: - hgmdSymptomScore - hgmdSymptomSimScore - hgmdSymMatchFlag - curationScoreHGMD - - according to: - hgmdGeneFound - hgmdVarFound - - ''' - print('\nIn HGMD curation now...') - - # change hgmdSymptomSimScore according to wether gene found in HGMD database to avoid info leaking - zero_score_bool = vardf['hgmdGeneFound']==0 - vardf.loc[zero_score_bool, 'hgmdVarFound'] = 0 - vardf.loc[zero_score_bool, 'hgmdSymptomSimScore'] = 0.0 - - # change hgmdSymptomScore according to wether variant found in HGMD database to avoid info leaking - zero_score_bool = vardf['hgmdVarFound']==0 - vardf.loc[zero_score_bool, 'hgmdSymptomScore'] = 0.0 - - # change hgmdSymMatchFlag accordingly - score_numeric = vardf['hgmdSymptomSimScore'].copy() - score_numeric[score_numeric=='-'] = np.NaN - score_numeric = score_numeric.astype(float) - flag_0_bool = score_numeric < 0.2 - vardf.loc[flag_0_bool, 'hgmdSymMatchFlag'] = 0 - vardf.loc[~flag_0_bool, 'hgmdSymMatchFlag'] = 1 - - # change final curation score - high_bool = (vardf['hgmdVarFound']==1) & (vardf['hgmdSymMatchFlag']==1) - medi_bool = ((vardf['hgmdVarFound']==1) & (vardf['hgmdSymMatchFlag']==0)) | ((vardf['hgmdVarFound']==0) & (vardf['hgmdGeneFound']==1) & (vardf['hgmdSymMatchFlag']==1)) - - vardf['curationScoreHGMD'] = 'Low' - vardf.loc[high_bool, 'curationScoreHGMD'] = 'High' - vardf.loc[medi_bool, 'curationScoreHGMD'] = 'Medium' - - return vardf - - - - - -def clinvarCurate(vardf): - ''' - When related value changed, - - change the following value: - clinVarSymMatchFlag - curationScoreClinVar - - according to: - clinVarVarFound - clinVarGeneFound - clinvarSignDesc - - ''' - print('\nIn ClinVar curation now...') - - # change clinVarSymMatchFlag - vardf.loc[vardf['clinVarGeneFound']==0, 'clinVarVarFound'] = 0 - one_bool = (vardf['clinVarSymMatchFlag']==1) & ((vardf['clinVarVarFound']==1) | (vardf['clinVarGeneFound']==1)) - vardf.loc[~one_bool, 'clinVarSymMatchFlag'] = 0 - - # change final curation score - - pathList=['Pathogenic','Likely pathogenic','Pathogenic, Affects','Pathogenic/Likely pathogenic, other', - 'Pathogenic/Likely pathogenic','Pathogenic/Likely pathogenic, drug response', - 'Pathogenic/Likely pathogenic, risk factor','Likely pathogenic, drug response','Likely pathogenic, risk factor','Likely pathogenic, association', - 'Likely pathogenic, other','Pathogenic, association, protective','Pathogenic, Affects','Pathogenic, association','Pathogenic, other', - 'Pathogenic, protective','Pathogenic, protective, risk factor','Pathogenic, risk factor', - 'Pathogenic/Likely pathogenic, other','Pathogenic/Likely pathogenic, risk factor'] - - #pathList=['Pathogenic','Likely pathogenic', 'Pathogenic/Likely pathogenic'] - benignList=['Benign', 'Likely benign', 'Benign/Likely benign', 'Benign, association', 'Benign, drug response', - 'Benign, other', 'Benign, protective', ' Benign/Likely benign, Affects', 'Benign/Likely benign, association', - 'Benign/Likely benign, drug response', 'Benign/Likely benign, drug response, risk factor', - 'Benign/Likely benign, other', 'Benign/Likely benign, protective', 'Benign/Likely benign, protective, risk factor', - 'Benign/Likely benign, risk factor', 'Likely benign, drug response, other','Likely benign, other', - 'Likely benign, other, risk factor', 'Likely benign, risk factor'] - - #right logic - #path_bool = vardf['clinvarSignDesc'].str.contains('Pathogenic', case=False) & (~vardf['clinvarSignDesc'].str.contains('Conflicting', case=False)) - #bngn_bool = vardf['clinvarSignDesc'].str.contains('Benign', case=False) & (~vardf['clinvarSignDesc'].str.contains('Conflicting', case=False)) - # wrong but current logic - path_bool = vardf['clinvarSignDesc'].isin(pathList) - bngn_bool = vardf['clinvarSignDesc'].isin(benignList) - - high_bool = (vardf['hgmdVarFound']==1) & path_bool & (vardf['hgmdSymMatchFlag']==1) - medi_bool = ((vardf['hgmdVarFound']==1) & path_bool & (vardf['hgmdSymMatchFlag']==0)) | \ - ((vardf['hgmdVarFound']==1) & (~path_bool) & (~bngn_bool) & (vardf['hgmdSymMatchFlag']==1) & (vardf['hgmdSymMatchFlag']==1)) | \ - ((vardf['hgmdVarFound']==0) & (vardf['hgmdGeneFound']==1) & (vardf['clinVarSymMatchFlag']==1)) - - - vardf['curationScoreHGMD'] = 'Low' - vardf.loc[high_bool, 'curationScoreHGMD'] = 'High' - vardf.loc[medi_bool, 'curationScoreHGMD'] = 'Medium' - - return vardf - - - - -def conservationCurate(vardf): - ''' - When related value changed, - - change the following value: - conservationScoreDGV - conservationScoreGnomad - conservationScoreOELof - - according to: - gnomadAF - gnomadAFg - dgvVarFound - gnomadGeneOELofUpper - - ''' - - # gnomad - gnomadAFVal = vardf['gnomadAF'].copy() - gnomadAFVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFVal ]) - #gnomadAFVal[gnomadAFVal=='-'] = np.NaN - gnomadAFVal = gnomadAFVal.astype(float) - - gnomadAFgVal = vardf['gnomadAFg'].copy() - gnomadAFgVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFgVal ]) - #gnomadAFgVal[gnomadAFgVal=='-'] = np.NaN - gnomadAFgVal = gnomadAFgVal.astype(float) - - low_bool = (gnomadAFVal>=0.01) | (gnomadAFgVal>=0.01) - vardf.loc[low_bool, 'conservationScoreGnomad'] = 'Low' - vardf.loc[~low_bool, 'conservationScoreGnomad'] = 'High' - - - # conservationScoreDGV - low_bool = (vardf['conservationScoreDGV'] == 'Low') & (vardf['dgvVarFound']==1) - vardf['conservationScoreDGV'] = 'High' - vardf.loc[low_bool,'conservationScoreDGV'] = 'Low' - - # conservationScoreOELof - gnomadGeneOELofUpperVal = vardf['gnomadGeneOELofUpper'].copy() - gnomadGeneOELofUpperVal[gnomadGeneOELofUpperVal=='-'] = np.NaN - gnomadGeneOELofUpperVal = gnomadGeneOELofUpperVal.astype(float) - - high_bool = gnomadGeneOELofUpperVal < 0.35 - vardf['conservationScoreOELof'] = 'Low' - vardf.loc[high_bool, 'conservationScoreOELof'] = 'High' - - return vardf - -def getValFromStr(valStr: str, select: str = 'min'): - """ - Function to convert string to float, - and takes care of situation when multiple values exist - - Some test example: - >> a = '-' - >> b = '-,0.012' - >> c = '0.0003' - >> d = '6.38284e-05' - >> e = '6.38284e-05,3.19142e-05' - >> f = '-,-' - >> getValFromStr(a) - '-' - >> getValFromStr(b) - '-' - >> getValFromStr(c) - 0.0003 - >> getValFromStr(d) - 6.38284e-05 - >> getValFromStr(e) - 3.19142e-05 - >> getValFromStr(f) - '-' - """ - select_method = {'min':min, 'max':max} - vals = valStr.split(',') - if '-' in vals: - return np.NaN - else: - vals = [float(i) for i in vals] - return select_method[select](vals) - - - - diff --git a/run/annotation/utils_1.py b/run/annotation/utils_1.py deleted file mode 100755 index 8dc67a4..0000000 --- a/run/annotation/utils_1.py +++ /dev/null @@ -1,536 +0,0 @@ -## UPDATE -# Changes made by Chaozhong noted as #CL -#from liftover import get_lifter -import pandas as pd -import numpy as np -import pprint -import json -import requests - -#class for variant -class Variant(): - """Create a class to create an object for each variant - Parameters: - A class name for initiation call - Output: - An object of class Variant - """ - def __init__(self): - self.chrom='-' - self.pos='-' - self.start='-' - self.stop='-' - self.ref='-' - self.alt='-' - self.hg19Chrom='-' - self.hg19Pos='-' - self.hg38Chrom='-' - self.hg38Pos='-' - self.varId='-' - self.varId_dash='-'#varinat ID separated by dash like '6-99365567-T-C' - - self.patientID = '-' - self.zyg='-' - self.geneSymbol='-' - self.geneEnsId='-' - self.rsId='-' - self.featureType='-' - self.gnomadAF='-' - self.gnomadAFg='-'#genome gnomad score - #dbnsfp attributes - self.CLIN_SIG='-' - self.CADD_phred='-'#phred score from dbnsfp - self.CADD_PHRED='-' - self.GERPpp_RS='-'#GERP plus plus - self.GERPpp_NR='-' - self.DANN_score='-' - self.FATHMM_pred='-' - self.FATHMM_score='-' - self.GTEx_V8_gene='-' - self.GTEx_V8_tissue='-' - self.Polyphen2_HDIV_score='-' - self.Polyphen2_HVAR_score='-' - self.REVEL_score='-' - self.SIFT_score='-' - - self.clinvar_AlleleID = '-' #CL added - self.clinvar_clnsig='-' - self.clinvar_CLNREVSTAT = '-' #CL added - self.clinvar_CLNSIGCONF = '-' #CL added - - self.fathmm_MKL_coding_score='-' - self.HGVSc='-' - self.HGVSp='-' - #LRT - self.LRT_score='-' - self.LRT_Omega='-' - #Phylo - self.phyloP100way_vertebrate='-' - self.M_CAP_score='-' - self.MutationAssessor_score='-' - self.MutationTaster_score='-' - self.ESP6500_AA_AC='-' - self.ESP6500_AA_AF='-' - self.ESP6500_EA_AC='-' - self.ESP6500_EA_AF='-' - - - #symtom info - self.SymptomMatched = {'omim':0, 'clinvar':0, 'hgmd':0} #list containing omimMatched, clinvarMatched, hgmdMatched - self.symptomScore = {}#for OMIM - self.symptomName = {}#for OMIM - self.omimSymptomSimScore='-' - self.omimSymMatchFlag= '-' - self.hgmdSymptomScore='-' - self.hgmdSymptomSimScore='-' - self.hgmdSymMatchFlag='-' - self.clinVarSymMatchFlag='-' - - #gnomad gene metrics from flat file - self.gnomadGeneZscore='-' - self.gnomadGenePLI='-' - self.gnomadGeneOELof='-'#O/E lof - self.gnomadGeneOELofUpper='-'#O/E lof upper - #conseqeunce and impact - self.IMPACT='-' - self.Consequence='-' - #omim - self.omimDict={} - self.omimGeneFound='-' - self.omimVarFound='-' - self.omimList=[] - self.omimGeneDict={} - self.omimAlleleDict={} - self.phenoList=[] - self.phenoInhList=[] - self.phenoMimList=[] - #HGMD - self.HGMDDict={} - self.hgmdGeneFound='-' - self.hgmdVarFound='-' - self.hgmdVarPhenIdList=[] - self.hgmdVarHPOIdList=[] - self.hgmdVarHPOStrList=[] - #clinvar - self.clinVarVarDict={} - self.clinVarGeneDict={} - self.clinVarVarFound='-' - self.clinVarGeneFound='-' - self.clinVarList=[] - self.clinvarTotalNumVars='-' - self.clinvarNumP='-'#number of clinvar variants pathogenic - self.clinvarNumLP='-'#number of clinvar variants likely pathogenic - self.clinvarNumLB='-' - self.clinvarNumB='-' - self.clinvarTitle='-'#title from the flat file - self.clinvarSignDesc='-'#significance description from the flat file - self.clinvarCondition='-' - #dgv - self.DGVDict={} - self.dgvDictList=[] - self.dgvTypeList=[] - self.dgvSubtypeList=[] - self.dgvVarFound='-' - #Decipher - self.DecipherDict={} - self.decipherDictList=[] - self.decipherDeletionObsList=[] - self.decipherStudyList=[] - self.decipherVarFound='-' - #the module scores. The scores will be broken down so they can be added as features - #curation score - self.curationScoreTotal='-' - self.curationScoreHGMD='-' - self.curationScoreOMIM='-' - self.curationScoreClinVar='-' - #conservation scores - self.conservationScoreTotal='-' - self.conservationScorePhylop='-' - self.conservationScoreLRT='-' - self.conservationScoreGerpPP='-' - self.conservationScoreCNV='-' - self.conservationScoreDGV='-' - self.conservationScoreDecipher='-' - self.conservationScoreGnomad='-' - self.conservationScoreGeneConstZ='-' - self.conservationScoreGeneConstPLi='-' - self.conservationScoreDomino='-' - self.conservationScoreOELof='-' - #module 3 scores - self.effectOnGeneScore='-' - self.geneDiseaseAssocScore='-' - self.modelOrganismScore='-' - - -#function to get var objects from dataframe read from a VEP annoataed file -def getVarObjFromVEPAnnotDf(df, genomeRef): - #create the objects - varObjList = [ Variant() for i in range(len(df.index)) ] - #create a list of var objects - i=0 - for index, rows in df.iterrows(): - print('rows:', rows.Uploaded_variation, rows.Location, rows.Gene, rows.Feature_type) - s=rows.Uploaded_variation.split('_') - transcriptId=rows.Feature - chrom=s[0] - pos=s[1] - #if it is hg38 get its hg19 coordinates - if genomeRef=='hg38': - pass - varObjList[i].hg38Chrom=chrom - varObjList[i].hg38pos=pos - retList=gethg19LocFromHg38(chr, pos) - retList=[newChrom, newPos] - varObjList[i].hg19Chrom=retList[0] - varObjList[i].hg19pos=retList[1] - chrom=retList[0] - pos=retList[1] - else: - varObjList[i].hg19Chrom=chrom - varObjList[i].hg19pos=pos - - tmp=s[2] - s=tmp.split('/') - refAllele=s[0] - altAllele=s[1] - print(chr,pos, tmp, refAllele, altAllele) - - varObjList[i].chrom=chrom - varObjList[i].pos=pos - varObjList[i].ref=refAllele - varObjList[i].alt=altAllele - #cerate a unique ID since we have transcripts - varId='_'.join([chrom,pos,refAllele,altAllele,transcriptId]) - varObjList[i].varId=varId - varObjList[i].varId_dash='-'.join([chrom,pos,refAllele,altAllele]) - varObjList[i].zyg=rows.ZYG - varObjList[i].geneSymbol=rows.SYMBOL - varObjList[i].geneEnsId=rows.Gene - varObjList[i].rsId=rows.Existing_variation - varObjList[i].GERPpp_RS=rows.GERPpp_RS - varObjList[i].featureType=rows.Feature_type - varObjList[i].gnomadAF=rows.gnomAD_AF - varObjList[i].CLIN_SIG=rows.CLIN_SIG - varObjList[i].HGVSc=rows.HGVSc - varObjList[i].HGVSp=rows.HGVSp - - #increment - i=i+1 - - #print('in def len of objects:', len(varObjList)) - return varObjList - -#function to get OMIM info using MArrvel API -def getOMIMUsingMarrvelAPI(varObj): - print('getOMIM:',varObj.varId_dash, varObj.geneSymbol, varObj.rsId) - url='http://marrvel.org/data/omim/gene/symbol/{}/variant/{}'.format(varObj.geneSymbol,varObj.varId_dash) - #Example: 6:99365567 T>C / FBXL4 - #url='http://marrvel.org/data/omim/gene/symbol/{}/variant/{}'.format('FBXL4','6-99365567-T-C') - #print('url:', url) - - req=requests.get(url) - #print('reqText:', req) - #print('resqStatusCode:', req.status_code) - varFound=0 - geneFound=0 - omimDict={} - if req.status_code!=200: - #print('OMIM query not found') - pass - else: - #the entry for the gene exists but need to check if the variants exists also using the rs ID - geneFound=1 - #print('reqText:', req.text) - dat=json.loads(req.text) - #search var in the list of dict - retVal=next((item for item in dat['allelicVariants'] if item["dbSnps"] == varObj.rsId), None) - if retVal is not None: - varFound=1 - omimDict=data - #print('varFoundFlag:',varFoundFlag) - - - retList=[varFound, geneFound, omimDict] - return retList - -def getClinVarUsingMarrvelAPI(varObj): - print('getClinvar:',varObj.varId_dash, varObj.geneSymbol, varObj.rsId) - #the APIs - #/clinvar/gene/symbol/:geneSymbol - #/clinvar/gene/variant/:variant - - #check if varaint annotated - url='http://marrvel.org/data/clinvar/variant/{}'.format(varObj.varId_dash) - req=requests.get(url) - #print('reqText:', req) - #print('reqText:', req.text) - varFound=0 - varDict={} - if req.text: - varFound=1 - varDict=json.loads(req.text) - else: - varFound=0 - - - #check if gene annotated - url='http://marrvel.org/data/clinvar/symbol/{}'.format(varObj.geneSymbol) - req=requests.get(url) - #print('reqText:', req) - #print('reqText:', req.text) - geneFound=0 - geneDict={} - if req.text: - geneFound=1 - geneDict=json.loads(req.text) - else: - geneFound=0 - - #return - retList=[varFound,varDict, geneFound, geneDict] - return retList - - -def getCurationScore(varObj): - #print('in curation score') - #get the curation score per each DB - #omim score - omimScore='' - if varObj.omimVarFound==1: - if varObj.omimSymMatchFlag==1: - omimScore='High' - else: - omimScore='Medium' - elif varObj.omimGeneFound==1: - if varObj.omimSymMatchFlag==1: - omimScore='Medium' - else: - omimScore='Low' - else: - omimScore='Low' - - #hgmd score - hgmdScore='' - if varObj.hgmdVarFound==1: - if varObj.hgmdSymMatchFlag==1: - hgmdScore='High' - else: - hgmdScore='Medium' - elif varObj.hgmdGeneFound==1: - if varObj.hgmdSymMatchFlag==1: - hgmdScore='Medium' - else: - hgmdScore='Low' - else: - hgmdScore='Low' - - - #clinvar score - #if anything is not in the path/likely path list then it should have a low score - clinVarScore='' - pathList=['Pathogenic','Likely pathogenic','Pathogenic, Affects','Pathogenic/Likely pathogenic, other', - 'Pathogenic/Likely pathogenic','Pathogenic/Likely pathogenic, drug response', - 'Pathogenic/Likely pathogenic, risk factor','Likely pathogenic, drug response','Likely pathogenic, risk factor','Likely pathogenic, association', - 'Likely pathogenic, other','Pathogenic, association, protective','Pathogenic, Affects','Pathogenic, association','Pathogenic, other', - 'Pathogenic, protective','Pathogenic, protective, risk factor','Pathogenic, risk factor', - 'Pathogenic/Likely pathogenic, other','Pathogenic/Likely pathogenic, risk factor'] - - #pathList=['Pathogenic','Likely pathogenic', 'Pathogenic/Likely pathogenic'] - benignList=['Benign', 'Likely benign', 'Benign/Likely benign', 'Benign, association', 'Benign, drug response', - 'Benign, other', 'Benign, protective', ' Benign/Likely benign, Affects', 'Benign/Likely benign, association', - 'Benign/Likely benign, drug response', 'Benign/Likely benign, drug response, risk factor', - 'Benign/Likely benign, other', 'Benign/Likely benign, protective', 'Benign/Likely benign, protective, risk factor', - 'Benign/Likely benign, risk factor', 'Likely benign, drug response, other','Likely benign, other', - 'Likely benign, other, risk factor', 'Likely benign, risk factor'] - - clinVarScore='' - if varObj.clinVarVarFound==1: - if varObj.clinvarSignDesc in pathList: - if varObj.clinVarSymMatchFlag==1: - clinVarScore='High' - else: - clinVarScore='Medium' - elif varObj.clinvarSignDesc in benignList: - clinVarScore='Low' - else: - if varObj.clinVarSymMatchFlag==1: - clinVarScore='Medium' - else: - clinVarScore='Low' - elif varObj.clinVarGeneFound==1: - if varObj.clinVarSymMatchFlag==1: - clinVarScore='Medium' - else: - clinVarScore='Low' - else: - clinVarScore='Low' - - - - #OLD - #pathList=['Pathogenic','Likely pathogenic'] - #if varObj.clinvarSignDesc not in pathList: - # clinVarScore='Low' - #elif varObj.clinVarVarFound==1: - # if varObj.clinVarSymMatchFlag==1: - # clinVarScore='High' - # else: - # clinVarScore='Medium' - #elif varObj.clinVarGeneFound==1: - # if varObj.clinVarSymMatchFlag==1: - # clinVarScore='Medium' - # else: - # clinVarScore='Low' - #else: - # clinVarScore='Low' - - #print('\tomimScore:', omimScore,'hgmdScore:',hgmdScore,'clinVarScore:',clinVarScore) - - #total score - totalScore='-' - #need more work on this total score - if 0: - #if omim, clinvar, HGMD avaiable then use omim > hgmd > clinvar for the final score - varFound=0 - geneFound=0 - if varObj.omimVarFound==1: - varFound=1 - geneFound=varObj.omimGeneFound - elif varObj.hgmdVarFound==1: - varFound=1 - geneFound=varObj.hgmdGeneFound - elif varObj.clinVarVarFound==1: - varFound=1 - geneFound=varObj.clinVarGeneFound - else: - totalScore='Low' - retList=[omimScore, hgmdScore, clinVarScore, totalScore] - return retList - - #print('\tgetCuration func varFound:', varFound,'gene found:', geneFound) - #continue total score and follow the flowchart - if varFound==1: - if symMatchFlag==1: - totalScore='High' - elif symMatchFlag==0: - totalScore='Low' - elif geneFound==1: - if symMatchFlag==1: - totalScore='High' - elif symMatchFlag==0: - totalScore='Low' - else: - totalScore='Low' - - #print('\t curation total score:', totalScore) - retList=[omimScore, hgmdScore, clinVarScore, totalScore] - return retList - -def gethg19LocFromHg38(chrom, pos): - converter = get_lifter('hg38', 'hg19') - pos = int(pos) - converter[chrom][pos] - - retVal=converter.query(chrom, pos) - #print('retVal:', retVal) - newChrom=retVal[0][0] - newChrom=newChrom.replace('chr', '') - newPos=retVal[0][1] - #print('newChr:',newChrom,'newPos:', newPos) - retList=[newChrom, newPos] - return retList - - -def getDGVUsingMarrvelAPI(varObj): - #varObj=varObjList[0] - print('v:',varObj.geneSymbol) - #DGV returns a list not a dict - url='http://marrvel.org/data/dgv/variant/{}'.format(varObj.varId_dash) - req=requests.get(url) - #print('req:', req.text) - dgvDictList=[] - #geneList=[] - typeList=[] - subtypeList=[] - if req.status_code!=200: - #print('OMIM query not found') - pass - else: - dat=json.loads(req.text) - dgvDictList=dat - for row in dat: - dgvType=row['type'] - typeList.append(dgvType) - dgvSubtype=row['subType'] - subtypeList.append(dgvSubtype) - #dgvGeneList=[d['symbol'] for d in row['genes']] - #geneList.append(dgvGeneList) - - retList=[dgvDictList,typeList,subtypeList] - return retList - - -def getDecipherUsingMarrvelAPI(varObj): - print('v:',varObj.geneSymbol) - url='http://marrvel.org/data/decipher/variant/{}'.format(varObj.varId_dash)#varId_dash - #decipher/variant/:variant - #decipher/genomloc/:hg19Chr/:hg19Start/:hg19Stop - req=requests.get(url) - #print('req:', req.text) - decipherDictList=[] - decipherDeletionObsList=[] - decipherStudyList=[] - if req.status_code!=200: - #print('Decipher query not found') - pass - else: - dat=json.loads(req.text) - decipherDictList=dat - for row in dat: - #print('\n row:', row) - deleteObs=row['deletion']['obs'] - decipherDeletionObsList.append(deleteObs) - deleteStudy=row['study'] - decipherStudyList.append(deleteObs) - #print('deleteObs:', deleteObs, 'study:',deleteStudy) - - retList=[decipherDictList,decipherDeletionObsList,decipherStudyList] - return retList - -def checkVarGeneFile(inFile, fileType): - #check if input files are in right format - pass - -def checkPatientFile(inFile, fileType): - #check if input files are in right format - pass - - -def changeVariantFormat(inVar): - #change the varaint format from 6:99365567 T>C to 6-99365567-T-C - #replace : > and space with - - inVar=inVar.replace(' ', '-') - inVar=inVar.replace(':', '-') - inVar=inVar.replace('>', '-') - return inVar - -def readVarGeneFile(inFile, fileType): - #read the var/gene file and return a data frame; if it is varList only then need to call - if fileType=='varList': - df=pd.read_csv(inFile, sep='\t') - print('df head:', df.head(), '\n') - #change variant format - df=df.apply(np.vectorize(changeVariantFormat)) - print('df head:', df.head(), '\n') - return df - -def checkVarGnomad(varId): - url='http://marrvel.org/data/gnomad/variant/{}'.format(varId) - req=requests.get(url) - pass - - -def getVarGroups(varList, groupSize): - #get a list of variant groups for parallel processing - for i in range(0, len(varList), groupSize): - yield varList[i:i + groupSize] diff --git a/run/annotation/utils_for_marrvel_flatfile.py b/run/annotation/utils_for_marrvel_flatfile.py deleted file mode 100755 index f1e3f94..0000000 --- a/run/annotation/utils_for_marrvel_flatfile.py +++ /dev/null @@ -1,1060 +0,0 @@ -## UPDATE -# Changes made by Chaozhong noted as #CL - -from utils_1 import * -from utils_for_marrvel_flatfile_module_2 import * -import numpy as np -import time -import re - -def getOMIMUsingMarrvelFlatFile(varObj, omimGeneList, omimAlleleList): - ''' - function to get OMIM info using marrrvel flat file - Params:a varaint object read from VEP annotation - - Returns: - List of OMIM annotations - ''' - #print('In OMIM call:') - #print('\tvar:', varObj.varId_dash, 'rsId:', varObj.rsId) - #check if rsId has a comma in it - inputSnpList=[] - if ',' in varObj.rsId: - inputSnpList = varObj.rsId.split(',') - else: - inputSnpList=varObj.rsId - #print('\tinputSnpList:', inputSnpList) - varFound=0 - geneFound=0 - omimDict={} - omimGeneDict={} - omimAlleleDict={} - phenoList=[] - phenoInhList=[] - phenoMimList=[] - #check gene - #keys: dict_keys(['phenotypes', 'allelicVariants', 'mimNumber', 'status', 'title', 'description', 'geneEntrezId', 'geneSymbol']) - if any(d['geneSymbol'] == varObj.geneSymbol for d in omimGeneList): - #print('\tgene:', varObj.geneSymbol, 'found') - geneFound=1 - omimGeneDict = next((sub for sub in omimGeneList if sub['geneSymbol'] == varObj.geneSymbol), None) - #print('\tgeneSymbol from dict:', omimGeneDict['geneSymbol']) - #print('\talleleic:', type(omimGeneDict['allelicVariants']), ' len:', len(omimGeneDict['allelicVariants']) ) - #check if snp found - snpList=[] - for a in omimGeneDict['allelicVariants']: - #print('a:', a) - #print('type:', type(a)) - if 'dbSnps' in a: - snpList.append(a['dbSnps']) - #print('\tsnpList:', snpList) - #print('\tlen snpList:', len(snpList)) - #check if input snpID matches the OMIM one - set1 = set(inputSnpList) - set2 = set(snpList) - if set1.intersection(set2): - varFound=1 - else: - varFound=0 - - #get disease info from OMIM - #print('\tphenotypes:', type(omimGeneDict['phenotypes']), ' len:', len(omimGeneDict['phenotypes']) ) - for a in omimGeneDict['phenotypes']: - #print('type:', type(a)) - pheno=a['phenotype'] - if 'phenotypeMimNumber' in a: - phenoMim=a['phenotypeMimNumber'] - else: - phenoMim='-' - if 'phenotypeInheritance' in a: - phenoInh=a['phenotypeInheritance'] - else: - phenoInh='-' - phenoList.append(pheno) - phenoInhList.append(phenoInh) - phenoMimList.append(str(phenoMim)) - #print('phenotype:', pheno,phenoMim,phenoInh) - - #print('\tvarFound:', varFound) - #print('\tphenoList:', phenoList) - #print('\tphenoInhList:', phenoInhList) - #print('\tphenoMimList:', phenoMimList) - - retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict, phenoList, phenoInhList, phenoMimList] - return retList - -def getClinVarUsingMarrvelFlatFile(varObj,clinvarAlleleDf,clinvarGeneDf): - ''' - function to get clinvar info using marrrvel flat file - Params:a varaint object read from VEP annotation - - Returns: - List of clinvar annotations - ''' - #print('in clinvar using flatfile') - #print('\tvar:',varObj.varId_dash) - varFound=0 - varDict={} - geneFound=0 - geneDict={} - clinvarTotalNumVars=0 - clinvarNumP=0 - clinvarNumLP=0 - clinvarNumLB=0 - clinvarNumB=0 - ## CL: no use but kept - clinvarTitle='' - clinvarSignDesc='' - clinvarCondition='' - - #print('in function type clinavrDf:', type(clinvarAlleleDf) ) - chromVal=int(varObj.chrom) - posVal=int(varObj.pos) - - """ #CL: Old implementation - #using inetger column not index - if 1: - vals=clinvarAlleleDf.loc[(clinvarAlleleDf['chr']==chromVal ) & - (clinvarAlleleDf['start'] == posVal ) & - (clinvarAlleleDf['stop'] == posVal )] - numRows=len(vals.index) - print('columns numRows:', numRows) - #using index - if 0: - idVal=str(chromVal)+'_'+str(posVal)+'_'+str(posVal) - #vals=clinvarAlleleDf.loc[(clinvarAlleleDf['id1']==idVal)] - try: - vals=clinvarAlleleDf.loc[idVal] - numRows=len(vals.index) - print('index numRows:', numRows) - except: - numRows=0 - - print('\tcheck var numRows:', numRows) - if numRows > 0: - varFound=1 - print('\tclinvar var found') - print('\t clinvar vals:', vals) - clinvarTitle=vals.iloc[0]['title'] - clinvarSignDesc=vals.iloc[0]['significance.description'] - clinvarCondition=vals.iloc[0]['condition'] - print('\ttitle:', clinvarTitle, 'signDes:', clinvarSignDesc, 'condition:', clinvarCondition) - else: - varFound=0 - """ - - #CL: check if var annotated in clinvar using VEP clinvar.vcf.gz custom annotation - if varObj.clinvar_AlleleID != '-': - varFound = 1 - #print('\tclinvar var found') - else: - varFound = 0 - - - - #check if gene annotated in clinvar using clinvarGeneDf - geneSymbol=varObj.geneSymbol - #print('\tgene symbol:', geneSymbol) - try: - vals=clinvarGeneDf.loc[geneSymbol] - numRows=len(vals.index) - except: - numRows=0 - #print('\tcheck gene numRows:', numRows) - if numRows>0: - #print('\tclinvar gene found') - geneFound=1 - clinvarTotalNumVars=vals['totalClinvarVars'] - clinvarNumP=vals['P'] - clinvarNumLP=vals['LP'] - clinvarNumLB=vals['LB'] - clinvarNumB=vals['B'] - #return - retList=[varFound,varDict, geneFound, geneDict, clinvarTotalNumVars,clinvarNumP,clinvarNumLP,clinvarNumLB, - clinvarNumB, clinvarTitle, clinvarSignDesc,clinvarCondition] - return retList - -def getHGMDUsingFlatFile(varObj,hgmdDf): - ''' - function to get HGMD from local flat file - Params: - varObj:a varaint object read from VEP annotation - hgmdDf: HGMD data frame read from local file (CL: now it refers to hgmdHPOScoreDf in main.py) - - Returns: - List of HGMD annotations - - Update by CL: - ''' - #print('\nin HGMD') - #print('\tvar:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol) - HGMDDict={} - hgmdGeneFound=0 - hgmdVarFound=0 - hgmdVarPhenIdList=[] - hgmdVarHPOIdList=[] - hgmdVarHPOStrList=[] - chromVal=varObj.chrom - posVal=int(varObj.pos) - startVal=int(varObj.start) - stopVal=int(varObj.stop) - #print('\tpos type:',type(varObj.start),'chrom:', type(chromVal) ) - - """ - #using int columns - if 1: - vals=hgmdDf[ ( hgmdDf['chromosome'] == chromVal ) & ( hgmdDf['startCoord']==startVal ) & (hgmdDf['endCoord']==stopVal) ] - numRows=len(vals.index) - #using index - if 0: - idVal=str(chromVal)+'_'+str(startVal)+'_'+str(stopVal) - try: - vals=hgmdVarDf.loc[idVal] - numRows=len(vals.index) - print('index numRows:', numRows) - except: - numRows=0 - - print('\tvar numRows:', numRows) - - - if numRows>0: - hgmdVarFound=1 - print('\tnumrows:',numRows) - print('\tvals:', vals) - if 'phen_id' in vals: - hgmdVarPhenIdList.extend(vals['phen_id'].tolist()) - if 'hpo_id' in vals: - hgmdVarHPOIdList.extend(vals['hpo_id'].tolist()) - if 'hpo_str' in vals: - hgmdVarHPOStrList.extend(vals['hpo_str'].tolist()) - print('\tvals:', vals) - """ - # CL: check VarFound - if varObj.hgmd_id != '-': - hgmdVarFound = 1 - else: - hgmdVarFound = 0 - - """ - #check gene - #vals=hgmdGeneDf[(hgmdGeneDf['gene']==varObj.geneSymbol)] - print('\t1 HGMD geneSymbol:', varObj.geneSymbol) - try: - print('\t2 HGMD geneSymbol:', varObj.geneSymbol) - vals=hgmdDf.loc[varObj.geneSymbol] - #vals=hgmdDf[ ( hgmdDf['gene'] == varObj.geneSymbol ) ] - numRows=len(vals.index) - except: - numRows=0 - - print('\tHGMD gene found numRows:', numRows) - if numRows>0: - hgmdGeneFound=1 - """ - # CL: check geneFound - if np.any(hgmdDf['gene_sym'].isin([varObj.geneSymbol])): - hgmdGeneFound = 1 - else: - hgmdGeneFound = 0 - - - #print('\thgmdVarFound:',hgmdVarFound,'hgmdGeneFound:',hgmdGeneFound, - # 'hgmdVarPhenIdList:',hgmdVarPhenIdList,'hgmdVarHPOIdList:',hgmdVarHPOIdList, - # 'hgmdVarHPOStrList:',hgmdVarHPOStrList) - #return - retList=[hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList] - return retList - -def getVarObjFromMarrvelFlatFile(varDf,genomeRef,clinvarGeneDf,clinvarAlleleDf, omimGeneList, omimAlleleList,hgmdDf): - ''' - Function to get the marrvel annotaion if not done in parallel. Use parallel option better - ''' - #dict_keys(['chr', 'pos', 'ref', 'alt', 'omim', 'clinvar', 'gtex', 'dgv', - #'decipher', 'decipherDisease', 'gnomad', 'dbnsfp']) - numVars=len(varDf.index) - print('type:', type(varDf), 'len/numVars:', numVars) - varObjList = [ Variant() for i in range(numVars) ] - - debugFlag=0 - - i=0 - for index, row in varDf.iterrows(): - print('type of row:', type(row)) - transcriptId=row.Feature - #s=row.Uploaded_variation.split('_') '1_10204_-/T' - s=row[0].split('_') - #print('s:', s) - chrom=s[0] - pos=int(s[1]) - tmp=s[2] - s=tmp.split('/') - ref=s[0] - alt=s[1] - #get the start and stop from second column like '1:10203-10204' - if '-' in row[1]: - s=row[1].split(':') - tmp=s[1] - s=tmp.split('-') - print('s:',s) - start=int(s[0]) - stop=int(s[1]) - else: - #start and stop the same - s=row[1].split(':') - start=int(s[1]) - stop=int(s[1]) - - - print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) - #change chrom X and Y and MT to numbers - if chrom=='X': - chrom=23 - elif chrom=='Y': - chrom=24 - elif chrom=='MT': - chrom=25 - chrom=int(chrom) - #if it is hg38 get its hg19 coordinates - if genomeRef=='hg38': - varObjList[i].hg38Chrom=chrom - varObjList[i].hg38Pos=pos - retList=gethg19LocFromHg38(chrom, pos) - retList=[newChrom, newPos] - varObjList[i].hg19Chrom=retList[0] - varObjList[i].hg19Pos=retList[1] - chrom=retList[0] - pos=retList[1] - #get the start - retList=gethg19LocFromHg38(chrom, start) - varObjList[i].start=int(retList[1]) - #get the stop - retList=gethg19LocFromHg38(chrom, stop) - varObjList[i].stop=int(retList[1]) - else: - varObjList[i].hg19Chrom=chrom - varObjList[i].hg19Pos=pos - varObjList[i].chrom=chrom - varObjList[i].pos=pos - varObjList[i].start=start - varObjList[i].stop=stop - - - geneSymbol=row.SYMBOL - print('gene:', geneSymbol) - varObjList[i].geneSymbol=geneSymbol - varObjList[i].CADD_phred=row.CADD_phred - varObjList[i].CADD_PHRED=row.CADD_PHRED - #assign - varObjList[i].ref=ref - varObjList[i].alt=alt - varObjList[i].varId_dash='-'.join([str(chrom),str(pos),ref,alt]) - varId='_'.join([str(chrom),str(pos),ref,alt,transcriptId]) - varObjList[i].varId=varId - varObjList[i].zyg=row.ZYG - varObjList[i].geneEnsId=row.Gene - varObjList[i].rsId=row.Existing_variation - varObjList[i].GERPpp_RS=row.GERPpp_RS - varObjList[i].featureType=row.Feature_type - varObjList[i].gnomadAF=row.gnomAD_AF - varObjList[i].gnomadAFg=row.gnomADg_AF - varObjList[i].CLIN_SIG=row.CLIN_SIG - varObjList[i].HGVSc=row.HGVSc - varObjList[i].HGVSp=row.HGVSp - #dbnsfp attributes - varObjList[i].GERPpp_NR=row.GERPpp_NR - varObjList[i].DANN_score=row.DANN_score - varObjList[i].FATHMM_pred=row.FATHMM_pred - varObjList[i].FATHMM_score=row.FATHMM_score - varObjList[i].GTEx_V8_gene=row.GTEx_V8_gene - varObjList[i].GTEx_V8_tissue=row.GTEx_V8_tissue - varObjList[i].Polyphen2_HDIV_score=row.Polyphen2_HDIV_score - varObjList[i].Polyphen2_HVAR_score=row.Polyphen2_HVAR_score - varObjList[i].REVEL_score=row.REVEL_score - varObjList[i].SIFT_score=row.SIFT_score - varObjList[i].clinvar_clnsig=row.clinvar_clnsig - varObjList[i].fathmm_MKL_coding_score=row.fathmm_MKL_coding_score - varObjList[i].LRT_score=row.LRT_score - varObjList[i].LRT_Omega=row.LRT_Omega - varObjList[i].phyloP100way_vertebrate=row.phyloP100way_vertebrate - varObjList[i].M_CAP_score=row.M_CAP_score - varObjList[i].MutationAssessor_score=row.MutationAssessor_score - varObjList[i].MutationTaster_score=row.MutationTaster_score - varObjList[i].ESP6500_AA_AC=row.ESP6500_AA_AC - varObjList[i].ESP6500_AA_AF=row.ESP6500_AA_AF - varObjList[i].ESP6500_EA_AC=row.ESP6500_EA_AC - varObjList[i].ESP6500_EA_AF=row.ESP6500_EA_AF - #dbnsfp - - #for module one will need OMIM, HGMD, clinvar - #get OMIM - if 1: - print('\nReading OMIM') - #varObjList[i].omimList=jsonDict['omim'] - #retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] - omimRet=getOMIMUsingMarrvelFlatFile(varObjList[i], omimGeneList, omimAlleleList) - varObjList[i].omimVarFound=omimRet[0] - varObjList[i].omimGeneFound=omimRet[1] - varObjList[i].omimDict=omimRet[2] - varObjList[i].omimGeneDict=omimRet[3] - varObjList[i].omimAlleleDict=omimRet[4] - varObjList[i].phenoList=omimRet[5] - varObjList[i].phenoInhList=omimRet[6] - varObjList[i].phenoMimList=omimRet[7] - print('OMIM res:') - print('\tgeneFound:',varObjList[i].omimGeneFound,'varFound:',varObjList[i].omimVarFound ) - - #get clinvar - if 1: - print('\nReading clinVar') - #clinVarList=jsonDict['clinvar'] - #varObjList[i].clinVarList=clinVarList - #retList=[varFound,varDict, geneFound, geneDict, clinvarTotalNumVars,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB] - clinVarRet=getClinVarUsingMarrvelFlatFile(varObjList[i], clinvarAlleleDf, clinvarGeneDf) - varObjList[i].clinVarVarFound=clinVarRet[0] - varObjList[i].clinVarVarDict=clinVarRet[1] - varObjList[i].clinVarGeneFound=clinVarRet[2] - varObjList[i].clinVarGeneDict=clinVarRet[3] - varObjList[i].clinvarTotalNumVars=clinVarRet[4] - varObjList[i].clinvarNumP=clinVarRet[5] - varObjList[i].clinvarNumLP=clinVarRet[6] - varObjList[i].clinvarNumLB=clinVarRet[7] - varObjList[i].clinvarNumB=clinVarRet[8] - varObjList[i].clinvarTitle= clinVarRet[9] - varObjList[i].clinvarSignDesc=clinVarRet[10] - varObjList[i].clinvarCondition=clinVarRet[11] - print('clinVar res:') - if debugFlag==1: - print('\tgeneFound::',varObjList[i].clinVarGeneFound,'varFound:',varObjList[i].clinVarVarFound) - print('\tnumVars:',varObjList[i].clinvarTotalNumVars,'numPathologic:',varObjList[i].clinvarNumP,'numBenign:',varObjList[i].clinvarNumB) - print('\tsignDesc:', varObjList[i].clinvarSignDesc) - - #get HGMD annotation - if 1: - print('\nReading HGMD') - hgmdRet=getHGMDUsingFlatFile(varObjList[i],hgmdDf) - #hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList - varObjList[i].hgmdVarFound=hgmdRet[0] - varObjList[i].hgmdGeneFound=hgmdRet[1] - varObjList[i].hgmdVarPhenIdList=hgmdRet[2] - varObjList[i].hgmdVarHPOIdList=hgmdRet[3] - varObjList[i].hgmdVarHPOStrList=hgmdRet[4] - print('HGMD results:') - print('\thgmdVarFound:',varObjList[i].hgmdVarFound,'hgmdGeneFound:',varObjList[i].hgmdGeneFound, - 'hgmdVarPhenIdList:',varObjList[i].hgmdVarPhenIdList,'hgmdVarHPOIdList:', - varObjList[i].hgmdVarHPOIdList, - 'hgmdVarHPOStrList:',varObjList[i].hgmdVarHPOStrList) - - #get DGV annotation - if 1: - print('Reading DGV') - dgvList=jsonDict['dgv'] - #print('dgvList:', dgvList) - dgvRet=getDGVUsingMarrvelAnnotScript(varObjList[i],dgvList) - varObjList[i].dgvDictList=dgvRet[0] - varObjList[i].dgvTypeList=dgvRet[1] - varObjList[i].dgvSubtypeList=dgvRet[2] - #get DECIPHER annotation - if 1: - print('Reading DECIPHER') - decipherList=jsonDict['decipher'] - decipherRet=getDecipherUsingMarrvelAnnotScript(varObjList[i], decipherList) - # - varObjList[i].decipherDictList=decipherRet[0] - varObjList[i].decipherDeletionObsList=decipherRet[1] - varObjList[i].decipherStudyList=decipherRet[2] - - #break for debug - i=i+1 - if debugFlag==1: - if i==2: - break - - - return varObjList - -def getVarObjListFromDict(varDictList, numVars): - ''' - Make a var obj list after paralle processing - the list has three levels since from pool get a first level list which is equal to the number of cores used - ''' - numProc=len(varDictList) - print('numProc:', numProc) - #initialize - varObjList = [ Variant() for i in range(numVars) ] - i=0 - for procList in varDictList: - for varDict in procList: - #print('i:', i) - #for varDict in dictList: - varObjList[i].chrom=varDict['chrom'] - varObjList[i].pos=varDict['pos'] - varObjList[i].start=varDict['start'] - varObjList[i].stop=varDict['stop'] - varObjList[i].ref=varDict['ref'] - varObjList[i].alt=varDict['alt'] - varObjList[i].hg19Chrom=varDict['hg19Chrom'] - varObjList[i].hg19Pos=varDict['hg19Pos'] - varObjList[i].varId=varDict['varId'] - varObjList[i].varId_dash=varDict['varId_dash'] - - #varObjList[i].patientID=varDict['patientID'] - varObjList[i].zyg=varDict['ZYG'] - varObjList[i].geneSymbol=varDict['geneSymbol'] - varObjList[i].CADD_phred=varDict['CADD_phred'] - varObjList[i].CADD_PHRED=varDict['CADD_PHRED'] - varObjList[i].geneEnsId=varDict['Gene'] - varObjList[i].rsId=varDict['Existing_variation'] - varObjList[i].GERPpp_RS=varDict['GERPpp_RS'] - varObjList[i].featureType=varDict['Feature_type'] - varObjList[i].gnomadAF=varDict['gnomadAF'] - varObjList[i].gnomadAFg=varDict['gnomadAFg'] - varObjList[i].CLIN_SIG=varDict['CLIN_SIG'] - varObjList[i].HGVSc=varDict['HGVSc'] - varObjList[i].HGVSp=varDict['HGVSp'] - - varObjList[i].VARIANT_CLASS=varDict['VARIANT_CLASS'] - varObjList[i].Feature=varDict['Feature'] - varObjList[i].hom=varDict['hom'] - varObjList[i].hgmd_rs=varDict['hgmd_rs'] - - varObjList[i].clin_dict=varDict['clin_dict'] - varObjList[i].clin_PLP=varDict['clin_PLP'] - varObjList[i].clin_PLP_perc=varDict['clin_PLP_perc'] - varObjList[i].spliceAI=varDict['spliceAI'] - varObjList[i].spliceAImax=varDict['spliceAImax'] - - - #dbnsfp attributes - varObjList[i].GERPpp_NR=varDict['GERPpp_NR'] - varObjList[i].DANN_score=varDict['DANN_score'] - varObjList[i].FATHMM_pred=varDict['FATHMM_pred'] - varObjList[i].FATHMM_score=varDict['FATHMM_score'] - varObjList[i].GTEx_V8_gene=varDict['GTEx_V8_gene'] - varObjList[i].GTEx_V8_tissue=varDict['GTEx_V8_tissue'] - varObjList[i].Polyphen2_HDIV_score=varDict['Polyphen2_HDIV_score'] - varObjList[i].Polyphen2_HVAR_score=varDict['Polyphen2_HVAR_score'] - varObjList[i].REVEL_score=varDict['REVEL_score'] - varObjList[i].SIFT_score=varDict['SIFT_score'] - #varObjList[i].clinvar_clnsig=varDict['clinvar_clnsig'] - - varObjList[i].clinvar_AlleleID = varDict['clinvar_AlleleID'] # Clinvar allele ID from clinvar.vcf.gz - varObjList[i].clinvar_clnsig = varDict['clinvar_clnsig'] #CL: Clinvar SIG from clinvar.vcf.gz - varObjList[i].clinvar_CLNREVSTAT = varDict['clinvar_CLNREVSTAT'] #CL: Clinvar STAT from clinvar.vcf.gz, for interface only - varObjList[i].clinvar_CLNSIGCONF = varDict['clinvar_CLNSIGCONF'] #CL: Clinvar STAT from clinvar.vcf.gz - varObjList[i].clin_code = varDict['clin_code'] #CL: feature for ai - - - varObjList[i].fathmm_MKL_coding_score=varDict['fathmm_MKL_coding_score'] - varObjList[i].LRT_score=varDict['LRT_score'] - varObjList[i].LRT_Omega=varDict['LRT_Omega'] - varObjList[i].phyloP100way_vertebrate=varDict['phyloP100way_vertebrate'] - varObjList[i].M_CAP_score=varDict['M_CAP_score'] - varObjList[i].MutationAssessor_score=varDict['MutationAssessor_score'] - varObjList[i].MutationTaster_score=varDict['MutationTaster_score'] - varObjList[i].ESP6500_AA_AC=varDict['ESP6500_AA_AC'] - varObjList[i].ESP6500_AA_AF=varDict['ESP6500_AA_AF'] - varObjList[i].ESP6500_EA_AC=varDict['ESP6500_EA_AC'] - varObjList[i].ESP6500_EA_AF=varDict['ESP6500_EA_AF'] - #conservation - varObjList[i].LRT_Omega=varDict['LRT_Omega'] - varObjList[i].LRT_score=varDict['LRT_score'] - varObjList[i].phyloP100way_vertebrate=varDict['phyloP100way_vertebrate'] - varObjList[i].IMPACT=varDict['IMPACT'] - varObjList[i].Consequence=varDict['Consequence'] - #DGV - varObjList[i].dgvDictList=varDict['dgvDictList'] - varObjList[i].dgvTypeList=varDict['dgvTypeList'] - varObjList[i].dgvSubtypeList=varDict['dgvSubtypeList'] - varObjList[i].dgvVarFound=varDict['dgvVarFound'] - #Decipher - varObjList[i].decipherDictList=varDict['decipherDictList'] - varObjList[i].decipherDeletionObsList=varDict['decipherDeletionObsList'] - varObjList[i].decipherStudyList=varDict['decipherStudyList'] - varObjList[i].decipherVarFound=varDict['decipherVarFound'] - #gnomadG - varObjList[i].gnomadGeneZscore=varDict['gnomadGeneZscore'] - varObjList[i].gnomadGenePLI=varDict['gnomadGenePLI'] - varObjList[i].gnomadGeneOELof=varDict['gnomadGeneOELof'] - varObjList[i].gnomadGeneOELofUpper=varDict['gnomadGeneOELofUpper'] - - varObjList[i].omimVarFound=varDict['omimVarFound'] - varObjList[i].omimGeneFound=varDict['omimGeneFound'] - varObjList[i].omimDict=varDict['omimDict'] - varObjList[i].omimGeneDict=varDict['omimGeneDict'] - varObjList[i].omimAlleleDict=varDict['omimAlleleDict'] - varObjList[i].phenoList=varDict['phenoList'] - varObjList[i].phenoInhList=varDict['phenoInhList'] - varObjList[i].phenoMimList=varDict['phenoMimList'] - - varObjList[i].clinVarVarFound=varDict['clinVarVarFound'] - varObjList[i].clinVarVarDict=varDict['clinVarVarDict'] - varObjList[i].clinVarGeneFound=varDict['clinVarGeneFound'] - varObjList[i].clinVarGeneDict=varDict['clinVarGeneDict'] - varObjList[i].clinvarTotalNumVars=varDict['clinvarTotalNumVars'] - varObjList[i].clinvarNumP=varDict['clinvarNumP'] - varObjList[i].clinvarNumLP=varDict['clinvarNumLP'] - varObjList[i].clinvarNumLB=varDict['clinvarNumLB'] - varObjList[i].clinvarNumB=varDict['clinvarNumB'] - varObjList[i].clinvarTitle= varDict['clinvarTitle'] - varObjList[i].clinvarSignDesc=varDict['clinvarSignDesc'] - varObjList[i].clinvarCondition=varDict['clinvarCondition'] - - varObjList[i].hgmdVarFound=varDict['hgmdVarFound'] - varObjList[i].hgmdGeneFound=varDict['hgmdGeneFound'] - varObjList[i].hgmdVarPhenIdList=varDict['hgmdVarPhenIdList'] - varObjList[i].hgmdVarHPOIdList=varDict['hgmdVarHPOIdList'] - varObjList[i].hgmdVarHPOStrList=varDict['hgmdVarHPOStrList'] - - varObjList[i].hgmd_id = varDict['hgmd_id'] # CL added - varObjList[i].hgmd_symbol = varDict['hgmd_symbol'] # CL added - varObjList[i].hgmd_PHEN = varDict['hgmd_PHEN'] # CL added - varObjList[i].hgmd_CLASS = varDict['hgmd_CLASS'] # CL added - - #symptom - varObjList[i].SymptomMatched=varDict['SymptomMatched'] - varObjList[i].symptomScore=varDict['symptomScore'] - varObjList[i].symptomName=varDict['symptomName'] - varObjList[i].omimSymptomSimScore=varDict['omimSymptomSimScore'] - varObjList[i].omimSymMatchFlag=varDict['omimSymMatchFlag'] - varObjList[i].hgmdSymptomScore=varDict['hgmdSymptomScore'] - varObjList[i].hgmdSymptomSimScore=varDict['hgmdSymptomSimScore'] - varObjList[i].hgmdSymMatchFlag=varDict['hgmdSymMatchFlag'] - varObjList[i].clinVarSymMatchFlag=varDict['clinVarSymMatchFlag'] - #increment - i=i+1 - #print('i index:', i) - #return - return varObjList - -def getAnnotateInfo_2(rowList,genomeRef,clinvarGeneDf,clinvarAlleleDf, omimGeneList, omimAlleleList,hgmdDf, moduleList, dgvDf, decipherDf, gnomadMetricsGeneDf): - ''' - Function to read the input annoatated varaint file and call the marrvel flat files to get annotations such as OMIM, DGV, Decipher - Parameters: - rowList: the input variants as a lits - other inputs are the flat file objects read locally instaed of an API - - Returns: - A dict of annoatated varaints - ''' - debugFlag=0 - #print('\nlen numVars:', len(rowList), 'typerow00:', type(rowList[0][0]), 'typerow[0]:', - # type(rowList[0]) ) - #print('len(row[0][0]):', len(rowList[0][0])) - #print('moduleList:', moduleList) - varObjList=[] - varDictList=[] - i=0 - #loop thru the rows - for row in rowList: - # CL 03-14-2023: commented all printing lines - #print('type of row:', type(row)) - varObj=Variant() - transcriptId=row.Feature - #s=row.Uploaded_variation.split('_') '1_10204_-/T' 1_1588250_T_A - ####row[0]: 21_11039079_C/A - ####s: ['21', '11039079', 'C/A'] - #print('row[0]:', row[0]) - - #two ways of input of first column either 1_1588250_T_A OR 21_11039079_C/A, so use the option flag - optFlag=0 - if(row[0].find('/')!=-1): - optFlag=1 - - if optFlag==0: - s=row[0].split('_') - #print('s:', s) - chrom=s[0] - pos=int(s[1]) - ref=s[2] - alt=s[3] - elif optFlag==1: - s=row[0].split('_') - #print('s:', s) - chrom=s[0] - pos=int(s[1]) - s=s[2].split('/') - ref=s[0] - alt=s[1] - - #get the start and stop from second column like '1:10203-10204' - if '-' in row[1]: - s=row[1].split(':') - tmp=s[1] - s=tmp.split('-') - #print('s:',s) - start=int(s[0]) - stop=int(s[1]) - else: - #start and stop the same - s=row[1].split(':') - start=int(s[1]) - stop=int(s[1]) - - #print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) - #change chrom X and Y and MT to numbers - if chrom=='X': - chrom=23 - elif chrom=='Y': - chrom=24 - elif chrom=='MT': - chrom=25 - elif re.search(r'GL', chrom): - chrom=26 - chrom=int(chrom) - - #if it is hg38 get its hg19 coordinates - # CL 03-14-2023: we have separate database for hg19 and hg38, - # we don't need to use LiftOver which is inaccurate - # related codes commented and modified - if genomeRef=='hg38': - varObj.hg38Chrom=chrom - varObj.hg38Pos=pos - varObj.chrom=chrom - varObj.pos=pos - varObj.start=start - varObj.stop=stop - - ''' - retList=gethg19LocFromHg38(chrom, pos)#called from the utils_1.py - # retList=[newChrom, newPos] - varObj.hg19Chrom=retList[0] - varObj.hg19Pos=retList[1] - varObj.chrom=retList[0] - varObj.pos=retList[1] - #get the start - retList=gethg19LocFromHg38(chrom, start) - varObj.start=int(retList[1]) - #get the stop - retList=gethg19LocFromHg38(chrom, stop) - varObj.stop=int(retList[1]) - ''' - else: - varObj.hg19Chrom=chrom - varObj.hg19Pos=pos - varObj.chrom=chrom - varObj.pos=pos - varObj.start=start - varObj.stop=stop - - - geneSymbol=row.SYMBOL - #print('gene:', geneSymbol) - varObj.geneSymbol=geneSymbol - varObj.CADD_phred=row.CADD_phred - varObj.CADD_PHRED=row.CADD_PHRED - - #assign - varObj.ref=ref - varObj.alt=alt - varObj.varId_dash='-'.join([str(chrom),str(start),ref,alt]) - #print('varId dash:', varObj.varId_dash) - varId='_'.join([str(chrom),str(pos),ref,alt,transcriptId]) - varObj.varId=varId - if 'ZYG' in row: - varObj.zyg=row.ZYG - varObj.geneEnsId=row.Gene - varObj.rsId=row.Existing_variation - varObj.GERPpp_RS=row.GERPpp_RS - varObj.featureType=row.Feature_type - varObj.gnomadAF=row.gnomAD_AF - varObj.gnomadAFg=row.gnomADg_AF - varObj.CLIN_SIG=row.CLIN_SIG #CL: useless but kept for now - varObj.LRT_Omega=row.LRT_Omega - varObj.LRT_score=row.LRT_score - varObj.phyloP100way_vertebrate=row.phyloP100way_vertebrate - varObj.IMPACT=row.IMPACT - varObj.Consequence=row.Consequence - varObj.HGVSc=row.HGVSc - varObj.HGVSp=row.HGVSp - #dbnsfp attributes - varObj.GERPpp_NR=row.GERPpp_NR - varObj.DANN_score=row.DANN_score - varObj.FATHMM_pred=row.FATHMM_pred - varObj.FATHMM_score=row.FATHMM_score - varObj.GTEx_V8_gene=row.GTEx_V8_gene - varObj.GTEx_V8_tissue=row.GTEx_V8_tissue - varObj.Polyphen2_HDIV_score=row.Polyphen2_HDIV_score - varObj.Polyphen2_HVAR_score=row.Polyphen2_HVAR_score - varObj.REVEL_score=row.REVEL_score - varObj.SIFT_score=row.SIFT_score - - varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz - varObj.clinvar_clnsig= row.clinvar_CLNSIG #CL: Clinvar SIG from clinvar.vcf.gz - #varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted - varObj.clinvar_CLNREVSTAT= row.clinvar_CLNREVSTAT #CL: Clinvar STAT from clinvar.vcf.gz, for interface only - varObj.clinvar_CLNSIGCONF= row.clinvar_CLNSIGCONF #CL: Clinvar SIGCONF from clinvar.vcf.gz - varObj.clin_code = row.clinvar_CLNSIG #CL: feature name for ai - - varObj.fathmm_MKL_coding_score=row.fathmm_MKL_coding_score - varObj.LRT_score=row.LRT_score - varObj.LRT_Omega=row.LRT_Omega - varObj.phyloP100way_vertebrate=row.phyloP100way_vertebrate - varObj.M_CAP_score=row.M_CAP_score - varObj.MutationAssessor_score=row.MutationAssessor_score - varObj.MutationTaster_score=row.MutationTaster_score - varObj.ESP6500_AA_AC=row.ESP6500_AA_AC - varObj.ESP6500_AA_AF=row.ESP6500_AA_AF - varObj.ESP6500_EA_AC=row.ESP6500_EA_AC - varObj.ESP6500_EA_AF=row.ESP6500_EA_AF - - varObj.VARIANT_CLASS=row.VARIANT_CLASS - varObj.Feature=row.Feature - varObj.hom=row.gnomADg_controls_nhomalt - varObj.hgmd_id=row.hgmd # CL added - varObj.hgmd_symbol=row.hgmd_GENE # CL added - varObj.hgmd_rs=row.hgmd_RANKSCORE - varObj.hgmd_PHEN=row.hgmd_PHEN # CL added - varObj.hgmd_CLASS=row.hgmd_CLASS # CL added - - if row.clinvar_CLNSIGCONF !="-": - clin_dict=dict() - for ro in row.clinvar_CLNSIGCONF.split("|_"): - temp=ro.split("(") - clin_dict[temp[0]]=int(temp[1][0]) - PLP_sum=clin_dict.get("Pathogenic", 0)+clin_dict.get("Likely_pathogenic", 0) - varObj.clin_dict=clin_dict - varObj.clin_PLP=PLP_sum - varObj.clin_PLP_perc=PLP_sum/sum(clin_dict.values()) - else: - if "benign" in row.clinvar_clnsig.lower(): - varObj.clin_PLP_perc=0 - elif "pathogenic" in row.clinvar_clnsig.lower(): - varObj.clin_PLP_perc=1 - else: - varObj.clin_PLP_perc="-" - varObj.clin_PLP="-" - varObj.clin_dict="-" - - - if row.SpliceAI_pred !="-": - varObj.spliceAI=row.SpliceAI_pred - temp=row.SpliceAI_pred.split("|") - varObj.spliceAImax=max(float(temp[1]),float(temp[2]),float(temp[3]),float(temp[4])) - else: - varObj.spliceAI="-" - varObj.spliceAImax="-" - - #get dgv - if 'conserve' in moduleList: - #print('\nGetting DGV') - dgvRet=getDGVUsingMarrvelFlatFile(varObj, dgvDf) - varObj.dgvDictList=dgvRet[0] - varObj.dgvTypeList=dgvRet[1] - varObj.dgvSubtypeList=dgvRet[2] - varObj.dgvVarFound=dgvRet[3] - - #get decipher - if 'conserve' in moduleList: - #print('\nGetting DECIPHER') - decipherRet=getDecipherUsingMarrvelFlatFile(varObj, decipherDf) - #[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] - varObj.decipherDictList=decipherRet[0] - varObj.decipherDeletionObsList=decipherRet[1] - varObj.decipherStudyList=decipherRet[2] - varObj.decipherVarFound=decipherRet[3] - - #get gnomad gene metrics from gnomad file - if 'conserve' in moduleList: - #print('\nGetting gnomad gene metrics') - gnomadGeneMetricRet=getGnomadGeneMetricFlatFile(varObj, gnomadMetricsGeneDf) - #[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] - varObj.gnomadGeneZscore=gnomadGeneMetricRet[0] - varObj.gnomadGenePLI=gnomadGeneMetricRet[1] - varObj.gnomadGeneOELof=gnomadGeneMetricRet[2]#O/E lof - varObj.gnomadGeneOELofUpper=gnomadGeneMetricRet[3]#O/E lof upper - - #for module one will need OMIM, HGMD, clinvar - #get OMIM - if 'curate' in moduleList: - #print('\nGetting OMIM') - #varObj.omimList=jsonDict['omim'] - #retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] - omimRet=getOMIMUsingMarrvelFlatFile(varObj, omimGeneList, omimAlleleList) - varObj.omimVarFound=omimRet[0] - varObj.omimGeneFound=omimRet[1] - varObj.omimDict=omimRet[2] - varObj.omimGeneDict=omimRet[3] - varObj.omimAlleleDict=omimRet[4] - varObj.phenoList=omimRet[5] - varObj.phenoInhList=omimRet[6] - varObj.phenoMimList=omimRet[7] - #print('OMIM res:') - #print('\tgeneFound:',varObj.omimGeneFound,'varFound:',varObj.omimVarFound ) - - #get clinvar - if 'curate' in moduleList: - #print('\nReading clinVar') - clinVarRet=getClinVarUsingMarrvelFlatFile(varObj, clinvarAlleleDf, clinvarGeneDf) - varObj.clinVarVarFound=clinVarRet[0] - varObj.clinVarVarDict=clinVarRet[1] - varObj.clinVarGeneFound=clinVarRet[2] - varObj.clinVarGeneDict=clinVarRet[3] - varObj.clinvarTotalNumVars=clinVarRet[4] - varObj.clinvarNumP=clinVarRet[5] - varObj.clinvarNumLP=clinVarRet[6] - varObj.clinvarNumLB=clinVarRet[7] - varObj.clinvarNumB=clinVarRet[8] - varObj.clinvarTitle= clinVarRet[9] - varObj.clinvarSignDesc= row.clinvar_CLNSIG #clinVarRet[10] #CL: changed to clinvar.vcf.gz annotation - varObj.clinvarCondition=clinVarRet[11] - #print('clinVar res:') - ''' - if debugFlag==1: - print('\tgeneFound::',varObj.clinVarGeneFound,'varFound:',varObj.clinVarVarFound) - print('\tnumVars:',varObj.clinvarTotalNumVars,'numPathologic:',varObj.clinvarNumP,'numBenign:',varObj.clinvarNumB) - print('\tsignDesc:', varObj.clinvarSignDesc) - ''' - - #get HGMD - if 'curate' in moduleList: - #print('\nReading HGMD') - hgmdRet=getHGMDUsingFlatFile(varObj,hgmdDf) - #hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList - varObj.hgmdVarFound=hgmdRet[0] - varObj.hgmdGeneFound=hgmdRet[1] - varObj.hgmdVarPhenIdList=hgmdRet[2] - varObj.hgmdVarHPOIdList=hgmdRet[3] - varObj.hgmdVarHPOStrList=hgmdRet[4] - #print('HGMD results:') - #print('\thgmdVarFound:',varObj.hgmdVarFound,'hgmdGeneFound:',varObj.hgmdGeneFound, - # 'hgmdVarPhenIdList:',varObj.hgmdVarPhenIdList,'hgmdVarHPOIdList:', - # varObj.hgmdVarHPOIdList, - # 'hgmdVarHPOStrList:',varObj.hgmdVarHPOStrList) - - #make a dict from the varObj. the varObj has one element for each row of the input VEP annotated file - varDict={ - 'hg19Chrom':varObj.hg19Chrom, - 'hg19Pos':varObj.hg19Pos, - 'chrom':varObj.chrom, - 'pos':varObj.pos, - 'start':varObj.start, - 'stop':varObj.stop, - 'geneSymbol':varObj.geneSymbol, - 'CADD_phred':varObj.CADD_phred, - 'CADD_PHRED':varObj.CADD_PHRED, - 'ref':varObj.ref, - 'alt':varObj.alt, - 'varId':varObj.varId, - 'ZYG':varObj.zyg, - 'HGVSc':varObj.HGVSc, - 'HGVSp':varObj.HGVSp, - 'Gene':varObj.geneEnsId, - 'Existing_variation':varObj.rsId, - 'GERPpp_RS':varObj.GERPpp_RS, - 'Feature_type':varObj.featureType, - 'gnomadAF':varObj.gnomadAF, - 'gnomadAFg':varObj.gnomadAFg, - 'CLIN_SIG':varObj.CLIN_SIG, - 'LRT_Omega':varObj.LRT_Omega, - 'LRT_score':varObj.LRT_score, - 'phyloP100way_vertebrate':varObj.phyloP100way_vertebrate, - #dbnsfp attributes - 'GERPpp_NR':varObj.GERPpp_NR, - 'DANN_score':varObj.DANN_score, - 'FATHMM_pred':varObj.FATHMM_pred, - 'FATHMM_score':varObj.FATHMM_score, - 'GTEx_V8_gene':varObj.GTEx_V8_gene, - 'GTEx_V8_tissue':varObj.GTEx_V8_tissue, - 'Polyphen2_HDIV_score':varObj.Polyphen2_HDIV_score, - 'Polyphen2_HVAR_score':varObj.Polyphen2_HVAR_score, - 'REVEL_score':varObj.REVEL_score, - 'SIFT_score':varObj.SIFT_score, - - 'clinvar_AlleleID': varObj.clinvar_AlleleID, # Clinvar allele ID from clinvar.vcf.gz - 'clinvar_clnsig': varObj.clinvar_clnsig, #CL: Clinvar SIG from clinvar.vcf.gz - 'clinvar_CLNREVSTAT': varObj.clinvar_CLNREVSTAT, #CL: Clinvar STAT from clinvar.vcf.gz, for interface only - 'clinvar_CLNSIGCONF': varObj.clinvar_CLNSIGCONF, #CL: Clinvar SIGCONF from clinvar.vcf.gz - 'clin_code': varObj.clin_code, #CL: feature for ai - - 'fathmm_MKL_coding_score':varObj.fathmm_MKL_coding_score, - 'LRT_score':varObj.LRT_score, - 'LRT_Omega':varObj.LRT_Omega, - 'phyloP100way_vertebrate':varObj.phyloP100way_vertebrate, - 'M_CAP_score':varObj.M_CAP_score, - 'MutationAssessor_score':varObj.MutationAssessor_score, - 'MutationTaster_score':varObj.MutationTaster_score, - 'ESP6500_AA_AC':varObj.ESP6500_AA_AC, - 'ESP6500_AA_AF':varObj.ESP6500_AA_AF, - 'ESP6500_EA_AC':varObj.ESP6500_EA_AC, - 'ESP6500_EA_AF':varObj.ESP6500_EA_AF, - #dbnsfp - 'gnomadGeneZscore':varObj.gnomadGeneZscore, - 'gnomadGenePLI':varObj.gnomadGenePLI, - 'gnomadGeneOELof':varObj.gnomadGeneOELof,#O/E lof - 'gnomadGeneOELofUpper':varObj.gnomadGeneOELofUpper,#O/E lof upper, - 'IMPACT':varObj.IMPACT, - 'Consequence':varObj.Consequence, - 'omimVarFound':varObj.omimVarFound, - 'omimGeneFound':varObj.omimGeneFound, - 'omimDict':varObj.omimDict, - 'omimGeneDict':varObj.omimGeneDict, - 'omimAlleleDict':varObj.omimAlleleDict, - 'phenoList':varObj.phenoList, - 'phenoInhList':varObj.phenoInhList, - 'phenoMimList':varObj.phenoMimList, - 'clinVarVarFound':varObj.clinVarVarFound, - 'clinVarVarDict':varObj.clinVarVarDict, - 'clinVarGeneFound':varObj.clinVarGeneFound, - 'clinVarGeneDict':varObj.clinVarGeneDict, - 'clinvarTotalNumVars':varObj.clinvarTotalNumVars, - 'clinvarNumP':varObj.clinvarNumP, - 'clinvarNumLP':varObj.clinvarNumLP, - 'clinvarNumLB':varObj.clinvarNumLB, - 'clinvarNumB':varObj.clinvarNumB, - 'clinvarTitle':varObj.clinvarTitle, - 'clinvarSignDesc':varObj.clinvarSignDesc, - 'clinvarCondition':varObj.clinvarCondition, - 'hgmdVarFound':varObj.hgmdVarFound, - 'hgmdGeneFound':varObj.hgmdGeneFound, - 'hgmdVarPhenIdList':varObj.hgmdVarPhenIdList, - 'hgmdVarHPOIdList':varObj.hgmdVarHPOIdList, - 'hgmdVarHPOStrList':varObj.hgmdVarHPOStrList, - 'varId_dash':varObj.varId_dash, - 'dgvDictList':varObj.dgvDictList, - 'dgvTypeList':varObj.dgvTypeList, - 'dgvSubtypeList':varObj.dgvSubtypeList, - 'dgvVarFound':varObj.dgvVarFound, - 'decipherDictList':varObj.decipherDictList, - 'decipherDeletionObsList':varObj.decipherDeletionObsList, - 'decipherStudyList':varObj.decipherStudyList, - 'decipherVarFound':varObj.decipherVarFound, - 'gnomadGeneZscore':varObj.gnomadGeneZscore, - 'gnomadGenePLI':varObj.gnomadGenePLI, - 'gnomadGeneOELof':varObj.gnomadGeneOELof, - 'gnomadGeneOELofUpper':varObj.gnomadGeneOELofUpper, - #symptom - 'SymptomMatched':varObj.SymptomMatched, - 'symptomScore':varObj.symptomScore, - 'symptomName':varObj.symptomName, - 'omimSymptomSimScore':varObj.omimSymptomSimScore, - 'omimSymMatchFlag':varObj.omimSymMatchFlag, - 'hgmdSymptomScore':varObj.hgmdSymptomScore, - 'hgmdSymptomSimScore':varObj.hgmdSymptomSimScore, - 'hgmdSymMatchFlag':varObj.hgmdSymMatchFlag, - 'clinVarSymMatchFlag':varObj.clinVarSymMatchFlag, - 'VARIANT_CLASS':varObj.VARIANT_CLASS, - 'Feature':varObj.Feature, - 'hom':varObj.hom, - 'hgmd_rs':varObj.hgmd_rs, - - 'hgmd_id':varObj.hgmd_id, # CL added - 'hgmd_symbol':varObj.hgmd_symbol, # CL added - 'hgmd_PHEN':varObj.hgmd_PHEN, # CL added - 'hgmd_CLASS':varObj.hgmd_CLASS, # CL added - - 'clin_dict':varObj.clin_dict, - 'clin_PLP':varObj.clin_PLP, - 'clin_PLP_perc':varObj.clin_PLP_perc, - 'spliceAI':varObj.spliceAI, - 'spliceAImax':varObj.spliceAImax - - } - varDictList.append(varDict) - #print('finsihed making dict') - #increment index - i=i+1 - - #return object - return varDictList - diff --git a/run/annotation/utils_for_marrvel_flatfile_module_2.py b/run/annotation/utils_for_marrvel_flatfile_module_2.py deleted file mode 100755 index bd3179a..0000000 --- a/run/annotation/utils_for_marrvel_flatfile_module_2.py +++ /dev/null @@ -1,186 +0,0 @@ -## UPDATE -# Changes made by Chaozhong noted as #CL - -from utils_1 import * -import numpy as np -import time -import re - -def getDecipherUsingMarrvelFlatFile(varObj, decipherDf): - ''' - Get Decipher annotation using marrvel flat file - Params: - varObj:variant object - decipherDf:dataframe of input flat file - Return: - Decipher list - ''' - #print('in DECIPHER call') - decipherDictList=[] - decipherDeletionObsList=[] - decipherStudyList=[] - decipherVarFound=0 - deletionObs='-' - #get the varaint object info from varObj - chromVal=int(varObj.chrom) - posVal=int(varObj.pos) - startVal=int(varObj.start) - stopVal=int(varObj.stop) - - # CL 03-14-2023: changed column names to be compatible with hg38 - #vals=decipherDf[ ( decipherDf['hg19Chr'] == chromVal ) & ( decipherDf['hg19Start']==startVal ) & (decipherDf['hg19Stop']==stopVal) ] - vals=decipherDf[ ( decipherDf['Chr'] == chromVal ) & ( decipherDf['Start']==startVal ) & (decipherDf['Stop']==stopVal) ] - numRows=len(vals.index) - - if numRows>0: - decipherVarFound=1 - deletionObs=vals.iloc[0]['deletion.obs'] - decipherDeletionObsList.append(deletionObs) - - #print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) - #print('\tdecipherVarFound:',decipherVarFound,'decipherDeletionObs:', deletionObs) - retList=[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] - return retList - -def getDGVUsingMarrvelFlatFile(varObj, dgvDf): - ''' - Get DGV annotation using marrvel flat file - Params: - varObj:variant object - dgvDf:dataframe of input flat file - Return: - DGV list - ''' - #print('in DGV call') - dgvDictList=[] - typeList=[] - subtypeList=[] - dgvVarFound=0 - dgvType='-' - dgvSubtype='-' - - chromVal=int(varObj.chrom) - posVal=int(varObj.pos) - startVal=int(varObj.start) - stopVal=int(varObj.stop) - - #CL 03-14-2023: changed column names to be compatible with hg38 - #vals=dgvDf[ ( dgvDf['hg19Chr'] == chromVal ) & ( dgvDf['hg19Start']<=startVal ) & (dgvDf['hg19Stop']>=stopVal) ] - vals=dgvDf[ ( dgvDf['Chr'] == chromVal ) & ( dgvDf['Start']<=startVal ) & (dgvDf['Stop']>=stopVal) ] - numRows=len(vals.index) - - if numRows>0: - dgvVarFound=1 - #print('\tnumrows:',numRows) - #print('\t type of vals:', type(vals)) - #print('\tvals:', vals) - dgvType=vals.iloc[0]['type'] - dgvSubtype=vals.iloc[0]['subType'] - - #print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) - #print('\tdgvVarFound:',dgvVarFound,'dgvType:', dgvType, 'dgvsubtype:', dgvSubtype) - typeList.append(dgvType) - subtypeList.append(dgvSubtype) - retList=[dgvDictList,typeList,subtypeList,dgvVarFound] - return retList - -def getGnomadGeneMetricFlatFile(varObj, gnomadMetricsGeneDf): - ''' - Get GnomadGeneMetric from flat file - Params: - varObj:variant object - gnomadMetricsGeneDf:dataframe of input flat file - Return: - gnomadMetricsGeneDf list - ''' - #print('in gnomadGeneMetricFlatFile call') - #check if gene in file - vals=gnomadMetricsGeneDf[gnomadMetricsGeneDf['gene'] == varObj.geneSymbol] - numRows=len(vals.index) - - if numRows>0:#pLI, oe_lof, oe_lof_upper,mis_z - gnomadGeneZscore=vals.iloc[0]['mis_z'] - gnomadGenePLI=vals.iloc[0]['pLI'] - gnomadGeneOELof=vals.iloc[0]['oe_lof'] - gnomadGeneOELofUpper=vals.iloc[0]['oe_lof_upper'] - else: - #get the values - gnomadGeneZscore='-' - gnomadGenePLI='-' - gnomadGeneOELof='-' - gnomadGeneOELofUpper='-' - - retList=[gnomadGeneZscore, gnomadGenePLI, gnomadGeneOELof, gnomadGeneOELofUpper] - #print('\t gene symbol:', varObj.geneSymbol, 'zScore:', gnomadGeneZscore, 'gnomadGenePLI:',gnomadGenePLI,'gnomadGeneOELof:', gnomadGeneOELof,'gnomadGeneOELofUpper:', gnomadGeneOELofUpper) - #print('\t gene symbol:', varObj.geneSymbol, 'zScore:', gnomadGeneZscore) - return retList - -def getConservationScore(varObj, diseaseInh): - ''' - Get conservation score - Params: - varObj:variant object - diseaseInh: the disease inheritance - Return: - return three conservation scores: conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof - ''' - #print('in conservation score call') - #create conservation scores - #set up thresholds - gnomadGFreqCut=0.01#genome g - gnomadEFreqCut=0.01#exome e - # GERP ranges - gerpPPCut=4 - #LRT cut - #phylo cut - - #gnomad - conservationScoreGnomad='-' - gnomadAFVal = getValFromStr(str(varObj.gnomadAF), 'min') - gnomadAFgVal = getValFromStr(str(varObj.gnomadAFg), 'min') - if gnomadAFVal!='-' and gnomadAFgVal!='-': - #gnomadAFVal=float(varObj.gnomadAF) - #gnomadAFgVal=float(varObj.gnomadAFg) - if gnomadAFVal<0.01 and gnomadAFgVal<0.01: - conservationScoreGnomad='High' - else: - conservationScoreGnomad='Low' - else: - conservationScoreGnomad='-' - - #DGV - conservationScoreDGV='-' - if 'deletion' in varObj.dgvSubtypeList or 'loss' in varObj.dgvSubtypeList: - conservationScoreDGV='Low' - else: - conservationScoreDGV='High' - - #gene O/E score - conservationScoreOELof='-' - if varObj.gnomadGeneOELofUpper!='-': - gnomadGeneOELofUpperVal=float(varObj.gnomadGeneOELofUpper) - if gnomadGeneOELofUpperVal<0.35: - conservationScoreOELof='High' - else: - conservationScoreOELof='Low' - - #return - retList=[conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] - return retList - -def getValFromStr(valStr: str, select: str = 'min'): - """ - Function to convert string to float, - and takes care of situation when multiple values exist - """ - select_method = {'min':min, 'max':max} - vals = valStr.split(',') - if '-' in vals: - return '-' - else: - vals = [float(i) for i in vals] - return select_method[select](vals) - - - - diff --git a/run/annotation/utils_for_symMatch.py b/run/annotation/utils_for_symMatch.py deleted file mode 100755 index 9cbc296..0000000 --- a/run/annotation/utils_for_symMatch.py +++ /dev/null @@ -1,143 +0,0 @@ -## UPDATE -# Changes made by Chaozhong noted as #CL - -from utils_1 import * - - -def omimSymMatch(varObj, omimHPOScoreDf, inFileType): - ''' - Find OMIM symptom match score - Param: - omimHPOScoreDf: this is read from user input OMIM symptom mathc file. Foe example:/houston_10t/dongxue/MARRVEL_AI/BG/HPO2Gene/out_May12021/UDN/HPOsimi_UDN630665_simi_0.tsv - inFileType:the type of input file. Will be used later. - Return: - Calculate the variant symptom score - ''' - #print('\nin omimSymMatch') - #print('type varObj:', type(varObj)) - #print('\tvar:', varObj.varId_dash) - simScore=0 - if 'phenotypes' in varObj.omimGeneDict.keys(): - #print('\t phenotypes key found') - for pheno in varObj.omimGeneDict['phenotypes']: - if 'phenotypeMimNumber' in pheno.keys(): - PminNum = pheno['phenotypeMimNumber'] - #print('\tPminNum:', PminNum) - #print('\ttmp1:', omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum]) - valDf=omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum] - numRows=len(valDf.index) - if numRows!=0: - simScore = omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum].iloc[0]['Similarity_Score'] - else: - simScore=0 - #print('\tsimScore:', simScore) - else: - simScore=0 - if simScore >= 0.2: #change the threshold to a user arg later - varObj.symptomScore[PminNum] = simScore - symptomList = omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum].iloc[0]['Disease_Name'].split(';') - symptomList = [i.strip().upper() for i in symptomList] #remove space and tranfer all words to capital - varObj.symptomName[PminNum] = symptomList - varObj.omimSymMatchFlag = 1 - else: - continue - #store the sim score we get - varObj.omimSymptomSimScore=simScore - #print('omimSymMatch results:') - #print('\tsymtomSimScore:', varObj.omimSymptomSimScore) - #print('\tsymptomScore:', varObj.symptomScore) - #print('\tsymptomName:', varObj.symptomName) - #print('\tomimSymMatchFlag:', varObj.omimSymMatchFlag) - -def hgmdSymMatch(varObj, hgmdHPOScoreDf): - ''' - Find HGMD symptom match score - Param: - omimHPOScoreDf: this is read from user input OMIM symptom mathc file. For example:/six_tera/chaozhong/module_1/out/UDN/HPOsimi_UDNUDN630665_simi_0.tsv - inFileType:the type of input file. Will be used later. - Return: - Calculate the variant symptom score - ''' - - - #print('\nin HGMDSymMatch') - #print('\tvar:', varObj.varId_dash) - hgmdSymptomSimScore='-' - """ old version - if varObj.hgmdVarFound: - var_tmp = varObj.varId_dash.split('-') - var_tmp = 'chr%s:%s %s>%s'%(var_tmp[0],var_tmp[1],var_tmp[2],var_tmp[3]) - print('\tvar_tmp:', var_tmp) - if var_tmp in hgmdHPOScoreDf['hgvs'].tolist(): - varDf = hgmdHPOScoreDf[hgmdHPOScoreDf['hgvs'] == var_tmp] - varScore = max(varDf['Similarity_Score'].tolist()) - varObj.hgmdSymptomScore = varScore - if varScore >= 0.2: - varObj.hgmdSymMatchFlag = 1 - hgmdSymptomSimScore=varScore - else: - pass - elif varObj.hgmdGeneFound: - if varObj.geneSymbol in hgmdHPOScoreDf['Gene'].tolist(): - geneDf = hgmdHPOScoreDf[hgmdHPOScoreDf['Gene'] == varObj.geneSymbol] - geneScore = max(geneDf['Similarity_Score'].tolist()) - if geneScore >= 0.2: - varObj.hgmdSymMatchFlag = 1 - hgmdSymptomSimScore=geneScore - #store - varObj.hgmdSymptomSimScore=hgmdSymptomSimScore - print('hgmdSymMatch results:') - print('\thgmdSymMatchFlag:', varObj.hgmdSymMatchFlag) - print('\thgmdSymptomSimScore:', varObj.hgmdSymptomSimScore) - """ - if np.any(hgmdHPOScoreDf['acc_num'].isin([varObj.hgmd_id])): - varDf = hgmdHPOScoreDf[hgmdHPOScoreDf['acc_num'] == varObj.hgmd_id] - varScore = max(varDf['Similarity_Score'].tolist()) - varObj.hgmdSymptomScore = varScore - if varScore >= 0.2: - varObj.hgmdSymMatchFlag = 1 - hgmdSymptomSimScore=varScore - - elif varObj.hgmdGeneFound: - geneDf = hgmdHPOScoreDf[hgmdHPOScoreDf['gene_sym'] == varObj.geneSymbol] - geneScore = max(geneDf['Similarity_Score'].tolist()) - if geneScore >= 0.2: - varObj.hgmdSymMatchFlag = 1 - hgmdSymptomSimScore=geneScore - - varObj.hgmdSymptomSimScore=hgmdSymptomSimScore - #print('hgmdSymMatch results:') - #print('\thgmdSymMatchFlag:', varObj.hgmdSymMatchFlag) - #print('\thgmdSymptomSimScore:', varObj.hgmdSymptomSimScore) - - - - -def clinVarSymMatch(varObj, inFileType): - ''' - Find clinvar symptom match score - Param: - Return: - Calculate the variant symptom score - ''' - #print('\nin clinvarSymMatch') - if varObj.clinVarVarFound: - #pheno = varObj.clinVarVarDict['condition'].strip().upper() - #print('clinvar condition:', varObj.clinvarCondition) - if type(varObj.clinvarCondition) is str: - pheno=varObj.clinvarCondition.strip().upper() - for PminNum in varObj.symptomName.keys(): - if pheno in varObj.symptomName[PminNum] or '#%s %s'%(PminNum,pheno) in varObj.symptomName[PminNum]: - varObj.clinVarSymMatchFlag = 1 - else: - varObj.clinVarSymMatchFlag = 0 - - #Need more checking on this one per gene condition - if varObj.clinVarGeneFound: - for var in varObj.clinVarGeneDict: - pheno = var['condition'].strip().upper() - for PminNum in varObj.symptomName.keys(): - if pheno in varObj.symptomName[PminNum] or '#%s %s'%(PminNum,pheno) in varObj.symptomName[PminNum]: - varObj.clinVarGeneSymMatchFlag = 1 - - #print('\tvarObj.clinVarVarSymMatchFlag:', varObj.clinVarSymMatchFlag) diff --git a/run/extraModel/__init__.py b/run/extraModel/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/run/extraModel/confidence.py b/run/extraModel/confidence.py deleted file mode 100755 index 08505d6..0000000 --- a/run/extraModel/confidence.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python -"""Script to estimate the confidence of whether a case is solved or not and assign -a confidence score to each prediction. -""" -__author__ = "Linhua Wang" -__email__ = "linhuaw@bcm.edu" -import pandas as pd -import os -import numpy as np -import scipy - -def get_causal_distribution(pred_fd, label_col='is_strong'): - # pred_fd: folder for prediction files only - fns = os.listdir(pred_fd) - # get top20 predictions - top20_preds = [] - for fn in fns: - top20_preds.append(pd.read_csv(f'{pred_fd}/{fn}', index_col=0).iloc[:20,:]) - top20_preds = pd.concat(top20_preds) - # get scores of causal variants - top20_causals = np.ravel(top20_preds.loc[top20_preds[label_col]==1, 'predict'].to_numpy()) - return top20_causals - -def assign_confidence_score(causal_distr, pred_df): - # pred_fd: folder for prediction files only - # pred_df: prediction data frame of the sample of interest - - prediction_scores = pred_df.predict.to_numpy() - - # assign confidence score using percentile - conf_scores = [scipy.stats.percentileofscore(causal_distr, i) for i in prediction_scores] - # assign confidence category by discretizing - conf_categories = [] - for conf_score in conf_scores: - if conf_score < 25: - conf_categories.append("Unsolved") - elif conf_score < 50: - conf_categories.append("Solved (Low)") - elif conf_score < 75: - conf_categories.append("Solved (Medium)") - else: - conf_categories.append("Solved (High)") - pred_df['confidence'] = conf_scores - pred_df['confidence level'] = conf_categories - pred_df.sort_values("confidence", ascending=False) - return pred_df - -# def determine_case_confidence(causal_distr, pred_df, label_col='is_strong'): -# pred_df = assign_confidence_score(causal_distr, pred_df, label_col) -# return pred_df['confidence level'].tolist()[0] \ No newline at end of file diff --git a/run/extraModel/generate_bivar_data.py b/run/extraModel/generate_bivar_data.py deleted file mode 100755 index 87268a6..0000000 --- a/run/extraModel/generate_bivar_data.py +++ /dev/null @@ -1,179 +0,0 @@ -import pandas as pd -import os -import numpy as np -from glob import glob -from tqdm import tqdm, trange -import logging -from multiprocessing import Pool -from os.path import exists -import shutil - -def process_sample(data_folder, sample_id, - default_pred, labeling=False, n_thread = 10): - - # recessive_folder = f'{data_folder}/recessive_matrix' - # if not os.path.exists(recessive_folder): - # os.mkdir(recessive_folder) - - recessive_folder = f'{data_folder}/recessive_matrix/' - if not os.path.exists(recessive_folder): - os.mkdir(recessive_folder) - - tmp_folder = f'{data_folder}/recessive_matrix/tmp/' - if not os.path.exists(tmp_folder): - os.mkdir(tmp_folder) - - ## read feature matrix for single var - feature_fn = f"/out/final_matrix/{sample_id}.csv" - #feature_df = [] - #for feature_fn in feature_fns: - if 'csv' in feature_fn: - feature_df = pd.read_csv(feature_fn, index_col=0, sep=",") - else: - feature_df = pd.read_csv(feature_fn, index_col=0, sep="\t") - #feature_df.append(df) - #feature_df = pd.concat(feature_df) - feature_df = feature_df.loc[~feature_df.index.duplicated(keep='first')] - - ## Group variants by Ens IDs - expanded_fn = f"/out/final_matrix_expanded/{sample_id}.expanded.csv.gz" - gene_dict = {} - #for expanded_fn in expanded_fns: - if 'csv' in expanded_fn: - df = pd.read_csv(expanded_fn, sep=",", index_col=0, compression='infer') - else: - df = pd.read_csv(expanded_fn, sep="\t", index_col=0, compression='infer') - - if df.shape[0] > 100000: - df = df.loc[df['IMPACT.from.Tier'] > 1] - - for varId, gene in zip(df.index.tolist(), df.geneEnsId.tolist()): - if gene.startswith('ENSG'): - if gene not in gene_dict: - gene_dict[gene] = {varId} - else: - gene_dict[gene].add(varId) - - ## create parameters and run bivar feature generation in parallel - params = [{'feature_df': feature_df, - 'default_pred': default_pred, - 'labeling': labeling, - 'out_folder': f'{tmp_folder}', - 'gene': gene, - 'varIDs': list(gene_dict[gene])} for gene in gene_dict - ] - print(f"Now starting to generate recessive feature matrix for {len(gene_dict)} genes, {feature_df.shape[0]} variants using {n_thread} threads.") - p = Pool(processes=n_thread) - - with tqdm(total=len(params)) as pbar: - for result in p.imap_unordered(process_gene, params): - pbar.update() - p.close() - p.join() - print("Recessive features for each gene finished, now putting together...") - - bivar_feature_mats = [] - fns = glob(tmp_folder + "/*.csv") - for fn in tqdm(fns, total=len(fns)): - df = pd.read_csv(fn, index_col=0) - bivar_feature_mats.append(df) - if bivar_feature_mats: - bivar_feature_mats = pd.concat(bivar_feature_mats) - bivar_feature_mats.to_csv(f"{recessive_folder}/{sample_id}.csv") - print("Recessive features saved, now removing tmp files...") - print("Recessive features generation finished...") - else: - print("No recessive variant pair found. End and remove temp files") - - shutil.rmtree(f'{tmp_folder}') - -def process_gene(param): - - feature_names = 'diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat' - feature_names = feature_names.split(",") - nFeatures = len(feature_names) - bivar_feature_mats = [] - - gene = param['gene'] - varIDs, labeling = param['varIDs'], param['labeling'] - - - feature_df, out_folder = param['feature_df'], param['out_folder'] - - seen_pairs = set() - - allVars = feature_df.index.tolist() - varIDs = [v for v in varIDs if v in allVars] - - if len(varIDs) == 0: - return - - if len(varIDs) > 6: - defaultPred = param['default_pred'].copy() - defaultPred = defaultPred.loc[varIDs,:].sort_values("predict", ascending=False) - varIDs = defaultPred.index.tolist()[:6] - - gene_feats = feature_df.loc[varIDs, feature_names].copy() - gene_feats = gene_feats.values - assert gene_feats.shape[0] == len(varIDs) - # print(gene_feats, varIDs) - - def generate_feature_vector(i, j): - fmat = np.zeros((2, nFeatures * 2 + 1)) - dist = np.absolute(int(varIDs[i].split("-")[1])-int(varIDs[j].split("-")[1])) - - fmat[0] = np.append(np.append(gene_feats[i], gene_feats[j]), [dist]) - fmat[1] = np.append(np.append(gene_feats[j], gene_feats[i]), [dist]) - - f1_features = [f'{f}_1' for f in feature_names] - f2_features = [f'{f}_2' for f in feature_names] - - return pd.DataFrame(data=fmat, columns=f1_features + f2_features + ['var_dist'], - index=[f'{varIDs[i]}_{varIDs[j]}', f'{varIDs[j]}_{varIDs[i]}']) - - - for i in range(len(varIDs)): - varii_feature_matrix = generate_feature_vector(i,i) - var1 = varIDs[i] - - if f'{var1}_{var1}' not in seen_pairs: - if feature_df.loc[var1, 'zyg'] == 2: - - if labeling: - if feature_df.loc[var1, 'is_strong'] == 1: - is_causal_wo_OMIM = 1 - else: - is_causal_wo_OMIM = 0 - varii_feature_matrix['is_causal'] = is_causal_wo_OMIM - - bivar_feature_mats.append(varii_feature_matrix) - seen_pairs.add(f'{var1}_{var1}') - - if i < len(varIDs) - 1: - for j in range(i+1, len(varIDs)): - var2 = varIDs[j] - if (f'{var1}_{var2}' not in seen_pairs) and (f'{var2}_{var1}' not in seen_pairs) and (var1 != var2): - varij_feature_matrix = generate_feature_vector(i, j) - - if labeling: - is_causal_wo_OMIM = 0 - - if (feature_df.loc[[var1, var2], 'is_strong'] == 1).all(): - if (feature_df.loc[[var1, var2], 'zyg'] == 1).all(): - is_causal_wo_OMIM = 1 - - varij_feature_matrix['is_causal'] = is_causal_wo_OMIM - - bivar_feature_mats.append(varij_feature_matrix) - - seen_pairs.add(f'{var1}_{var2}') - seen_pairs.add(f'{var2}_{var1}') - else: - pass - - if len(bivar_feature_mats) > 0: - bivar_feature_mats = pd.concat(bivar_feature_mats, axis=0) - bivar_feature_mats = bivar_feature_mats.drop_duplicates() - bivar_feature_mats.to_csv(f"{out_folder}/{gene}.csv") - - return \ No newline at end of file diff --git a/run/extraModel/integrate_output.py b/run/extraModel/integrate_output.py deleted file mode 100755 index 3c3c6cc..0000000 --- a/run/extraModel/integrate_output.py +++ /dev/null @@ -1,150 +0,0 @@ -import pandas as pd -from glob import glob -import os - -def process_recessive_matrix(df): - df['varPair'] = df.index.copy() - df['var1'] = df.varPair.apply(lambda x: x.split("_")[0]) - df['var2'] = df.varPair.apply(lambda x: x.split("_")[1]) - var_grps = df.groupby("var1") - - result = [] - for _, var_grp in var_grps: - var_grp = var_grp.sort_values("predict", ascending=False) - result.append(var_grp.iloc[[0],:]) - result = pd.concat(result) - result.set_index("var1", inplace=True) - return result - -def integrate_output(prj_folder, data_folder, sample_id): - ## Group variants by Ens IDs - expanded_fn = f"/out/final_matrix_expanded/{sample_id}.expanded.csv.gz" - #expanded_df = [] - # for expanded_fn in expanded_fns: - if 'csv' in expanded_fn: - expanded_df = pd.read_csv(expanded_fn, sep=",", index_col=0, compression='infer') - else: - expanded_df = pd.read_csv(expanded_fn, sep="\t", index_col=0, compression='infer') - #expanded_df.append(df) - #expanded_df = pd.concat(expanded_df) - # expanded_df = expanded_df.loc[~expanded_df.index.duplicated(keep='first')] - ## read predictions for default model - default_fn = f"{data_folder}/{sample_id}_default_predictions.csv" - default_pred = pd.read_csv(default_fn, index_col=0) - default_raw_pred_dict = dict(zip(default_pred.index.tolist(), default_pred.predict.tolist())) - default_conf_dict = dict(zip(default_pred.index.tolist(), default_pred['confidence'].tolist())) - default_conf_lvl_dict = dict(zip(default_pred.index.tolist(), default_pred['confidence level'].tolist())) - default_rank_dict = dict(zip(default_pred.index.tolist(), default_pred.ranking.tolist())) - - expanded_df['predict (default)'] = expanded_df.index.map(default_raw_pred_dict) - expanded_df['confidence (default)'] = expanded_df.index.map(default_conf_dict) - expanded_df['confidence level (default)'] = expanded_df.index.map(default_conf_lvl_dict) - expanded_df['ranking (default)'] = expanded_df.index.map(default_rank_dict) - - ## read predictions for novel disease model - nd_fn = f"{data_folder}/{sample_id}_nd_predictions.csv" - nd_pred = pd.read_csv(nd_fn, index_col=0) - nd_raw_pred_dict = dict(zip(nd_pred.index.tolist(), nd_pred.predict.tolist())) - nd_conf_dict = dict(zip(nd_pred.index.tolist(), nd_pred['confidence'].tolist())) - nd_rank_dict = dict(zip(nd_pred.index.tolist(), nd_pred.ranking.tolist())) - nd_conf_lvl_dict = dict(zip(nd_pred.index.tolist(), nd_pred['confidence level'].tolist())) - - expanded_df['predict (nd)'] = expanded_df.index.map(nd_raw_pred_dict) - expanded_df['confidence (nd)'] = expanded_df.index.map(nd_conf_dict) - expanded_df['ranking (nd)'] = expanded_df.index.map(nd_rank_dict) - expanded_df['confidence level (nd)'] = expanded_df.index.map(nd_conf_lvl_dict) - - ## recessive predictions for default model - recessive_fn = f"{data_folder}/{sample_id}_recessive_predictions.csv" - if os.path.exists(recessive_fn): - recessive_pred = pd.read_csv(recessive_fn, index_col=0) - recessive_pred = process_recessive_matrix(recessive_pred) - - recessive_pred_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred.predict.tolist())) - recessive_conf_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred['confidence'].tolist())) - recessive_rank_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred.ranking.tolist())) - recessive_other_var = dict(zip(recessive_pred.index.tolist(), recessive_pred.var2.tolist())) - recessive_conf_lvl_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred['confidence level'].tolist())) - - rec_preds, rec_confs, rec_ranks, rec_var2s = [], [], [], [] - rec_conf_lvls = [] - for var in expanded_df.index.tolist(): - if var in recessive_pred_dict: - rec_preds.append(recessive_pred_dict[var]) - rec_confs.append(recessive_conf_dict[var]) - rec_ranks.append(recessive_rank_dict[var]) - rec_var2s.append(recessive_other_var[var]) - rec_conf_lvls.append(recessive_conf_lvl_dict[var]) - else: - rec_preds.append(-1) - rec_confs.append(-1) - rec_ranks.append(99999) - rec_var2s.append('NA') - rec_conf_lvls.append('Unsolved') - - expanded_df['predict (recessive)'] = rec_preds - expanded_df['confidence (recessive)'] = rec_confs - expanded_df['ranking (recessive)'] = rec_ranks - expanded_df['recessive var2'] = rec_var2s - expanded_df['confidence level (recessive)'] = rec_conf_lvls - else: - expanded_df['predict (recessive)'] = -1 - expanded_df['confidence (recessive)'] = -1 - expanded_df['ranking (recessive)'] = 99999 - expanded_df['recessive var2'] = 'NA' - expanded_df['confidence level (recessive)'] = 'Unsolved' - - ## novel disease recessive - nd_recessive_fn = f"{data_folder}/{sample_id}_nd_recessive_predictions.csv" - if os.path.exists(nd_recessive_fn): - nd_recessive_pred = pd.read_csv(nd_recessive_fn, index_col=0) - nd_recessive_pred = process_recessive_matrix(nd_recessive_pred) - - nd_recessive_pred_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.predict.tolist())) - nd_recessive_conf_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred['confidence'].tolist())) - nd_recessive_rank_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.ranking.tolist())) - nd_recessive_conf_lvl_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred['confidence level'].tolist())) - nd_recessive_other_var = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.var2.tolist())) - - nd_rec_preds, nd_rec_confs, nd_rec_ranks, nd_rec_var2s = [], [], [], [] - nd_rec_lvls = [] - for var in expanded_df.index.tolist(): - if var in nd_recessive_pred_dict: - nd_rec_preds.append(nd_recessive_pred_dict[var]) - nd_rec_confs.append(nd_recessive_conf_dict[var]) - nd_rec_ranks.append(nd_recessive_rank_dict[var]) - nd_rec_var2s.append(nd_recessive_other_var[var]) - nd_rec_lvls.append(nd_recessive_conf_lvl_dict[var]) - else: - nd_rec_preds.append(-1) - nd_rec_confs.append(-1) - nd_rec_ranks.append(99999) - nd_rec_var2s.append('NA') - nd_rec_lvls.append('Unsolved') - - expanded_df['predict (nd recessive)'] = nd_rec_preds - expanded_df['confidence (nd recessive)'] = nd_rec_confs - expanded_df['confidence level (nd recessive)'] = nd_rec_lvls - expanded_df['ranking (nd recessive)'] = nd_rec_ranks - expanded_df['nd recessive var2'] = nd_rec_var2s - else: - expanded_df['predict (nd recessive)'] = -1 - expanded_df['confidence (nd recessive)'] = -1 - expanded_df['confidence level (nd recessive)'] = 99999 - expanded_df['ranking (nd recessive)'] = 'NA' - expanded_df['nd recessive var2'] = 'Unsolved' - - return expanded_df - -if __name__ == "__main__": - from tqdm import tqdm - import os - prj_folder = 'projects/Texome/' - data_folder = 'data/Texome/' - sample_ids = os.listdir(data_folder) - for sample_id in tqdm(sample_ids): - integrated_df = integrate_output(prj_folder, data_folder, sample_id) - if not os.path.exists(f"{prj_folder}/AIM_integrated"): - os.mkdir(f"{prj_folder}/AIM_integrated") - integrated_df.to_csv(f'{prj_folder}/AIM_integrated/{sample_id}_integrated.csv') - diff --git a/run/extraModel/main.py b/run/extraModel/main.py deleted file mode 100755 index 258aa73..0000000 --- a/run/extraModel/main.py +++ /dev/null @@ -1,120 +0,0 @@ -import argparse -import os -import pandas as pd -from confidence import * -import joblib -from time import time -import generate_bivar_data -from datetime import datetime -from multiprocessing import Pool -from tqdm import tqdm -from scipy.stats import rankdata -from integrate_output import * - -parser = argparse.ArgumentParser() - -parser.add_argument('-id', metavar='I', type=str, - help = 'sample ID') - -parser.add_argument('-n_cpu', type=int, default=10, - help = 'folders containing all extended final matrices') - -args = parser.parse_args() - -#st_time = time() -#prj_name = args.project -sample_id = args.id -n_cpu = args.n_cpu - -out_folder = '/out/conf_4Model' - -if not os.path.exists(out_folder): - os.mkdir(out_folder) - -print(f"### Results will be saved at: {out_folder}.") -print(f"### Process started at: {datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}.") - -model_names = ['default', 'recessive', 'nd', 'nd_recessive'] -model_dict = {} - -for mn in model_names: - model = joblib.load(f"/run/data_dependencies/model_inputs/{mn}/final_model.job") - ref_panel = joblib.load(f"/run/data_dependencies/model_inputs/{mn}/reference_panel.job") - features = open(f"/run/data_dependencies/model_inputs/{mn}/features.csv", 'r').readline().split(",") - model_dict[mn] = {'model': model, 'ref': ref_panel, 'features': features} - #if not os.path.exists(f"./{out_folder}/{mn}"): - # os.mkdir(f"./{out_folder}/{mn}") - -print(f"### All models, features, reference panels loaded.") - -print(f"### Start generating recessive matrix and running predictions.") - -def assign_ranking(df): - pred_df = df.copy() - pred_df = pred_df.sort_values("predict", ascending=False) - min_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='min').astype(int) - max_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='max').astype(int) - pred_df['min_ranking'] = min_rankings - pred_df['max_ranking'] = max_rankings - pred_df['ranking'] = max_rankings - return pred_df - - -def AIM(data_folder, sample_id, n_thread): - feature_fn = f'/out/final_matrix/{sample_id}.csv' - - if not os.path.exists(feature_fn): - print(f"{feature_fn} does not exist.") - return - - df = pd.read_csv(feature_fn, index_col=0) - - for mn in ['default', 'nd']: - df_pred = df.copy() - if 'predict' in df_pred.columns: - df_pred = df_pred.drop(columns=['predict']) - predict = model_dict[mn]['model'].predict_proba(df_pred.loc[:, model_dict[mn]['features']])[:, 1] - df_pred.insert(loc=df_pred.shape[1]-1,column='predict',value=predict) - df_pred = assign_confidence_score(model_dict[mn]['ref'], df_pred) - df_pred = df_pred.sort_values('confidence', ascending=False) - df_pred = assign_ranking(df_pred) - df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") - - print(f"### Start processing recessive data and make predictions.") - - - default_pred = pd.read_csv(f"{out_folder}/{sample_id}_default_predictions.csv", index_col=0) - - generate_bivar_data.process_sample( data_folder = out_folder, - sample_id = sample_id, - default_pred = default_pred, - labeling=False, n_thread = n_thread) - - recessive_feature_file = f"{out_folder}/recessive_matrix/{sample_id}.csv" - if os.path.exists(recessive_feature_file): - # AIM found recessive variant pairs and generated the matrix - df = pd.read_csv(recessive_feature_file, index_col=0) - for mn in ['recessive', 'nd_recessive']: - df_pred = df.copy() - if 'predict' in df_pred.columns: - df_pred = df_pred.drop(columns=['predict']) - predict = model_dict[mn]['model'].predict_proba(df_pred.loc[:, model_dict[mn]['features']])[:, 1] - df_pred.insert(loc=df_pred.shape[1]-1,column='predict',value=predict) - df_pred = assign_confidence_score(model_dict[mn]['ref'], df_pred) - df_pred = df_pred.sort_values('confidence', ascending=False) - df_pred = assign_ranking(df_pred) - df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") - else: - # AIM found no recessive variant pair - pass - - print(f"Integrating all information for sample {sample_id}...") - ######### Construction Paused here ######### - integrated_df = integrate_output(out_folder, data_folder, sample_id) - if not os.path.exists(f"{out_folder}/integrated"): - os.mkdir(f"{out_folder}/integrated") - integrated_df.to_csv(f'{out_folder}/integrated/{sample_id}_integrated.csv') - return - -#for sample_id in tqdm(sample_folders): -AIM(out_folder, sample_id, n_thread = n_cpu) diff --git a/run/fillna_tier.py b/run/fillna_tier.py deleted file mode 100755 index d0e2148..0000000 --- a/run/fillna_tier.py +++ /dev/null @@ -1,682 +0,0 @@ -from random import sample -import numpy as np -import pandas as pd - -__author__ = "Chaozhong Liu" -__email__ = "chaozhol@bcm.edu" - -###Feature engineering and incorporation of tier module outputs. -###fills NA w/ 0 - -def getValFromStr(valStr: str, select: str = 'min'): - select_method = {'min':min, 'max':max} - vals = valStr.split(',') - if '-' in vals or '.' in vals: - return np.NaN - else: - vals = [float(i) for i in vals] - return select_method[select](vals) - -def feature_engineering(score_file, tier_file): - variable_name = ['varId_dash', 'hgmdSymptomScore', - 'omimSymMatchFlag', 'hgmdSymMatchFlag', 'clinVarSymMatchFlag', - 'omimGeneFound', 'omimVarFound', 'hgmdGeneFound', 'hgmdVarFound', - 'clinVarVarFound', 'clinVarGeneFound', - 'clinvarTotalNumVars','clinvarNumP', 'clinvarNumLP', 'clinvarNumLB', 'clinvarNumB', - 'dgvVarFound','decipherVarFound', - 'curationScoreHGMD', 'curationScoreOMIM', 'curationScoreClinVar', - 'conservationScoreDGV', 'omimSymptomSimScore', 'hgmdSymptomSimScore', 'clin_code', - 'GERPpp_RS', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'phyloP100way_vertebrate', - 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELof', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_phred', - 'CADD_PHRED', 'DANN_score', 'REVEL_score', 'fathmm_MKL_coding_score', 'conservationScoreGnomad', - 'conservationScoreOELof', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'SIFT_score', 'zyg', - 'FATHMM_score', 'M_CAP_score', 'MutationAssessor_score', 'ESP6500_AA_AF', - 'ESP6500_EA_AF', 'hom', 'hgmd_rs', 'spliceAImax', 'clin_PLP_perc', 'Consequence', - 'nc_ClinVar_Exp', 'c_ClinVar_Exp', 'c_HGMD_Exp', 'nc_HGMD_Exp', - 'nc_isPLP', 'nc_isBLB', 'c_isPLP', 'c_isBLB', - 'nc_CLNREVSTAT', 'c_CLNREVSTAT', - 'nc_RANKSCORE', 'c_RANKSCORE', 'CLASS', "phrank"] - - - - #sample_name = score_file.split('/')[-1].split('_')[1] - #patient = pd.read_csv(score_file,sep='\t') - feature_stat = pd.read_csv('/run/data_dependencies/annotate/feature_stats.csv', index_col=0) - - patient = score_file.copy() - - patient = patient[variable_name] - patient = patient.fillna('-') - indel_index = [len(i.split('-')[-1]) != 1 or len(i.split('-')[-2]) != 1 for i in patient['varId_dash'].to_list()] - - patient.loc[patient['phrank'] == '-','phrank'] = 0 - patient['phrank'] = patient['phrank'].astype('float64') - - patient.loc[patient['hgmdSymptomScore'] == '-','hgmdSymptomScore'] = 0 - patient['hgmdSymptomScore'] = patient['hgmdSymptomScore'].astype('float64') - - patient.loc[patient['omimSymMatchFlag'] == '-','omimSymMatchFlag'] = 0 - patient['omimSymMatchFlag'] = patient['omimSymMatchFlag'].astype('float64') - - patient.loc[patient['hgmdSymMatchFlag'] == '-','hgmdSymMatchFlag'] = 0 - patient['hgmdSymMatchFlag'] = patient['hgmdSymMatchFlag'].astype('float64') - - patient.loc[patient['clinVarSymMatchFlag'] == '-','clinVarSymMatchFlag'] = 0 - patient['clinVarSymMatchFlag'] = patient['clinVarSymMatchFlag'].astype('float64') - - - patient['clinvarNumLP'] = (patient['clinvarNumLP']+patient['clinvarNumP']) / patient['clinvarTotalNumVars'] - patient['clinvarNumLP'] = patient['clinvarNumLP'].fillna(0.0) - patient['clinvarNumP'] = patient['clinvarNumP'] / patient['clinvarTotalNumVars'] - patient['clinvarNumP'] = patient['clinvarNumP'].fillna(0.0) - patient['clinvarNumLB'] = (patient['clinvarNumLB']+patient['clinvarNumB']) / patient['clinvarTotalNumVars'] - patient['clinvarNumLB'] = patient['clinvarNumLB'].fillna(1.0) - patient['clinvarNumB'] = patient['clinvarNumB'] / patient['clinvarTotalNumVars'] - patient['clinvarNumB'] = patient['clinvarNumB'].fillna(1.0) - patient = patient.drop(columns=['clinvarTotalNumVars']) - variable_name.remove('clinvarTotalNumVars') - - - patient.loc[patient['curationScoreHGMD'] == 'Low','curationScoreHGMD'] = 1 - patient.loc[patient['curationScoreHGMD'] == 'Medium','curationScoreHGMD'] = 2 - patient.loc[patient['curationScoreHGMD'] == 'High','curationScoreHGMD'] = 3 - patient['curationScoreHGMD'] = patient['curationScoreHGMD'].astype('float64') - - patient.loc[patient['curationScoreOMIM'] == 'Low','curationScoreOMIM'] = 1 - patient.loc[patient['curationScoreOMIM'] == 'Medium','curationScoreOMIM'] = 2 - patient.loc[patient['curationScoreOMIM'] == 'High','curationScoreOMIM'] = 3 - patient['curationScoreOMIM'] = patient['curationScoreOMIM'].astype('float64') - - patient.loc[patient['curationScoreClinVar'] == 'Low','curationScoreClinVar'] = 1 - patient.loc[patient['curationScoreClinVar'] == 'Medium','curationScoreClinVar'] = 2 - patient.loc[patient['curationScoreClinVar'] == 'High','curationScoreClinVar'] = 3 - patient['curationScoreClinVar'] = patient['curationScoreClinVar'].astype('float64') - - patient.loc[patient['conservationScoreDGV'] == 'Low','conservationScoreDGV'] = 1 - patient.loc[patient['conservationScoreDGV'] == 'Medium','conservationScoreDGV'] = 2 - patient.loc[patient['conservationScoreDGV'] == 'High','conservationScoreDGV'] = 3 - patient['conservationScoreDGV'] = patient['conservationScoreDGV'].astype('float64') - - - patient.loc[patient['omimSymptomSimScore'] == '-','omimSymptomSimScore'] = 0.0 - patient['omimSymptomSimScore'] = patient['omimSymptomSimScore'].astype('float64') - - patient.loc[patient['hgmdSymptomSimScore'] == '-','hgmdSymptomSimScore'] = 0.0 - patient['hgmdSymptomSimScore'] = patient['hgmdSymptomSimScore'].astype('float64') - - - patient['isB/LB'] = 0 - patient['isP/LP'] = 0 - patient.loc[patient['clin_code'].str.contains('Benign'),'isB/LB'] = 1 - patient.loc[patient['clin_code'].str.contains('Likely_benign'),'isB/LB'] = 1 - patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isB/LB'] = 0 - - patient.loc[patient['clin_code'].str.contains('Likely_pathogenic'),'isP/LP'] = 1 - patient.loc[patient['clin_code'].str.contains('Pathogenic'),'isP/LP'] = 1 - patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isP/LP'] = 0 - - patient.loc[patient['clin_PLP_perc']!='-','isP/LP'] = patient.loc[patient['clin_PLP_perc']!='-','clin_PLP_perc'].astype('float64') - - variable_name.append('isB/LB') - variable_name.append('isP/LP') - variable_name.remove('clin_PLP_perc') - variable_name.remove('clin_code') - - - patient.loc[patient['GERPpp_RS'] == '-', 'GERPpp_RS'] = np.NaN - if np.all(pd.isna(patient['GERPpp_RS'])): - # Single variant query - patient.loc[(pd.isna(patient['GERPpp_RS'])) & np.array(indel_index), 'GERPpp_RS'] = feature_stat.loc['GERPpp_RS', 'mean'] - patient.loc[pd.isna(patient['GERPpp_RS']), 'GERPpp_RS'] = feature_stat.loc['GERPpp_RS', 'mean'] - patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') - else: - # Patient whole VCF - patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') - patient.loc[(pd.isna(patient['GERPpp_RS'])) & np.array(indel_index), 'GERPpp_RS'] = patient['GERPpp_RS'].describe()['mean'] - patient.loc[pd.isna(patient['GERPpp_RS']), 'GERPpp_RS'] = patient['GERPpp_RS'].describe()['mean'] - - - gnomadAFVal = patient['gnomadAF'].copy() - gnomadAFVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFVal ]) - gnomadAFVal = gnomadAFVal.astype(float) - patient['gnomadAF'] = gnomadAFVal - - gnomadAFgVal = patient['gnomadAFg'].copy() - gnomadAFgVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFgVal ]) - gnomadAFgVal = gnomadAFgVal.astype(float) - patient['gnomadAFg'] = gnomadAFgVal - patient.loc[patient['gnomadAFg'].isna(), 'gnomadAFg'] = patient.loc[patient['gnomadAFg'].isna(), 'gnomadAF'] - patient.loc[patient['gnomadAF'].isna(), 'gnomadAF'] = patient.loc[patient['gnomadAF'].isna(), 'gnomadAFg'] - patient['gnomadAF'] = patient['gnomadAF'].fillna(0.0) - patient['gnomadAFg'] = patient['gnomadAFg'].fillna(0.0) - - #patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '-', 'gnomadAF'] #0.0 # - #patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '.', 'gnomadAF'] # - #patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '-', 'gnomadAFg'] #0.0 # - #patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '.', 'gnomadAFg'] - #patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = 0.0 - #patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = 0.0 - #patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = 0.0 - #patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = 0.0 - #patient['gnomadAFg'] = patient['gnomadAFg'].astype('float64') - #patient['gnomadAF'] = patient['gnomadAF'].astype('float64') - #patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] - - - patient.loc[patient['LRT_score'] == '-', 'LRT_score'] = np.NaN - if np.all(pd.isna(patient['LRT_score'])): - # Single variant query - patient.loc[(pd.isna(patient['LRT_score'])) & np.array(indel_index), 'LRT_score'] = feature_stat.loc['LRT_score', 'mean'] - patient.loc[pd.isna(patient['LRT_score']), 'LRT_score'] = feature_stat.loc['LRT_score', 'mean'] - patient['LRT_score'] = patient['LRT_score'].astype('float64') - else: - # Patient whole VCF - patient['LRT_score'] = patient['LRT_score'].astype('float64') - patient.loc[(pd.isna(patient['LRT_score'])) & np.array(indel_index), 'LRT_score'] = patient['LRT_score'].describe()['mean'] - patient.loc[pd.isna(patient['LRT_score']), 'LRT_score'] = patient['LRT_score'].describe()['mean'] - - - patient.loc[patient['LRT_Omega'] == '-', 'LRT_Omega'] = np.NaN - if np.all(pd.isna(patient['LRT_Omega'])): - # Single variant query - patient.loc[(pd.isna(patient['LRT_Omega'])) & np.array(indel_index), 'LRT_Omega'] = feature_stat.loc['LRT_Omega', 'mean'] - patient.loc[pd.isna(patient['LRT_Omega']), 'LRT_Omega'] = feature_stat.loc['LRT_Omega', 'mean'] - patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') - else: - # Patient whole VCF - patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') - patient.loc[(pd.isna(patient['LRT_Omega'])) & np.array(indel_index), 'LRT_Omega'] = patient['LRT_Omega'].describe()['mean'] - patient.loc[pd.isna(patient['LRT_Omega']), 'LRT_Omega'] = patient['LRT_Omega'].describe()['mean'] - - - patient.loc[patient['phyloP100way_vertebrate'] == '-', 'phyloP100way_vertebrate'] = np.NaN - if np.all(pd.isna(patient['phyloP100way_vertebrate'])): - # Single variant query - patient.loc[(pd.isna(patient['phyloP100way_vertebrate'])) & np.array(indel_index), 'phyloP100way_vertebrate'] = feature_stat.loc['phyloP100way_vertebrate', 'mean'] - patient.loc[pd.isna(patient['phyloP100way_vertebrate']), 'phyloP100way_vertebrate'] = feature_stat.loc['phyloP100way_vertebrate', 'mean'] - patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') - else: - # Patient whole VCF - patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') - patient.loc[(pd.isna(patient['phyloP100way_vertebrate'])) & np.array(indel_index), 'phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].describe()['mean'] - patient.loc[pd.isna(patient['phyloP100way_vertebrate']), 'phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].describe()['mean'] - - - patient.loc[patient['gnomadGeneZscore'] == '-', 'gnomadGeneZscore'] = np.NaN - if np.all(pd.isna(patient['gnomadGeneZscore'])): - # Single variant query - patient.loc[pd.isna(patient['gnomadGeneZscore']), 'gnomadGeneZscore'] = feature_stat.loc['gnomadGeneZscore', 'mean'] - patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') - else: - # Patient whole VCF - patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') - patient.loc[pd.isna(patient['gnomadGeneZscore']), 'gnomadGeneZscore'] = patient['gnomadGeneZscore'].describe()['mean'] - - - patient.loc[patient['gnomadGenePLI'] == '-', 'gnomadGenePLI'] = np.NaN - if np.all(pd.isna(patient['gnomadGenePLI'])): - # Single variant query - patient.loc[pd.isna(patient['gnomadGenePLI']), 'gnomadGenePLI'] = feature_stat.loc['gnomadGenePLI', 'mean'] - patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') - else: - # Patient whole VCF - patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') - patient.loc[pd.isna(patient['gnomadGenePLI']), 'gnomadGenePLI'] = patient['gnomadGenePLI'].describe()['mean'] - - - patient.loc[patient['gnomadGeneOELof'] == '-', 'gnomadGeneOELof'] = np.NaN - if np.all(pd.isna(patient['gnomadGeneOELof'])): - # Single variant query - patient.loc[pd.isna(patient['gnomadGeneOELof']), 'gnomadGeneOELof'] = feature_stat.loc['gnomadGeneOELof', 'mean'] - patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') - else: - # Patient whole VCF - patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') - patient.loc[pd.isna(patient['gnomadGeneOELof']), 'gnomadGeneOELof'] = patient['gnomadGeneOELof'].describe()['mean'] - - patient.loc[patient['gnomadGeneOELofUpper'] == '-', 'gnomadGeneOELofUpper'] = np.NaN - if np.all(pd.isna(patient['gnomadGeneOELofUpper'])): - # Single variant query - patient.loc[pd.isna(patient['gnomadGeneOELofUpper']), 'gnomadGeneOELofUpper'] = feature_stat.loc['gnomadGeneOELofUpper', 'mean'] - patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') - else: - # Patient whole VCF - patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') - patient.loc[pd.isna(patient['gnomadGeneOELofUpper']), 'gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].describe()['mean'] - - - patient.loc[patient['IMPACT'] == '-','IMPACT'] = 0 - patient.loc[patient['IMPACT'] == 'MODIFIER','IMPACT'] = 1 - patient.loc[patient['IMPACT'] == 'LOW','IMPACT'] = 2 - patient.loc[patient['IMPACT'] == 'MODERATE','IMPACT'] = 3 - patient.loc[patient['IMPACT'] == 'HIGH','IMPACT'] = 4 - patient['IMPACT'] = patient['IMPACT'].astype('float64') - - - #"CADD_phred" - patient.loc[patient['CADD_phred'] == '-', 'CADD_phred'] = np.NaN - if np.all(pd.isna(patient['CADD_phred'])): - # Single variant query - patient.loc[(pd.isna(patient['CADD_phred'])) & np.array(indel_index), 'CADD_phred'] = feature_stat.loc['CADD_phred', 'mean'] - patient.loc[pd.isna(patient['CADD_phred']), 'CADD_phred'] = feature_stat.loc['CADD_phred', 'mean'] - patient['CADD_phred'] = patient['CADD_phred'].astype('float64') - else: - # Patient whole VCF - patient['CADD_phred'] = patient['CADD_phred'].astype('float64') - patient.loc[(pd.isna(patient['CADD_phred'])) & np.array(indel_index), 'CADD_phred'] = patient['CADD_phred'].describe()['mean'] - patient.loc[pd.isna(patient['CADD_phred']), 'CADD_phred'] = patient['CADD_phred'].describe()['mean'] - - - patient.loc[patient['CADD_PHRED'] == '-', 'CADD_PHRED'] = np.NaN - if np.all(pd.isna(patient['CADD_PHRED'])): - # Single variant query - patient.loc[(pd.isna(patient['CADD_PHRED'])) & np.array(indel_index), 'CADD_PHRED'] = feature_stat.loc['CADD_PHRED', 'mean'] - patient.loc[pd.isna(patient['CADD_PHRED']), 'CADD_PHRED'] = feature_stat.loc['CADD_PHRED', 'mean'] - patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') - else: - # Patient whole VCF - patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') - patient.loc[(pd.isna(patient['CADD_PHRED'])) & np.array(indel_index), 'CADD_PHRED'] = patient['CADD_PHRED'].describe()['mean'] - patient.loc[pd.isna(patient['CADD_PHRED']), 'CADD_PHRED'] = patient['CADD_PHRED'].describe()['mean'] - - - #DANN_score - patient.loc[patient['DANN_score'] == '-', 'DANN_score'] = np.NaN - if np.all(pd.isna(patient['DANN_score'])): - # Single variant query - patient.loc[(pd.isna(patient['DANN_score'])) & np.array(indel_index), 'DANN_score'] = feature_stat.loc['DANN_score', 'mean'] - patient.loc[pd.isna(patient['DANN_score']), 'DANN_score'] = feature_stat.loc['DANN_score', 'mean'] - patient['DANN_score'] = patient['DANN_score'].astype('float64') - else: - # Patient whole VCF - patient['DANN_score'] = patient['DANN_score'].astype('float64') - patient.loc[(pd.isna(patient['DANN_score'])) & np.array(indel_index), 'DANN_score'] = patient['DANN_score'].describe()['mean'] - patient.loc[pd.isna(patient['DANN_score']), 'DANN_score'] = patient['DANN_score'].describe()['mean'] - - - #REVEL_score - #patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') - patient.loc[patient['REVEL_score'] == '-', 'REVEL_score'] = np.NaN - for i in patient[~patient['REVEL_score'].isna()].index: - score_list = patient.loc[i, 'REVEL_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'REVEL_score'] = np.NaN - else: - patient.loc[i, 'REVEL_score'] = max(score_list) - - if np.all(pd.isna(patient['REVEL_score'])): - # Single variant query - patient.loc[(pd.isna(patient['REVEL_score'])) & np.array(indel_index), 'REVEL_score'] = feature_stat.loc['REVEL_score', 'mean'] - patient.loc[pd.isna(patient['REVEL_score']), 'REVEL_score'] = feature_stat.loc['REVEL_score', 'mean'] - patient['REVEL_score'] = patient['REVEL_score'].astype('float64') - else: - # Patient whole VCF - patient['REVEL_score'] = patient['REVEL_score'].astype('float64') - patient.loc[(patient['REVEL_score'].isna()) & np.array(indel_index), 'REVEL_score'] = patient['REVEL_score'].describe()['mean'] - patient.loc[pd.isna(patient['REVEL_score']), 'REVEL_score'] = patient['REVEL_score'].describe()['mean'] - - - - #fathmm_MKL_coding_score - patient.loc[patient['fathmm_MKL_coding_score'] == '-', 'fathmm_MKL_coding_score'] = np.NaN - if np.all(pd.isna(patient['fathmm_MKL_coding_score'])): - # Single variant query - patient.loc[(pd.isna(patient['fathmm_MKL_coding_score'])) & np.array(indel_index), 'fathmm_MKL_coding_score'] = feature_stat.loc['fathmm_MKL_coding_score', 'mean'] - patient.loc[pd.isna(patient['fathmm_MKL_coding_score']), 'fathmm_MKL_coding_score'] = feature_stat.loc['fathmm_MKL_coding_score', 'mean'] - patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') - else: - # Patient whole VCF - patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') - patient.loc[(pd.isna(patient['fathmm_MKL_coding_score'])) & np.array(indel_index), 'fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].describe()['mean'] - patient.loc[pd.isna(patient['fathmm_MKL_coding_score']), 'fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].describe()['mean'] - - - #conservationScoreGnomad - patient.loc[patient['conservationScoreGnomad'] == '-','conservationScoreGnomad'] = 1 - patient.loc[patient['conservationScoreGnomad'] == 'Low','conservationScoreGnomad'] = 1 - patient.loc[patient['conservationScoreGnomad'] == 'High','conservationScoreGnomad'] = 2 - patient['conservationScoreGnomad'] = patient['conservationScoreGnomad'].astype('float64') - - #conservationScoreOELof - patient.loc[patient['conservationScoreOELof'] == '-','conservationScoreOELof'] = 1 - patient.loc[patient['conservationScoreOELof'] == 'Low','conservationScoreOELof'] = 1 - patient.loc[patient['conservationScoreOELof'] == 'High','conservationScoreOELof'] = 2 - patient['conservationScoreOELof'] = patient['conservationScoreOELof'].astype('float64') - - - #Polyphen2_HDIV_score - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') - patient.loc[patient['Polyphen2_HDIV_score'] == '-','Polyphen2_HDIV_score'] = np.NaN - for i in patient[~patient['Polyphen2_HDIV_score'].isna()].index: - score_list = patient.loc[i, 'Polyphen2_HDIV_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'Polyphen2_HDIV_score'] = np.NaN - else: - patient.loc[i, 'Polyphen2_HDIV_score'] = max(score_list) - - if np.all(pd.isna(patient['Polyphen2_HDIV_score'])): - # Single variant query - patient.loc[(pd.isna(patient['Polyphen2_HDIV_score'])) & np.array(indel_index), 'Polyphen2_HDIV_score'] = feature_stat.loc['Polyphen2_HDIV_score', '50%'] - patient.loc[pd.isna(patient['Polyphen2_HDIV_score']), 'Polyphen2_HDIV_score'] = feature_stat.loc['Polyphen2_HDIV_score', '50%'] - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') - else: - # Patient whole VCF - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') - patient.loc[(pd.isna(patient['Polyphen2_HDIV_score'])) & np.array(indel_index), 'Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].describe()['50%'] - patient.loc[pd.isna(patient['Polyphen2_HDIV_score']), 'Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].describe()['50%'] - - - #Polyphen2_HVAR_score - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'] #.str.split(',') - patient.loc[patient['Polyphen2_HVAR_score'] == '-','Polyphen2_HVAR_score'] = np.NaN - for i in patient[~patient['Polyphen2_HVAR_score'].isna()].index: - score_list = patient.loc[i, 'Polyphen2_HVAR_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'Polyphen2_HVAR_score'] = np.NaN - else: - patient.loc[i, 'Polyphen2_HVAR_score'] = max(score_list) - - if np.all(pd.isna(patient['Polyphen2_HVAR_score'])): - # Single variant query - patient.loc[(pd.isna(patient['Polyphen2_HVAR_score'])) & np.array(indel_index), 'Polyphen2_HVAR_score'] = feature_stat.loc['Polyphen2_HVAR_score', '50%'] - patient.loc[pd.isna(patient['Polyphen2_HVAR_score']), 'Polyphen2_HVAR_score'] = feature_stat.loc['Polyphen2_HVAR_score', '50%'] - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') - else: - # Patient whole VCF - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') - patient.loc[(pd.isna(patient['Polyphen2_HVAR_score'])) & np.array(indel_index), 'Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].describe()['50%'] - patient.loc[pd.isna(patient['Polyphen2_HVAR_score']), 'Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].describe()['50%'] - - - #SIFT_score - patient['SIFT_score'] = patient['SIFT_score'] #.str.split(',') - patient.loc[patient['SIFT_score'] == '-','SIFT_score'] = np.NaN - for i in patient[~patient['SIFT_score'].isna()].index: - score_list = patient.loc[i, 'SIFT_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'SIFT_score'] = np.NaN - else: - patient.loc[i, 'SIFT_score'] = min(score_list) - - if np.all(pd.isna(patient['SIFT_score'])): - # Single variant query - patient.loc[(pd.isna(patient['SIFT_score'])) & np.array(indel_index), 'SIFT_score'] = feature_stat.loc['SIFT_score', '50%'] - patient.loc[pd.isna(patient['SIFT_score']), 'SIFT_score'] = feature_stat.loc['SIFT_score', '50%'] - patient['SIFT_score'] = patient['SIFT_score'].astype('float64') - else: - # Patient whole VCF - patient['SIFT_score'] = patient['SIFT_score'].astype('float64') - patient.loc[(pd.isna(patient['SIFT_score'])) & np.array(indel_index), 'SIFT_score'] = patient['SIFT_score'].describe()['50%'] - patient.loc[pd.isna(patient['SIFT_score']), 'SIFT_score'] = patient['SIFT_score'].describe()['50%'] - - - patient.loc[patient['zyg'] == 'HET','zyg'] = 1 - patient.loc[patient['zyg'] == 'HOM','zyg'] = 2 - patient.loc[patient['zyg'] == '-','zyg'] = 0 - - patient['zyg'] = patient['zyg'].astype('float64') - - - #patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN - #patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') - #patient.loc[(pd.isna(patient['GERPpp_NR'])) & np.array(indel_index), 'GERPpp_NR'] = patient.loc[patient['GERPpp_NR'] != -100, 'GERPpp_NR'].describe()['mean'] - #patient.loc[pd.isna(patient['GERPpp_NR']), 'GERPpp_NR'] = patient['GERPpp_NR'].describe()['min'] - - - #FATHMM_score - patient['FATHMM_score'] = patient['FATHMM_score'] #.str.split(',') - patient.loc[patient['FATHMM_score'] == '-','FATHMM_score'] = np.NaN - for i in patient[~patient['FATHMM_score'].isna()].index: - score_list = patient.loc[i, 'FATHMM_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'FATHMM_score'] = np.NaN - else: - patient.loc[i, 'FATHMM_score'] = min(score_list) - - if np.all(pd.isna(patient['FATHMM_score'])): - # Single variant query - patient.loc[(pd.isna(patient['FATHMM_score'])) & np.array(indel_index), 'FATHMM_score'] = feature_stat.loc['FATHMM_score', '50%'] - patient.loc[pd.isna(patient['FATHMM_score']), 'FATHMM_score'] = feature_stat.loc['FATHMM_score', '50%'] - patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') - else: - # Patient whole VCF - patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') - patient.loc[(pd.isna(patient['FATHMM_score'])) & np.array(indel_index), 'FATHMM_score'] = patient['FATHMM_score'].describe()['50%'] - patient.loc[pd.isna(patient['FATHMM_score']), 'FATHMM_score'] = patient['FATHMM_score'].describe()['50%'] - - - #M_CAP_score - patient.loc[patient['M_CAP_score'] == '-', 'M_CAP_score'] = np.NaN - if np.all(pd.isna(patient['M_CAP_score'])): - # Single variant query - patient.loc[(pd.isna(patient['M_CAP_score'])) & np.array(indel_index), 'M_CAP_score'] = feature_stat.loc['M_CAP_score', 'mean'] - patient.loc[pd.isna(patient['M_CAP_score']), 'M_CAP_score'] = feature_stat.loc['M_CAP_score', 'mean'] - patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') - else: - # Patient whole VCF - patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') - patient.loc[(pd.isna(patient['M_CAP_score'])) & np.array(indel_index), 'M_CAP_score'] = patient['M_CAP_score'].describe()['mean'] - patient.loc[pd.isna(patient['M_CAP_score']), 'M_CAP_score'] = patient['M_CAP_score'].describe()['mean'] - - - #MutationAssessor_score - patient['MutationAssessor_score'] = patient['MutationAssessor_score'] #.str.split(',') - patient.loc[patient['MutationAssessor_score'] == '-','MutationAssessor_score'] = np.NaN - for i in patient[~patient['MutationAssessor_score'].isna()].index: - score_list = patient.loc[i, 'MutationAssessor_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'MutationAssessor_score'] = np.NaN - else: - patient.loc[i, 'MutationAssessor_score'] = max(score_list) - - if np.all(pd.isna(patient['MutationAssessor_score'])): - # Single variant query - patient.loc[(pd.isna(patient['MutationAssessor_score'])) & np.array(indel_index), 'MutationAssessor_score'] = feature_stat.loc['MutationAssessor_score', '50%'] - patient.loc[pd.isna(patient['MutationAssessor_score']), 'MutationAssessor_score'] = feature_stat.loc['MutationAssessor_score', '50%'] - patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') - else: - # Patient whole VCF - patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') - patient.loc[(pd.isna(patient['MutationAssessor_score'])) & np.array(indel_index), 'MutationAssessor_score'] = patient['MutationAssessor_score'].describe()['50%'] - patient.loc[pd.isna(patient['MutationAssessor_score']), 'MutationAssessor_score'] = patient['MutationAssessor_score'].describe()['50%'] - - - #ESP6500_AA_AF - patient.loc[patient['ESP6500_AA_AF'] == '-', 'ESP6500_AA_AF'] = 0.0 - patient['ESP6500_AA_AF'] = patient['ESP6500_AA_AF'].astype('float64') - - - #ESP6500_AA_AF - patient.loc[patient['ESP6500_EA_AF'] == '-', 'ESP6500_EA_AF'] = 0.0 - patient['ESP6500_EA_AF'] = patient['ESP6500_EA_AF'].astype('float64') - - - #hom - patient.loc[patient['hom'] == '-','hom'] = np.NaN - for i in patient[~patient['hom'].isna()].index: - score_list = patient.loc[i, 'hom'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'hom'] = np.NaN - else: - patient.loc[i, 'hom'] = max(score_list) - patient['hom'] = patient['hom'].fillna(0) - patient['hom'] = patient['hom'].astype('float64') - - patient['hgmd_rs'] = patient['hgmd_rs'].apply(lambda x: x.split(",")[0] if type(x) == str else str(x)) - - patient.loc[patient['hgmd_rs'] == '-','hgmd_rs'] = 0 - - patient['hgmd_rs'] = patient['hgmd_rs'].astype('float64') - - - #spliceAImax - patient.loc[patient['spliceAImax'] == '-', 'spliceAImax'] = np.NaN - if np.all(pd.isna(patient['spliceAImax'])): - # Single variant query - patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = feature_stat.loc['spliceAImax', 'mean'] - patient['spliceAImax'] = patient['spliceAImax'].astype('float64') - else: - # Patient whole VCF - patient['spliceAImax'] = patient['spliceAImax'].astype('float64') - patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = patient.loc[~pd.isna(patient['spliceAImax']), 'spliceAImax'].describe()['mean'] - - - #Consequence - consequence = ['transcript_ablation', 'splice_acceptor_variant', 'splice_donor_variant', 'stop_gained', - 'frameshift_variant', 'stop_lost', 'start_lost', 'transcript_amplification', 'inframe_insertion', - 'inframe_deletion', 'missense_variant', 'protein_altering_variant', 'splice_region_variant', - 'splice_donor_5th_base_variant', 'splice_donor_region_variant'] - - for cons in consequence: - patient['cons_%s'%cons] = patient['Consequence'].str.contains(cons).astype('int') - variable_name.append('cons_%s'%cons) - - variable_name.remove('Consequence') - - - #nc_isPLP - patient.loc[patient['nc_isPLP']!=True, 'nc_isPLP'] = 0 - patient.loc[patient['nc_isPLP']==True, 'nc_isPLP'] = 1 - patient['nc_isPLP'] = patient['nc_isPLP'].astype('float64') - - #nc_isBLB - patient.loc[patient['nc_isBLB']!=True, 'nc_isBLB'] = 0 - patient.loc[patient['nc_isBLB']==True, 'nc_isBLB'] = 1 - patient['nc_isBLB'] = patient['nc_isBLB'].astype('float64') - - #c_isPLP - patient.loc[patient['c_isPLP']!=True, 'c_isPLP'] = 0 - patient.loc[patient['c_isPLP']==True, 'c_isPLP'] = 1 - patient['c_isPLP'] = patient['c_isPLP'].astype('float64') - - #c_isBLB - patient.loc[patient['c_isBLB']!=True, 'c_isBLB'] = 0 - patient.loc[patient['c_isBLB']==True, 'c_isBLB'] = 1 - patient['c_isBLB'] = patient['c_isBLB'].astype('float64') - - - #nc_ClinVar_Exp - patient.loc[patient['nc_ClinVar_Exp']!='nonCoding','nc_ClinVar_Exp'] = 0 - patient.loc[patient['nc_ClinVar_Exp']=='nonCoding','nc_ClinVar_Exp'] = 1 - patient['nc_ClinVar_Exp'] = patient['nc_ClinVar_Exp'].astype('float64') - - #nc_HGMD_Exp - patient.loc[patient['nc_HGMD_Exp']!='nonCoding','nc_HGMD_Exp'] = 0 - patient.loc[patient['nc_HGMD_Exp']=='nonCoding','nc_HGMD_Exp'] = 1 - patient['nc_HGMD_Exp'] = patient['nc_HGMD_Exp'].astype('float64') - - #c_ClinVar_Exp - c_clin_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange'] - - for exp in c_clin_exp: - - patient['c_ClinVar_Exp_%s'%exp] = patient['c_ClinVar_Exp'].str.contains(exp).astype('int') - variable_name.append('c_ClinVar_Exp_%s'%exp) - - variable_name.remove('c_ClinVar_Exp') - - #c_HGMD_Exp - c_hgmd_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange', 'Stop_Loss', 'Start_Loss'] - - for exp in c_hgmd_exp: - - patient['c_HGMD_Exp_%s'%exp] = patient['c_HGMD_Exp'].str.contains(exp).astype('int') - variable_name.append('c_HGMD_Exp_%s'%exp) - - variable_name.remove('c_HGMD_Exp') - - - #nc_CLNREVSTAT - CLN_STAT = ['-', 'no_assertion_provided', 'no_assertion_criteria_provided','no_assertion_for_the_individual_variant', - 'criteria_provided,_single_submitter', 'criteria_provided,_conflicting_interpretations', - 'criteria_provided,_multiple_submitters,_no_conflicts', 'reviewed_by_expert_panel', 'practice_guideline'] - - CLN_STAT_SCORE = [0,0,0,0,1,1,2,3,4] - - for i in range(len(CLN_STAT)): - patient.loc[patient['nc_CLNREVSTAT'] == CLN_STAT[i], 'nc_CLNREVSTAT'] = CLN_STAT_SCORE[i] - patient.loc[patient['c_CLNREVSTAT'] == CLN_STAT[i], 'c_CLNREVSTAT'] = CLN_STAT_SCORE[i] - - patient['nc_CLNREVSTAT'] = patient['nc_CLNREVSTAT'].astype('float64') - patient['c_CLNREVSTAT'] = patient['c_CLNREVSTAT'].astype('float64') - - - #nc_RANKSCORE - patient.loc[patient['nc_RANKSCORE'] == '-', 'nc_RANKSCORE'] = 0 - patient['nc_RANKSCORE'] = patient['nc_RANKSCORE'].astype('float64') - - #nc_RANKSCORE - patient.loc[patient['c_RANKSCORE'] == '-', 'c_RANKSCORE'] = 0 - patient['c_RANKSCORE'] = patient['c_RANKSCORE'].astype('float64') - - #CLASS - patient.loc[patient['CLASS'] == '-', 'CLASS'] = 0 - patient.loc[patient['CLASS'] == 'DM?', 'CLASS'] = 1 - patient.loc[patient['CLASS'] == 'DM', 'CLASS'] = 2 - patient['CLASS'] = patient['CLASS'].astype('float64') - - - - patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', - 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] - patient = patient.groupby(['varId_dash'], sort=False)[variable_name[1:]].max() - patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', - 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] - - - #adding in tier features - #tier = pd.read_csv(tier_file, sep='\t') - tier=tier_file.copy() - tier_vars = ['IMPACT.from.Tier','TierAD','TierAR','TierAR.adj', - 'No.Var.HM','No.Var.H','No.Var.M','No.Var.L', - 'AD.matched','AR.matched', - 'recessive', 'dominant'] #, - #'OMIM.Inheritance.both', 'OMIM.Inheritance.others'] - """ CL: deleted - # recessive and dominant is enough - tier['OMIM.Inheritance.recessive'] = 0 - tier['OMIM.Inheritance.dominant'] = 0 - tier['OMIM.Inheritance.both'] = 0 - tier['OMIM.Inheritance.others'] = 0 - tier.loc[tier['OMIM.Inheritance'] == 'recessive', 'OMIM.Inheritance.recessive'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'dominant', 'OMIM.Inheritance.dominant'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'both', 'OMIM.Inheritance.both'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'others', 'OMIM.Inheritance.others'] = 1 - """ - - tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] - tier = tier.groupby(['Uploaded_variation'], sort=False)[tier_vars].max() - tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] - - patient = pd.concat([patient, tier], axis=1) - patient.loc[patient['IMPACT.from.Tier'].isna(), 'IMPACT.from.Tier'] = 1 - patient.loc[patient['TierAD'].isna(), 'TierAD'] = 4 - patient.loc[patient['TierAR'].isna(), 'TierAR'] = 4 - patient.loc[patient['TierAR.adj'].isna(), 'TierAR.adj'] = 4 - - for var in tier_vars[4:8]: - patient.loc[patient[var].isna(), var] = 0 - - patient.loc[patient['AD.matched'].isna(), 'AD.matched'] = 0 - patient.loc[patient['AR.matched'].isna(), 'AR.matched'] = 0 - - patient.loc[patient['recessive'].isna(), 'recessive'] = 0 - patient.loc[patient['dominant'].isna(), 'dominant'] = 0 - #patient.loc[patient['OMIM.Inheritance.both'].isna(), 'OMIM.Inheritance.both'] = 0 ##CL: deleted - #patient.loc[patient['OMIM.Inheritance.others'].isna(), 'OMIM.Inheritance.others'] = 0 ##CL: deleted - - return patient - - - - - - - diff --git a/run/generate_new_matrix_2.py b/run/generate_new_matrix_2.py deleted file mode 100755 index 10b61e0..0000000 --- a/run/generate_new_matrix_2.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -import os -import pandas as pd -import numpy as np -import sys - -#from marrvel_score_recalc import * -from add_c_nc import add_c_nc -from fillna_tier import feature_engineering as fillna -from mod5_diffusion import diffusion, diffuseSample -#from labelling import add_label -from simple_repeat_anno import simple_repeat_anno - -### read score file ### -score=pd.read_csv('/out/rami-test/%s_scores.csv'%(sys.argv[1])) - -### add more clin and hgmd features ### -merged = add_c_nc(score, sys.argv[2]) - -### run diffusion module 5 ### -if os.path.getsize(f"/out/phrank/{sys.argv[1]}.txt") == 0: - phrank_empty = True -else: - phrank_empty = False - mod5 = diffuseSample(sys.argv[1], merged, "/out/phrank/") - -### get original coordinates ### -merged["varId"]=merged["varId"].apply(lambda x: x.split("_E")[0]) - -### read tier ### -tier_f=pd.read_csv("/out/tier-test-false/"+sys.argv[1]+"_Tier.v2.tsv", sep="\t") - -### add phrank calculated elsewhere for final combination ### -if phrank_empty: - merged['phrank'] = 0 -else: - phr=pd.read_csv("/out/phrank/"+sys.argv[1]+".txt", sep="\t", names=["ENSG", "phrank"]) - merged=merged.merge(phr,left_on="geneEnsId", right_on="ENSG", how="left") - -### save intermediate file for merging ### -merged.to_csv("/out/scores/"+sys.argv[1]+".txt.gz", compression="gzip", sep="\t") - -### run Chaozhong's feature engineering code ### -iff=fillna(merged,tier_f) - -### add diffusion module data ### -if phrank_empty: - iff.insert(loc=0, column='diffuse_Phrank_STRING', value=0) -else: - iff.insert(loc=0, column='diffuse_Phrank_STRING', value=mod5['diffuse_Phrank_STRING']) - -### remove chr 26 ### -iff= iff.loc[~iff.index.str.startswith('26')] -#iff.to_csv(sys.argv[1]+".out.txt", sep="\t") - -### run chaozhong's simple repeat annotation ### -iff=simple_repeat_anno(sys.argv[1], iff, f"/run/data_dependencies/merge_expand/{sys.argv[2]}/simpleRepeats.{sys.argv[2]}.bed") - -### delete intermediate files ### -os.remove("/out/"+sys.argv[1]+".bed") -os.remove("/out/"+sys.argv[1]+".sp.bed") - -### save final file for prediction ### -iff.to_csv("/out/"+sys.argv[1]+".matrix.txt", sep="\t") diff --git a/run/keepna_tier.py b/run/keepna_tier.py deleted file mode 100755 index e67d65c..0000000 --- a/run/keepna_tier.py +++ /dev/null @@ -1,443 +0,0 @@ -from random import sample -import numpy as np -import pandas as pd - -__author__ = "Chaozhong Liu" -__email__ = "chaozhol@bcm.edu" - -###feature engineering and incorporation of tier features -###keeps NA fields - -def feature_engineering(score_file, tier_file): - variable_name = ['varId_dash', 'hgmdSymptomScore', - 'omimSymMatchFlag', 'hgmdSymMatchFlag', 'clinVarSymMatchFlag', - 'omimGeneFound', 'omimVarFound', 'hgmdGeneFound', 'hgmdVarFound', - 'clinVarVarFound', 'clinVarGeneFound', - 'clinvarTotalNumVars','clinvarNumP', 'clinvarNumLP', 'clinvarNumLB', 'clinvarNumB', - 'dgvVarFound','decipherVarFound', - 'curationScoreHGMD', 'curationScoreOMIM', 'curationScoreClinVar', - 'conservationScoreDGV', 'omimSymptomSimScore', 'hgmdSymptomSimScore', 'clin_code', - 'GERPpp_RS', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'phyloP100way_vertebrate', - 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELof', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_phred', - 'CADD_PHRED', 'DANN_score', 'REVEL_score', 'fathmm_MKL_coding_score', 'conservationScoreGnomad', - 'conservationScoreOELof', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'SIFT_score', 'zyg', - 'FATHMM_score', 'M_CAP_score', 'MutationAssessor_score', 'ESP6500_AA_AF', - 'ESP6500_EA_AF', 'hom', 'hgmd_rs', 'spliceAImax', 'clin_PLP_perc', 'Consequence', - 'nc_ClinVar_Exp', 'c_ClinVar_Exp', 'c_HGMD_Exp', 'nc_HGMD_Exp', - 'nc_isPLP', 'nc_isBLB', 'c_isPLP', 'c_isBLB', - 'nc_CLNREVSTAT', 'c_CLNREVSTAT', - 'nc_RANKSCORE', 'c_RANKSCORE', 'CLASS', "phrank"] - - - - #sample_name = score_file.split('/')[-1].split('_')[1] - #patient = pd.read_csv(score_file,sep='\t') - patient = score_file - - patient = patient[variable_name] - patient = patient.fillna('-') - indel_index = [len(i.split('-')[-1]) != 1 or len(i.split('-')[-2]) != 1 for i in patient['varId_dash'].to_list()] - - patient.loc[patient['phrank'] == '-','phrank'] = np.NaN - patient['phrank'] = patient['phrank'].astype('float64') - - patient.loc[patient['hgmdSymptomScore'] == '-','hgmdSymptomScore'] = np.NaN - patient['hgmdSymptomScore'] = patient['hgmdSymptomScore'].astype('float64') - - patient.loc[patient['omimSymMatchFlag'] == '-','omimSymMatchFlag'] = np.NaN - patient['omimSymMatchFlag'] = patient['omimSymMatchFlag'].astype('float64') - - patient.loc[patient['hgmdSymMatchFlag'] == '-','hgmdSymMatchFlag'] = np.NaN - patient['hgmdSymMatchFlag'] = patient['hgmdSymMatchFlag'].astype('float64') - - patient.loc[patient['clinVarSymMatchFlag'] == '-','clinVarSymMatchFlag'] = np.NaN - patient['clinVarSymMatchFlag'] = patient['clinVarSymMatchFlag'].astype('float64') - - - patient['clinvarNumLP'] = (patient['clinvarNumLP']+patient['clinvarNumP']) / patient['clinvarTotalNumVars'] - patient['clinvarNumP'] = patient['clinvarNumP'] / patient['clinvarTotalNumVars'] - patient['clinvarNumLB'] = (patient['clinvarNumLB']+patient['clinvarNumB']) / patient['clinvarTotalNumVars'] - patient['clinvarNumB'] = patient['clinvarNumB'] / patient['clinvarTotalNumVars'] - patient = patient.drop(columns=['clinvarTotalNumVars']) - variable_name.remove('clinvarTotalNumVars') - - - patient.loc[patient['curationScoreHGMD'] == 'Low','curationScoreHGMD'] = 1 - patient.loc[patient['curationScoreHGMD'] == 'Medium','curationScoreHGMD'] = 2 - patient.loc[patient['curationScoreHGMD'] == 'High','curationScoreHGMD'] = 3 - patient['curationScoreHGMD'] = patient['curationScoreHGMD'].astype('float64') - - patient.loc[patient['curationScoreOMIM'] == 'Low','curationScoreOMIM'] = 1 - patient.loc[patient['curationScoreOMIM'] == 'Medium','curationScoreOMIM'] = 2 - patient.loc[patient['curationScoreOMIM'] == 'High','curationScoreOMIM'] = 3 - patient['curationScoreOMIM'] = patient['curationScoreOMIM'].astype('float64') - - patient.loc[patient['curationScoreClinVar'] == 'Low','curationScoreClinVar'] = 1 - patient.loc[patient['curationScoreClinVar'] == 'Medium','curationScoreClinVar'] = 2 - patient.loc[patient['curationScoreClinVar'] == 'High','curationScoreClinVar'] = 3 - patient['curationScoreClinVar'] = patient['curationScoreClinVar'].astype('float64') - - patient.loc[patient['conservationScoreDGV'] == 'Low','conservationScoreDGV'] = 1 - patient.loc[patient['conservationScoreDGV'] == 'Medium','conservationScoreDGV'] = 2 - patient.loc[patient['conservationScoreDGV'] == 'High','conservationScoreDGV'] = 3 - patient['conservationScoreDGV'] = patient['conservationScoreDGV'].astype('float64') - - - patient.loc[patient['omimSymptomSimScore'] == '-','omimSymptomSimScore'] = np.NaN - patient['omimSymptomSimScore'] = patient['omimSymptomSimScore'].astype('float64') - - patient.loc[patient['hgmdSymptomSimScore'] == '-','hgmdSymptomSimScore'] = np.NaN - patient['hgmdSymptomSimScore'] = patient['hgmdSymptomSimScore'].astype('float64') - - - patient['isB/LB'] = 0 - patient['isP/LP'] = 0 - patient.loc[patient['clin_code'].str.contains('Benign'),'isB/LB'] = 1 - patient.loc[patient['clin_code'].str.contains('Likely_benign'),'isB/LB'] = 1 - patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isB/LB'] = 0 - - patient.loc[patient['clin_code'].str.contains('Likely_pathogenic'),'isP/LP'] = 1 - patient.loc[patient['clin_code'].str.contains('Pathogenic'),'isP/LP'] = 1 - patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isP/LP'] = 0 - - patient.loc[patient['clin_PLP_perc']!='-','isP/LP'] = patient.loc[patient['clin_PLP_perc']!='-','clin_PLP_perc'].astype('float64') - - variable_name.append('isB/LB') - variable_name.append('isP/LP') - variable_name.remove('clin_PLP_perc') - variable_name.remove('clin_code') - - - patient.loc[patient['GERPpp_RS'] == '-', 'GERPpp_RS'] = np.NaN - patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') - - - patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '-', 'gnomadAF'] #0.0 # - patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '.', 'gnomadAF'] # - patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '-', 'gnomadAFg'] #0.0 # - patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '.', 'gnomadAFg'] - patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = np.NaN - patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = np.NaN - patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = np.NaN - patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = np.NaN - patient['gnomadAFg'] = patient['gnomadAFg'].astype('float64') - patient['gnomadAF'] = patient['gnomadAF'].astype('float64') - #patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] - - - patient.loc[patient['LRT_score'] == '-', 'LRT_score'] = np.NaN - patient['LRT_score'] = patient['LRT_score'].astype('float64') - - - patient.loc[patient['LRT_Omega'] == '-', 'LRT_Omega'] = np.NaN - patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') - - - patient.loc[patient['phyloP100way_vertebrate'] == '-', 'phyloP100way_vertebrate'] = np.NaN - patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') - - - patient.loc[patient['gnomadGeneZscore'] == '-', 'gnomadGeneZscore'] = np.NaN - patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') - - patient.loc[patient['gnomadGenePLI'] == '-', 'gnomadGenePLI'] = np.NaN - patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') - - patient.loc[patient['gnomadGeneOELof'] == '-', 'gnomadGeneOELof'] = np.NaN - patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') - - patient.loc[patient['gnomadGeneOELofUpper'] == '-', 'gnomadGeneOELofUpper'] = np.NaN - patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') - - - patient.loc[patient['IMPACT'] == '-','IMPACT'] = np.NaN - patient.loc[patient['IMPACT'] == 'MODIFIER','IMPACT'] = 1 - patient.loc[patient['IMPACT'] == 'LOW','IMPACT'] = 2 - patient.loc[patient['IMPACT'] == 'MODERATE','IMPACT'] = 3 - patient.loc[patient['IMPACT'] == 'HIGH','IMPACT'] = 4 - patient['IMPACT'] = patient['IMPACT'].astype('float64') - - - #"CADD_phred" - patient.loc[patient['CADD_phred'] == '-', 'CADD_phred'] = np.NaN - patient['CADD_phred'] = patient['CADD_phred'].astype('float64') - - patient.loc[patient['CADD_PHRED'] == '-', 'CADD_PHRED'] = np.NaN - patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') - - - #DANN_score - patient.loc[patient['DANN_score'] == '-', 'DANN_score'] = np.NaN - patient['DANN_score'] = patient['DANN_score'].astype('float64') - - - #REVEL_score - patient.loc[patient['REVEL_score'] == '-', 'REVEL_score'] = np.NaN - patient['REVEL_score'] = patient['REVEL_score'].astype('float64') - - - #fathmm_MKL_coding_score - patient.loc[patient['fathmm_MKL_coding_score'] == '-', 'fathmm_MKL_coding_score'] = np.NaN - patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') - - - #conservationScoreGnomad - patient.loc[patient['conservationScoreGnomad'] == '-','conservationScoreGnomad'] = np.NaN - patient.loc[patient['conservationScoreGnomad'] == 'Low','conservationScoreGnomad'] = 1 - patient.loc[patient['conservationScoreGnomad'] == 'High','conservationScoreGnomad'] = 2 - patient['conservationScoreGnomad'] = patient['conservationScoreGnomad'].astype('float64') - - #conservationScoreOELof - patient.loc[patient['conservationScoreOELof'] == '-','conservationScoreOELof'] = np.NaN - patient.loc[patient['conservationScoreOELof'] == 'Low','conservationScoreOELof'] = 1 - patient.loc[patient['conservationScoreOELof'] == 'High','conservationScoreOELof'] = 2 - patient['conservationScoreOELof'] = patient['conservationScoreOELof'].astype('float64') - - - #Polyphen2_HDIV_score - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') - patient.loc[patient['Polyphen2_HDIV_score'] == '-','Polyphen2_HDIV_score'] = -100.0 - for i in patient[patient['Polyphen2_HDIV_score'] != -100.0].index: - score_list = patient.loc[i, 'Polyphen2_HDIV_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'Polyphen2_HDIV_score'] = np.NaN - else: - patient.loc[i, 'Polyphen2_HDIV_score'] = max(score_list) - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') - patient.loc[patient['Polyphen2_HVAR_score'] == -100, 'Polyphen2_HVAR_score'] = np.NaN - - #Polyphen2_HVAR_score - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'] #.str.split(',') - patient.loc[patient['Polyphen2_HVAR_score'] == '-','Polyphen2_HVAR_score'] = -100.0 - for i in patient[patient['Polyphen2_HVAR_score'] != -100.0].index: - score_list = patient.loc[i, 'Polyphen2_HVAR_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'Polyphen2_HVAR_score'] = np.NaN - else: - patient.loc[i, 'Polyphen2_HVAR_score'] = max(score_list) - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') - patient.loc[patient['Polyphen2_HVAR_score'] == -100, 'Polyphen2_HVAR_score'] = np.NaN - - - #SIFT_score - patient['SIFT_score'] = patient['SIFT_score'] #.str.split(',') - patient.loc[patient['SIFT_score'] == '-','SIFT_score'] = -100.0 - for i in patient[patient['SIFT_score'] != -100.0].index: - score_list = patient.loc[i, 'SIFT_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'SIFT_score'] = np.NaN - else: - patient.loc[i, 'SIFT_score'] = min(score_list) - patient['SIFT_score'] = patient['SIFT_score'].astype('float64') - patient.loc[patient['SIFT_score'] == -100, 'SIFT_score'] = np.NaN - - - patient.loc[patient['zyg'] == 'HET','zyg'] = 1 - patient.loc[patient['zyg'] == 'HOM','zyg'] = 2 - patient.loc[patient['zyg'] == '-','zyg'] = np.NaN - - patient['zyg'] = patient['zyg'].astype('float64') - - - #patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN - #patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') - - - #FATHMM_score - patient['FATHMM_score'] = patient['FATHMM_score'] #.str.split(',') - patient.loc[patient['FATHMM_score'] == '-','FATHMM_score'] = -100.0 - for i in patient[patient['FATHMM_score'] != -100.0].index: - score_list = patient.loc[i, 'FATHMM_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'FATHMM_score'] = np.NaN - else: - patient.loc[i, 'FATHMM_score'] = min(score_list) - patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') - patient.loc[patient['FATHMM_score'] == -100, 'FATHMM_score'] = np.NaN - - - #M_CAP_score - patient.loc[patient['M_CAP_score'] == '-', 'M_CAP_score'] = np.NaN - patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') - - - #MutationAssessor_score - patient['MutationAssessor_score'] = patient['MutationAssessor_score'] #.str.split(',') - patient.loc[patient['MutationAssessor_score'] == '-','MutationAssessor_score'] = -100.0 - for i in patient[patient['MutationAssessor_score'] != -100.0].index: - score_list = patient.loc[i, 'MutationAssessor_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] - if score_list == []: - patient.loc[i, 'MutationAssessor_score'] = np.NaN - else: - patient.loc[i, 'MutationAssessor_score'] = max(score_list) - patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') - patient.loc[patient['MutationAssessor_score'] == -100, 'MutationAssessor_score'] = np.NaN - - - - #ESP6500_AA_AF - patient.loc[patient['ESP6500_AA_AF'] == '-', 'ESP6500_AA_AF'] = np.NaN - patient['ESP6500_AA_AF'] = patient['ESP6500_AA_AF'].astype('float64') - - - #ESP6500_AA_AF - patient.loc[patient['ESP6500_EA_AF'] == '-', 'ESP6500_EA_AF'] = np.NaN - patient['ESP6500_EA_AF'] = patient['ESP6500_EA_AF'].astype('float64') - - - #hom - patient.loc[patient['hom'] == '-', 'hom'] = np.NaN - patient['hom'] = patient['hom'].astype('float64') - - patient['hgmd_rs'] = patient['hgmd_rs'].apply(lambda x: x.split(",")[0]) - patient.loc[patient['hgmd_rs'] == '-','hgmd_rs'] = np.NaN - patient['hgmd_rs'] = patient['hgmd_rs'].astype('float64') - - - #spliceAImax - patient.loc[patient['spliceAImax'] == '-', 'spliceAImax'] = np.NaN - patient['spliceAImax'] = patient['spliceAImax'].astype('float64') - patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = patient.loc[~pd.isna(patient['spliceAImax']), 'spliceAImax'].describe()['min'] - - - #Consequence - consequence = ['transcript_ablation', 'splice_acceptor_variant', 'splice_donor_variant', 'stop_gained', - 'frameshift_variant', 'stop_lost', 'start_lost', 'transcript_amplification', 'inframe_insertion', - 'inframe_deletion', 'missense_variant', 'protein_altering_variant', 'splice_region_variant', - 'splice_donor_5th_base_variant', 'splice_donor_region_variant'] - - for cons in consequence: - patient['cons_%s'%cons] = patient['Consequence'].str.contains(cons).astype('int') - variable_name.append('cons_%s'%cons) - - variable_name.remove('Consequence') - - - #nc_isPLP - patient.loc[patient['nc_isPLP']!=True, 'nc_isPLP'] = 0 - patient.loc[patient['nc_isPLP']==True, 'nc_isPLP'] = 1 - patient['nc_isPLP'] = patient['nc_isPLP'].astype('float64') - - #nc_isBLB - patient.loc[patient['nc_isBLB']!=True, 'nc_isBLB'] = 0 - patient.loc[patient['nc_isBLB']==True, 'nc_isBLB'] = 1 - patient['nc_isBLB'] = patient['nc_isBLB'].astype('float64') - - #c_isPLP - patient.loc[patient['c_isPLP']!=True, 'c_isPLP'] = 0 - patient.loc[patient['c_isPLP']==True, 'c_isPLP'] = 1 - patient['c_isPLP'] = patient['c_isPLP'].astype('float64') - - #c_isBLB - patient.loc[patient['c_isBLB']!=True, 'c_isBLB'] = 0 - patient.loc[patient['c_isBLB']==True, 'c_isBLB'] = 1 - patient['c_isBLB'] = patient['c_isBLB'].astype('float64') - - - #nc_ClinVar_Exp - patient.loc[patient['nc_ClinVar_Exp']!='nonCoding','nc_ClinVar_Exp'] = 0 - patient.loc[patient['nc_ClinVar_Exp']=='nonCoding','nc_ClinVar_Exp'] = 1 - patient['nc_ClinVar_Exp'] = patient['nc_ClinVar_Exp'].astype('float64') - - #nc_HGMD_Exp - patient.loc[patient['nc_HGMD_Exp']!='nonCoding','nc_HGMD_Exp'] = 0 - patient.loc[patient['nc_HGMD_Exp']=='nonCoding','nc_HGMD_Exp'] = 1 - patient['nc_HGMD_Exp'] = patient['nc_HGMD_Exp'].astype('float64') - - #c_ClinVar_Exp - c_clin_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange'] - - for exp in c_clin_exp: - - patient['c_ClinVar_Exp_%s'%exp] = patient['c_ClinVar_Exp'].str.contains(exp).astype('int') - variable_name.append('c_ClinVar_Exp_%s'%exp) - - variable_name.remove('c_ClinVar_Exp') - - #c_HGMD_Exp - c_hgmd_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange', 'Stop_Loss', 'Start_Loss'] - - for exp in c_hgmd_exp: - - patient['c_HGMD_Exp_%s'%exp] = patient['c_HGMD_Exp'].str.contains(exp).astype('int') - variable_name.append('c_HGMD_Exp_%s'%exp) - - variable_name.remove('c_HGMD_Exp') - - - #nc_CLNREVSTAT - CLN_STAT = ['no_assertion_provided', 'no_assertion_criteria_provided','no_assertion_for_the_individual_variant', - 'criteria_provided,_single_submitter', 'criteria_provided,_conflicting_interpretations', - 'criteria_provided,_multiple_submitters,_no_conflicts', 'reviewed_by_expert_panel', 'practice_guideline'] - - CLN_STAT_SCORE = [0,0,0,1,1,2,3,4] - patient.loc[patient['nc_CLNREVSTAT'] == '-', 'nc_CLNREVSTAT'] = np.NaN - patient.loc[patient['c_CLNREVSTAT'] == '-', 'c_CLNREVSTAT'] = np.NaN - - for i in range(len(CLN_STAT)): - patient.loc[patient['nc_CLNREVSTAT'] == CLN_STAT[i], 'nc_CLNREVSTAT'] = CLN_STAT_SCORE[i] - patient.loc[patient['c_CLNREVSTAT'] == CLN_STAT[i], 'c_CLNREVSTAT'] = CLN_STAT_SCORE[i] - - patient['nc_CLNREVSTAT'] = patient['nc_CLNREVSTAT'].astype('float64') - patient['c_CLNREVSTAT'] = patient['c_CLNREVSTAT'].astype('float64') - - - #nc_RANKSCORE - patient.loc[patient['nc_RANKSCORE'] == '-', 'nc_RANKSCORE'] = np.NaN - patient['nc_RANKSCORE'] = patient['nc_RANKSCORE'].astype('float64') - - #nc_RANKSCORE - patient.loc[patient['c_RANKSCORE'] == '-', 'c_RANKSCORE'] = np.NaN - patient['c_RANKSCORE'] = patient['c_RANKSCORE'].astype('float64') - - #CLASS - patient.loc[patient['CLASS'] == '-', 'CLASS'] = np.NaN - patient.loc[patient['CLASS'] == 'DM?', 'CLASS'] = 1 - patient.loc[patient['CLASS'] == 'DM', 'CLASS'] = 2 - patient['CLASS'] = patient['CLASS'].astype('float64') - - - patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', - 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] - patient = patient.groupby(['varId_dash'], sort=False)[variable_name[1:]].max() - patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', - 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] - - - #adding in tier features - #tier = pd.read_csv(tier_file, sep='\t') - tier=tier_file - tier_vars = ['IMPACT.from.Tier','TierAD','TierAR','TierAR.adj', - 'No.Var.HM','No.Var.H','No.Var.M','No.Var.L', - 'AD.matched','AR.matched', - 'OMIM.Inheritance.recessive', 'OMIM.Inheritance.dominant', - 'OMIM.Inheritance.both', 'OMIM.Inheritance.others'] - - tier['OMIM.Inheritance.recessive'] = 0 - tier['OMIM.Inheritance.dominant'] = 0 - tier['OMIM.Inheritance.both'] = 0 - tier['OMIM.Inheritance.others'] = 0 - tier.loc[tier['OMIM.Inheritance'] == 'recessive', 'OMIM.Inheritance.recessive'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'dominant', 'OMIM.Inheritance.dominant'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'both', 'OMIM.Inheritance.both'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'others', 'OMIM.Inheritance.others'] = 1 - - tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] - tier = tier.groupby(['Uploaded_variation'], sort=False)[tier_vars].max() - tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] - - patient = pd.concat([patient, tier], axis=1) - - - return patient - - - - - - diff --git a/run/labelling.py b/run/labelling.py deleted file mode 100755 index eb372f4..0000000 --- a/run/labelling.py +++ /dev/null @@ -1,62 +0,0 @@ -import numpy as np -import pandas as pd - -__author__ = "Chaozhong Liu" -__email__ = "chaozhol@bcm.edu" - -###adds known strong/weak labels to matrix (not used in production pipeline) - -def add_label(feature_mtx, label_file, sample_id): - """ - feature_mtx: file path of matrix after feature engineering - - label_file: path of file containing all diagnosed causal variants with columns "BG_ID", "var" and "Confidence" - - sample_id: id of sample to be labeled - """ - - patient = feature_mtx - # is_causal - including strong and weak - label_df = pd.read_csv(label_file) - label_df = label_df[label_df['BG_ID'] == sample_id] - label = label_df['var'].values.tolist() - label = list(set(label)) - - if len(label) == 0: - print('No causal variant found in label file: %s'%(sample_id)) - return - - if sum(patient.index.isin(label)) == 0: - print('True label not found in matrix: %s.'%(sample_id)) - return - - - patient['is_causal'] = 0 - patient.loc[patient.index.isin(label),'is_causal'] = 1 - - - # is_strong - including strong only - label_df = pd.read_csv(label_file) - label_df = label_df.loc[label_df['Confidence']=='Strong',:] - label_df = label_df[label_df['BG_ID'] == sample_id] - label = label_df['var'].values.tolist() - label = list(set(label)) - - patient['is_strong'] = 0 - - if len(label) == 0: - print('No strong causal variant found in label file: %s'%(sample_id)) - return patient - - - if sum(patient.index.isin(label)) == 0: - print('Strong label not found in matrix: %s.'%(sample_id)) - return patient - - - patient.loc[patient.index.isin(label),'is_strong'] = 1 - - - return patient - - diff --git a/run/merge_rm.py b/run/merge_rm.py deleted file mode 100755 index fb06026..0000000 --- a/run/merge_rm.py +++ /dev/null @@ -1,25 +0,0 @@ -import pandas as pd -import sys - -###merge score files with final rank matrix - -#read rank matrix and score file -rm=pd.read_csv("/out/final_matrix/"+sys.argv[1]+".csv") -annot=pd.read_csv("/out/scores/"+sys.argv[1]+".txt.gz", sep="\t", compression="gzip") - -#annot=pd.read_csv("/local/LucianLi/rami-test/r1_"+sys.argv[1]+"_scores.txt", sep="\t", compression="gzip") - -#get columns from score file -annot=annot[["varId","varId_dash", "geneSymbol", "geneEnsId", "rsId", "HGVSc", "HGVSp", "IMPACT", "Consequence", "phenoList", "phenoInhList", "clin_code", "clinvarCondition"]] - -#get original coordinates -annot["varId"]=annot["varId"].apply(lambda x: x.split("_E")[0]) - -#rename for final output -annot=annot.rename(columns={"varId":"origId", "IMPACT": "IMPACT_text", "clin_code": "clinvarSignDesc"}) - -#merge -test=rm.merge(annot, right_on="varId_dash", left_on="Unnamed: 0", how="left") - -test.to_csv("/out/final_matrix_expanded/"+sys.argv[1]+".expanded.csv.gz", index=False, compression="gzip") - diff --git a/run/mod5_diffusion.py b/run/mod5_diffusion.py deleted file mode 100755 index 9cfbec1..0000000 --- a/run/mod5_diffusion.py +++ /dev/null @@ -1,147 +0,0 @@ -from os.path import exists -from scipy import stats -from os import mkdir -import numpy as np -import pandas as pd - -__author__ = "Dongxue Mao; Linhua Wang" -__email__ = "dmao@bcm.edu; linhuaw@bcm.edu" - - -#runs diffusion module - -def diffusion(nn, y, alpha = 0.5, max_iter = 100): - """Helper function that runs the network diffusion. - - Parameters: - ---------- - nn: 2D-numpy array, normalized version of the adjacency matrix of the network of interest - y: list of float, initial heat of each node - alpha: float, parameter for diffusion, representing the percentage of heat diffused out at each iteration - max_iter: integer, max number of iterations of the diffusion - - Return: - ------- - F: 2D-numpy array, diffused network - """ - alpha = 0.5 - max_iter = 100 - y = y.to_numpy() - F = y - ## initialize an empty array for the heat - Fs = np.empty((nn.shape[0], 0), float) - ## restart - fY = (1-alpha) * y - for i in range(0, max_iter): - Fs = np.append(Fs, F, axis=1) - F = alpha * nn @ Fs[:,[i]] + fY - return(F) - -def diffuseSample(ID, Anno_df, Phrank_folder): - """Method that runs diffusion network for a specific sample. - - Parameters: - ---------- - ID: str, sample ID. - Anno_folder: str, path to the folder that contains the annotation file for module 1 & 2. - Phrank_folder: str, path to the folder that contains Phrank gene ranking score for diffusion. - - Return: - ------- - Pandas Data Frame, index indicates variant ID, - columnns contatins diffused phrank scores normalized as percentile. - """ - - ## Get the PPI network for diffusion - cor_path = "/run/data_dependencies/mod5_diffusion/combined_score.hdf5" - cor_df = pd.read_hdf(cor_path, mode='r') - cor_df = cor_df[(cor_df.T != 0).sum() > 1] - cor_df = cor_df.loc[cor_df.index, cor_df.index] - cor = cor_df.values - cor_GeneID = pd.DataFrame({"ID":cor_df.columns.tolist()}) - - Phrank_path = Phrank_folder + ID.split('.')[0] + ".txt" - ## Validate Phrank file's existence - if not exists(Phrank_path): - print("File %s not exists, skipping..." %Phrank_path) - return - - Phrank = pd.read_csv(Phrank_path, sep = "\t", header = None) - Phrank = Phrank.rename({0:"Ensembl_Gene_ID",1:"Score"}, axis = "columns") - ## remove rows without Ensembl_Gene_ID - Phrank["Ensembl_Gene_ID"].replace('', np.nan, inplace=True) - Phrank.dropna(subset=["Ensembl_Gene_ID"], inplace= True) - ## remove duplicated genes and keep the highest similarity score - Phrank = Phrank.sort_values('Score', ascending=False).drop_duplicates('Ensembl_Gene_ID').sort_index() - simi = Phrank.rename({0:"Ensembl_Gene_ID","Score":"Similarity_Score"}, axis = "columns") - - - ## load module 1 & 2 file as reference - #geneFiltFn = "%s/%s.txt.gz" %(Anno_folder, ID) - - ## Validate annotation file's existence - #if not exists(geneFiltFn): - #print("Module 1-2 file not exists for %s, skipping..." %ID) - #return - - #m12_df = pd.read_csv(geneFiltFn, sep="\t", compression="gzip") - m12_df=Anno_df - m12_genes = [g for g in m12_df.loc[:, "geneEnsId"].tolist() if "ENSG" in g] - m12_gc = len(set(m12_genes)) - - ## Filter Genes - ## Keep all genes in filtered GeneID in module 1 and 2 and Phrank, but not in cor_GeneID - m12_wSimi = list(set(Phrank["Ensembl_Gene_ID"]) & set(m12_genes)) - m12_wSimi_woCor = list(set(m12_wSimi) - (set(cor_GeneID["ID"]))) - m12_wSimi_woCor = simi[simi["Ensembl_Gene_ID"].isin(m12_wSimi_woCor)] - ## normalized the cor matrix - net = abs(cor) - D = 1/np.sqrt(net.sum(axis = 1)) - D2 = np.diag(D) - net_norm = D2 @ net @ D2 - - ## Set Y as similarity score - Y = cor_GeneID.merge(simi, left_on = "ID", right_on = "Ensembl_Gene_ID", how = "left") - Y = Y[["ID","Similarity_Score"]].fillna(0) - Y.set_index("ID", inplace = True) - - ## Start diffusion - diff_res = diffusion(net_norm,Y,0.5,100) - - ## Combine diffusion result with GeneID - diff_wGeneID = pd.DataFrame(diff_res, columns=["Final_Heat"]) - diff_wGeneID["GeneID"] = cor_GeneID - - ## remove the genes with zero heat - diff_wGeneID = diff_wGeneID[diff_wGeneID["Final_Heat"] != 0] - diff_wGeneID = diff_wGeneID[["GeneID","Final_Heat"]] - - ## combine diffusion result with similarity scores - FinalHeat_wSimi = pd.concat([m12_wSimi_woCor,diff_wGeneID],ignore_index=True, sort=True) - - ## remove diffusion result with non-vep genes - diff_wGeneID_in_m12 = diff_wGeneID[diff_wGeneID['GeneID'].isin(m12_genes)] - m12_wSimi_woCor = m12_wSimi_woCor.rename(columns = {"Similarity_Score":"Final_Heat"}) - diff_wGeneID_in_m12 = diff_wGeneID_in_m12.rename(columns = {"GeneID":"Ensembl_Gene_ID"}) - FinalHeat_wSimi = pd.concat([m12_wSimi_woCor,diff_wGeneID_in_m12],ignore_index=True, sort=True) - FinalHeat_wSimi = FinalHeat_wSimi.sort_values(by = 'Final_Heat', ascending = False) - - ## Get the diffusion scores for genes in the annotation data, 0 if not existed - score_ordered = [] - gene_ordered = m12_df.loc[:, "geneEnsId"].tolist() - for gene in gene_ordered: - if gene in FinalHeat_wSimi.Ensembl_Gene_ID.tolist(): - score_ordered.append(FinalHeat_wSimi.loc[FinalHeat_wSimi.Ensembl_Gene_ID == gene, 'Final_Heat'].to_numpy()[0]) - else: - score_ordered.append(0) - - ## Normalize to have a z-score (max 1, min 0) - scores = stats.rankdata(score_ordered, 'max')/len(score_ordered) - - m12_df['diffuse_Phrank_STRING'] = scores - m12_df = m12_df.drop_duplicates(subset = ["varId_dash"]) - m12_df = m12_df[["varId_dash", "diffuse_Phrank_STRING"]] - m12_df = m12_df.set_index("varId_dash", drop=True) - return m12_df - - diff --git a/run/phenoSim.R b/run/phenoSim.R deleted file mode 100755 index e721259..0000000 --- a/run/phenoSim.R +++ /dev/null @@ -1,133 +0,0 @@ -rm(list=ls()) -library(ontologyIndex) -library(ontologySimilarity) -args = commandArgs(trailingOnly=TRUE) - -id=args[1] -code=args[2] -refg=args[3] -#dat <- read.csv('/run/data_dependencies/omim_annotate/HGMD_feature_20190510.csv') -#dat <- dat[!is.na(dat$hpo_id),] -dat <- read.csv(paste0('/run/data_dependencies/omim_annotate/',refg,'/HGMD_phen.tsv'), - sep='\t') - -library(dplyr) -get_HPO_list <- function(df1){ - df2 <- df1 %>% - dplyr::group_by(acc_num, phen_id, gene_sym) %>% - dplyr::summarise(HPO = paste0(hpo_id, collapse = " ")) %>% - dplyr::ungroup() %>% as.data.frame() - df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) - #df2$Gene <- lapply(df2$Gene, function(i) unique(strsplit(i, ",")[[1]])) - #rownames(df2) <- ifelse(is.na(df2$OMIM_ID), df2$Disease_Name, df2$OMIM_ID) - return(df2) -} - - -# Load HPO_obo -HPO_obo <- get_OBO("/run/data_dependencies/omim_annotate/hp.obo",propagate_relationships = c('is_a','part_of'), extract_tags = "minimal") - -# set simi_thresh -simi_thresh <- 0 -# Set pwd: -out_pwd <- "./" - - -# In public release, there might be empty HGMD phenotype file -if (dim(dat)[1] == 0) { - col_names <- c('acc_num', 'phen_id', 'gene_sym', 'HPO', 'HPO_list', 'Similarity_Score') - dat2 <- data.frame(matrix(nrow = 0, ncol = length(col_names))) - colnames(dat2)<- col_names - -} else { - dat <- dat[!is.na(dat$hpo_id),] - dat2_ori <- get_HPO_list(dat) - #dat2_ori <- dat2_ori[with(dat2_ori,order(hgvs)),] - #dat2_ori$Gene <- unlist(lapply(dat2_ori$Gene, function(x) x[[1]])) - dat2 <- dat2_ori - - # Load patient HPO - HPO.file <- file.path(id) - HPO.orig <- read.table(HPO.file, sep = "\t", fill=T, header=F, stringsAsFactors=F) - HPO <- HPO.orig$V1 - - # remove terms without a HPO ID - HPO <- HPO[grepl("HP:", HPO)] - HPO <- list(HPO) - - sim_mat <- get_asym_sim_grid(HPO, dat2$HPO_list, ontology=HPO_obo) - dat2$Similarity_Score <- as.vector(sim_mat) - dat2$HPO_list <- unlist(lapply(dat2$HPO_list, function(x) paste0(unlist(x), collapse = "|"))) - dat2 <- dat2[order(dat2$Similarity_Score, decreasing = T),] -} - -output_file_name2 <- paste0("/out/",code,"-cz") -write.table(dat2, output_file_name2, sep = "\t", quote = F, row.names = F) - - - - -# Load OMIM gene-disease data -genemap2 <-readRDS("/run/data_dependencies/omim_annotate/hg19/genemap2_v2022.rds") - -## ---- Load OMIM Phenotype ---- -HPO_orig <- read.table("/run/data_dependencies/omim_annotate/hg19/HPO_OMIM.tsv", sep="\t", header= T, stringsAsFactors = FALSE, comment.char = "", fill = TRUE, quote = "\"") -OMIM_HPO <- HPO_orig[,c('OMIM_ID', 'DiseaseName', 'HPO_ID')] -# rename colnames -colnames(OMIM_HPO) = c("OMIM_ID", "Disease_Name", "HPO_ID") -OMIM_HPO_cl <- unique(OMIM_HPO) - - -# Function to get_HPO_list -get_HPO_list <- function(df1){ - df2 <- df1 %>% - dplyr::group_by(OMIM_ID, Disease_Name) %>% - dplyr::summarise(HPO = paste0(HPO_ID, collapse = " ")) %>% - dplyr::ungroup() %>% as.data.frame() - df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) - #rownames(df2) <- ifelse(is.na(df2$OMIM_ID), df2$Disease_Name, df2$OMIM_ID) - return(df2) -} -# Prepare OMIM HPO list for ontology similarity comparision -OMIM_HPO_all <- get_HPO_list(OMIM_HPO_cl[, c("OMIM_ID", "Disease_Name","HPO_ID")]) - -HPO.file <- file.path(id) -#HPO.file <- '/mnt/atlas_local/sasidhar/data/1127895/P-031-101_HPOTerms.txt' -HPO.orig <- read.table(HPO.file, sep = "\t", fill=T, header=F, stringsAsFactors=F) -HPO <- HPO.orig$V1 -# remove terms without a HPO ID -HPO <- HPO[grepl("HP:", HPO)] -HPO <- list(HPO) - - -# Generate HPO list -#Unknown_Disease <- data.frame(Disease_Name = "Unknown", -# HPO_ID = HPO, -# OMIM_ID = 'NA') - -#Unknown_Disease_all <- get_HPO_list(Unknown_Disease) - -# compare disease similarity -#sim_mat <- get_asym_sim_grid(Unknown_Disease_all$HPO_list, OMIM_HPO_all$HPO_list, ontology=HPO_obo) -sim_mat <- get_asym_sim_grid(HPO, OMIM_HPO_all$HPO_list, ontology=HPO_obo) - - -# OMIM_HPO_all$HPO_term <- HPO_obo$name[OMIM_HPO_all$HPO] -OMIM_HPO_all$Similarity_Score <- as.vector(sim_mat) -# convert HPO ID to HPO term -OMIM_HPO_all$HPO_term <- unlist(lapply(OMIM_HPO_all$HPO_list, function(x) paste0(HPO_obo$name[unlist(x)], collapse = "|"))) - -## Add gene - disease relationship -OMIM_HPO_all_wGene <- merge(unique(genemap2[,c("Pheno_ID","Approved_Gene_Symbol","Ensembl_Gene_ID","Entrez_Gene_ID")]), - OMIM_HPO_all[,c("OMIM_ID","Disease_Name","Similarity_Score","HPO_term")], by.y = "OMIM_ID", by.x = "Pheno_ID") -colnames(OMIM_HPO_all_wGene)[2] <- 'Gene_Symbol' -OMIM_HPO_all_order <-OMIM_HPO_all_wGene[order(OMIM_HPO_all_wGene$Similarity_Score, decreasing = T),] -# write.table(OMIM_HPO_all_order, paste0(unknown_disease_path,"HPOsimi_all_",output_file_name), sep = "\t", quote = F, row.names = F) - -# OMIM_HPO_all_filt <- head(OMIM_HPO_all_order, n = No_candidate) -OMIM_HPO_all_filt <- OMIM_HPO_all_order[OMIM_HPO_all_order$Similarity_Score >= simi_thresh,] -output_file_name2 <- paste0("/out/",code,"-dx") -write.table(OMIM_HPO_all_filt, output_file_name2, sep = "\t", quote = F, row.names = F) - - - diff --git a/run/phrank/src/location_to_gene.py b/run/phrank/src/location_to_gene.py deleted file mode 100755 index 926d6b1..0000000 --- a/run/phrank/src/location_to_gene.py +++ /dev/null @@ -1,89 +0,0 @@ -import sys -import os -from collections import defaultdict - - -if sys.argv[2] == 'hg19': - GENE_LOCS = "/run/data_dependencies/phrank/hg19/grch37_symbol_to_location.txt" -else: - GENE_LOCS = "/run/data_dependencies/phrank/hg38/grch38_symbol_to_location.txt" - -def load_loc_maps(locmap): - by_start = defaultdict(list) - by_end = defaultdict(list) - by_symbol = defaultdict(lambda: defaultdict(lambda: None)) - for line in open(locmap): - lineData = line.strip().split("\t") - symbol = lineData[0] - if "." in symbol: continue - chrom = lineData[1] - start = int(lineData[2]) - end = int(lineData[3]) - if not by_symbol[chrom][symbol]: by_symbol[chrom][symbol] = [start, end] - else: - if by_symbol[chrom][symbol][0] > start: by_symbol[chrom][symbol][0] = start - if by_symbol[chrom][symbol][1] < end: by_symbol[chrom][symbol][1] = end - by_start[chrom].append((start, end, symbol)) - by_end[chrom].append((end, start, symbol)) - for chrom in by_start.keys(): - by_start[chrom].sort() - by_end[chrom].sort() - return by_start, by_end, by_symbol - -def binary_search(loc_to_symbol, start, end): - minimum = 0 #Inclusive - maximum = len(loc_to_symbol) #Exclusive - returnSet = set() - index = None - while minimum < maximum: - index = (minimum + maximum) // 2 - location = loc_to_symbol[index] - if location[0] < start: - minimum = index + 1 - continue - if location[0] > end: - maximum = index - continue - break - if index is None: return returnSet - returnSet.add(loc_to_symbol[index][2]) - i = index - 1 - while i >= 0: - location = loc_to_symbol[i] - i -= 1 - if location[0] < start: break - returnSet.add(location[2]) - i = index + 1 - while i < len(loc_to_symbol): - location = loc_to_symbol[i] - i += 1 - if location[0] > end: break - returnSet.add(location[2]) - return returnSet - -def find_overlapping_genes(by_symbol, by_start, by_end): - gene_overlaps = defaultdict(set) - for chrom in by_symbol.keys(): - for symbol in by_symbol[chrom].keys(): - start, end = by_symbol[chrom][symbol] - start_overlaps = binary_search(by_start[chrom], start, end) - end_overlaps = binary_search(by_end[chrom], start, end) - gene_overlaps[symbol] = (start_overlaps | end_overlaps) - set([symbol]) - return gene_overlaps - -def main(variants): - by_start, by_end, by_symbol = load_loc_maps(GENE_LOCS) - #atxn1start = by_symbol["6"]["ENSG00000124788"][0] - #atxn1end = by_symbol["6"]["ENSG00000124788"][1] - for line in open(variants): - lineData = line.strip().split(":") - chrom = lineData[0] - pos = int(lineData[1]) - start_overlaps = binary_search(by_start[chrom], pos, pos) - end_overlaps = binary_search(by_end[chrom], pos, pos) - genes = start_overlaps | end_overlaps - for gene in genes: print(line.strip() + "\t" + gene) - -if __name__ == "__main__": - main(sys.argv[1]) - diff --git a/run/phrank/src/run_phrank.py b/run/phrank/src/run_phrank.py deleted file mode 100755 index d356ae4..0000000 --- a/run/phrank/src/run_phrank.py +++ /dev/null @@ -1,34 +0,0 @@ -import sys -import os -from collections import defaultdict -from phrank import * - -DAGFILE = f"/run/data_dependencies/phrank/{sys.argv[3]}/child_to_parent.txt" -DISEASEANNOTATIONS = f"/run/data_dependencies/phrank/{sys.argv[3]}/disease_to_pheno.txt" -GENEANNOTATIONS = f"/run/data_dependencies/phrank/{sys.argv[3]}/gene_to_phenotype.txt" -DISEASEGENE = f"/run/data_dependencies/phrank/{sys.argv[3]}/disease_to_gene.txt" - -#/houston_10t/cole/MARRVEL/udn/phrank/data/knowledgebase/gene_to_symbol.txt -#/houston_10t/cole/MARRVEL/udn/phrank/data/knowledgebase/hpo_to_name.txt - -def load_list(listfile): - returnList = [] - for line in open(listfile): - returnList.append(line.strip()) - return returnList - -def load_set(listfile): - returnSet = set() - for line in open(listfile): - returnSet.add(line.strip().split("\t")[0]) - return returnSet - -def main(genelist, phenotypelist): - p = Phrank(DAGFILE, DISEASEANNOTATIONS, DISEASEGENE, GENEANNOTATIONS) - genes = load_set(genelist) - phenos = load_set(phenotypelist) - ranking = p.rank_genes(genes, phenos) - for item in ranking: print(str(item[1]) + "\t" + str(item[0])) - -if __name__ == "__main__": main(sys.argv[1], sys.argv[2]) - diff --git a/run/predict_new/.py b/run/predict_new/.py deleted file mode 100755 index 1c1b44b..0000000 --- a/run/predict_new/.py +++ /dev/null @@ -1,14 +0,0 @@ -from utilities import * -import warnings -import json -import os -from glob import glob -from datetime import datetime -import joblib -from confidence import * - -model=joblib.load("/home/lul0614/pipeline/predict_new/final_model_wo_bg_val.job") -features=pd.read_csv("features.csv").columns -out=rank_patient(model, "/local/LucianLi/final-all/989068_fillna_tierFalse.txt", features, train_causals=None) - -out.to_csv("test.csv") \ No newline at end of file diff --git a/run/predict_new/causal_distr.py b/run/predict_new/causal_distr.py deleted file mode 100755 index 80ba2aa..0000000 --- a/run/predict_new/causal_distr.py +++ /dev/null @@ -1,14 +0,0 @@ -from utilities import * -import warnings -import json -import os -from glob import glob -from datetime import datetime -import joblib -from confidence import * - - -causal_distr = get_causal_distribution('/houston_30t/alexw/MARRVEL_ML/results/rf_fillNA_tierNDB_is_causal_version1_tuneFalse_05-31-2022/BG_pred_raw_rank', - label_col='is_strong') - -pd.DataFrame(causal_distr).to_csv('causal_distr.txt',index=False,header=False) \ No newline at end of file diff --git a/run/predict_new/causal_distr.txt b/run/predict_new/causal_distr.txt deleted file mode 100755 index bfb814a..0000000 --- a/run/predict_new/causal_distr.txt +++ /dev/null @@ -1,74 +0,0 @@ -0.7766666666666666 -0.07333333333333332 -0.01 -0.06666666666666668 -0.03666666666666667 -0.6866666666666666 -0.016666666666666666 -0.0033333333333333327 -0.07333333333333332 -0.21666666666666667 -0.06333333333333334 -0.0033333333333333327 -0.57 -0.12333333333333335 -0.04 -0.7366666666666667 -0.11666666666666667 -0.13333333333333333 -0.4033333333333333 -0.5966666666666667 -0.13 -0.05 -0.2433333333333333 -0.11333333333333333 -0.24 -0.36666666666666653 -0.2433333333333333 -0.3933333333333333 -0.7933333333333333 -0.08 -0.06666666666666668 -0.28 -0.15333333333333332 -0.12666666666666668 -0.7266666666666667 -0.06333333333333334 -0.5633333333333334 -0.67 -0.016666666666666666 -0.36 -0.21666666666666667 -0.6166666666666667 -0.02333333333333333 -0.6233333333333333 -0.09333333333333334 -0.4133333333333333 -0.35 -0.2933333333333333 -0.006666666666666667 -0.7233333333333334 -0.7233333333333334 -0.06 -0.5133333333333333 -0.4033333333333333 -0.2533333333333333 -0.14333333333333334 -0.06 -0.17 -0.02 -0.26 -0.5233333333333333 -0.016666666666666666 -0.07 -0.15 -0.15 -0.01 -0.49 -0.57 -0.013333333333333334 -0.11333333333333333 -0.5433333333333333 -0.45 -0.2933333333333333 -0.68 diff --git a/run/predict_new/confidence.py b/run/predict_new/confidence.py deleted file mode 100755 index 3ebf265..0000000 --- a/run/predict_new/confidence.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python -"""Script to estimate the confidence of whether a case is solved or not and assign -a confidence score to each prediction. -""" -__author__ = "Linhua Wang" -__email__ = "linhuaw@bcm.edu" -import pandas as pd -import os -import numpy as np -import scipy - -def get_causal_distribution(pred_fd, label_col='is_strong'): - # pred_fd: folder for prediction files only - fns = os.listdir(pred_fd) - # get top20 predictions - top20_preds = [] - for fn in fns: - top20_preds.append(pd.read_csv(f'{pred_fd}/{fn}', index_col=0).iloc[:20,:]) - top20_preds = pd.concat(top20_preds) - # get scores of causal variants - top20_causals = np.ravel(top20_preds.loc[top20_preds[label_col]==1, 'predict'].to_numpy()) - return top20_causals - -def assign_confidence_score(causal_distr, pred_df, label_col='is_strong'): - # pred_fd: folder for prediction files only - # pred_df: prediction data frame of the sample of interest - - prediction_scores = pred_df.predict.to_numpy() - - # assign confidence score using percentile - conf_scores = [scipy.stats.percentileofscore(causal_distr, i) for i in prediction_scores] - # assign confidence category by discretizing - conf_categories = [] - for conf_score in conf_scores: - if conf_score < 25: - conf_categories.append("Unsolved") - elif conf_score < 50: - conf_categories.append("Solved (Low)") - elif conf_score < 75: - conf_categories.append("Solved (Medium)") - else: - conf_categories.append("Solved (High)") - pred_df['confidence'] = conf_scores - pred_df['confidence level'] = conf_categories - pred_df.sort_values("confidence", ascending=False) - return pred_df - -# def determine_case_confidence(causal_distr, pred_df, label_col='is_strong'): -# pred_df = assign_confidence_score(causal_distr, pred_df, label_col) -# return pred_df['confidence level'].tolist()[0] \ No newline at end of file diff --git a/run/predict_new/features.csv b/run/predict_new/features.csv deleted file mode 100755 index b3fe29e..0000000 --- a/run/predict_new/features.csv +++ /dev/null @@ -1 +0,0 @@ -diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat \ No newline at end of file diff --git a/run/predict_new/main.py b/run/predict_new/main.py deleted file mode 100755 index 7edc4c8..0000000 --- a/run/predict_new/main.py +++ /dev/null @@ -1,162 +0,0 @@ -from utilities import * -import warnings -import json -import os -from glob import glob -from datetime import datetime -import joblib -from confidence import * - -def test(final_model, val_fns, features, test_label, out_folder, mode, train_causals, train_pred_folder): - if train_pred_folder is not None: - causal_distr = get_causal_distribution(train_pred_folder, label_col=test_label) - else: - causal_distr = None - - causal_rank_raw = rank_test_patients(final_model, val_fns, features, test_label, causal_distr = causal_distr, - out_folder=f"{out_folder}/{mode}_pred_raw_rank", train_causals=None) - - f_raw, eval_df_raw = plot_topK_performance(causal_rank_raw) - f_raw.savefig(f"{out_folder}/{mode}_test_perf_raw.png", dpi=100) - eval_df_raw.to_csv(f"{out_folder}/{mode}_test_perf_raw.csv") - - ## evaluate only on non-seen variants -# causal_rank_exclude = rank_test_patients(final_model, val_fns, features, test_label,train_pred_folder, -# out_folder=f"{out_folder}/{mode}_pred_exclude_rank", -# train_causals=train_causals) - -# f_exclude, eval_df_exclude = plot_topK_performance(causal_rank_exclude) -# f_exclude.savefig(f"{out_folder}/{mode}_test_perf_exclude.png", dpi=100) -# eval_df_exclude.to_csv(f"{out_folder}/{mode}_test_perf_exclude.csv") - -def main(feature_fillna=None, feature_tier_with_clinvar=None, - train_label=None, test_label=None, out_folder=None, - tune=True, train_test=None): - """ - feature_fillna: bool, fill na feature or not - feature_tier_with_clinvar: bool, tier feature use clinvar/hgmd info or not - train_label: str, label to be used in training (is_causal, is_strong) - test_label: str, label to be used in training (is_causal, is_strong) - out_folder: if not specified, will be created automatically - tune: bool, whether or not tuning is needed - train_test: train/test split strategy, - if str, must be one of ['BG_val', 'version1', 'leave_one_out'] - if int/float, represent number/fraction of test samples to be randomly used. - """ - ### -> load parameters - f = open("meta.json") - meta = json.load(f) - data_folder = meta["data_folder"] - classifier = meta['classifier'] - if train_test is None: - train_test = meta['train_test'] - if feature_fillna is None: - feature_fillna = meta["feature_fillna"] - if feature_tier_with_clinvar is None: - feature_tier_with_clinvar = meta["feature_tier_with_clinvar"] - if train_label is None: - train_label = meta["train_label"] - if test_label is None: - test_label = meta["test_label"] - if out_folder is None: - fillna = "fillNA" if feature_fillna else "withNA" - tier = 'tierDB' if feature_tier_with_clinvar else 'tierNDB' - dateid = datetime.now().strftime('%m-%d-%Y') - fd = '_'.join([classifier, fillna, tier, train_label, train_test, f'tune{tune}', dateid]) - out_folder = f"./results/{fd}" - - if not os.path.exists("./results/"): - os.mkdir("./results/") - if not os.path.exists(out_folder): - os.mkdir(out_folder) - - print(f"### Classifier: {classifier}.") - print(f"### DataHub: {data_folder}.") - print(f"### TrainTestMode: {train_test}.") - print(f"### Features' NA filled: {feature_fillna}.") - print(f"### Tier used disease DB: {feature_tier_with_clinvar}.") - print(f"### Results will be saved at: {out_folder}.") - print(f"### Process started at: {datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}.") - - ### <- load parameters - if feature_fillna and feature_tier_with_clinvar: - ext = "fillna_tierTrue.txt" - fns = glob(f"{data_folder}/*_{ext}") - elif feature_fillna and (not feature_tier_with_clinvar): - ext = "fillna_tierFalse.txt" - fns = glob(f"{data_folder}/*_{ext}") - elif (not feature_fillna) and feature_tier_with_clinvar: - ext = "keepna_tierTrue.txt" - fns = glob(f"{data_folder}/*_{ext}") - else: - ext = "keepna_tierFalse.txt" - fns = glob(f"{data_folder}/*_{ext}") - - ### [Start] ---> Get training and testing files - ## Files BG provided for additional validation are taken out of training - fns = [os.path.abspath(fn) for fn in fns] # take absolute paths - sample_meta = pd.read_csv(meta["sample_meta_fn"], sep=" ") - bg_samples = sample_meta.loc[sample_meta.val==1,'BG_ID'].tolist() - print(len(bg_samples)) - ## Testing files - bg_val_fns = [os.path.abspath(f"{data_folder}/{vid}_{ext}") for vid in bg_samples] - udn_val_fns = [fn for fn in fns if 'UDN' in fn] - ddd_val_fns = [fn for fn in fns if 'DDD' in fn] - print(f'#fns={len(fns)}, #BG validation={len(bg_val_fns)}, #UDN validation={len(udn_val_fns)}, #DDD validation={len(ddd_val_fns)}') - ## Training files - train_fns = list(set(fns) - set(udn_val_fns) - set(bg_val_fns) - set(ddd_val_fns)) - ### <- Get training and testing files [End] - - # if isinstance(train_sample_num, int) and (len(train_fns) > train_sample_num): - # train_fns = np.random.choice(train_fns, train_sample_num, replace=False) - if tune: - validation_performances = tune_params(train_fns, classifier=classifier) - validation_performances.to_csv(f"{out_folder}/param_tuning_perfs.csv") - val_best_params = validation_performances.iloc[0, :].tolist() - if classifier == 'xgb': - n_est, md, classifier = int(val_best_params[0]), int(val_best_params[1]), float(val_best_params[2]) - else: - n_est, md = int(val_best_params[0]), int(val_best_params[1]) - else: - ### Fix parameter tuned, TO BE REMOVED --> - if classifier == 'xgb': - n_est, lr, md = 300, 0.1, None - print(f"n_est={n_est}, lr={lr}, md={md}") - elif classifier == 'rf': - n_est, md = 300, None - ### <-- Fix parameter tuned, TO BE REMOVED - - if classifier == 'xgb': - trainX, trainY, neg_pos_ratio = get_training_data(train_fns, train_label) - features = trainX.columns.tolist() - print(f"[Final XGB] Training with {len(train_fns)} samples.") - final_model = train_XGBoost(trainX, trainY, n_est, lr, md, neg_pos_ratio) - causal_idx = np.where(trainY == 1)[0] - train_causals = trainX.iloc[causal_idx,:] - train_causals.to_csv(f"{out_folder}/train_causal_df.csv") - train_causals = train_causals.index.tolist() - elif classifier == 'rf': - final_model, features, train_causals = train_minibatch_RF(train_fns, n_estimator=n_est, max_depth=md, - label_col=train_label, batch=2000) - print(f"[Final RF] Trained, #trees = {final_model.get_params('n_estimators')}") - - joblib.dump(final_model, f"{out_folder}/final_model_wo_bg_val.job") - - with open(f"{out_folder}/features.csv", "w") as feature_f: - feature_f.write(",".join(features)) - - # ### predict on training samples - # test(final_model, train_fns, features, test_label, out_folder, 'Training', train_causals, train_pred_folder=None) - ### BG validation - test(final_model, bg_val_fns, features, test_label, out_folder, 'BG', train_causals, train_pred_folder=None) - ### UDN validation - test(final_model, udn_val_fns, features, test_label, out_folder, 'UDN', train_causals, train_pred_folder=f"{out_folder}/BG_pred_raw_rank") - ### DDD validation - test(final_model, ddd_val_fns, features, test_label, out_folder, 'DDD', train_causals, train_pred_folder=f"{out_folder}/BG_pred_raw_rank") - - - -if __name__ == "__main__": - warnings.filterwarnings('ignore') - main(tune=False) - \ No newline at end of file diff --git a/run/predict_new/run_final.py b/run/predict_new/run_final.py deleted file mode 100755 index 9baf945..0000000 --- a/run/predict_new/run_final.py +++ /dev/null @@ -1,21 +0,0 @@ -from utilities import * -import warnings -import json -import os -from glob import glob -from datetime import datetime -import joblib -from confidence import * -import sys - -#read in precalulated model -model=joblib.load("/run/data_dependencies/predict_new/hg19/final_model_wo_bg_val.job") - -#read in features to use -features=list(pd.read_csv("/run/data_dependencies/predict_new/hg19/features.csv")) - -#predict with Linhua's code -out=rank_patient(model, "/out/"+sys.argv[1]+".matrix.txt", features, train_causals=None) - -#write out -out.to_csv("/out/final_matrix/"+sys.argv[1]+".csv") \ No newline at end of file diff --git a/run/predict_new/test.csv b/run/predict_new/test.csv deleted file mode 100755 index 4955d87..0000000 --- a/run/predict_new/test.csv +++ /dev/null @@ -1,17460 +0,0 @@ -Unnamed: 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as RF -import itertools -from confidence import * - -def get_training_data(fns, label_col): - """Read in training data, drop features with specified mode in the meta['feature_mode'] json. - Allowed modes are ['all', 'raw', 'var_disase_db_off', 'gene_disase_db_off'] - """ - np.random.seed(0) - ### READ PARAMETERS - f = open("meta.json") - meta = json.load(f) - feature_mode = meta["feature_mode"] - feature_meta = pd.read_csv(meta["feature_meta_fn"], sep=',') - ### READ training samples - t0 = time() - print("[TrainVal-Train] Start reading training data.") - dfs = [] - for i in trange(len(fns)): - fn = fns[i] - df = pd.read_csv(fn, index_col=0, sep="\t") - dfs.append(df) - dfs = pd.concat(dfs) - - t1 = time() - print(f"[TrainVal-Train] Completed reading training data in {(t1-t0):.2f} seconds.") - train_Y = dfs[label_col].to_numpy().astype(int) - neg_pos_ratios = [(train_Y == 0).sum()/(train_Y == 1).sum()] - - ## Drop features based on feature mode - if feature_mode == "all": - features = feature_meta.Feature.tolist() - elif feature_mode == "raw": - features = feature_meta.loc[feature_meta.Engineered != 1,:].Feature.tolist() - elif feature_mode == "var_disase_db_off": - features = feature_meta.loc[feature_meta.DiseaseDB_VariantLevel != 1,:].Feature.tolist() - elif feature_mode == "gene_disase_db_off": - features = feature_meta.loc[feature_meta.DiseaseDB_GeneLevel != 1,:].Feature.tolist() - else: - print(f"Wrong feature mode entered, should be one of\ - ['all', 'raw', 'var_disease_db_off', 'gene_disease_db_off'],\ - your input is {feature_mode}." - ) - sys.exit(0) - train_X = dfs[features] - print("[TrainVal-Train] Training data obtained.") - return train_X, train_Y, np.mean(neg_pos_ratios) - -def tune_params(fns, classifier='xgb'): - ### READ PARAMETERS - f = open("meta.json") - meta = json.load(f) - train_label = meta["train_label"] - test_label = meta["test_label"] - train_val_frac = meta["train_val_frac"] - sep2 = "\t" - - train_fns_sampled = list(np.random.choice(fns, int(train_val_frac * len(fns)))) - val_fns_sampled = list(set(fns) - set(train_fns_sampled)) - - print(f"[TrainVal-Train] train {len(train_fns_sampled)} samples with label {train_label},\ - validate {len(val_fns_sampled)} samples with label {test_label}.") - - para_train_dfs = [pd.read_csv(train_fn, usecols=[train_label], sep=sep2) for train_fn in train_fns_sampled] - para_train_dfs = pd.concat(para_train_dfs) - if classifier == 'xgb': - ## tune parameters - n_estimators = [200, 300] - learning_rates = list(np.logspace(0.001, 0.2, num=10, endpoint=True)) - max_depths = [None] - params = list(itertools.product(n_estimators, learning_rates, max_depths)) - elif classifier == 'rf': - n_estimators = [100, 200, 300, 400] - max_depths = [None] - params = list(itertools.product(n_estimators, max_depths)) - - train_X, train_Y, neg_pos_ratio = get_training_data(train_fns_sampled, train_label) - perf_dfs = [] - for param in params: - t0 = time() - if classifier == 'xgb': - n_est, lr, md = param - print(f"[TrainVal-Train] n_trees: {n_est}, lr: {lr}, max_depth: {md}, n/p ratio: {neg_pos_ratio}.") - clf = train_XGBoost(train_X, train_Y, n_est, lr, md, neg_pos_ratio) - - elif classifier == 'rf': - n_est, md = param - print(f"[TrainVal-Train] n_trees: {n_est}, max_depth: {md}, n/p ratio: {neg_pos_ratio}.") - clf, _, _ = train_minibatch_RF(train_X,train_Y, n_est, md, neg_pos_ratio) - t1 = time() - print(f"[TrainVal-Train] done in {(t1-t0):.2f} seconds.") - rank_dfs = rank_test_patients(clf, val_fns_sampled, train_X.columns.tolist(), test_label) - obj_df = objective(rank_dfs) - if classifier == 'xgb': - perf_df = pd.DataFrame(data=[[n_est, md,lr, obj_df['objective'], - obj_df['var_top1_acc'], obj_df['var_top5_acc'], obj_df['var_top10_acc'] ]], - columns=['n_estimator', 'max_depth', 'learning_rate', - 'objective', 'var_top1', 'var_top5', 'var_top10']) - else: - perf_df = pd.DataFrame(data=[[n_est, md, obj_df['objective'], - obj_df['var_top1_acc'], obj_df['var_top5_acc'], obj_df['var_top10_acc'] ]], - columns=['n_estimator', 'max_depth', - 'objective', 'var_top1', 'var_top5', 'var_top10']) - perf_dfs.append(perf_df) - t2 = time() - print(f"[TrainVal-Validate] done in {(t2-t1):.2f} seconds, objective = {obj_df['objective']}.") - perf_dfs = pd.concat(perf_dfs).sort_values(["objective", "var_top1", "var_top5", "var_top10"], ascending=False) - return perf_dfs - -def quantify_perf(test_perfs, topK=5, mode='variant_level'): - if mode == 'sample_level': - sample_perfs = test_perfs.groupby('identifier')[['ranking']].min() - else: - sample_perfs = test_perfs.copy() - - topK_acc = (sample_perfs['ranking'] <= topK).sum() / sample_perfs.shape[0] - return topK_acc - -def plot_topK_performance(rank_dfs): - f = plt.figure(figsize=(10,4)) - topks = range(1, 101) - var_accs = [] - sample_accs = [] - - for i in topks: - var_accs.append(quantify_perf(rank_dfs, topK=i)) - sample_accs.append(quantify_perf(rank_dfs, topK=i, mode='sample_level')) - df = pd.DataFrame({"top_k": topks, "var_accuracy": var_accs, "sample_accuracy": sample_accs}) - ax1 = plt.subplot(1,2,1) - ax1.plot(topks, var_accs, linestyle='--', marker='o', color='blue') - ax1.set_xlabel("Top K", fontsize=14) - ax1.set_ylabel("Variant Accuracy", fontsize=14) - ax2 = plt.subplot(1,2,2) - ax2.plot(topks, sample_accs, linestyle='--', marker='o', color='green') - ax2.set_xlabel("Top K", fontsize=14) - ax2.set_ylabel("Sample Accuracy", fontsize=14) - plt.subplots_adjust(wspace=0.3) - return f, df - -def objective(rank_dfs): - ### READ PARAMETERS - f = open("meta.json") - meta = json.load(f) - alpha = meta["objective_params"]["alpha"] - beta = meta["objective_params"]["beta"] - gamma = meta["objective_params"]["gamma"] - f.close() - ### Get accuracy and calculate objective - vl1 = quantify_perf(rank_dfs, topK=1) - vl5 = quantify_perf(rank_dfs, topK=5) - vl10 = quantify_perf(rank_dfs, topK=10) - sl1 = quantify_perf(rank_dfs, topK=1, mode='sample_level') - sl5 = quantify_perf(rank_dfs, topK=5, mode='sample_level') - sl10 = quantify_perf(rank_dfs, topK=10, mode='sample_level') - obj_score = alpha * vl1 + beta * (vl5 - vl1) + gamma * (vl10 - vl5) - result = {"var_top1_acc": vl1, - "var_top5_acc": vl5, - "var_top10_acc": vl10, - "sample_top1_acc": sl1, - "sample_top5_acc": sl5, - "sample_top10_acc": sl10, - "objective": obj_score} - return result - -def train_XGBoost(X, Y, n_estimator, lr, max_depth, ratio): - xgb.config_context(verbosity=0) - bst = xgb.XGBClassifier(max_depth = max_depth, - n_estimators=n_estimator, - learning_rate= lr, - scale_pos_weight = ratio, - verbosity=0, n_jobs=-1) - bst.fit(X, Y) - return bst - -def train_RF(X, Y, n_estimator=None, max_depth=None, ratio=None, model=None): - if model is None: - model = RF(class_weight = {0:1, 1:ratio}, random_state=2021, n_jobs=-1, - n_estimators=n_estimator, max_depth=max_depth, warm_start=True) - model.fit(X, Y) - return model - -def train_minibatch_RF(fns, n_estimator, max_depth, label_col, batch=500): - k = ceil(len(fns) / batch) - model = RF(random_state=2021, n_jobs=-1, - n_estimators=n_estimator, - max_depth=max_depth, warm_start=True) - train_causals = [] - for i in range(k): - if i < k-1: - batch_fns = fns[i*batch:(i+1)*batch] - else: - batch_fns = fns[i*batch:] - trainX, trainY, neg_pos_ratio = get_training_data(batch_fns, label_col=label_col) - features = trainX.columns.tolist() - model.set_params(**{'class_weight': {0:1, 1:neg_pos_ratio}, - 'n_estimators': n_estimator}) - model.fit(trainX, trainY) - n_estimator += 10 - train_causals += trainX.index[np.where(trainY==1)[0]].tolist() - return model, features, train_causals - - -def rank_patient(model, fn, features, train_causals=None): - df = pd.read_csv(fn, sep="\t", index_col=0) ## File name format can be a parameter in the future - identifier = fn.split("/")[-1].split(".")[0] - patient_features = df.loc[:, features] - patient_preds = model.predict_proba(patient_features)[:, 1] - pred_df = df.copy() - pred_df["predict"] = patient_preds - - if train_causals is not None: - pred_df = pred_df.loc[~pred_df.index.isin(train_causals),:] - pred_df = pred_df.sort_values("predict", ascending=False) - min_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='min').astype(int) - max_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='max').astype(int) - pred_df['min_ranking'] = min_rankings - pred_df['max_ranking'] = max_rankings - pred_df['ranking'] = max_rankings - pred_df['identifier'] = identifier - return pred_df - -def rank_test_patients(model, fns, features, label_col, causal_distr = None, - out_folder=None, train_causals=None): - print(out_folder) - causal_rank_dfs = [] - for i in trange(len(fns)): - fn = fns[i] - - if not os.path.exists(fn): - continue - - identifier = fn.split("/")[-1].split("_")[0] - rank_df = rank_patient(model, fn, features, train_causals) - - if causal_distr is not None: - rank_df = assign_confidence_score(causal_distr, rank_df, label_col='is_strong') - - if out_folder is not None: - if not os.path.exists(out_folder): - os.mkdir(out_folder) - - rank_df.to_csv(f"{out_folder}/{identifier}_variant_rank.csv") - - causal_rank = get_causal_rank(rank_df, label_col) - - if causal_rank is not None: - causal_rank_dfs.append(causal_rank) - - causal_rank_dfs = pd.concat(causal_rank_dfs) - return causal_rank_dfs - -def get_causal_rank(pred_df, label_col): - if (pred_df[label_col] == 1).sum() == 0: - return None - return pred_df.loc[pred_df[label_col] == 1,:] \ No newline at end of file diff --git a/run/proc.sh b/run/proc.sh deleted file mode 100755 index 4e535ac..0000000 --- a/run/proc.sh +++ /dev/null @@ -1,284 +0,0 @@ -#!/bin/bash -#$1 submission id -#$2 ref genome -#$3 memory limitation - -#make output subdirectories -echo $(date +"%T") > /out/whole_log.txt -mkdir -m777 /out/phrank -mkdir -m777 /out/input -mkdir -m777 /out/tier-test-false -mkdir -m777 /out/rami-test -mkdir -m777 /out/scores -mkdir -m777 /out/final_matrix -mkdir -m777 /out/final_matrix_expanded - -REF_DIR=$2 -CHUNK_RAM=$3 -KEEP_INTERMEDIATE=${4:-True} - -if [ "$REF_DIR" = "hg19" ] -then - REF_GEN="GRCh37" -else - REF_GEN="GRCh38" -fi - -#Check if hpo.txt is empty or has no valid HPO ID -if [[ -z $(egrep 'HP:[0-9]{7}' /input/hpo.txt) ]] ; then - #Replace it with HP:0000001 - echo "HP:0000001" > /input/hpo.txt -fi - - -cp /input/vcf.gz /out/vcf.gz ### New Step: Copying file to output directory - - -# Initial path of the VCF file -INPUT_VCF_PATH="/out/vcf.gz" ### Changed: Adjust path to new output location - -#Check if the file exists -if [ -e "$INPUT_VCF_PATH" ]; then - #Use file command to determine the file type - INPUT_VCF_TYPE=$(file -b "$INPUT_VCF_PATH") - - # Determine if the file is gzip or bgzip format - if echo "$INPUT_VCF_TYPE" | grep -q 'gzip compressed data'; then - # Check if it's bgzip format for tabix compatibility - if echo "$INPUT_VCF_TYPE" | grep -q 'BGZF'; then - echo "The file is in BGZF format, ready for tabix." - tabix -p vcf "$INPUT_VCF_PATH" ### Adjusted Step: Only index if BGZF - else - echo "GZIP format detected, converting to BGZF." - gunzip -c "$INPUT_VCF_PATH" | bgzip > "${INPUT_VCF_PATH%.gz}" - mv "${INPUT_VCF_PATH%.gz}" "$INPUT_VCF_PATH" ### Ensuring the correct .gz extension - tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file - fi - elif echo "$INPUT_VCF_TYPE" | grep -q 'ASCII text'; then - echo "Plain VCF file detected, compressing and indexing." - bgzip -c "$INPUT_VCF_PATH" > "${INPUT_VCF_PATH}.gz" - mv "${INPUT_VCF_PATH}.gz" "$INPUT_VCF_PATH" ### Replace uncompressed with compressed - tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file - fi -else - echo "The file $INPUT_VCF_PATH does not exist." -fi - - - -echo "VCF pre-processing" -#annotate with new chromosomes and preserve original coordinates in ID -bcftools annotate --rename-chrs /run/data_dependencies/bcf_annotate/chrmap.txt -x ID /out/vcf.gz -Oz -o /out/$1-annot.txt -bcftools annotate --set-id +'%CHROM\_%POS\_%REF\_%FIRST_ALT' /out/$1-annot.txt -Oz -o /out/$1-add-id.vcf.gz - - -echo "VCF quality filtering" -#run quality filters #removed 27th line -bcftools filter /out/$1-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o /out/$1.filt.vcf.gz - -#check number of variants left -variant_count=$(bcftools view -H /out/$1.filt.vcf.gz | wc -l) -if [ "$variant_count" -gt 0 ]; then - echo "Quality filtering completed successfully. Variants passing the filters: $variant_count" -else - echo "The VCF file doesn't have FILTER annotation, or all variants filtered." - echo "Pipeline will proceed with unfiltered VCF file." - cp /out/$1-add-id.vcf.gz /out/$1.filt.vcf.gz -fi - -echo "Remove mitochondrial and unknown chromosome variants" -#gunzip /out/$1.filt.vcf.gz -#grep -vE "chrM" /out/$1.filt.vcf > /out/$1.filt.rmMT.vcf -tabix -p vcf /out/$1.filt.vcf.gz -bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y /out/$1.filt.vcf.gz -o /out/$1.filt.rmMT.vcf - -#Phrank annotation -echo "Phrank scoring" -zcat /out/vcf.gz | awk 'substr($0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > /out/$1-var.txt -cat /out/$1-var.txt | sed 's/chr//g' | sort -u > /out/$1-var-filt.txt - - -#annotate with ENSG -#echo "ensg annotates" -python2.7 /run/phrank/src/location_to_gene.py /out/$1-var-filt.txt ${REF_DIR} | sed 's/:/\t/g' | sed 's/X\t/23\t/g' | sed 's/Y\t/24\t/g' | sed 's/MT\t/25\t/g' > /out/$1-ensmbl.txt - - -#echo "gene-sym" -cat /out/$1-ensmbl.txt | sort -k5,5 | join -1 5 -2 1 - /run/data_dependencies/phrank/hg19/ensembl_to_symbol.txt | sed 's/ /\t/g' | cut -f2- > /out/$1-genesym.txt -cat /out/$1-genesym.txt | cut -f5 | sort -u | join -t$'\t' -1 1 -2 2 - /run/data_dependencies/phrank/hg19/gene_to_symbol_sorted.txt | cut -f2 | sort -u > /out/$1-gene.txt - - -# #run phrank scoring -#echo "phrank scoring" -python3.8 /run/phrank/src/run_phrank.py /out/$1-gene.txt /input/hpo.txt ${REF_DIR} > /out/phrank/$1.txt - - -# #annotate with OMIM and HPO path -echo "Calculating phenotype similarities" -Rscript /run/phenoSim.R /input/hpo.txt $1 ${REF_DIR} > /dev/null - - -#Filter proband vcf with blacklist -echo "Filtering VCF with blacklist" -bgzip /out/$1.filt.rmMT.vcf - -tabix /out/$1.filt.rmMT.vcf.gz -mkdir -m777 /out/isec_tmp1 -bcftools isec -p /out/isec_tmp1 -w 1 -Oz \ -/out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.genomes.vcf.gz - -tabix /out/isec_tmp1/0000.vcf.gz -mkdir -m777 /out/isec_tmp2 -bcftools isec -p /out/isec_tmp2 -w 1 -Oz \ -/out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.exomes.vcf.gz - -tabix /out/isec_tmp2/0000.vcf.gz -mkdir -m777 /out/isec_tmp3 -bcftools isec -p /out/isec_tmp3 -Ov \ -/out/isec_tmp1/0000.vcf.gz /out/isec_tmp2/0000.vcf.gz - -mv /out/isec_tmp3/0002.vcf /out/$1.filt.rmBL.vcf -rm -rf /out/isec_tmp1 -rm -rf /out/isec_tmp2 -rm -rf /out/isec_tmp3 - - -#annotate with vep -echo "VEP annotation" - -if [ "$REF_DIR" = "hg38" ] -then - - /opt/vep/src/ensembl-vep/vep \ - --dir_cache /run/data_dependencies/vep/hg38/ \ - --dir_plugins /run/data_dependencies/vep/hg38/Plugins/ \ - --fork 10 --everything --format vcf \ - --cache --offline --tab --force_overwrite \ - --species homo_sapiens --assembly GRCh38 \ - --custom /run/data_dependencies/vep/hg38/gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ - --custom /run/data_dependencies/vep/hg38/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ - --custom /run/data_dependencies/vep/hg38/HGMD_Pro_2022.2_hg38.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ - --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg38/new_tabbed_revel_grch38.tsv.gz,ALL \ - --plugin SpliceAI,snv=/run/data_dependencies/vep/hg38/spliceai_scores.masked.snv.hg38.vcf.gz,indel=/run/data_dependencies/vep/hg38/spliceai_scores.masked.indel.hg38.vcf.gz,cutoff=0.5 \ - --plugin CADD,/run/data_dependencies/vep/hg38/hg38_whole_genome_SNV.tsv.gz,ALL \ - --plugin dbNSFP,/run/data_dependencies/vep/hg38/dbNSFP4.1a_grch38.gz,ALL \ - --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf -else - - /opt/vep/src/ensembl-vep/vep \ - --dir_cache /run/data_dependencies/vep/hg19/ \ - --dir_plugins /run/data_dependencies/vep/hg19/Plugins/ \ - --fork 10 --everything --format vcf \ - --cache --offline --tab --force_overwrite \ - --species homo_sapiens --assembly GRCh37 \ - --custom /run/data_dependencies/vep/hg19/gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ - --custom /run/data_dependencies/vep/hg19/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ - --custom /run/data_dependencies/vep/hg19/HGMD_Pro_2022.2_hg19.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ - --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg19/new_tabbed_revel.tsv.gz,ALL \ - --plugin SpliceAI,snv=/run/data_dependencies/vep/hg19/spliceai_scores.masked.snv.hg19.vcf.gz,indel=/run/data_dependencies/vep/hg19/spliceai_scores.masked.indel.hg19.vcf.gz,cutoff=0.5 \ - --plugin CADD,/run/data_dependencies/vep/hg19/hg19_whole_genome_SNVs.tsv.gz,ALL \ - --plugin dbNSFP,/run/data_dependencies/vep/hg19/dbNSFP4.3a_grch37.gz,ALL \ - --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf -fi - -echo "Feature engineering" -#annotate w/ marrvel flatfiles -AIM_FREE_RAM=$(free -g | awk 'NR==2{printf $7}') - -if (( AIM_FREE_RAM > CHUNK_RAM )); then - RAM_MAX=${CHUNK_RAM} -else - RAM_MAX=${AIM_FREE_RAM} -fi - -python3.8 /run/split_chunks.py $1 $RAM_MAX - -while read -r INDEX LINEH LINEA LINEB -do - sed -n -e "${LINEH}p" -e "${LINEA},${LINEB}p" /out/$1-vep.txt > /out/$1-vep-${INDEX}.txt - - python3.8 /run/annotation/main.py \ - -outPrefix /out/rami-test/r1 \ - -patientID $1 \ - -patientHPOsimiOMIM /out/$1-dx \ - -patientHPOsimiHGMD /out/$1-cz \ - -varFile /out/$1-vep-$INDEX.txt \ - -inFileType vepAnnotTab \ - -patientFileType one \ - -genomeRef ${REF_DIR} \ - -diseaseInh AD \ - -modules curate,conserve - - if [ $INDEX -gt 1 ]; then - sed -n "2,\$p" /out/rami-test/$1_scores.csv > /out/rami-test/$1_scores_$INDEX.csv - rm /out/rami-test/$1_scores.csv - sed -n "2,\$p" /out/rami-test/r1_$1_scores.txt > /out/rami-test/r1_$1_scores_$INDEX.txt - rm /out/rami-test/r1_$1_scores.txt - else - mv /out/rami-test/$1_scores.csv /out/rami-test/$1_scores_$INDEX.csv - mv /out/rami-test/r1_$1_scores.txt /out/rami-test/r1_$1_scores_$INDEX.txt - fi -done < /out/vep_split.txt - - -# Combine all $1_scores_$INDEX.csv -for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) -do - cat /out/rami-test/$1_scores_$INDEX.csv -done > /out/rami-test/$1_scores.csv - -for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) -do - cat /out/rami-test/r1_$1_scores_$INDEX.txt -done > /out/rami-test/r1_$1_scores.txt - -rm /out/$1-vep-*.txt -rm /out/rami-test/r1_$1_scores_*.txt -rm /out/rami-test/$1_scores_*.csv - -Rscript /run/VarTierDiseaseDBFalse.R $1 ${REF_DIR} - -python3.8 /run/generate_new_matrix_2.py $1 ${REF_DIR} - -echo "Making prediction" -python3.8 /run/predict_new/run_final.py $1 - -python3.8 /run/merge_rm.py $1 - -python3.8 /run/extraModel/main.py -id $1 - - -echo $(date +"%T") >> /out/whole_log.txt - -# delete intermediate files -if [ "$KEEP_INTERMEDIATE" = "False" ]; then - rm /out/$1-add-id.vcf.gz - rm /out/$1-annot.txt - rm /out/$1-cz - rm /out/$1-dx - rm /out/$1-ensmbl.txt - rm /out/$1.filt.rmBL.vcf - rm /out/$1.filt.rmMT.vcf.gz - rm /out/$1.filt.rmMT.vcf.gz.tbi - rm /out/$1.filt.vcf - rm /out/$1-genesym.txt - rm /out/$1-gene.txt - rm /out/$1.matrix.txt - rm /out/$1-var-filt.txt - rm /out/$1-var.txt - rm /out/$1-vep.txt - rm /out/$1-vep.txt* - rm /out/vep_split.txt - rm -r /out/final_matrix - rm -r /out/final_matrix_expanded - rm -r /out/input - rm -r /out/phrank - rm -r /out/rami-test - rm -r /out/scores - rm -r /out/tier-test-false -fi - -mv /out/conf_4Model/*.csv /out/ -mv /out/conf_4Model/integrated/*.csv /out/ -rm -r /out/conf_4Model - diff --git a/run/simple_repeat_anno.py b/run/simple_repeat_anno.py deleted file mode 100755 index 39d9d17..0000000 --- a/run/simple_repeat_anno.py +++ /dev/null @@ -1,29 +0,0 @@ -import pandas as pd -import numpy as np -import os - -#annotates a feature matrix with a simple repeat column - -def simple_repeat_anno(sample_id, feature_file, sp_bed_file): - sample = feature_file - sample_bed = pd.DataFrame({'chr':sample.index.str.split('-').str[0], - 'start':sample.index.str.split('-').str[1], - 'end':sample.index.str.split('-').str[1], - 'ref':sample.index.str.split('-').str[2], - 'alt':sample.index.str.split('-').str[3]}) - sample_bed.to_csv('/out/%s.bed'%(sample_id),index=False,header=False,sep='\t') - - # bedtools needed - os.system('/run/bedtools intersect -a /out/%s.bed -b %s -wa > /out/%s.sp.bed'%(sample_id, sp_bed_file, sample_id)) - - if os.stat('/out/%s.sp.bed'%(sample_id)).st_size == 0: - sample['simple_repeat'] = 0 - else: - sample_sp = pd.read_csv('/out/%s.sp.bed'%(sample_id),sep='\t',header=None) - sample_sp['var_dash'] = sample_sp[0].astype(str) + '-' + sample_sp[2].astype(str) + '-' + sample_sp[3].astype(str) + '-' + sample_sp[4].astype(str) - sample['simple_repeat'] = sample.index.isin(sample_sp['var_dash'].to_numpy()).astype(int) - - return sample - - - diff --git a/run/split_chunks.py b/run/split_chunks.py deleted file mode 100644 index 0837bb8..0000000 --- a/run/split_chunks.py +++ /dev/null @@ -1,44 +0,0 @@ -import math -import os -import sys -import subprocess - -patientID = sys.argv[1] -RAMlimit = float(sys.argv[2]) - -#numHeaderSkip=354#this depends on the type of annotation used -numHeaderSkip=0 -with open(f"/out/{patientID}-vep.txt",'r') as F: - for line in F: - if line.startswith('##'): - numHeaderSkip += 1 - else: - break - -# 06/30/2023: Process the vep annotation result chunk by chunk -command = f'wc -l /out/{patientID}-vep.txt | cut -d" " -f1' -nlines = int(subprocess.check_output(command, shell=True, text=True).strip()) - numHeaderSkip - 1 - -command = f"du /out/{patientID}-vep.txt" -filesize = int(subprocess.check_output(command, shell=True, text=True).strip().split('\t')[0]) - -num_chunk = math.ceil( (filesize/1024) / (RAMlimit * 1024 / 23) ) - -chunk_lines = math.ceil(nlines/num_chunk) - -lines = [] -if num_chunk > 1: - for i in range(num_chunk-1): - lines.append(f"{i+1}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + i*chunk_lines)}\t{int(numHeaderSkip+1 + (i+1)*chunk_lines)}") - - -lines.append(f"{num_chunk}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + (num_chunk-1)*chunk_lines)}\t{int(nlines+numHeaderSkip+1)}") - -vep_split = open("/out/vep_split.txt", 'w') -vep_split.write('\n'.join(lines)) -vep_split.write('\n') -vep_split.close() - - - - From fc9a0903a293e2c46e747d18e2e1dfb0abf3116f Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 11:44:08 -0500 Subject: [PATCH 05/71] enable docker. --- .gitignore | 8 +++++++- nextflow.config | 10 +++++++++- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 7bf348b..3d081c6 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,11 @@ +.gitignore bcftools-1.9.tar.bz2 run/bedtools .vscode/ .DS_Store -*/.DS_Store \ No newline at end of file +*/.DS_Store +.nextflow* +.nextflow/ +work/ +test_data/ +test_ref/ diff --git a/nextflow.config b/nextflow.config index 4828b9e..e2d7510 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,5 +1,7 @@ params { - run_id = null + run_id = 1 + + ref_dir = "/home/hwan/AI_MARRVEL/test_ref/grch37_symbol_to_location.txt" input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" input_hpo = null outdir = "/home/hwan/AI_MARRVEL/test_output/" @@ -7,4 +9,10 @@ params { // for data dependency chrmap = "/home/hwan/AI_MARRVEL/test_ref/chrmap.txt" +} + +docker.enabled = true + +process { + container = "chaozhongliu/aim-lite:latest" } \ No newline at end of file From 02caca1251b26234b1e69f8bf7752a42d273e908 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 12:05:48 -0500 Subject: [PATCH 06/71] add phrank scoring. --- main.nf | 49 ++++++++++++++++++++++++++++++++++++++++++++++++- nextflow.config | 12 ++++++++++-- 2 files changed, 58 insertions(+), 3 deletions(-) diff --git a/main.nf b/main.nf index 6218f4a..56814a6 100644 --- a/main.nf +++ b/main.nf @@ -53,7 +53,20 @@ process VCF_PRE_PROCESS { bcftools annotate --rename-chrs $chrmap -x ID $vcf -Oz -o ${params.run_id}-annot # Annotate with new IDs based on CHROM, POS, REF, ALT - bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o ${params.run_id}.filt.vcf.gz + bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o add-id.vcf.gz + + #run quality filters + bcftools filter add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o ${params.run_id}.filt.vcf.gz + + #check number of variants left + variant_count=\$(bcftools view -H ${params.run_id}.filt.vcf.gz | wc -l) + if [ "\${variant_count}" -gt 0 ]; then + echo "Quality filtering completed successfully. Variants passing the filters: \${variant_count}" + else + echo "The VCF file doesn't have FILTER annotation, or all variants filtered." + echo "Pipeline will proceed with unfiltered VCF file." + cp add-id.vcf.gz ${params.run_id}.filt.vcf.gz + fi """ } @@ -101,9 +114,37 @@ process ANNOT_ENSMBLE { sed 's/:/\\t/g' | sed 's/X\\t/23\\t/g' | sed 's/Y\\t/24\\t/g' | \\ sed 's/MT\\t/25\\t/g' > ${params.run_id}-ensmbl.txt """ +} +process TO_GENE_SYM { + input: + path ensmbl + path ref_to_sym + path ref_sorted_sym + + output: + path "*-gene.txt" + + script: + """ + cat $ensmbl | sort -k5,5 | join -1 5 -2 1 - $ref_to_sym | sed 's/ /\\t/g' | cut -f2- > genesym.txt + cat genesym.txt | cut -f5 | sort -u | join -t\$'\\t' -1 1 -2 2 - $ref_sorted_sym | cut -f2 | sort -u > ${params.run_id}-gene.txt + """ } +process PHRANK_SCORING { + input: + path gene + path hpo + path ref_dir + output: + path "${params.run_id}.phrank.txt.txt" publishDir "${params.outdir}/phrank/", mode: 'copy' + + script: + """ + python3.8 ${workflow.projectDir}/scripts/phrank/src/run_phrank.py $gene $hpo $ref_dir > ${params.run_id}.phrank.txt + """ +} workflow { INDEX_VCF(params.input_vcf) @@ -111,4 +152,10 @@ workflow { REMOVE_MITO_AND_UNKOWN_CHR(VCF_PRE_PROCESS.out) ANNOT_PHRANK(VCF_PRE_PROCESS.out) ANNOT_ENSMBLE(params.ref_dir, ANNOT_PHRANK.out) + TO_GENE_SYM(ANNOT_ENSMBLE.out, params.ref_to_sym, params.ref_sorted_sym) + PHRANK_SCORING(TO_GENE_SYM, params.hpo, + params.phrank_dagfile, + params.phrank_disease_annotation, + params.phrank_gene_annotation, + params.phrank_disease_gene) } \ No newline at end of file diff --git a/nextflow.config b/nextflow.config index e2d7510..b9397ae 100644 --- a/nextflow.config +++ b/nextflow.config @@ -2,13 +2,21 @@ params { run_id = 1 ref_dir = "/home/hwan/AI_MARRVEL/test_ref/grch37_symbol_to_location.txt" + ref_to_sym = "/home/hwan/AI_MARRVEL/test_ref/ensembl_to_symbol.txt" + ref_sorted_sym = "/home/hwan/AI_MARRVEL/test_ref/gene_to_symbol_sorted.txt" input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" - input_hpo = null + input_hpo = "/home/hwan/AI_MARRVEL/test_data/hpos.txt" outdir = "/home/hwan/AI_MARRVEL/test_output/" - // for data dependency chrmap = "/home/hwan/AI_MARRVEL/test_ref/chrmap.txt" + + // for phrank + phrank_dagfile = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/child_to_parent.txt" + phrank_disease_annotation = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/disease_to_pheno.txt" + phrank_gene_annotation = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/gene_to_phenotype.txt" + phrank_disease_gene = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/disease_to_gene.txt" + } docker.enabled = true From 1aa5585d6c7a9bd7b88eeaf8d4a0628048f5cf88 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 12:06:17 -0500 Subject: [PATCH 07/71] arrange newlines. --- main.nf | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/main.nf b/main.nf index 56814a6..21975af 100644 --- a/main.nf +++ b/main.nf @@ -86,6 +86,7 @@ process REMOVE_MITO_AND_UNKOWN_CHR { """ } + process ANNOT_PHRANK { input: path vcf @@ -100,6 +101,7 @@ process ANNOT_PHRANK { """ } + process ANNOT_ENSMBLE { input: path ref @@ -116,6 +118,7 @@ process ANNOT_ENSMBLE { """ } + process TO_GENE_SYM { input: path ensmbl @@ -132,6 +135,7 @@ process TO_GENE_SYM { """ } + process PHRANK_SCORING { input: path gene @@ -146,6 +150,9 @@ process PHRANK_SCORING { """ } + +process + workflow { INDEX_VCF(params.input_vcf) VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) From 915d84494ce24f3739a78e7d60de72de44f24dcd Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 12:24:07 -0500 Subject: [PATCH 08/71] add OMIM simmiarity calculation. --- main.nf | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/main.nf b/main.nf index 21975af..af685ef 100644 --- a/main.nf +++ b/main.nf @@ -151,7 +151,24 @@ process PHRANK_SCORING { } -process +process HPO_SIM { + input: + path hpo + path omim_hgmd_phen + path omim_obo + path omim_genemap2 + path omim_pheno + + output: + path "*-cz" + path "*-dx" + + script: + """ + Rscript ${workflow.projectDir}/scripts/phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno + """ + +} workflow { INDEX_VCF(params.input_vcf) @@ -165,4 +182,9 @@ workflow { params.phrank_disease_annotation, params.phrank_gene_annotation, params.phrank_disease_gene) + HPO_SIM(params.hpo, + params.omim_hgmd_phen, + params.omim_obo, + params.omim_genemap2, + params.omim_pheno) } \ No newline at end of file From 6cdc51a637b350bed7e4d90b1ca24a16b83df437 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 12:24:21 -0500 Subject: [PATCH 09/71] add OMIM and HPO refs. --- nextflow.config | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/nextflow.config b/nextflow.config index b9397ae..3f39a08 100644 --- a/nextflow.config +++ b/nextflow.config @@ -17,6 +17,11 @@ params { phrank_gene_annotation = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/gene_to_phenotype.txt" phrank_disease_gene = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/disease_to_gene.txt" + // OMIM and HPO + omim_hgmd_phen = "/home/hwan/AI_MARRVEL/refs/omim_annotate/hg19/HGMD_phen.tsv" + omim_obo = "/home/hwan/AI_MARRVEL/refs/omim_annotate/hp.obo" + omim_genemap2 = "/home/hwan/AI_MARRVEL/refs/omim_annotate/hg19/genemap2_v2022.rds" + omim_pheno = "/home/hwan/AI_MARRVEL/refs/omim_annotate/hg19/HPO_OMIM.tsv" } docker.enabled = true From 5d5822c8f6f1338b6eb13d0dc0b14c7f85941453 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 12:26:02 -0500 Subject: [PATCH 10/71] change for file paths. --- scripts/phenoSim.R | 41 ++++++++++++++------------------ scripts/phrank/src/run_phrank.py | 13 +++++----- 2 files changed, 24 insertions(+), 30 deletions(-) diff --git a/scripts/phenoSim.R b/scripts/phenoSim.R index e721259..13a0003 100755 --- a/scripts/phenoSim.R +++ b/scripts/phenoSim.R @@ -3,13 +3,14 @@ library(ontologyIndex) library(ontologySimilarity) args = commandArgs(trailingOnly=TRUE) -id=args[1] -code=args[2] -refg=args[3] -#dat <- read.csv('/run/data_dependencies/omim_annotate/HGMD_feature_20190510.csv') -#dat <- dat[!is.na(dat$hpo_id),] -dat <- read.csv(paste0('/run/data_dependencies/omim_annotate/',refg,'/HGMD_phen.tsv'), - sep='\t') +PATIENT_HPO=args[1] +OMIM_HGMD=args[2] +OMIM_OBO=args[3] +OMIM_PHENO=args[4] +OUTFILE_CZ_NAME=args[5] +OUTFILE_DX_NAME=args[6] + +dat <- read.csv(OMIM_HGMD,sep='\t') library(dplyr) get_HPO_list <- function(df1){ @@ -18,19 +19,16 @@ get_HPO_list <- function(df1){ dplyr::summarise(HPO = paste0(hpo_id, collapse = " ")) %>% dplyr::ungroup() %>% as.data.frame() df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) - #df2$Gene <- lapply(df2$Gene, function(i) unique(strsplit(i, ",")[[1]])) - #rownames(df2) <- ifelse(is.na(df2$OMIM_ID), df2$Disease_Name, df2$OMIM_ID) + return(df2) } # Load HPO_obo -HPO_obo <- get_OBO("/run/data_dependencies/omim_annotate/hp.obo",propagate_relationships = c('is_a','part_of'), extract_tags = "minimal") +HPO_obo <- get_OBO(OMIM_OBO,propagate_relationships = c('is_a','part_of'), extract_tags = "minimal") # set simi_thresh simi_thresh <- 0 -# Set pwd: -out_pwd <- "./" # In public release, there might be empty HGMD phenotype file @@ -47,8 +45,7 @@ if (dim(dat)[1] == 0) { dat2 <- dat2_ori # Load patient HPO - HPO.file <- file.path(id) - HPO.orig <- read.table(HPO.file, sep = "\t", fill=T, header=F, stringsAsFactors=F) + HPO.orig <- read.table(PATIENT_HPO, sep = "\t", fill=T, header=F, stringsAsFactors=F) HPO <- HPO.orig$V1 # remove terms without a HPO ID @@ -61,17 +58,17 @@ if (dim(dat)[1] == 0) { dat2 <- dat2[order(dat2$Similarity_Score, decreasing = T),] } -output_file_name2 <- paste0("/out/",code,"-cz") -write.table(dat2, output_file_name2, sep = "\t", quote = F, row.names = F) + +write.table(dat2, OUTFILE_CZ_NAME, sep = "\t", quote = F, row.names = F) # Load OMIM gene-disease data -genemap2 <-readRDS("/run/data_dependencies/omim_annotate/hg19/genemap2_v2022.rds") +genemap2 <-readRDS() ## ---- Load OMIM Phenotype ---- -HPO_orig <- read.table("/run/data_dependencies/omim_annotate/hg19/HPO_OMIM.tsv", sep="\t", header= T, stringsAsFactors = FALSE, comment.char = "", fill = TRUE, quote = "\"") +HPO_orig <- read.table(OMIM_PHENO, sep="\t", header= T, stringsAsFactors = FALSE, comment.char = "", fill = TRUE, quote = "\"") OMIM_HPO <- HPO_orig[,c('OMIM_ID', 'DiseaseName', 'HPO_ID')] # rename colnames colnames(OMIM_HPO) = c("OMIM_ID", "Disease_Name", "HPO_ID") @@ -91,9 +88,7 @@ get_HPO_list <- function(df1){ # Prepare OMIM HPO list for ontology similarity comparision OMIM_HPO_all <- get_HPO_list(OMIM_HPO_cl[, c("OMIM_ID", "Disease_Name","HPO_ID")]) -HPO.file <- file.path(id) -#HPO.file <- '/mnt/atlas_local/sasidhar/data/1127895/P-031-101_HPOTerms.txt' -HPO.orig <- read.table(HPO.file, sep = "\t", fill=T, header=F, stringsAsFactors=F) +HPO.orig <- read.table(PATIENT_HPO, sep = "\t", fill=T, header=F, stringsAsFactors=F) HPO <- HPO.orig$V1 # remove terms without a HPO ID HPO <- HPO[grepl("HP:", HPO)] @@ -126,8 +121,8 @@ OMIM_HPO_all_order <-OMIM_HPO_all_wGene[order(OMIM_HPO_all_wGene$Similarity_Scor # OMIM_HPO_all_filt <- head(OMIM_HPO_all_order, n = No_candidate) OMIM_HPO_all_filt <- OMIM_HPO_all_order[OMIM_HPO_all_order$Similarity_Score >= simi_thresh,] -output_file_name2 <- paste0("/out/",code,"-dx") -write.table(OMIM_HPO_all_filt, output_file_name2, sep = "\t", quote = F, row.names = F) + +write.table(OMIM_HPO_all_filt, OUTFILE_DX_NAME, sep = "\t", quote = F, row.names = F) diff --git a/scripts/phrank/src/run_phrank.py b/scripts/phrank/src/run_phrank.py index d356ae4..f34a0db 100755 --- a/scripts/phrank/src/run_phrank.py +++ b/scripts/phrank/src/run_phrank.py @@ -3,13 +3,11 @@ from collections import defaultdict from phrank import * -DAGFILE = f"/run/data_dependencies/phrank/{sys.argv[3]}/child_to_parent.txt" -DISEASEANNOTATIONS = f"/run/data_dependencies/phrank/{sys.argv[3]}/disease_to_pheno.txt" -GENEANNOTATIONS = f"/run/data_dependencies/phrank/{sys.argv[3]}/gene_to_phenotype.txt" -DISEASEGENE = f"/run/data_dependencies/phrank/{sys.argv[3]}/disease_to_gene.txt" +DAGFILE = sys.argv[3] +DISEASEANNOTATIONS = sys.argv[4] +GENEANNOTATIONS = sys.argv[5] +DISEASEGENE = sys.argv[6] -#/houston_10t/cole/MARRVEL/udn/phrank/data/knowledgebase/gene_to_symbol.txt -#/houston_10t/cole/MARRVEL/udn/phrank/data/knowledgebase/hpo_to_name.txt def load_list(listfile): returnList = [] @@ -30,5 +28,6 @@ def main(genelist, phenotypelist): ranking = p.rank_genes(genes, phenos) for item in ranking: print(str(item[1]) + "\t" + str(item[0])) -if __name__ == "__main__": main(sys.argv[1], sys.argv[2]) +if __name__ == "__main__": + main(sys.argv[1], sys.argv[2]) From 411232c12cda74a256635b3d68fe293a1ee91f54 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 12:31:25 -0500 Subject: [PATCH 11/71] add output paths. --- main.nf | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/main.nf b/main.nf index af685ef..adeb2ac 100644 --- a/main.nf +++ b/main.nf @@ -165,7 +165,8 @@ process HPO_SIM { script: """ - Rscript ${workflow.projectDir}/scripts/phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno + Rscript ${workflow.projectDir}/scripts/phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ + ${params.run_id}-cz ${params.run_id}-dx """ } From 43af2f27f0af2fe36d6ae71c0e5786207bef35d6 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 23 May 2024 12:31:40 -0500 Subject: [PATCH 12/71] forgot to add genemap2 path. --- scripts/phenoSim.R | 21 ++++++--------------- 1 file changed, 6 insertions(+), 15 deletions(-) diff --git a/scripts/phenoSim.R b/scripts/phenoSim.R index 13a0003..5fa33e6 100755 --- a/scripts/phenoSim.R +++ b/scripts/phenoSim.R @@ -6,9 +6,10 @@ args = commandArgs(trailingOnly=TRUE) PATIENT_HPO=args[1] OMIM_HGMD=args[2] OMIM_OBO=args[3] -OMIM_PHENO=args[4] -OUTFILE_CZ_NAME=args[5] -OUTFILE_DX_NAME=args[6] +OMIM_GENEMAP2=args[4] +OMIM_PHENO=args[5] +OUTFILE_CZ_NAME=args[6] +OUTFILE_DX_NAME=args[7] dat <- read.csv(OMIM_HGMD,sep='\t') @@ -40,8 +41,6 @@ if (dim(dat)[1] == 0) { } else { dat <- dat[!is.na(dat$hpo_id),] dat2_ori <- get_HPO_list(dat) - #dat2_ori <- dat2_ori[with(dat2_ori,order(hgvs)),] - #dat2_ori$Gene <- unlist(lapply(dat2_ori$Gene, function(x) x[[1]])) dat2 <- dat2_ori # Load patient HPO @@ -95,26 +94,18 @@ HPO <- HPO[grepl("HP:", HPO)] HPO <- list(HPO) -# Generate HPO list -#Unknown_Disease <- data.frame(Disease_Name = "Unknown", -# HPO_ID = HPO, -# OMIM_ID = 'NA') - -#Unknown_Disease_all <- get_HPO_list(Unknown_Disease) - -# compare disease similarity -#sim_mat <- get_asym_sim_grid(Unknown_Disease_all$HPO_list, OMIM_HPO_all$HPO_list, ontology=HPO_obo) sim_mat <- get_asym_sim_grid(HPO, OMIM_HPO_all$HPO_list, ontology=HPO_obo) -# OMIM_HPO_all$HPO_term <- HPO_obo$name[OMIM_HPO_all$HPO] OMIM_HPO_all$Similarity_Score <- as.vector(sim_mat) + # convert HPO ID to HPO term OMIM_HPO_all$HPO_term <- unlist(lapply(OMIM_HPO_all$HPO_list, function(x) paste0(HPO_obo$name[unlist(x)], collapse = "|"))) ## Add gene - disease relationship OMIM_HPO_all_wGene <- merge(unique(genemap2[,c("Pheno_ID","Approved_Gene_Symbol","Ensembl_Gene_ID","Entrez_Gene_ID")]), OMIM_HPO_all[,c("OMIM_ID","Disease_Name","Similarity_Score","HPO_term")], by.y = "OMIM_ID", by.x = "Pheno_ID") + colnames(OMIM_HPO_all_wGene)[2] <- 'Gene_Symbol' OMIM_HPO_all_order <-OMIM_HPO_all_wGene[order(OMIM_HPO_all_wGene$Similarity_Score, decreasing = T),] # write.table(OMIM_HPO_all_order, paste0(unknown_disease_path,"HPOsimi_all_",output_file_name), sep = "\t", quote = F, row.names = F) From faf3b95e9e66e02ce7910d4b1c0262ae698cdcab Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 24 May 2024 11:53:37 -0500 Subject: [PATCH 13/71] add PHRANK SCORING and HPO SIM calc. --- main.nf | 26 +++++++++++++++++--------- nextflow.config | 30 +++++++++++++++++------------- scripts/phenoSim.R | 2 +- 3 files changed, 35 insertions(+), 23 deletions(-) diff --git a/main.nf b/main.nf index adeb2ac..d8cb226 100644 --- a/main.nf +++ b/main.nf @@ -137,16 +137,23 @@ process TO_GENE_SYM { process PHRANK_SCORING { + publishDir "${params.outdir}/phrank/", mode: 'copy' + input: path gene path hpo - path ref_dir + path dagfile + path disease_annotation + path gene_annotation + path disease_gene + output: - path "${params.run_id}.phrank.txt.txt" publishDir "${params.outdir}/phrank/", mode: 'copy' + path "${params.run_id}.phrank.txt" script: """ - python3.8 ${workflow.projectDir}/scripts/phrank/src/run_phrank.py $gene $hpo $ref_dir > ${params.run_id}.phrank.txt + python3.8 ${workflow.projectDir}/scripts/phrank/src/run_phrank.py \\ + $gene $hpo $dagfile $disease_annotation $gene_annotation $disease_gene > ${params.run_id}.phrank.txt """ } @@ -178,12 +185,13 @@ workflow { ANNOT_PHRANK(VCF_PRE_PROCESS.out) ANNOT_ENSMBLE(params.ref_dir, ANNOT_PHRANK.out) TO_GENE_SYM(ANNOT_ENSMBLE.out, params.ref_to_sym, params.ref_sorted_sym) - PHRANK_SCORING(TO_GENE_SYM, params.hpo, - params.phrank_dagfile, - params.phrank_disease_annotation, - params.phrank_gene_annotation, - params.phrank_disease_gene) - HPO_SIM(params.hpo, + PHRANK_SCORING( TO_GENE_SYM.out, + params.input_hpo, + params.phrank_dagfile, + params.phrank_disease_annotation, + params.phrank_gene_annotation, + params.phrank_disease_gene) + HPO_SIM(params.input_hpo, params.omim_hgmd_phen, params.omim_obo, params.omim_genemap2, diff --git a/nextflow.config b/nextflow.config index 3f39a08..5bb26b7 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,27 +1,31 @@ params { run_id = 1 - ref_dir = "/home/hwan/AI_MARRVEL/test_ref/grch37_symbol_to_location.txt" - ref_to_sym = "/home/hwan/AI_MARRVEL/test_ref/ensembl_to_symbol.txt" - ref_sorted_sym = "/home/hwan/AI_MARRVEL/test_ref/gene_to_symbol_sorted.txt" + ref_ver = "hg19" + ref_dir = "/home/hwan/aim_data_dependencies_04_24_2024_sasi" input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" + input_hpo = "/home/hwan/AI_MARRVEL/test_data/hpos.txt" outdir = "/home/hwan/AI_MARRVEL/test_output/" - + // for data dependency - chrmap = "/home/hwan/AI_MARRVEL/test_ref/chrmap.txt" + chrmap = "${ref_dir}/data_public/bcf_annotate/chrmap.txt" + + ref_loc = "${ref_dir}/phrank/${ref_ver}/grch37_symbol_to_location.txt" + ref_to_sym = "${ref_dir}/phrank/${ref_ver}/ensembl_to_symbol.txt" + ref_sorted_sym = "${ref_dir}/phrank/${ref_ver}/gene_to_symbol_sorted.txt" // for phrank - phrank_dagfile = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/child_to_parent.txt" - phrank_disease_annotation = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/disease_to_pheno.txt" - phrank_gene_annotation = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/gene_to_phenotype.txt" - phrank_disease_gene = "/home/hwan/AI_MARRVEL/refs/phrank/hg19/disease_to_gene.txt" + phrank_dagfile = "${ref_dir}/phrank/${ref_ver}/child_to_parent.txt" + phrank_disease_annotation = "${ref_dir}/phrank/${ref_ver}/disease_to_pheno.txt" + phrank_gene_annotation = "${ref_dir}/phrank/${ref_ver}/gene_to_phenotype.txt" + phrank_disease_gene = "${ref_dir}/phrank/${ref_ver}/disease_to_gene.txt" // OMIM and HPO - omim_hgmd_phen = "/home/hwan/AI_MARRVEL/refs/omim_annotate/hg19/HGMD_phen.tsv" - omim_obo = "/home/hwan/AI_MARRVEL/refs/omim_annotate/hp.obo" - omim_genemap2 = "/home/hwan/AI_MARRVEL/refs/omim_annotate/hg19/genemap2_v2022.rds" - omim_pheno = "/home/hwan/AI_MARRVEL/refs/omim_annotate/hg19/HPO_OMIM.tsv" + omim_hgmd_phen = "${ref_dir}/omim_annotate/${ref_ver}/HGMD_phen.tsv" + omim_obo = "${ref_dir}/omim_annotate/hp.obo" + omim_genemap2 = "${ref_dir}/omim_annotate/${ref_ver}/genemap2_v2022.rds" + omim_pheno = "${ref_dir}/omim_annotate/${ref_ver}/HPO_OMIM.tsv" } docker.enabled = true diff --git a/scripts/phenoSim.R b/scripts/phenoSim.R index 5fa33e6..298e741 100755 --- a/scripts/phenoSim.R +++ b/scripts/phenoSim.R @@ -64,7 +64,7 @@ write.table(dat2, OUTFILE_CZ_NAME, sep = "\t", quote = F, row.names = F) # Load OMIM gene-disease data -genemap2 <-readRDS() +genemap2 <-readRDS(OMIM_GENEMAP2) ## ---- Load OMIM Phenotype ---- HPO_orig <- read.table(OMIM_PHENO, sep="\t", header= T, stringsAsFactors = FALSE, comment.char = "", fill = TRUE, quote = "\"") From ef5dc974d820987ad4dca132fc9011f8fddb34f5 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 24 May 2024 12:20:22 -0500 Subject: [PATCH 14/71] add GNOMAD references --- nextflow.config | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/nextflow.config b/nextflow.config index 5bb26b7..03c9f02 100644 --- a/nextflow.config +++ b/nextflow.config @@ -3,9 +3,10 @@ params { ref_ver = "hg19" ref_dir = "/home/hwan/aim_data_dependencies_04_24_2024_sasi" - input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" + input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" input_hpo = "/home/hwan/AI_MARRVEL/test_data/hpos.txt" + outdir = "/home/hwan/AI_MARRVEL/test_output/" // for data dependency @@ -26,6 +27,12 @@ params { omim_obo = "${ref_dir}/omim_annotate/hp.obo" omim_genemap2 = "${ref_dir}/omim_annotate/${ref_ver}/genemap2_v2022.rds" omim_pheno = "${ref_dir}/omim_annotate/${ref_ver}/HPO_OMIM.tsv" + + // GNOMAD VCF + ref_gnomad_genome = "${ref_dir}/filter_vep/${ref_ver}/gnomad.${ref_ver}.blacklist.genomes.vcf.gz" + ref_gnomad_genome_idx = "${ref_gnomad_genome}.tbi" + ref_gnomad_exome = "${ref_dir}/filter_vep/${ref_ver}/gnomad.${ref_ver}.blacklist.exomes.vcf.gz" + ref_gnomad_exome_idx = "${ref_gnomad_exome}.tbi" } docker.enabled = true From 3c3686dd76843c620d9bb8e2ef6ef3c60fa0e939 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 24 May 2024 12:20:46 -0500 Subject: [PATCH 15/71] add FILTER_PROBAND --- main.nf | 50 +++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 49 insertions(+), 1 deletion(-) diff --git a/main.nf b/main.nf index d8cb226..aaaf4b6 100644 --- a/main.nf +++ b/main.nf @@ -72,17 +72,22 @@ process VCF_PRE_PROCESS { process REMOVE_MITO_AND_UNKOWN_CHR { + publishDir "${params.outdir}/vcf/", mode: 'copy' input: path vcf output: - path "${params.run_id}.filt.rmMT.vcf" + path "${params.run_id}.filt.rmMT.vcf.gz" + path "${params.run_id}.filt.rmMT.vcf.gz.tbi" script: """ tabix -p vcf $vcf bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y $vcf -o ${params.run_id}.filt.rmMT.vcf + + bgzip ${params.run_id}.filt.rmMT.vcf + tabix ${params.run_id}.filt.rmMT.vcf.gz """ } @@ -178,6 +183,41 @@ process HPO_SIM { } +process { + publishDir "${params.outdir}/vcf/", mode: 'copy' + + input: + path vcf + path tbi + path ref_gnomad_genome + path ref_gnomad_genome_idx + path ref_gnomad_exome + path ref_gnomad_exome_idx + + output: + path "*.filt.rmBL.vcf" + + script: + """ + mkdir -m777 isec_tmp1 + bcftools isec -p isec_tmp1 -w 1 -Oz \\ + $vcf $ref_gnomad_genome + # tabix isec_tmp1/0000.vcf.gz + + mkdir -m777 isec_tmp2 + bcftools isec -p isec_tmp2 -w 1 -Oz \\ + $vcf $ref_gnomad_exome + # tabix isec_tmp2/0000.vcf.gz + + mkdir -m777 isec_tmp3 + bcftools isec -p isec_tmp3 -Ov \\ + isec_tmp1/0000.vcf.gz isec_tmp2/0000.vcf.gz + + mv isec_tmp3/0002.vcf ${params.run_id}.filt.rmBL.vcf + """ + +} + workflow { INDEX_VCF(params.input_vcf) VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) @@ -196,4 +236,12 @@ workflow { params.omim_obo, params.omim_genemap2, params.omim_pheno) + FILTER_PROBAND( + REMOVE_MITO_AND_UNKOWN_CHR.out[0], + REMOVE_MITO_AND_UNKOWN_CHR.out[1], + params.ref_gnomad_genome, + params.ref_gnomad_genome_idx, + params.ref_gnomad_exome, + params.ref_gnomad_exome_idx + ) } \ No newline at end of file From 67cf7c7248abd5878c6b4581af8f3817d8346ba2 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 24 May 2024 15:05:15 -0500 Subject: [PATCH 16/71] add VEP_ANNOTATE --- main.nf | 68 ++++++++++++++++++++++++++++++++++++++++++++++++- nextflow.config | 17 +++++++++++++ 2 files changed, 84 insertions(+), 1 deletion(-) diff --git a/main.nf b/main.nf index aaaf4b6..1c89401 100644 --- a/main.nf +++ b/main.nf @@ -183,7 +183,7 @@ process HPO_SIM { } -process { +process FILTER_PROBAND { publishDir "${params.outdir}/vcf/", mode: 'copy' input: @@ -218,24 +218,75 @@ process { } +process VEP_ANNOTATE { + publishDir "${params.outdir}/vep/", mode: "copy" + + input: + path vcf + path vep_dir_cache + path vep_dir_plugins + path vep_custom_gnomad + path vep_custom_clinvar + path vep_custom_hgmd + path vep_plugin_revel + path vep_plugin_spliceai_snv + path vep_plugin_spliceai_indel + path vep_plugin_cadd + path vep_plugin_dbnsfp + path vep_idx + + output: + path "*-vep.txt" + + script: + def ref_assembly = (params.ref_ver == 'hg38') ? 'GRCh38' : 'GRCh37' + """ + /opt/vep/src/ensembl-vep/vep \\ + --dir_cache ${vep_dir_cache} \\ + --dir_plugins ${vep_dir_plugins} \\ + --fork {params.num_vep_processors} \\ + --everything --format vcf \\ + --cache --offline --tab --force_overwrite \\ + --species homo_sapiens --assembly ${ref_assembly} \\ + --custom ${vep_custom_gnomad},gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomal \\ + --custom ${vep_custom_clinvar},clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \\ + --custom ${vep_custom_hgmd},hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \\ + --af_gnomad \\ + --plugin REVEL,${vep_plugin_revel},ALL \\ + --plugin SpliceAI,snv=${vep_plugin_spliceai_snv},indel=${vep_plugin_spliceai_indel},cutoff=0.5 \\ + --plugin CADD,${vep_plugin_cadd},ALL \\ + --plugin dbNSFP,${vep_plugin_dbnsfp},ALL \\ + --individual all --output_file ${params.run_id}-vep.txt --input_file $vcf + """ +} + + workflow { INDEX_VCF(params.input_vcf) + VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) + REMOVE_MITO_AND_UNKOWN_CHR(VCF_PRE_PROCESS.out) + ANNOT_PHRANK(VCF_PRE_PROCESS.out) + ANNOT_ENSMBLE(params.ref_dir, ANNOT_PHRANK.out) + TO_GENE_SYM(ANNOT_ENSMBLE.out, params.ref_to_sym, params.ref_sorted_sym) + PHRANK_SCORING( TO_GENE_SYM.out, params.input_hpo, params.phrank_dagfile, params.phrank_disease_annotation, params.phrank_gene_annotation, params.phrank_disease_gene) + HPO_SIM(params.input_hpo, params.omim_hgmd_phen, params.omim_obo, params.omim_genemap2, params.omim_pheno) + FILTER_PROBAND( REMOVE_MITO_AND_UNKOWN_CHR.out[0], REMOVE_MITO_AND_UNKOWN_CHR.out[1], @@ -244,4 +295,19 @@ workflow { params.ref_gnomad_exome, params.ref_gnomad_exome_idx ) + + VEP_ANNOTATE( + FILTER_PROBAND.out, + params.vep_dir_cache, + params.vep_dir_plugins, + params.vep_custom_gnomad, + params.vep_custom_clinvar, + params.vep_custom_hgmd, + params.vep_plugin_revel, + params.vep_plugin_spliceai_snv, + params.vep_plugin_spliceai_indel, + params.vep_plugin_cadd, + params.vep_plugin_dbnsfp, + file(params.vep_idx) + ) } \ No newline at end of file diff --git a/nextflow.config b/nextflow.config index 03c9f02..cc7c379 100644 --- a/nextflow.config +++ b/nextflow.config @@ -33,6 +33,23 @@ params { ref_gnomad_genome_idx = "${ref_gnomad_genome}.tbi" ref_gnomad_exome = "${ref_dir}/filter_vep/${ref_ver}/gnomad.${ref_ver}.blacklist.exomes.vcf.gz" ref_gnomad_exome_idx = "${ref_gnomad_exome}.tbi" + + // VEP + vep_num_processors = 16 + vep_ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" + vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" + vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" + vep_dir_cache = "${ref_dir}/vep/${ref_ver}/" + vep_dir_plugins = "${ref_dir}/vep/${ref_ver}/Plugins/" + vep_custom_gnomad = "${ref_dir}/vep/${ref_ver}/${vep_gnomad_name}" // changed for hg38 + vep_custom_clinvar = "${ref_dir}/vep/${ref_ver}/clinvar_20220730.vcf.gz" + vep_custom_hgmd = "${ref_dir}/vep/${ref_ver}/HGMD_Pro_2022.2_${ref_ver}.vcf.gz" + vep_plugin_revel = "${ref_dir}/vep/${ref_ver}/new_tabbed_revel_${vep_ref_assembly}.tsv.gz" // changed for hg19 + vep_plugin_spliceai_snv = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.snv.${ref_ver}.vcf.gz" + vep_plugin_spliceai_indel = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.indel.${ref_ver}.vcf.gz" + vep_plugin_cadd = "${ref_dir}/vep/${ref_ver}/${ref_ver}_whole_genome_SNV.tsv.gz" + vep_plugin_dbnsfp = "${ref_dir}/vep/${ref_ver}/${vep_dbnsfp_name}" + vep_idx = "${ref_dir}/vep/${ref_ver}/*.tbi" } docker.enabled = true From a2d570fbadbcb9cd9b82c290412aed63c41d996a Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 24 May 2024 15:50:12 -0500 Subject: [PATCH 17/71] not sure why is is not working. --- main.nf | 18 ++++++++++++------ nextflow.config | 5 ++--- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/main.nf b/main.nf index 1c89401..5bdd391 100644 --- a/main.nf +++ b/main.nf @@ -219,7 +219,8 @@ process FILTER_PROBAND { } process VEP_ANNOTATE { - publishDir "${params.outdir}/vep/", mode: "copy" + cpus 10 + // publishDir "${params.outdir}/vep/", mode: "copy" input: path vcf @@ -244,10 +245,13 @@ process VEP_ANNOTATE { /opt/vep/src/ensembl-vep/vep \\ --dir_cache ${vep_dir_cache} \\ --dir_plugins ${vep_dir_plugins} \\ - --fork {params.num_vep_processors} \\ - --everything --format vcf \\ - --cache --offline --tab --force_overwrite \\ - --species homo_sapiens --assembly ${ref_assembly} \\ + --fork ${task.cpus} \\ + --everything \\ + --format vcf \\ + -database \\ + --cache \\ + --tab --force_overwrite \\ + species homo_sapiens --assembly ${ref_assembly} \\ --custom ${vep_custom_gnomad},gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomal \\ --custom ${vep_custom_clinvar},clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \\ --custom ${vep_custom_hgmd},hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \\ @@ -256,7 +260,9 @@ process VEP_ANNOTATE { --plugin SpliceAI,snv=${vep_plugin_spliceai_snv},indel=${vep_plugin_spliceai_indel},cutoff=0.5 \\ --plugin CADD,${vep_plugin_cadd},ALL \\ --plugin dbNSFP,${vep_plugin_dbnsfp},ALL \\ - --individual all --output_file ${params.run_id}-vep.txt --input_file $vcf + --individual all --output_file ${params.run_id}-vep.txt --input_file $vcf \\ + --verbose \\ + --show_cache_info """ } diff --git a/nextflow.config b/nextflow.config index cc7c379..ec623e1 100644 --- a/nextflow.config +++ b/nextflow.config @@ -35,19 +35,18 @@ params { ref_gnomad_exome_idx = "${ref_gnomad_exome}.tbi" // VEP - vep_num_processors = 16 vep_ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" vep_dir_cache = "${ref_dir}/vep/${ref_ver}/" vep_dir_plugins = "${ref_dir}/vep/${ref_ver}/Plugins/" - vep_custom_gnomad = "${ref_dir}/vep/${ref_ver}/${vep_gnomad_name}" // changed for hg38 + vep_custom_gnomad = "${ref_dir}/vep/${ref_ver}/${vep_gnomad_name}" vep_custom_clinvar = "${ref_dir}/vep/${ref_ver}/clinvar_20220730.vcf.gz" vep_custom_hgmd = "${ref_dir}/vep/${ref_ver}/HGMD_Pro_2022.2_${ref_ver}.vcf.gz" vep_plugin_revel = "${ref_dir}/vep/${ref_ver}/new_tabbed_revel_${vep_ref_assembly}.tsv.gz" // changed for hg19 vep_plugin_spliceai_snv = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.snv.${ref_ver}.vcf.gz" vep_plugin_spliceai_indel = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.indel.${ref_ver}.vcf.gz" - vep_plugin_cadd = "${ref_dir}/vep/${ref_ver}/${ref_ver}_whole_genome_SNV.tsv.gz" + vep_plugin_cadd = "${ref_dir}/vep/${ref_ver}/${ref_ver}_whole_genome_SNV.tsv.gz" // changed for hg19 vep_plugin_dbnsfp = "${ref_dir}/vep/${ref_ver}/${vep_dbnsfp_name}" vep_idx = "${ref_dir}/vep/${ref_ver}/*.tbi" } From 840c5776c5c6a1a71e241df5c388c9887e7af8a8 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 24 May 2024 17:19:16 -0500 Subject: [PATCH 18/71] it was a problem for parameters. --- main.nf | 17 +++++------------ nextflow.config | 4 +++- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/main.nf b/main.nf index 5bdd391..b310a2f 100644 --- a/main.nf +++ b/main.nf @@ -245,24 +245,17 @@ process VEP_ANNOTATE { /opt/vep/src/ensembl-vep/vep \\ --dir_cache ${vep_dir_cache} \\ --dir_plugins ${vep_dir_plugins} \\ - --fork ${task.cpus} \\ - --everything \\ - --format vcf \\ - -database \\ - --cache \\ - --tab --force_overwrite \\ - species homo_sapiens --assembly ${ref_assembly} \\ + --fork ${task.cpus} --everything --format vcf \\ + --cache --offline --tab --force_overwrite \\ + --species homo_sapiens --assembly ${ref_assembly} \\ --custom ${vep_custom_gnomad},gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomal \\ --custom ${vep_custom_clinvar},clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \\ --custom ${vep_custom_hgmd},hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \\ - --af_gnomad \\ - --plugin REVEL,${vep_plugin_revel},ALL \\ + --af_gnomad --plugin REVEL,${vep_plugin_revel},ALL \\ --plugin SpliceAI,snv=${vep_plugin_spliceai_snv},indel=${vep_plugin_spliceai_indel},cutoff=0.5 \\ --plugin CADD,${vep_plugin_cadd},ALL \\ --plugin dbNSFP,${vep_plugin_dbnsfp},ALL \\ - --individual all --output_file ${params.run_id}-vep.txt --input_file $vcf \\ - --verbose \\ - --show_cache_info + --individual all --output_file ${params.run_id}-vep.txt --input_file $vcf """ } diff --git a/nextflow.config b/nextflow.config index ec623e1..e0a6940 100644 --- a/nextflow.config +++ b/nextflow.config @@ -38,6 +38,8 @@ params { vep_ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" + vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" + vep_dir_cache = "${ref_dir}/vep/${ref_ver}/" vep_dir_plugins = "${ref_dir}/vep/${ref_ver}/Plugins/" vep_custom_gnomad = "${ref_dir}/vep/${ref_ver}/${vep_gnomad_name}" @@ -46,7 +48,7 @@ params { vep_plugin_revel = "${ref_dir}/vep/${ref_ver}/new_tabbed_revel_${vep_ref_assembly}.tsv.gz" // changed for hg19 vep_plugin_spliceai_snv = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.snv.${ref_ver}.vcf.gz" vep_plugin_spliceai_indel = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.indel.${ref_ver}.vcf.gz" - vep_plugin_cadd = "${ref_dir}/vep/${ref_ver}/${ref_ver}_whole_genome_SNV.tsv.gz" // changed for hg19 + vep_plugin_cadd = "${ref_dir}/vep/${ref_ver}/${vep_cadd_name}" // changed for hg19 vep_plugin_dbnsfp = "${ref_dir}/vep/${ref_ver}/${vep_dbnsfp_name}" vep_idx = "${ref_dir}/vep/${ref_ver}/*.tbi" } From b638076ddac0eddb5c53381d5fd0a83b98fe56e3 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 24 May 2024 18:56:00 -0500 Subject: [PATCH 19/71] let's wait until I get something. --- main.nf | 58 ++++++++++++++++++++++++++++++++++++-- nextflow.config | 5 ++++ scripts/annotation/main.py | 45 ++++++----------------------- scripts/split_chunks.py | 10 +++---- 4 files changed, 75 insertions(+), 43 deletions(-) diff --git a/main.nf b/main.nf index b310a2f..02347fa 100644 --- a/main.nf +++ b/main.nf @@ -220,7 +220,7 @@ process FILTER_PROBAND { process VEP_ANNOTATE { cpus 10 - // publishDir "${params.outdir}/vep/", mode: "copy" + publishDir "${params.outdir}/vep/", mode: "copy" input: path vcf @@ -259,6 +259,51 @@ process VEP_ANNOTATE { """ } +process FEATURE_ENGINEERING { + input: + path vep + path omim_sim + path hgmd_sim + path ref_annot_dir + // not sure why projectDir is not working + path script_chunking + path script_annot + + output: + path "*_scores_*.txt" + + script: + """ + #AIM_FREE_RAM=\$(free -g | awk 'NR==2{printf \$7}') + + python3.8 $script_chunking $vep 256 + + while read -r INDEX LINEH LINEA LINEB + do + sed -n -e "\${LINEH}p" -e "\${LINEA},\${LINEB}p" $vep > vep-\${INDEX}.txt + + python3.8 main.py \\ + -outPrefix r1 \\ + -patientID $vep \\ + -patientHPOsimiOMIM $omim_sim \\ + -patientHPOsimiHGMD $hgmd_sim \\ + -varFile vep-\${INDEX}.txt \\ + -inFileType vepAnnotTab \\ + -patientFileType one \\ + -genomeRef ${params.ref_ver} \\ + -diseaseInh AD \\ + -modules curate,conserve + + if [ \$INDEX -gt 1 ]; then + sed -n "2,\$p" scores.csv > scores_\$INDEX.csv + sed -n "2,\$p" r1_scores.txt > r1_scores_\$INDEX.txt + else + mv scores.csv scores_\$INDEX.csv + mv r1_scores.csv r1_scores\$INDEX.csv + fi + done < vep_split.txt + """ +} workflow { INDEX_VCF(params.input_vcf) @@ -309,4 +354,13 @@ workflow { params.vep_plugin_dbnsfp, file(params.vep_idx) ) -} \ No newline at end of file + + FEATURE_ENGINEERING( + VEP_ANNOTATE.out, + HPO_SIM.out[0], + HPO_SIM.out[1], + params.ref_annot_dir, + params.script_chunking, + file(params.script_annot) + ) +} diff --git a/nextflow.config b/nextflow.config index e0a6940..c5b56fe 100644 --- a/nextflow.config +++ b/nextflow.config @@ -51,6 +51,11 @@ params { vep_plugin_cadd = "${ref_dir}/vep/${ref_ver}/${vep_cadd_name}" // changed for hg19 vep_plugin_dbnsfp = "${ref_dir}/vep/${ref_ver}/${vep_dbnsfp_name}" vep_idx = "${ref_dir}/vep/${ref_ver}/*.tbi" + + script_chunking = "${projectDir}/scripts/split_chunks.py" + script_annot = "${projectDir}/scripts/annotation/*.py" + + ref_annot_dir = "${ref_dir}/annotate" } docker.enabled = true diff --git a/scripts/annotation/main.py b/scripts/annotation/main.py index c167909..4f5b653 100644 --- a/scripts/annotation/main.py +++ b/scripts/annotation/main.py @@ -81,7 +81,7 @@ def main(): #need to write user options to a log file #read the gnomad gene metrics file - fileName='/run/data_dependencies/annotate/anno_hg19/gnomad.v2.1.1.lof_metrics.by_gene.txt' + fileName='annotate/anno_hg19/gnomad.v2.1.1.lof_metrics.by_gene.txt' #fileName='/database_test/gnomad.v2.1.1.lof_metrics.by_gene.txt' gnomadMetricsGeneDf=pd.read_csv(fileName, sep='\t') #print('gnomadMetricsGeneDf shape:', gnomadMetricsGeneDf.shape) @@ -184,9 +184,9 @@ def main(): # Q: Why we use the same for hg19 and hg38? # A: if args.genomeRef == 'hg38': - fileName='/run/data_dependencies/annotate/anno_hg19/gene_clinvar.csv' + fileName='annotate/anno_hg19/gene_clinvar.csv' else: - fileName='/run/data_dependencies/annotate/anno_hg19/gene_clinvar.csv' + fileName='annotate/anno_hg19/gene_clinvar.csv' #fileName='/database_test/gene_clinvar.csv' #print('\nfileName:', fileName) clinvarGeneDf=pd.read_csv(fileName, sep=',') @@ -195,38 +195,11 @@ def main(): clinvarGeneDf.set_index(['symbol'], inplace = True,drop=False) #print('clinvarGenes dim:', clinvarGeneDf.shape)#41754 x 10,, entrezId,symbol,chr,hg19Start,hg19Stop,totalClinvarVars,P,LP,LB,B #print(clinvarGeneDf.head()) - - ### CL: this part was substituted by VEP custom annotation from clinvar.vcf.gz - """ - #read the clinvar variant file - fileName='/run/annotation/clinvar.csv' - print('\nfileName:', fileName) - clinvarAlleleDf=pd.read_csv(fileName, sep=',') - print('clinvarAllele dim:', clinvarAlleleDf.shape)# (507196, 5) title chr start stop significance.description - - #convert float to int - clinvarAlleleDf = clinvarAlleleDf.fillna(0) - clinvarAlleleDf['start'] = clinvarAlleleDf['start'].astype(int) - clinvarAlleleDf['stop'] = clinvarAlleleDf['stop'].astype(int) - #in chr column change X and Y to numbers - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('X',23) - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('Y',24) - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('MT',25) - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].replace('GL.*','26',regex=True ) - #make chr as int - clinvarAlleleDf['chr']=clinvarAlleleDf['chr'].astype(int) - #make index - clinvarAlleleDf['id1']=clinvarAlleleDf['chr'].astype(str)+ '_' +clinvarAlleleDf['start'].astype(str)+'_'+ clinvarAlleleDf['stop'].astype(str) - # setting first name as index column - clinvarAlleleDf.set_index("id1", inplace = True,drop=False) - #make sure start and stop are int - print('type chr:', type(clinvarAlleleDf['chr'][0]), 'start:', type(clinvarAlleleDf['start'][0]), 'stop:', type(clinvarAlleleDf['stop'][0])) - """ #read OMIM if 'curate' in moduleList: #read the OMIM gene file - fileName='/run/data_dependencies/annotate/anno_hg19/gene_omim.json' + fileName='annotate/anno_hg19/gene_omim.json' #fileName='/database_test/gene_omim.json' #print('\nfileName:', fileName) with open(fileName) as f: @@ -250,7 +223,7 @@ def main(): break #read the OMIM allele file - fileName='/run/data_dependencies/annotate/anno_hg19/omim_alleric_variants.json' + fileName='annotate/anno_hg19/omim_alleric_variants.json' #fileName='/database_test/omim_alleric_variants.json' #print('\nfileName:', fileName) with open(fileName) as f: @@ -270,9 +243,9 @@ def main(): if 'conserve' in moduleList: print('reading DGV flat file') if args.genomeRef == 'hg38': - fileName='/run/data_dependencies/annotate/anno_hg38/dgv.csv' + fileName='annotate/anno_hg38/dgv.csv' else: - fileName='/run/data_dependencies/annotate/anno_hg19/dgv.csv' + fileName='annotate/anno_hg19/dgv.csv' #fileName='/database_test/dgv.csv' dgvDf=pd.read_csv(fileName, sep=',') #print('dgvDf shape:', dgvDf.shape) @@ -310,9 +283,9 @@ def main(): if 'conserve' in moduleList: print('reading Decipher flat file') if args.genomeRef == 'hg38': - fileName='/run/data_dependencies/annotate/anno_hg38/decipher.csv' + fileName='annotate/anno_hg38/decipher.csv' else: - fileName='/run/data_dependencies/annotate/anno_hg19/decipher.csv' + fileName='annotate/anno_hg19/decipher.csv' #fileName='/database_test/decipher.csv' decipherDf=pd.read_csv(fileName, sep=',') #print('decipherDf shape:', decipherDf.shape) diff --git a/scripts/split_chunks.py b/scripts/split_chunks.py index 0837bb8..162fed5 100644 --- a/scripts/split_chunks.py +++ b/scripts/split_chunks.py @@ -3,12 +3,12 @@ import sys import subprocess -patientID = sys.argv[1] +VEP_FILE = sys.argv[1] RAMlimit = float(sys.argv[2]) #numHeaderSkip=354#this depends on the type of annotation used numHeaderSkip=0 -with open(f"/out/{patientID}-vep.txt",'r') as F: +with open(VEP_FILE,'r') as F: for line in F: if line.startswith('##'): numHeaderSkip += 1 @@ -16,10 +16,10 @@ break # 06/30/2023: Process the vep annotation result chunk by chunk -command = f'wc -l /out/{patientID}-vep.txt | cut -d" " -f1' +command = f'wc -l {VEP_FILE} | cut -d" " -f1' nlines = int(subprocess.check_output(command, shell=True, text=True).strip()) - numHeaderSkip - 1 -command = f"du /out/{patientID}-vep.txt" +command = f"du {VEP_FILE}" filesize = int(subprocess.check_output(command, shell=True, text=True).strip().split('\t')[0]) num_chunk = math.ceil( (filesize/1024) / (RAMlimit * 1024 / 23) ) @@ -34,7 +34,7 @@ lines.append(f"{num_chunk}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + (num_chunk-1)*chunk_lines)}\t{int(nlines+numHeaderSkip+1)}") -vep_split = open("/out/vep_split.txt", 'w') +vep_split = open("vep_split.txt", 'w') vep_split.write('\n'.join(lines)) vep_split.write('\n') vep_split.close() From 036a3cfb1df78110905bc22569b76bae24524498 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Tue, 28 May 2024 12:37:25 -0500 Subject: [PATCH 20/71] fix a typo --- main.nf | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/main.nf b/main.nf index 02347fa..a7f97e9 100644 --- a/main.nf +++ b/main.nf @@ -248,7 +248,7 @@ process VEP_ANNOTATE { --fork ${task.cpus} --everything --format vcf \\ --cache --offline --tab --force_overwrite \\ --species homo_sapiens --assembly ${ref_assembly} \\ - --custom ${vep_custom_gnomad},gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomal \\ + --custom ${vep_custom_gnomad},gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \\ --custom ${vep_custom_clinvar},clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \\ --custom ${vep_custom_hgmd},hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \\ --af_gnomad --plugin REVEL,${vep_plugin_revel},ALL \\ @@ -260,6 +260,8 @@ process VEP_ANNOTATE { } process FEATURE_ENGINEERING { + cpus 10 + memory '64 GB' input: path vep path omim_sim From 0ef764756d7d8e9abfaec225c61a3b6eff501f6a Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Tue, 28 May 2024 17:27:03 -0500 Subject: [PATCH 21/71] fixed another typo --- main.nf | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/main.nf b/main.nf index a7f97e9..dcd5aa6 100644 --- a/main.nf +++ b/main.nf @@ -272,7 +272,7 @@ process FEATURE_ENGINEERING { path script_annot output: - path "*_scores_*.txt" + path "*_scores.txt" script: """ @@ -296,14 +296,25 @@ process FEATURE_ENGINEERING { -diseaseInh AD \\ -modules curate,conserve - if [ \$INDEX -gt 1 ]; then - sed -n "2,\$p" scores.csv > scores_\$INDEX.csv - sed -n "2,\$p" r1_scores.txt > r1_scores_\$INDEX.txt + if [ \${INDEX} -gt 1 ]; then + sed -n "2,\$p" scores.csv > scores_\${INDEX}.csv + sed -n "2,\$p" r1_scores.txt > r1_scores_\${INDEX}.txt else - mv scores.csv scores_\$INDEX.csv - mv r1_scores.csv r1_scores\$INDEX.csv + mv scores.csv scores_\${INDEX}.csv + mv r1_scores.csv r1_scores\${INDEX}.csv fi done < vep_split.txt + + + for INDEX in \$(cut -d\$'\t' -f1 vep_split.txt) + do + cat scores_\${INDEX}.csv + done > scores.csv + + for INDEX in $(cut -d$'\t' -f1 vep_split.txt) + do + cat r1_scores_\${INDEX}.txt + done > r1_scores.txt """ } From 09032c1c83eaf5491e5b468f8da69c8897e0fd42 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Tue, 28 May 2024 23:43:35 -0500 Subject: [PATCH 22/71] found the bug, but haven't compared the output and the other parts. --- main.nf | 13 ++++++------- nextflow.config | 6 +++--- scripts/annotation/main.py | 8 ++++---- 3 files changed, 13 insertions(+), 14 deletions(-) diff --git a/main.nf b/main.nf index dcd5aa6..2af5018 100644 --- a/main.nf +++ b/main.nf @@ -109,8 +109,8 @@ process ANNOT_PHRANK { process ANNOT_ENSMBLE { input: - path ref path vcf + path ref output: path "*-ensmbl.txt" @@ -264,8 +264,8 @@ process FEATURE_ENGINEERING { memory '64 GB' input: path vep - path omim_sim path hgmd_sim + path omim_sim path ref_annot_dir // not sure why projectDir is not working path script_chunking @@ -286,7 +286,6 @@ process FEATURE_ENGINEERING { python3.8 main.py \\ -outPrefix r1 \\ - -patientID $vep \\ -patientHPOsimiOMIM $omim_sim \\ -patientHPOsimiHGMD $hgmd_sim \\ -varFile vep-\${INDEX}.txt \\ @@ -301,17 +300,17 @@ process FEATURE_ENGINEERING { sed -n "2,\$p" r1_scores.txt > r1_scores_\${INDEX}.txt else mv scores.csv scores_\${INDEX}.csv - mv r1_scores.csv r1_scores\${INDEX}.csv + mv r1_scores.txt r1_scores_\${INDEX}.txt fi done < vep_split.txt - for INDEX in \$(cut -d\$'\t' -f1 vep_split.txt) + for INDEX in \$(cut -d\$'\\t' -f1 vep_split.txt) do cat scores_\${INDEX}.csv done > scores.csv - for INDEX in $(cut -d$'\t' -f1 vep_split.txt) + for INDEX in \$(cut -d\$'\\t' -f1 vep_split.txt) do cat r1_scores_\${INDEX}.txt done > r1_scores.txt @@ -327,7 +326,7 @@ workflow { ANNOT_PHRANK(VCF_PRE_PROCESS.out) - ANNOT_ENSMBLE(params.ref_dir, ANNOT_PHRANK.out) + ANNOT_ENSMBLE(ANNOT_PHRANK.out, params.ref_loc) TO_GENE_SYM(ANNOT_ENSMBLE.out, params.ref_to_sym, params.ref_sorted_sym) diff --git a/nextflow.config b/nextflow.config index c5b56fe..eb74829 100644 --- a/nextflow.config +++ b/nextflow.config @@ -2,6 +2,7 @@ params { run_id = 1 ref_ver = "hg19" + ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" ref_dir = "/home/hwan/aim_data_dependencies_04_24_2024_sasi" input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" @@ -12,7 +13,7 @@ params { // for data dependency chrmap = "${ref_dir}/data_public/bcf_annotate/chrmap.txt" - ref_loc = "${ref_dir}/phrank/${ref_ver}/grch37_symbol_to_location.txt" + ref_loc = "${ref_dir}/phrank/${ref_ver}/${ref_assembly}_symbol_to_location.txt" ref_to_sym = "${ref_dir}/phrank/${ref_ver}/ensembl_to_symbol.txt" ref_sorted_sym = "${ref_dir}/phrank/${ref_ver}/gene_to_symbol_sorted.txt" @@ -35,7 +36,6 @@ params { ref_gnomad_exome_idx = "${ref_gnomad_exome}.tbi" // VEP - vep_ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" @@ -45,7 +45,7 @@ params { vep_custom_gnomad = "${ref_dir}/vep/${ref_ver}/${vep_gnomad_name}" vep_custom_clinvar = "${ref_dir}/vep/${ref_ver}/clinvar_20220730.vcf.gz" vep_custom_hgmd = "${ref_dir}/vep/${ref_ver}/HGMD_Pro_2022.2_${ref_ver}.vcf.gz" - vep_plugin_revel = "${ref_dir}/vep/${ref_ver}/new_tabbed_revel_${vep_ref_assembly}.tsv.gz" // changed for hg19 + vep_plugin_revel = "${ref_dir}/vep/${ref_ver}/new_tabbed_revel_${ref_assembly}.tsv.gz" // changed for hg19 vep_plugin_spliceai_snv = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.snv.${ref_ver}.vcf.gz" vep_plugin_spliceai_indel = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.indel.${ref_ver}.vcf.gz" vep_plugin_cadd = "${ref_dir}/vep/${ref_ver}/${vep_cadd_name}" // changed for hg19 diff --git a/scripts/annotation/main.py b/scripts/annotation/main.py index 4f5b653..0216b0e 100644 --- a/scripts/annotation/main.py +++ b/scripts/annotation/main.py @@ -58,7 +58,6 @@ def main(): parser.add_argument("-inFileType", "--inFileType", help="Proivde type of input file:vcf, vepAnnotTab") parser.add_argument("-patientFile", "--patientFile", help="Proivde HPO IDs")#currently not used parser.add_argument("-patientFileType", "--patientFileType", help="Proivde type of file:one, two")#currently not used - parser.add_argument("-patientID", "--patientID", help="Proivde patientID") parser.add_argument("-patientHPOsimiOMIM", "--patientHPOsimiOMIM", help="Proivde patient HPO similarity file-OMIM") parser.add_argument("-patientHPOsimiHGMD", "--patientHPOsimiHGMD", help="Proivde patient HPO similarity file-HGMD") parser.add_argument("-diseaseInh", "--diseaseInh", help="Proivde disease Inheritance:AD, AR, XD, XR") @@ -423,7 +422,7 @@ def main(): df = pd.DataFrame([t.__dict__ for t in varObjList ]) print('shape of output file:',df.shape) #write - fileName=args.outPrefix+'_'+args.patientID+'_scores.txt' + fileName=args.outPrefix+'_scores.txt' print('out file name:', fileName) df.to_csv(fileName, sep='\t', index=False) @@ -431,7 +430,7 @@ def main(): process_time = end_time - start_time print('pipeline time:', process_time) #log file for times - fileName=args.outPrefix+'_'+args.patientID+'_log.txt' + fileName=args.outPrefix+'_log.txt' f = open(fileName, "w") f.write("Process time:"+str(process_time)+ ' seconds and in mins:'+ str(process_time/60)+"\n") f.close() @@ -449,7 +448,8 @@ def main(): score = clinvarCurate(score) score = conservationCurate(score) - score.to_csv('/out/rami-test/%s_scores.csv'%(args.patientID), index=False) + # score.to_csv('/out/rami-test/%s_scores.csv'%(args.patientID), index=False) + score.to_csv('scores.csv', index=False) exit() From 66e6c82425fba9905de129712d5e8f711b4ca885 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Wed, 29 May 2024 09:52:52 -0500 Subject: [PATCH 23/71] change the cache mode to lenient. --- nextflow.config | 1 + 1 file changed, 1 insertion(+) diff --git a/nextflow.config b/nextflow.config index eb74829..8584490 100644 --- a/nextflow.config +++ b/nextflow.config @@ -62,4 +62,5 @@ docker.enabled = true process { container = "chaozhongliu/aim-lite:latest" + cache = "lenient" } \ No newline at end of file From db05985a997bf96a44cbccde71b09c853ef3298b Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Wed, 29 May 2024 09:53:26 -0500 Subject: [PATCH 24/71] change all wildcard to params.run_id --- main.nf | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/main.nf b/main.nf index 2af5018..a12eb36 100644 --- a/main.nf +++ b/main.nf @@ -97,7 +97,7 @@ process ANNOT_PHRANK { path vcf output: - path "*-var-filt.txt" + path "${params.run_id}-var-filt.txt" script: """ @@ -113,7 +113,7 @@ process ANNOT_ENSMBLE { path ref output: - path "*-ensmbl.txt" + path "${params.run_id}-ensmbl.txt" script: """ @@ -131,7 +131,7 @@ process TO_GENE_SYM { path ref_sorted_sym output: - path "*-gene.txt" + path "${params.run_id}-gene.txt" script: """ @@ -172,8 +172,8 @@ process HPO_SIM { path omim_pheno output: - path "*-cz" - path "*-dx" + path "${params.run_id}-cz" + path "${params.run_id}-dx" script: """ @@ -195,7 +195,7 @@ process FILTER_PROBAND { path ref_gnomad_exome_idx output: - path "*.filt.rmBL.vcf" + path "${params.run_id}.filt.rmBL.vcf" script: """ @@ -237,7 +237,7 @@ process VEP_ANNOTATE { path vep_idx output: - path "*-vep.txt" + path "${params.run_id}-vep.txt" script: def ref_assembly = (params.ref_ver == 'hg38') ? 'GRCh38' : 'GRCh37' From b26768969f239ac0b11142c71e540e0b98257281 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Wed, 29 May 2024 15:42:32 -0500 Subject: [PATCH 25/71] added prediction, need to validate. --- {scripts => bin}/VarTierDiseaseDBFalse.R | 19 ++--- {scripts => bin}/add_c_nc.py | 16 ++-- .../annotation/marrvel_score_recalc.py | 0 {scripts => bin}/annotation/utils_1.py | 0 .../annotation/utils_for_marrvel_flatfile.py | 4 +- .../utils_for_marrvel_flatfile_module_2.py | 2 +- .../annotation/utils_for_symMatch.py | 2 +- {scripts => bin}/extraModel/__init__.py | 0 {scripts => bin}/extraModel/confidence.py | 0 .../extraModel/generate_bivar_data.py | 4 +- .../extraModel/integrate_output.py | 2 +- .../main.py => bin/extraModel_main.py | 25 +++--- scripts/annotation/main.py => bin/feature.py | 9 ++- {scripts => bin}/fillna_tier.py | 2 +- {scripts => bin}/generate_new_matrix_2.py | 23 +++--- {scripts => bin}/keepna_tier.py | 0 {scripts => bin}/merge_rm.py | 8 +- {scripts => bin}/mod5_diffusion.py | 5 +- {scripts => bin}/predict_new/confidence.py | 0 {scripts => bin}/predict_new/features.csv | 0 {scripts => bin}/predict_new/main.py | 0 {scripts => bin}/predict_new/test.csv | 0 {scripts => bin}/predict_new/utilities.py | 2 +- bin/run_final.py | 22 ++++++ {scripts => bin}/simple_repeat_anno.py | 8 +- .../split_vep_chunks.py | 2 + main.nf | 78 ++++++++++++++++--- nextflow.config | 5 ++ scripts/predict_new/.py | 14 ---- scripts/predict_new/causal_distr.py | 14 ---- scripts/predict_new/causal_distr.txt | 74 ------------------ scripts/predict_new/run_final.py | 21 ----- 32 files changed, 162 insertions(+), 199 deletions(-) rename {scripts => bin}/VarTierDiseaseDBFalse.R (95%) mode change 100644 => 100755 rename {scripts => bin}/add_c_nc.py (70%) rename {scripts => bin}/annotation/marrvel_score_recalc.py (100%) rename {scripts => bin}/annotation/utils_1.py (100%) rename {scripts => bin}/annotation/utils_for_marrvel_flatfile.py (99%) rename {scripts => bin}/annotation/utils_for_marrvel_flatfile_module_2.py (99%) rename {scripts => bin}/annotation/utils_for_symMatch.py (99%) rename {scripts => bin}/extraModel/__init__.py (100%) rename {scripts => bin}/extraModel/confidence.py (100%) rename {scripts => bin}/extraModel/generate_bivar_data.py (98%) rename {scripts => bin}/extraModel/integrate_output.py (99%) rename scripts/extraModel/main.py => bin/extraModel_main.py (84%) rename scripts/annotation/main.py => bin/feature.py (98%) mode change 100644 => 100755 rename {scripts => bin}/fillna_tier.py (99%) rename {scripts => bin}/generate_new_matrix_2.py (66%) rename {scripts => bin}/keepna_tier.py (100%) rename {scripts => bin}/merge_rm.py (63%) rename {scripts => bin}/mod5_diffusion.py (97%) rename {scripts => bin}/predict_new/confidence.py (100%) rename {scripts => bin}/predict_new/features.csv (100%) rename {scripts => bin}/predict_new/main.py (100%) rename {scripts => bin}/predict_new/test.csv (100%) rename {scripts => bin}/predict_new/utilities.py (99%) create mode 100755 bin/run_final.py rename {scripts => bin}/simple_repeat_anno.py (72%) rename scripts/split_chunks.py => bin/split_vep_chunks.py (97%) mode change 100644 => 100755 delete mode 100755 scripts/predict_new/.py delete mode 100755 scripts/predict_new/causal_distr.py delete mode 100755 scripts/predict_new/causal_distr.txt delete mode 100755 scripts/predict_new/run_final.py diff --git a/scripts/VarTierDiseaseDBFalse.R b/bin/VarTierDiseaseDBFalse.R old mode 100644 new mode 100755 similarity index 95% rename from scripts/VarTierDiseaseDBFalse.R rename to bin/VarTierDiseaseDBFalse.R index e53439c..eb8b7d2 --- a/scripts/VarTierDiseaseDBFalse.R +++ b/bin/VarTierDiseaseDBFalse.R @@ -1,3 +1,4 @@ +#!/usr/bin/env Rscript # Run Tier # Updates history #### @@ -33,29 +34,25 @@ IncludeClinVarHGMD <- FALSE # load database: OMIM inheritance #### # genemap2.Inh.F <- readRDS(file.path("/run/genemap2.Inh.F.rds")) -#genemap2.Inh.F <- read.delim2(file.path("/run/data_dependencies/var_tier/hg19/genemap2.Inh.F.txt")) +#genemap2.Inh.F <- read.delim2(file.path("var_tier/hg19/genemap2.Inh.F.txt")) # genemap2.Inh.F <- read.delim2("/houston_10t/dongxue/MARRVEL_AI/resource/OMIM/out/genemap2.Inh.F.txt") args = commandArgs(trailingOnly=TRUE) -refg <- args[2] +refg <- args[1] # load database: OMIM inheritance #### # genemap2.Inh.F <- readRDS(file.path("/run/genemap2.Inh.F.rds")) -genemap2.Inh.F <- read.delim2(file.path(paste0('/run/data_dependencies/var_tier/',refg,'/genemap2.Inh.F.txt'))) +genemap2.Inh.F <- read.delim2(file.path(paste0('var_tier/',refg,'/genemap2.Inh.F.txt'))) # set the input output file / directory #### # in.f.path <- "/out/input/" -in.f.path <- "/out/rami-test/" -out.f.path <- "/out/tier-test-false/" # When test the code without docker #in.f.path <- out.f.path <- "/out/" -# in.f <- paste0(args[1],"_Tier") -# in.f <- paste0(args[1],"_scores.csv") -in.f <- paste0(args[1],"_scores.csv") -out.f <- paste0(args[1],"_Tier.v2.tsv") +in.f <- "scores.csv" +out.f <-"Tier.v2.tsv" # load libraries #### library("data.table") @@ -67,7 +64,7 @@ library("dplyr") anno.columns <- c('varId_dash','zyg','geneSymbol','geneEnsId','gnomadAF','gnomadAFg','omimSymptomSimScore','hgmdSymptomSimScore', 'IMPACT','Consequence','hgmdVarFound','clinvarSignDesc','spliceAImax') -anno.orig <- read.table(file.path(in.f.path,in.f), stringsAsFactors = F, sep = ",", header = T) +anno.orig <- read.table(in.f, stringsAsFactors = F, sep = ",", header = T) # To test: # score.path <- "/houston_20t/chaozhong/MARRVEL_AI_2022/explain/906246_raw.csv" # score.path <- "/houston_20t/chaozhong/MARRVEL_AI_2022/opt_09302022/output/rami-test/906246_scores.csv" @@ -371,7 +368,7 @@ VEP.Tier.wInh$recessive[is.na(VEP.Tier.wInh$recessive)] <- 0 VEP.Tier.wInh$AD.matched <- ifelse(VEP.Tier.wInh$TierAD <=2 & (VEP.Tier.wInh$dominant == 1),1,0) VEP.Tier.wInh$AR.matched <- ifelse(VEP.Tier.wInh$TierAR <=2 & (VEP.Tier.wInh$recessive == 1),1,0) -write.table(VEP.Tier.wInh,file.path(out.f.path,out.f), sep = "\t", quote = F, col.names = T, row.names = F) +write.table(VEP.Tier.wInh,out.f, sep = "\t", quote = F, col.names = T, row.names = F) # write.table(VEP.Tier.wInh,"/houston_20t/dongxue/MARRVEL_AI/V2/FeatureEng/TestOut/906246_Tier.tsv", sep = "\t", quote = F, col.names = T, row.names = F) # write.table(VEP.Tier.wInh,"/houston_30t/dongxue/AI/UDN_Sample_2023/ModifiedAIMCode/NDVG_causal_Tier.v2.tsv", sep = "\t", quote = F, col.names = T, row.names = F) diff --git a/scripts/add_c_nc.py b/bin/add_c_nc.py similarity index 70% rename from scripts/add_c_nc.py rename to bin/add_c_nc.py index dc43bd8..c526721 100755 --- a/scripts/add_c_nc.py +++ b/bin/add_c_nc.py @@ -9,15 +9,15 @@ def add_c_nc(score, ref): #read data files if ref == 'hg38': - clin_c=pd.read_csv("/run/data_dependencies/merge_expand/hg38/clin_c.tsv.gz", sep="\t") - clin_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg38/clin_nc.tsv.gz", sep="\t") - hgmd_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t") - hgmd_c=pd.read_csv("/run/data_dependencies/merge_expand/hg38/hgmd_c.tsv.gz", sep="\t") + clin_c=pd.read_csv("merge_expand/hg38/clin_c.tsv.gz", sep="\t") + clin_nc=pd.read_csv("merge_expand/hg38/clin_nc.tsv.gz", sep="\t") + hgmd_nc=pd.read_csv("merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t") + hgmd_c=pd.read_csv("merge_expand/hg38/hgmd_c.tsv.gz", sep="\t") else: - clin_c=pd.read_csv("/run/data_dependencies/merge_expand/hg19/clin_c.tsv.gz", sep="\t") - clin_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg19/clin_nc.tsv.gz", sep="\t") - hgmd_nc=pd.read_csv("/run/data_dependencies/merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t") - hgmd_c=pd.read_csv("/run/data_dependencies/merge_expand/hg19/hgmd_c.tsv.gz", sep="\t") + clin_c=pd.read_csv("merge_expand/hg19/clin_c.tsv.gz", sep="\t") + clin_nc=pd.read_csv("merge_expand/hg19/clin_nc.tsv.gz", sep="\t") + hgmd_nc=pd.read_csv("merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t") + hgmd_c=pd.read_csv("merge_expand/hg19/hgmd_c.tsv.gz", sep="\t") a = score.pos.values diff --git a/scripts/annotation/marrvel_score_recalc.py b/bin/annotation/marrvel_score_recalc.py similarity index 100% rename from scripts/annotation/marrvel_score_recalc.py rename to bin/annotation/marrvel_score_recalc.py diff --git a/scripts/annotation/utils_1.py b/bin/annotation/utils_1.py similarity index 100% rename from scripts/annotation/utils_1.py rename to bin/annotation/utils_1.py diff --git a/scripts/annotation/utils_for_marrvel_flatfile.py b/bin/annotation/utils_for_marrvel_flatfile.py similarity index 99% rename from scripts/annotation/utils_for_marrvel_flatfile.py rename to bin/annotation/utils_for_marrvel_flatfile.py index f1e3f94..adf8c20 100755 --- a/scripts/annotation/utils_for_marrvel_flatfile.py +++ b/bin/annotation/utils_for_marrvel_flatfile.py @@ -1,8 +1,8 @@ ## UPDATE # Changes made by Chaozhong noted as #CL -from utils_1 import * -from utils_for_marrvel_flatfile_module_2 import * +from annotation.utils_1 import * +from annotation.utils_for_marrvel_flatfile_module_2 import * import numpy as np import time import re diff --git a/scripts/annotation/utils_for_marrvel_flatfile_module_2.py b/bin/annotation/utils_for_marrvel_flatfile_module_2.py similarity index 99% rename from scripts/annotation/utils_for_marrvel_flatfile_module_2.py rename to bin/annotation/utils_for_marrvel_flatfile_module_2.py index bd3179a..a2d5154 100755 --- a/scripts/annotation/utils_for_marrvel_flatfile_module_2.py +++ b/bin/annotation/utils_for_marrvel_flatfile_module_2.py @@ -1,7 +1,7 @@ ## UPDATE # Changes made by Chaozhong noted as #CL -from utils_1 import * +from annotation.utils_1 import * import numpy as np import time import re diff --git a/scripts/annotation/utils_for_symMatch.py b/bin/annotation/utils_for_symMatch.py similarity index 99% rename from scripts/annotation/utils_for_symMatch.py rename to bin/annotation/utils_for_symMatch.py index 9cbc296..03a54b5 100755 --- a/scripts/annotation/utils_for_symMatch.py +++ b/bin/annotation/utils_for_symMatch.py @@ -1,7 +1,7 @@ ## UPDATE # Changes made by Chaozhong noted as #CL -from utils_1 import * +from annotation.utils_1 import * def omimSymMatch(varObj, omimHPOScoreDf, inFileType): diff --git a/scripts/extraModel/__init__.py b/bin/extraModel/__init__.py similarity index 100% rename from scripts/extraModel/__init__.py rename to bin/extraModel/__init__.py diff --git a/scripts/extraModel/confidence.py b/bin/extraModel/confidence.py similarity index 100% rename from scripts/extraModel/confidence.py rename to bin/extraModel/confidence.py diff --git a/scripts/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py similarity index 98% rename from scripts/extraModel/generate_bivar_data.py rename to bin/extraModel/generate_bivar_data.py index 87268a6..62dbd7c 100755 --- a/scripts/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -24,7 +24,7 @@ def process_sample(data_folder, sample_id, os.mkdir(tmp_folder) ## read feature matrix for single var - feature_fn = f"/out/final_matrix/{sample_id}.csv" + feature_fn = f"{sample_id}.csv" #feature_df = [] #for feature_fn in feature_fns: if 'csv' in feature_fn: @@ -36,7 +36,7 @@ def process_sample(data_folder, sample_id, feature_df = feature_df.loc[~feature_df.index.duplicated(keep='first')] ## Group variants by Ens IDs - expanded_fn = f"/out/final_matrix_expanded/{sample_id}.expanded.csv.gz" + expanded_fn = f"final_matrix_expanded/{sample_id}.expanded.csv.gz" gene_dict = {} #for expanded_fn in expanded_fns: if 'csv' in expanded_fn: diff --git a/scripts/extraModel/integrate_output.py b/bin/extraModel/integrate_output.py similarity index 99% rename from scripts/extraModel/integrate_output.py rename to bin/extraModel/integrate_output.py index 3c3c6cc..4b18852 100755 --- a/scripts/extraModel/integrate_output.py +++ b/bin/extraModel/integrate_output.py @@ -18,7 +18,7 @@ def process_recessive_matrix(df): def integrate_output(prj_folder, data_folder, sample_id): ## Group variants by Ens IDs - expanded_fn = f"/out/final_matrix_expanded/{sample_id}.expanded.csv.gz" + expanded_fn = f"final_matrix_expanded/{sample_id}.expanded.csv.gz" #expanded_df = [] # for expanded_fn in expanded_fns: if 'csv' in expanded_fn: diff --git a/scripts/extraModel/main.py b/bin/extraModel_main.py similarity index 84% rename from scripts/extraModel/main.py rename to bin/extraModel_main.py index 258aa73..be18f59 100755 --- a/scripts/extraModel/main.py +++ b/bin/extraModel_main.py @@ -1,15 +1,16 @@ +#!/usr/bin/env python3.8 import argparse import os import pandas as pd -from confidence import * +from extraModel.confidence import * import joblib from time import time -import generate_bivar_data +from extraModel.generate_bivar_data import * from datetime import datetime from multiprocessing import Pool from tqdm import tqdm from scipy.stats import rankdata -from integrate_output import * +from extraModel.integrate_output import * parser = argparse.ArgumentParser() @@ -26,7 +27,7 @@ sample_id = args.id n_cpu = args.n_cpu -out_folder = '/out/conf_4Model' +out_folder = 'conf_4Model' if not os.path.exists(out_folder): os.mkdir(out_folder) @@ -38,9 +39,9 @@ model_dict = {} for mn in model_names: - model = joblib.load(f"/run/data_dependencies/model_inputs/{mn}/final_model.job") - ref_panel = joblib.load(f"/run/data_dependencies/model_inputs/{mn}/reference_panel.job") - features = open(f"/run/data_dependencies/model_inputs/{mn}/features.csv", 'r').readline().split(",") + model = joblib.load(f"model_inputs/{mn}/final_model.job") + ref_panel = joblib.load(f"model_inputs/{mn}/reference_panel.job") + features = open(f"model_inputs/{mn}/features.csv", 'r').readline().split(",") model_dict[mn] = {'model': model, 'ref': ref_panel, 'features': features} #if not os.path.exists(f"./{out_folder}/{mn}"): # os.mkdir(f"./{out_folder}/{mn}") @@ -61,7 +62,7 @@ def assign_ranking(df): def AIM(data_folder, sample_id, n_thread): - feature_fn = f'/out/final_matrix/{sample_id}.csv' + feature_fn = f'{sample_id}.csv' if not os.path.exists(feature_fn): print(f"{feature_fn} does not exist.") @@ -85,10 +86,10 @@ def AIM(data_folder, sample_id, n_thread): default_pred = pd.read_csv(f"{out_folder}/{sample_id}_default_predictions.csv", index_col=0) - generate_bivar_data.process_sample( data_folder = out_folder, - sample_id = sample_id, - default_pred = default_pred, - labeling=False, n_thread = n_thread) + process_sample( data_folder = out_folder, + sample_id = sample_id, + default_pred = default_pred, + labeling=False, n_thread = n_thread) recessive_feature_file = f"{out_folder}/recessive_matrix/{sample_id}.csv" if os.path.exists(recessive_feature_file): diff --git a/scripts/annotation/main.py b/bin/feature.py old mode 100644 new mode 100755 similarity index 98% rename from scripts/annotation/main.py rename to bin/feature.py index 0216b0e..b42dad5 --- a/scripts/annotation/main.py +++ b/bin/feature.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3.8 ## UPDATE # Changes made by Chaozhong noted as #CL # 1. added clinvar feature and changed database to clinvar.vcf.gz in VEP annotation @@ -15,14 +16,14 @@ import requests import subprocess #local methods -from utils_1 import * +from annotation.utils_1 import * #from utils_for_marrvel_annotate_script import * -from utils_for_symMatch import * -from utils_for_marrvel_flatfile import * +from annotation.utils_for_symMatch import * +from annotation.utils_for_marrvel_flatfile import * #from utils_for_marrvel_flatfile_module_3 import * import multiprocessing from functools import partial -from marrvel_score_recalc import * +from annotation.marrvel_score_recalc import * ############################################################# diff --git a/scripts/fillna_tier.py b/bin/fillna_tier.py similarity index 99% rename from scripts/fillna_tier.py rename to bin/fillna_tier.py index d0e2148..ab4bb6a 100755 --- a/scripts/fillna_tier.py +++ b/bin/fillna_tier.py @@ -41,7 +41,7 @@ def feature_engineering(score_file, tier_file): #sample_name = score_file.split('/')[-1].split('_')[1] #patient = pd.read_csv(score_file,sep='\t') - feature_stat = pd.read_csv('/run/data_dependencies/annotate/feature_stats.csv', index_col=0) + feature_stat = pd.read_csv('annotate/feature_stats.csv', index_col=0) patient = score_file.copy() diff --git a/scripts/generate_new_matrix_2.py b/bin/generate_new_matrix_2.py similarity index 66% rename from scripts/generate_new_matrix_2.py rename to bin/generate_new_matrix_2.py index 10b61e0..b4bfaa5 100755 --- a/scripts/generate_new_matrix_2.py +++ b/bin/generate_new_matrix_2.py @@ -1,4 +1,4 @@ -#!/usr/bin/env python +#!/usr/bin/env python3.8 # coding: utf-8 import os @@ -14,33 +14,34 @@ from simple_repeat_anno import simple_repeat_anno ### read score file ### -score=pd.read_csv('/out/rami-test/%s_scores.csv'%(sys.argv[1])) +score=pd.read_csv('scores.csv') ### add more clin and hgmd features ### merged = add_c_nc(score, sys.argv[2]) +path_phrank = sys.argv[1]+".phrank.txt" ### run diffusion module 5 ### -if os.path.getsize(f"/out/phrank/{sys.argv[1]}.txt") == 0: +if os.path.getsize(path_phrank) == 0: phrank_empty = True else: phrank_empty = False - mod5 = diffuseSample(sys.argv[1], merged, "/out/phrank/") + mod5 = diffuseSample(sys.argv[1], merged, ".") ### get original coordinates ### merged["varId"]=merged["varId"].apply(lambda x: x.split("_E")[0]) ### read tier ### -tier_f=pd.read_csv("/out/tier-test-false/"+sys.argv[1]+"_Tier.v2.tsv", sep="\t") +tier_f=pd.read_csv("Tier.v2.tsv", sep="\t") ### add phrank calculated elsewhere for final combination ### if phrank_empty: merged['phrank'] = 0 else: - phr=pd.read_csv("/out/phrank/"+sys.argv[1]+".txt", sep="\t", names=["ENSG", "phrank"]) + phr=pd.read_csv(path_phrank, sep="\t", names=["ENSG", "phrank"]) merged=merged.merge(phr,left_on="geneEnsId", right_on="ENSG", how="left") ### save intermediate file for merging ### -merged.to_csv("/out/scores/"+sys.argv[1]+".txt.gz", compression="gzip", sep="\t") +merged.to_csv("scores.txt.gz", compression="gzip", sep="\t") ### run Chaozhong's feature engineering code ### iff=fillna(merged,tier_f) @@ -56,11 +57,11 @@ #iff.to_csv(sys.argv[1]+".out.txt", sep="\t") ### run chaozhong's simple repeat annotation ### -iff=simple_repeat_anno(sys.argv[1], iff, f"/run/data_dependencies/merge_expand/{sys.argv[2]}/simpleRepeats.{sys.argv[2]}.bed") +iff=simple_repeat_anno(sys.argv[1], iff, f"merge_expand/{sys.argv[2]}/simpleRepeats.{sys.argv[2]}.bed") ### delete intermediate files ### -os.remove("/out/"+sys.argv[1]+".bed") -os.remove("/out/"+sys.argv[1]+".sp.bed") +# os.remove(sys.argv[1]+".bed") +# os.remove(sys.argv[1]+".sp.bed") ### save final file for prediction ### -iff.to_csv("/out/"+sys.argv[1]+".matrix.txt", sep="\t") +iff.to_csv(sys.argv[1]+".matrix.txt", sep="\t") diff --git a/scripts/keepna_tier.py b/bin/keepna_tier.py similarity index 100% rename from scripts/keepna_tier.py rename to bin/keepna_tier.py diff --git a/scripts/merge_rm.py b/bin/merge_rm.py similarity index 63% rename from scripts/merge_rm.py rename to bin/merge_rm.py index fb06026..6df6b37 100755 --- a/scripts/merge_rm.py +++ b/bin/merge_rm.py @@ -1,13 +1,13 @@ +#!/usr/bin/env python3.8 import pandas as pd import sys ###merge score files with final rank matrix #read rank matrix and score file -rm=pd.read_csv("/out/final_matrix/"+sys.argv[1]+".csv") -annot=pd.read_csv("/out/scores/"+sys.argv[1]+".txt.gz", sep="\t", compression="gzip") +rm=pd.read_csv(sys.argv[1]+".csv") +annot=pd.read_csv("scores.txt.gz", sep="\t", compression="gzip") -#annot=pd.read_csv("/local/LucianLi/rami-test/r1_"+sys.argv[1]+"_scores.txt", sep="\t", compression="gzip") #get columns from score file annot=annot[["varId","varId_dash", "geneSymbol", "geneEnsId", "rsId", "HGVSc", "HGVSp", "IMPACT", "Consequence", "phenoList", "phenoInhList", "clin_code", "clinvarCondition"]] @@ -21,5 +21,5 @@ #merge test=rm.merge(annot, right_on="varId_dash", left_on="Unnamed: 0", how="left") -test.to_csv("/out/final_matrix_expanded/"+sys.argv[1]+".expanded.csv.gz", index=False, compression="gzip") +test.to_csv("final_matrix_expanded/"+sys.argv[1]+".expanded.csv.gz", index=False, compression="gzip") diff --git a/scripts/mod5_diffusion.py b/bin/mod5_diffusion.py similarity index 97% rename from scripts/mod5_diffusion.py rename to bin/mod5_diffusion.py index 9cfbec1..4c6fb5f 100755 --- a/scripts/mod5_diffusion.py +++ b/bin/mod5_diffusion.py @@ -53,14 +53,15 @@ def diffuseSample(ID, Anno_df, Phrank_folder): """ ## Get the PPI network for diffusion - cor_path = "/run/data_dependencies/mod5_diffusion/combined_score.hdf5" + cor_path = "mod5_diffusion/combined_score.hdf5" cor_df = pd.read_hdf(cor_path, mode='r') cor_df = cor_df[(cor_df.T != 0).sum() > 1] cor_df = cor_df.loc[cor_df.index, cor_df.index] cor = cor_df.values cor_GeneID = pd.DataFrame({"ID":cor_df.columns.tolist()}) - Phrank_path = Phrank_folder + ID.split('.')[0] + ".txt" + # Phrank_path = Phrank_folder + ID.split('.')[0] + ".txt" + Phrank_path = ID + ".phrank.txt" ## Validate Phrank file's existence if not exists(Phrank_path): print("File %s not exists, skipping..." %Phrank_path) diff --git a/scripts/predict_new/confidence.py b/bin/predict_new/confidence.py similarity index 100% rename from scripts/predict_new/confidence.py rename to bin/predict_new/confidence.py diff --git a/scripts/predict_new/features.csv b/bin/predict_new/features.csv similarity index 100% rename from scripts/predict_new/features.csv rename to bin/predict_new/features.csv diff --git a/scripts/predict_new/main.py b/bin/predict_new/main.py similarity index 100% rename from scripts/predict_new/main.py rename to bin/predict_new/main.py diff --git a/scripts/predict_new/test.csv b/bin/predict_new/test.csv similarity index 100% rename from scripts/predict_new/test.csv rename to bin/predict_new/test.csv diff --git a/scripts/predict_new/utilities.py b/bin/predict_new/utilities.py similarity index 99% rename from scripts/predict_new/utilities.py rename to bin/predict_new/utilities.py index 3a89743..a1174c3 100755 --- a/scripts/predict_new/utilities.py +++ b/bin/predict_new/utilities.py @@ -11,7 +11,7 @@ from matplotlib import pyplot as plt from sklearn.ensemble import RandomForestClassifier as RF import itertools -from confidence import * +from predict_new.confidence import * def get_training_data(fns, label_col): """Read in training data, drop features with specified mode in the meta['feature_mode'] json. diff --git a/bin/run_final.py b/bin/run_final.py new file mode 100755 index 0000000..fc835f9 --- /dev/null +++ b/bin/run_final.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3.8 +from predict_new.utilities import * +import warnings +import json +import os +from glob import glob +from datetime import datetime +import joblib +from predict_new.confidence import * +import sys + +#read in precalulated model +model=joblib.load("predict_new/hg19/final_model_wo_bg_val.job") + +#read in features to use +features=list(pd.read_csv("predict_new/hg19/features.csv")) + +#predict with Linhua's code +out=rank_patient(model, sys.argv[1]+".matrix.txt", features, train_causals=None) + +#write out +out.to_csv(sys.argv[1]+".csv") \ No newline at end of file diff --git a/scripts/simple_repeat_anno.py b/bin/simple_repeat_anno.py similarity index 72% rename from scripts/simple_repeat_anno.py rename to bin/simple_repeat_anno.py index 39d9d17..a695f46 100755 --- a/scripts/simple_repeat_anno.py +++ b/bin/simple_repeat_anno.py @@ -11,15 +11,15 @@ def simple_repeat_anno(sample_id, feature_file, sp_bed_file): 'end':sample.index.str.split('-').str[1], 'ref':sample.index.str.split('-').str[2], 'alt':sample.index.str.split('-').str[3]}) - sample_bed.to_csv('/out/%s.bed'%(sample_id),index=False,header=False,sep='\t') + sample_bed.to_csv('%s.bed'%(sample_id),index=False,header=False,sep='\t') # bedtools needed - os.system('/run/bedtools intersect -a /out/%s.bed -b %s -wa > /out/%s.sp.bed'%(sample_id, sp_bed_file, sample_id)) + os.system('/run/bedtools intersect -a %s.bed -b %s -wa > %s.sp.bed'%(sample_id, sp_bed_file, sample_id)) - if os.stat('/out/%s.sp.bed'%(sample_id)).st_size == 0: + if os.stat('%s.sp.bed'%(sample_id)).st_size == 0: sample['simple_repeat'] = 0 else: - sample_sp = pd.read_csv('/out/%s.sp.bed'%(sample_id),sep='\t',header=None) + sample_sp = pd.read_csv('%s.sp.bed'%(sample_id),sep='\t',header=None) sample_sp['var_dash'] = sample_sp[0].astype(str) + '-' + sample_sp[2].astype(str) + '-' + sample_sp[3].astype(str) + '-' + sample_sp[4].astype(str) sample['simple_repeat'] = sample.index.isin(sample_sp['var_dash'].to_numpy()).astype(int) diff --git a/scripts/split_chunks.py b/bin/split_vep_chunks.py old mode 100644 new mode 100755 similarity index 97% rename from scripts/split_chunks.py rename to bin/split_vep_chunks.py index 162fed5..25d296e --- a/scripts/split_chunks.py +++ b/bin/split_vep_chunks.py @@ -1,3 +1,5 @@ +#!/usr/bin/env python3.8 + import math import os import sys diff --git a/main.nf b/main.nf index a12eb36..8789c1b 100644 --- a/main.nf +++ b/main.nf @@ -259,7 +259,7 @@ process VEP_ANNOTATE { """ } -process FEATURE_ENGINEERING { +process FEATURE_ENGINEERING_PART1 { cpus 10 memory '64 GB' input: @@ -268,23 +268,22 @@ process FEATURE_ENGINEERING { path omim_sim path ref_annot_dir // not sure why projectDir is not working - path script_chunking - path script_annot output: - path "*_scores.txt" + path "scores.csv" + path "r1_scores.txt" script: """ - #AIM_FREE_RAM=\$(free -g | awk 'NR==2{printf \$7}') + AIM_FREE_RAM=\$(free -g | awk 'NR==2{printf \$7}') - python3.8 $script_chunking $vep 256 + split_vep_chunks.py $vep \$AIM_FREE_RAM while read -r INDEX LINEH LINEA LINEB do sed -n -e "\${LINEH}p" -e "\${LINEA},\${LINEB}p" $vep > vep-\${INDEX}.txt - python3.8 main.py \\ + feature.py \\ -outPrefix r1 \\ -patientHPOsimiOMIM $omim_sim \\ -patientHPOsimiHGMD $hgmd_sim \\ @@ -317,6 +316,48 @@ process FEATURE_ENGINEERING { """ } +process FEATURE_ENGINEERING_PART2 { + input: + path scores + path r1_scores + path phrank + path ref_annot_dir + path ref_var_tier_dir + path ref_merge_expand_dir + path ref_mod5_diffusion_dir + + output: + path "${params.run_id}.matrix.txt" + path "scores.txt.gz" + + script: + """ + VarTierDiseaseDBFalse.R ${params.ref_ver} + generate_new_matrix_2.py ${params.run_id} ${params.ref_ver} + """ +} + +process PREDICTION { + debug true + + input: + path matrix + path scores + path ref_predict_new_dir + path ref_model_inputs_dir + + script: + """ + mkdir recessive_matrix + mkdir final_matrix_expanded + mkdir conf_4Model + run_final.py ${params.run_id} + merge_rm.py ${params.run_id} + extraModel_main.py -id ${params.run_id} + """ +} + + workflow { INDEX_VCF(params.input_vcf) @@ -367,12 +408,27 @@ workflow { file(params.vep_idx) ) - FEATURE_ENGINEERING( + FEATURE_ENGINEERING_PART1 ( // will rename it once we have analyzed/review the part VEP_ANNOTATE.out, HPO_SIM.out[0], HPO_SIM.out[1], - params.ref_annot_dir, - params.script_chunking, - file(params.script_annot) + file(params.ref_annot_dir) + ) + + FEATURE_ENGINEERING_PART2 ( + FEATURE_ENGINEERING_PART1.out[0], + FEATURE_ENGINEERING_PART1.out[1], + PHRANK_SCORING.out, + file(params.ref_annot_dir), + file(params.ref_var_tier_dir), + file(params.ref_merge_expand_dir), + file(params.ref_mod5_diffusion_dir) + ) + + PREDICTION( + FEATURE_ENGINEERING_PART2.out[0], + FEATURE_ENGINEERING_PART2.out[1], + file(params.ref_predict_new_dir), + file(params.ref_model_inputs_dir) ) } diff --git a/nextflow.config b/nextflow.config index 8584490..9c8b3db 100644 --- a/nextflow.config +++ b/nextflow.config @@ -56,6 +56,11 @@ params { script_annot = "${projectDir}/scripts/annotation/*.py" ref_annot_dir = "${ref_dir}/annotate" + ref_var_tier_dir = "${ref_dir}/var_tier" + ref_merge_expand_dir = "${ref_dir}/merge_expand" + ref_mod5_diffusion_dir = "${ref_dir}/mod5_diffusion" + ref_predict_new_dir = "${ref_dir}/predict_new" + ref_model_inputs_dir = "${ref_dir}/model_inputs" } docker.enabled = true diff --git a/scripts/predict_new/.py b/scripts/predict_new/.py deleted file mode 100755 index 1c1b44b..0000000 --- a/scripts/predict_new/.py +++ /dev/null @@ -1,14 +0,0 @@ -from utilities import * -import warnings -import json -import os -from glob import glob -from datetime import datetime -import joblib -from confidence import * - -model=joblib.load("/home/lul0614/pipeline/predict_new/final_model_wo_bg_val.job") -features=pd.read_csv("features.csv").columns -out=rank_patient(model, "/local/LucianLi/final-all/989068_fillna_tierFalse.txt", features, train_causals=None) - -out.to_csv("test.csv") \ No newline at end of file diff --git a/scripts/predict_new/causal_distr.py b/scripts/predict_new/causal_distr.py deleted file mode 100755 index 80ba2aa..0000000 --- a/scripts/predict_new/causal_distr.py +++ /dev/null @@ -1,14 +0,0 @@ -from utilities import * -import warnings -import json -import os -from glob import glob -from datetime import datetime -import joblib -from confidence import * - - -causal_distr = get_causal_distribution('/houston_30t/alexw/MARRVEL_ML/results/rf_fillNA_tierNDB_is_causal_version1_tuneFalse_05-31-2022/BG_pred_raw_rank', - label_col='is_strong') - -pd.DataFrame(causal_distr).to_csv('causal_distr.txt',index=False,header=False) \ No newline at end of file diff --git a/scripts/predict_new/causal_distr.txt b/scripts/predict_new/causal_distr.txt deleted file mode 100755 index bfb814a..0000000 --- a/scripts/predict_new/causal_distr.txt +++ /dev/null @@ -1,74 +0,0 @@ -0.7766666666666666 -0.07333333333333332 -0.01 -0.06666666666666668 -0.03666666666666667 -0.6866666666666666 -0.016666666666666666 -0.0033333333333333327 -0.07333333333333332 -0.21666666666666667 -0.06333333333333334 -0.0033333333333333327 -0.57 -0.12333333333333335 -0.04 -0.7366666666666667 -0.11666666666666667 -0.13333333333333333 -0.4033333333333333 -0.5966666666666667 -0.13 -0.05 -0.2433333333333333 -0.11333333333333333 -0.24 -0.36666666666666653 -0.2433333333333333 -0.3933333333333333 -0.7933333333333333 -0.08 -0.06666666666666668 -0.28 -0.15333333333333332 -0.12666666666666668 -0.7266666666666667 -0.06333333333333334 -0.5633333333333334 -0.67 -0.016666666666666666 -0.36 -0.21666666666666667 -0.6166666666666667 -0.02333333333333333 -0.6233333333333333 -0.09333333333333334 -0.4133333333333333 -0.35 -0.2933333333333333 -0.006666666666666667 -0.7233333333333334 -0.7233333333333334 -0.06 -0.5133333333333333 -0.4033333333333333 -0.2533333333333333 -0.14333333333333334 -0.06 -0.17 -0.02 -0.26 -0.5233333333333333 -0.016666666666666666 -0.07 -0.15 -0.15 -0.01 -0.49 -0.57 -0.013333333333333334 -0.11333333333333333 -0.5433333333333333 -0.45 -0.2933333333333333 -0.68 diff --git a/scripts/predict_new/run_final.py b/scripts/predict_new/run_final.py deleted file mode 100755 index 9baf945..0000000 --- a/scripts/predict_new/run_final.py +++ /dev/null @@ -1,21 +0,0 @@ -from utilities import * -import warnings -import json -import os -from glob import glob -from datetime import datetime -import joblib -from confidence import * -import sys - -#read in precalulated model -model=joblib.load("/run/data_dependencies/predict_new/hg19/final_model_wo_bg_val.job") - -#read in features to use -features=list(pd.read_csv("/run/data_dependencies/predict_new/hg19/features.csv")) - -#predict with Linhua's code -out=rank_patient(model, "/out/"+sys.argv[1]+".matrix.txt", features, train_causals=None) - -#write out -out.to_csv("/out/final_matrix/"+sys.argv[1]+".csv") \ No newline at end of file From ecad35a3587e33f2083a4c04b56bb288857889e9 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Wed, 29 May 2024 15:57:08 -0500 Subject: [PATCH 26/71] replace phrank scripts to bin. --- {scripts/phrank/src => bin}/location_to_gene.py | 0 {scripts/phrank/src => bin}/run_phrank.py | 1 + main.nf | 4 ++-- 3 files changed, 3 insertions(+), 2 deletions(-) rename {scripts/phrank/src => bin}/location_to_gene.py (100%) rename {scripts/phrank/src => bin}/run_phrank.py (96%) diff --git a/scripts/phrank/src/location_to_gene.py b/bin/location_to_gene.py similarity index 100% rename from scripts/phrank/src/location_to_gene.py rename to bin/location_to_gene.py diff --git a/scripts/phrank/src/run_phrank.py b/bin/run_phrank.py similarity index 96% rename from scripts/phrank/src/run_phrank.py rename to bin/run_phrank.py index f34a0db..bb9809d 100755 --- a/scripts/phrank/src/run_phrank.py +++ b/bin/run_phrank.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3.8 import sys import os from collections import defaultdict diff --git a/main.nf b/main.nf index 8789c1b..053a819 100644 --- a/main.nf +++ b/main.nf @@ -117,7 +117,7 @@ process ANNOT_ENSMBLE { script: """ - python2.7 ${workflow.projectDir}/scripts/phrank/src/location_to_gene.py $vcf $ref | \\ + location_to_gene.py $vcf $ref | \\ sed 's/:/\\t/g' | sed 's/X\\t/23\\t/g' | sed 's/Y\\t/24\\t/g' | \\ sed 's/MT\\t/25\\t/g' > ${params.run_id}-ensmbl.txt """ @@ -157,7 +157,7 @@ process PHRANK_SCORING { script: """ - python3.8 ${workflow.projectDir}/scripts/phrank/src/run_phrank.py \\ + run_phrank.py \\ $gene $hpo $dagfile $disease_annotation $gene_annotation $disease_gene > ${params.run_id}.phrank.txt """ } From 741cf4ab856ce22e440fd9b68c88a05a15ac3c89 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Wed, 29 May 2024 16:29:11 -0500 Subject: [PATCH 27/71] all scripts have been moved to bin --- main.nf | 2 +- scripts/labelling.py | 62 ---------------------- scripts/phenoSim.R | 119 ------------------------------------------- 3 files changed, 1 insertion(+), 182 deletions(-) delete mode 100755 scripts/labelling.py delete mode 100755 scripts/phenoSim.R diff --git a/main.nf b/main.nf index 053a819..abdd8fb 100644 --- a/main.nf +++ b/main.nf @@ -177,7 +177,7 @@ process HPO_SIM { script: """ - Rscript ${workflow.projectDir}/scripts/phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ + phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ ${params.run_id}-cz ${params.run_id}-dx """ diff --git a/scripts/labelling.py b/scripts/labelling.py deleted file mode 100755 index eb372f4..0000000 --- a/scripts/labelling.py +++ /dev/null @@ -1,62 +0,0 @@ -import numpy as np -import pandas as pd - -__author__ = "Chaozhong Liu" -__email__ = "chaozhol@bcm.edu" - -###adds known strong/weak labels to matrix (not used in production pipeline) - -def add_label(feature_mtx, label_file, sample_id): - """ - feature_mtx: file path of matrix after feature engineering - - label_file: path of file containing all diagnosed causal variants with columns "BG_ID", "var" and "Confidence" - - sample_id: id of sample to be labeled - """ - - patient = feature_mtx - # is_causal - including strong and weak - label_df = pd.read_csv(label_file) - label_df = label_df[label_df['BG_ID'] == sample_id] - label = label_df['var'].values.tolist() - label = list(set(label)) - - if len(label) == 0: - print('No causal variant found in label file: %s'%(sample_id)) - return - - if sum(patient.index.isin(label)) == 0: - print('True label not found in matrix: %s.'%(sample_id)) - return - - - patient['is_causal'] = 0 - patient.loc[patient.index.isin(label),'is_causal'] = 1 - - - # is_strong - including strong only - label_df = pd.read_csv(label_file) - label_df = label_df.loc[label_df['Confidence']=='Strong',:] - label_df = label_df[label_df['BG_ID'] == sample_id] - label = label_df['var'].values.tolist() - label = list(set(label)) - - patient['is_strong'] = 0 - - if len(label) == 0: - print('No strong causal variant found in label file: %s'%(sample_id)) - return patient - - - if sum(patient.index.isin(label)) == 0: - print('Strong label not found in matrix: %s.'%(sample_id)) - return patient - - - patient.loc[patient.index.isin(label),'is_strong'] = 1 - - - return patient - - diff --git a/scripts/phenoSim.R b/scripts/phenoSim.R deleted file mode 100755 index 298e741..0000000 --- a/scripts/phenoSim.R +++ /dev/null @@ -1,119 +0,0 @@ -rm(list=ls()) -library(ontologyIndex) -library(ontologySimilarity) -args = commandArgs(trailingOnly=TRUE) - -PATIENT_HPO=args[1] -OMIM_HGMD=args[2] -OMIM_OBO=args[3] -OMIM_GENEMAP2=args[4] -OMIM_PHENO=args[5] -OUTFILE_CZ_NAME=args[6] -OUTFILE_DX_NAME=args[7] - -dat <- read.csv(OMIM_HGMD,sep='\t') - -library(dplyr) -get_HPO_list <- function(df1){ - df2 <- df1 %>% - dplyr::group_by(acc_num, phen_id, gene_sym) %>% - dplyr::summarise(HPO = paste0(hpo_id, collapse = " ")) %>% - dplyr::ungroup() %>% as.data.frame() - df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) - - return(df2) -} - - -# Load HPO_obo -HPO_obo <- get_OBO(OMIM_OBO,propagate_relationships = c('is_a','part_of'), extract_tags = "minimal") - -# set simi_thresh -simi_thresh <- 0 - - -# In public release, there might be empty HGMD phenotype file -if (dim(dat)[1] == 0) { - col_names <- c('acc_num', 'phen_id', 'gene_sym', 'HPO', 'HPO_list', 'Similarity_Score') - dat2 <- data.frame(matrix(nrow = 0, ncol = length(col_names))) - colnames(dat2)<- col_names - -} else { - dat <- dat[!is.na(dat$hpo_id),] - dat2_ori <- get_HPO_list(dat) - dat2 <- dat2_ori - - # Load patient HPO - HPO.orig <- read.table(PATIENT_HPO, sep = "\t", fill=T, header=F, stringsAsFactors=F) - HPO <- HPO.orig$V1 - - # remove terms without a HPO ID - HPO <- HPO[grepl("HP:", HPO)] - HPO <- list(HPO) - - sim_mat <- get_asym_sim_grid(HPO, dat2$HPO_list, ontology=HPO_obo) - dat2$Similarity_Score <- as.vector(sim_mat) - dat2$HPO_list <- unlist(lapply(dat2$HPO_list, function(x) paste0(unlist(x), collapse = "|"))) - dat2 <- dat2[order(dat2$Similarity_Score, decreasing = T),] -} - - -write.table(dat2, OUTFILE_CZ_NAME, sep = "\t", quote = F, row.names = F) - - - - -# Load OMIM gene-disease data -genemap2 <-readRDS(OMIM_GENEMAP2) - -## ---- Load OMIM Phenotype ---- -HPO_orig <- read.table(OMIM_PHENO, sep="\t", header= T, stringsAsFactors = FALSE, comment.char = "", fill = TRUE, quote = "\"") -OMIM_HPO <- HPO_orig[,c('OMIM_ID', 'DiseaseName', 'HPO_ID')] -# rename colnames -colnames(OMIM_HPO) = c("OMIM_ID", "Disease_Name", "HPO_ID") -OMIM_HPO_cl <- unique(OMIM_HPO) - - -# Function to get_HPO_list -get_HPO_list <- function(df1){ - df2 <- df1 %>% - dplyr::group_by(OMIM_ID, Disease_Name) %>% - dplyr::summarise(HPO = paste0(HPO_ID, collapse = " ")) %>% - dplyr::ungroup() %>% as.data.frame() - df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) - #rownames(df2) <- ifelse(is.na(df2$OMIM_ID), df2$Disease_Name, df2$OMIM_ID) - return(df2) -} -# Prepare OMIM HPO list for ontology similarity comparision -OMIM_HPO_all <- get_HPO_list(OMIM_HPO_cl[, c("OMIM_ID", "Disease_Name","HPO_ID")]) - -HPO.orig <- read.table(PATIENT_HPO, sep = "\t", fill=T, header=F, stringsAsFactors=F) -HPO <- HPO.orig$V1 -# remove terms without a HPO ID -HPO <- HPO[grepl("HP:", HPO)] -HPO <- list(HPO) - - -sim_mat <- get_asym_sim_grid(HPO, OMIM_HPO_all$HPO_list, ontology=HPO_obo) - - -OMIM_HPO_all$Similarity_Score <- as.vector(sim_mat) - -# convert HPO ID to HPO term -OMIM_HPO_all$HPO_term <- unlist(lapply(OMIM_HPO_all$HPO_list, function(x) paste0(HPO_obo$name[unlist(x)], collapse = "|"))) - -## Add gene - disease relationship -OMIM_HPO_all_wGene <- merge(unique(genemap2[,c("Pheno_ID","Approved_Gene_Symbol","Ensembl_Gene_ID","Entrez_Gene_ID")]), - OMIM_HPO_all[,c("OMIM_ID","Disease_Name","Similarity_Score","HPO_term")], by.y = "OMIM_ID", by.x = "Pheno_ID") - -colnames(OMIM_HPO_all_wGene)[2] <- 'Gene_Symbol' -OMIM_HPO_all_order <-OMIM_HPO_all_wGene[order(OMIM_HPO_all_wGene$Similarity_Score, decreasing = T),] -# write.table(OMIM_HPO_all_order, paste0(unknown_disease_path,"HPOsimi_all_",output_file_name), sep = "\t", quote = F, row.names = F) - -# OMIM_HPO_all_filt <- head(OMIM_HPO_all_order, n = No_candidate) -OMIM_HPO_all_filt <- OMIM_HPO_all_order[OMIM_HPO_all_order$Similarity_Score >= simi_thresh,] - -write.table(OMIM_HPO_all_filt, OUTFILE_DX_NAME, sep = "\t", quote = F, row.names = F) - - - From 372dc481439ce81f3558808c50ea5df3b7aee1a6 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Wed, 29 May 2024 16:29:11 -0500 Subject: [PATCH 28/71] all scripts have been moved to bin --- {scripts => bin}/phenoSim.R | 1 + main.nf | 2 +- scripts/labelling.py | 62 ------------------------------------- 3 files changed, 2 insertions(+), 63 deletions(-) rename {scripts => bin}/phenoSim.R (99%) delete mode 100755 scripts/labelling.py diff --git a/scripts/phenoSim.R b/bin/phenoSim.R similarity index 99% rename from scripts/phenoSim.R rename to bin/phenoSim.R index 298e741..b04f099 100755 --- a/scripts/phenoSim.R +++ b/bin/phenoSim.R @@ -1,3 +1,4 @@ +#!/usr/bin/env Rscript rm(list=ls()) library(ontologyIndex) library(ontologySimilarity) diff --git a/main.nf b/main.nf index 053a819..abdd8fb 100644 --- a/main.nf +++ b/main.nf @@ -177,7 +177,7 @@ process HPO_SIM { script: """ - Rscript ${workflow.projectDir}/scripts/phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ + phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ ${params.run_id}-cz ${params.run_id}-dx """ diff --git a/scripts/labelling.py b/scripts/labelling.py deleted file mode 100755 index eb372f4..0000000 --- a/scripts/labelling.py +++ /dev/null @@ -1,62 +0,0 @@ -import numpy as np -import pandas as pd - -__author__ = "Chaozhong Liu" -__email__ = "chaozhol@bcm.edu" - -###adds known strong/weak labels to matrix (not used in production pipeline) - -def add_label(feature_mtx, label_file, sample_id): - """ - feature_mtx: file path of matrix after feature engineering - - label_file: path of file containing all diagnosed causal variants with columns "BG_ID", "var" and "Confidence" - - sample_id: id of sample to be labeled - """ - - patient = feature_mtx - # is_causal - including strong and weak - label_df = pd.read_csv(label_file) - label_df = label_df[label_df['BG_ID'] == sample_id] - label = label_df['var'].values.tolist() - label = list(set(label)) - - if len(label) == 0: - print('No causal variant found in label file: %s'%(sample_id)) - return - - if sum(patient.index.isin(label)) == 0: - print('True label not found in matrix: %s.'%(sample_id)) - return - - - patient['is_causal'] = 0 - patient.loc[patient.index.isin(label),'is_causal'] = 1 - - - # is_strong - including strong only - label_df = pd.read_csv(label_file) - label_df = label_df.loc[label_df['Confidence']=='Strong',:] - label_df = label_df[label_df['BG_ID'] == sample_id] - label = label_df['var'].values.tolist() - label = list(set(label)) - - patient['is_strong'] = 0 - - if len(label) == 0: - print('No strong causal variant found in label file: %s'%(sample_id)) - return patient - - - if sum(patient.index.isin(label)) == 0: - print('Strong label not found in matrix: %s.'%(sample_id)) - return patient - - - patient.loc[patient.index.isin(label),'is_strong'] = 1 - - - return patient - - From 70c49c4c7e6f1c537902080c4c655ad691a7844c Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 30 May 2024 10:04:45 -0500 Subject: [PATCH 29/71] remove __init__.py - empty --- bin/extraModel/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100755 bin/extraModel/__init__.py diff --git a/bin/extraModel/__init__.py b/bin/extraModel/__init__.py deleted file mode 100755 index e69de29..0000000 From 5b44d1c1820725aff768647cd4f2175d14056213 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 30 May 2024 12:49:22 -0500 Subject: [PATCH 30/71] lint the python files. --- bin/add_c_nc.py | 92 +- bin/annotation/marrvel_score_recalc.py | 356 ++-- bin/annotation/utils_1.py | 889 ++++---- bin/annotation/utils_for_marrvel_flatfile.py | 1828 +++++++++-------- .../utils_for_marrvel_flatfile_module_2.py | 258 +-- bin/annotation/utils_for_symMatch.py | 138 +- bin/extraModel/confidence.py | 23 +- bin/extraModel/generate_bivar_data.py | 152 +- bin/extraModel/integrate_output.py | 186 +- bin/extraModel_main.py | 105 +- bin/feature.py | 597 +++--- bin/fillna_tier.py | 1404 ++++++++----- bin/generate_new_matrix_2.py | 37 +- bin/keepna_tier.py | 976 +++++---- bin/location_to_gene.py | 151 +- bin/merge_rm.py | 63 +- bin/mod5_diffusion.py | 97 +- bin/predict_new/confidence.py | 25 +- bin/predict_new/main.py | 140 +- bin/predict_new/utilities.py | 273 ++- bin/run_final.py | 16 +- bin/run_phrank.py | 33 +- bin/simple_repeat_anno.py | 47 +- bin/split_vep_chunks.py | 42 +- 24 files changed, 4549 insertions(+), 3379 deletions(-) diff --git a/bin/add_c_nc.py b/bin/add_c_nc.py index c526721..8c4b74d 100755 --- a/bin/add_c_nc.py +++ b/bin/add_c_nc.py @@ -4,71 +4,83 @@ import numpy as np - def add_c_nc(score, ref): - #read data files - if ref == 'hg38': - clin_c=pd.read_csv("merge_expand/hg38/clin_c.tsv.gz", sep="\t") - clin_nc=pd.read_csv("merge_expand/hg38/clin_nc.tsv.gz", sep="\t") - hgmd_nc=pd.read_csv("merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t") - hgmd_c=pd.read_csv("merge_expand/hg38/hgmd_c.tsv.gz", sep="\t") + # read data files + if ref == "hg38": + clin_c = pd.read_csv("merge_expand/hg38/clin_c.tsv.gz", sep="\t") + clin_nc = pd.read_csv("merge_expand/hg38/clin_nc.tsv.gz", sep="\t") + hgmd_nc = pd.read_csv("merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t") + hgmd_c = pd.read_csv("merge_expand/hg38/hgmd_c.tsv.gz", sep="\t") else: - clin_c=pd.read_csv("merge_expand/hg19/clin_c.tsv.gz", sep="\t") - clin_nc=pd.read_csv("merge_expand/hg19/clin_nc.tsv.gz", sep="\t") - hgmd_nc=pd.read_csv("merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t") - hgmd_c=pd.read_csv("merge_expand/hg19/hgmd_c.tsv.gz", sep="\t") - + clin_c = pd.read_csv("merge_expand/hg19/clin_c.tsv.gz", sep="\t") + clin_nc = pd.read_csv("merge_expand/hg19/clin_nc.tsv.gz", sep="\t") + hgmd_nc = pd.read_csv("merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t") + hgmd_c = pd.read_csv("merge_expand/hg19/hgmd_c.tsv.gz", sep="\t") a = score.pos.values ac = score.chrom.values - temp=score[["varId"]] + temp = score[["varId"]] bh = clin_nc.new_stop.values bl = clin_nc.new_start.values - bc= clin_nc.new_chr.values + bc = clin_nc.new_chr.values - i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) &(ac[:, None]==bc)) + i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) & (ac[:, None] == bc)) - cln=pd.concat([ - temp.loc[i, :].reset_index(drop=True), - clin_nc.loc[j, :].reset_index(drop=True) - ], axis=1) + cln = pd.concat( + [ + temp.loc[i, :].reset_index(drop=True), + clin_nc.loc[j, :].reset_index(drop=True), + ], + axis=1, + ) # Take into account When HGMD data base is empty if hgmd_nc.shape[0] == 0: pass - + else: - #merge by region + # merge by region a = score.pos.values ac = score.chrom.values - temp=score[["varId"]] + temp = score[["varId"]] bh = hgmd_nc.new_stop.values bl = hgmd_nc.new_start.values - bc= hgmd_nc.new_chr.values - i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) &(ac[:, None]==bc)) + bc = hgmd_nc.new_chr.values + i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) & (ac[:, None] == bc)) - hgmd=pd.concat([ - temp.loc[i, :].reset_index(drop=True), - hgmd_nc.loc[j, :].reset_index(drop=True) - ], axis=1) + hgmd = pd.concat( + [ + temp.loc[i, :].reset_index(drop=True), + hgmd_nc.loc[j, :].reset_index(drop=True), + ], + axis=1, + ) - - merged=score.merge(cln, how="left", on="varId") + merged = score.merge(cln, how="left", on="varId") if hgmd_nc.shape[0] == 0: - merged['nc_HGMD_Exp'] = np.NaN - merged['nc_RANKSCORE'] = np.NaN + merged["nc_HGMD_Exp"] = np.NaN + merged["nc_RANKSCORE"] = np.NaN else: - merged=merged.merge(hgmd, how="left", on="varId") - + merged = merged.merge(hgmd, how="left", on="varId") + if hgmd_c.shape[0] == 0: - merged['c_HGMD_Exp'] = np.NaN - merged['c_RANKSCORE'] = np.NaN - merged['CLASS'] = np.NaN + merged["c_HGMD_Exp"] = np.NaN + merged["c_RANKSCORE"] = np.NaN + merged["CLASS"] = np.NaN else: - merged=merged.merge(hgmd_c, how="left", left_on=["chrom", "pos", "ref", "alt"], right_on=["new_chr", "new_pos", "ref", "alt"]) - merged=merged.merge(clin_c, how="left", left_on=["chrom", "pos", "ref", "alt"], right_on=["new_chr", "new_pos", "ref", "alt"]) + merged = merged.merge( + hgmd_c, + how="left", + left_on=["chrom", "pos", "ref", "alt"], + right_on=["new_chr", "new_pos", "ref", "alt"], + ) + merged = merged.merge( + clin_c, + how="left", + left_on=["chrom", "pos", "ref", "alt"], + right_on=["new_chr", "new_pos", "ref", "alt"], + ) return merged - diff --git a/bin/annotation/marrvel_score_recalc.py b/bin/annotation/marrvel_score_recalc.py index 2e413bf..9c224a8 100755 --- a/bin/annotation/marrvel_score_recalc.py +++ b/bin/annotation/marrvel_score_recalc.py @@ -4,36 +4,97 @@ import numpy as np - - def load_raw_matrix(score): - #score_file = '/out/rami-test/r1_%s_scores.txt'%(ID) #'/out/rami-test/r1_%s_scores.txt'%(ID) - #score = pd.read_csv(score_file, sep='\t') - - raw_features = ["chrom", "pos", "ref", - "alt",'varId','varId_dash','zyg','geneSymbol','geneEnsId','gnomadAF','gnomadAFg','CADD_phred', - 'CADD_PHRED','GERPpp_RS','GERPpp_NR','DANN_score','FATHMM_pred','FATHMM_score', - 'Polyphen2_HDIV_score','Polyphen2_HVAR_score','REVEL_score','SIFT_score', - 'fathmm_MKL_coding_score','LRT_score','LRT_Omega','phyloP100way_vertebrate', - 'M_CAP_score','MutationAssessor_score','MutationTaster_score','ESP6500_AA_AF', - 'ESP6500_EA_AF','symptomName','omimSymptomSimScore','omimSymMatchFlag','hgmdSymptomScore', - 'hgmdSymptomSimScore','hgmdSymMatchFlag','clinVarSymMatchFlag','gnomadGeneZscore', - 'gnomadGenePLI','gnomadGeneOELof','gnomadGeneOELofUpper','IMPACT','Consequence', - 'omimGeneFound','omimVarFound','hgmdGeneFound','hgmdVarFound', - 'clinVarVarFound','clinVarGeneFound','clinvarTotalNumVars', - 'clinvarNumP','clinvarNumLP','clinvarNumLB','clinvarNumB','clinvarSignDesc', - 'clinvarCondition','dgvVarFound','decipherVarFound','curationScoreHGMD','curationScoreOMIM', - 'curationScoreClinVar','conservationScoreDGV','conservationScoreGnomad','conservationScoreOELof', - 'hom','hgmd_rs','clin_dict','clin_PLP','clin_PLP_perc','spliceAImax','clin_code','hgmd_id', - 'hgmd_CLASS','rsId', 'HGVSc', 'HGVSp', 'phenoList', 'phenoInhList'] - - return score.loc[:,raw_features].copy() - - + # score_file = '/out/rami-test/r1_%s_scores.txt'%(ID) #'/out/rami-test/r1_%s_scores.txt'%(ID) + # score = pd.read_csv(score_file, sep='\t') + + raw_features = [ + "chrom", + "pos", + "ref", + "alt", + "varId", + "varId_dash", + "zyg", + "geneSymbol", + "geneEnsId", + "gnomadAF", + "gnomadAFg", + "CADD_phred", + "CADD_PHRED", + "GERPpp_RS", + "GERPpp_NR", + "DANN_score", + "FATHMM_pred", + "FATHMM_score", + "Polyphen2_HDIV_score", + "Polyphen2_HVAR_score", + "REVEL_score", + "SIFT_score", + "fathmm_MKL_coding_score", + "LRT_score", + "LRT_Omega", + "phyloP100way_vertebrate", + "M_CAP_score", + "MutationAssessor_score", + "MutationTaster_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + "symptomName", + "omimSymptomSimScore", + "omimSymMatchFlag", + "hgmdSymptomScore", + "hgmdSymptomSimScore", + "hgmdSymMatchFlag", + "clinVarSymMatchFlag", + "gnomadGeneZscore", + "gnomadGenePLI", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "IMPACT", + "Consequence", + "omimGeneFound", + "omimVarFound", + "hgmdGeneFound", + "hgmdVarFound", + "clinVarVarFound", + "clinVarGeneFound", + "clinvarTotalNumVars", + "clinvarNumP", + "clinvarNumLP", + "clinvarNumLB", + "clinvarNumB", + "clinvarSignDesc", + "clinvarCondition", + "dgvVarFound", + "decipherVarFound", + "curationScoreHGMD", + "curationScoreOMIM", + "curationScoreClinVar", + "conservationScoreDGV", + "conservationScoreGnomad", + "conservationScoreOELof", + "hom", + "hgmd_rs", + "clin_dict", + "clin_PLP", + "clin_PLP_perc", + "spliceAImax", + "clin_code", + "hgmd_id", + "hgmd_CLASS", + "rsId", + "HGVSc", + "HGVSp", + "phenoList", + "phenoInhList", + ] + + return score.loc[:, raw_features].copy() def omimCurate(vardf): - ''' + """ When related value changed, change the following value: @@ -45,34 +106,36 @@ def omimCurate(vardf): omimGeneFound omimVarFound - ''' - print('\nIn OMIM curation now...') + """ + print("\nIn OMIM curation now...") # change omimSymptomSimScore according to wether gene found in OMIM database to avoid info leaking - zero_score_bool = vardf['omimGeneFound']==0 - vardf.loc[zero_score_bool, 'omimVarFound'] = 0 - vardf.loc[zero_score_bool, 'omimSymptomSimScore'] = 0.0 + zero_score_bool = vardf["omimGeneFound"] == 0 + vardf.loc[zero_score_bool, "omimVarFound"] = 0 + vardf.loc[zero_score_bool, "omimSymptomSimScore"] = 0.0 # change omimSymMatchFlag accordingly - flag_0_bool = vardf['omimSymptomSimScore'] < 0.2 - vardf.loc[flag_0_bool, 'omimSymMatchFlag'] = 0 - vardf.loc[~flag_0_bool, 'omimSymMatchFlag'] = 1 + flag_0_bool = vardf["omimSymptomSimScore"] < 0.2 + vardf.loc[flag_0_bool, "omimSymMatchFlag"] = 0 + vardf.loc[~flag_0_bool, "omimSymMatchFlag"] = 1 # change final curation score - high_bool = (vardf['omimVarFound']==1) & (vardf['omimSymMatchFlag']==1) - medi_bool = ((vardf['omimVarFound']==1) & (vardf['omimSymMatchFlag']==0)) | ((vardf['omimVarFound']==0) & (vardf['omimGeneFound']==1) & (vardf['omimSymMatchFlag']==1)) + high_bool = (vardf["omimVarFound"] == 1) & (vardf["omimSymMatchFlag"] == 1) + medi_bool = ((vardf["omimVarFound"] == 1) & (vardf["omimSymMatchFlag"] == 0)) | ( + (vardf["omimVarFound"] == 0) + & (vardf["omimGeneFound"] == 1) + & (vardf["omimSymMatchFlag"] == 1) + ) - vardf['curationScoreOMIM'] = 'Low' - vardf.loc[high_bool, 'curationScoreOMIM'] = 'High' - vardf.loc[medi_bool, 'curationScoreOMIM'] = 'Medium' + vardf["curationScoreOMIM"] = "Low" + vardf.loc[high_bool, "curationScoreOMIM"] = "High" + vardf.loc[medi_bool, "curationScoreOMIM"] = "Medium" return vardf - - def hgmdCurate(vardf): - ''' + """ When related value changed, change the following value: @@ -85,42 +148,43 @@ def hgmdCurate(vardf): hgmdGeneFound hgmdVarFound - ''' - print('\nIn HGMD curation now...') + """ + print("\nIn HGMD curation now...") # change hgmdSymptomSimScore according to wether gene found in HGMD database to avoid info leaking - zero_score_bool = vardf['hgmdGeneFound']==0 - vardf.loc[zero_score_bool, 'hgmdVarFound'] = 0 - vardf.loc[zero_score_bool, 'hgmdSymptomSimScore'] = 0.0 + zero_score_bool = vardf["hgmdGeneFound"] == 0 + vardf.loc[zero_score_bool, "hgmdVarFound"] = 0 + vardf.loc[zero_score_bool, "hgmdSymptomSimScore"] = 0.0 # change hgmdSymptomScore according to wether variant found in HGMD database to avoid info leaking - zero_score_bool = vardf['hgmdVarFound']==0 - vardf.loc[zero_score_bool, 'hgmdSymptomScore'] = 0.0 + zero_score_bool = vardf["hgmdVarFound"] == 0 + vardf.loc[zero_score_bool, "hgmdSymptomScore"] = 0.0 # change hgmdSymMatchFlag accordingly - score_numeric = vardf['hgmdSymptomSimScore'].copy() - score_numeric[score_numeric=='-'] = np.NaN + score_numeric = vardf["hgmdSymptomSimScore"].copy() + score_numeric[score_numeric == "-"] = np.NaN score_numeric = score_numeric.astype(float) flag_0_bool = score_numeric < 0.2 - vardf.loc[flag_0_bool, 'hgmdSymMatchFlag'] = 0 - vardf.loc[~flag_0_bool, 'hgmdSymMatchFlag'] = 1 + vardf.loc[flag_0_bool, "hgmdSymMatchFlag"] = 0 + vardf.loc[~flag_0_bool, "hgmdSymMatchFlag"] = 1 # change final curation score - high_bool = (vardf['hgmdVarFound']==1) & (vardf['hgmdSymMatchFlag']==1) - medi_bool = ((vardf['hgmdVarFound']==1) & (vardf['hgmdSymMatchFlag']==0)) | ((vardf['hgmdVarFound']==0) & (vardf['hgmdGeneFound']==1) & (vardf['hgmdSymMatchFlag']==1)) + high_bool = (vardf["hgmdVarFound"] == 1) & (vardf["hgmdSymMatchFlag"] == 1) + medi_bool = ((vardf["hgmdVarFound"] == 1) & (vardf["hgmdSymMatchFlag"] == 0)) | ( + (vardf["hgmdVarFound"] == 0) + & (vardf["hgmdGeneFound"] == 1) + & (vardf["hgmdSymMatchFlag"] == 1) + ) - vardf['curationScoreHGMD'] = 'Low' - vardf.loc[high_bool, 'curationScoreHGMD'] = 'High' - vardf.loc[medi_bool, 'curationScoreHGMD'] = 'Medium' + vardf["curationScoreHGMD"] = "Low" + vardf.loc[high_bool, "curationScoreHGMD"] = "High" + vardf.loc[medi_bool, "curationScoreHGMD"] = "Medium" return vardf - - - def clinvarCurate(vardf): - ''' + """ When related value changed, change the following value: @@ -132,55 +196,99 @@ def clinvarCurate(vardf): clinVarGeneFound clinvarSignDesc - ''' - print('\nIn ClinVar curation now...') + """ + print("\nIn ClinVar curation now...") # change clinVarSymMatchFlag - vardf.loc[vardf['clinVarGeneFound']==0, 'clinVarVarFound'] = 0 - one_bool = (vardf['clinVarSymMatchFlag']==1) & ((vardf['clinVarVarFound']==1) | (vardf['clinVarGeneFound']==1)) - vardf.loc[~one_bool, 'clinVarSymMatchFlag'] = 0 + vardf.loc[vardf["clinVarGeneFound"] == 0, "clinVarVarFound"] = 0 + one_bool = (vardf["clinVarSymMatchFlag"] == 1) & ( + (vardf["clinVarVarFound"] == 1) | (vardf["clinVarGeneFound"] == 1) + ) + vardf.loc[~one_bool, "clinVarSymMatchFlag"] = 0 # change final curation score - pathList=['Pathogenic','Likely pathogenic','Pathogenic, Affects','Pathogenic/Likely pathogenic, other', - 'Pathogenic/Likely pathogenic','Pathogenic/Likely pathogenic, drug response', - 'Pathogenic/Likely pathogenic, risk factor','Likely pathogenic, drug response','Likely pathogenic, risk factor','Likely pathogenic, association', - 'Likely pathogenic, other','Pathogenic, association, protective','Pathogenic, Affects','Pathogenic, association','Pathogenic, other', - 'Pathogenic, protective','Pathogenic, protective, risk factor','Pathogenic, risk factor', - 'Pathogenic/Likely pathogenic, other','Pathogenic/Likely pathogenic, risk factor'] - - #pathList=['Pathogenic','Likely pathogenic', 'Pathogenic/Likely pathogenic'] - benignList=['Benign', 'Likely benign', 'Benign/Likely benign', 'Benign, association', 'Benign, drug response', - 'Benign, other', 'Benign, protective', ' Benign/Likely benign, Affects', 'Benign/Likely benign, association', - 'Benign/Likely benign, drug response', 'Benign/Likely benign, drug response, risk factor', - 'Benign/Likely benign, other', 'Benign/Likely benign, protective', 'Benign/Likely benign, protective, risk factor', - 'Benign/Likely benign, risk factor', 'Likely benign, drug response, other','Likely benign, other', - 'Likely benign, other, risk factor', 'Likely benign, risk factor'] - - #right logic - #path_bool = vardf['clinvarSignDesc'].str.contains('Pathogenic', case=False) & (~vardf['clinvarSignDesc'].str.contains('Conflicting', case=False)) - #bngn_bool = vardf['clinvarSignDesc'].str.contains('Benign', case=False) & (~vardf['clinvarSignDesc'].str.contains('Conflicting', case=False)) + pathList = [ + "Pathogenic", + "Likely pathogenic", + "Pathogenic, Affects", + "Pathogenic/Likely pathogenic, other", + "Pathogenic/Likely pathogenic", + "Pathogenic/Likely pathogenic, drug response", + "Pathogenic/Likely pathogenic, risk factor", + "Likely pathogenic, drug response", + "Likely pathogenic, risk factor", + "Likely pathogenic, association", + "Likely pathogenic, other", + "Pathogenic, association, protective", + "Pathogenic, Affects", + "Pathogenic, association", + "Pathogenic, other", + "Pathogenic, protective", + "Pathogenic, protective, risk factor", + "Pathogenic, risk factor", + "Pathogenic/Likely pathogenic, other", + "Pathogenic/Likely pathogenic, risk factor", + ] + + # pathList=['Pathogenic','Likely pathogenic', 'Pathogenic/Likely pathogenic'] + benignList = [ + "Benign", + "Likely benign", + "Benign/Likely benign", + "Benign, association", + "Benign, drug response", + "Benign, other", + "Benign, protective", + " Benign/Likely benign, Affects", + "Benign/Likely benign, association", + "Benign/Likely benign, drug response", + "Benign/Likely benign, drug response, risk factor", + "Benign/Likely benign, other", + "Benign/Likely benign, protective", + "Benign/Likely benign, protective, risk factor", + "Benign/Likely benign, risk factor", + "Likely benign, drug response, other", + "Likely benign, other", + "Likely benign, other, risk factor", + "Likely benign, risk factor", + ] + + # right logic + # path_bool = vardf['clinvarSignDesc'].str.contains('Pathogenic', case=False) & (~vardf['clinvarSignDesc'].str.contains('Conflicting', case=False)) + # bngn_bool = vardf['clinvarSignDesc'].str.contains('Benign', case=False) & (~vardf['clinvarSignDesc'].str.contains('Conflicting', case=False)) # wrong but current logic - path_bool = vardf['clinvarSignDesc'].isin(pathList) - bngn_bool = vardf['clinvarSignDesc'].isin(benignList) - - high_bool = (vardf['hgmdVarFound']==1) & path_bool & (vardf['hgmdSymMatchFlag']==1) - medi_bool = ((vardf['hgmdVarFound']==1) & path_bool & (vardf['hgmdSymMatchFlag']==0)) | \ - ((vardf['hgmdVarFound']==1) & (~path_bool) & (~bngn_bool) & (vardf['hgmdSymMatchFlag']==1) & (vardf['hgmdSymMatchFlag']==1)) | \ - ((vardf['hgmdVarFound']==0) & (vardf['hgmdGeneFound']==1) & (vardf['clinVarSymMatchFlag']==1)) - - - vardf['curationScoreHGMD'] = 'Low' - vardf.loc[high_bool, 'curationScoreHGMD'] = 'High' - vardf.loc[medi_bool, 'curationScoreHGMD'] = 'Medium' + path_bool = vardf["clinvarSignDesc"].isin(pathList) + bngn_bool = vardf["clinvarSignDesc"].isin(benignList) + + high_bool = ( + (vardf["hgmdVarFound"] == 1) & path_bool & (vardf["hgmdSymMatchFlag"] == 1) + ) + medi_bool = ( + ((vardf["hgmdVarFound"] == 1) & path_bool & (vardf["hgmdSymMatchFlag"] == 0)) + | ( + (vardf["hgmdVarFound"] == 1) + & (~path_bool) + & (~bngn_bool) + & (vardf["hgmdSymMatchFlag"] == 1) + & (vardf["hgmdSymMatchFlag"] == 1) + ) + | ( + (vardf["hgmdVarFound"] == 0) + & (vardf["hgmdGeneFound"] == 1) + & (vardf["clinVarSymMatchFlag"] == 1) + ) + ) + + vardf["curationScoreHGMD"] = "Low" + vardf.loc[high_bool, "curationScoreHGMD"] = "High" + vardf.loc[medi_bool, "curationScoreHGMD"] = "Medium" return vardf - - def conservationCurate(vardf): - ''' + """ When related value changed, change the following value: @@ -194,41 +302,41 @@ def conservationCurate(vardf): dgvVarFound gnomadGeneOELofUpper - ''' + """ # gnomad - gnomadAFVal = vardf['gnomadAF'].copy() - gnomadAFVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFVal ]) - #gnomadAFVal[gnomadAFVal=='-'] = np.NaN + gnomadAFVal = vardf["gnomadAF"].copy() + gnomadAFVal = np.array([getValFromStr(str(i), "min") for i in gnomadAFVal]) + # gnomadAFVal[gnomadAFVal=='-'] = np.NaN gnomadAFVal = gnomadAFVal.astype(float) - gnomadAFgVal = vardf['gnomadAFg'].copy() - gnomadAFgVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFgVal ]) - #gnomadAFgVal[gnomadAFgVal=='-'] = np.NaN + gnomadAFgVal = vardf["gnomadAFg"].copy() + gnomadAFgVal = np.array([getValFromStr(str(i), "min") for i in gnomadAFgVal]) + # gnomadAFgVal[gnomadAFgVal=='-'] = np.NaN gnomadAFgVal = gnomadAFgVal.astype(float) - low_bool = (gnomadAFVal>=0.01) | (gnomadAFgVal>=0.01) - vardf.loc[low_bool, 'conservationScoreGnomad'] = 'Low' - vardf.loc[~low_bool, 'conservationScoreGnomad'] = 'High' - + low_bool = (gnomadAFVal >= 0.01) | (gnomadAFgVal >= 0.01) + vardf.loc[low_bool, "conservationScoreGnomad"] = "Low" + vardf.loc[~low_bool, "conservationScoreGnomad"] = "High" # conservationScoreDGV - low_bool = (vardf['conservationScoreDGV'] == 'Low') & (vardf['dgvVarFound']==1) - vardf['conservationScoreDGV'] = 'High' - vardf.loc[low_bool,'conservationScoreDGV'] = 'Low' + low_bool = (vardf["conservationScoreDGV"] == "Low") & (vardf["dgvVarFound"] == 1) + vardf["conservationScoreDGV"] = "High" + vardf.loc[low_bool, "conservationScoreDGV"] = "Low" # conservationScoreOELof - gnomadGeneOELofUpperVal = vardf['gnomadGeneOELofUpper'].copy() - gnomadGeneOELofUpperVal[gnomadGeneOELofUpperVal=='-'] = np.NaN + gnomadGeneOELofUpperVal = vardf["gnomadGeneOELofUpper"].copy() + gnomadGeneOELofUpperVal[gnomadGeneOELofUpperVal == "-"] = np.NaN gnomadGeneOELofUpperVal = gnomadGeneOELofUpperVal.astype(float) high_bool = gnomadGeneOELofUpperVal < 0.35 - vardf['conservationScoreOELof'] = 'Low' - vardf.loc[high_bool, 'conservationScoreOELof'] = 'High' + vardf["conservationScoreOELof"] = "Low" + vardf.loc[high_bool, "conservationScoreOELof"] = "High" return vardf -def getValFromStr(valStr: str, select: str = 'min'): + +def getValFromStr(valStr: str, select: str = "min"): """ Function to convert string to float, and takes care of situation when multiple values exist @@ -253,14 +361,10 @@ def getValFromStr(valStr: str, select: str = 'min'): >> getValFromStr(f) '-' """ - select_method = {'min':min, 'max':max} - vals = valStr.split(',') - if '-' in vals: + select_method = {"min": min, "max": max} + vals = valStr.split(",") + if "-" in vals: return np.NaN else: vals = [float(i) for i in vals] return select_method[select](vals) - - - - diff --git a/bin/annotation/utils_1.py b/bin/annotation/utils_1.py index 8dc67a4..fa45532 100755 --- a/bin/annotation/utils_1.py +++ b/bin/annotation/utils_1.py @@ -1,536 +1,587 @@ ## UPDATE # Changes made by Chaozhong noted as #CL -#from liftover import get_lifter +# from liftover import get_lifter import pandas as pd import numpy as np import pprint import json import requests -#class for variant -class Variant(): + +# class for variant +class Variant: """Create a class to create an object for each variant Parameters: A class name for initiation call Output: An object of class Variant """ + def __init__(self): - self.chrom='-' - self.pos='-' - self.start='-' - self.stop='-' - self.ref='-' - self.alt='-' - self.hg19Chrom='-' - self.hg19Pos='-' - self.hg38Chrom='-' - self.hg38Pos='-' - self.varId='-' - self.varId_dash='-'#varinat ID separated by dash like '6-99365567-T-C' - - self.patientID = '-' - self.zyg='-' - self.geneSymbol='-' - self.geneEnsId='-' - self.rsId='-' - self.featureType='-' - self.gnomadAF='-' - self.gnomadAFg='-'#genome gnomad score - #dbnsfp attributes - self.CLIN_SIG='-' - self.CADD_phred='-'#phred score from dbnsfp - self.CADD_PHRED='-' - self.GERPpp_RS='-'#GERP plus plus - self.GERPpp_NR='-' - self.DANN_score='-' - self.FATHMM_pred='-' - self.FATHMM_score='-' - self.GTEx_V8_gene='-' - self.GTEx_V8_tissue='-' - self.Polyphen2_HDIV_score='-' - self.Polyphen2_HVAR_score='-' - self.REVEL_score='-' - self.SIFT_score='-' - - self.clinvar_AlleleID = '-' #CL added - self.clinvar_clnsig='-' - self.clinvar_CLNREVSTAT = '-' #CL added - self.clinvar_CLNSIGCONF = '-' #CL added - - self.fathmm_MKL_coding_score='-' - self.HGVSc='-' - self.HGVSp='-' - #LRT - self.LRT_score='-' - self.LRT_Omega='-' - #Phylo - self.phyloP100way_vertebrate='-' - self.M_CAP_score='-' - self.MutationAssessor_score='-' - self.MutationTaster_score='-' - self.ESP6500_AA_AC='-' - self.ESP6500_AA_AF='-' - self.ESP6500_EA_AC='-' - self.ESP6500_EA_AF='-' - - - #symtom info - self.SymptomMatched = {'omim':0, 'clinvar':0, 'hgmd':0} #list containing omimMatched, clinvarMatched, hgmdMatched - self.symptomScore = {}#for OMIM - self.symptomName = {}#for OMIM - self.omimSymptomSimScore='-' - self.omimSymMatchFlag= '-' - self.hgmdSymptomScore='-' - self.hgmdSymptomSimScore='-' - self.hgmdSymMatchFlag='-' - self.clinVarSymMatchFlag='-' - - #gnomad gene metrics from flat file - self.gnomadGeneZscore='-' - self.gnomadGenePLI='-' - self.gnomadGeneOELof='-'#O/E lof - self.gnomadGeneOELofUpper='-'#O/E lof upper - #conseqeunce and impact - self.IMPACT='-' - self.Consequence='-' - #omim - self.omimDict={} - self.omimGeneFound='-' - self.omimVarFound='-' - self.omimList=[] - self.omimGeneDict={} - self.omimAlleleDict={} - self.phenoList=[] - self.phenoInhList=[] - self.phenoMimList=[] - #HGMD - self.HGMDDict={} - self.hgmdGeneFound='-' - self.hgmdVarFound='-' - self.hgmdVarPhenIdList=[] - self.hgmdVarHPOIdList=[] - self.hgmdVarHPOStrList=[] - #clinvar - self.clinVarVarDict={} - self.clinVarGeneDict={} - self.clinVarVarFound='-' - self.clinVarGeneFound='-' - self.clinVarList=[] - self.clinvarTotalNumVars='-' - self.clinvarNumP='-'#number of clinvar variants pathogenic - self.clinvarNumLP='-'#number of clinvar variants likely pathogenic - self.clinvarNumLB='-' - self.clinvarNumB='-' - self.clinvarTitle='-'#title from the flat file - self.clinvarSignDesc='-'#significance description from the flat file - self.clinvarCondition='-' - #dgv - self.DGVDict={} - self.dgvDictList=[] - self.dgvTypeList=[] - self.dgvSubtypeList=[] - self.dgvVarFound='-' - #Decipher - self.DecipherDict={} - self.decipherDictList=[] - self.decipherDeletionObsList=[] - self.decipherStudyList=[] - self.decipherVarFound='-' - #the module scores. The scores will be broken down so they can be added as features - #curation score - self.curationScoreTotal='-' - self.curationScoreHGMD='-' - self.curationScoreOMIM='-' - self.curationScoreClinVar='-' - #conservation scores - self.conservationScoreTotal='-' - self.conservationScorePhylop='-' - self.conservationScoreLRT='-' - self.conservationScoreGerpPP='-' - self.conservationScoreCNV='-' - self.conservationScoreDGV='-' - self.conservationScoreDecipher='-' - self.conservationScoreGnomad='-' - self.conservationScoreGeneConstZ='-' - self.conservationScoreGeneConstPLi='-' - self.conservationScoreDomino='-' - self.conservationScoreOELof='-' - #module 3 scores - self.effectOnGeneScore='-' - self.geneDiseaseAssocScore='-' - self.modelOrganismScore='-' - - -#function to get var objects from dataframe read from a VEP annoataed file + self.chrom = "-" + self.pos = "-" + self.start = "-" + self.stop = "-" + self.ref = "-" + self.alt = "-" + self.hg19Chrom = "-" + self.hg19Pos = "-" + self.hg38Chrom = "-" + self.hg38Pos = "-" + self.varId = "-" + self.varId_dash = "-" # varinat ID separated by dash like '6-99365567-T-C' + + self.patientID = "-" + self.zyg = "-" + self.geneSymbol = "-" + self.geneEnsId = "-" + self.rsId = "-" + self.featureType = "-" + self.gnomadAF = "-" + self.gnomadAFg = "-" # genome gnomad score + # dbnsfp attributes + self.CLIN_SIG = "-" + self.CADD_phred = "-" # phred score from dbnsfp + self.CADD_PHRED = "-" + self.GERPpp_RS = "-" # GERP plus plus + self.GERPpp_NR = "-" + self.DANN_score = "-" + self.FATHMM_pred = "-" + self.FATHMM_score = "-" + self.GTEx_V8_gene = "-" + self.GTEx_V8_tissue = "-" + self.Polyphen2_HDIV_score = "-" + self.Polyphen2_HVAR_score = "-" + self.REVEL_score = "-" + self.SIFT_score = "-" + + self.clinvar_AlleleID = "-" # CL added + self.clinvar_clnsig = "-" + self.clinvar_CLNREVSTAT = "-" # CL added + self.clinvar_CLNSIGCONF = "-" # CL added + + self.fathmm_MKL_coding_score = "-" + self.HGVSc = "-" + self.HGVSp = "-" + # LRT + self.LRT_score = "-" + self.LRT_Omega = "-" + # Phylo + self.phyloP100way_vertebrate = "-" + self.M_CAP_score = "-" + self.MutationAssessor_score = "-" + self.MutationTaster_score = "-" + self.ESP6500_AA_AC = "-" + self.ESP6500_AA_AF = "-" + self.ESP6500_EA_AC = "-" + self.ESP6500_EA_AF = "-" + + # symtom info + self.SymptomMatched = { + "omim": 0, + "clinvar": 0, + "hgmd": 0, + } # list containing omimMatched, clinvarMatched, hgmdMatched + self.symptomScore = {} # for OMIM + self.symptomName = {} # for OMIM + self.omimSymptomSimScore = "-" + self.omimSymMatchFlag = "-" + self.hgmdSymptomScore = "-" + self.hgmdSymptomSimScore = "-" + self.hgmdSymMatchFlag = "-" + self.clinVarSymMatchFlag = "-" + + # gnomad gene metrics from flat file + self.gnomadGeneZscore = "-" + self.gnomadGenePLI = "-" + self.gnomadGeneOELof = "-" # O/E lof + self.gnomadGeneOELofUpper = "-" # O/E lof upper + # conseqeunce and impact + self.IMPACT = "-" + self.Consequence = "-" + # omim + self.omimDict = {} + self.omimGeneFound = "-" + self.omimVarFound = "-" + self.omimList = [] + self.omimGeneDict = {} + self.omimAlleleDict = {} + self.phenoList = [] + self.phenoInhList = [] + self.phenoMimList = [] + # HGMD + self.HGMDDict = {} + self.hgmdGeneFound = "-" + self.hgmdVarFound = "-" + self.hgmdVarPhenIdList = [] + self.hgmdVarHPOIdList = [] + self.hgmdVarHPOStrList = [] + # clinvar + self.clinVarVarDict = {} + self.clinVarGeneDict = {} + self.clinVarVarFound = "-" + self.clinVarGeneFound = "-" + self.clinVarList = [] + self.clinvarTotalNumVars = "-" + self.clinvarNumP = "-" # number of clinvar variants pathogenic + self.clinvarNumLP = "-" # number of clinvar variants likely pathogenic + self.clinvarNumLB = "-" + self.clinvarNumB = "-" + self.clinvarTitle = "-" # title from the flat file + self.clinvarSignDesc = "-" # significance description from the flat file + self.clinvarCondition = "-" + # dgv + self.DGVDict = {} + self.dgvDictList = [] + self.dgvTypeList = [] + self.dgvSubtypeList = [] + self.dgvVarFound = "-" + # Decipher + self.DecipherDict = {} + self.decipherDictList = [] + self.decipherDeletionObsList = [] + self.decipherStudyList = [] + self.decipherVarFound = "-" + # the module scores. The scores will be broken down so they can be added as features + # curation score + self.curationScoreTotal = "-" + self.curationScoreHGMD = "-" + self.curationScoreOMIM = "-" + self.curationScoreClinVar = "-" + # conservation scores + self.conservationScoreTotal = "-" + self.conservationScorePhylop = "-" + self.conservationScoreLRT = "-" + self.conservationScoreGerpPP = "-" + self.conservationScoreCNV = "-" + self.conservationScoreDGV = "-" + self.conservationScoreDecipher = "-" + self.conservationScoreGnomad = "-" + self.conservationScoreGeneConstZ = "-" + self.conservationScoreGeneConstPLi = "-" + self.conservationScoreDomino = "-" + self.conservationScoreOELof = "-" + # module 3 scores + self.effectOnGeneScore = "-" + self.geneDiseaseAssocScore = "-" + self.modelOrganismScore = "-" + + +# function to get var objects from dataframe read from a VEP annoataed file def getVarObjFromVEPAnnotDf(df, genomeRef): - #create the objects - varObjList = [ Variant() for i in range(len(df.index)) ] - #create a list of var objects - i=0 + # create the objects + varObjList = [Variant() for i in range(len(df.index))] + # create a list of var objects + i = 0 for index, rows in df.iterrows(): - print('rows:', rows.Uploaded_variation, rows.Location, rows.Gene, rows.Feature_type) - s=rows.Uploaded_variation.split('_') - transcriptId=rows.Feature - chrom=s[0] - pos=s[1] - #if it is hg38 get its hg19 coordinates - if genomeRef=='hg38': + print( + "rows:", + rows.Uploaded_variation, + rows.Location, + rows.Gene, + rows.Feature_type, + ) + s = rows.Uploaded_variation.split("_") + transcriptId = rows.Feature + chrom = s[0] + pos = s[1] + # if it is hg38 get its hg19 coordinates + if genomeRef == "hg38": pass - varObjList[i].hg38Chrom=chrom - varObjList[i].hg38pos=pos - retList=gethg19LocFromHg38(chr, pos) - retList=[newChrom, newPos] - varObjList[i].hg19Chrom=retList[0] - varObjList[i].hg19pos=retList[1] - chrom=retList[0] - pos=retList[1] + varObjList[i].hg38Chrom = chrom + varObjList[i].hg38pos = pos + retList = gethg19LocFromHg38(chr, pos) + retList = [newChrom, newPos] + varObjList[i].hg19Chrom = retList[0] + varObjList[i].hg19pos = retList[1] + chrom = retList[0] + pos = retList[1] else: - varObjList[i].hg19Chrom=chrom - varObjList[i].hg19pos=pos - - tmp=s[2] - s=tmp.split('/') - refAllele=s[0] - altAllele=s[1] - print(chr,pos, tmp, refAllele, altAllele) - - varObjList[i].chrom=chrom - varObjList[i].pos=pos - varObjList[i].ref=refAllele - varObjList[i].alt=altAllele - #cerate a unique ID since we have transcripts - varId='_'.join([chrom,pos,refAllele,altAllele,transcriptId]) - varObjList[i].varId=varId - varObjList[i].varId_dash='-'.join([chrom,pos,refAllele,altAllele]) - varObjList[i].zyg=rows.ZYG - varObjList[i].geneSymbol=rows.SYMBOL - varObjList[i].geneEnsId=rows.Gene - varObjList[i].rsId=rows.Existing_variation - varObjList[i].GERPpp_RS=rows.GERPpp_RS - varObjList[i].featureType=rows.Feature_type - varObjList[i].gnomadAF=rows.gnomAD_AF - varObjList[i].CLIN_SIG=rows.CLIN_SIG - varObjList[i].HGVSc=rows.HGVSc - varObjList[i].HGVSp=rows.HGVSp - - #increment - i=i+1 - - #print('in def len of objects:', len(varObjList)) + varObjList[i].hg19Chrom = chrom + varObjList[i].hg19pos = pos + + tmp = s[2] + s = tmp.split("/") + refAllele = s[0] + altAllele = s[1] + print(chr, pos, tmp, refAllele, altAllele) + + varObjList[i].chrom = chrom + varObjList[i].pos = pos + varObjList[i].ref = refAllele + varObjList[i].alt = altAllele + # cerate a unique ID since we have transcripts + varId = "_".join([chrom, pos, refAllele, altAllele, transcriptId]) + varObjList[i].varId = varId + varObjList[i].varId_dash = "-".join([chrom, pos, refAllele, altAllele]) + varObjList[i].zyg = rows.ZYG + varObjList[i].geneSymbol = rows.SYMBOL + varObjList[i].geneEnsId = rows.Gene + varObjList[i].rsId = rows.Existing_variation + varObjList[i].GERPpp_RS = rows.GERPpp_RS + varObjList[i].featureType = rows.Feature_type + varObjList[i].gnomadAF = rows.gnomAD_AF + varObjList[i].CLIN_SIG = rows.CLIN_SIG + varObjList[i].HGVSc = rows.HGVSc + varObjList[i].HGVSp = rows.HGVSp + + # increment + i = i + 1 + + # print('in def len of objects:', len(varObjList)) return varObjList -#function to get OMIM info using MArrvel API + +# function to get OMIM info using MArrvel API def getOMIMUsingMarrvelAPI(varObj): - print('getOMIM:',varObj.varId_dash, varObj.geneSymbol, varObj.rsId) - url='http://marrvel.org/data/omim/gene/symbol/{}/variant/{}'.format(varObj.geneSymbol,varObj.varId_dash) - #Example: 6:99365567 T>C / FBXL4 - #url='http://marrvel.org/data/omim/gene/symbol/{}/variant/{}'.format('FBXL4','6-99365567-T-C') - #print('url:', url) - - req=requests.get(url) - #print('reqText:', req) - #print('resqStatusCode:', req.status_code) - varFound=0 - geneFound=0 - omimDict={} - if req.status_code!=200: - #print('OMIM query not found') + print("getOMIM:", varObj.varId_dash, varObj.geneSymbol, varObj.rsId) + url = "http://marrvel.org/data/omim/gene/symbol/{}/variant/{}".format( + varObj.geneSymbol, varObj.varId_dash + ) + # Example: 6:99365567 T>C / FBXL4 + # url='http://marrvel.org/data/omim/gene/symbol/{}/variant/{}'.format('FBXL4','6-99365567-T-C') + # print('url:', url) + + req = requests.get(url) + # print('reqText:', req) + # print('resqStatusCode:', req.status_code) + varFound = 0 + geneFound = 0 + omimDict = {} + if req.status_code != 200: + # print('OMIM query not found') pass else: - #the entry for the gene exists but need to check if the variants exists also using the rs ID - geneFound=1 - #print('reqText:', req.text) - dat=json.loads(req.text) - #search var in the list of dict - retVal=next((item for item in dat['allelicVariants'] if item["dbSnps"] == varObj.rsId), None) + # the entry for the gene exists but need to check if the variants exists also using the rs ID + geneFound = 1 + # print('reqText:', req.text) + dat = json.loads(req.text) + # search var in the list of dict + retVal = next( + (item for item in dat["allelicVariants"] if item["dbSnps"] == varObj.rsId), + None, + ) if retVal is not None: - varFound=1 - omimDict=data - #print('varFoundFlag:',varFoundFlag) - + varFound = 1 + omimDict = data + # print('varFoundFlag:',varFoundFlag) - retList=[varFound, geneFound, omimDict] + retList = [varFound, geneFound, omimDict] return retList + def getClinVarUsingMarrvelAPI(varObj): - print('getClinvar:',varObj.varId_dash, varObj.geneSymbol, varObj.rsId) - #the APIs - #/clinvar/gene/symbol/:geneSymbol - #/clinvar/gene/variant/:variant - - #check if varaint annotated - url='http://marrvel.org/data/clinvar/variant/{}'.format(varObj.varId_dash) - req=requests.get(url) - #print('reqText:', req) - #print('reqText:', req.text) - varFound=0 - varDict={} + print("getClinvar:", varObj.varId_dash, varObj.geneSymbol, varObj.rsId) + # the APIs + # /clinvar/gene/symbol/:geneSymbol + # /clinvar/gene/variant/:variant + + # check if varaint annotated + url = "http://marrvel.org/data/clinvar/variant/{}".format(varObj.varId_dash) + req = requests.get(url) + # print('reqText:', req) + # print('reqText:', req.text) + varFound = 0 + varDict = {} if req.text: - varFound=1 - varDict=json.loads(req.text) + varFound = 1 + varDict = json.loads(req.text) else: - varFound=0 - - - #check if gene annotated - url='http://marrvel.org/data/clinvar/symbol/{}'.format(varObj.geneSymbol) - req=requests.get(url) - #print('reqText:', req) - #print('reqText:', req.text) - geneFound=0 - geneDict={} + varFound = 0 + + # check if gene annotated + url = "http://marrvel.org/data/clinvar/symbol/{}".format(varObj.geneSymbol) + req = requests.get(url) + # print('reqText:', req) + # print('reqText:', req.text) + geneFound = 0 + geneDict = {} if req.text: - geneFound=1 - geneDict=json.loads(req.text) + geneFound = 1 + geneDict = json.loads(req.text) else: - geneFound=0 + geneFound = 0 - #return - retList=[varFound,varDict, geneFound, geneDict] + # return + retList = [varFound, varDict, geneFound, geneDict] return retList def getCurationScore(varObj): - #print('in curation score') - #get the curation score per each DB - #omim score - omimScore='' - if varObj.omimVarFound==1: - if varObj.omimSymMatchFlag==1: - omimScore='High' + # print('in curation score') + # get the curation score per each DB + # omim score + omimScore = "" + if varObj.omimVarFound == 1: + if varObj.omimSymMatchFlag == 1: + omimScore = "High" else: - omimScore='Medium' - elif varObj.omimGeneFound==1: - if varObj.omimSymMatchFlag==1: - omimScore='Medium' + omimScore = "Medium" + elif varObj.omimGeneFound == 1: + if varObj.omimSymMatchFlag == 1: + omimScore = "Medium" else: - omimScore='Low' + omimScore = "Low" else: - omimScore='Low' + omimScore = "Low" - #hgmd score - hgmdScore='' - if varObj.hgmdVarFound==1: - if varObj.hgmdSymMatchFlag==1: - hgmdScore='High' + # hgmd score + hgmdScore = "" + if varObj.hgmdVarFound == 1: + if varObj.hgmdSymMatchFlag == 1: + hgmdScore = "High" else: - hgmdScore='Medium' - elif varObj.hgmdGeneFound==1: - if varObj.hgmdSymMatchFlag==1: - hgmdScore='Medium' + hgmdScore = "Medium" + elif varObj.hgmdGeneFound == 1: + if varObj.hgmdSymMatchFlag == 1: + hgmdScore = "Medium" else: - hgmdScore='Low' + hgmdScore = "Low" else: - hgmdScore='Low' - - - #clinvar score - #if anything is not in the path/likely path list then it should have a low score - clinVarScore='' - pathList=['Pathogenic','Likely pathogenic','Pathogenic, Affects','Pathogenic/Likely pathogenic, other', - 'Pathogenic/Likely pathogenic','Pathogenic/Likely pathogenic, drug response', - 'Pathogenic/Likely pathogenic, risk factor','Likely pathogenic, drug response','Likely pathogenic, risk factor','Likely pathogenic, association', - 'Likely pathogenic, other','Pathogenic, association, protective','Pathogenic, Affects','Pathogenic, association','Pathogenic, other', - 'Pathogenic, protective','Pathogenic, protective, risk factor','Pathogenic, risk factor', - 'Pathogenic/Likely pathogenic, other','Pathogenic/Likely pathogenic, risk factor'] - - #pathList=['Pathogenic','Likely pathogenic', 'Pathogenic/Likely pathogenic'] - benignList=['Benign', 'Likely benign', 'Benign/Likely benign', 'Benign, association', 'Benign, drug response', - 'Benign, other', 'Benign, protective', ' Benign/Likely benign, Affects', 'Benign/Likely benign, association', - 'Benign/Likely benign, drug response', 'Benign/Likely benign, drug response, risk factor', - 'Benign/Likely benign, other', 'Benign/Likely benign, protective', 'Benign/Likely benign, protective, risk factor', - 'Benign/Likely benign, risk factor', 'Likely benign, drug response, other','Likely benign, other', - 'Likely benign, other, risk factor', 'Likely benign, risk factor'] - - clinVarScore='' - if varObj.clinVarVarFound==1: + hgmdScore = "Low" + + # clinvar score + # if anything is not in the path/likely path list then it should have a low score + clinVarScore = "" + pathList = [ + "Pathogenic", + "Likely pathogenic", + "Pathogenic, Affects", + "Pathogenic/Likely pathogenic, other", + "Pathogenic/Likely pathogenic", + "Pathogenic/Likely pathogenic, drug response", + "Pathogenic/Likely pathogenic, risk factor", + "Likely pathogenic, drug response", + "Likely pathogenic, risk factor", + "Likely pathogenic, association", + "Likely pathogenic, other", + "Pathogenic, association, protective", + "Pathogenic, Affects", + "Pathogenic, association", + "Pathogenic, other", + "Pathogenic, protective", + "Pathogenic, protective, risk factor", + "Pathogenic, risk factor", + "Pathogenic/Likely pathogenic, other", + "Pathogenic/Likely pathogenic, risk factor", + ] + + # pathList=['Pathogenic','Likely pathogenic', 'Pathogenic/Likely pathogenic'] + benignList = [ + "Benign", + "Likely benign", + "Benign/Likely benign", + "Benign, association", + "Benign, drug response", + "Benign, other", + "Benign, protective", + " Benign/Likely benign, Affects", + "Benign/Likely benign, association", + "Benign/Likely benign, drug response", + "Benign/Likely benign, drug response, risk factor", + "Benign/Likely benign, other", + "Benign/Likely benign, protective", + "Benign/Likely benign, protective, risk factor", + "Benign/Likely benign, risk factor", + "Likely benign, drug response, other", + "Likely benign, other", + "Likely benign, other, risk factor", + "Likely benign, risk factor", + ] + + clinVarScore = "" + if varObj.clinVarVarFound == 1: if varObj.clinvarSignDesc in pathList: - if varObj.clinVarSymMatchFlag==1: - clinVarScore='High' + if varObj.clinVarSymMatchFlag == 1: + clinVarScore = "High" else: - clinVarScore='Medium' + clinVarScore = "Medium" elif varObj.clinvarSignDesc in benignList: - clinVarScore='Low' + clinVarScore = "Low" else: - if varObj.clinVarSymMatchFlag==1: - clinVarScore='Medium' + if varObj.clinVarSymMatchFlag == 1: + clinVarScore = "Medium" else: - clinVarScore='Low' - elif varObj.clinVarGeneFound==1: - if varObj.clinVarSymMatchFlag==1: - clinVarScore='Medium' + clinVarScore = "Low" + elif varObj.clinVarGeneFound == 1: + if varObj.clinVarSymMatchFlag == 1: + clinVarScore = "Medium" else: - clinVarScore='Low' + clinVarScore = "Low" else: - clinVarScore='Low' - - - - #OLD - #pathList=['Pathogenic','Likely pathogenic'] - #if varObj.clinvarSignDesc not in pathList: + clinVarScore = "Low" + + # OLD + # pathList=['Pathogenic','Likely pathogenic'] + # if varObj.clinvarSignDesc not in pathList: # clinVarScore='Low' - #elif varObj.clinVarVarFound==1: + # elif varObj.clinVarVarFound==1: # if varObj.clinVarSymMatchFlag==1: # clinVarScore='High' # else: # clinVarScore='Medium' - #elif varObj.clinVarGeneFound==1: + # elif varObj.clinVarGeneFound==1: # if varObj.clinVarSymMatchFlag==1: # clinVarScore='Medium' # else: # clinVarScore='Low' - #else: + # else: # clinVarScore='Low' - #print('\tomimScore:', omimScore,'hgmdScore:',hgmdScore,'clinVarScore:',clinVarScore) + # print('\tomimScore:', omimScore,'hgmdScore:',hgmdScore,'clinVarScore:',clinVarScore) - #total score - totalScore='-' - #need more work on this total score + # total score + totalScore = "-" + # need more work on this total score if 0: - #if omim, clinvar, HGMD avaiable then use omim > hgmd > clinvar for the final score - varFound=0 - geneFound=0 - if varObj.omimVarFound==1: - varFound=1 - geneFound=varObj.omimGeneFound - elif varObj.hgmdVarFound==1: - varFound=1 - geneFound=varObj.hgmdGeneFound - elif varObj.clinVarVarFound==1: - varFound=1 - geneFound=varObj.clinVarGeneFound + # if omim, clinvar, HGMD avaiable then use omim > hgmd > clinvar for the final score + varFound = 0 + geneFound = 0 + if varObj.omimVarFound == 1: + varFound = 1 + geneFound = varObj.omimGeneFound + elif varObj.hgmdVarFound == 1: + varFound = 1 + geneFound = varObj.hgmdGeneFound + elif varObj.clinVarVarFound == 1: + varFound = 1 + geneFound = varObj.clinVarGeneFound else: - totalScore='Low' - retList=[omimScore, hgmdScore, clinVarScore, totalScore] + totalScore = "Low" + retList = [omimScore, hgmdScore, clinVarScore, totalScore] return retList - #print('\tgetCuration func varFound:', varFound,'gene found:', geneFound) - #continue total score and follow the flowchart - if varFound==1: - if symMatchFlag==1: - totalScore='High' - elif symMatchFlag==0: - totalScore='Low' - elif geneFound==1: - if symMatchFlag==1: - totalScore='High' - elif symMatchFlag==0: - totalScore='Low' + # print('\tgetCuration func varFound:', varFound,'gene found:', geneFound) + # continue total score and follow the flowchart + if varFound == 1: + if symMatchFlag == 1: + totalScore = "High" + elif symMatchFlag == 0: + totalScore = "Low" + elif geneFound == 1: + if symMatchFlag == 1: + totalScore = "High" + elif symMatchFlag == 0: + totalScore = "Low" else: - totalScore='Low' + totalScore = "Low" - #print('\t curation total score:', totalScore) - retList=[omimScore, hgmdScore, clinVarScore, totalScore] + # print('\t curation total score:', totalScore) + retList = [omimScore, hgmdScore, clinVarScore, totalScore] return retList + def gethg19LocFromHg38(chrom, pos): - converter = get_lifter('hg38', 'hg19') + converter = get_lifter("hg38", "hg19") pos = int(pos) converter[chrom][pos] - retVal=converter.query(chrom, pos) - #print('retVal:', retVal) - newChrom=retVal[0][0] - newChrom=newChrom.replace('chr', '') - newPos=retVal[0][1] - #print('newChr:',newChrom,'newPos:', newPos) - retList=[newChrom, newPos] + retVal = converter.query(chrom, pos) + # print('retVal:', retVal) + newChrom = retVal[0][0] + newChrom = newChrom.replace("chr", "") + newPos = retVal[0][1] + # print('newChr:',newChrom,'newPos:', newPos) + retList = [newChrom, newPos] return retList def getDGVUsingMarrvelAPI(varObj): - #varObj=varObjList[0] - print('v:',varObj.geneSymbol) - #DGV returns a list not a dict - url='http://marrvel.org/data/dgv/variant/{}'.format(varObj.varId_dash) - req=requests.get(url) - #print('req:', req.text) - dgvDictList=[] - #geneList=[] - typeList=[] - subtypeList=[] - if req.status_code!=200: - #print('OMIM query not found') + # varObj=varObjList[0] + print("v:", varObj.geneSymbol) + # DGV returns a list not a dict + url = "http://marrvel.org/data/dgv/variant/{}".format(varObj.varId_dash) + req = requests.get(url) + # print('req:', req.text) + dgvDictList = [] + # geneList=[] + typeList = [] + subtypeList = [] + if req.status_code != 200: + # print('OMIM query not found') pass else: - dat=json.loads(req.text) - dgvDictList=dat + dat = json.loads(req.text) + dgvDictList = dat for row in dat: - dgvType=row['type'] + dgvType = row["type"] typeList.append(dgvType) - dgvSubtype=row['subType'] + dgvSubtype = row["subType"] subtypeList.append(dgvSubtype) - #dgvGeneList=[d['symbol'] for d in row['genes']] - #geneList.append(dgvGeneList) + # dgvGeneList=[d['symbol'] for d in row['genes']] + # geneList.append(dgvGeneList) - retList=[dgvDictList,typeList,subtypeList] + retList = [dgvDictList, typeList, subtypeList] return retList def getDecipherUsingMarrvelAPI(varObj): - print('v:',varObj.geneSymbol) - url='http://marrvel.org/data/decipher/variant/{}'.format(varObj.varId_dash)#varId_dash - #decipher/variant/:variant - #decipher/genomloc/:hg19Chr/:hg19Start/:hg19Stop - req=requests.get(url) - #print('req:', req.text) - decipherDictList=[] - decipherDeletionObsList=[] - decipherStudyList=[] - if req.status_code!=200: - #print('Decipher query not found') + print("v:", varObj.geneSymbol) + url = "http://marrvel.org/data/decipher/variant/{}".format( + varObj.varId_dash + ) # varId_dash + # decipher/variant/:variant + # decipher/genomloc/:hg19Chr/:hg19Start/:hg19Stop + req = requests.get(url) + # print('req:', req.text) + decipherDictList = [] + decipherDeletionObsList = [] + decipherStudyList = [] + if req.status_code != 200: + # print('Decipher query not found') pass else: - dat=json.loads(req.text) - decipherDictList=dat + dat = json.loads(req.text) + decipherDictList = dat for row in dat: - #print('\n row:', row) - deleteObs=row['deletion']['obs'] + # print('\n row:', row) + deleteObs = row["deletion"]["obs"] decipherDeletionObsList.append(deleteObs) - deleteStudy=row['study'] + deleteStudy = row["study"] decipherStudyList.append(deleteObs) - #print('deleteObs:', deleteObs, 'study:',deleteStudy) + # print('deleteObs:', deleteObs, 'study:',deleteStudy) - retList=[decipherDictList,decipherDeletionObsList,decipherStudyList] + retList = [decipherDictList, decipherDeletionObsList, decipherStudyList] return retList + def checkVarGeneFile(inFile, fileType): - #check if input files are in right format + # check if input files are in right format pass + def checkPatientFile(inFile, fileType): - #check if input files are in right format + # check if input files are in right format pass def changeVariantFormat(inVar): - #change the varaint format from 6:99365567 T>C to 6-99365567-T-C - #replace : > and space with - - inVar=inVar.replace(' ', '-') - inVar=inVar.replace(':', '-') - inVar=inVar.replace('>', '-') + # change the varaint format from 6:99365567 T>C to 6-99365567-T-C + # replace : > and space with - + inVar = inVar.replace(" ", "-") + inVar = inVar.replace(":", "-") + inVar = inVar.replace(">", "-") return inVar + def readVarGeneFile(inFile, fileType): - #read the var/gene file and return a data frame; if it is varList only then need to call - if fileType=='varList': - df=pd.read_csv(inFile, sep='\t') - print('df head:', df.head(), '\n') - #change variant format - df=df.apply(np.vectorize(changeVariantFormat)) - print('df head:', df.head(), '\n') + # read the var/gene file and return a data frame; if it is varList only then need to call + if fileType == "varList": + df = pd.read_csv(inFile, sep="\t") + print("df head:", df.head(), "\n") + # change variant format + df = df.apply(np.vectorize(changeVariantFormat)) + print("df head:", df.head(), "\n") return df + def checkVarGnomad(varId): - url='http://marrvel.org/data/gnomad/variant/{}'.format(varId) - req=requests.get(url) + url = "http://marrvel.org/data/gnomad/variant/{}".format(varId) + req = requests.get(url) pass def getVarGroups(varList, groupSize): - #get a list of variant groups for parallel processing + # get a list of variant groups for parallel processing for i in range(0, len(varList), groupSize): - yield varList[i:i + groupSize] + yield varList[i : i + groupSize] diff --git a/bin/annotation/utils_for_marrvel_flatfile.py b/bin/annotation/utils_for_marrvel_flatfile.py index adf8c20..e8ee648 100755 --- a/bin/annotation/utils_for_marrvel_flatfile.py +++ b/bin/annotation/utils_for_marrvel_flatfile.py @@ -1,115 +1,129 @@ ## UPDATE # Changes made by Chaozhong noted as #CL -from annotation.utils_1 import * +from annotation.utils_1 import * from annotation.utils_for_marrvel_flatfile_module_2 import * import numpy as np import time import re + def getOMIMUsingMarrvelFlatFile(varObj, omimGeneList, omimAlleleList): - ''' + """ function to get OMIM info using marrrvel flat file Params:a varaint object read from VEP annotation - + Returns: List of OMIM annotations - ''' - #print('In OMIM call:') - #print('\tvar:', varObj.varId_dash, 'rsId:', varObj.rsId) - #check if rsId has a comma in it - inputSnpList=[] - if ',' in varObj.rsId: - inputSnpList = varObj.rsId.split(',') + """ + # print('In OMIM call:') + # print('\tvar:', varObj.varId_dash, 'rsId:', varObj.rsId) + # check if rsId has a comma in it + inputSnpList = [] + if "," in varObj.rsId: + inputSnpList = varObj.rsId.split(",") else: - inputSnpList=varObj.rsId - #print('\tinputSnpList:', inputSnpList) - varFound=0 - geneFound=0 - omimDict={} - omimGeneDict={} - omimAlleleDict={} - phenoList=[] - phenoInhList=[] - phenoMimList=[] - #check gene - #keys: dict_keys(['phenotypes', 'allelicVariants', 'mimNumber', 'status', 'title', 'description', 'geneEntrezId', 'geneSymbol']) - if any(d['geneSymbol'] == varObj.geneSymbol for d in omimGeneList): - #print('\tgene:', varObj.geneSymbol, 'found') - geneFound=1 - omimGeneDict = next((sub for sub in omimGeneList if sub['geneSymbol'] == varObj.geneSymbol), None) - #print('\tgeneSymbol from dict:', omimGeneDict['geneSymbol']) - #print('\talleleic:', type(omimGeneDict['allelicVariants']), ' len:', len(omimGeneDict['allelicVariants']) ) - #check if snp found - snpList=[] - for a in omimGeneDict['allelicVariants']: - #print('a:', a) - #print('type:', type(a)) - if 'dbSnps' in a: - snpList.append(a['dbSnps']) - #print('\tsnpList:', snpList) - #print('\tlen snpList:', len(snpList)) - #check if input snpID matches the OMIM one - set1 = set(inputSnpList) - set2 = set(snpList) - if set1.intersection(set2): - varFound=1 - else: - varFound=0 - - #get disease info from OMIM - #print('\tphenotypes:', type(omimGeneDict['phenotypes']), ' len:', len(omimGeneDict['phenotypes']) ) - for a in omimGeneDict['phenotypes']: - #print('type:', type(a)) - pheno=a['phenotype'] - if 'phenotypeMimNumber' in a: - phenoMim=a['phenotypeMimNumber'] - else: - phenoMim='-' - if 'phenotypeInheritance' in a: - phenoInh=a['phenotypeInheritance'] - else: - phenoInh='-' - phenoList.append(pheno) - phenoInhList.append(phenoInh) - phenoMimList.append(str(phenoMim)) - #print('phenotype:', pheno,phenoMim,phenoInh) - - #print('\tvarFound:', varFound) - #print('\tphenoList:', phenoList) - #print('\tphenoInhList:', phenoInhList) - #print('\tphenoMimList:', phenoMimList) - - retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict, phenoList, phenoInhList, phenoMimList] + inputSnpList = varObj.rsId + # print('\tinputSnpList:', inputSnpList) + varFound = 0 + geneFound = 0 + omimDict = {} + omimGeneDict = {} + omimAlleleDict = {} + phenoList = [] + phenoInhList = [] + phenoMimList = [] + # check gene + # keys: dict_keys(['phenotypes', 'allelicVariants', 'mimNumber', 'status', 'title', 'description', 'geneEntrezId', 'geneSymbol']) + if any(d["geneSymbol"] == varObj.geneSymbol for d in omimGeneList): + # print('\tgene:', varObj.geneSymbol, 'found') + geneFound = 1 + omimGeneDict = next( + (sub for sub in omimGeneList if sub["geneSymbol"] == varObj.geneSymbol), + None, + ) + # print('\tgeneSymbol from dict:', omimGeneDict['geneSymbol']) + # print('\talleleic:', type(omimGeneDict['allelicVariants']), ' len:', len(omimGeneDict['allelicVariants']) ) + # check if snp found + snpList = [] + for a in omimGeneDict["allelicVariants"]: + # print('a:', a) + # print('type:', type(a)) + if "dbSnps" in a: + snpList.append(a["dbSnps"]) + # print('\tsnpList:', snpList) + # print('\tlen snpList:', len(snpList)) + # check if input snpID matches the OMIM one + set1 = set(inputSnpList) + set2 = set(snpList) + if set1.intersection(set2): + varFound = 1 + else: + varFound = 0 + + # get disease info from OMIM + # print('\tphenotypes:', type(omimGeneDict['phenotypes']), ' len:', len(omimGeneDict['phenotypes']) ) + for a in omimGeneDict["phenotypes"]: + # print('type:', type(a)) + pheno = a["phenotype"] + if "phenotypeMimNumber" in a: + phenoMim = a["phenotypeMimNumber"] + else: + phenoMim = "-" + if "phenotypeInheritance" in a: + phenoInh = a["phenotypeInheritance"] + else: + phenoInh = "-" + phenoList.append(pheno) + phenoInhList.append(phenoInh) + phenoMimList.append(str(phenoMim)) + # print('phenotype:', pheno,phenoMim,phenoInh) + + # print('\tvarFound:', varFound) + # print('\tphenoList:', phenoList) + # print('\tphenoInhList:', phenoInhList) + # print('\tphenoMimList:', phenoMimList) + + retList = [ + varFound, + geneFound, + omimDict, + omimGeneDict, + omimAlleleDict, + phenoList, + phenoInhList, + phenoMimList, + ] return retList -def getClinVarUsingMarrvelFlatFile(varObj,clinvarAlleleDf,clinvarGeneDf): - ''' + +def getClinVarUsingMarrvelFlatFile(varObj, clinvarAlleleDf, clinvarGeneDf): + """ function to get clinvar info using marrrvel flat file Params:a varaint object read from VEP annotation - + Returns: List of clinvar annotations - ''' - #print('in clinvar using flatfile') - #print('\tvar:',varObj.varId_dash) - varFound=0 - varDict={} - geneFound=0 - geneDict={} - clinvarTotalNumVars=0 - clinvarNumP=0 - clinvarNumLP=0 - clinvarNumLB=0 - clinvarNumB=0 + """ + # print('in clinvar using flatfile') + # print('\tvar:',varObj.varId_dash) + varFound = 0 + varDict = {} + geneFound = 0 + geneDict = {} + clinvarTotalNumVars = 0 + clinvarNumP = 0 + clinvarNumLP = 0 + clinvarNumLB = 0 + clinvarNumB = 0 ## CL: no use but kept - clinvarTitle='' - clinvarSignDesc='' - clinvarCondition='' + clinvarTitle = "" + clinvarSignDesc = "" + clinvarCondition = "" - #print('in function type clinavrDf:', type(clinvarAlleleDf) ) - chromVal=int(varObj.chrom) - posVal=int(varObj.pos) + # print('in function type clinavrDf:', type(clinvarAlleleDf) ) + chromVal = int(varObj.chrom) + posVal = int(varObj.pos) """ #CL: Old implementation #using inetger column not index @@ -143,62 +157,73 @@ def getClinVarUsingMarrvelFlatFile(varObj,clinvarAlleleDf,clinvarGeneDf): varFound=0 """ - #CL: check if var annotated in clinvar using VEP clinvar.vcf.gz custom annotation - if varObj.clinvar_AlleleID != '-': + # CL: check if var annotated in clinvar using VEP clinvar.vcf.gz custom annotation + if varObj.clinvar_AlleleID != "-": varFound = 1 - #print('\tclinvar var found') + # print('\tclinvar var found') else: varFound = 0 - - - #check if gene annotated in clinvar using clinvarGeneDf - geneSymbol=varObj.geneSymbol - #print('\tgene symbol:', geneSymbol) + # check if gene annotated in clinvar using clinvarGeneDf + geneSymbol = varObj.geneSymbol + # print('\tgene symbol:', geneSymbol) try: - vals=clinvarGeneDf.loc[geneSymbol] - numRows=len(vals.index) + vals = clinvarGeneDf.loc[geneSymbol] + numRows = len(vals.index) except: - numRows=0 - #print('\tcheck gene numRows:', numRows) - if numRows>0: - #print('\tclinvar gene found') - geneFound=1 - clinvarTotalNumVars=vals['totalClinvarVars'] - clinvarNumP=vals['P'] - clinvarNumLP=vals['LP'] - clinvarNumLB=vals['LB'] - clinvarNumB=vals['B'] - #return - retList=[varFound,varDict, geneFound, geneDict, clinvarTotalNumVars,clinvarNumP,clinvarNumLP,clinvarNumLB, - clinvarNumB, clinvarTitle, clinvarSignDesc,clinvarCondition] + numRows = 0 + # print('\tcheck gene numRows:', numRows) + if numRows > 0: + # print('\tclinvar gene found') + geneFound = 1 + clinvarTotalNumVars = vals["totalClinvarVars"] + clinvarNumP = vals["P"] + clinvarNumLP = vals["LP"] + clinvarNumLB = vals["LB"] + clinvarNumB = vals["B"] + # return + retList = [ + varFound, + varDict, + geneFound, + geneDict, + clinvarTotalNumVars, + clinvarNumP, + clinvarNumLP, + clinvarNumLB, + clinvarNumB, + clinvarTitle, + clinvarSignDesc, + clinvarCondition, + ] return retList -def getHGMDUsingFlatFile(varObj,hgmdDf): - ''' + +def getHGMDUsingFlatFile(varObj, hgmdDf): + """ function to get HGMD from local flat file Params: varObj:a varaint object read from VEP annotation hgmdDf: HGMD data frame read from local file (CL: now it refers to hgmdHPOScoreDf in main.py) - + Returns: List of HGMD annotations Update by CL: - ''' - #print('\nin HGMD') - #print('\tvar:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol) - HGMDDict={} - hgmdGeneFound=0 - hgmdVarFound=0 - hgmdVarPhenIdList=[] - hgmdVarHPOIdList=[] - hgmdVarHPOStrList=[] - chromVal=varObj.chrom - posVal=int(varObj.pos) - startVal=int(varObj.start) - stopVal=int(varObj.stop) - #print('\tpos type:',type(varObj.start),'chrom:', type(chromVal) ) + """ + # print('\nin HGMD') + # print('\tvar:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol) + HGMDDict = {} + hgmdGeneFound = 0 + hgmdVarFound = 0 + hgmdVarPhenIdList = [] + hgmdVarHPOIdList = [] + hgmdVarHPOStrList = [] + chromVal = varObj.chrom + posVal = int(varObj.pos) + startVal = int(varObj.start) + stopVal = int(varObj.stop) + # print('\tpos type:',type(varObj.start),'chrom:', type(chromVal) ) """ #using int columns @@ -231,7 +256,7 @@ def getHGMDUsingFlatFile(varObj,hgmdDf): print('\tvals:', vals) """ # CL: check VarFound - if varObj.hgmd_id != '-': + if varObj.hgmd_id != "-": hgmdVarFound = 1 else: hgmdVarFound = 0 @@ -253,469 +278,544 @@ def getHGMDUsingFlatFile(varObj,hgmdDf): hgmdGeneFound=1 """ # CL: check geneFound - if np.any(hgmdDf['gene_sym'].isin([varObj.geneSymbol])): + if np.any(hgmdDf["gene_sym"].isin([varObj.geneSymbol])): hgmdGeneFound = 1 else: hgmdGeneFound = 0 - - #print('\thgmdVarFound:',hgmdVarFound,'hgmdGeneFound:',hgmdGeneFound, + # print('\thgmdVarFound:',hgmdVarFound,'hgmdGeneFound:',hgmdGeneFound, # 'hgmdVarPhenIdList:',hgmdVarPhenIdList,'hgmdVarHPOIdList:',hgmdVarHPOIdList, # 'hgmdVarHPOStrList:',hgmdVarHPOStrList) - #return - retList=[hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList] + # return + retList = [ + hgmdVarFound, + hgmdGeneFound, + hgmdVarPhenIdList, + hgmdVarHPOIdList, + hgmdVarHPOStrList, + ] return retList -def getVarObjFromMarrvelFlatFile(varDf,genomeRef,clinvarGeneDf,clinvarAlleleDf, omimGeneList, omimAlleleList,hgmdDf): - ''' + +def getVarObjFromMarrvelFlatFile( + varDf, + genomeRef, + clinvarGeneDf, + clinvarAlleleDf, + omimGeneList, + omimAlleleList, + hgmdDf, +): + """ Function to get the marrvel annotaion if not done in parallel. Use parallel option better - ''' - #dict_keys(['chr', 'pos', 'ref', 'alt', 'omim', 'clinvar', 'gtex', 'dgv', + """ + # dict_keys(['chr', 'pos', 'ref', 'alt', 'omim', 'clinvar', 'gtex', 'dgv', #'decipher', 'decipherDisease', 'gnomad', 'dbnsfp']) - numVars=len(varDf.index) - print('type:', type(varDf), 'len/numVars:', numVars) - varObjList = [ Variant() for i in range(numVars) ] + numVars = len(varDf.index) + print("type:", type(varDf), "len/numVars:", numVars) + varObjList = [Variant() for i in range(numVars)] - debugFlag=0 + debugFlag = 0 - i=0 + i = 0 for index, row in varDf.iterrows(): - print('type of row:', type(row)) - transcriptId=row.Feature - #s=row.Uploaded_variation.split('_') '1_10204_-/T' - s=row[0].split('_') - #print('s:', s) - chrom=s[0] - pos=int(s[1]) - tmp=s[2] - s=tmp.split('/') - ref=s[0] - alt=s[1] - #get the start and stop from second column like '1:10203-10204' - if '-' in row[1]: - s=row[1].split(':') - tmp=s[1] - s=tmp.split('-') - print('s:',s) - start=int(s[0]) - stop=int(s[1]) + print("type of row:", type(row)) + transcriptId = row.Feature + # s=row.Uploaded_variation.split('_') '1_10204_-/T' + s = row[0].split("_") + # print('s:', s) + chrom = s[0] + pos = int(s[1]) + tmp = s[2] + s = tmp.split("/") + ref = s[0] + alt = s[1] + # get the start and stop from second column like '1:10203-10204' + if "-" in row[1]: + s = row[1].split(":") + tmp = s[1] + s = tmp.split("-") + print("s:", s) + start = int(s[0]) + stop = int(s[1]) else: - #start and stop the same - s=row[1].split(':') - start=int(s[1]) - stop=int(s[1]) - - - print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) - #change chrom X and Y and MT to numbers - if chrom=='X': - chrom=23 - elif chrom=='Y': - chrom=24 - elif chrom=='MT': - chrom=25 - chrom=int(chrom) - #if it is hg38 get its hg19 coordinates - if genomeRef=='hg38': - varObjList[i].hg38Chrom=chrom - varObjList[i].hg38Pos=pos - retList=gethg19LocFromHg38(chrom, pos) - retList=[newChrom, newPos] - varObjList[i].hg19Chrom=retList[0] - varObjList[i].hg19Pos=retList[1] - chrom=retList[0] - pos=retList[1] - #get the start - retList=gethg19LocFromHg38(chrom, start) - varObjList[i].start=int(retList[1]) - #get the stop - retList=gethg19LocFromHg38(chrom, stop) - varObjList[i].stop=int(retList[1]) + # start and stop the same + s = row[1].split(":") + start = int(s[1]) + stop = int(s[1]) + + print( + "chrom:", + chrom, + "pos:", + pos, + "ref:", + ref, + "alt:", + alt, + "start:", + start, + "stop:", + stop, + ) + # change chrom X and Y and MT to numbers + if chrom == "X": + chrom = 23 + elif chrom == "Y": + chrom = 24 + elif chrom == "MT": + chrom = 25 + chrom = int(chrom) + # if it is hg38 get its hg19 coordinates + if genomeRef == "hg38": + varObjList[i].hg38Chrom = chrom + varObjList[i].hg38Pos = pos + retList = gethg19LocFromHg38(chrom, pos) + retList = [newChrom, newPos] + varObjList[i].hg19Chrom = retList[0] + varObjList[i].hg19Pos = retList[1] + chrom = retList[0] + pos = retList[1] + # get the start + retList = gethg19LocFromHg38(chrom, start) + varObjList[i].start = int(retList[1]) + # get the stop + retList = gethg19LocFromHg38(chrom, stop) + varObjList[i].stop = int(retList[1]) else: - varObjList[i].hg19Chrom=chrom - varObjList[i].hg19Pos=pos - varObjList[i].chrom=chrom - varObjList[i].pos=pos - varObjList[i].start=start - varObjList[i].stop=stop - - - geneSymbol=row.SYMBOL - print('gene:', geneSymbol) - varObjList[i].geneSymbol=geneSymbol - varObjList[i].CADD_phred=row.CADD_phred - varObjList[i].CADD_PHRED=row.CADD_PHRED - #assign - varObjList[i].ref=ref - varObjList[i].alt=alt - varObjList[i].varId_dash='-'.join([str(chrom),str(pos),ref,alt]) - varId='_'.join([str(chrom),str(pos),ref,alt,transcriptId]) - varObjList[i].varId=varId - varObjList[i].zyg=row.ZYG - varObjList[i].geneEnsId=row.Gene - varObjList[i].rsId=row.Existing_variation - varObjList[i].GERPpp_RS=row.GERPpp_RS - varObjList[i].featureType=row.Feature_type - varObjList[i].gnomadAF=row.gnomAD_AF - varObjList[i].gnomadAFg=row.gnomADg_AF - varObjList[i].CLIN_SIG=row.CLIN_SIG - varObjList[i].HGVSc=row.HGVSc - varObjList[i].HGVSp=row.HGVSp - #dbnsfp attributes - varObjList[i].GERPpp_NR=row.GERPpp_NR - varObjList[i].DANN_score=row.DANN_score - varObjList[i].FATHMM_pred=row.FATHMM_pred - varObjList[i].FATHMM_score=row.FATHMM_score - varObjList[i].GTEx_V8_gene=row.GTEx_V8_gene - varObjList[i].GTEx_V8_tissue=row.GTEx_V8_tissue - varObjList[i].Polyphen2_HDIV_score=row.Polyphen2_HDIV_score - varObjList[i].Polyphen2_HVAR_score=row.Polyphen2_HVAR_score - varObjList[i].REVEL_score=row.REVEL_score - varObjList[i].SIFT_score=row.SIFT_score - varObjList[i].clinvar_clnsig=row.clinvar_clnsig - varObjList[i].fathmm_MKL_coding_score=row.fathmm_MKL_coding_score - varObjList[i].LRT_score=row.LRT_score - varObjList[i].LRT_Omega=row.LRT_Omega - varObjList[i].phyloP100way_vertebrate=row.phyloP100way_vertebrate - varObjList[i].M_CAP_score=row.M_CAP_score - varObjList[i].MutationAssessor_score=row.MutationAssessor_score - varObjList[i].MutationTaster_score=row.MutationTaster_score - varObjList[i].ESP6500_AA_AC=row.ESP6500_AA_AC - varObjList[i].ESP6500_AA_AF=row.ESP6500_AA_AF - varObjList[i].ESP6500_EA_AC=row.ESP6500_EA_AC - varObjList[i].ESP6500_EA_AF=row.ESP6500_EA_AF - #dbnsfp - - #for module one will need OMIM, HGMD, clinvar - #get OMIM + varObjList[i].hg19Chrom = chrom + varObjList[i].hg19Pos = pos + varObjList[i].chrom = chrom + varObjList[i].pos = pos + varObjList[i].start = start + varObjList[i].stop = stop + + geneSymbol = row.SYMBOL + print("gene:", geneSymbol) + varObjList[i].geneSymbol = geneSymbol + varObjList[i].CADD_phred = row.CADD_phred + varObjList[i].CADD_PHRED = row.CADD_PHRED + # assign + varObjList[i].ref = ref + varObjList[i].alt = alt + varObjList[i].varId_dash = "-".join([str(chrom), str(pos), ref, alt]) + varId = "_".join([str(chrom), str(pos), ref, alt, transcriptId]) + varObjList[i].varId = varId + varObjList[i].zyg = row.ZYG + varObjList[i].geneEnsId = row.Gene + varObjList[i].rsId = row.Existing_variation + varObjList[i].GERPpp_RS = row.GERPpp_RS + varObjList[i].featureType = row.Feature_type + varObjList[i].gnomadAF = row.gnomAD_AF + varObjList[i].gnomadAFg = row.gnomADg_AF + varObjList[i].CLIN_SIG = row.CLIN_SIG + varObjList[i].HGVSc = row.HGVSc + varObjList[i].HGVSp = row.HGVSp + # dbnsfp attributes + varObjList[i].GERPpp_NR = row.GERPpp_NR + varObjList[i].DANN_score = row.DANN_score + varObjList[i].FATHMM_pred = row.FATHMM_pred + varObjList[i].FATHMM_score = row.FATHMM_score + varObjList[i].GTEx_V8_gene = row.GTEx_V8_gene + varObjList[i].GTEx_V8_tissue = row.GTEx_V8_tissue + varObjList[i].Polyphen2_HDIV_score = row.Polyphen2_HDIV_score + varObjList[i].Polyphen2_HVAR_score = row.Polyphen2_HVAR_score + varObjList[i].REVEL_score = row.REVEL_score + varObjList[i].SIFT_score = row.SIFT_score + varObjList[i].clinvar_clnsig = row.clinvar_clnsig + varObjList[i].fathmm_MKL_coding_score = row.fathmm_MKL_coding_score + varObjList[i].LRT_score = row.LRT_score + varObjList[i].LRT_Omega = row.LRT_Omega + varObjList[i].phyloP100way_vertebrate = row.phyloP100way_vertebrate + varObjList[i].M_CAP_score = row.M_CAP_score + varObjList[i].MutationAssessor_score = row.MutationAssessor_score + varObjList[i].MutationTaster_score = row.MutationTaster_score + varObjList[i].ESP6500_AA_AC = row.ESP6500_AA_AC + varObjList[i].ESP6500_AA_AF = row.ESP6500_AA_AF + varObjList[i].ESP6500_EA_AC = row.ESP6500_EA_AC + varObjList[i].ESP6500_EA_AF = row.ESP6500_EA_AF + # dbnsfp + + # for module one will need OMIM, HGMD, clinvar + # get OMIM if 1: - print('\nReading OMIM') - #varObjList[i].omimList=jsonDict['omim'] - #retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] - omimRet=getOMIMUsingMarrvelFlatFile(varObjList[i], omimGeneList, omimAlleleList) - varObjList[i].omimVarFound=omimRet[0] - varObjList[i].omimGeneFound=omimRet[1] - varObjList[i].omimDict=omimRet[2] - varObjList[i].omimGeneDict=omimRet[3] - varObjList[i].omimAlleleDict=omimRet[4] - varObjList[i].phenoList=omimRet[5] - varObjList[i].phenoInhList=omimRet[6] - varObjList[i].phenoMimList=omimRet[7] - print('OMIM res:') - print('\tgeneFound:',varObjList[i].omimGeneFound,'varFound:',varObjList[i].omimVarFound ) - - #get clinvar + print("\nReading OMIM") + # varObjList[i].omimList=jsonDict['omim'] + # retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] + omimRet = getOMIMUsingMarrvelFlatFile( + varObjList[i], omimGeneList, omimAlleleList + ) + varObjList[i].omimVarFound = omimRet[0] + varObjList[i].omimGeneFound = omimRet[1] + varObjList[i].omimDict = omimRet[2] + varObjList[i].omimGeneDict = omimRet[3] + varObjList[i].omimAlleleDict = omimRet[4] + varObjList[i].phenoList = omimRet[5] + varObjList[i].phenoInhList = omimRet[6] + varObjList[i].phenoMimList = omimRet[7] + print("OMIM res:") + print( + "\tgeneFound:", + varObjList[i].omimGeneFound, + "varFound:", + varObjList[i].omimVarFound, + ) + + # get clinvar if 1: - print('\nReading clinVar') - #clinVarList=jsonDict['clinvar'] - #varObjList[i].clinVarList=clinVarList - #retList=[varFound,varDict, geneFound, geneDict, clinvarTotalNumVars,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB] - clinVarRet=getClinVarUsingMarrvelFlatFile(varObjList[i], clinvarAlleleDf, clinvarGeneDf) - varObjList[i].clinVarVarFound=clinVarRet[0] - varObjList[i].clinVarVarDict=clinVarRet[1] - varObjList[i].clinVarGeneFound=clinVarRet[2] - varObjList[i].clinVarGeneDict=clinVarRet[3] - varObjList[i].clinvarTotalNumVars=clinVarRet[4] - varObjList[i].clinvarNumP=clinVarRet[5] - varObjList[i].clinvarNumLP=clinVarRet[6] - varObjList[i].clinvarNumLB=clinVarRet[7] - varObjList[i].clinvarNumB=clinVarRet[8] - varObjList[i].clinvarTitle= clinVarRet[9] - varObjList[i].clinvarSignDesc=clinVarRet[10] - varObjList[i].clinvarCondition=clinVarRet[11] - print('clinVar res:') - if debugFlag==1: - print('\tgeneFound::',varObjList[i].clinVarGeneFound,'varFound:',varObjList[i].clinVarVarFound) - print('\tnumVars:',varObjList[i].clinvarTotalNumVars,'numPathologic:',varObjList[i].clinvarNumP,'numBenign:',varObjList[i].clinvarNumB) - print('\tsignDesc:', varObjList[i].clinvarSignDesc) - - #get HGMD annotation + print("\nReading clinVar") + # clinVarList=jsonDict['clinvar'] + # varObjList[i].clinVarList=clinVarList + # retList=[varFound,varDict, geneFound, geneDict, clinvarTotalNumVars,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB] + clinVarRet = getClinVarUsingMarrvelFlatFile( + varObjList[i], clinvarAlleleDf, clinvarGeneDf + ) + varObjList[i].clinVarVarFound = clinVarRet[0] + varObjList[i].clinVarVarDict = clinVarRet[1] + varObjList[i].clinVarGeneFound = clinVarRet[2] + varObjList[i].clinVarGeneDict = clinVarRet[3] + varObjList[i].clinvarTotalNumVars = clinVarRet[4] + varObjList[i].clinvarNumP = clinVarRet[5] + varObjList[i].clinvarNumLP = clinVarRet[6] + varObjList[i].clinvarNumLB = clinVarRet[7] + varObjList[i].clinvarNumB = clinVarRet[8] + varObjList[i].clinvarTitle = clinVarRet[9] + varObjList[i].clinvarSignDesc = clinVarRet[10] + varObjList[i].clinvarCondition = clinVarRet[11] + print("clinVar res:") + if debugFlag == 1: + print( + "\tgeneFound::", + varObjList[i].clinVarGeneFound, + "varFound:", + varObjList[i].clinVarVarFound, + ) + print( + "\tnumVars:", + varObjList[i].clinvarTotalNumVars, + "numPathologic:", + varObjList[i].clinvarNumP, + "numBenign:", + varObjList[i].clinvarNumB, + ) + print("\tsignDesc:", varObjList[i].clinvarSignDesc) + + # get HGMD annotation if 1: - print('\nReading HGMD') - hgmdRet=getHGMDUsingFlatFile(varObjList[i],hgmdDf) - #hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList - varObjList[i].hgmdVarFound=hgmdRet[0] - varObjList[i].hgmdGeneFound=hgmdRet[1] - varObjList[i].hgmdVarPhenIdList=hgmdRet[2] - varObjList[i].hgmdVarHPOIdList=hgmdRet[3] - varObjList[i].hgmdVarHPOStrList=hgmdRet[4] - print('HGMD results:') - print('\thgmdVarFound:',varObjList[i].hgmdVarFound,'hgmdGeneFound:',varObjList[i].hgmdGeneFound, - 'hgmdVarPhenIdList:',varObjList[i].hgmdVarPhenIdList,'hgmdVarHPOIdList:', - varObjList[i].hgmdVarHPOIdList, - 'hgmdVarHPOStrList:',varObjList[i].hgmdVarHPOStrList) - - #get DGV annotation + print("\nReading HGMD") + hgmdRet = getHGMDUsingFlatFile(varObjList[i], hgmdDf) + # hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList + varObjList[i].hgmdVarFound = hgmdRet[0] + varObjList[i].hgmdGeneFound = hgmdRet[1] + varObjList[i].hgmdVarPhenIdList = hgmdRet[2] + varObjList[i].hgmdVarHPOIdList = hgmdRet[3] + varObjList[i].hgmdVarHPOStrList = hgmdRet[4] + print("HGMD results:") + print( + "\thgmdVarFound:", + varObjList[i].hgmdVarFound, + "hgmdGeneFound:", + varObjList[i].hgmdGeneFound, + "hgmdVarPhenIdList:", + varObjList[i].hgmdVarPhenIdList, + "hgmdVarHPOIdList:", + varObjList[i].hgmdVarHPOIdList, + "hgmdVarHPOStrList:", + varObjList[i].hgmdVarHPOStrList, + ) + + # get DGV annotation if 1: - print('Reading DGV') - dgvList=jsonDict['dgv'] - #print('dgvList:', dgvList) - dgvRet=getDGVUsingMarrvelAnnotScript(varObjList[i],dgvList) - varObjList[i].dgvDictList=dgvRet[0] - varObjList[i].dgvTypeList=dgvRet[1] - varObjList[i].dgvSubtypeList=dgvRet[2] - #get DECIPHER annotation + print("Reading DGV") + dgvList = jsonDict["dgv"] + # print('dgvList:', dgvList) + dgvRet = getDGVUsingMarrvelAnnotScript(varObjList[i], dgvList) + varObjList[i].dgvDictList = dgvRet[0] + varObjList[i].dgvTypeList = dgvRet[1] + varObjList[i].dgvSubtypeList = dgvRet[2] + # get DECIPHER annotation if 1: - print('Reading DECIPHER') - decipherList=jsonDict['decipher'] - decipherRet=getDecipherUsingMarrvelAnnotScript(varObjList[i], decipherList) + print("Reading DECIPHER") + decipherList = jsonDict["decipher"] + decipherRet = getDecipherUsingMarrvelAnnotScript( + varObjList[i], decipherList + ) # - varObjList[i].decipherDictList=decipherRet[0] - varObjList[i].decipherDeletionObsList=decipherRet[1] - varObjList[i].decipherStudyList=decipherRet[2] - - #break for debug - i=i+1 - if debugFlag==1: - if i==2: + varObjList[i].decipherDictList = decipherRet[0] + varObjList[i].decipherDeletionObsList = decipherRet[1] + varObjList[i].decipherStudyList = decipherRet[2] + + # break for debug + i = i + 1 + if debugFlag == 1: + if i == 2: break - return varObjList + def getVarObjListFromDict(varDictList, numVars): - ''' - Make a var obj list after paralle processing + """ + Make a var obj list after paralle processing the list has three levels since from pool get a first level list which is equal to the number of cores used - ''' - numProc=len(varDictList) - print('numProc:', numProc) - #initialize - varObjList = [ Variant() for i in range(numVars) ] - i=0 + """ + numProc = len(varDictList) + print("numProc:", numProc) + # initialize + varObjList = [Variant() for i in range(numVars)] + i = 0 for procList in varDictList: for varDict in procList: - #print('i:', i) - #for varDict in dictList: - varObjList[i].chrom=varDict['chrom'] - varObjList[i].pos=varDict['pos'] - varObjList[i].start=varDict['start'] - varObjList[i].stop=varDict['stop'] - varObjList[i].ref=varDict['ref'] - varObjList[i].alt=varDict['alt'] - varObjList[i].hg19Chrom=varDict['hg19Chrom'] - varObjList[i].hg19Pos=varDict['hg19Pos'] - varObjList[i].varId=varDict['varId'] - varObjList[i].varId_dash=varDict['varId_dash'] - - #varObjList[i].patientID=varDict['patientID'] - varObjList[i].zyg=varDict['ZYG'] - varObjList[i].geneSymbol=varDict['geneSymbol'] - varObjList[i].CADD_phred=varDict['CADD_phred'] - varObjList[i].CADD_PHRED=varDict['CADD_PHRED'] - varObjList[i].geneEnsId=varDict['Gene'] - varObjList[i].rsId=varDict['Existing_variation'] - varObjList[i].GERPpp_RS=varDict['GERPpp_RS'] - varObjList[i].featureType=varDict['Feature_type'] - varObjList[i].gnomadAF=varDict['gnomadAF'] - varObjList[i].gnomadAFg=varDict['gnomadAFg'] - varObjList[i].CLIN_SIG=varDict['CLIN_SIG'] - varObjList[i].HGVSc=varDict['HGVSc'] - varObjList[i].HGVSp=varDict['HGVSp'] - - varObjList[i].VARIANT_CLASS=varDict['VARIANT_CLASS'] - varObjList[i].Feature=varDict['Feature'] - varObjList[i].hom=varDict['hom'] - varObjList[i].hgmd_rs=varDict['hgmd_rs'] - - varObjList[i].clin_dict=varDict['clin_dict'] - varObjList[i].clin_PLP=varDict['clin_PLP'] - varObjList[i].clin_PLP_perc=varDict['clin_PLP_perc'] - varObjList[i].spliceAI=varDict['spliceAI'] - varObjList[i].spliceAImax=varDict['spliceAImax'] - - - #dbnsfp attributes - varObjList[i].GERPpp_NR=varDict['GERPpp_NR'] - varObjList[i].DANN_score=varDict['DANN_score'] - varObjList[i].FATHMM_pred=varDict['FATHMM_pred'] - varObjList[i].FATHMM_score=varDict['FATHMM_score'] - varObjList[i].GTEx_V8_gene=varDict['GTEx_V8_gene'] - varObjList[i].GTEx_V8_tissue=varDict['GTEx_V8_tissue'] - varObjList[i].Polyphen2_HDIV_score=varDict['Polyphen2_HDIV_score'] - varObjList[i].Polyphen2_HVAR_score=varDict['Polyphen2_HVAR_score'] - varObjList[i].REVEL_score=varDict['REVEL_score'] - varObjList[i].SIFT_score=varDict['SIFT_score'] - #varObjList[i].clinvar_clnsig=varDict['clinvar_clnsig'] - - varObjList[i].clinvar_AlleleID = varDict['clinvar_AlleleID'] # Clinvar allele ID from clinvar.vcf.gz - varObjList[i].clinvar_clnsig = varDict['clinvar_clnsig'] #CL: Clinvar SIG from clinvar.vcf.gz - varObjList[i].clinvar_CLNREVSTAT = varDict['clinvar_CLNREVSTAT'] #CL: Clinvar STAT from clinvar.vcf.gz, for interface only - varObjList[i].clinvar_CLNSIGCONF = varDict['clinvar_CLNSIGCONF'] #CL: Clinvar STAT from clinvar.vcf.gz - varObjList[i].clin_code = varDict['clin_code'] #CL: feature for ai - - - varObjList[i].fathmm_MKL_coding_score=varDict['fathmm_MKL_coding_score'] - varObjList[i].LRT_score=varDict['LRT_score'] - varObjList[i].LRT_Omega=varDict['LRT_Omega'] - varObjList[i].phyloP100way_vertebrate=varDict['phyloP100way_vertebrate'] - varObjList[i].M_CAP_score=varDict['M_CAP_score'] - varObjList[i].MutationAssessor_score=varDict['MutationAssessor_score'] - varObjList[i].MutationTaster_score=varDict['MutationTaster_score'] - varObjList[i].ESP6500_AA_AC=varDict['ESP6500_AA_AC'] - varObjList[i].ESP6500_AA_AF=varDict['ESP6500_AA_AF'] - varObjList[i].ESP6500_EA_AC=varDict['ESP6500_EA_AC'] - varObjList[i].ESP6500_EA_AF=varDict['ESP6500_EA_AF'] - #conservation - varObjList[i].LRT_Omega=varDict['LRT_Omega'] - varObjList[i].LRT_score=varDict['LRT_score'] - varObjList[i].phyloP100way_vertebrate=varDict['phyloP100way_vertebrate'] - varObjList[i].IMPACT=varDict['IMPACT'] - varObjList[i].Consequence=varDict['Consequence'] - #DGV - varObjList[i].dgvDictList=varDict['dgvDictList'] - varObjList[i].dgvTypeList=varDict['dgvTypeList'] - varObjList[i].dgvSubtypeList=varDict['dgvSubtypeList'] - varObjList[i].dgvVarFound=varDict['dgvVarFound'] - #Decipher - varObjList[i].decipherDictList=varDict['decipherDictList'] - varObjList[i].decipherDeletionObsList=varDict['decipherDeletionObsList'] - varObjList[i].decipherStudyList=varDict['decipherStudyList'] - varObjList[i].decipherVarFound=varDict['decipherVarFound'] - #gnomadG - varObjList[i].gnomadGeneZscore=varDict['gnomadGeneZscore'] - varObjList[i].gnomadGenePLI=varDict['gnomadGenePLI'] - varObjList[i].gnomadGeneOELof=varDict['gnomadGeneOELof'] - varObjList[i].gnomadGeneOELofUpper=varDict['gnomadGeneOELofUpper'] - - varObjList[i].omimVarFound=varDict['omimVarFound'] - varObjList[i].omimGeneFound=varDict['omimGeneFound'] - varObjList[i].omimDict=varDict['omimDict'] - varObjList[i].omimGeneDict=varDict['omimGeneDict'] - varObjList[i].omimAlleleDict=varDict['omimAlleleDict'] - varObjList[i].phenoList=varDict['phenoList'] - varObjList[i].phenoInhList=varDict['phenoInhList'] - varObjList[i].phenoMimList=varDict['phenoMimList'] - - varObjList[i].clinVarVarFound=varDict['clinVarVarFound'] - varObjList[i].clinVarVarDict=varDict['clinVarVarDict'] - varObjList[i].clinVarGeneFound=varDict['clinVarGeneFound'] - varObjList[i].clinVarGeneDict=varDict['clinVarGeneDict'] - varObjList[i].clinvarTotalNumVars=varDict['clinvarTotalNumVars'] - varObjList[i].clinvarNumP=varDict['clinvarNumP'] - varObjList[i].clinvarNumLP=varDict['clinvarNumLP'] - varObjList[i].clinvarNumLB=varDict['clinvarNumLB'] - varObjList[i].clinvarNumB=varDict['clinvarNumB'] - varObjList[i].clinvarTitle= varDict['clinvarTitle'] - varObjList[i].clinvarSignDesc=varDict['clinvarSignDesc'] - varObjList[i].clinvarCondition=varDict['clinvarCondition'] - - varObjList[i].hgmdVarFound=varDict['hgmdVarFound'] - varObjList[i].hgmdGeneFound=varDict['hgmdGeneFound'] - varObjList[i].hgmdVarPhenIdList=varDict['hgmdVarPhenIdList'] - varObjList[i].hgmdVarHPOIdList=varDict['hgmdVarHPOIdList'] - varObjList[i].hgmdVarHPOStrList=varDict['hgmdVarHPOStrList'] - - varObjList[i].hgmd_id = varDict['hgmd_id'] # CL added - varObjList[i].hgmd_symbol = varDict['hgmd_symbol'] # CL added - varObjList[i].hgmd_PHEN = varDict['hgmd_PHEN'] # CL added - varObjList[i].hgmd_CLASS = varDict['hgmd_CLASS'] # CL added - - #symptom - varObjList[i].SymptomMatched=varDict['SymptomMatched'] - varObjList[i].symptomScore=varDict['symptomScore'] - varObjList[i].symptomName=varDict['symptomName'] - varObjList[i].omimSymptomSimScore=varDict['omimSymptomSimScore'] - varObjList[i].omimSymMatchFlag=varDict['omimSymMatchFlag'] - varObjList[i].hgmdSymptomScore=varDict['hgmdSymptomScore'] - varObjList[i].hgmdSymptomSimScore=varDict['hgmdSymptomSimScore'] - varObjList[i].hgmdSymMatchFlag=varDict['hgmdSymMatchFlag'] - varObjList[i].clinVarSymMatchFlag=varDict['clinVarSymMatchFlag'] - #increment - i=i+1 - #print('i index:', i) - #return + # print('i:', i) + # for varDict in dictList: + varObjList[i].chrom = varDict["chrom"] + varObjList[i].pos = varDict["pos"] + varObjList[i].start = varDict["start"] + varObjList[i].stop = varDict["stop"] + varObjList[i].ref = varDict["ref"] + varObjList[i].alt = varDict["alt"] + varObjList[i].hg19Chrom = varDict["hg19Chrom"] + varObjList[i].hg19Pos = varDict["hg19Pos"] + varObjList[i].varId = varDict["varId"] + varObjList[i].varId_dash = varDict["varId_dash"] + + # varObjList[i].patientID=varDict['patientID'] + varObjList[i].zyg = varDict["ZYG"] + varObjList[i].geneSymbol = varDict["geneSymbol"] + varObjList[i].CADD_phred = varDict["CADD_phred"] + varObjList[i].CADD_PHRED = varDict["CADD_PHRED"] + varObjList[i].geneEnsId = varDict["Gene"] + varObjList[i].rsId = varDict["Existing_variation"] + varObjList[i].GERPpp_RS = varDict["GERPpp_RS"] + varObjList[i].featureType = varDict["Feature_type"] + varObjList[i].gnomadAF = varDict["gnomadAF"] + varObjList[i].gnomadAFg = varDict["gnomadAFg"] + varObjList[i].CLIN_SIG = varDict["CLIN_SIG"] + varObjList[i].HGVSc = varDict["HGVSc"] + varObjList[i].HGVSp = varDict["HGVSp"] + + varObjList[i].VARIANT_CLASS = varDict["VARIANT_CLASS"] + varObjList[i].Feature = varDict["Feature"] + varObjList[i].hom = varDict["hom"] + varObjList[i].hgmd_rs = varDict["hgmd_rs"] + + varObjList[i].clin_dict = varDict["clin_dict"] + varObjList[i].clin_PLP = varDict["clin_PLP"] + varObjList[i].clin_PLP_perc = varDict["clin_PLP_perc"] + varObjList[i].spliceAI = varDict["spliceAI"] + varObjList[i].spliceAImax = varDict["spliceAImax"] + + # dbnsfp attributes + varObjList[i].GERPpp_NR = varDict["GERPpp_NR"] + varObjList[i].DANN_score = varDict["DANN_score"] + varObjList[i].FATHMM_pred = varDict["FATHMM_pred"] + varObjList[i].FATHMM_score = varDict["FATHMM_score"] + varObjList[i].GTEx_V8_gene = varDict["GTEx_V8_gene"] + varObjList[i].GTEx_V8_tissue = varDict["GTEx_V8_tissue"] + varObjList[i].Polyphen2_HDIV_score = varDict["Polyphen2_HDIV_score"] + varObjList[i].Polyphen2_HVAR_score = varDict["Polyphen2_HVAR_score"] + varObjList[i].REVEL_score = varDict["REVEL_score"] + varObjList[i].SIFT_score = varDict["SIFT_score"] + # varObjList[i].clinvar_clnsig=varDict['clinvar_clnsig'] + + varObjList[i].clinvar_AlleleID = varDict[ + "clinvar_AlleleID" + ] # Clinvar allele ID from clinvar.vcf.gz + varObjList[i].clinvar_clnsig = varDict[ + "clinvar_clnsig" + ] # CL: Clinvar SIG from clinvar.vcf.gz + varObjList[i].clinvar_CLNREVSTAT = varDict[ + "clinvar_CLNREVSTAT" + ] # CL: Clinvar STAT from clinvar.vcf.gz, for interface only + varObjList[i].clinvar_CLNSIGCONF = varDict[ + "clinvar_CLNSIGCONF" + ] # CL: Clinvar STAT from clinvar.vcf.gz + varObjList[i].clin_code = varDict["clin_code"] # CL: feature for ai + + varObjList[i].fathmm_MKL_coding_score = varDict["fathmm_MKL_coding_score"] + varObjList[i].LRT_score = varDict["LRT_score"] + varObjList[i].LRT_Omega = varDict["LRT_Omega"] + varObjList[i].phyloP100way_vertebrate = varDict["phyloP100way_vertebrate"] + varObjList[i].M_CAP_score = varDict["M_CAP_score"] + varObjList[i].MutationAssessor_score = varDict["MutationAssessor_score"] + varObjList[i].MutationTaster_score = varDict["MutationTaster_score"] + varObjList[i].ESP6500_AA_AC = varDict["ESP6500_AA_AC"] + varObjList[i].ESP6500_AA_AF = varDict["ESP6500_AA_AF"] + varObjList[i].ESP6500_EA_AC = varDict["ESP6500_EA_AC"] + varObjList[i].ESP6500_EA_AF = varDict["ESP6500_EA_AF"] + # conservation + varObjList[i].LRT_Omega = varDict["LRT_Omega"] + varObjList[i].LRT_score = varDict["LRT_score"] + varObjList[i].phyloP100way_vertebrate = varDict["phyloP100way_vertebrate"] + varObjList[i].IMPACT = varDict["IMPACT"] + varObjList[i].Consequence = varDict["Consequence"] + # DGV + varObjList[i].dgvDictList = varDict["dgvDictList"] + varObjList[i].dgvTypeList = varDict["dgvTypeList"] + varObjList[i].dgvSubtypeList = varDict["dgvSubtypeList"] + varObjList[i].dgvVarFound = varDict["dgvVarFound"] + # Decipher + varObjList[i].decipherDictList = varDict["decipherDictList"] + varObjList[i].decipherDeletionObsList = varDict["decipherDeletionObsList"] + varObjList[i].decipherStudyList = varDict["decipherStudyList"] + varObjList[i].decipherVarFound = varDict["decipherVarFound"] + # gnomadG + varObjList[i].gnomadGeneZscore = varDict["gnomadGeneZscore"] + varObjList[i].gnomadGenePLI = varDict["gnomadGenePLI"] + varObjList[i].gnomadGeneOELof = varDict["gnomadGeneOELof"] + varObjList[i].gnomadGeneOELofUpper = varDict["gnomadGeneOELofUpper"] + + varObjList[i].omimVarFound = varDict["omimVarFound"] + varObjList[i].omimGeneFound = varDict["omimGeneFound"] + varObjList[i].omimDict = varDict["omimDict"] + varObjList[i].omimGeneDict = varDict["omimGeneDict"] + varObjList[i].omimAlleleDict = varDict["omimAlleleDict"] + varObjList[i].phenoList = varDict["phenoList"] + varObjList[i].phenoInhList = varDict["phenoInhList"] + varObjList[i].phenoMimList = varDict["phenoMimList"] + + varObjList[i].clinVarVarFound = varDict["clinVarVarFound"] + varObjList[i].clinVarVarDict = varDict["clinVarVarDict"] + varObjList[i].clinVarGeneFound = varDict["clinVarGeneFound"] + varObjList[i].clinVarGeneDict = varDict["clinVarGeneDict"] + varObjList[i].clinvarTotalNumVars = varDict["clinvarTotalNumVars"] + varObjList[i].clinvarNumP = varDict["clinvarNumP"] + varObjList[i].clinvarNumLP = varDict["clinvarNumLP"] + varObjList[i].clinvarNumLB = varDict["clinvarNumLB"] + varObjList[i].clinvarNumB = varDict["clinvarNumB"] + varObjList[i].clinvarTitle = varDict["clinvarTitle"] + varObjList[i].clinvarSignDesc = varDict["clinvarSignDesc"] + varObjList[i].clinvarCondition = varDict["clinvarCondition"] + + varObjList[i].hgmdVarFound = varDict["hgmdVarFound"] + varObjList[i].hgmdGeneFound = varDict["hgmdGeneFound"] + varObjList[i].hgmdVarPhenIdList = varDict["hgmdVarPhenIdList"] + varObjList[i].hgmdVarHPOIdList = varDict["hgmdVarHPOIdList"] + varObjList[i].hgmdVarHPOStrList = varDict["hgmdVarHPOStrList"] + + varObjList[i].hgmd_id = varDict["hgmd_id"] # CL added + varObjList[i].hgmd_symbol = varDict["hgmd_symbol"] # CL added + varObjList[i].hgmd_PHEN = varDict["hgmd_PHEN"] # CL added + varObjList[i].hgmd_CLASS = varDict["hgmd_CLASS"] # CL added + + # symptom + varObjList[i].SymptomMatched = varDict["SymptomMatched"] + varObjList[i].symptomScore = varDict["symptomScore"] + varObjList[i].symptomName = varDict["symptomName"] + varObjList[i].omimSymptomSimScore = varDict["omimSymptomSimScore"] + varObjList[i].omimSymMatchFlag = varDict["omimSymMatchFlag"] + varObjList[i].hgmdSymptomScore = varDict["hgmdSymptomScore"] + varObjList[i].hgmdSymptomSimScore = varDict["hgmdSymptomSimScore"] + varObjList[i].hgmdSymMatchFlag = varDict["hgmdSymMatchFlag"] + varObjList[i].clinVarSymMatchFlag = varDict["clinVarSymMatchFlag"] + # increment + i = i + 1 + # print('i index:', i) + # return return varObjList -def getAnnotateInfo_2(rowList,genomeRef,clinvarGeneDf,clinvarAlleleDf, omimGeneList, omimAlleleList,hgmdDf, moduleList, dgvDf, decipherDf, gnomadMetricsGeneDf): - ''' + +def getAnnotateInfo_2( + rowList, + genomeRef, + clinvarGeneDf, + clinvarAlleleDf, + omimGeneList, + omimAlleleList, + hgmdDf, + moduleList, + dgvDf, + decipherDf, + gnomadMetricsGeneDf, +): + """ Function to read the input annoatated varaint file and call the marrvel flat files to get annotations such as OMIM, DGV, Decipher Parameters: rowList: the input variants as a lits other inputs are the flat file objects read locally instaed of an API - + Returns: A dict of annoatated varaints - ''' - debugFlag=0 - #print('\nlen numVars:', len(rowList), 'typerow00:', type(rowList[0][0]), 'typerow[0]:', + """ + debugFlag = 0 + # print('\nlen numVars:', len(rowList), 'typerow00:', type(rowList[0][0]), 'typerow[0]:', # type(rowList[0]) ) - #print('len(row[0][0]):', len(rowList[0][0])) - #print('moduleList:', moduleList) - varObjList=[] - varDictList=[] - i=0 - #loop thru the rows + # print('len(row[0][0]):', len(rowList[0][0])) + # print('moduleList:', moduleList) + varObjList = [] + varDictList = [] + i = 0 + # loop thru the rows for row in rowList: # CL 03-14-2023: commented all printing lines - #print('type of row:', type(row)) - varObj=Variant() - transcriptId=row.Feature - #s=row.Uploaded_variation.split('_') '1_10204_-/T' 1_1588250_T_A + # print('type of row:', type(row)) + varObj = Variant() + transcriptId = row.Feature + # s=row.Uploaded_variation.split('_') '1_10204_-/T' 1_1588250_T_A ####row[0]: 21_11039079_C/A ####s: ['21', '11039079', 'C/A'] - #print('row[0]:', row[0]) - - #two ways of input of first column either 1_1588250_T_A OR 21_11039079_C/A, so use the option flag - optFlag=0 - if(row[0].find('/')!=-1): - optFlag=1 - - if optFlag==0: - s=row[0].split('_') - #print('s:', s) - chrom=s[0] - pos=int(s[1]) - ref=s[2] - alt=s[3] - elif optFlag==1: - s=row[0].split('_') - #print('s:', s) - chrom=s[0] - pos=int(s[1]) - s=s[2].split('/') - ref=s[0] - alt=s[1] - - #get the start and stop from second column like '1:10203-10204' - if '-' in row[1]: - s=row[1].split(':') - tmp=s[1] - s=tmp.split('-') - #print('s:',s) - start=int(s[0]) - stop=int(s[1]) + # print('row[0]:', row[0]) + + # two ways of input of first column either 1_1588250_T_A OR 21_11039079_C/A, so use the option flag + optFlag = 0 + if row[0].find("/") != -1: + optFlag = 1 + + if optFlag == 0: + s = row[0].split("_") + # print('s:', s) + chrom = s[0] + pos = int(s[1]) + ref = s[2] + alt = s[3] + elif optFlag == 1: + s = row[0].split("_") + # print('s:', s) + chrom = s[0] + pos = int(s[1]) + s = s[2].split("/") + ref = s[0] + alt = s[1] + + # get the start and stop from second column like '1:10203-10204' + if "-" in row[1]: + s = row[1].split(":") + tmp = s[1] + s = tmp.split("-") + # print('s:',s) + start = int(s[0]) + stop = int(s[1]) else: - #start and stop the same - s=row[1].split(':') - start=int(s[1]) - stop=int(s[1]) - - #print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) - #change chrom X and Y and MT to numbers - if chrom=='X': - chrom=23 - elif chrom=='Y': - chrom=24 - elif chrom=='MT': - chrom=25 - elif re.search(r'GL', chrom): - chrom=26 - chrom=int(chrom) - - #if it is hg38 get its hg19 coordinates + # start and stop the same + s = row[1].split(":") + start = int(s[1]) + stop = int(s[1]) + + # print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) + # change chrom X and Y and MT to numbers + if chrom == "X": + chrom = 23 + elif chrom == "Y": + chrom = 24 + elif chrom == "MT": + chrom = 25 + elif re.search(r"GL", chrom): + chrom = 26 + chrom = int(chrom) + + # if it is hg38 get its hg19 coordinates # CL 03-14-2023: we have separate database for hg19 and hg38, # we don't need to use LiftOver which is inaccurate # related codes commented and modified - if genomeRef=='hg38': - varObj.hg38Chrom=chrom - varObj.hg38Pos=pos - varObj.chrom=chrom - varObj.pos=pos - varObj.start=start - varObj.stop=stop - - ''' + if genomeRef == "hg38": + varObj.hg38Chrom = chrom + varObj.hg38Pos = pos + varObj.chrom = chrom + varObj.pos = pos + varObj.start = start + varObj.stop = stop + + """ retList=gethg19LocFromHg38(chrom, pos)#called from the utils_1.py # retList=[newChrom, newPos] varObj.hg19Chrom=retList[0] @@ -728,333 +828,341 @@ def getAnnotateInfo_2(rowList,genomeRef,clinvarGeneDf,clinvarAlleleDf, omimGeneL #get the stop retList=gethg19LocFromHg38(chrom, stop) varObj.stop=int(retList[1]) - ''' + """ else: - varObj.hg19Chrom=chrom - varObj.hg19Pos=pos - varObj.chrom=chrom - varObj.pos=pos - varObj.start=start - varObj.stop=stop - - - geneSymbol=row.SYMBOL - #print('gene:', geneSymbol) - varObj.geneSymbol=geneSymbol - varObj.CADD_phred=row.CADD_phred - varObj.CADD_PHRED=row.CADD_PHRED - - #assign - varObj.ref=ref - varObj.alt=alt - varObj.varId_dash='-'.join([str(chrom),str(start),ref,alt]) - #print('varId dash:', varObj.varId_dash) - varId='_'.join([str(chrom),str(pos),ref,alt,transcriptId]) - varObj.varId=varId - if 'ZYG' in row: - varObj.zyg=row.ZYG - varObj.geneEnsId=row.Gene - varObj.rsId=row.Existing_variation - varObj.GERPpp_RS=row.GERPpp_RS - varObj.featureType=row.Feature_type - varObj.gnomadAF=row.gnomAD_AF - varObj.gnomadAFg=row.gnomADg_AF - varObj.CLIN_SIG=row.CLIN_SIG #CL: useless but kept for now - varObj.LRT_Omega=row.LRT_Omega - varObj.LRT_score=row.LRT_score - varObj.phyloP100way_vertebrate=row.phyloP100way_vertebrate - varObj.IMPACT=row.IMPACT - varObj.Consequence=row.Consequence - varObj.HGVSc=row.HGVSc - varObj.HGVSp=row.HGVSp - #dbnsfp attributes - varObj.GERPpp_NR=row.GERPpp_NR - varObj.DANN_score=row.DANN_score - varObj.FATHMM_pred=row.FATHMM_pred - varObj.FATHMM_score=row.FATHMM_score - varObj.GTEx_V8_gene=row.GTEx_V8_gene - varObj.GTEx_V8_tissue=row.GTEx_V8_tissue - varObj.Polyphen2_HDIV_score=row.Polyphen2_HDIV_score - varObj.Polyphen2_HVAR_score=row.Polyphen2_HVAR_score - varObj.REVEL_score=row.REVEL_score - varObj.SIFT_score=row.SIFT_score - - varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz - varObj.clinvar_clnsig= row.clinvar_CLNSIG #CL: Clinvar SIG from clinvar.vcf.gz - #varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted - varObj.clinvar_CLNREVSTAT= row.clinvar_CLNREVSTAT #CL: Clinvar STAT from clinvar.vcf.gz, for interface only - varObj.clinvar_CLNSIGCONF= row.clinvar_CLNSIGCONF #CL: Clinvar SIGCONF from clinvar.vcf.gz - varObj.clin_code = row.clinvar_CLNSIG #CL: feature name for ai - - varObj.fathmm_MKL_coding_score=row.fathmm_MKL_coding_score - varObj.LRT_score=row.LRT_score - varObj.LRT_Omega=row.LRT_Omega - varObj.phyloP100way_vertebrate=row.phyloP100way_vertebrate - varObj.M_CAP_score=row.M_CAP_score - varObj.MutationAssessor_score=row.MutationAssessor_score - varObj.MutationTaster_score=row.MutationTaster_score - varObj.ESP6500_AA_AC=row.ESP6500_AA_AC - varObj.ESP6500_AA_AF=row.ESP6500_AA_AF - varObj.ESP6500_EA_AC=row.ESP6500_EA_AC - varObj.ESP6500_EA_AF=row.ESP6500_EA_AF - - varObj.VARIANT_CLASS=row.VARIANT_CLASS - varObj.Feature=row.Feature - varObj.hom=row.gnomADg_controls_nhomalt - varObj.hgmd_id=row.hgmd # CL added - varObj.hgmd_symbol=row.hgmd_GENE # CL added - varObj.hgmd_rs=row.hgmd_RANKSCORE - varObj.hgmd_PHEN=row.hgmd_PHEN # CL added - varObj.hgmd_CLASS=row.hgmd_CLASS # CL added - - if row.clinvar_CLNSIGCONF !="-": - clin_dict=dict() + varObj.hg19Chrom = chrom + varObj.hg19Pos = pos + varObj.chrom = chrom + varObj.pos = pos + varObj.start = start + varObj.stop = stop + + geneSymbol = row.SYMBOL + # print('gene:', geneSymbol) + varObj.geneSymbol = geneSymbol + varObj.CADD_phred = row.CADD_phred + varObj.CADD_PHRED = row.CADD_PHRED + + # assign + varObj.ref = ref + varObj.alt = alt + varObj.varId_dash = "-".join([str(chrom), str(start), ref, alt]) + # print('varId dash:', varObj.varId_dash) + varId = "_".join([str(chrom), str(pos), ref, alt, transcriptId]) + varObj.varId = varId + if "ZYG" in row: + varObj.zyg = row.ZYG + varObj.geneEnsId = row.Gene + varObj.rsId = row.Existing_variation + varObj.GERPpp_RS = row.GERPpp_RS + varObj.featureType = row.Feature_type + varObj.gnomadAF = row.gnomAD_AF + varObj.gnomadAFg = row.gnomADg_AF + varObj.CLIN_SIG = row.CLIN_SIG # CL: useless but kept for now + varObj.LRT_Omega = row.LRT_Omega + varObj.LRT_score = row.LRT_score + varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate + varObj.IMPACT = row.IMPACT + varObj.Consequence = row.Consequence + varObj.HGVSc = row.HGVSc + varObj.HGVSp = row.HGVSp + # dbnsfp attributes + varObj.GERPpp_NR = row.GERPpp_NR + varObj.DANN_score = row.DANN_score + varObj.FATHMM_pred = row.FATHMM_pred + varObj.FATHMM_score = row.FATHMM_score + varObj.GTEx_V8_gene = row.GTEx_V8_gene + varObj.GTEx_V8_tissue = row.GTEx_V8_tissue + varObj.Polyphen2_HDIV_score = row.Polyphen2_HDIV_score + varObj.Polyphen2_HVAR_score = row.Polyphen2_HVAR_score + varObj.REVEL_score = row.REVEL_score + varObj.SIFT_score = row.SIFT_score + + varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz + varObj.clinvar_clnsig = ( + row.clinvar_CLNSIG + ) # CL: Clinvar SIG from clinvar.vcf.gz + # varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted + varObj.clinvar_CLNREVSTAT = ( + row.clinvar_CLNREVSTAT + ) # CL: Clinvar STAT from clinvar.vcf.gz, for interface only + varObj.clinvar_CLNSIGCONF = ( + row.clinvar_CLNSIGCONF + ) # CL: Clinvar SIGCONF from clinvar.vcf.gz + varObj.clin_code = row.clinvar_CLNSIG # CL: feature name for ai + + varObj.fathmm_MKL_coding_score = row.fathmm_MKL_coding_score + varObj.LRT_score = row.LRT_score + varObj.LRT_Omega = row.LRT_Omega + varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate + varObj.M_CAP_score = row.M_CAP_score + varObj.MutationAssessor_score = row.MutationAssessor_score + varObj.MutationTaster_score = row.MutationTaster_score + varObj.ESP6500_AA_AC = row.ESP6500_AA_AC + varObj.ESP6500_AA_AF = row.ESP6500_AA_AF + varObj.ESP6500_EA_AC = row.ESP6500_EA_AC + varObj.ESP6500_EA_AF = row.ESP6500_EA_AF + + varObj.VARIANT_CLASS = row.VARIANT_CLASS + varObj.Feature = row.Feature + varObj.hom = row.gnomADg_controls_nhomalt + varObj.hgmd_id = row.hgmd # CL added + varObj.hgmd_symbol = row.hgmd_GENE # CL added + varObj.hgmd_rs = row.hgmd_RANKSCORE + varObj.hgmd_PHEN = row.hgmd_PHEN # CL added + varObj.hgmd_CLASS = row.hgmd_CLASS # CL added + + if row.clinvar_CLNSIGCONF != "-": + clin_dict = dict() for ro in row.clinvar_CLNSIGCONF.split("|_"): - temp=ro.split("(") - clin_dict[temp[0]]=int(temp[1][0]) - PLP_sum=clin_dict.get("Pathogenic", 0)+clin_dict.get("Likely_pathogenic", 0) - varObj.clin_dict=clin_dict - varObj.clin_PLP=PLP_sum - varObj.clin_PLP_perc=PLP_sum/sum(clin_dict.values()) + temp = ro.split("(") + clin_dict[temp[0]] = int(temp[1][0]) + PLP_sum = clin_dict.get("Pathogenic", 0) + clin_dict.get( + "Likely_pathogenic", 0 + ) + varObj.clin_dict = clin_dict + varObj.clin_PLP = PLP_sum + varObj.clin_PLP_perc = PLP_sum / sum(clin_dict.values()) else: if "benign" in row.clinvar_clnsig.lower(): - varObj.clin_PLP_perc=0 + varObj.clin_PLP_perc = 0 elif "pathogenic" in row.clinvar_clnsig.lower(): - varObj.clin_PLP_perc=1 + varObj.clin_PLP_perc = 1 else: - varObj.clin_PLP_perc="-" - varObj.clin_PLP="-" - varObj.clin_dict="-" - - - if row.SpliceAI_pred !="-": - varObj.spliceAI=row.SpliceAI_pred - temp=row.SpliceAI_pred.split("|") - varObj.spliceAImax=max(float(temp[1]),float(temp[2]),float(temp[3]),float(temp[4])) + varObj.clin_PLP_perc = "-" + varObj.clin_PLP = "-" + varObj.clin_dict = "-" + + if row.SpliceAI_pred != "-": + varObj.spliceAI = row.SpliceAI_pred + temp = row.SpliceAI_pred.split("|") + varObj.spliceAImax = max( + float(temp[1]), float(temp[2]), float(temp[3]), float(temp[4]) + ) else: - varObj.spliceAI="-" - varObj.spliceAImax="-" - - #get dgv - if 'conserve' in moduleList: - #print('\nGetting DGV') - dgvRet=getDGVUsingMarrvelFlatFile(varObj, dgvDf) - varObj.dgvDictList=dgvRet[0] - varObj.dgvTypeList=dgvRet[1] - varObj.dgvSubtypeList=dgvRet[2] - varObj.dgvVarFound=dgvRet[3] - - #get decipher - if 'conserve' in moduleList: - #print('\nGetting DECIPHER') - decipherRet=getDecipherUsingMarrvelFlatFile(varObj, decipherDf) - #[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] - varObj.decipherDictList=decipherRet[0] - varObj.decipherDeletionObsList=decipherRet[1] - varObj.decipherStudyList=decipherRet[2] - varObj.decipherVarFound=decipherRet[3] - - #get gnomad gene metrics from gnomad file - if 'conserve' in moduleList: - #print('\nGetting gnomad gene metrics') - gnomadGeneMetricRet=getGnomadGeneMetricFlatFile(varObj, gnomadMetricsGeneDf) - #[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] - varObj.gnomadGeneZscore=gnomadGeneMetricRet[0] - varObj.gnomadGenePLI=gnomadGeneMetricRet[1] - varObj.gnomadGeneOELof=gnomadGeneMetricRet[2]#O/E lof - varObj.gnomadGeneOELofUpper=gnomadGeneMetricRet[3]#O/E lof upper - - #for module one will need OMIM, HGMD, clinvar - #get OMIM - if 'curate' in moduleList: - #print('\nGetting OMIM') - #varObj.omimList=jsonDict['omim'] - #retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] - omimRet=getOMIMUsingMarrvelFlatFile(varObj, omimGeneList, omimAlleleList) - varObj.omimVarFound=omimRet[0] - varObj.omimGeneFound=omimRet[1] - varObj.omimDict=omimRet[2] - varObj.omimGeneDict=omimRet[3] - varObj.omimAlleleDict=omimRet[4] - varObj.phenoList=omimRet[5] - varObj.phenoInhList=omimRet[6] - varObj.phenoMimList=omimRet[7] - #print('OMIM res:') - #print('\tgeneFound:',varObj.omimGeneFound,'varFound:',varObj.omimVarFound ) - - #get clinvar - if 'curate' in moduleList: - #print('\nReading clinVar') - clinVarRet=getClinVarUsingMarrvelFlatFile(varObj, clinvarAlleleDf, clinvarGeneDf) - varObj.clinVarVarFound=clinVarRet[0] - varObj.clinVarVarDict=clinVarRet[1] - varObj.clinVarGeneFound=clinVarRet[2] - varObj.clinVarGeneDict=clinVarRet[3] - varObj.clinvarTotalNumVars=clinVarRet[4] - varObj.clinvarNumP=clinVarRet[5] - varObj.clinvarNumLP=clinVarRet[6] - varObj.clinvarNumLB=clinVarRet[7] - varObj.clinvarNumB=clinVarRet[8] - varObj.clinvarTitle= clinVarRet[9] - varObj.clinvarSignDesc= row.clinvar_CLNSIG #clinVarRet[10] #CL: changed to clinvar.vcf.gz annotation - varObj.clinvarCondition=clinVarRet[11] - #print('clinVar res:') - ''' + varObj.spliceAI = "-" + varObj.spliceAImax = "-" + + # get dgv + if "conserve" in moduleList: + # print('\nGetting DGV') + dgvRet = getDGVUsingMarrvelFlatFile(varObj, dgvDf) + varObj.dgvDictList = dgvRet[0] + varObj.dgvTypeList = dgvRet[1] + varObj.dgvSubtypeList = dgvRet[2] + varObj.dgvVarFound = dgvRet[3] + + # get decipher + if "conserve" in moduleList: + # print('\nGetting DECIPHER') + decipherRet = getDecipherUsingMarrvelFlatFile(varObj, decipherDf) + # [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] + varObj.decipherDictList = decipherRet[0] + varObj.decipherDeletionObsList = decipherRet[1] + varObj.decipherStudyList = decipherRet[2] + varObj.decipherVarFound = decipherRet[3] + + # get gnomad gene metrics from gnomad file + if "conserve" in moduleList: + # print('\nGetting gnomad gene metrics') + gnomadGeneMetricRet = getGnomadGeneMetricFlatFile( + varObj, gnomadMetricsGeneDf + ) + # [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] + varObj.gnomadGeneZscore = gnomadGeneMetricRet[0] + varObj.gnomadGenePLI = gnomadGeneMetricRet[1] + varObj.gnomadGeneOELof = gnomadGeneMetricRet[2] # O/E lof + varObj.gnomadGeneOELofUpper = gnomadGeneMetricRet[3] # O/E lof upper + + # for module one will need OMIM, HGMD, clinvar + # get OMIM + if "curate" in moduleList: + # print('\nGetting OMIM') + # varObj.omimList=jsonDict['omim'] + # retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] + omimRet = getOMIMUsingMarrvelFlatFile(varObj, omimGeneList, omimAlleleList) + varObj.omimVarFound = omimRet[0] + varObj.omimGeneFound = omimRet[1] + varObj.omimDict = omimRet[2] + varObj.omimGeneDict = omimRet[3] + varObj.omimAlleleDict = omimRet[4] + varObj.phenoList = omimRet[5] + varObj.phenoInhList = omimRet[6] + varObj.phenoMimList = omimRet[7] + # print('OMIM res:') + # print('\tgeneFound:',varObj.omimGeneFound,'varFound:',varObj.omimVarFound ) + + # get clinvar + if "curate" in moduleList: + # print('\nReading clinVar') + clinVarRet = getClinVarUsingMarrvelFlatFile( + varObj, clinvarAlleleDf, clinvarGeneDf + ) + varObj.clinVarVarFound = clinVarRet[0] + varObj.clinVarVarDict = clinVarRet[1] + varObj.clinVarGeneFound = clinVarRet[2] + varObj.clinVarGeneDict = clinVarRet[3] + varObj.clinvarTotalNumVars = clinVarRet[4] + varObj.clinvarNumP = clinVarRet[5] + varObj.clinvarNumLP = clinVarRet[6] + varObj.clinvarNumLB = clinVarRet[7] + varObj.clinvarNumB = clinVarRet[8] + varObj.clinvarTitle = clinVarRet[9] + varObj.clinvarSignDesc = ( + row.clinvar_CLNSIG + ) # clinVarRet[10] #CL: changed to clinvar.vcf.gz annotation + varObj.clinvarCondition = clinVarRet[11] + # print('clinVar res:') + """ if debugFlag==1: print('\tgeneFound::',varObj.clinVarGeneFound,'varFound:',varObj.clinVarVarFound) print('\tnumVars:',varObj.clinvarTotalNumVars,'numPathologic:',varObj.clinvarNumP,'numBenign:',varObj.clinvarNumB) print('\tsignDesc:', varObj.clinvarSignDesc) - ''' - - #get HGMD - if 'curate' in moduleList: - #print('\nReading HGMD') - hgmdRet=getHGMDUsingFlatFile(varObj,hgmdDf) - #hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList - varObj.hgmdVarFound=hgmdRet[0] - varObj.hgmdGeneFound=hgmdRet[1] - varObj.hgmdVarPhenIdList=hgmdRet[2] - varObj.hgmdVarHPOIdList=hgmdRet[3] - varObj.hgmdVarHPOStrList=hgmdRet[4] - #print('HGMD results:') - #print('\thgmdVarFound:',varObj.hgmdVarFound,'hgmdGeneFound:',varObj.hgmdGeneFound, + """ + + # get HGMD + if "curate" in moduleList: + # print('\nReading HGMD') + hgmdRet = getHGMDUsingFlatFile(varObj, hgmdDf) + # hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList + varObj.hgmdVarFound = hgmdRet[0] + varObj.hgmdGeneFound = hgmdRet[1] + varObj.hgmdVarPhenIdList = hgmdRet[2] + varObj.hgmdVarHPOIdList = hgmdRet[3] + varObj.hgmdVarHPOStrList = hgmdRet[4] + # print('HGMD results:') + # print('\thgmdVarFound:',varObj.hgmdVarFound,'hgmdGeneFound:',varObj.hgmdGeneFound, # 'hgmdVarPhenIdList:',varObj.hgmdVarPhenIdList,'hgmdVarHPOIdList:', # varObj.hgmdVarHPOIdList, # 'hgmdVarHPOStrList:',varObj.hgmdVarHPOStrList) - #make a dict from the varObj. the varObj has one element for each row of the input VEP annotated file - varDict={ - 'hg19Chrom':varObj.hg19Chrom, - 'hg19Pos':varObj.hg19Pos, - 'chrom':varObj.chrom, - 'pos':varObj.pos, - 'start':varObj.start, - 'stop':varObj.stop, - 'geneSymbol':varObj.geneSymbol, - 'CADD_phred':varObj.CADD_phred, - 'CADD_PHRED':varObj.CADD_PHRED, - 'ref':varObj.ref, - 'alt':varObj.alt, - 'varId':varObj.varId, - 'ZYG':varObj.zyg, - 'HGVSc':varObj.HGVSc, - 'HGVSp':varObj.HGVSp, - 'Gene':varObj.geneEnsId, - 'Existing_variation':varObj.rsId, - 'GERPpp_RS':varObj.GERPpp_RS, - 'Feature_type':varObj.featureType, - 'gnomadAF':varObj.gnomadAF, - 'gnomadAFg':varObj.gnomadAFg, - 'CLIN_SIG':varObj.CLIN_SIG, - 'LRT_Omega':varObj.LRT_Omega, - 'LRT_score':varObj.LRT_score, - 'phyloP100way_vertebrate':varObj.phyloP100way_vertebrate, - #dbnsfp attributes - 'GERPpp_NR':varObj.GERPpp_NR, - 'DANN_score':varObj.DANN_score, - 'FATHMM_pred':varObj.FATHMM_pred, - 'FATHMM_score':varObj.FATHMM_score, - 'GTEx_V8_gene':varObj.GTEx_V8_gene, - 'GTEx_V8_tissue':varObj.GTEx_V8_tissue, - 'Polyphen2_HDIV_score':varObj.Polyphen2_HDIV_score, - 'Polyphen2_HVAR_score':varObj.Polyphen2_HVAR_score, - 'REVEL_score':varObj.REVEL_score, - 'SIFT_score':varObj.SIFT_score, - - 'clinvar_AlleleID': varObj.clinvar_AlleleID, # Clinvar allele ID from clinvar.vcf.gz - 'clinvar_clnsig': varObj.clinvar_clnsig, #CL: Clinvar SIG from clinvar.vcf.gz - 'clinvar_CLNREVSTAT': varObj.clinvar_CLNREVSTAT, #CL: Clinvar STAT from clinvar.vcf.gz, for interface only - 'clinvar_CLNSIGCONF': varObj.clinvar_CLNSIGCONF, #CL: Clinvar SIGCONF from clinvar.vcf.gz - 'clin_code': varObj.clin_code, #CL: feature for ai - - 'fathmm_MKL_coding_score':varObj.fathmm_MKL_coding_score, - 'LRT_score':varObj.LRT_score, - 'LRT_Omega':varObj.LRT_Omega, - 'phyloP100way_vertebrate':varObj.phyloP100way_vertebrate, - 'M_CAP_score':varObj.M_CAP_score, - 'MutationAssessor_score':varObj.MutationAssessor_score, - 'MutationTaster_score':varObj.MutationTaster_score, - 'ESP6500_AA_AC':varObj.ESP6500_AA_AC, - 'ESP6500_AA_AF':varObj.ESP6500_AA_AF, - 'ESP6500_EA_AC':varObj.ESP6500_EA_AC, - 'ESP6500_EA_AF':varObj.ESP6500_EA_AF, - #dbnsfp - 'gnomadGeneZscore':varObj.gnomadGeneZscore, - 'gnomadGenePLI':varObj.gnomadGenePLI, - 'gnomadGeneOELof':varObj.gnomadGeneOELof,#O/E lof - 'gnomadGeneOELofUpper':varObj.gnomadGeneOELofUpper,#O/E lof upper, - 'IMPACT':varObj.IMPACT, - 'Consequence':varObj.Consequence, - 'omimVarFound':varObj.omimVarFound, - 'omimGeneFound':varObj.omimGeneFound, - 'omimDict':varObj.omimDict, - 'omimGeneDict':varObj.omimGeneDict, - 'omimAlleleDict':varObj.omimAlleleDict, - 'phenoList':varObj.phenoList, - 'phenoInhList':varObj.phenoInhList, - 'phenoMimList':varObj.phenoMimList, - 'clinVarVarFound':varObj.clinVarVarFound, - 'clinVarVarDict':varObj.clinVarVarDict, - 'clinVarGeneFound':varObj.clinVarGeneFound, - 'clinVarGeneDict':varObj.clinVarGeneDict, - 'clinvarTotalNumVars':varObj.clinvarTotalNumVars, - 'clinvarNumP':varObj.clinvarNumP, - 'clinvarNumLP':varObj.clinvarNumLP, - 'clinvarNumLB':varObj.clinvarNumLB, - 'clinvarNumB':varObj.clinvarNumB, - 'clinvarTitle':varObj.clinvarTitle, - 'clinvarSignDesc':varObj.clinvarSignDesc, - 'clinvarCondition':varObj.clinvarCondition, - 'hgmdVarFound':varObj.hgmdVarFound, - 'hgmdGeneFound':varObj.hgmdGeneFound, - 'hgmdVarPhenIdList':varObj.hgmdVarPhenIdList, - 'hgmdVarHPOIdList':varObj.hgmdVarHPOIdList, - 'hgmdVarHPOStrList':varObj.hgmdVarHPOStrList, - 'varId_dash':varObj.varId_dash, - 'dgvDictList':varObj.dgvDictList, - 'dgvTypeList':varObj.dgvTypeList, - 'dgvSubtypeList':varObj.dgvSubtypeList, - 'dgvVarFound':varObj.dgvVarFound, - 'decipherDictList':varObj.decipherDictList, - 'decipherDeletionObsList':varObj.decipherDeletionObsList, - 'decipherStudyList':varObj.decipherStudyList, - 'decipherVarFound':varObj.decipherVarFound, - 'gnomadGeneZscore':varObj.gnomadGeneZscore, - 'gnomadGenePLI':varObj.gnomadGenePLI, - 'gnomadGeneOELof':varObj.gnomadGeneOELof, - 'gnomadGeneOELofUpper':varObj.gnomadGeneOELofUpper, - #symptom - 'SymptomMatched':varObj.SymptomMatched, - 'symptomScore':varObj.symptomScore, - 'symptomName':varObj.symptomName, - 'omimSymptomSimScore':varObj.omimSymptomSimScore, - 'omimSymMatchFlag':varObj.omimSymMatchFlag, - 'hgmdSymptomScore':varObj.hgmdSymptomScore, - 'hgmdSymptomSimScore':varObj.hgmdSymptomSimScore, - 'hgmdSymMatchFlag':varObj.hgmdSymMatchFlag, - 'clinVarSymMatchFlag':varObj.clinVarSymMatchFlag, - 'VARIANT_CLASS':varObj.VARIANT_CLASS, - 'Feature':varObj.Feature, - 'hom':varObj.hom, - 'hgmd_rs':varObj.hgmd_rs, - - 'hgmd_id':varObj.hgmd_id, # CL added - 'hgmd_symbol':varObj.hgmd_symbol, # CL added - 'hgmd_PHEN':varObj.hgmd_PHEN, # CL added - 'hgmd_CLASS':varObj.hgmd_CLASS, # CL added - - 'clin_dict':varObj.clin_dict, - 'clin_PLP':varObj.clin_PLP, - 'clin_PLP_perc':varObj.clin_PLP_perc, - 'spliceAI':varObj.spliceAI, - 'spliceAImax':varObj.spliceAImax - + # make a dict from the varObj. the varObj has one element for each row of the input VEP annotated file + varDict = { + "hg19Chrom": varObj.hg19Chrom, + "hg19Pos": varObj.hg19Pos, + "chrom": varObj.chrom, + "pos": varObj.pos, + "start": varObj.start, + "stop": varObj.stop, + "geneSymbol": varObj.geneSymbol, + "CADD_phred": varObj.CADD_phred, + "CADD_PHRED": varObj.CADD_PHRED, + "ref": varObj.ref, + "alt": varObj.alt, + "varId": varObj.varId, + "ZYG": varObj.zyg, + "HGVSc": varObj.HGVSc, + "HGVSp": varObj.HGVSp, + "Gene": varObj.geneEnsId, + "Existing_variation": varObj.rsId, + "GERPpp_RS": varObj.GERPpp_RS, + "Feature_type": varObj.featureType, + "gnomadAF": varObj.gnomadAF, + "gnomadAFg": varObj.gnomadAFg, + "CLIN_SIG": varObj.CLIN_SIG, + "LRT_Omega": varObj.LRT_Omega, + "LRT_score": varObj.LRT_score, + "phyloP100way_vertebrate": varObj.phyloP100way_vertebrate, + # dbnsfp attributes + "GERPpp_NR": varObj.GERPpp_NR, + "DANN_score": varObj.DANN_score, + "FATHMM_pred": varObj.FATHMM_pred, + "FATHMM_score": varObj.FATHMM_score, + "GTEx_V8_gene": varObj.GTEx_V8_gene, + "GTEx_V8_tissue": varObj.GTEx_V8_tissue, + "Polyphen2_HDIV_score": varObj.Polyphen2_HDIV_score, + "Polyphen2_HVAR_score": varObj.Polyphen2_HVAR_score, + "REVEL_score": varObj.REVEL_score, + "SIFT_score": varObj.SIFT_score, + "clinvar_AlleleID": varObj.clinvar_AlleleID, # Clinvar allele ID from clinvar.vcf.gz + "clinvar_clnsig": varObj.clinvar_clnsig, # CL: Clinvar SIG from clinvar.vcf.gz + "clinvar_CLNREVSTAT": varObj.clinvar_CLNREVSTAT, # CL: Clinvar STAT from clinvar.vcf.gz, for interface only + "clinvar_CLNSIGCONF": varObj.clinvar_CLNSIGCONF, # CL: Clinvar SIGCONF from clinvar.vcf.gz + "clin_code": varObj.clin_code, # CL: feature for ai + "fathmm_MKL_coding_score": varObj.fathmm_MKL_coding_score, + "LRT_score": varObj.LRT_score, + "LRT_Omega": varObj.LRT_Omega, + "phyloP100way_vertebrate": varObj.phyloP100way_vertebrate, + "M_CAP_score": varObj.M_CAP_score, + "MutationAssessor_score": varObj.MutationAssessor_score, + "MutationTaster_score": varObj.MutationTaster_score, + "ESP6500_AA_AC": varObj.ESP6500_AA_AC, + "ESP6500_AA_AF": varObj.ESP6500_AA_AF, + "ESP6500_EA_AC": varObj.ESP6500_EA_AC, + "ESP6500_EA_AF": varObj.ESP6500_EA_AF, + # dbnsfp + "gnomadGeneZscore": varObj.gnomadGeneZscore, + "gnomadGenePLI": varObj.gnomadGenePLI, + "gnomadGeneOELof": varObj.gnomadGeneOELof, # O/E lof + "gnomadGeneOELofUpper": varObj.gnomadGeneOELofUpper, # O/E lof upper, + "IMPACT": varObj.IMPACT, + "Consequence": varObj.Consequence, + "omimVarFound": varObj.omimVarFound, + "omimGeneFound": varObj.omimGeneFound, + "omimDict": varObj.omimDict, + "omimGeneDict": varObj.omimGeneDict, + "omimAlleleDict": varObj.omimAlleleDict, + "phenoList": varObj.phenoList, + "phenoInhList": varObj.phenoInhList, + "phenoMimList": varObj.phenoMimList, + "clinVarVarFound": varObj.clinVarVarFound, + "clinVarVarDict": varObj.clinVarVarDict, + "clinVarGeneFound": varObj.clinVarGeneFound, + "clinVarGeneDict": varObj.clinVarGeneDict, + "clinvarTotalNumVars": varObj.clinvarTotalNumVars, + "clinvarNumP": varObj.clinvarNumP, + "clinvarNumLP": varObj.clinvarNumLP, + "clinvarNumLB": varObj.clinvarNumLB, + "clinvarNumB": varObj.clinvarNumB, + "clinvarTitle": varObj.clinvarTitle, + "clinvarSignDesc": varObj.clinvarSignDesc, + "clinvarCondition": varObj.clinvarCondition, + "hgmdVarFound": varObj.hgmdVarFound, + "hgmdGeneFound": varObj.hgmdGeneFound, + "hgmdVarPhenIdList": varObj.hgmdVarPhenIdList, + "hgmdVarHPOIdList": varObj.hgmdVarHPOIdList, + "hgmdVarHPOStrList": varObj.hgmdVarHPOStrList, + "varId_dash": varObj.varId_dash, + "dgvDictList": varObj.dgvDictList, + "dgvTypeList": varObj.dgvTypeList, + "dgvSubtypeList": varObj.dgvSubtypeList, + "dgvVarFound": varObj.dgvVarFound, + "decipherDictList": varObj.decipherDictList, + "decipherDeletionObsList": varObj.decipherDeletionObsList, + "decipherStudyList": varObj.decipherStudyList, + "decipherVarFound": varObj.decipherVarFound, + "gnomadGeneZscore": varObj.gnomadGeneZscore, + "gnomadGenePLI": varObj.gnomadGenePLI, + "gnomadGeneOELof": varObj.gnomadGeneOELof, + "gnomadGeneOELofUpper": varObj.gnomadGeneOELofUpper, + # symptom + "SymptomMatched": varObj.SymptomMatched, + "symptomScore": varObj.symptomScore, + "symptomName": varObj.symptomName, + "omimSymptomSimScore": varObj.omimSymptomSimScore, + "omimSymMatchFlag": varObj.omimSymMatchFlag, + "hgmdSymptomScore": varObj.hgmdSymptomScore, + "hgmdSymptomSimScore": varObj.hgmdSymptomSimScore, + "hgmdSymMatchFlag": varObj.hgmdSymMatchFlag, + "clinVarSymMatchFlag": varObj.clinVarSymMatchFlag, + "VARIANT_CLASS": varObj.VARIANT_CLASS, + "Feature": varObj.Feature, + "hom": varObj.hom, + "hgmd_rs": varObj.hgmd_rs, + "hgmd_id": varObj.hgmd_id, # CL added + "hgmd_symbol": varObj.hgmd_symbol, # CL added + "hgmd_PHEN": varObj.hgmd_PHEN, # CL added + "hgmd_CLASS": varObj.hgmd_CLASS, # CL added + "clin_dict": varObj.clin_dict, + "clin_PLP": varObj.clin_PLP, + "clin_PLP_perc": varObj.clin_PLP_perc, + "spliceAI": varObj.spliceAI, + "spliceAImax": varObj.spliceAImax, } varDictList.append(varDict) - #print('finsihed making dict') - #increment index - i=i+1 - - #return object - return varDictList + # print('finsihed making dict') + # increment index + i = i + 1 + # return object + return varDictList diff --git a/bin/annotation/utils_for_marrvel_flatfile_module_2.py b/bin/annotation/utils_for_marrvel_flatfile_module_2.py index a2d5154..ef1179f 100755 --- a/bin/annotation/utils_for_marrvel_flatfile_module_2.py +++ b/bin/annotation/utils_for_marrvel_flatfile_module_2.py @@ -1,186 +1,200 @@ ## UPDATE # Changes made by Chaozhong noted as #CL -from annotation.utils_1 import * +from annotation.utils_1 import * import numpy as np import time import re + def getDecipherUsingMarrvelFlatFile(varObj, decipherDf): - ''' + """ Get Decipher annotation using marrvel flat file Params: varObj:variant object decipherDf:dataframe of input flat file Return: Decipher list - ''' - #print('in DECIPHER call') - decipherDictList=[] - decipherDeletionObsList=[] - decipherStudyList=[] - decipherVarFound=0 - deletionObs='-' - #get the varaint object info from varObj - chromVal=int(varObj.chrom) - posVal=int(varObj.pos) - startVal=int(varObj.start) - stopVal=int(varObj.stop) + """ + # print('in DECIPHER call') + decipherDictList = [] + decipherDeletionObsList = [] + decipherStudyList = [] + decipherVarFound = 0 + deletionObs = "-" + # get the varaint object info from varObj + chromVal = int(varObj.chrom) + posVal = int(varObj.pos) + startVal = int(varObj.start) + stopVal = int(varObj.stop) # CL 03-14-2023: changed column names to be compatible with hg38 - #vals=decipherDf[ ( decipherDf['hg19Chr'] == chromVal ) & ( decipherDf['hg19Start']==startVal ) & (decipherDf['hg19Stop']==stopVal) ] - vals=decipherDf[ ( decipherDf['Chr'] == chromVal ) & ( decipherDf['Start']==startVal ) & (decipherDf['Stop']==stopVal) ] - numRows=len(vals.index) - - if numRows>0: - decipherVarFound=1 - deletionObs=vals.iloc[0]['deletion.obs'] + # vals=decipherDf[ ( decipherDf['hg19Chr'] == chromVal ) & ( decipherDf['hg19Start']==startVal ) & (decipherDf['hg19Stop']==stopVal) ] + vals = decipherDf[ + (decipherDf["Chr"] == chromVal) + & (decipherDf["Start"] == startVal) + & (decipherDf["Stop"] == stopVal) + ] + numRows = len(vals.index) + + if numRows > 0: + decipherVarFound = 1 + deletionObs = vals.iloc[0]["deletion.obs"] decipherDeletionObsList.append(deletionObs) - #print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) - #print('\tdecipherVarFound:',decipherVarFound,'decipherDeletionObs:', deletionObs) - retList=[decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] + # print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) + # print('\tdecipherVarFound:',decipherVarFound,'decipherDeletionObs:', deletionObs) + retList = [ + decipherDictList, + decipherDeletionObsList, + decipherStudyList, + decipherVarFound, + ] return retList + def getDGVUsingMarrvelFlatFile(varObj, dgvDf): - ''' + """ Get DGV annotation using marrvel flat file Params: varObj:variant object dgvDf:dataframe of input flat file Return: DGV list - ''' - #print('in DGV call') - dgvDictList=[] - typeList=[] - subtypeList=[] - dgvVarFound=0 - dgvType='-' - dgvSubtype='-' - - chromVal=int(varObj.chrom) - posVal=int(varObj.pos) - startVal=int(varObj.start) - stopVal=int(varObj.stop) - - #CL 03-14-2023: changed column names to be compatible with hg38 - #vals=dgvDf[ ( dgvDf['hg19Chr'] == chromVal ) & ( dgvDf['hg19Start']<=startVal ) & (dgvDf['hg19Stop']>=stopVal) ] - vals=dgvDf[ ( dgvDf['Chr'] == chromVal ) & ( dgvDf['Start']<=startVal ) & (dgvDf['Stop']>=stopVal) ] - numRows=len(vals.index) - - if numRows>0: - dgvVarFound=1 - #print('\tnumrows:',numRows) - #print('\t type of vals:', type(vals)) - #print('\tvals:', vals) - dgvType=vals.iloc[0]['type'] - dgvSubtype=vals.iloc[0]['subType'] - - #print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) - #print('\tdgvVarFound:',dgvVarFound,'dgvType:', dgvType, 'dgvsubtype:', dgvSubtype) + """ + # print('in DGV call') + dgvDictList = [] + typeList = [] + subtypeList = [] + dgvVarFound = 0 + dgvType = "-" + dgvSubtype = "-" + + chromVal = int(varObj.chrom) + posVal = int(varObj.pos) + startVal = int(varObj.start) + stopVal = int(varObj.stop) + + # CL 03-14-2023: changed column names to be compatible with hg38 + # vals=dgvDf[ ( dgvDf['hg19Chr'] == chromVal ) & ( dgvDf['hg19Start']<=startVal ) & (dgvDf['hg19Stop']>=stopVal) ] + vals = dgvDf[ + (dgvDf["Chr"] == chromVal) + & (dgvDf["Start"] <= startVal) + & (dgvDf["Stop"] >= stopVal) + ] + numRows = len(vals.index) + + if numRows > 0: + dgvVarFound = 1 + # print('\tnumrows:',numRows) + # print('\t type of vals:', type(vals)) + # print('\tvals:', vals) + dgvType = vals.iloc[0]["type"] + dgvSubtype = vals.iloc[0]["subType"] + + # print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) + # print('\tdgvVarFound:',dgvVarFound,'dgvType:', dgvType, 'dgvsubtype:', dgvSubtype) typeList.append(dgvType) subtypeList.append(dgvSubtype) - retList=[dgvDictList,typeList,subtypeList,dgvVarFound] + retList = [dgvDictList, typeList, subtypeList, dgvVarFound] return retList + def getGnomadGeneMetricFlatFile(varObj, gnomadMetricsGeneDf): - ''' + """ Get GnomadGeneMetric from flat file Params: varObj:variant object gnomadMetricsGeneDf:dataframe of input flat file Return: gnomadMetricsGeneDf list - ''' - #print('in gnomadGeneMetricFlatFile call') - #check if gene in file - vals=gnomadMetricsGeneDf[gnomadMetricsGeneDf['gene'] == varObj.geneSymbol] - numRows=len(vals.index) - - if numRows>0:#pLI, oe_lof, oe_lof_upper,mis_z - gnomadGeneZscore=vals.iloc[0]['mis_z'] - gnomadGenePLI=vals.iloc[0]['pLI'] - gnomadGeneOELof=vals.iloc[0]['oe_lof'] - gnomadGeneOELofUpper=vals.iloc[0]['oe_lof_upper'] + """ + # print('in gnomadGeneMetricFlatFile call') + # check if gene in file + vals = gnomadMetricsGeneDf[gnomadMetricsGeneDf["gene"] == varObj.geneSymbol] + numRows = len(vals.index) + + if numRows > 0: # pLI, oe_lof, oe_lof_upper,mis_z + gnomadGeneZscore = vals.iloc[0]["mis_z"] + gnomadGenePLI = vals.iloc[0]["pLI"] + gnomadGeneOELof = vals.iloc[0]["oe_lof"] + gnomadGeneOELofUpper = vals.iloc[0]["oe_lof_upper"] else: - #get the values - gnomadGeneZscore='-' - gnomadGenePLI='-' - gnomadGeneOELof='-' - gnomadGeneOELofUpper='-' - - retList=[gnomadGeneZscore, gnomadGenePLI, gnomadGeneOELof, gnomadGeneOELofUpper] - #print('\t gene symbol:', varObj.geneSymbol, 'zScore:', gnomadGeneZscore, 'gnomadGenePLI:',gnomadGenePLI,'gnomadGeneOELof:', gnomadGeneOELof,'gnomadGeneOELofUpper:', gnomadGeneOELofUpper) - #print('\t gene symbol:', varObj.geneSymbol, 'zScore:', gnomadGeneZscore) + # get the values + gnomadGeneZscore = "-" + gnomadGenePLI = "-" + gnomadGeneOELof = "-" + gnomadGeneOELofUpper = "-" + + retList = [gnomadGeneZscore, gnomadGenePLI, gnomadGeneOELof, gnomadGeneOELofUpper] + # print('\t gene symbol:', varObj.geneSymbol, 'zScore:', gnomadGeneZscore, 'gnomadGenePLI:',gnomadGenePLI,'gnomadGeneOELof:', gnomadGeneOELof,'gnomadGeneOELofUpper:', gnomadGeneOELofUpper) + # print('\t gene symbol:', varObj.geneSymbol, 'zScore:', gnomadGeneZscore) return retList + def getConservationScore(varObj, diseaseInh): - ''' + """ Get conservation score Params: varObj:variant object diseaseInh: the disease inheritance Return: return three conservation scores: conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof - ''' - #print('in conservation score call') - #create conservation scores - #set up thresholds - gnomadGFreqCut=0.01#genome g - gnomadEFreqCut=0.01#exome e + """ + # print('in conservation score call') + # create conservation scores + # set up thresholds + gnomadGFreqCut = 0.01 # genome g + gnomadEFreqCut = 0.01 # exome e # GERP ranges - gerpPPCut=4 - #LRT cut - #phylo cut - - #gnomad - conservationScoreGnomad='-' - gnomadAFVal = getValFromStr(str(varObj.gnomadAF), 'min') - gnomadAFgVal = getValFromStr(str(varObj.gnomadAFg), 'min') - if gnomadAFVal!='-' and gnomadAFgVal!='-': - #gnomadAFVal=float(varObj.gnomadAF) - #gnomadAFgVal=float(varObj.gnomadAFg) - if gnomadAFVal<0.01 and gnomadAFgVal<0.01: - conservationScoreGnomad='High' + gerpPPCut = 4 + # LRT cut + # phylo cut + + # gnomad + conservationScoreGnomad = "-" + gnomadAFVal = getValFromStr(str(varObj.gnomadAF), "min") + gnomadAFgVal = getValFromStr(str(varObj.gnomadAFg), "min") + if gnomadAFVal != "-" and gnomadAFgVal != "-": + # gnomadAFVal=float(varObj.gnomadAF) + # gnomadAFgVal=float(varObj.gnomadAFg) + if gnomadAFVal < 0.01 and gnomadAFgVal < 0.01: + conservationScoreGnomad = "High" else: - conservationScoreGnomad='Low' + conservationScoreGnomad = "Low" else: - conservationScoreGnomad='-' + conservationScoreGnomad = "-" - #DGV - conservationScoreDGV='-' - if 'deletion' in varObj.dgvSubtypeList or 'loss' in varObj.dgvSubtypeList: - conservationScoreDGV='Low' + # DGV + conservationScoreDGV = "-" + if "deletion" in varObj.dgvSubtypeList or "loss" in varObj.dgvSubtypeList: + conservationScoreDGV = "Low" else: - conservationScoreDGV='High' - - #gene O/E score - conservationScoreOELof='-' - if varObj.gnomadGeneOELofUpper!='-': - gnomadGeneOELofUpperVal=float(varObj.gnomadGeneOELofUpper) - if gnomadGeneOELofUpperVal<0.35: - conservationScoreOELof='High' + conservationScoreDGV = "High" + + # gene O/E score + conservationScoreOELof = "-" + if varObj.gnomadGeneOELofUpper != "-": + gnomadGeneOELofUpperVal = float(varObj.gnomadGeneOELofUpper) + if gnomadGeneOELofUpperVal < 0.35: + conservationScoreOELof = "High" else: - conservationScoreOELof='Low' + conservationScoreOELof = "Low" - #return - retList=[conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] + # return + retList = [conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] return retList -def getValFromStr(valStr: str, select: str = 'min'): + +def getValFromStr(valStr: str, select: str = "min"): """ Function to convert string to float, and takes care of situation when multiple values exist """ - select_method = {'min':min, 'max':max} - vals = valStr.split(',') - if '-' in vals: - return '-' + select_method = {"min": min, "max": max} + vals = valStr.split(",") + if "-" in vals: + return "-" else: vals = [float(i) for i in vals] return select_method[select](vals) - - - - diff --git a/bin/annotation/utils_for_symMatch.py b/bin/annotation/utils_for_symMatch.py index 03a54b5..3914cc4 100755 --- a/bin/annotation/utils_for_symMatch.py +++ b/bin/annotation/utils_for_symMatch.py @@ -1,68 +1,76 @@ ## UPDATE # Changes made by Chaozhong noted as #CL -from annotation.utils_1 import * +from annotation.utils_1 import * def omimSymMatch(varObj, omimHPOScoreDf, inFileType): - ''' + """ Find OMIM symptom match score Param: omimHPOScoreDf: this is read from user input OMIM symptom mathc file. Foe example:/houston_10t/dongxue/MARRVEL_AI/BG/HPO2Gene/out_May12021/UDN/HPOsimi_UDN630665_simi_0.tsv - inFileType:the type of input file. Will be used later. + inFileType:the type of input file. Will be used later. Return: Calculate the variant symptom score - ''' - #print('\nin omimSymMatch') - #print('type varObj:', type(varObj)) - #print('\tvar:', varObj.varId_dash) - simScore=0 - if 'phenotypes' in varObj.omimGeneDict.keys(): - #print('\t phenotypes key found') - for pheno in varObj.omimGeneDict['phenotypes']: - if 'phenotypeMimNumber' in pheno.keys(): - PminNum = pheno['phenotypeMimNumber'] - #print('\tPminNum:', PminNum) - #print('\ttmp1:', omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum]) - valDf=omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum] - numRows=len(valDf.index) - if numRows!=0: - simScore = omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum].iloc[0]['Similarity_Score'] + """ + # print('\nin omimSymMatch') + # print('type varObj:', type(varObj)) + # print('\tvar:', varObj.varId_dash) + simScore = 0 + if "phenotypes" in varObj.omimGeneDict.keys(): + # print('\t phenotypes key found') + for pheno in varObj.omimGeneDict["phenotypes"]: + if "phenotypeMimNumber" in pheno.keys(): + PminNum = pheno["phenotypeMimNumber"] + # print('\tPminNum:', PminNum) + # print('\ttmp1:', omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum]) + valDf = omimHPOScoreDf[omimHPOScoreDf["Pheno_ID"] == PminNum] + numRows = len(valDf.index) + if numRows != 0: + simScore = omimHPOScoreDf[ + omimHPOScoreDf["Pheno_ID"] == PminNum + ].iloc[0]["Similarity_Score"] else: - simScore=0 - #print('\tsimScore:', simScore) + simScore = 0 + # print('\tsimScore:', simScore) else: - simScore=0 - if simScore >= 0.2: #change the threshold to a user arg later + simScore = 0 + if simScore >= 0.2: # change the threshold to a user arg later varObj.symptomScore[PminNum] = simScore - symptomList = omimHPOScoreDf[omimHPOScoreDf['Pheno_ID'] == PminNum].iloc[0]['Disease_Name'].split(';') - symptomList = [i.strip().upper() for i in symptomList] #remove space and tranfer all words to capital + symptomList = ( + omimHPOScoreDf[omimHPOScoreDf["Pheno_ID"] == PminNum] + .iloc[0]["Disease_Name"] + .split(";") + ) + symptomList = [ + i.strip().upper() for i in symptomList + ] # remove space and tranfer all words to capital varObj.symptomName[PminNum] = symptomList varObj.omimSymMatchFlag = 1 else: continue - #store the sim score we get - varObj.omimSymptomSimScore=simScore - #print('omimSymMatch results:') - #print('\tsymtomSimScore:', varObj.omimSymptomSimScore) - #print('\tsymptomScore:', varObj.symptomScore) - #print('\tsymptomName:', varObj.symptomName) - #print('\tomimSymMatchFlag:', varObj.omimSymMatchFlag) + # store the sim score we get + varObj.omimSymptomSimScore = simScore + # print('omimSymMatch results:') + # print('\tsymtomSimScore:', varObj.omimSymptomSimScore) + # print('\tsymptomScore:', varObj.symptomScore) + # print('\tsymptomName:', varObj.symptomName) + # print('\tomimSymMatchFlag:', varObj.omimSymMatchFlag) + def hgmdSymMatch(varObj, hgmdHPOScoreDf): - ''' + """ Find HGMD symptom match score Param: omimHPOScoreDf: this is read from user input OMIM symptom mathc file. For example:/six_tera/chaozhong/module_1/out/UDN/HPOsimi_UDNUDN630665_simi_0.tsv - inFileType:the type of input file. Will be used later. + inFileType:the type of input file. Will be used later. Return: Calculate the variant symptom score - ''' + """ - - #print('\nin HGMDSymMatch') - #print('\tvar:', varObj.varId_dash) - hgmdSymptomSimScore='-' + # print('\nin HGMDSymMatch') + # print('\tvar:', varObj.varId_dash) + hgmdSymptomSimScore = "-" """ old version if varObj.hgmdVarFound: var_tmp = varObj.varId_dash.split('-') @@ -90,54 +98,58 @@ def hgmdSymMatch(varObj, hgmdHPOScoreDf): print('\thgmdSymMatchFlag:', varObj.hgmdSymMatchFlag) print('\thgmdSymptomSimScore:', varObj.hgmdSymptomSimScore) """ - if np.any(hgmdHPOScoreDf['acc_num'].isin([varObj.hgmd_id])): - varDf = hgmdHPOScoreDf[hgmdHPOScoreDf['acc_num'] == varObj.hgmd_id] - varScore = max(varDf['Similarity_Score'].tolist()) + if np.any(hgmdHPOScoreDf["acc_num"].isin([varObj.hgmd_id])): + varDf = hgmdHPOScoreDf[hgmdHPOScoreDf["acc_num"] == varObj.hgmd_id] + varScore = max(varDf["Similarity_Score"].tolist()) varObj.hgmdSymptomScore = varScore if varScore >= 0.2: varObj.hgmdSymMatchFlag = 1 - hgmdSymptomSimScore=varScore + hgmdSymptomSimScore = varScore elif varObj.hgmdGeneFound: - geneDf = hgmdHPOScoreDf[hgmdHPOScoreDf['gene_sym'] == varObj.geneSymbol] - geneScore = max(geneDf['Similarity_Score'].tolist()) + geneDf = hgmdHPOScoreDf[hgmdHPOScoreDf["gene_sym"] == varObj.geneSymbol] + geneScore = max(geneDf["Similarity_Score"].tolist()) if geneScore >= 0.2: varObj.hgmdSymMatchFlag = 1 - hgmdSymptomSimScore=geneScore - - varObj.hgmdSymptomSimScore=hgmdSymptomSimScore - #print('hgmdSymMatch results:') - #print('\thgmdSymMatchFlag:', varObj.hgmdSymMatchFlag) - #print('\thgmdSymptomSimScore:', varObj.hgmdSymptomSimScore) - + hgmdSymptomSimScore = geneScore + varObj.hgmdSymptomSimScore = hgmdSymptomSimScore + # print('hgmdSymMatch results:') + # print('\thgmdSymMatchFlag:', varObj.hgmdSymMatchFlag) + # print('\thgmdSymptomSimScore:', varObj.hgmdSymptomSimScore) def clinVarSymMatch(varObj, inFileType): - ''' + """ Find clinvar symptom match score Param: Return: Calculate the variant symptom score - ''' - #print('\nin clinvarSymMatch') + """ + # print('\nin clinvarSymMatch') if varObj.clinVarVarFound: - #pheno = varObj.clinVarVarDict['condition'].strip().upper() - #print('clinvar condition:', varObj.clinvarCondition) + # pheno = varObj.clinVarVarDict['condition'].strip().upper() + # print('clinvar condition:', varObj.clinvarCondition) if type(varObj.clinvarCondition) is str: - pheno=varObj.clinvarCondition.strip().upper() + pheno = varObj.clinvarCondition.strip().upper() for PminNum in varObj.symptomName.keys(): - if pheno in varObj.symptomName[PminNum] or '#%s %s'%(PminNum,pheno) in varObj.symptomName[PminNum]: + if ( + pheno in varObj.symptomName[PminNum] + or "#%s %s" % (PminNum, pheno) in varObj.symptomName[PminNum] + ): varObj.clinVarSymMatchFlag = 1 else: varObj.clinVarSymMatchFlag = 0 - #Need more checking on this one per gene condition + # Need more checking on this one per gene condition if varObj.clinVarGeneFound: for var in varObj.clinVarGeneDict: - pheno = var['condition'].strip().upper() + pheno = var["condition"].strip().upper() for PminNum in varObj.symptomName.keys(): - if pheno in varObj.symptomName[PminNum] or '#%s %s'%(PminNum,pheno) in varObj.symptomName[PminNum]: + if ( + pheno in varObj.symptomName[PminNum] + or "#%s %s" % (PminNum, pheno) in varObj.symptomName[PminNum] + ): varObj.clinVarGeneSymMatchFlag = 1 - #print('\tvarObj.clinVarVarSymMatchFlag:', varObj.clinVarSymMatchFlag) + # print('\tvarObj.clinVarVarSymMatchFlag:', varObj.clinVarSymMatchFlag) diff --git a/bin/extraModel/confidence.py b/bin/extraModel/confidence.py index 08505d6..f0e5c79 100755 --- a/bin/extraModel/confidence.py +++ b/bin/extraModel/confidence.py @@ -9,26 +9,32 @@ import numpy as np import scipy -def get_causal_distribution(pred_fd, label_col='is_strong'): + +def get_causal_distribution(pred_fd, label_col="is_strong"): # pred_fd: folder for prediction files only fns = os.listdir(pred_fd) # get top20 predictions top20_preds = [] for fn in fns: - top20_preds.append(pd.read_csv(f'{pred_fd}/{fn}', index_col=0).iloc[:20,:]) + top20_preds.append(pd.read_csv(f"{pred_fd}/{fn}", index_col=0).iloc[:20, :]) top20_preds = pd.concat(top20_preds) # get scores of causal variants - top20_causals = np.ravel(top20_preds.loc[top20_preds[label_col]==1, 'predict'].to_numpy()) + top20_causals = np.ravel( + top20_preds.loc[top20_preds[label_col] == 1, "predict"].to_numpy() + ) return top20_causals + def assign_confidence_score(causal_distr, pred_df): # pred_fd: folder for prediction files only # pred_df: prediction data frame of the sample of interest - + prediction_scores = pred_df.predict.to_numpy() # assign confidence score using percentile - conf_scores = [scipy.stats.percentileofscore(causal_distr, i) for i in prediction_scores] + conf_scores = [ + scipy.stats.percentileofscore(causal_distr, i) for i in prediction_scores + ] # assign confidence category by discretizing conf_categories = [] for conf_score in conf_scores: @@ -40,11 +46,12 @@ def assign_confidence_score(causal_distr, pred_df): conf_categories.append("Solved (Medium)") else: conf_categories.append("Solved (High)") - pred_df['confidence'] = conf_scores - pred_df['confidence level'] = conf_categories + pred_df["confidence"] = conf_scores + pred_df["confidence level"] = conf_categories pred_df.sort_values("confidence", ascending=False) return pred_df + # def determine_case_confidence(causal_distr, pred_df, label_col='is_strong'): # pred_df = assign_confidence_score(causal_distr, pred_df, label_col) -# return pred_df['confidence level'].tolist()[0] \ No newline at end of file +# return pred_df['confidence level'].tolist()[0] diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index 62dbd7c..78cb9c1 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -8,63 +8,69 @@ from os.path import exists import shutil -def process_sample(data_folder, sample_id, - default_pred, labeling=False, n_thread = 10): + +def process_sample(data_folder, sample_id, default_pred, labeling=False, n_thread=10): # recessive_folder = f'{data_folder}/recessive_matrix' # if not os.path.exists(recessive_folder): # os.mkdir(recessive_folder) - recessive_folder = f'{data_folder}/recessive_matrix/' + recessive_folder = f"{data_folder}/recessive_matrix/" if not os.path.exists(recessive_folder): os.mkdir(recessive_folder) - tmp_folder = f'{data_folder}/recessive_matrix/tmp/' + tmp_folder = f"{data_folder}/recessive_matrix/tmp/" if not os.path.exists(tmp_folder): os.mkdir(tmp_folder) ## read feature matrix for single var feature_fn = f"{sample_id}.csv" - #feature_df = [] - #for feature_fn in feature_fns: - if 'csv' in feature_fn: + # feature_df = [] + # for feature_fn in feature_fns: + if "csv" in feature_fn: feature_df = pd.read_csv(feature_fn, index_col=0, sep=",") else: feature_df = pd.read_csv(feature_fn, index_col=0, sep="\t") - #feature_df.append(df) - #feature_df = pd.concat(feature_df) - feature_df = feature_df.loc[~feature_df.index.duplicated(keep='first')] + # feature_df.append(df) + # feature_df = pd.concat(feature_df) + feature_df = feature_df.loc[~feature_df.index.duplicated(keep="first")] ## Group variants by Ens IDs expanded_fn = f"final_matrix_expanded/{sample_id}.expanded.csv.gz" gene_dict = {} - #for expanded_fn in expanded_fns: - if 'csv' in expanded_fn: - df = pd.read_csv(expanded_fn, sep=",", index_col=0, compression='infer') + # for expanded_fn in expanded_fns: + if "csv" in expanded_fn: + df = pd.read_csv(expanded_fn, sep=",", index_col=0, compression="infer") else: - df = pd.read_csv(expanded_fn, sep="\t", index_col=0, compression='infer') - + df = pd.read_csv(expanded_fn, sep="\t", index_col=0, compression="infer") + if df.shape[0] > 100000: - df = df.loc[df['IMPACT.from.Tier'] > 1] + df = df.loc[df["IMPACT.from.Tier"] > 1] for varId, gene in zip(df.index.tolist(), df.geneEnsId.tolist()): - if gene.startswith('ENSG'): + if gene.startswith("ENSG"): if gene not in gene_dict: gene_dict[gene] = {varId} else: gene_dict[gene].add(varId) - + ## create parameters and run bivar feature generation in parallel - params = [{'feature_df': feature_df, - 'default_pred': default_pred, - 'labeling': labeling, - 'out_folder': f'{tmp_folder}', - 'gene': gene, - 'varIDs': list(gene_dict[gene])} for gene in gene_dict - ] - print(f"Now starting to generate recessive feature matrix for {len(gene_dict)} genes, {feature_df.shape[0]} variants using {n_thread} threads.") + params = [ + { + "feature_df": feature_df, + "default_pred": default_pred, + "labeling": labeling, + "out_folder": f"{tmp_folder}", + "gene": gene, + "varIDs": list(gene_dict[gene]), + } + for gene in gene_dict + ] + print( + f"Now starting to generate recessive feature matrix for {len(gene_dict)} genes, {feature_df.shape[0]} variants using {n_thread} threads." + ) p = Pool(processes=n_thread) - + with tqdm(total=len(params)) as pbar: for result in p.imap_unordered(process_gene, params): pbar.update() @@ -84,24 +90,24 @@ def process_sample(data_folder, sample_id, print("Recessive features generation finished...") else: print("No recessive variant pair found. End and remove temp files") - - shutil.rmtree(f'{tmp_folder}') + + shutil.rmtree(f"{tmp_folder}") + def process_gene(param): - - feature_names = 'diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat' + + feature_names = "diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat" feature_names = feature_names.split(",") nFeatures = len(feature_names) bivar_feature_mats = [] - gene = param['gene'] - varIDs, labeling = param['varIDs'], param['labeling'] + gene = param["gene"] + varIDs, labeling = param["varIDs"], param["labeling"] + + feature_df, out_folder = param["feature_df"], param["out_folder"] - - feature_df, out_folder = param['feature_df'], param['out_folder'] + seen_pairs = set() - seen_pairs = set() - allVars = feature_df.index.tolist() varIDs = [v for v in varIDs if v in allVars] @@ -109,8 +115,8 @@ def process_gene(param): return if len(varIDs) > 6: - defaultPred = param['default_pred'].copy() - defaultPred = defaultPred.loc[varIDs,:].sort_values("predict", ascending=False) + defaultPred = param["default_pred"].copy() + defaultPred = defaultPred.loc[varIDs, :].sort_values("predict", ascending=False) varIDs = defaultPred.index.tolist()[:6] gene_feats = feature_df.loc[varIDs, feature_names].copy() @@ -120,60 +126,66 @@ def process_gene(param): def generate_feature_vector(i, j): fmat = np.zeros((2, nFeatures * 2 + 1)) - dist = np.absolute(int(varIDs[i].split("-")[1])-int(varIDs[j].split("-")[1])) - + dist = np.absolute(int(varIDs[i].split("-")[1]) - int(varIDs[j].split("-")[1])) + fmat[0] = np.append(np.append(gene_feats[i], gene_feats[j]), [dist]) fmat[1] = np.append(np.append(gene_feats[j], gene_feats[i]), [dist]) - f1_features = [f'{f}_1' for f in feature_names] - f2_features = [f'{f}_2' for f in feature_names] - - return pd.DataFrame(data=fmat, columns=f1_features + f2_features + ['var_dist'], - index=[f'{varIDs[i]}_{varIDs[j]}', f'{varIDs[j]}_{varIDs[i]}']) - + f1_features = [f"{f}_1" for f in feature_names] + f2_features = [f"{f}_2" for f in feature_names] + + return pd.DataFrame( + data=fmat, + columns=f1_features + f2_features + ["var_dist"], + index=[f"{varIDs[i]}_{varIDs[j]}", f"{varIDs[j]}_{varIDs[i]}"], + ) for i in range(len(varIDs)): - varii_feature_matrix = generate_feature_vector(i,i) + varii_feature_matrix = generate_feature_vector(i, i) var1 = varIDs[i] - - if f'{var1}_{var1}' not in seen_pairs: - if feature_df.loc[var1, 'zyg'] == 2: - if labeling: - if feature_df.loc[var1, 'is_strong'] == 1: + if f"{var1}_{var1}" not in seen_pairs: + if feature_df.loc[var1, "zyg"] == 2: + + if labeling: + if feature_df.loc[var1, "is_strong"] == 1: is_causal_wo_OMIM = 1 else: is_causal_wo_OMIM = 0 - varii_feature_matrix['is_causal'] = is_causal_wo_OMIM - + varii_feature_matrix["is_causal"] = is_causal_wo_OMIM + bivar_feature_mats.append(varii_feature_matrix) - seen_pairs.add(f'{var1}_{var1}') - + seen_pairs.add(f"{var1}_{var1}") + if i < len(varIDs) - 1: - for j in range(i+1, len(varIDs)): + for j in range(i + 1, len(varIDs)): var2 = varIDs[j] - if (f'{var1}_{var2}' not in seen_pairs) and (f'{var2}_{var1}' not in seen_pairs) and (var1 != var2): + if ( + (f"{var1}_{var2}" not in seen_pairs) + and (f"{var2}_{var1}" not in seen_pairs) + and (var1 != var2) + ): varij_feature_matrix = generate_feature_vector(i, j) - + if labeling: is_causal_wo_OMIM = 0 - if (feature_df.loc[[var1, var2], 'is_strong'] == 1).all(): - if (feature_df.loc[[var1, var2], 'zyg'] == 1).all(): - is_causal_wo_OMIM = 1 + if (feature_df.loc[[var1, var2], "is_strong"] == 1).all(): + if (feature_df.loc[[var1, var2], "zyg"] == 1).all(): + is_causal_wo_OMIM = 1 - varij_feature_matrix['is_causal'] = is_causal_wo_OMIM + varij_feature_matrix["is_causal"] = is_causal_wo_OMIM bivar_feature_mats.append(varij_feature_matrix) - seen_pairs.add(f'{var1}_{var2}') - seen_pairs.add(f'{var2}_{var1}') + seen_pairs.add(f"{var1}_{var2}") + seen_pairs.add(f"{var2}_{var1}") else: pass - if len(bivar_feature_mats) > 0: + if len(bivar_feature_mats) > 0: bivar_feature_mats = pd.concat(bivar_feature_mats, axis=0) bivar_feature_mats = bivar_feature_mats.drop_duplicates() - bivar_feature_mats.to_csv(f"{out_folder}/{gene}.csv") - - return \ No newline at end of file + bivar_feature_mats.to_csv(f"{out_folder}/{gene}.csv") + + return diff --git a/bin/extraModel/integrate_output.py b/bin/extraModel/integrate_output.py index 4b18852..ca5b5ce 100755 --- a/bin/extraModel/integrate_output.py +++ b/bin/extraModel/integrate_output.py @@ -2,57 +2,75 @@ from glob import glob import os + def process_recessive_matrix(df): - df['varPair'] = df.index.copy() - df['var1'] = df.varPair.apply(lambda x: x.split("_")[0]) - df['var2'] = df.varPair.apply(lambda x: x.split("_")[1]) + df["varPair"] = df.index.copy() + df["var1"] = df.varPair.apply(lambda x: x.split("_")[0]) + df["var2"] = df.varPair.apply(lambda x: x.split("_")[1]) var_grps = df.groupby("var1") result = [] for _, var_grp in var_grps: var_grp = var_grp.sort_values("predict", ascending=False) - result.append(var_grp.iloc[[0],:]) + result.append(var_grp.iloc[[0], :]) result = pd.concat(result) result.set_index("var1", inplace=True) return result + def integrate_output(prj_folder, data_folder, sample_id): ## Group variants by Ens IDs expanded_fn = f"final_matrix_expanded/{sample_id}.expanded.csv.gz" - #expanded_df = [] - # for expanded_fn in expanded_fns: - if 'csv' in expanded_fn: - expanded_df = pd.read_csv(expanded_fn, sep=",", index_col=0, compression='infer') + # expanded_df = [] + # for expanded_fn in expanded_fns: + if "csv" in expanded_fn: + expanded_df = pd.read_csv( + expanded_fn, sep=",", index_col=0, compression="infer" + ) else: - expanded_df = pd.read_csv(expanded_fn, sep="\t", index_col=0, compression='infer') - #expanded_df.append(df) - #expanded_df = pd.concat(expanded_df) + expanded_df = pd.read_csv( + expanded_fn, sep="\t", index_col=0, compression="infer" + ) + # expanded_df.append(df) + # expanded_df = pd.concat(expanded_df) # expanded_df = expanded_df.loc[~expanded_df.index.duplicated(keep='first')] ## read predictions for default model default_fn = f"{data_folder}/{sample_id}_default_predictions.csv" - default_pred = pd.read_csv(default_fn, index_col=0) - default_raw_pred_dict = dict(zip(default_pred.index.tolist(), default_pred.predict.tolist())) - default_conf_dict = dict(zip(default_pred.index.tolist(), default_pred['confidence'].tolist())) - default_conf_lvl_dict = dict(zip(default_pred.index.tolist(), default_pred['confidence level'].tolist())) - default_rank_dict = dict(zip(default_pred.index.tolist(), default_pred.ranking.tolist())) - - expanded_df['predict (default)'] = expanded_df.index.map(default_raw_pred_dict) - expanded_df['confidence (default)'] = expanded_df.index.map(default_conf_dict) - expanded_df['confidence level (default)'] = expanded_df.index.map(default_conf_lvl_dict) - expanded_df['ranking (default)'] = expanded_df.index.map(default_rank_dict) + default_pred = pd.read_csv(default_fn, index_col=0) + default_raw_pred_dict = dict( + zip(default_pred.index.tolist(), default_pred.predict.tolist()) + ) + default_conf_dict = dict( + zip(default_pred.index.tolist(), default_pred["confidence"].tolist()) + ) + default_conf_lvl_dict = dict( + zip(default_pred.index.tolist(), default_pred["confidence level"].tolist()) + ) + default_rank_dict = dict( + zip(default_pred.index.tolist(), default_pred.ranking.tolist()) + ) + + expanded_df["predict (default)"] = expanded_df.index.map(default_raw_pred_dict) + expanded_df["confidence (default)"] = expanded_df.index.map(default_conf_dict) + expanded_df["confidence level (default)"] = expanded_df.index.map( + default_conf_lvl_dict + ) + expanded_df["ranking (default)"] = expanded_df.index.map(default_rank_dict) ## read predictions for novel disease model nd_fn = f"{data_folder}/{sample_id}_nd_predictions.csv" - nd_pred = pd.read_csv(nd_fn, index_col=0) + nd_pred = pd.read_csv(nd_fn, index_col=0) nd_raw_pred_dict = dict(zip(nd_pred.index.tolist(), nd_pred.predict.tolist())) - nd_conf_dict = dict(zip(nd_pred.index.tolist(), nd_pred['confidence'].tolist())) + nd_conf_dict = dict(zip(nd_pred.index.tolist(), nd_pred["confidence"].tolist())) nd_rank_dict = dict(zip(nd_pred.index.tolist(), nd_pred.ranking.tolist())) - nd_conf_lvl_dict = dict(zip(nd_pred.index.tolist(), nd_pred['confidence level'].tolist())) + nd_conf_lvl_dict = dict( + zip(nd_pred.index.tolist(), nd_pred["confidence level"].tolist()) + ) - expanded_df['predict (nd)'] = expanded_df.index.map(nd_raw_pred_dict) - expanded_df['confidence (nd)'] = expanded_df.index.map(nd_conf_dict) - expanded_df['ranking (nd)'] = expanded_df.index.map(nd_rank_dict) - expanded_df['confidence level (nd)'] = expanded_df.index.map(nd_conf_lvl_dict) + expanded_df["predict (nd)"] = expanded_df.index.map(nd_raw_pred_dict) + expanded_df["confidence (nd)"] = expanded_df.index.map(nd_conf_dict) + expanded_df["ranking (nd)"] = expanded_df.index.map(nd_rank_dict) + expanded_df["confidence level (nd)"] = expanded_df.index.map(nd_conf_lvl_dict) ## recessive predictions for default model recessive_fn = f"{data_folder}/{sample_id}_recessive_predictions.csv" @@ -60,11 +78,24 @@ def integrate_output(prj_folder, data_folder, sample_id): recessive_pred = pd.read_csv(recessive_fn, index_col=0) recessive_pred = process_recessive_matrix(recessive_pred) - recessive_pred_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred.predict.tolist())) - recessive_conf_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred['confidence'].tolist())) - recessive_rank_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred.ranking.tolist())) - recessive_other_var = dict(zip(recessive_pred.index.tolist(), recessive_pred.var2.tolist())) - recessive_conf_lvl_dict = dict(zip(recessive_pred.index.tolist(), recessive_pred['confidence level'].tolist())) + recessive_pred_dict = dict( + zip(recessive_pred.index.tolist(), recessive_pred.predict.tolist()) + ) + recessive_conf_dict = dict( + zip(recessive_pred.index.tolist(), recessive_pred["confidence"].tolist()) + ) + recessive_rank_dict = dict( + zip(recessive_pred.index.tolist(), recessive_pred.ranking.tolist()) + ) + recessive_other_var = dict( + zip(recessive_pred.index.tolist(), recessive_pred.var2.tolist()) + ) + recessive_conf_lvl_dict = dict( + zip( + recessive_pred.index.tolist(), + recessive_pred["confidence level"].tolist(), + ) + ) rec_preds, rec_confs, rec_ranks, rec_var2s = [], [], [], [] rec_conf_lvls = [] @@ -79,32 +110,48 @@ def integrate_output(prj_folder, data_folder, sample_id): rec_preds.append(-1) rec_confs.append(-1) rec_ranks.append(99999) - rec_var2s.append('NA') - rec_conf_lvls.append('Unsolved') - - expanded_df['predict (recessive)'] = rec_preds - expanded_df['confidence (recessive)'] = rec_confs - expanded_df['ranking (recessive)'] = rec_ranks - expanded_df['recessive var2'] = rec_var2s - expanded_df['confidence level (recessive)'] = rec_conf_lvls + rec_var2s.append("NA") + rec_conf_lvls.append("Unsolved") + + expanded_df["predict (recessive)"] = rec_preds + expanded_df["confidence (recessive)"] = rec_confs + expanded_df["ranking (recessive)"] = rec_ranks + expanded_df["recessive var2"] = rec_var2s + expanded_df["confidence level (recessive)"] = rec_conf_lvls else: - expanded_df['predict (recessive)'] = -1 - expanded_df['confidence (recessive)'] = -1 - expanded_df['ranking (recessive)'] = 99999 - expanded_df['recessive var2'] = 'NA' - expanded_df['confidence level (recessive)'] = 'Unsolved' + expanded_df["predict (recessive)"] = -1 + expanded_df["confidence (recessive)"] = -1 + expanded_df["ranking (recessive)"] = 99999 + expanded_df["recessive var2"] = "NA" + expanded_df["confidence level (recessive)"] = "Unsolved" - ## novel disease recessive + ## novel disease recessive nd_recessive_fn = f"{data_folder}/{sample_id}_nd_recessive_predictions.csv" if os.path.exists(nd_recessive_fn): nd_recessive_pred = pd.read_csv(nd_recessive_fn, index_col=0) nd_recessive_pred = process_recessive_matrix(nd_recessive_pred) - nd_recessive_pred_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.predict.tolist())) - nd_recessive_conf_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred['confidence'].tolist())) - nd_recessive_rank_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.ranking.tolist())) - nd_recessive_conf_lvl_dict = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred['confidence level'].tolist())) - nd_recessive_other_var = dict(zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.var2.tolist())) + nd_recessive_pred_dict = dict( + zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.predict.tolist()) + ) + nd_recessive_conf_dict = dict( + zip( + nd_recessive_pred.index.tolist(), + nd_recessive_pred["confidence"].tolist(), + ) + ) + nd_recessive_rank_dict = dict( + zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.ranking.tolist()) + ) + nd_recessive_conf_lvl_dict = dict( + zip( + nd_recessive_pred.index.tolist(), + nd_recessive_pred["confidence level"].tolist(), + ) + ) + nd_recessive_other_var = dict( + zip(nd_recessive_pred.index.tolist(), nd_recessive_pred.var2.tolist()) + ) nd_rec_preds, nd_rec_confs, nd_rec_ranks, nd_rec_var2s = [], [], [], [] nd_rec_lvls = [] @@ -119,32 +166,33 @@ def integrate_output(prj_folder, data_folder, sample_id): nd_rec_preds.append(-1) nd_rec_confs.append(-1) nd_rec_ranks.append(99999) - nd_rec_var2s.append('NA') - nd_rec_lvls.append('Unsolved') - - expanded_df['predict (nd recessive)'] = nd_rec_preds - expanded_df['confidence (nd recessive)'] = nd_rec_confs - expanded_df['confidence level (nd recessive)'] = nd_rec_lvls - expanded_df['ranking (nd recessive)'] = nd_rec_ranks - expanded_df['nd recessive var2'] = nd_rec_var2s + nd_rec_var2s.append("NA") + nd_rec_lvls.append("Unsolved") + + expanded_df["predict (nd recessive)"] = nd_rec_preds + expanded_df["confidence (nd recessive)"] = nd_rec_confs + expanded_df["confidence level (nd recessive)"] = nd_rec_lvls + expanded_df["ranking (nd recessive)"] = nd_rec_ranks + expanded_df["nd recessive var2"] = nd_rec_var2s else: - expanded_df['predict (nd recessive)'] = -1 - expanded_df['confidence (nd recessive)'] = -1 - expanded_df['confidence level (nd recessive)'] = 99999 - expanded_df['ranking (nd recessive)'] = 'NA' - expanded_df['nd recessive var2'] = 'Unsolved' + expanded_df["predict (nd recessive)"] = -1 + expanded_df["confidence (nd recessive)"] = -1 + expanded_df["confidence level (nd recessive)"] = 99999 + expanded_df["ranking (nd recessive)"] = "NA" + expanded_df["nd recessive var2"] = "Unsolved" return expanded_df + if __name__ == "__main__": from tqdm import tqdm import os - prj_folder = 'projects/Texome/' - data_folder = 'data/Texome/' + + prj_folder = "projects/Texome/" + data_folder = "data/Texome/" sample_ids = os.listdir(data_folder) for sample_id in tqdm(sample_ids): integrated_df = integrate_output(prj_folder, data_folder, sample_id) if not os.path.exists(f"{prj_folder}/AIM_integrated"): os.mkdir(f"{prj_folder}/AIM_integrated") - integrated_df.to_csv(f'{prj_folder}/AIM_integrated/{sample_id}_integrated.csv') - + integrated_df.to_csv(f"{prj_folder}/AIM_integrated/{sample_id}_integrated.csv") diff --git a/bin/extraModel_main.py b/bin/extraModel_main.py index be18f59..e489951 100755 --- a/bin/extraModel_main.py +++ b/bin/extraModel_main.py @@ -14,20 +14,23 @@ parser = argparse.ArgumentParser() -parser.add_argument('-id', metavar='I', type=str, - help = 'sample ID') +parser.add_argument("-id", metavar="I", type=str, help="sample ID") -parser.add_argument('-n_cpu', type=int, default=10, - help = 'folders containing all extended final matrices') +parser.add_argument( + "-n_cpu", + type=int, + default=10, + help="folders containing all extended final matrices", +) args = parser.parse_args() -#st_time = time() -#prj_name = args.project +# st_time = time() +# prj_name = args.project sample_id = args.id n_cpu = args.n_cpu -out_folder = 'conf_4Model' +out_folder = "conf_4Model" if not os.path.exists(out_folder): os.mkdir(out_folder) @@ -35,74 +38,83 @@ print(f"### Results will be saved at: {out_folder}.") print(f"### Process started at: {datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}.") -model_names = ['default', 'recessive', 'nd', 'nd_recessive'] -model_dict = {} +model_names = ["default", "recessive", "nd", "nd_recessive"] +model_dict = {} for mn in model_names: model = joblib.load(f"model_inputs/{mn}/final_model.job") ref_panel = joblib.load(f"model_inputs/{mn}/reference_panel.job") - features = open(f"model_inputs/{mn}/features.csv", 'r').readline().split(",") - model_dict[mn] = {'model': model, 'ref': ref_panel, 'features': features} - #if not os.path.exists(f"./{out_folder}/{mn}"): + features = open(f"model_inputs/{mn}/features.csv", "r").readline().split(",") + model_dict[mn] = {"model": model, "ref": ref_panel, "features": features} + # if not os.path.exists(f"./{out_folder}/{mn}"): # os.mkdir(f"./{out_folder}/{mn}") print(f"### All models, features, reference panels loaded.") print(f"### Start generating recessive matrix and running predictions.") + def assign_ranking(df): pred_df = df.copy() pred_df = pred_df.sort_values("predict", ascending=False) - min_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='min').astype(int) - max_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='max').astype(int) - pred_df['min_ranking'] = min_rankings - pred_df['max_ranking'] = max_rankings - pred_df['ranking'] = max_rankings + min_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method="min").astype(int) + max_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method="max").astype(int) + pred_df["min_ranking"] = min_rankings + pred_df["max_ranking"] = max_rankings + pred_df["ranking"] = max_rankings return pred_df - + def AIM(data_folder, sample_id, n_thread): - feature_fn = f'{sample_id}.csv' + feature_fn = f"{sample_id}.csv" if not os.path.exists(feature_fn): print(f"{feature_fn} does not exist.") return - - df = pd.read_csv(feature_fn, index_col=0) - for mn in ['default', 'nd']: + df = pd.read_csv(feature_fn, index_col=0) + + for mn in ["default", "nd"]: df_pred = df.copy() - if 'predict' in df_pred.columns: - df_pred = df_pred.drop(columns=['predict']) - predict = model_dict[mn]['model'].predict_proba(df_pred.loc[:, model_dict[mn]['features']])[:, 1] - df_pred.insert(loc=df_pred.shape[1]-1,column='predict',value=predict) - df_pred = assign_confidence_score(model_dict[mn]['ref'], df_pred) - df_pred = df_pred.sort_values('confidence', ascending=False) + if "predict" in df_pred.columns: + df_pred = df_pred.drop(columns=["predict"]) + predict = model_dict[mn]["model"].predict_proba( + df_pred.loc[:, model_dict[mn]["features"]] + )[:, 1] + df_pred.insert(loc=df_pred.shape[1] - 1, column="predict", value=predict) + df_pred = assign_confidence_score(model_dict[mn]["ref"], df_pred) + df_pred = df_pred.sort_values("confidence", ascending=False) df_pred = assign_ranking(df_pred) df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") - + print(f"### Start processing recessive data and make predictions.") - - default_pred = pd.read_csv(f"{out_folder}/{sample_id}_default_predictions.csv", index_col=0) + default_pred = pd.read_csv( + f"{out_folder}/{sample_id}_default_predictions.csv", index_col=0 + ) - process_sample( data_folder = out_folder, - sample_id = sample_id, - default_pred = default_pred, - labeling=False, n_thread = n_thread) + process_sample( + data_folder=out_folder, + sample_id=sample_id, + default_pred=default_pred, + labeling=False, + n_thread=n_thread, + ) recessive_feature_file = f"{out_folder}/recessive_matrix/{sample_id}.csv" if os.path.exists(recessive_feature_file): # AIM found recessive variant pairs and generated the matrix - df = pd.read_csv(recessive_feature_file, index_col=0) - for mn in ['recessive', 'nd_recessive']: + df = pd.read_csv(recessive_feature_file, index_col=0) + for mn in ["recessive", "nd_recessive"]: df_pred = df.copy() - if 'predict' in df_pred.columns: - df_pred = df_pred.drop(columns=['predict']) - predict = model_dict[mn]['model'].predict_proba(df_pred.loc[:, model_dict[mn]['features']])[:, 1] - df_pred.insert(loc=df_pred.shape[1]-1,column='predict',value=predict) - df_pred = assign_confidence_score(model_dict[mn]['ref'], df_pred) - df_pred = df_pred.sort_values('confidence', ascending=False) + if "predict" in df_pred.columns: + df_pred = df_pred.drop(columns=["predict"]) + predict = model_dict[mn]["model"].predict_proba( + df_pred.loc[:, model_dict[mn]["features"]] + )[:, 1] + df_pred.insert(loc=df_pred.shape[1] - 1, column="predict", value=predict) + df_pred = assign_confidence_score(model_dict[mn]["ref"], df_pred) + df_pred = df_pred.sort_values("confidence", ascending=False) df_pred = assign_ranking(df_pred) df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") else: @@ -114,8 +126,9 @@ def AIM(data_folder, sample_id, n_thread): integrated_df = integrate_output(out_folder, data_folder, sample_id) if not os.path.exists(f"{out_folder}/integrated"): os.mkdir(f"{out_folder}/integrated") - integrated_df.to_csv(f'{out_folder}/integrated/{sample_id}_integrated.csv') + integrated_df.to_csv(f"{out_folder}/integrated/{sample_id}_integrated.csv") return -#for sample_id in tqdm(sample_folders): -AIM(out_folder, sample_id, n_thread = n_cpu) + +# for sample_id in tqdm(sample_folders): +AIM(out_folder, sample_id, n_thread=n_cpu) diff --git a/bin/feature.py b/bin/feature.py index b42dad5..7206dd3 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -15,12 +15,15 @@ import sys import requests import subprocess -#local methods -from annotation.utils_1 import * -#from utils_for_marrvel_annotate_script import * + +# local methods +from annotation.utils_1 import * + +# from utils_for_marrvel_annotate_script import * from annotation.utils_for_symMatch import * from annotation.utils_for_marrvel_flatfile import * -#from utils_for_marrvel_flatfile_module_3 import * + +# from utils_for_marrvel_flatfile_module_3 import * import multiprocessing from functools import partial from annotation.marrvel_score_recalc import * @@ -38,76 +41,98 @@ ############################################################# - - def checkUserArgs(inArgs): - #check the user arguments and if anything necessary is missing + # check the user arguments and if anything necessary is missing pass def main(): - #example of running the script: - #python3 main.py -outPrefix ./test/r1 \ - #-patientID UDN630665 -patientHPOsimiOMIM /houston_10t/dongxue/MARRVEL_AI/BG/HPO2Gene/out_May12021/UDN/HPOsimi_UDN630665_simi_0.tsv \ - #-patientHPOsimiHGMD /six_tera/chaozhong/module_1/out/UDN/HPOsimi_UDNUDN630665_simi_0.tsv \ - #-varFile /houston_20t/rami/vep_PassVarQual_filt_UDN_Apr2521/540200-UDN630665-P_HGYTFBCXY-2-ID01-vep_gnomADg_gnomADe_splice_clin_hgmd_filt.txt \ - #-inFileType vepAnnotTab -patientFile testPatientFile.txt -patientFileType one -genomeRef hg19 -diseaseInh AD -modules curate,conserve - - + # example of running the script: + # python3 main.py -outPrefix ./test/r1 \ + # -patientID UDN630665 -patientHPOsimiOMIM /houston_10t/dongxue/MARRVEL_AI/BG/HPO2Gene/out_May12021/UDN/HPOsimi_UDN630665_simi_0.tsv \ + # -patientHPOsimiHGMD /six_tera/chaozhong/module_1/out/UDN/HPOsimi_UDNUDN630665_simi_0.tsv \ + # -varFile /houston_20t/rami/vep_PassVarQual_filt_UDN_Apr2521/540200-UDN630665-P_HGYTFBCXY-2-ID01-vep_gnomADg_gnomADe_splice_clin_hgmd_filt.txt \ + # -inFileType vepAnnotTab -patientFile testPatientFile.txt -patientFileType one -genomeRef hg19 -diseaseInh AD -modules curate,conserve + parser = argparse.ArgumentParser() - parser.add_argument("-varFile", "--varFile", help="Proivde input variant file") - parser.add_argument("-inFileType", "--inFileType", help="Proivde type of input file:vcf, vepAnnotTab") - parser.add_argument("-patientFile", "--patientFile", help="Proivde HPO IDs")#currently not used - parser.add_argument("-patientFileType", "--patientFileType", help="Proivde type of file:one, two")#currently not used - parser.add_argument("-patientHPOsimiOMIM", "--patientHPOsimiOMIM", help="Proivde patient HPO similarity file-OMIM") - parser.add_argument("-patientHPOsimiHGMD", "--patientHPOsimiHGMD", help="Proivde patient HPO similarity file-HGMD") - parser.add_argument("-diseaseInh", "--diseaseInh", help="Proivde disease Inheritance:AD, AR, XD, XR") - parser.add_argument("-modules", "--modules", help="Select modules to run:all,curate,conserve,effectOnGene") - parser.add_argument("-genomeRef", "--genomeRef", help="Proivde genome ref: hg19, hg38") - parser.add_argument("-outPrefix", "--outPrefix", help="Proivde prefix for output files") + parser.add_argument("-varFile", "--varFile", help="Proivde input variant file") + parser.add_argument( + "-inFileType", + "--inFileType", + help="Proivde type of input file:vcf, vepAnnotTab", + ) + parser.add_argument( + "-patientFile", "--patientFile", help="Proivde HPO IDs" + ) # currently not used + parser.add_argument( + "-patientFileType", "--patientFileType", help="Proivde type of file:one, two" + ) # currently not used + parser.add_argument( + "-patientHPOsimiOMIM", + "--patientHPOsimiOMIM", + help="Proivde patient HPO similarity file-OMIM", + ) + parser.add_argument( + "-patientHPOsimiHGMD", + "--patientHPOsimiHGMD", + help="Proivde patient HPO similarity file-HGMD", + ) + parser.add_argument( + "-diseaseInh", "--diseaseInh", help="Proivde disease Inheritance:AD, AR, XD, XR" + ) + parser.add_argument( + "-modules", + "--modules", + help="Select modules to run:all,curate,conserve,effectOnGene", + ) + parser.add_argument( + "-genomeRef", "--genomeRef", help="Proivde genome ref: hg19, hg38" + ) + parser.add_argument( + "-outPrefix", "--outPrefix", help="Proivde prefix for output files" + ) args = parser.parse_args() - #check the user args + # check the user args checkUserArgs(args) - print('input file:', args.varFile) - print('type of input file:', args.inFileType) - print('outPrefix:', args.outPrefix) - print('modules:', args.modules) - moduleList=args.modules.split(',') - print('modules list:', moduleList) - - #start time + print("input file:", args.varFile) + print("type of input file:", args.inFileType) + print("outPrefix:", args.outPrefix) + print("modules:", args.modules) + moduleList = args.modules.split(",") + print("modules list:", moduleList) + + # start time start_time = time.time() - #need to write user options to a log file - - #read the gnomad gene metrics file - fileName='annotate/anno_hg19/gnomad.v2.1.1.lof_metrics.by_gene.txt' - #fileName='/database_test/gnomad.v2.1.1.lof_metrics.by_gene.txt' - gnomadMetricsGeneDf=pd.read_csv(fileName, sep='\t') - #print('gnomadMetricsGeneDf shape:', gnomadMetricsGeneDf.shape) - - #set up flags - #debug flag; if 1 then can check/test the code - debugFlag=0 - #print('debugFlag:', debugFlag) - #use flat files to read the marrvel databases instead of using API - flatFileFlag=1 - #use marrvel annotation script and API to get marrvel info. this is slower - annotateFlag=0 - - #the directory for marrvel database flat file - #flatFilesDir='/home/dongxue/hdd_10t/MARRVEL_AI/resource/MARRVEL/' - - #initialization - dgvDf=[] - decipherDf=[] - hgmdDf=[] - omimHPOScoreDf=[] - hgmdHPOScoreDf=[] - clinvarGeneDf=[] - clinvarAlleleDf=[] - omimAlleleList=[] - + # need to write user options to a log file + + # read the gnomad gene metrics file + fileName = "annotate/anno_hg19/gnomad.v2.1.1.lof_metrics.by_gene.txt" + # fileName='/database_test/gnomad.v2.1.1.lof_metrics.by_gene.txt' + gnomadMetricsGeneDf = pd.read_csv(fileName, sep="\t") + # print('gnomadMetricsGeneDf shape:', gnomadMetricsGeneDf.shape) + + # set up flags + # debug flag; if 1 then can check/test the code + debugFlag = 0 + # print('debugFlag:', debugFlag) + # use flat files to read the marrvel databases instead of using API + flatFileFlag = 1 + # use marrvel annotation script and API to get marrvel info. this is slower + annotateFlag = 0 + + # the directory for marrvel database flat file + # flatFilesDir='/home/dongxue/hdd_10t/MARRVEL_AI/resource/MARRVEL/' + + # initialization + dgvDf = [] + decipherDf = [] + hgmdDf = [] + omimHPOScoreDf = [] + hgmdHPOScoreDf = [] + clinvarGeneDf = [] + clinvarAlleleDf = [] + omimAlleleList = [] ## CL: It's no longer needed to extract info from HGMD_allmut.txt """ @@ -168,106 +193,111 @@ def main(): """ - #read HPO files - if 'curate' in moduleList: - fileName=args.patientHPOsimiOMIM - omimHPOScoreDf = pd.read_csv(fileName, sep='\t') - print('patientHPOsimi-OMIM dimension:',omimHPOScoreDf.shape) + # read HPO files + if "curate" in moduleList: + fileName = args.patientHPOsimiOMIM + omimHPOScoreDf = pd.read_csv(fileName, sep="\t") + print("patientHPOsimi-OMIM dimension:", omimHPOScoreDf.shape) - fileName=args.patientHPOsimiHGMD - hgmdHPOScoreDf = pd.read_csv(fileName, sep='\t') - print('patientHPOsimi-HGMD dimension:',hgmdHPOScoreDf.shape) + fileName = args.patientHPOsimiHGMD + hgmdHPOScoreDf = pd.read_csv(fileName, sep="\t") + print("patientHPOsimi-HGMD dimension:", hgmdHPOScoreDf.shape) - #read the clinvar genes file - if 'curate' in moduleList: + # read the clinvar genes file + if "curate" in moduleList: # Q: Why we use the same for hg19 and hg38? - # A: - if args.genomeRef == 'hg38': - fileName='annotate/anno_hg19/gene_clinvar.csv' + # A: + if args.genomeRef == "hg38": + fileName = "annotate/anno_hg19/gene_clinvar.csv" else: - fileName='annotate/anno_hg19/gene_clinvar.csv' - #fileName='/database_test/gene_clinvar.csv' - #print('\nfileName:', fileName) - clinvarGeneDf=pd.read_csv(fileName, sep=',') - #sort by gene name + fileName = "annotate/anno_hg19/gene_clinvar.csv" + # fileName='/database_test/gene_clinvar.csv' + # print('\nfileName:', fileName) + clinvarGeneDf = pd.read_csv(fileName, sep=",") + # sort by gene name clinvarGeneDf.sort_values("symbol", inplace=True) - clinvarGeneDf.set_index(['symbol'], inplace = True,drop=False) - #print('clinvarGenes dim:', clinvarGeneDf.shape)#41754 x 10,, entrezId,symbol,chr,hg19Start,hg19Stop,totalClinvarVars,P,LP,LB,B - #print(clinvarGeneDf.head()) - - #read OMIM - if 'curate' in moduleList: - #read the OMIM gene file - fileName='annotate/anno_hg19/gene_omim.json' - #fileName='/database_test/gene_omim.json' - #print('\nfileName:', fileName) + clinvarGeneDf.set_index(["symbol"], inplace=True, drop=False) + # print('clinvarGenes dim:', clinvarGeneDf.shape)#41754 x 10,, entrezId,symbol,chr,hg19Start,hg19Stop,totalClinvarVars,P,LP,LB,B + # print(clinvarGeneDf.head()) + + # read OMIM + if "curate" in moduleList: + # read the OMIM gene file + fileName = "annotate/anno_hg19/gene_omim.json" + # fileName='/database_test/gene_omim.json' + # print('\nfileName:', fileName) with open(fileName) as f: - omimGeneList=json.load(f) - #print('type omimGeneList:', type(omimGeneList), 'len:', len(omimGeneList)) - #check the type of data and keys - if debugFlag==1: + omimGeneList = json.load(f) + # print('type omimGeneList:', type(omimGeneList), 'len:', len(omimGeneList)) + # check the type of data and keys + if debugFlag == 1: for omimGeneDict in omimGeneList: - print('type of omimGeneDict:', type(omimGeneDict)) - print('keys:', omimGeneDict.keys()) + print("type of omimGeneDict:", type(omimGeneDict)) + print("keys:", omimGeneDict.keys()) for keyVal in omimGeneDict.keys(): - print('keyVal:', keyVal) - print('\tsubkeys type:', type(omimGeneDict[keyVal]) ) - if isinstance(omimGeneDict[keyVal], list): - print('\n\t\tfound list') - print('\t\t type:',type(omimGeneDict[keyVal])) - #print('\t\t type:',omimGeneDict[keyVal]) - #for row in omimGeneDict[keyVal]: + print("keyVal:", keyVal) + print("\tsubkeys type:", type(omimGeneDict[keyVal])) + if isinstance(omimGeneDict[keyVal], list): + print("\n\t\tfound list") + print("\t\t type:", type(omimGeneDict[keyVal])) + # print('\t\t type:',omimGeneDict[keyVal]) + # for row in omimGeneDict[keyVal]: # print('row keys:', row.keys()) break - #read the OMIM allele file - fileName='annotate/anno_hg19/omim_alleric_variants.json' - #fileName='/database_test/omim_alleric_variants.json' - #print('\nfileName:', fileName) + # read the OMIM allele file + fileName = "annotate/anno_hg19/omim_alleric_variants.json" + # fileName='/database_test/omim_alleric_variants.json' + # print('\nfileName:', fileName) with open(fileName) as f: - omimAlleleList=json.load(f) - if debugFlag==1: - print('type of omim allelic :', type(omimAlleleList), 'len:', len(omimAlleleList)) + omimAlleleList = json.load(f) + if debugFlag == 1: + print( + "type of omim allelic :", + type(omimAlleleList), + "len:", + len(omimAlleleList), + ) for omimAlleleDict in omimAlleleList: - print('type of omimAlleleDict:', type(omimAlleleDict)) - #check the keys - print('keys:', omimAlleleDict.keys()) + print("type of omimAlleleDict:", type(omimAlleleDict)) + # check the keys + print("keys:", omimAlleleDict.keys()) for keyVal in omimAlleleDict.keys(): - print('keyVal:', keyVal) - print('\tsubkeys type:', type(omimAlleleDict[keyVal]) ) + print("keyVal:", keyVal) + print("\tsubkeys type:", type(omimAlleleDict[keyVal])) break - - #read DGV database - if 'conserve' in moduleList: - print('reading DGV flat file') - if args.genomeRef == 'hg38': - fileName='annotate/anno_hg38/dgv.csv' - else: - fileName='annotate/anno_hg19/dgv.csv' - #fileName='/database_test/dgv.csv' - dgvDf=pd.read_csv(fileName, sep=',') - #print('dgvDf shape:', dgvDf.shape) - #print('dgvHead:', dgvDf.head()) - #hg19Chr hg19Start hg19Stop - dgvDf=dgvDf.fillna(0) - - #if args.genomeRef == 'hg19': - dgvDf.columns = ['Chr', 'Start', 'Stop'] + dgvDf.columns.tolist()[3:] - - dgvDf['Start']=dgvDf['Start'].astype(int) - dgvDf['Stop']=dgvDf['Stop'].astype(int) - dgvDf['Chr']=dgvDf['Chr'].replace('X',23 ) - dgvDf['Chr']=dgvDf['Chr'].replace('Y',24 ) - dgvDf['Chr']=dgvDf['Chr'].replace('MT',25 ) - dgvDf['Chr']=dgvDf['Chr'].replace('GL.*',26,regex=True ) - #make chr as int - #dgvDf = dgvDf.loc[dgvDf['Chr'].isin(list(range(27))),:].copy() - dgvDf['Chr']=dgvDf['Chr'].astype(int) - - ''' CL: change column name to be compatible with hg38 + # read DGV database + if "conserve" in moduleList: + print("reading DGV flat file") + if args.genomeRef == "hg38": + fileName = "annotate/anno_hg38/dgv.csv" + else: + fileName = "annotate/anno_hg19/dgv.csv" + # fileName='/database_test/dgv.csv' + dgvDf = pd.read_csv(fileName, sep=",") + # print('dgvDf shape:', dgvDf.shape) + # print('dgvHead:', dgvDf.head()) + # hg19Chr hg19Start hg19Stop + dgvDf = dgvDf.fillna(0) + + # if args.genomeRef == 'hg19': + dgvDf.columns = ["Chr", "Start", "Stop"] + dgvDf.columns.tolist()[3:] + + dgvDf["Start"] = dgvDf["Start"].astype(int) + dgvDf["Stop"] = dgvDf["Stop"].astype(int) + dgvDf["Chr"] = dgvDf["Chr"].replace("X", 23) + dgvDf["Chr"] = dgvDf["Chr"].replace("Y", 24) + dgvDf["Chr"] = dgvDf["Chr"].replace("MT", 25) + dgvDf["Chr"] = dgvDf["Chr"].replace("GL.*", 26, regex=True) + + # make chr as int + # dgvDf = dgvDf.loc[dgvDf['Chr'].isin(list(range(27))),:].copy() + dgvDf["Chr"] = dgvDf["Chr"].astype(int) + + """ CL: change column name to be compatible with hg38 dgvDf['hg19Start']=dgvDf['hg19Start'].astype(int) dgvDf['hg19Stop']=dgvDf['hg19Stop'].astype(int) dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('X',23 ) @@ -276,35 +306,35 @@ def main(): dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('GL.*','26',regex=True ) #make chr as int dgvDf['hg19Chr']=dgvDf['hg19Chr'].astype(int) - ''' - print('finsihed reading DGV') - - #read DECIPHER database - if 'conserve' in moduleList: - print('reading Decipher flat file') - if args.genomeRef == 'hg38': - fileName='annotate/anno_hg38/decipher.csv' + """ + print("finsihed reading DGV") + + # read DECIPHER database + if "conserve" in moduleList: + print("reading Decipher flat file") + if args.genomeRef == "hg38": + fileName = "annotate/anno_hg38/decipher.csv" else: - fileName='annotate/anno_hg19/decipher.csv' - #fileName='/database_test/decipher.csv' - decipherDf=pd.read_csv(fileName, sep=',') - #print('decipherDf shape:', decipherDf.shape) - #hg19Chr,hg19Start,hg19Stop - decipherDf=decipherDf.fillna(0) - - #if args.genomeRef == 'hg19': - decipherDf.columns = ['Chr', 'Start', 'Stop'] + decipherDf.columns.tolist()[3:] - - decipherDf['Start']=decipherDf['Start'].astype(int) - decipherDf['Stop']=decipherDf['Stop'].astype(int) - decipherDf['Chr']=decipherDf['Chr'].replace('X',23 ) - decipherDf['Chr']=decipherDf['Chr'].replace('Y',24 ) - decipherDf['Chr']=decipherDf['Chr'].replace('MT',25 ) - decipherDf['Chr']=decipherDf['Chr'].replace('GL.*','26',regex=True ) - #make chr as int - decipherDf['Chr']=decipherDf['Chr'].astype(int) - - ''' CL: change column name to be compatible with hg38 + fileName = "annotate/anno_hg19/decipher.csv" + # fileName='/database_test/decipher.csv' + decipherDf = pd.read_csv(fileName, sep=",") + # print('decipherDf shape:', decipherDf.shape) + # hg19Chr,hg19Start,hg19Stop + decipherDf = decipherDf.fillna(0) + + # if args.genomeRef == 'hg19': + decipherDf.columns = ["Chr", "Start", "Stop"] + decipherDf.columns.tolist()[3:] + + decipherDf["Start"] = decipherDf["Start"].astype(int) + decipherDf["Stop"] = decipherDf["Stop"].astype(int) + decipherDf["Chr"] = decipherDf["Chr"].replace("X", 23) + decipherDf["Chr"] = decipherDf["Chr"].replace("Y", 24) + decipherDf["Chr"] = decipherDf["Chr"].replace("MT", 25) + decipherDf["Chr"] = decipherDf["Chr"].replace("GL.*", "26", regex=True) + # make chr as int + decipherDf["Chr"] = decipherDf["Chr"].astype(int) + + """ CL: change column name to be compatible with hg38 decipherDf['hg19Start']=decipherDf['hg19Start'].astype(int) decipherDf['hg19Stop']=decipherDf['hg19Stop'].astype(int) decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('X',23 ) @@ -313,134 +343,154 @@ def main(): decipherDf['hg19Chr']=decipherDf['hg19Chr'].replace('GL.*','26',regex=True ) #make chr as int decipherDf['hg19Chr']=decipherDf['hg19Chr'].astype(int) - ''' - print('finsihed reading DECIPHER') - - #read the input variant file - if args.inFileType=='vepAnnotTab' and flatFileFlag==1: - #numHeaderSkip=354#this depends on the type of annotation used - numHeaderSkip=0 - with open(args.varFile,'r') as F: + """ + print("finsihed reading DECIPHER") + + # read the input variant file + if args.inFileType == "vepAnnotTab" and flatFileFlag == 1: + # numHeaderSkip=354#this depends on the type of annotation used + numHeaderSkip = 0 + with open(args.varFile, "r") as F: for line in F: - if line.startswith('##'): + if line.startswith("##"): numHeaderSkip += 1 else: break - print('input annoatated varFile:', args.varFile) + print("input annoatated varFile:", args.varFile) t1 = time.time() - varDf = pd.read_csv(args.varFile,sep='\t', skiprows=numHeaderSkip,error_bad_lines=False) - #varDf=varDf[0:10]#do this if need to have a small test - print('shape:', varDf.shape) + varDf = pd.read_csv( + args.varFile, sep="\t", skiprows=numHeaderSkip, error_bad_lines=False + ) + # varDf=varDf[0:10]#do this if need to have a small test + print("shape:", varDf.shape) t2 = time.time() inputReadTime = t2 - t1 - inputNumRows=len(varDf.index) - - #print('head:', varDf.head) - #print('column names:', varDf.columns) - #update the GERp column names since cant use ++ in the varaible name - if 'GERP++_RS' in varDf.columns: - print('found GERP++RS') - varDf['GERPpp_RS'] = varDf['GERP++_RS'] - if 'GERP++_NR' in varDf.columns: - print('found GERP++NR') - varDf['GERPpp_NR'] = varDf['GERP++_NR'] - #update column names which have - in it - if 'fathmm-MKL_coding_score' in varDf.columns: - varDf['fathmm_MKL_coding_score']=varDf['fathmm-MKL_coding_score'] - if 'M-CAP_score' in varDf.columns: - varDf['M_CAP_score']=varDf['M-CAP_score'] - - - print('start making variant objects') - numCores=8 + inputNumRows = len(varDf.index) + + # print('head:', varDf.head) + # print('column names:', varDf.columns) + # update the GERp column names since cant use ++ in the varaible name + if "GERP++_RS" in varDf.columns: + print("found GERP++RS") + varDf["GERPpp_RS"] = varDf["GERP++_RS"] + if "GERP++_NR" in varDf.columns: + print("found GERP++NR") + varDf["GERPpp_NR"] = varDf["GERP++_NR"] + # update column names which have - in it + if "fathmm-MKL_coding_score" in varDf.columns: + varDf["fathmm_MKL_coding_score"] = varDf["fathmm-MKL_coding_score"] + if "M-CAP_score" in varDf.columns: + varDf["M_CAP_score"] = varDf["M-CAP_score"] + + print("start making variant objects") + numCores = 8 p1 = time.time() - #store rows into list for parallel function - inputList =[] + # store rows into list for parallel function + inputList = [] for index, rows in varDf.iterrows(): inputList.append(rows) p2 = time.time() makeListTime = p2 - p1 - print('makeListTime :', makeListTime) - print('type inputList:', type(inputList[0]), 'len inputList:', len(inputList)) - #group or chunks for parallel function - groupSize=min((len(inputList) // numCores) + (len(inputList) %numCores), 1000) - print('groupSize:', groupSize) - groupList=list( getVarGroups(inputList, groupSize) ) - print('type groupList:', type(groupList), 'len:', len(groupList)) - - #call parallel function, pass it the main function "getAnnotateInfo_2" from utils_for_marrvel_flatfile.py and any needed files. - #this function will get the marrvel annoatations from marrvel flat files - parallelFlag=1 - if parallelFlag==1: + print("makeListTime :", makeListTime) + print("type inputList:", type(inputList[0]), "len inputList:", len(inputList)) + # group or chunks for parallel function + groupSize = min( + (len(inputList) // numCores) + (len(inputList) % numCores), 1000 + ) + print("groupSize:", groupSize) + groupList = list(getVarGroups(inputList, groupSize)) + print("type groupList:", type(groupList), "len:", len(groupList)) + + # call parallel function, pass it the main function "getAnnotateInfo_2" from utils_for_marrvel_flatfile.py and any needed files. + # this function will get the marrvel annoatations from marrvel flat files + parallelFlag = 1 + if parallelFlag == 1: pool = multiprocessing.Pool(processes=numCores) # CL: hgmdDf changed to hgmdHPOScoreDf - func = partial(getAnnotateInfo_2, genomeRef=args.genomeRef,clinvarGeneDf=clinvarGeneDf, clinvarAlleleDf=clinvarAlleleDf, - omimGeneList=omimGeneList, omimAlleleList=omimAlleleList,hgmdDf=hgmdHPOScoreDf, moduleList=moduleList, - dgvDf=dgvDf, decipherDf=decipherDf, gnomadMetricsGeneDf=gnomadMetricsGeneDf) - varDictList=pool.map(func,groupList) + func = partial( + getAnnotateInfo_2, + genomeRef=args.genomeRef, + clinvarGeneDf=clinvarGeneDf, + clinvarAlleleDf=clinvarAlleleDf, + omimGeneList=omimGeneList, + omimAlleleList=omimAlleleList, + hgmdDf=hgmdHPOScoreDf, + moduleList=moduleList, + dgvDf=dgvDf, + decipherDf=decipherDf, + gnomadMetricsGeneDf=gnomadMetricsGeneDf, + ) + varDictList = pool.map(func, groupList) pool.close() else: pass - print('finsihed getAnnotateInfo_2') - #make a var obj list after paralle processing for each group, need a better more efficient way for doing this !! + print("finsihed getAnnotateInfo_2") + # make a var obj list after paralle processing for each group, need a better more efficient way for doing this !! t1 = time.time() varObjList = getVarObjListFromDict(varDictList, len(varDf.index)) t2 = time.time() makeVarObjListTime = t2 - t1 - #print('len varObjList:', len(varObjList)) - print('makeVarObjListTime 1:', makeVarObjListTime) + # print('len varObjList:', len(varObjList)) + print("makeVarObjListTime 1:", makeVarObjListTime) - #we now have the variant object list "varObjList" with annotations; loop thru it and get the scores + # we now have the variant object list "varObjList" with annotations; loop thru it and get the scores for varObj in varObjList: - #print('var:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol, 'var-gene-pLI:', varObj.gnomadGenePLI) - #the curate score is under the utils_1.py file - if 'curate' in moduleList: - #print('finding symptom matching') + # print('var:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol, 'var-gene-pLI:', varObj.gnomadGenePLI) + # the curate score is under the utils_1.py file + if "curate" in moduleList: + # print('finding symptom matching') omimSymMatch(varObj, omimHPOScoreDf, args.inFileType) hgmdSymMatch(varObj, hgmdHPOScoreDf) - clinVarSymMatch(varObj,args.inFileType) - #OMIM and clinvar info - retList=getCurationScore(varObj)#retList=[omimScore, hgmdScore, clinVarScore, totalScore] - varObj.curationScoreOMIM=retList[0] - varObj.curationScoreHGMD=retList[1] - varObj.curationScoreClinVar=retList[2] - varObj.curationScoreTotal=retList[3] - #print('curation score total:', varObj.curationScoreTotal) - #conserve score is under - if 'conserve' in moduleList: - retList=getConservationScore(varObj, args.diseaseInh) - #retList=[conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] - varObj.conservationScoreGnomad=retList[0] - varObj.conservationScoreDGV=retList[1] - varObj.conservationScoreOELof=retList[2] - #print('conservation score GERP++:',varObj.conservationScoreGerpPP,'DGV:',varObj.conservationScoreDGV,) - #print('conserve score DGV:',varObj.conservationScoreDGV,'conserve score gnomad:', varObj.conservationScoreGnomad) - - - #write the objects info scores to a file - print('writing results') - df = pd.DataFrame([t.__dict__ for t in varObjList ]) - print('shape of output file:',df.shape) - #write - fileName=args.outPrefix+'_scores.txt' - print('out file name:', fileName) - df.to_csv(fileName, sep='\t', index=False) + clinVarSymMatch(varObj, args.inFileType) + # OMIM and clinvar info + retList = getCurationScore( + varObj + ) # retList=[omimScore, hgmdScore, clinVarScore, totalScore] + varObj.curationScoreOMIM = retList[0] + varObj.curationScoreHGMD = retList[1] + varObj.curationScoreClinVar = retList[2] + varObj.curationScoreTotal = retList[3] + # print('curation score total:', varObj.curationScoreTotal) + # conserve score is under + if "conserve" in moduleList: + retList = getConservationScore(varObj, args.diseaseInh) + # retList=[conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] + varObj.conservationScoreGnomad = retList[0] + varObj.conservationScoreDGV = retList[1] + varObj.conservationScoreOELof = retList[2] + # print('conservation score GERP++:',varObj.conservationScoreGerpPP,'DGV:',varObj.conservationScoreDGV,) + # print('conserve score DGV:',varObj.conservationScoreDGV,'conserve score gnomad:', varObj.conservationScoreGnomad) + + # write the objects info scores to a file + print("writing results") + df = pd.DataFrame([t.__dict__ for t in varObjList]) + print("shape of output file:", df.shape) + # write + fileName = args.outPrefix + "_scores.txt" + print("out file name:", fileName) + df.to_csv(fileName, sep="\t", index=False) end_time = time.time() process_time = end_time - start_time - print('pipeline time:', process_time) - #log file for times - fileName=args.outPrefix+'_log.txt' + print("pipeline time:", process_time) + # log file for times + fileName = args.outPrefix + "_log.txt" f = open(fileName, "w") - f.write("Process time:"+str(process_time)+ ' seconds and in mins:'+ str(process_time/60)+"\n") + f.write( + "Process time:" + + str(process_time) + + " seconds and in mins:" + + str(process_time / 60) + + "\n" + ) f.close() - print('log file name:', fileName) - print('input read time:', inputReadTime) - print('input num rows:', inputNumRows) - print('makeListTime from pd:', makeListTime) - print('makeVarObjListTime 1:', makeVarObjListTime) - print('m:', moduleList) + print("log file name:", fileName) + print("input read time:", inputReadTime) + print("input num rows:", inputNumRows) + print("makeListTime from pd:", makeListTime) + print("makeVarObjListTime 1:", makeVarObjListTime) + print("m:", moduleList) print("Score re-calculation:") score = load_raw_matrix(df) @@ -450,12 +500,9 @@ def main(): score = conservationCurate(score) # score.to_csv('/out/rami-test/%s_scores.csv'%(args.patientID), index=False) - score.to_csv('scores.csv', index=False) - + score.to_csv("scores.csv", index=False) exit() - main() - diff --git a/bin/fillna_tier.py b/bin/fillna_tier.py index ab4bb6a..de88306 100755 --- a/bin/fillna_tier.py +++ b/bin/fillna_tier.py @@ -5,640 +5,969 @@ __author__ = "Chaozhong Liu" __email__ = "chaozhol@bcm.edu" -###Feature engineering and incorporation of tier module outputs. +###Feature engineering and incorporation of tier module outputs. ###fills NA w/ 0 -def getValFromStr(valStr: str, select: str = 'min'): - select_method = {'min':min, 'max':max} - vals = valStr.split(',') - if '-' in vals or '.' in vals: + +def getValFromStr(valStr: str, select: str = "min"): + select_method = {"min": min, "max": max} + vals = valStr.split(",") + if "-" in vals or "." in vals: return np.NaN else: vals = [float(i) for i in vals] return select_method[select](vals) + def feature_engineering(score_file, tier_file): - variable_name = ['varId_dash', 'hgmdSymptomScore', - 'omimSymMatchFlag', 'hgmdSymMatchFlag', 'clinVarSymMatchFlag', - 'omimGeneFound', 'omimVarFound', 'hgmdGeneFound', 'hgmdVarFound', - 'clinVarVarFound', 'clinVarGeneFound', - 'clinvarTotalNumVars','clinvarNumP', 'clinvarNumLP', 'clinvarNumLB', 'clinvarNumB', - 'dgvVarFound','decipherVarFound', - 'curationScoreHGMD', 'curationScoreOMIM', 'curationScoreClinVar', - 'conservationScoreDGV', 'omimSymptomSimScore', 'hgmdSymptomSimScore', 'clin_code', - 'GERPpp_RS', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'phyloP100way_vertebrate', - 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELof', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_phred', - 'CADD_PHRED', 'DANN_score', 'REVEL_score', 'fathmm_MKL_coding_score', 'conservationScoreGnomad', - 'conservationScoreOELof', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'SIFT_score', 'zyg', - 'FATHMM_score', 'M_CAP_score', 'MutationAssessor_score', 'ESP6500_AA_AF', - 'ESP6500_EA_AF', 'hom', 'hgmd_rs', 'spliceAImax', 'clin_PLP_perc', 'Consequence', - 'nc_ClinVar_Exp', 'c_ClinVar_Exp', 'c_HGMD_Exp', 'nc_HGMD_Exp', - 'nc_isPLP', 'nc_isBLB', 'c_isPLP', 'c_isBLB', - 'nc_CLNREVSTAT', 'c_CLNREVSTAT', - 'nc_RANKSCORE', 'c_RANKSCORE', 'CLASS', "phrank"] - - - - #sample_name = score_file.split('/')[-1].split('_')[1] - #patient = pd.read_csv(score_file,sep='\t') - feature_stat = pd.read_csv('annotate/feature_stats.csv', index_col=0) + variable_name = [ + "varId_dash", + "hgmdSymptomScore", + "omimSymMatchFlag", + "hgmdSymMatchFlag", + "clinVarSymMatchFlag", + "omimGeneFound", + "omimVarFound", + "hgmdGeneFound", + "hgmdVarFound", + "clinVarVarFound", + "clinVarGeneFound", + "clinvarTotalNumVars", + "clinvarNumP", + "clinvarNumLP", + "clinvarNumLB", + "clinvarNumB", + "dgvVarFound", + "decipherVarFound", + "curationScoreHGMD", + "curationScoreOMIM", + "curationScoreClinVar", + "conservationScoreDGV", + "omimSymptomSimScore", + "hgmdSymptomSimScore", + "clin_code", + "GERPpp_RS", + "gnomadAF", + "gnomadAFg", + "LRT_score", + "LRT_Omega", + "phyloP100way_vertebrate", + "gnomadGeneZscore", + "gnomadGenePLI", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "IMPACT", + "CADD_phred", + "CADD_PHRED", + "DANN_score", + "REVEL_score", + "fathmm_MKL_coding_score", + "conservationScoreGnomad", + "conservationScoreOELof", + "Polyphen2_HDIV_score", + "Polyphen2_HVAR_score", + "SIFT_score", + "zyg", + "FATHMM_score", + "M_CAP_score", + "MutationAssessor_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + "hom", + "hgmd_rs", + "spliceAImax", + "clin_PLP_perc", + "Consequence", + "nc_ClinVar_Exp", + "c_ClinVar_Exp", + "c_HGMD_Exp", + "nc_HGMD_Exp", + "nc_isPLP", + "nc_isBLB", + "c_isPLP", + "c_isBLB", + "nc_CLNREVSTAT", + "c_CLNREVSTAT", + "nc_RANKSCORE", + "c_RANKSCORE", + "CLASS", + "phrank", + ] + + # sample_name = score_file.split('/')[-1].split('_')[1] + # patient = pd.read_csv(score_file,sep='\t') + feature_stat = pd.read_csv("annotate/feature_stats.csv", index_col=0) patient = score_file.copy() patient = patient[variable_name] - patient = patient.fillna('-') - indel_index = [len(i.split('-')[-1]) != 1 or len(i.split('-')[-2]) != 1 for i in patient['varId_dash'].to_list()] - - patient.loc[patient['phrank'] == '-','phrank'] = 0 - patient['phrank'] = patient['phrank'].astype('float64') - - patient.loc[patient['hgmdSymptomScore'] == '-','hgmdSymptomScore'] = 0 - patient['hgmdSymptomScore'] = patient['hgmdSymptomScore'].astype('float64') - - patient.loc[patient['omimSymMatchFlag'] == '-','omimSymMatchFlag'] = 0 - patient['omimSymMatchFlag'] = patient['omimSymMatchFlag'].astype('float64') - - patient.loc[patient['hgmdSymMatchFlag'] == '-','hgmdSymMatchFlag'] = 0 - patient['hgmdSymMatchFlag'] = patient['hgmdSymMatchFlag'].astype('float64') - - patient.loc[patient['clinVarSymMatchFlag'] == '-','clinVarSymMatchFlag'] = 0 - patient['clinVarSymMatchFlag'] = patient['clinVarSymMatchFlag'].astype('float64') - - - patient['clinvarNumLP'] = (patient['clinvarNumLP']+patient['clinvarNumP']) / patient['clinvarTotalNumVars'] - patient['clinvarNumLP'] = patient['clinvarNumLP'].fillna(0.0) - patient['clinvarNumP'] = patient['clinvarNumP'] / patient['clinvarTotalNumVars'] - patient['clinvarNumP'] = patient['clinvarNumP'].fillna(0.0) - patient['clinvarNumLB'] = (patient['clinvarNumLB']+patient['clinvarNumB']) / patient['clinvarTotalNumVars'] - patient['clinvarNumLB'] = patient['clinvarNumLB'].fillna(1.0) - patient['clinvarNumB'] = patient['clinvarNumB'] / patient['clinvarTotalNumVars'] - patient['clinvarNumB'] = patient['clinvarNumB'].fillna(1.0) - patient = patient.drop(columns=['clinvarTotalNumVars']) - variable_name.remove('clinvarTotalNumVars') - - - patient.loc[patient['curationScoreHGMD'] == 'Low','curationScoreHGMD'] = 1 - patient.loc[patient['curationScoreHGMD'] == 'Medium','curationScoreHGMD'] = 2 - patient.loc[patient['curationScoreHGMD'] == 'High','curationScoreHGMD'] = 3 - patient['curationScoreHGMD'] = patient['curationScoreHGMD'].astype('float64') - - patient.loc[patient['curationScoreOMIM'] == 'Low','curationScoreOMIM'] = 1 - patient.loc[patient['curationScoreOMIM'] == 'Medium','curationScoreOMIM'] = 2 - patient.loc[patient['curationScoreOMIM'] == 'High','curationScoreOMIM'] = 3 - patient['curationScoreOMIM'] = patient['curationScoreOMIM'].astype('float64') - - patient.loc[patient['curationScoreClinVar'] == 'Low','curationScoreClinVar'] = 1 - patient.loc[patient['curationScoreClinVar'] == 'Medium','curationScoreClinVar'] = 2 - patient.loc[patient['curationScoreClinVar'] == 'High','curationScoreClinVar'] = 3 - patient['curationScoreClinVar'] = patient['curationScoreClinVar'].astype('float64') - - patient.loc[patient['conservationScoreDGV'] == 'Low','conservationScoreDGV'] = 1 - patient.loc[patient['conservationScoreDGV'] == 'Medium','conservationScoreDGV'] = 2 - patient.loc[patient['conservationScoreDGV'] == 'High','conservationScoreDGV'] = 3 - patient['conservationScoreDGV'] = patient['conservationScoreDGV'].astype('float64') - - - patient.loc[patient['omimSymptomSimScore'] == '-','omimSymptomSimScore'] = 0.0 - patient['omimSymptomSimScore'] = patient['omimSymptomSimScore'].astype('float64') - - patient.loc[patient['hgmdSymptomSimScore'] == '-','hgmdSymptomSimScore'] = 0.0 - patient['hgmdSymptomSimScore'] = patient['hgmdSymptomSimScore'].astype('float64') - - - patient['isB/LB'] = 0 - patient['isP/LP'] = 0 - patient.loc[patient['clin_code'].str.contains('Benign'),'isB/LB'] = 1 - patient.loc[patient['clin_code'].str.contains('Likely_benign'),'isB/LB'] = 1 - patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isB/LB'] = 0 - - patient.loc[patient['clin_code'].str.contains('Likely_pathogenic'),'isP/LP'] = 1 - patient.loc[patient['clin_code'].str.contains('Pathogenic'),'isP/LP'] = 1 - patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isP/LP'] = 0 - - patient.loc[patient['clin_PLP_perc']!='-','isP/LP'] = patient.loc[patient['clin_PLP_perc']!='-','clin_PLP_perc'].astype('float64') - - variable_name.append('isB/LB') - variable_name.append('isP/LP') - variable_name.remove('clin_PLP_perc') - variable_name.remove('clin_code') - - - patient.loc[patient['GERPpp_RS'] == '-', 'GERPpp_RS'] = np.NaN - if np.all(pd.isna(patient['GERPpp_RS'])): + patient = patient.fillna("-") + indel_index = [ + len(i.split("-")[-1]) != 1 or len(i.split("-")[-2]) != 1 + for i in patient["varId_dash"].to_list() + ] + + patient.loc[patient["phrank"] == "-", "phrank"] = 0 + patient["phrank"] = patient["phrank"].astype("float64") + + patient.loc[patient["hgmdSymptomScore"] == "-", "hgmdSymptomScore"] = 0 + patient["hgmdSymptomScore"] = patient["hgmdSymptomScore"].astype("float64") + + patient.loc[patient["omimSymMatchFlag"] == "-", "omimSymMatchFlag"] = 0 + patient["omimSymMatchFlag"] = patient["omimSymMatchFlag"].astype("float64") + + patient.loc[patient["hgmdSymMatchFlag"] == "-", "hgmdSymMatchFlag"] = 0 + patient["hgmdSymMatchFlag"] = patient["hgmdSymMatchFlag"].astype("float64") + + patient.loc[patient["clinVarSymMatchFlag"] == "-", "clinVarSymMatchFlag"] = 0 + patient["clinVarSymMatchFlag"] = patient["clinVarSymMatchFlag"].astype("float64") + + patient["clinvarNumLP"] = ( + patient["clinvarNumLP"] + patient["clinvarNumP"] + ) / patient["clinvarTotalNumVars"] + patient["clinvarNumLP"] = patient["clinvarNumLP"].fillna(0.0) + patient["clinvarNumP"] = patient["clinvarNumP"] / patient["clinvarTotalNumVars"] + patient["clinvarNumP"] = patient["clinvarNumP"].fillna(0.0) + patient["clinvarNumLB"] = ( + patient["clinvarNumLB"] + patient["clinvarNumB"] + ) / patient["clinvarTotalNumVars"] + patient["clinvarNumLB"] = patient["clinvarNumLB"].fillna(1.0) + patient["clinvarNumB"] = patient["clinvarNumB"] / patient["clinvarTotalNumVars"] + patient["clinvarNumB"] = patient["clinvarNumB"].fillna(1.0) + patient = patient.drop(columns=["clinvarTotalNumVars"]) + variable_name.remove("clinvarTotalNumVars") + + patient.loc[patient["curationScoreHGMD"] == "Low", "curationScoreHGMD"] = 1 + patient.loc[patient["curationScoreHGMD"] == "Medium", "curationScoreHGMD"] = 2 + patient.loc[patient["curationScoreHGMD"] == "High", "curationScoreHGMD"] = 3 + patient["curationScoreHGMD"] = patient["curationScoreHGMD"].astype("float64") + + patient.loc[patient["curationScoreOMIM"] == "Low", "curationScoreOMIM"] = 1 + patient.loc[patient["curationScoreOMIM"] == "Medium", "curationScoreOMIM"] = 2 + patient.loc[patient["curationScoreOMIM"] == "High", "curationScoreOMIM"] = 3 + patient["curationScoreOMIM"] = patient["curationScoreOMIM"].astype("float64") + + patient.loc[patient["curationScoreClinVar"] == "Low", "curationScoreClinVar"] = 1 + patient.loc[patient["curationScoreClinVar"] == "Medium", "curationScoreClinVar"] = 2 + patient.loc[patient["curationScoreClinVar"] == "High", "curationScoreClinVar"] = 3 + patient["curationScoreClinVar"] = patient["curationScoreClinVar"].astype("float64") + + patient.loc[patient["conservationScoreDGV"] == "Low", "conservationScoreDGV"] = 1 + patient.loc[patient["conservationScoreDGV"] == "Medium", "conservationScoreDGV"] = 2 + patient.loc[patient["conservationScoreDGV"] == "High", "conservationScoreDGV"] = 3 + patient["conservationScoreDGV"] = patient["conservationScoreDGV"].astype("float64") + + patient.loc[patient["omimSymptomSimScore"] == "-", "omimSymptomSimScore"] = 0.0 + patient["omimSymptomSimScore"] = patient["omimSymptomSimScore"].astype("float64") + + patient.loc[patient["hgmdSymptomSimScore"] == "-", "hgmdSymptomSimScore"] = 0.0 + patient["hgmdSymptomSimScore"] = patient["hgmdSymptomSimScore"].astype("float64") + + patient["isB/LB"] = 0 + patient["isP/LP"] = 0 + patient.loc[patient["clin_code"].str.contains("Benign"), "isB/LB"] = 1 + patient.loc[patient["clin_code"].str.contains("Likely_benign"), "isB/LB"] = 1 + patient.loc[ + patient["clin_code"].str.contains( + "Conflicting_interpretations_of_pathogenicity" + ), + "isB/LB", + ] = 0 + + patient.loc[patient["clin_code"].str.contains("Likely_pathogenic"), "isP/LP"] = 1 + patient.loc[patient["clin_code"].str.contains("Pathogenic"), "isP/LP"] = 1 + patient.loc[ + patient["clin_code"].str.contains( + "Conflicting_interpretations_of_pathogenicity" + ), + "isP/LP", + ] = 0 + + patient.loc[patient["clin_PLP_perc"] != "-", "isP/LP"] = patient.loc[ + patient["clin_PLP_perc"] != "-", "clin_PLP_perc" + ].astype("float64") + + variable_name.append("isB/LB") + variable_name.append("isP/LP") + variable_name.remove("clin_PLP_perc") + variable_name.remove("clin_code") + + patient.loc[patient["GERPpp_RS"] == "-", "GERPpp_RS"] = np.NaN + if np.all(pd.isna(patient["GERPpp_RS"])): # Single variant query - patient.loc[(pd.isna(patient['GERPpp_RS'])) & np.array(indel_index), 'GERPpp_RS'] = feature_stat.loc['GERPpp_RS', 'mean'] - patient.loc[pd.isna(patient['GERPpp_RS']), 'GERPpp_RS'] = feature_stat.loc['GERPpp_RS', 'mean'] - patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') + patient.loc[ + (pd.isna(patient["GERPpp_RS"])) & np.array(indel_index), "GERPpp_RS" + ] = feature_stat.loc["GERPpp_RS", "mean"] + patient.loc[pd.isna(patient["GERPpp_RS"]), "GERPpp_RS"] = feature_stat.loc[ + "GERPpp_RS", "mean" + ] + patient["GERPpp_RS"] = patient["GERPpp_RS"].astype("float64") else: # Patient whole VCF - patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') - patient.loc[(pd.isna(patient['GERPpp_RS'])) & np.array(indel_index), 'GERPpp_RS'] = patient['GERPpp_RS'].describe()['mean'] - patient.loc[pd.isna(patient['GERPpp_RS']), 'GERPpp_RS'] = patient['GERPpp_RS'].describe()['mean'] - - - gnomadAFVal = patient['gnomadAF'].copy() - gnomadAFVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFVal ]) + patient["GERPpp_RS"] = patient["GERPpp_RS"].astype("float64") + patient.loc[ + (pd.isna(patient["GERPpp_RS"])) & np.array(indel_index), "GERPpp_RS" + ] = patient["GERPpp_RS"].describe()["mean"] + patient.loc[pd.isna(patient["GERPpp_RS"]), "GERPpp_RS"] = patient[ + "GERPpp_RS" + ].describe()["mean"] + + gnomadAFVal = patient["gnomadAF"].copy() + gnomadAFVal = np.array([getValFromStr(str(i), "min") for i in gnomadAFVal]) gnomadAFVal = gnomadAFVal.astype(float) - patient['gnomadAF'] = gnomadAFVal + patient["gnomadAF"] = gnomadAFVal - gnomadAFgVal = patient['gnomadAFg'].copy() - gnomadAFgVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFgVal ]) + gnomadAFgVal = patient["gnomadAFg"].copy() + gnomadAFgVal = np.array([getValFromStr(str(i), "min") for i in gnomadAFgVal]) gnomadAFgVal = gnomadAFgVal.astype(float) - patient['gnomadAFg'] = gnomadAFgVal - patient.loc[patient['gnomadAFg'].isna(), 'gnomadAFg'] = patient.loc[patient['gnomadAFg'].isna(), 'gnomadAF'] - patient.loc[patient['gnomadAF'].isna(), 'gnomadAF'] = patient.loc[patient['gnomadAF'].isna(), 'gnomadAFg'] - patient['gnomadAF'] = patient['gnomadAF'].fillna(0.0) - patient['gnomadAFg'] = patient['gnomadAFg'].fillna(0.0) - - #patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '-', 'gnomadAF'] #0.0 # - #patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '.', 'gnomadAF'] # - #patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '-', 'gnomadAFg'] #0.0 # - #patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '.', 'gnomadAFg'] - #patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = 0.0 - #patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = 0.0 - #patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = 0.0 - #patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = 0.0 - #patient['gnomadAFg'] = patient['gnomadAFg'].astype('float64') - #patient['gnomadAF'] = patient['gnomadAF'].astype('float64') - #patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] - - - patient.loc[patient['LRT_score'] == '-', 'LRT_score'] = np.NaN - if np.all(pd.isna(patient['LRT_score'])): + patient["gnomadAFg"] = gnomadAFgVal + patient.loc[patient["gnomadAFg"].isna(), "gnomadAFg"] = patient.loc[ + patient["gnomadAFg"].isna(), "gnomadAF" + ] + patient.loc[patient["gnomadAF"].isna(), "gnomadAF"] = patient.loc[ + patient["gnomadAF"].isna(), "gnomadAFg" + ] + patient["gnomadAF"] = patient["gnomadAF"].fillna(0.0) + patient["gnomadAFg"] = patient["gnomadAFg"].fillna(0.0) + + # patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '-', 'gnomadAF'] #0.0 # + # patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '.', 'gnomadAF'] # + # patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '-', 'gnomadAFg'] #0.0 # + # patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '.', 'gnomadAFg'] + # patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = 0.0 + # patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = 0.0 + # patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = 0.0 + # patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = 0.0 + # patient['gnomadAFg'] = patient['gnomadAFg'].astype('float64') + # patient['gnomadAF'] = patient['gnomadAF'].astype('float64') + # patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] + + patient.loc[patient["LRT_score"] == "-", "LRT_score"] = np.NaN + if np.all(pd.isna(patient["LRT_score"])): # Single variant query - patient.loc[(pd.isna(patient['LRT_score'])) & np.array(indel_index), 'LRT_score'] = feature_stat.loc['LRT_score', 'mean'] - patient.loc[pd.isna(patient['LRT_score']), 'LRT_score'] = feature_stat.loc['LRT_score', 'mean'] - patient['LRT_score'] = patient['LRT_score'].astype('float64') + patient.loc[ + (pd.isna(patient["LRT_score"])) & np.array(indel_index), "LRT_score" + ] = feature_stat.loc["LRT_score", "mean"] + patient.loc[pd.isna(patient["LRT_score"]), "LRT_score"] = feature_stat.loc[ + "LRT_score", "mean" + ] + patient["LRT_score"] = patient["LRT_score"].astype("float64") else: # Patient whole VCF - patient['LRT_score'] = patient['LRT_score'].astype('float64') - patient.loc[(pd.isna(patient['LRT_score'])) & np.array(indel_index), 'LRT_score'] = patient['LRT_score'].describe()['mean'] - patient.loc[pd.isna(patient['LRT_score']), 'LRT_score'] = patient['LRT_score'].describe()['mean'] - - - patient.loc[patient['LRT_Omega'] == '-', 'LRT_Omega'] = np.NaN - if np.all(pd.isna(patient['LRT_Omega'])): + patient["LRT_score"] = patient["LRT_score"].astype("float64") + patient.loc[ + (pd.isna(patient["LRT_score"])) & np.array(indel_index), "LRT_score" + ] = patient["LRT_score"].describe()["mean"] + patient.loc[pd.isna(patient["LRT_score"]), "LRT_score"] = patient[ + "LRT_score" + ].describe()["mean"] + + patient.loc[patient["LRT_Omega"] == "-", "LRT_Omega"] = np.NaN + if np.all(pd.isna(patient["LRT_Omega"])): # Single variant query - patient.loc[(pd.isna(patient['LRT_Omega'])) & np.array(indel_index), 'LRT_Omega'] = feature_stat.loc['LRT_Omega', 'mean'] - patient.loc[pd.isna(patient['LRT_Omega']), 'LRT_Omega'] = feature_stat.loc['LRT_Omega', 'mean'] - patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') + patient.loc[ + (pd.isna(patient["LRT_Omega"])) & np.array(indel_index), "LRT_Omega" + ] = feature_stat.loc["LRT_Omega", "mean"] + patient.loc[pd.isna(patient["LRT_Omega"]), "LRT_Omega"] = feature_stat.loc[ + "LRT_Omega", "mean" + ] + patient["LRT_Omega"] = patient["LRT_Omega"].astype("float64") else: # Patient whole VCF - patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') - patient.loc[(pd.isna(patient['LRT_Omega'])) & np.array(indel_index), 'LRT_Omega'] = patient['LRT_Omega'].describe()['mean'] - patient.loc[pd.isna(patient['LRT_Omega']), 'LRT_Omega'] = patient['LRT_Omega'].describe()['mean'] - - - patient.loc[patient['phyloP100way_vertebrate'] == '-', 'phyloP100way_vertebrate'] = np.NaN - if np.all(pd.isna(patient['phyloP100way_vertebrate'])): + patient["LRT_Omega"] = patient["LRT_Omega"].astype("float64") + patient.loc[ + (pd.isna(patient["LRT_Omega"])) & np.array(indel_index), "LRT_Omega" + ] = patient["LRT_Omega"].describe()["mean"] + patient.loc[pd.isna(patient["LRT_Omega"]), "LRT_Omega"] = patient[ + "LRT_Omega" + ].describe()["mean"] + + patient.loc[ + patient["phyloP100way_vertebrate"] == "-", "phyloP100way_vertebrate" + ] = np.NaN + if np.all(pd.isna(patient["phyloP100way_vertebrate"])): # Single variant query - patient.loc[(pd.isna(patient['phyloP100way_vertebrate'])) & np.array(indel_index), 'phyloP100way_vertebrate'] = feature_stat.loc['phyloP100way_vertebrate', 'mean'] - patient.loc[pd.isna(patient['phyloP100way_vertebrate']), 'phyloP100way_vertebrate'] = feature_stat.loc['phyloP100way_vertebrate', 'mean'] - patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') + patient.loc[ + (pd.isna(patient["phyloP100way_vertebrate"])) & np.array(indel_index), + "phyloP100way_vertebrate", + ] = feature_stat.loc["phyloP100way_vertebrate", "mean"] + patient.loc[ + pd.isna(patient["phyloP100way_vertebrate"]), "phyloP100way_vertebrate" + ] = feature_stat.loc["phyloP100way_vertebrate", "mean"] + patient["phyloP100way_vertebrate"] = patient["phyloP100way_vertebrate"].astype( + "float64" + ) else: # Patient whole VCF - patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') - patient.loc[(pd.isna(patient['phyloP100way_vertebrate'])) & np.array(indel_index), 'phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].describe()['mean'] - patient.loc[pd.isna(patient['phyloP100way_vertebrate']), 'phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].describe()['mean'] - - - patient.loc[patient['gnomadGeneZscore'] == '-', 'gnomadGeneZscore'] = np.NaN - if np.all(pd.isna(patient['gnomadGeneZscore'])): + patient["phyloP100way_vertebrate"] = patient["phyloP100way_vertebrate"].astype( + "float64" + ) + patient.loc[ + (pd.isna(patient["phyloP100way_vertebrate"])) & np.array(indel_index), + "phyloP100way_vertebrate", + ] = patient["phyloP100way_vertebrate"].describe()["mean"] + patient.loc[ + pd.isna(patient["phyloP100way_vertebrate"]), "phyloP100way_vertebrate" + ] = patient["phyloP100way_vertebrate"].describe()["mean"] + + patient.loc[patient["gnomadGeneZscore"] == "-", "gnomadGeneZscore"] = np.NaN + if np.all(pd.isna(patient["gnomadGeneZscore"])): # Single variant query - patient.loc[pd.isna(patient['gnomadGeneZscore']), 'gnomadGeneZscore'] = feature_stat.loc['gnomadGeneZscore', 'mean'] - patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') + patient.loc[pd.isna(patient["gnomadGeneZscore"]), "gnomadGeneZscore"] = ( + feature_stat.loc["gnomadGeneZscore", "mean"] + ) + patient["gnomadGeneZscore"] = patient["gnomadGeneZscore"].astype("float64") else: # Patient whole VCF - patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') - patient.loc[pd.isna(patient['gnomadGeneZscore']), 'gnomadGeneZscore'] = patient['gnomadGeneZscore'].describe()['mean'] + patient["gnomadGeneZscore"] = patient["gnomadGeneZscore"].astype("float64") + patient.loc[pd.isna(patient["gnomadGeneZscore"]), "gnomadGeneZscore"] = patient[ + "gnomadGeneZscore" + ].describe()["mean"] - - patient.loc[patient['gnomadGenePLI'] == '-', 'gnomadGenePLI'] = np.NaN - if np.all(pd.isna(patient['gnomadGenePLI'])): + patient.loc[patient["gnomadGenePLI"] == "-", "gnomadGenePLI"] = np.NaN + if np.all(pd.isna(patient["gnomadGenePLI"])): # Single variant query - patient.loc[pd.isna(patient['gnomadGenePLI']), 'gnomadGenePLI'] = feature_stat.loc['gnomadGenePLI', 'mean'] - patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') + patient.loc[pd.isna(patient["gnomadGenePLI"]), "gnomadGenePLI"] = ( + feature_stat.loc["gnomadGenePLI", "mean"] + ) + patient["gnomadGenePLI"] = patient["gnomadGenePLI"].astype("float64") else: # Patient whole VCF - patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') - patient.loc[pd.isna(patient['gnomadGenePLI']), 'gnomadGenePLI'] = patient['gnomadGenePLI'].describe()['mean'] + patient["gnomadGenePLI"] = patient["gnomadGenePLI"].astype("float64") + patient.loc[pd.isna(patient["gnomadGenePLI"]), "gnomadGenePLI"] = patient[ + "gnomadGenePLI" + ].describe()["mean"] - - patient.loc[patient['gnomadGeneOELof'] == '-', 'gnomadGeneOELof'] = np.NaN - if np.all(pd.isna(patient['gnomadGeneOELof'])): + patient.loc[patient["gnomadGeneOELof"] == "-", "gnomadGeneOELof"] = np.NaN + if np.all(pd.isna(patient["gnomadGeneOELof"])): # Single variant query - patient.loc[pd.isna(patient['gnomadGeneOELof']), 'gnomadGeneOELof'] = feature_stat.loc['gnomadGeneOELof', 'mean'] - patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') + patient.loc[pd.isna(patient["gnomadGeneOELof"]), "gnomadGeneOELof"] = ( + feature_stat.loc["gnomadGeneOELof", "mean"] + ) + patient["gnomadGeneOELof"] = patient["gnomadGeneOELof"].astype("float64") else: # Patient whole VCF - patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') - patient.loc[pd.isna(patient['gnomadGeneOELof']), 'gnomadGeneOELof'] = patient['gnomadGeneOELof'].describe()['mean'] - - patient.loc[patient['gnomadGeneOELofUpper'] == '-', 'gnomadGeneOELofUpper'] = np.NaN - if np.all(pd.isna(patient['gnomadGeneOELofUpper'])): + patient["gnomadGeneOELof"] = patient["gnomadGeneOELof"].astype("float64") + patient.loc[pd.isna(patient["gnomadGeneOELof"]), "gnomadGeneOELof"] = patient[ + "gnomadGeneOELof" + ].describe()["mean"] + + patient.loc[patient["gnomadGeneOELofUpper"] == "-", "gnomadGeneOELofUpper"] = np.NaN + if np.all(pd.isna(patient["gnomadGeneOELofUpper"])): # Single variant query - patient.loc[pd.isna(patient['gnomadGeneOELofUpper']), 'gnomadGeneOELofUpper'] = feature_stat.loc['gnomadGeneOELofUpper', 'mean'] - patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') + patient.loc[ + pd.isna(patient["gnomadGeneOELofUpper"]), "gnomadGeneOELofUpper" + ] = feature_stat.loc["gnomadGeneOELofUpper", "mean"] + patient["gnomadGeneOELofUpper"] = patient["gnomadGeneOELofUpper"].astype( + "float64" + ) else: # Patient whole VCF - patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') - patient.loc[pd.isna(patient['gnomadGeneOELofUpper']), 'gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].describe()['mean'] - - - patient.loc[patient['IMPACT'] == '-','IMPACT'] = 0 - patient.loc[patient['IMPACT'] == 'MODIFIER','IMPACT'] = 1 - patient.loc[patient['IMPACT'] == 'LOW','IMPACT'] = 2 - patient.loc[patient['IMPACT'] == 'MODERATE','IMPACT'] = 3 - patient.loc[patient['IMPACT'] == 'HIGH','IMPACT'] = 4 - patient['IMPACT'] = patient['IMPACT'].astype('float64') - - - #"CADD_phred" - patient.loc[patient['CADD_phred'] == '-', 'CADD_phred'] = np.NaN - if np.all(pd.isna(patient['CADD_phred'])): + patient["gnomadGeneOELofUpper"] = patient["gnomadGeneOELofUpper"].astype( + "float64" + ) + patient.loc[ + pd.isna(patient["gnomadGeneOELofUpper"]), "gnomadGeneOELofUpper" + ] = patient["gnomadGeneOELofUpper"].describe()["mean"] + + patient.loc[patient["IMPACT"] == "-", "IMPACT"] = 0 + patient.loc[patient["IMPACT"] == "MODIFIER", "IMPACT"] = 1 + patient.loc[patient["IMPACT"] == "LOW", "IMPACT"] = 2 + patient.loc[patient["IMPACT"] == "MODERATE", "IMPACT"] = 3 + patient.loc[patient["IMPACT"] == "HIGH", "IMPACT"] = 4 + patient["IMPACT"] = patient["IMPACT"].astype("float64") + + # "CADD_phred" + patient.loc[patient["CADD_phred"] == "-", "CADD_phred"] = np.NaN + if np.all(pd.isna(patient["CADD_phred"])): # Single variant query - patient.loc[(pd.isna(patient['CADD_phred'])) & np.array(indel_index), 'CADD_phred'] = feature_stat.loc['CADD_phred', 'mean'] - patient.loc[pd.isna(patient['CADD_phred']), 'CADD_phred'] = feature_stat.loc['CADD_phred', 'mean'] - patient['CADD_phred'] = patient['CADD_phred'].astype('float64') + patient.loc[ + (pd.isna(patient["CADD_phred"])) & np.array(indel_index), "CADD_phred" + ] = feature_stat.loc["CADD_phred", "mean"] + patient.loc[pd.isna(patient["CADD_phred"]), "CADD_phred"] = feature_stat.loc[ + "CADD_phred", "mean" + ] + patient["CADD_phred"] = patient["CADD_phred"].astype("float64") else: # Patient whole VCF - patient['CADD_phred'] = patient['CADD_phred'].astype('float64') - patient.loc[(pd.isna(patient['CADD_phred'])) & np.array(indel_index), 'CADD_phred'] = patient['CADD_phred'].describe()['mean'] - patient.loc[pd.isna(patient['CADD_phred']), 'CADD_phred'] = patient['CADD_phred'].describe()['mean'] - - - patient.loc[patient['CADD_PHRED'] == '-', 'CADD_PHRED'] = np.NaN - if np.all(pd.isna(patient['CADD_PHRED'])): + patient["CADD_phred"] = patient["CADD_phred"].astype("float64") + patient.loc[ + (pd.isna(patient["CADD_phred"])) & np.array(indel_index), "CADD_phred" + ] = patient["CADD_phred"].describe()["mean"] + patient.loc[pd.isna(patient["CADD_phred"]), "CADD_phred"] = patient[ + "CADD_phred" + ].describe()["mean"] + + patient.loc[patient["CADD_PHRED"] == "-", "CADD_PHRED"] = np.NaN + if np.all(pd.isna(patient["CADD_PHRED"])): # Single variant query - patient.loc[(pd.isna(patient['CADD_PHRED'])) & np.array(indel_index), 'CADD_PHRED'] = feature_stat.loc['CADD_PHRED', 'mean'] - patient.loc[pd.isna(patient['CADD_PHRED']), 'CADD_PHRED'] = feature_stat.loc['CADD_PHRED', 'mean'] - patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') + patient.loc[ + (pd.isna(patient["CADD_PHRED"])) & np.array(indel_index), "CADD_PHRED" + ] = feature_stat.loc["CADD_PHRED", "mean"] + patient.loc[pd.isna(patient["CADD_PHRED"]), "CADD_PHRED"] = feature_stat.loc[ + "CADD_PHRED", "mean" + ] + patient["CADD_PHRED"] = patient["CADD_PHRED"].astype("float64") else: # Patient whole VCF - patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') - patient.loc[(pd.isna(patient['CADD_PHRED'])) & np.array(indel_index), 'CADD_PHRED'] = patient['CADD_PHRED'].describe()['mean'] - patient.loc[pd.isna(patient['CADD_PHRED']), 'CADD_PHRED'] = patient['CADD_PHRED'].describe()['mean'] - - - #DANN_score - patient.loc[patient['DANN_score'] == '-', 'DANN_score'] = np.NaN - if np.all(pd.isna(patient['DANN_score'])): + patient["CADD_PHRED"] = patient["CADD_PHRED"].astype("float64") + patient.loc[ + (pd.isna(patient["CADD_PHRED"])) & np.array(indel_index), "CADD_PHRED" + ] = patient["CADD_PHRED"].describe()["mean"] + patient.loc[pd.isna(patient["CADD_PHRED"]), "CADD_PHRED"] = patient[ + "CADD_PHRED" + ].describe()["mean"] + + # DANN_score + patient.loc[patient["DANN_score"] == "-", "DANN_score"] = np.NaN + if np.all(pd.isna(patient["DANN_score"])): # Single variant query - patient.loc[(pd.isna(patient['DANN_score'])) & np.array(indel_index), 'DANN_score'] = feature_stat.loc['DANN_score', 'mean'] - patient.loc[pd.isna(patient['DANN_score']), 'DANN_score'] = feature_stat.loc['DANN_score', 'mean'] - patient['DANN_score'] = patient['DANN_score'].astype('float64') + patient.loc[ + (pd.isna(patient["DANN_score"])) & np.array(indel_index), "DANN_score" + ] = feature_stat.loc["DANN_score", "mean"] + patient.loc[pd.isna(patient["DANN_score"]), "DANN_score"] = feature_stat.loc[ + "DANN_score", "mean" + ] + patient["DANN_score"] = patient["DANN_score"].astype("float64") else: # Patient whole VCF - patient['DANN_score'] = patient['DANN_score'].astype('float64') - patient.loc[(pd.isna(patient['DANN_score'])) & np.array(indel_index), 'DANN_score'] = patient['DANN_score'].describe()['mean'] - patient.loc[pd.isna(patient['DANN_score']), 'DANN_score'] = patient['DANN_score'].describe()['mean'] - - - #REVEL_score - #patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') - patient.loc[patient['REVEL_score'] == '-', 'REVEL_score'] = np.NaN - for i in patient[~patient['REVEL_score'].isna()].index: - score_list = patient.loc[i, 'REVEL_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient["DANN_score"] = patient["DANN_score"].astype("float64") + patient.loc[ + (pd.isna(patient["DANN_score"])) & np.array(indel_index), "DANN_score" + ] = patient["DANN_score"].describe()["mean"] + patient.loc[pd.isna(patient["DANN_score"]), "DANN_score"] = patient[ + "DANN_score" + ].describe()["mean"] + + # REVEL_score + # patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') + patient.loc[patient["REVEL_score"] == "-", "REVEL_score"] = np.NaN + for i in patient[~patient["REVEL_score"].isna()].index: + score_list = patient.loc[i, "REVEL_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'REVEL_score'] = np.NaN + patient.loc[i, "REVEL_score"] = np.NaN else: - patient.loc[i, 'REVEL_score'] = max(score_list) + patient.loc[i, "REVEL_score"] = max(score_list) - if np.all(pd.isna(patient['REVEL_score'])): + if np.all(pd.isna(patient["REVEL_score"])): # Single variant query - patient.loc[(pd.isna(patient['REVEL_score'])) & np.array(indel_index), 'REVEL_score'] = feature_stat.loc['REVEL_score', 'mean'] - patient.loc[pd.isna(patient['REVEL_score']), 'REVEL_score'] = feature_stat.loc['REVEL_score', 'mean'] - patient['REVEL_score'] = patient['REVEL_score'].astype('float64') + patient.loc[ + (pd.isna(patient["REVEL_score"])) & np.array(indel_index), "REVEL_score" + ] = feature_stat.loc["REVEL_score", "mean"] + patient.loc[pd.isna(patient["REVEL_score"]), "REVEL_score"] = feature_stat.loc[ + "REVEL_score", "mean" + ] + patient["REVEL_score"] = patient["REVEL_score"].astype("float64") else: # Patient whole VCF - patient['REVEL_score'] = patient['REVEL_score'].astype('float64') - patient.loc[(patient['REVEL_score'].isna()) & np.array(indel_index), 'REVEL_score'] = patient['REVEL_score'].describe()['mean'] - patient.loc[pd.isna(patient['REVEL_score']), 'REVEL_score'] = patient['REVEL_score'].describe()['mean'] - - - - #fathmm_MKL_coding_score - patient.loc[patient['fathmm_MKL_coding_score'] == '-', 'fathmm_MKL_coding_score'] = np.NaN - if np.all(pd.isna(patient['fathmm_MKL_coding_score'])): + patient["REVEL_score"] = patient["REVEL_score"].astype("float64") + patient.loc[ + (patient["REVEL_score"].isna()) & np.array(indel_index), "REVEL_score" + ] = patient["REVEL_score"].describe()["mean"] + patient.loc[pd.isna(patient["REVEL_score"]), "REVEL_score"] = patient[ + "REVEL_score" + ].describe()["mean"] + + # fathmm_MKL_coding_score + patient.loc[ + patient["fathmm_MKL_coding_score"] == "-", "fathmm_MKL_coding_score" + ] = np.NaN + if np.all(pd.isna(patient["fathmm_MKL_coding_score"])): # Single variant query - patient.loc[(pd.isna(patient['fathmm_MKL_coding_score'])) & np.array(indel_index), 'fathmm_MKL_coding_score'] = feature_stat.loc['fathmm_MKL_coding_score', 'mean'] - patient.loc[pd.isna(patient['fathmm_MKL_coding_score']), 'fathmm_MKL_coding_score'] = feature_stat.loc['fathmm_MKL_coding_score', 'mean'] - patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') + patient.loc[ + (pd.isna(patient["fathmm_MKL_coding_score"])) & np.array(indel_index), + "fathmm_MKL_coding_score", + ] = feature_stat.loc["fathmm_MKL_coding_score", "mean"] + patient.loc[ + pd.isna(patient["fathmm_MKL_coding_score"]), "fathmm_MKL_coding_score" + ] = feature_stat.loc["fathmm_MKL_coding_score", "mean"] + patient["fathmm_MKL_coding_score"] = patient["fathmm_MKL_coding_score"].astype( + "float64" + ) else: # Patient whole VCF - patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') - patient.loc[(pd.isna(patient['fathmm_MKL_coding_score'])) & np.array(indel_index), 'fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].describe()['mean'] - patient.loc[pd.isna(patient['fathmm_MKL_coding_score']), 'fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].describe()['mean'] - - - #conservationScoreGnomad - patient.loc[patient['conservationScoreGnomad'] == '-','conservationScoreGnomad'] = 1 - patient.loc[patient['conservationScoreGnomad'] == 'Low','conservationScoreGnomad'] = 1 - patient.loc[patient['conservationScoreGnomad'] == 'High','conservationScoreGnomad'] = 2 - patient['conservationScoreGnomad'] = patient['conservationScoreGnomad'].astype('float64') - - #conservationScoreOELof - patient.loc[patient['conservationScoreOELof'] == '-','conservationScoreOELof'] = 1 - patient.loc[patient['conservationScoreOELof'] == 'Low','conservationScoreOELof'] = 1 - patient.loc[patient['conservationScoreOELof'] == 'High','conservationScoreOELof'] = 2 - patient['conservationScoreOELof'] = patient['conservationScoreOELof'].astype('float64') - - - #Polyphen2_HDIV_score - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') - patient.loc[patient['Polyphen2_HDIV_score'] == '-','Polyphen2_HDIV_score'] = np.NaN - for i in patient[~patient['Polyphen2_HDIV_score'].isna()].index: - score_list = patient.loc[i, 'Polyphen2_HDIV_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient["fathmm_MKL_coding_score"] = patient["fathmm_MKL_coding_score"].astype( + "float64" + ) + patient.loc[ + (pd.isna(patient["fathmm_MKL_coding_score"])) & np.array(indel_index), + "fathmm_MKL_coding_score", + ] = patient["fathmm_MKL_coding_score"].describe()["mean"] + patient.loc[ + pd.isna(patient["fathmm_MKL_coding_score"]), "fathmm_MKL_coding_score" + ] = patient["fathmm_MKL_coding_score"].describe()["mean"] + + # conservationScoreGnomad + patient.loc[ + patient["conservationScoreGnomad"] == "-", "conservationScoreGnomad" + ] = 1 + patient.loc[ + patient["conservationScoreGnomad"] == "Low", "conservationScoreGnomad" + ] = 1 + patient.loc[ + patient["conservationScoreGnomad"] == "High", "conservationScoreGnomad" + ] = 2 + patient["conservationScoreGnomad"] = patient["conservationScoreGnomad"].astype( + "float64" + ) + + # conservationScoreOELof + patient.loc[patient["conservationScoreOELof"] == "-", "conservationScoreOELof"] = 1 + patient.loc[ + patient["conservationScoreOELof"] == "Low", "conservationScoreOELof" + ] = 1 + patient.loc[ + patient["conservationScoreOELof"] == "High", "conservationScoreOELof" + ] = 2 + patient["conservationScoreOELof"] = patient["conservationScoreOELof"].astype( + "float64" + ) + + # Polyphen2_HDIV_score + patient["Polyphen2_HDIV_score"] = patient["Polyphen2_HDIV_score"] # .str.split(',') + patient.loc[patient["Polyphen2_HDIV_score"] == "-", "Polyphen2_HDIV_score"] = np.NaN + for i in patient[~patient["Polyphen2_HDIV_score"].isna()].index: + score_list = patient.loc[i, "Polyphen2_HDIV_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'Polyphen2_HDIV_score'] = np.NaN + patient.loc[i, "Polyphen2_HDIV_score"] = np.NaN else: - patient.loc[i, 'Polyphen2_HDIV_score'] = max(score_list) + patient.loc[i, "Polyphen2_HDIV_score"] = max(score_list) - if np.all(pd.isna(patient['Polyphen2_HDIV_score'])): + if np.all(pd.isna(patient["Polyphen2_HDIV_score"])): # Single variant query - patient.loc[(pd.isna(patient['Polyphen2_HDIV_score'])) & np.array(indel_index), 'Polyphen2_HDIV_score'] = feature_stat.loc['Polyphen2_HDIV_score', '50%'] - patient.loc[pd.isna(patient['Polyphen2_HDIV_score']), 'Polyphen2_HDIV_score'] = feature_stat.loc['Polyphen2_HDIV_score', '50%'] - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') + patient.loc[ + (pd.isna(patient["Polyphen2_HDIV_score"])) & np.array(indel_index), + "Polyphen2_HDIV_score", + ] = feature_stat.loc["Polyphen2_HDIV_score", "50%"] + patient.loc[ + pd.isna(patient["Polyphen2_HDIV_score"]), "Polyphen2_HDIV_score" + ] = feature_stat.loc["Polyphen2_HDIV_score", "50%"] + patient["Polyphen2_HDIV_score"] = patient["Polyphen2_HDIV_score"].astype( + "float64" + ) else: # Patient whole VCF - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') - patient.loc[(pd.isna(patient['Polyphen2_HDIV_score'])) & np.array(indel_index), 'Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].describe()['50%'] - patient.loc[pd.isna(patient['Polyphen2_HDIV_score']), 'Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].describe()['50%'] - - - #Polyphen2_HVAR_score - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'] #.str.split(',') - patient.loc[patient['Polyphen2_HVAR_score'] == '-','Polyphen2_HVAR_score'] = np.NaN - for i in patient[~patient['Polyphen2_HVAR_score'].isna()].index: - score_list = patient.loc[i, 'Polyphen2_HVAR_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient["Polyphen2_HDIV_score"] = patient["Polyphen2_HDIV_score"].astype( + "float64" + ) + patient.loc[ + (pd.isna(patient["Polyphen2_HDIV_score"])) & np.array(indel_index), + "Polyphen2_HDIV_score", + ] = patient["Polyphen2_HDIV_score"].describe()["50%"] + patient.loc[ + pd.isna(patient["Polyphen2_HDIV_score"]), "Polyphen2_HDIV_score" + ] = patient["Polyphen2_HDIV_score"].describe()["50%"] + + # Polyphen2_HVAR_score + patient["Polyphen2_HVAR_score"] = patient["Polyphen2_HVAR_score"] # .str.split(',') + patient.loc[patient["Polyphen2_HVAR_score"] == "-", "Polyphen2_HVAR_score"] = np.NaN + for i in patient[~patient["Polyphen2_HVAR_score"].isna()].index: + score_list = patient.loc[i, "Polyphen2_HVAR_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'Polyphen2_HVAR_score'] = np.NaN + patient.loc[i, "Polyphen2_HVAR_score"] = np.NaN else: - patient.loc[i, 'Polyphen2_HVAR_score'] = max(score_list) + patient.loc[i, "Polyphen2_HVAR_score"] = max(score_list) - if np.all(pd.isna(patient['Polyphen2_HVAR_score'])): + if np.all(pd.isna(patient["Polyphen2_HVAR_score"])): # Single variant query - patient.loc[(pd.isna(patient['Polyphen2_HVAR_score'])) & np.array(indel_index), 'Polyphen2_HVAR_score'] = feature_stat.loc['Polyphen2_HVAR_score', '50%'] - patient.loc[pd.isna(patient['Polyphen2_HVAR_score']), 'Polyphen2_HVAR_score'] = feature_stat.loc['Polyphen2_HVAR_score', '50%'] - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') + patient.loc[ + (pd.isna(patient["Polyphen2_HVAR_score"])) & np.array(indel_index), + "Polyphen2_HVAR_score", + ] = feature_stat.loc["Polyphen2_HVAR_score", "50%"] + patient.loc[ + pd.isna(patient["Polyphen2_HVAR_score"]), "Polyphen2_HVAR_score" + ] = feature_stat.loc["Polyphen2_HVAR_score", "50%"] + patient["Polyphen2_HVAR_score"] = patient["Polyphen2_HVAR_score"].astype( + "float64" + ) else: # Patient whole VCF - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') - patient.loc[(pd.isna(patient['Polyphen2_HVAR_score'])) & np.array(indel_index), 'Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].describe()['50%'] - patient.loc[pd.isna(patient['Polyphen2_HVAR_score']), 'Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].describe()['50%'] - - - #SIFT_score - patient['SIFT_score'] = patient['SIFT_score'] #.str.split(',') - patient.loc[patient['SIFT_score'] == '-','SIFT_score'] = np.NaN - for i in patient[~patient['SIFT_score'].isna()].index: - score_list = patient.loc[i, 'SIFT_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient["Polyphen2_HVAR_score"] = patient["Polyphen2_HVAR_score"].astype( + "float64" + ) + patient.loc[ + (pd.isna(patient["Polyphen2_HVAR_score"])) & np.array(indel_index), + "Polyphen2_HVAR_score", + ] = patient["Polyphen2_HVAR_score"].describe()["50%"] + patient.loc[ + pd.isna(patient["Polyphen2_HVAR_score"]), "Polyphen2_HVAR_score" + ] = patient["Polyphen2_HVAR_score"].describe()["50%"] + + # SIFT_score + patient["SIFT_score"] = patient["SIFT_score"] # .str.split(',') + patient.loc[patient["SIFT_score"] == "-", "SIFT_score"] = np.NaN + for i in patient[~patient["SIFT_score"].isna()].index: + score_list = patient.loc[i, "SIFT_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'SIFT_score'] = np.NaN + patient.loc[i, "SIFT_score"] = np.NaN else: - patient.loc[i, 'SIFT_score'] = min(score_list) + patient.loc[i, "SIFT_score"] = min(score_list) - if np.all(pd.isna(patient['SIFT_score'])): + if np.all(pd.isna(patient["SIFT_score"])): # Single variant query - patient.loc[(pd.isna(patient['SIFT_score'])) & np.array(indel_index), 'SIFT_score'] = feature_stat.loc['SIFT_score', '50%'] - patient.loc[pd.isna(patient['SIFT_score']), 'SIFT_score'] = feature_stat.loc['SIFT_score', '50%'] - patient['SIFT_score'] = patient['SIFT_score'].astype('float64') + patient.loc[ + (pd.isna(patient["SIFT_score"])) & np.array(indel_index), "SIFT_score" + ] = feature_stat.loc["SIFT_score", "50%"] + patient.loc[pd.isna(patient["SIFT_score"]), "SIFT_score"] = feature_stat.loc[ + "SIFT_score", "50%" + ] + patient["SIFT_score"] = patient["SIFT_score"].astype("float64") else: # Patient whole VCF - patient['SIFT_score'] = patient['SIFT_score'].astype('float64') - patient.loc[(pd.isna(patient['SIFT_score'])) & np.array(indel_index), 'SIFT_score'] = patient['SIFT_score'].describe()['50%'] - patient.loc[pd.isna(patient['SIFT_score']), 'SIFT_score'] = patient['SIFT_score'].describe()['50%'] - - - patient.loc[patient['zyg'] == 'HET','zyg'] = 1 - patient.loc[patient['zyg'] == 'HOM','zyg'] = 2 - patient.loc[patient['zyg'] == '-','zyg'] = 0 - - patient['zyg'] = patient['zyg'].astype('float64') - - - #patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN - #patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') - #patient.loc[(pd.isna(patient['GERPpp_NR'])) & np.array(indel_index), 'GERPpp_NR'] = patient.loc[patient['GERPpp_NR'] != -100, 'GERPpp_NR'].describe()['mean'] - #patient.loc[pd.isna(patient['GERPpp_NR']), 'GERPpp_NR'] = patient['GERPpp_NR'].describe()['min'] - - - #FATHMM_score - patient['FATHMM_score'] = patient['FATHMM_score'] #.str.split(',') - patient.loc[patient['FATHMM_score'] == '-','FATHMM_score'] = np.NaN - for i in patient[~patient['FATHMM_score'].isna()].index: - score_list = patient.loc[i, 'FATHMM_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient["SIFT_score"] = patient["SIFT_score"].astype("float64") + patient.loc[ + (pd.isna(patient["SIFT_score"])) & np.array(indel_index), "SIFT_score" + ] = patient["SIFT_score"].describe()["50%"] + patient.loc[pd.isna(patient["SIFT_score"]), "SIFT_score"] = patient[ + "SIFT_score" + ].describe()["50%"] + + patient.loc[patient["zyg"] == "HET", "zyg"] = 1 + patient.loc[patient["zyg"] == "HOM", "zyg"] = 2 + patient.loc[patient["zyg"] == "-", "zyg"] = 0 + + patient["zyg"] = patient["zyg"].astype("float64") + + # patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN + # patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') + # patient.loc[(pd.isna(patient['GERPpp_NR'])) & np.array(indel_index), 'GERPpp_NR'] = patient.loc[patient['GERPpp_NR'] != -100, 'GERPpp_NR'].describe()['mean'] + # patient.loc[pd.isna(patient['GERPpp_NR']), 'GERPpp_NR'] = patient['GERPpp_NR'].describe()['min'] + + # FATHMM_score + patient["FATHMM_score"] = patient["FATHMM_score"] # .str.split(',') + patient.loc[patient["FATHMM_score"] == "-", "FATHMM_score"] = np.NaN + for i in patient[~patient["FATHMM_score"].isna()].index: + score_list = patient.loc[i, "FATHMM_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'FATHMM_score'] = np.NaN + patient.loc[i, "FATHMM_score"] = np.NaN else: - patient.loc[i, 'FATHMM_score'] = min(score_list) + patient.loc[i, "FATHMM_score"] = min(score_list) - if np.all(pd.isna(patient['FATHMM_score'])): + if np.all(pd.isna(patient["FATHMM_score"])): # Single variant query - patient.loc[(pd.isna(patient['FATHMM_score'])) & np.array(indel_index), 'FATHMM_score'] = feature_stat.loc['FATHMM_score', '50%'] - patient.loc[pd.isna(patient['FATHMM_score']), 'FATHMM_score'] = feature_stat.loc['FATHMM_score', '50%'] - patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') + patient.loc[ + (pd.isna(patient["FATHMM_score"])) & np.array(indel_index), "FATHMM_score" + ] = feature_stat.loc["FATHMM_score", "50%"] + patient.loc[pd.isna(patient["FATHMM_score"]), "FATHMM_score"] = ( + feature_stat.loc["FATHMM_score", "50%"] + ) + patient["FATHMM_score"] = patient["FATHMM_score"].astype("float64") else: # Patient whole VCF - patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') - patient.loc[(pd.isna(patient['FATHMM_score'])) & np.array(indel_index), 'FATHMM_score'] = patient['FATHMM_score'].describe()['50%'] - patient.loc[pd.isna(patient['FATHMM_score']), 'FATHMM_score'] = patient['FATHMM_score'].describe()['50%'] - - - #M_CAP_score - patient.loc[patient['M_CAP_score'] == '-', 'M_CAP_score'] = np.NaN - if np.all(pd.isna(patient['M_CAP_score'])): + patient["FATHMM_score"] = patient["FATHMM_score"].astype("float64") + patient.loc[ + (pd.isna(patient["FATHMM_score"])) & np.array(indel_index), "FATHMM_score" + ] = patient["FATHMM_score"].describe()["50%"] + patient.loc[pd.isna(patient["FATHMM_score"]), "FATHMM_score"] = patient[ + "FATHMM_score" + ].describe()["50%"] + + # M_CAP_score + patient.loc[patient["M_CAP_score"] == "-", "M_CAP_score"] = np.NaN + if np.all(pd.isna(patient["M_CAP_score"])): # Single variant query - patient.loc[(pd.isna(patient['M_CAP_score'])) & np.array(indel_index), 'M_CAP_score'] = feature_stat.loc['M_CAP_score', 'mean'] - patient.loc[pd.isna(patient['M_CAP_score']), 'M_CAP_score'] = feature_stat.loc['M_CAP_score', 'mean'] - patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') + patient.loc[ + (pd.isna(patient["M_CAP_score"])) & np.array(indel_index), "M_CAP_score" + ] = feature_stat.loc["M_CAP_score", "mean"] + patient.loc[pd.isna(patient["M_CAP_score"]), "M_CAP_score"] = feature_stat.loc[ + "M_CAP_score", "mean" + ] + patient["M_CAP_score"] = patient["M_CAP_score"].astype("float64") else: # Patient whole VCF - patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') - patient.loc[(pd.isna(patient['M_CAP_score'])) & np.array(indel_index), 'M_CAP_score'] = patient['M_CAP_score'].describe()['mean'] - patient.loc[pd.isna(patient['M_CAP_score']), 'M_CAP_score'] = patient['M_CAP_score'].describe()['mean'] - - - #MutationAssessor_score - patient['MutationAssessor_score'] = patient['MutationAssessor_score'] #.str.split(',') - patient.loc[patient['MutationAssessor_score'] == '-','MutationAssessor_score'] = np.NaN - for i in patient[~patient['MutationAssessor_score'].isna()].index: - score_list = patient.loc[i, 'MutationAssessor_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient["M_CAP_score"] = patient["M_CAP_score"].astype("float64") + patient.loc[ + (pd.isna(patient["M_CAP_score"])) & np.array(indel_index), "M_CAP_score" + ] = patient["M_CAP_score"].describe()["mean"] + patient.loc[pd.isna(patient["M_CAP_score"]), "M_CAP_score"] = patient[ + "M_CAP_score" + ].describe()["mean"] + + # MutationAssessor_score + patient["MutationAssessor_score"] = patient[ + "MutationAssessor_score" + ] # .str.split(',') + patient.loc[patient["MutationAssessor_score"] == "-", "MutationAssessor_score"] = ( + np.NaN + ) + for i in patient[~patient["MutationAssessor_score"].isna()].index: + score_list = patient.loc[i, "MutationAssessor_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'MutationAssessor_score'] = np.NaN + patient.loc[i, "MutationAssessor_score"] = np.NaN else: - patient.loc[i, 'MutationAssessor_score'] = max(score_list) + patient.loc[i, "MutationAssessor_score"] = max(score_list) - if np.all(pd.isna(patient['MutationAssessor_score'])): + if np.all(pd.isna(patient["MutationAssessor_score"])): # Single variant query - patient.loc[(pd.isna(patient['MutationAssessor_score'])) & np.array(indel_index), 'MutationAssessor_score'] = feature_stat.loc['MutationAssessor_score', '50%'] - patient.loc[pd.isna(patient['MutationAssessor_score']), 'MutationAssessor_score'] = feature_stat.loc['MutationAssessor_score', '50%'] - patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') + patient.loc[ + (pd.isna(patient["MutationAssessor_score"])) & np.array(indel_index), + "MutationAssessor_score", + ] = feature_stat.loc["MutationAssessor_score", "50%"] + patient.loc[ + pd.isna(patient["MutationAssessor_score"]), "MutationAssessor_score" + ] = feature_stat.loc["MutationAssessor_score", "50%"] + patient["MutationAssessor_score"] = patient["MutationAssessor_score"].astype( + "float64" + ) else: # Patient whole VCF - patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') - patient.loc[(pd.isna(patient['MutationAssessor_score'])) & np.array(indel_index), 'MutationAssessor_score'] = patient['MutationAssessor_score'].describe()['50%'] - patient.loc[pd.isna(patient['MutationAssessor_score']), 'MutationAssessor_score'] = patient['MutationAssessor_score'].describe()['50%'] - - - #ESP6500_AA_AF - patient.loc[patient['ESP6500_AA_AF'] == '-', 'ESP6500_AA_AF'] = 0.0 - patient['ESP6500_AA_AF'] = patient['ESP6500_AA_AF'].astype('float64') - - - #ESP6500_AA_AF - patient.loc[patient['ESP6500_EA_AF'] == '-', 'ESP6500_EA_AF'] = 0.0 - patient['ESP6500_EA_AF'] = patient['ESP6500_EA_AF'].astype('float64') - - - #hom - patient.loc[patient['hom'] == '-','hom'] = np.NaN - for i in patient[~patient['hom'].isna()].index: - score_list = patient.loc[i, 'hom'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient["MutationAssessor_score"] = patient["MutationAssessor_score"].astype( + "float64" + ) + patient.loc[ + (pd.isna(patient["MutationAssessor_score"])) & np.array(indel_index), + "MutationAssessor_score", + ] = patient["MutationAssessor_score"].describe()["50%"] + patient.loc[ + pd.isna(patient["MutationAssessor_score"]), "MutationAssessor_score" + ] = patient["MutationAssessor_score"].describe()["50%"] + + # ESP6500_AA_AF + patient.loc[patient["ESP6500_AA_AF"] == "-", "ESP6500_AA_AF"] = 0.0 + patient["ESP6500_AA_AF"] = patient["ESP6500_AA_AF"].astype("float64") + + # ESP6500_AA_AF + patient.loc[patient["ESP6500_EA_AF"] == "-", "ESP6500_EA_AF"] = 0.0 + patient["ESP6500_EA_AF"] = patient["ESP6500_EA_AF"].astype("float64") + + # hom + patient.loc[patient["hom"] == "-", "hom"] = np.NaN + for i in patient[~patient["hom"].isna()].index: + score_list = patient.loc[i, "hom"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'hom'] = np.NaN + patient.loc[i, "hom"] = np.NaN else: - patient.loc[i, 'hom'] = max(score_list) - patient['hom'] = patient['hom'].fillna(0) - patient['hom'] = patient['hom'].astype('float64') - - patient['hgmd_rs'] = patient['hgmd_rs'].apply(lambda x: x.split(",")[0] if type(x) == str else str(x)) + patient.loc[i, "hom"] = max(score_list) + patient["hom"] = patient["hom"].fillna(0) + patient["hom"] = patient["hom"].astype("float64") - patient.loc[patient['hgmd_rs'] == '-','hgmd_rs'] = 0 + patient["hgmd_rs"] = patient["hgmd_rs"].apply( + lambda x: x.split(",")[0] if type(x) == str else str(x) + ) - patient['hgmd_rs'] = patient['hgmd_rs'].astype('float64') + patient.loc[patient["hgmd_rs"] == "-", "hgmd_rs"] = 0 + patient["hgmd_rs"] = patient["hgmd_rs"].astype("float64") - #spliceAImax - patient.loc[patient['spliceAImax'] == '-', 'spliceAImax'] = np.NaN - if np.all(pd.isna(patient['spliceAImax'])): + # spliceAImax + patient.loc[patient["spliceAImax"] == "-", "spliceAImax"] = np.NaN + if np.all(pd.isna(patient["spliceAImax"])): # Single variant query - patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = feature_stat.loc['spliceAImax', 'mean'] - patient['spliceAImax'] = patient['spliceAImax'].astype('float64') + patient.loc[pd.isna(patient["spliceAImax"]), "spliceAImax"] = feature_stat.loc[ + "spliceAImax", "mean" + ] + patient["spliceAImax"] = patient["spliceAImax"].astype("float64") else: # Patient whole VCF - patient['spliceAImax'] = patient['spliceAImax'].astype('float64') - patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = patient.loc[~pd.isna(patient['spliceAImax']), 'spliceAImax'].describe()['mean'] - - - #Consequence - consequence = ['transcript_ablation', 'splice_acceptor_variant', 'splice_donor_variant', 'stop_gained', - 'frameshift_variant', 'stop_lost', 'start_lost', 'transcript_amplification', 'inframe_insertion', - 'inframe_deletion', 'missense_variant', 'protein_altering_variant', 'splice_region_variant', - 'splice_donor_5th_base_variant', 'splice_donor_region_variant'] + patient["spliceAImax"] = patient["spliceAImax"].astype("float64") + patient.loc[pd.isna(patient["spliceAImax"]), "spliceAImax"] = patient.loc[ + ~pd.isna(patient["spliceAImax"]), "spliceAImax" + ].describe()["mean"] + + # Consequence + consequence = [ + "transcript_ablation", + "splice_acceptor_variant", + "splice_donor_variant", + "stop_gained", + "frameshift_variant", + "stop_lost", + "start_lost", + "transcript_amplification", + "inframe_insertion", + "inframe_deletion", + "missense_variant", + "protein_altering_variant", + "splice_region_variant", + "splice_donor_5th_base_variant", + "splice_donor_region_variant", + ] for cons in consequence: - patient['cons_%s'%cons] = patient['Consequence'].str.contains(cons).astype('int') - variable_name.append('cons_%s'%cons) - - variable_name.remove('Consequence') - - - #nc_isPLP - patient.loc[patient['nc_isPLP']!=True, 'nc_isPLP'] = 0 - patient.loc[patient['nc_isPLP']==True, 'nc_isPLP'] = 1 - patient['nc_isPLP'] = patient['nc_isPLP'].astype('float64') - - #nc_isBLB - patient.loc[patient['nc_isBLB']!=True, 'nc_isBLB'] = 0 - patient.loc[patient['nc_isBLB']==True, 'nc_isBLB'] = 1 - patient['nc_isBLB'] = patient['nc_isBLB'].astype('float64') - - #c_isPLP - patient.loc[patient['c_isPLP']!=True, 'c_isPLP'] = 0 - patient.loc[patient['c_isPLP']==True, 'c_isPLP'] = 1 - patient['c_isPLP'] = patient['c_isPLP'].astype('float64') - - #c_isBLB - patient.loc[patient['c_isBLB']!=True, 'c_isBLB'] = 0 - patient.loc[patient['c_isBLB']==True, 'c_isBLB'] = 1 - patient['c_isBLB'] = patient['c_isBLB'].astype('float64') - - - #nc_ClinVar_Exp - patient.loc[patient['nc_ClinVar_Exp']!='nonCoding','nc_ClinVar_Exp'] = 0 - patient.loc[patient['nc_ClinVar_Exp']=='nonCoding','nc_ClinVar_Exp'] = 1 - patient['nc_ClinVar_Exp'] = patient['nc_ClinVar_Exp'].astype('float64') - - #nc_HGMD_Exp - patient.loc[patient['nc_HGMD_Exp']!='nonCoding','nc_HGMD_Exp'] = 0 - patient.loc[patient['nc_HGMD_Exp']=='nonCoding','nc_HGMD_Exp'] = 1 - patient['nc_HGMD_Exp'] = patient['nc_HGMD_Exp'].astype('float64') - - #c_ClinVar_Exp - c_clin_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange'] + patient["cons_%s" % cons] = ( + patient["Consequence"].str.contains(cons).astype("int") + ) + variable_name.append("cons_%s" % cons) + + variable_name.remove("Consequence") + + # nc_isPLP + patient.loc[patient["nc_isPLP"] != True, "nc_isPLP"] = 0 + patient.loc[patient["nc_isPLP"] == True, "nc_isPLP"] = 1 + patient["nc_isPLP"] = patient["nc_isPLP"].astype("float64") + + # nc_isBLB + patient.loc[patient["nc_isBLB"] != True, "nc_isBLB"] = 0 + patient.loc[patient["nc_isBLB"] == True, "nc_isBLB"] = 1 + patient["nc_isBLB"] = patient["nc_isBLB"].astype("float64") + + # c_isPLP + patient.loc[patient["c_isPLP"] != True, "c_isPLP"] = 0 + patient.loc[patient["c_isPLP"] == True, "c_isPLP"] = 1 + patient["c_isPLP"] = patient["c_isPLP"].astype("float64") + + # c_isBLB + patient.loc[patient["c_isBLB"] != True, "c_isBLB"] = 0 + patient.loc[patient["c_isBLB"] == True, "c_isBLB"] = 1 + patient["c_isBLB"] = patient["c_isBLB"].astype("float64") + + # nc_ClinVar_Exp + patient.loc[patient["nc_ClinVar_Exp"] != "nonCoding", "nc_ClinVar_Exp"] = 0 + patient.loc[patient["nc_ClinVar_Exp"] == "nonCoding", "nc_ClinVar_Exp"] = 1 + patient["nc_ClinVar_Exp"] = patient["nc_ClinVar_Exp"].astype("float64") + + # nc_HGMD_Exp + patient.loc[patient["nc_HGMD_Exp"] != "nonCoding", "nc_HGMD_Exp"] = 0 + patient.loc[patient["nc_HGMD_Exp"] == "nonCoding", "nc_HGMD_Exp"] = 1 + patient["nc_HGMD_Exp"] = patient["nc_HGMD_Exp"].astype("float64") + + # c_ClinVar_Exp + c_clin_exp = ["Del_to_Missense", "Different_pChange", "Same_pChange"] for exp in c_clin_exp: - patient['c_ClinVar_Exp_%s'%exp] = patient['c_ClinVar_Exp'].str.contains(exp).astype('int') - variable_name.append('c_ClinVar_Exp_%s'%exp) + patient["c_ClinVar_Exp_%s" % exp] = ( + patient["c_ClinVar_Exp"].str.contains(exp).astype("int") + ) + variable_name.append("c_ClinVar_Exp_%s" % exp) - variable_name.remove('c_ClinVar_Exp') + variable_name.remove("c_ClinVar_Exp") - #c_HGMD_Exp - c_hgmd_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange', 'Stop_Loss', 'Start_Loss'] + # c_HGMD_Exp + c_hgmd_exp = [ + "Del_to_Missense", + "Different_pChange", + "Same_pChange", + "Stop_Loss", + "Start_Loss", + ] for exp in c_hgmd_exp: - patient['c_HGMD_Exp_%s'%exp] = patient['c_HGMD_Exp'].str.contains(exp).astype('int') - variable_name.append('c_HGMD_Exp_%s'%exp) + patient["c_HGMD_Exp_%s" % exp] = ( + patient["c_HGMD_Exp"].str.contains(exp).astype("int") + ) + variable_name.append("c_HGMD_Exp_%s" % exp) - variable_name.remove('c_HGMD_Exp') + variable_name.remove("c_HGMD_Exp") + # nc_CLNREVSTAT + CLN_STAT = [ + "-", + "no_assertion_provided", + "no_assertion_criteria_provided", + "no_assertion_for_the_individual_variant", + "criteria_provided,_single_submitter", + "criteria_provided,_conflicting_interpretations", + "criteria_provided,_multiple_submitters,_no_conflicts", + "reviewed_by_expert_panel", + "practice_guideline", + ] - #nc_CLNREVSTAT - CLN_STAT = ['-', 'no_assertion_provided', 'no_assertion_criteria_provided','no_assertion_for_the_individual_variant', - 'criteria_provided,_single_submitter', 'criteria_provided,_conflicting_interpretations', - 'criteria_provided,_multiple_submitters,_no_conflicts', 'reviewed_by_expert_panel', 'practice_guideline'] - - CLN_STAT_SCORE = [0,0,0,0,1,1,2,3,4] + CLN_STAT_SCORE = [0, 0, 0, 0, 1, 1, 2, 3, 4] for i in range(len(CLN_STAT)): - patient.loc[patient['nc_CLNREVSTAT'] == CLN_STAT[i], 'nc_CLNREVSTAT'] = CLN_STAT_SCORE[i] - patient.loc[patient['c_CLNREVSTAT'] == CLN_STAT[i], 'c_CLNREVSTAT'] = CLN_STAT_SCORE[i] - - patient['nc_CLNREVSTAT'] = patient['nc_CLNREVSTAT'].astype('float64') - patient['c_CLNREVSTAT'] = patient['c_CLNREVSTAT'].astype('float64') - - - #nc_RANKSCORE - patient.loc[patient['nc_RANKSCORE'] == '-', 'nc_RANKSCORE'] = 0 - patient['nc_RANKSCORE'] = patient['nc_RANKSCORE'].astype('float64') - - #nc_RANKSCORE - patient.loc[patient['c_RANKSCORE'] == '-', 'c_RANKSCORE'] = 0 - patient['c_RANKSCORE'] = patient['c_RANKSCORE'].astype('float64') - - #CLASS - patient.loc[patient['CLASS'] == '-', 'CLASS'] = 0 - patient.loc[patient['CLASS'] == 'DM?', 'CLASS'] = 1 - patient.loc[patient['CLASS'] == 'DM', 'CLASS'] = 2 - patient['CLASS'] = patient['CLASS'].astype('float64') - - - - patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', - 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] - patient = patient.groupby(['varId_dash'], sort=False)[variable_name[1:]].max() - patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', - 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] - - - #adding in tier features - #tier = pd.read_csv(tier_file, sep='\t') - tier=tier_file.copy() - tier_vars = ['IMPACT.from.Tier','TierAD','TierAR','TierAR.adj', - 'No.Var.HM','No.Var.H','No.Var.M','No.Var.L', - 'AD.matched','AR.matched', - 'recessive', 'dominant'] #, - #'OMIM.Inheritance.both', 'OMIM.Inheritance.others'] + patient.loc[patient["nc_CLNREVSTAT"] == CLN_STAT[i], "nc_CLNREVSTAT"] = ( + CLN_STAT_SCORE[i] + ) + patient.loc[patient["c_CLNREVSTAT"] == CLN_STAT[i], "c_CLNREVSTAT"] = ( + CLN_STAT_SCORE[i] + ) + + patient["nc_CLNREVSTAT"] = patient["nc_CLNREVSTAT"].astype("float64") + patient["c_CLNREVSTAT"] = patient["c_CLNREVSTAT"].astype("float64") + + # nc_RANKSCORE + patient.loc[patient["nc_RANKSCORE"] == "-", "nc_RANKSCORE"] = 0 + patient["nc_RANKSCORE"] = patient["nc_RANKSCORE"].astype("float64") + + # nc_RANKSCORE + patient.loc[patient["c_RANKSCORE"] == "-", "c_RANKSCORE"] = 0 + patient["c_RANKSCORE"] = patient["c_RANKSCORE"].astype("float64") + + # CLASS + patient.loc[patient["CLASS"] == "-", "CLASS"] = 0 + patient.loc[patient["CLASS"] == "DM?", "CLASS"] = 1 + patient.loc[patient["CLASS"] == "DM", "CLASS"] = 2 + patient["CLASS"] = patient["CLASS"].astype("float64") + + patient.loc[ + :, + [ + "gnomadAF", + "gnomadAFg", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "SIFT_score", + "FATHMM_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + ], + ] = -patient.loc[ + :, + [ + "gnomadAF", + "gnomadAFg", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "SIFT_score", + "FATHMM_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + ], + ] + patient = patient.groupby(["varId_dash"], sort=False)[variable_name[1:]].max() + patient.loc[ + :, + [ + "gnomadAF", + "gnomadAFg", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "SIFT_score", + "FATHMM_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + ], + ] = -patient.loc[ + :, + [ + "gnomadAF", + "gnomadAFg", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "SIFT_score", + "FATHMM_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + ], + ] + + # adding in tier features + # tier = pd.read_csv(tier_file, sep='\t') + tier = tier_file.copy() + tier_vars = [ + "IMPACT.from.Tier", + "TierAD", + "TierAR", + "TierAR.adj", + "No.Var.HM", + "No.Var.H", + "No.Var.M", + "No.Var.L", + "AD.matched", + "AR.matched", + "recessive", + "dominant", + ] # , + #'OMIM.Inheritance.both', 'OMIM.Inheritance.others'] """ CL: deleted # recessive and dominant is enough tier['OMIM.Inheritance.recessive'] = 0 @@ -651,32 +980,29 @@ def feature_engineering(score_file, tier_file): tier.loc[tier['OMIM.Inheritance'] == 'others', 'OMIM.Inheritance.others'] = 1 """ - tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] - tier = tier.groupby(['Uploaded_variation'], sort=False)[tier_vars].max() - tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] - + tier.loc[:, ["TierAD", "TierAR", "TierAR.adj"]] = -tier.loc[ + :, ["TierAD", "TierAR", "TierAR.adj"] + ] + tier = tier.groupby(["Uploaded_variation"], sort=False)[tier_vars].max() + tier.loc[:, ["TierAD", "TierAR", "TierAR.adj"]] = -tier.loc[ + :, ["TierAD", "TierAR", "TierAR.adj"] + ] + patient = pd.concat([patient, tier], axis=1) - patient.loc[patient['IMPACT.from.Tier'].isna(), 'IMPACT.from.Tier'] = 1 - patient.loc[patient['TierAD'].isna(), 'TierAD'] = 4 - patient.loc[patient['TierAR'].isna(), 'TierAR'] = 4 - patient.loc[patient['TierAR.adj'].isna(), 'TierAR.adj'] = 4 + patient.loc[patient["IMPACT.from.Tier"].isna(), "IMPACT.from.Tier"] = 1 + patient.loc[patient["TierAD"].isna(), "TierAD"] = 4 + patient.loc[patient["TierAR"].isna(), "TierAR"] = 4 + patient.loc[patient["TierAR.adj"].isna(), "TierAR.adj"] = 4 for var in tier_vars[4:8]: patient.loc[patient[var].isna(), var] = 0 - patient.loc[patient['AD.matched'].isna(), 'AD.matched'] = 0 - patient.loc[patient['AR.matched'].isna(), 'AR.matched'] = 0 + patient.loc[patient["AD.matched"].isna(), "AD.matched"] = 0 + patient.loc[patient["AR.matched"].isna(), "AR.matched"] = 0 - patient.loc[patient['recessive'].isna(), 'recessive'] = 0 - patient.loc[patient['dominant'].isna(), 'dominant'] = 0 - #patient.loc[patient['OMIM.Inheritance.both'].isna(), 'OMIM.Inheritance.both'] = 0 ##CL: deleted - #patient.loc[patient['OMIM.Inheritance.others'].isna(), 'OMIM.Inheritance.others'] = 0 ##CL: deleted + patient.loc[patient["recessive"].isna(), "recessive"] = 0 + patient.loc[patient["dominant"].isna(), "dominant"] = 0 + # patient.loc[patient['OMIM.Inheritance.both'].isna(), 'OMIM.Inheritance.both'] = 0 ##CL: deleted + # patient.loc[patient['OMIM.Inheritance.others'].isna(), 'OMIM.Inheritance.others'] = 0 ##CL: deleted return patient - - - - - - - diff --git a/bin/generate_new_matrix_2.py b/bin/generate_new_matrix_2.py index b4bfaa5..436fa0f 100755 --- a/bin/generate_new_matrix_2.py +++ b/bin/generate_new_matrix_2.py @@ -6,20 +6,21 @@ import numpy as np import sys -#from marrvel_score_recalc import * +# from marrvel_score_recalc import * from add_c_nc import add_c_nc from fillna_tier import feature_engineering as fillna from mod5_diffusion import diffusion, diffuseSample -#from labelling import add_label + +# from labelling import add_label from simple_repeat_anno import simple_repeat_anno ### read score file ### -score=pd.read_csv('scores.csv') +score = pd.read_csv("scores.csv") ### add more clin and hgmd features ### merged = add_c_nc(score, sys.argv[2]) -path_phrank = sys.argv[1]+".phrank.txt" +path_phrank = sys.argv[1] + ".phrank.txt" ### run diffusion module 5 ### if os.path.getsize(path_phrank) == 0: phrank_empty = True @@ -28,40 +29,44 @@ mod5 = diffuseSample(sys.argv[1], merged, ".") ### get original coordinates ### -merged["varId"]=merged["varId"].apply(lambda x: x.split("_E")[0]) +merged["varId"] = merged["varId"].apply(lambda x: x.split("_E")[0]) ### read tier ### -tier_f=pd.read_csv("Tier.v2.tsv", sep="\t") +tier_f = pd.read_csv("Tier.v2.tsv", sep="\t") ### add phrank calculated elsewhere for final combination ### if phrank_empty: - merged['phrank'] = 0 + merged["phrank"] = 0 else: - phr=pd.read_csv(path_phrank, sep="\t", names=["ENSG", "phrank"]) - merged=merged.merge(phr,left_on="geneEnsId", right_on="ENSG", how="left") + phr = pd.read_csv(path_phrank, sep="\t", names=["ENSG", "phrank"]) + merged = merged.merge(phr, left_on="geneEnsId", right_on="ENSG", how="left") ### save intermediate file for merging ### merged.to_csv("scores.txt.gz", compression="gzip", sep="\t") ### run Chaozhong's feature engineering code ### -iff=fillna(merged,tier_f) +iff = fillna(merged, tier_f) ### add diffusion module data ### if phrank_empty: - iff.insert(loc=0, column='diffuse_Phrank_STRING', value=0) + iff.insert(loc=0, column="diffuse_Phrank_STRING", value=0) else: - iff.insert(loc=0, column='diffuse_Phrank_STRING', value=mod5['diffuse_Phrank_STRING']) + iff.insert( + loc=0, column="diffuse_Phrank_STRING", value=mod5["diffuse_Phrank_STRING"] + ) ### remove chr 26 ### -iff= iff.loc[~iff.index.str.startswith('26')] -#iff.to_csv(sys.argv[1]+".out.txt", sep="\t") +iff = iff.loc[~iff.index.str.startswith("26")] +# iff.to_csv(sys.argv[1]+".out.txt", sep="\t") ### run chaozhong's simple repeat annotation ### -iff=simple_repeat_anno(sys.argv[1], iff, f"merge_expand/{sys.argv[2]}/simpleRepeats.{sys.argv[2]}.bed") +iff = simple_repeat_anno( + sys.argv[1], iff, f"merge_expand/{sys.argv[2]}/simpleRepeats.{sys.argv[2]}.bed" +) ### delete intermediate files ### # os.remove(sys.argv[1]+".bed") # os.remove(sys.argv[1]+".sp.bed") ### save final file for prediction ### -iff.to_csv(sys.argv[1]+".matrix.txt", sep="\t") +iff.to_csv(sys.argv[1] + ".matrix.txt", sep="\t") diff --git a/bin/keepna_tier.py b/bin/keepna_tier.py index e67d65c..22a720b 100755 --- a/bin/keepna_tier.py +++ b/bin/keepna_tier.py @@ -5,439 +5,613 @@ __author__ = "Chaozhong Liu" __email__ = "chaozhol@bcm.edu" -###feature engineering and incorporation of tier features +###feature engineering and incorporation of tier features ###keeps NA fields + def feature_engineering(score_file, tier_file): - variable_name = ['varId_dash', 'hgmdSymptomScore', - 'omimSymMatchFlag', 'hgmdSymMatchFlag', 'clinVarSymMatchFlag', - 'omimGeneFound', 'omimVarFound', 'hgmdGeneFound', 'hgmdVarFound', - 'clinVarVarFound', 'clinVarGeneFound', - 'clinvarTotalNumVars','clinvarNumP', 'clinvarNumLP', 'clinvarNumLB', 'clinvarNumB', - 'dgvVarFound','decipherVarFound', - 'curationScoreHGMD', 'curationScoreOMIM', 'curationScoreClinVar', - 'conservationScoreDGV', 'omimSymptomSimScore', 'hgmdSymptomSimScore', 'clin_code', - 'GERPpp_RS', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'phyloP100way_vertebrate', - 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELof', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_phred', - 'CADD_PHRED', 'DANN_score', 'REVEL_score', 'fathmm_MKL_coding_score', 'conservationScoreGnomad', - 'conservationScoreOELof', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'SIFT_score', 'zyg', - 'FATHMM_score', 'M_CAP_score', 'MutationAssessor_score', 'ESP6500_AA_AF', - 'ESP6500_EA_AF', 'hom', 'hgmd_rs', 'spliceAImax', 'clin_PLP_perc', 'Consequence', - 'nc_ClinVar_Exp', 'c_ClinVar_Exp', 'c_HGMD_Exp', 'nc_HGMD_Exp', - 'nc_isPLP', 'nc_isBLB', 'c_isPLP', 'c_isBLB', - 'nc_CLNREVSTAT', 'c_CLNREVSTAT', - 'nc_RANKSCORE', 'c_RANKSCORE', 'CLASS', "phrank"] - - - - #sample_name = score_file.split('/')[-1].split('_')[1] - #patient = pd.read_csv(score_file,sep='\t') + variable_name = [ + "varId_dash", + "hgmdSymptomScore", + "omimSymMatchFlag", + "hgmdSymMatchFlag", + "clinVarSymMatchFlag", + "omimGeneFound", + "omimVarFound", + "hgmdGeneFound", + "hgmdVarFound", + "clinVarVarFound", + "clinVarGeneFound", + "clinvarTotalNumVars", + "clinvarNumP", + "clinvarNumLP", + "clinvarNumLB", + "clinvarNumB", + "dgvVarFound", + "decipherVarFound", + "curationScoreHGMD", + "curationScoreOMIM", + "curationScoreClinVar", + "conservationScoreDGV", + "omimSymptomSimScore", + "hgmdSymptomSimScore", + "clin_code", + "GERPpp_RS", + "gnomadAF", + "gnomadAFg", + "LRT_score", + "LRT_Omega", + "phyloP100way_vertebrate", + "gnomadGeneZscore", + "gnomadGenePLI", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "IMPACT", + "CADD_phred", + "CADD_PHRED", + "DANN_score", + "REVEL_score", + "fathmm_MKL_coding_score", + "conservationScoreGnomad", + "conservationScoreOELof", + "Polyphen2_HDIV_score", + "Polyphen2_HVAR_score", + "SIFT_score", + "zyg", + "FATHMM_score", + "M_CAP_score", + "MutationAssessor_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + "hom", + "hgmd_rs", + "spliceAImax", + "clin_PLP_perc", + "Consequence", + "nc_ClinVar_Exp", + "c_ClinVar_Exp", + "c_HGMD_Exp", + "nc_HGMD_Exp", + "nc_isPLP", + "nc_isBLB", + "c_isPLP", + "c_isBLB", + "nc_CLNREVSTAT", + "c_CLNREVSTAT", + "nc_RANKSCORE", + "c_RANKSCORE", + "CLASS", + "phrank", + ] + + # sample_name = score_file.split('/')[-1].split('_')[1] + # patient = pd.read_csv(score_file,sep='\t') patient = score_file patient = patient[variable_name] - patient = patient.fillna('-') - indel_index = [len(i.split('-')[-1]) != 1 or len(i.split('-')[-2]) != 1 for i in patient['varId_dash'].to_list()] - - patient.loc[patient['phrank'] == '-','phrank'] = np.NaN - patient['phrank'] = patient['phrank'].astype('float64') - - patient.loc[patient['hgmdSymptomScore'] == '-','hgmdSymptomScore'] = np.NaN - patient['hgmdSymptomScore'] = patient['hgmdSymptomScore'].astype('float64') - - patient.loc[patient['omimSymMatchFlag'] == '-','omimSymMatchFlag'] = np.NaN - patient['omimSymMatchFlag'] = patient['omimSymMatchFlag'].astype('float64') - - patient.loc[patient['hgmdSymMatchFlag'] == '-','hgmdSymMatchFlag'] = np.NaN - patient['hgmdSymMatchFlag'] = patient['hgmdSymMatchFlag'].astype('float64') - - patient.loc[patient['clinVarSymMatchFlag'] == '-','clinVarSymMatchFlag'] = np.NaN - patient['clinVarSymMatchFlag'] = patient['clinVarSymMatchFlag'].astype('float64') - - - patient['clinvarNumLP'] = (patient['clinvarNumLP']+patient['clinvarNumP']) / patient['clinvarTotalNumVars'] - patient['clinvarNumP'] = patient['clinvarNumP'] / patient['clinvarTotalNumVars'] - patient['clinvarNumLB'] = (patient['clinvarNumLB']+patient['clinvarNumB']) / patient['clinvarTotalNumVars'] - patient['clinvarNumB'] = patient['clinvarNumB'] / patient['clinvarTotalNumVars'] - patient = patient.drop(columns=['clinvarTotalNumVars']) - variable_name.remove('clinvarTotalNumVars') - - - patient.loc[patient['curationScoreHGMD'] == 'Low','curationScoreHGMD'] = 1 - patient.loc[patient['curationScoreHGMD'] == 'Medium','curationScoreHGMD'] = 2 - patient.loc[patient['curationScoreHGMD'] == 'High','curationScoreHGMD'] = 3 - patient['curationScoreHGMD'] = patient['curationScoreHGMD'].astype('float64') - - patient.loc[patient['curationScoreOMIM'] == 'Low','curationScoreOMIM'] = 1 - patient.loc[patient['curationScoreOMIM'] == 'Medium','curationScoreOMIM'] = 2 - patient.loc[patient['curationScoreOMIM'] == 'High','curationScoreOMIM'] = 3 - patient['curationScoreOMIM'] = patient['curationScoreOMIM'].astype('float64') - - patient.loc[patient['curationScoreClinVar'] == 'Low','curationScoreClinVar'] = 1 - patient.loc[patient['curationScoreClinVar'] == 'Medium','curationScoreClinVar'] = 2 - patient.loc[patient['curationScoreClinVar'] == 'High','curationScoreClinVar'] = 3 - patient['curationScoreClinVar'] = patient['curationScoreClinVar'].astype('float64') - - patient.loc[patient['conservationScoreDGV'] == 'Low','conservationScoreDGV'] = 1 - patient.loc[patient['conservationScoreDGV'] == 'Medium','conservationScoreDGV'] = 2 - patient.loc[patient['conservationScoreDGV'] == 'High','conservationScoreDGV'] = 3 - patient['conservationScoreDGV'] = patient['conservationScoreDGV'].astype('float64') - - - patient.loc[patient['omimSymptomSimScore'] == '-','omimSymptomSimScore'] = np.NaN - patient['omimSymptomSimScore'] = patient['omimSymptomSimScore'].astype('float64') - - patient.loc[patient['hgmdSymptomSimScore'] == '-','hgmdSymptomSimScore'] = np.NaN - patient['hgmdSymptomSimScore'] = patient['hgmdSymptomSimScore'].astype('float64') - - - patient['isB/LB'] = 0 - patient['isP/LP'] = 0 - patient.loc[patient['clin_code'].str.contains('Benign'),'isB/LB'] = 1 - patient.loc[patient['clin_code'].str.contains('Likely_benign'),'isB/LB'] = 1 - patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isB/LB'] = 0 - - patient.loc[patient['clin_code'].str.contains('Likely_pathogenic'),'isP/LP'] = 1 - patient.loc[patient['clin_code'].str.contains('Pathogenic'),'isP/LP'] = 1 - patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isP/LP'] = 0 - - patient.loc[patient['clin_PLP_perc']!='-','isP/LP'] = patient.loc[patient['clin_PLP_perc']!='-','clin_PLP_perc'].astype('float64') - - variable_name.append('isB/LB') - variable_name.append('isP/LP') - variable_name.remove('clin_PLP_perc') - variable_name.remove('clin_code') - - - patient.loc[patient['GERPpp_RS'] == '-', 'GERPpp_RS'] = np.NaN - patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') - - - patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '-', 'gnomadAF'] #0.0 # - patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '.', 'gnomadAF'] # - patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '-', 'gnomadAFg'] #0.0 # - patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '.', 'gnomadAFg'] - patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = np.NaN - patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = np.NaN - patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = np.NaN - patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = np.NaN - patient['gnomadAFg'] = patient['gnomadAFg'].astype('float64') - patient['gnomadAF'] = patient['gnomadAF'].astype('float64') - #patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] - - - patient.loc[patient['LRT_score'] == '-', 'LRT_score'] = np.NaN - patient['LRT_score'] = patient['LRT_score'].astype('float64') - - - patient.loc[patient['LRT_Omega'] == '-', 'LRT_Omega'] = np.NaN - patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') - - - patient.loc[patient['phyloP100way_vertebrate'] == '-', 'phyloP100way_vertebrate'] = np.NaN - patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') - - - patient.loc[patient['gnomadGeneZscore'] == '-', 'gnomadGeneZscore'] = np.NaN - patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') - - patient.loc[patient['gnomadGenePLI'] == '-', 'gnomadGenePLI'] = np.NaN - patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') - - patient.loc[patient['gnomadGeneOELof'] == '-', 'gnomadGeneOELof'] = np.NaN - patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') - - patient.loc[patient['gnomadGeneOELofUpper'] == '-', 'gnomadGeneOELofUpper'] = np.NaN - patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') - - - patient.loc[patient['IMPACT'] == '-','IMPACT'] = np.NaN - patient.loc[patient['IMPACT'] == 'MODIFIER','IMPACT'] = 1 - patient.loc[patient['IMPACT'] == 'LOW','IMPACT'] = 2 - patient.loc[patient['IMPACT'] == 'MODERATE','IMPACT'] = 3 - patient.loc[patient['IMPACT'] == 'HIGH','IMPACT'] = 4 - patient['IMPACT'] = patient['IMPACT'].astype('float64') - - - #"CADD_phred" - patient.loc[patient['CADD_phred'] == '-', 'CADD_phred'] = np.NaN - patient['CADD_phred'] = patient['CADD_phred'].astype('float64') - - patient.loc[patient['CADD_PHRED'] == '-', 'CADD_PHRED'] = np.NaN - patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') - - - #DANN_score - patient.loc[patient['DANN_score'] == '-', 'DANN_score'] = np.NaN - patient['DANN_score'] = patient['DANN_score'].astype('float64') - - - #REVEL_score - patient.loc[patient['REVEL_score'] == '-', 'REVEL_score'] = np.NaN - patient['REVEL_score'] = patient['REVEL_score'].astype('float64') - - - #fathmm_MKL_coding_score - patient.loc[patient['fathmm_MKL_coding_score'] == '-', 'fathmm_MKL_coding_score'] = np.NaN - patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') - - - #conservationScoreGnomad - patient.loc[patient['conservationScoreGnomad'] == '-','conservationScoreGnomad'] = np.NaN - patient.loc[patient['conservationScoreGnomad'] == 'Low','conservationScoreGnomad'] = 1 - patient.loc[patient['conservationScoreGnomad'] == 'High','conservationScoreGnomad'] = 2 - patient['conservationScoreGnomad'] = patient['conservationScoreGnomad'].astype('float64') - - #conservationScoreOELof - patient.loc[patient['conservationScoreOELof'] == '-','conservationScoreOELof'] = np.NaN - patient.loc[patient['conservationScoreOELof'] == 'Low','conservationScoreOELof'] = 1 - patient.loc[patient['conservationScoreOELof'] == 'High','conservationScoreOELof'] = 2 - patient['conservationScoreOELof'] = patient['conservationScoreOELof'].astype('float64') - - - #Polyphen2_HDIV_score - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') - patient.loc[patient['Polyphen2_HDIV_score'] == '-','Polyphen2_HDIV_score'] = -100.0 - for i in patient[patient['Polyphen2_HDIV_score'] != -100.0].index: - score_list = patient.loc[i, 'Polyphen2_HDIV_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient = patient.fillna("-") + indel_index = [ + len(i.split("-")[-1]) != 1 or len(i.split("-")[-2]) != 1 + for i in patient["varId_dash"].to_list() + ] + + patient.loc[patient["phrank"] == "-", "phrank"] = np.NaN + patient["phrank"] = patient["phrank"].astype("float64") + + patient.loc[patient["hgmdSymptomScore"] == "-", "hgmdSymptomScore"] = np.NaN + patient["hgmdSymptomScore"] = patient["hgmdSymptomScore"].astype("float64") + + patient.loc[patient["omimSymMatchFlag"] == "-", "omimSymMatchFlag"] = np.NaN + patient["omimSymMatchFlag"] = patient["omimSymMatchFlag"].astype("float64") + + patient.loc[patient["hgmdSymMatchFlag"] == "-", "hgmdSymMatchFlag"] = np.NaN + patient["hgmdSymMatchFlag"] = patient["hgmdSymMatchFlag"].astype("float64") + + patient.loc[patient["clinVarSymMatchFlag"] == "-", "clinVarSymMatchFlag"] = np.NaN + patient["clinVarSymMatchFlag"] = patient["clinVarSymMatchFlag"].astype("float64") + + patient["clinvarNumLP"] = ( + patient["clinvarNumLP"] + patient["clinvarNumP"] + ) / patient["clinvarTotalNumVars"] + patient["clinvarNumP"] = patient["clinvarNumP"] / patient["clinvarTotalNumVars"] + patient["clinvarNumLB"] = ( + patient["clinvarNumLB"] + patient["clinvarNumB"] + ) / patient["clinvarTotalNumVars"] + patient["clinvarNumB"] = patient["clinvarNumB"] / patient["clinvarTotalNumVars"] + patient = patient.drop(columns=["clinvarTotalNumVars"]) + variable_name.remove("clinvarTotalNumVars") + + patient.loc[patient["curationScoreHGMD"] == "Low", "curationScoreHGMD"] = 1 + patient.loc[patient["curationScoreHGMD"] == "Medium", "curationScoreHGMD"] = 2 + patient.loc[patient["curationScoreHGMD"] == "High", "curationScoreHGMD"] = 3 + patient["curationScoreHGMD"] = patient["curationScoreHGMD"].astype("float64") + + patient.loc[patient["curationScoreOMIM"] == "Low", "curationScoreOMIM"] = 1 + patient.loc[patient["curationScoreOMIM"] == "Medium", "curationScoreOMIM"] = 2 + patient.loc[patient["curationScoreOMIM"] == "High", "curationScoreOMIM"] = 3 + patient["curationScoreOMIM"] = patient["curationScoreOMIM"].astype("float64") + + patient.loc[patient["curationScoreClinVar"] == "Low", "curationScoreClinVar"] = 1 + patient.loc[patient["curationScoreClinVar"] == "Medium", "curationScoreClinVar"] = 2 + patient.loc[patient["curationScoreClinVar"] == "High", "curationScoreClinVar"] = 3 + patient["curationScoreClinVar"] = patient["curationScoreClinVar"].astype("float64") + + patient.loc[patient["conservationScoreDGV"] == "Low", "conservationScoreDGV"] = 1 + patient.loc[patient["conservationScoreDGV"] == "Medium", "conservationScoreDGV"] = 2 + patient.loc[patient["conservationScoreDGV"] == "High", "conservationScoreDGV"] = 3 + patient["conservationScoreDGV"] = patient["conservationScoreDGV"].astype("float64") + + patient.loc[patient["omimSymptomSimScore"] == "-", "omimSymptomSimScore"] = np.NaN + patient["omimSymptomSimScore"] = patient["omimSymptomSimScore"].astype("float64") + + patient.loc[patient["hgmdSymptomSimScore"] == "-", "hgmdSymptomSimScore"] = np.NaN + patient["hgmdSymptomSimScore"] = patient["hgmdSymptomSimScore"].astype("float64") + + patient["isB/LB"] = 0 + patient["isP/LP"] = 0 + patient.loc[patient["clin_code"].str.contains("Benign"), "isB/LB"] = 1 + patient.loc[patient["clin_code"].str.contains("Likely_benign"), "isB/LB"] = 1 + patient.loc[ + patient["clin_code"].str.contains( + "Conflicting_interpretations_of_pathogenicity" + ), + "isB/LB", + ] = 0 + + patient.loc[patient["clin_code"].str.contains("Likely_pathogenic"), "isP/LP"] = 1 + patient.loc[patient["clin_code"].str.contains("Pathogenic"), "isP/LP"] = 1 + patient.loc[ + patient["clin_code"].str.contains( + "Conflicting_interpretations_of_pathogenicity" + ), + "isP/LP", + ] = 0 + + patient.loc[patient["clin_PLP_perc"] != "-", "isP/LP"] = patient.loc[ + patient["clin_PLP_perc"] != "-", "clin_PLP_perc" + ].astype("float64") + + variable_name.append("isB/LB") + variable_name.append("isP/LP") + variable_name.remove("clin_PLP_perc") + variable_name.remove("clin_code") + + patient.loc[patient["GERPpp_RS"] == "-", "GERPpp_RS"] = np.NaN + patient["GERPpp_RS"] = patient["GERPpp_RS"].astype("float64") + + patient.loc[patient["gnomadAFg"] == "-", "gnomadAFg"] = patient.loc[ + patient["gnomadAFg"] == "-", "gnomadAF" + ] # 0.0 # + patient.loc[patient["gnomadAFg"] == ".", "gnomadAFg"] = patient.loc[ + patient["gnomadAFg"] == ".", "gnomadAF" + ] # + patient.loc[patient["gnomadAF"] == "-", "gnomadAF"] = patient.loc[ + patient["gnomadAF"] == "-", "gnomadAFg" + ] # 0.0 # + patient.loc[patient["gnomadAF"] == ".", "gnomadAF"] = patient.loc[ + patient["gnomadAF"] == ".", "gnomadAFg" + ] + patient.loc[patient["gnomadAFg"] == "-", "gnomadAFg"] = np.NaN + patient.loc[patient["gnomadAFg"] == ".", "gnomadAFg"] = np.NaN + patient.loc[patient["gnomadAF"] == "-", "gnomadAF"] = np.NaN + patient.loc[patient["gnomadAF"] == ".", "gnomadAF"] = np.NaN + patient["gnomadAFg"] = patient["gnomadAFg"].astype("float64") + patient["gnomadAF"] = patient["gnomadAF"].astype("float64") + # patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] + + patient.loc[patient["LRT_score"] == "-", "LRT_score"] = np.NaN + patient["LRT_score"] = patient["LRT_score"].astype("float64") + + patient.loc[patient["LRT_Omega"] == "-", "LRT_Omega"] = np.NaN + patient["LRT_Omega"] = patient["LRT_Omega"].astype("float64") + + patient.loc[ + patient["phyloP100way_vertebrate"] == "-", "phyloP100way_vertebrate" + ] = np.NaN + patient["phyloP100way_vertebrate"] = patient["phyloP100way_vertebrate"].astype( + "float64" + ) + + patient.loc[patient["gnomadGeneZscore"] == "-", "gnomadGeneZscore"] = np.NaN + patient["gnomadGeneZscore"] = patient["gnomadGeneZscore"].astype("float64") + + patient.loc[patient["gnomadGenePLI"] == "-", "gnomadGenePLI"] = np.NaN + patient["gnomadGenePLI"] = patient["gnomadGenePLI"].astype("float64") + + patient.loc[patient["gnomadGeneOELof"] == "-", "gnomadGeneOELof"] = np.NaN + patient["gnomadGeneOELof"] = patient["gnomadGeneOELof"].astype("float64") + + patient.loc[patient["gnomadGeneOELofUpper"] == "-", "gnomadGeneOELofUpper"] = np.NaN + patient["gnomadGeneOELofUpper"] = patient["gnomadGeneOELofUpper"].astype("float64") + + patient.loc[patient["IMPACT"] == "-", "IMPACT"] = np.NaN + patient.loc[patient["IMPACT"] == "MODIFIER", "IMPACT"] = 1 + patient.loc[patient["IMPACT"] == "LOW", "IMPACT"] = 2 + patient.loc[patient["IMPACT"] == "MODERATE", "IMPACT"] = 3 + patient.loc[patient["IMPACT"] == "HIGH", "IMPACT"] = 4 + patient["IMPACT"] = patient["IMPACT"].astype("float64") + + # "CADD_phred" + patient.loc[patient["CADD_phred"] == "-", "CADD_phred"] = np.NaN + patient["CADD_phred"] = patient["CADD_phred"].astype("float64") + + patient.loc[patient["CADD_PHRED"] == "-", "CADD_PHRED"] = np.NaN + patient["CADD_PHRED"] = patient["CADD_PHRED"].astype("float64") + + # DANN_score + patient.loc[patient["DANN_score"] == "-", "DANN_score"] = np.NaN + patient["DANN_score"] = patient["DANN_score"].astype("float64") + + # REVEL_score + patient.loc[patient["REVEL_score"] == "-", "REVEL_score"] = np.NaN + patient["REVEL_score"] = patient["REVEL_score"].astype("float64") + + # fathmm_MKL_coding_score + patient.loc[ + patient["fathmm_MKL_coding_score"] == "-", "fathmm_MKL_coding_score" + ] = np.NaN + patient["fathmm_MKL_coding_score"] = patient["fathmm_MKL_coding_score"].astype( + "float64" + ) + + # conservationScoreGnomad + patient.loc[ + patient["conservationScoreGnomad"] == "-", "conservationScoreGnomad" + ] = np.NaN + patient.loc[ + patient["conservationScoreGnomad"] == "Low", "conservationScoreGnomad" + ] = 1 + patient.loc[ + patient["conservationScoreGnomad"] == "High", "conservationScoreGnomad" + ] = 2 + patient["conservationScoreGnomad"] = patient["conservationScoreGnomad"].astype( + "float64" + ) + + # conservationScoreOELof + patient.loc[patient["conservationScoreOELof"] == "-", "conservationScoreOELof"] = ( + np.NaN + ) + patient.loc[ + patient["conservationScoreOELof"] == "Low", "conservationScoreOELof" + ] = 1 + patient.loc[ + patient["conservationScoreOELof"] == "High", "conservationScoreOELof" + ] = 2 + patient["conservationScoreOELof"] = patient["conservationScoreOELof"].astype( + "float64" + ) + + # Polyphen2_HDIV_score + patient["Polyphen2_HDIV_score"] = patient["Polyphen2_HDIV_score"] # .str.split(',') + patient.loc[patient["Polyphen2_HDIV_score"] == "-", "Polyphen2_HDIV_score"] = -100.0 + for i in patient[patient["Polyphen2_HDIV_score"] != -100.0].index: + score_list = patient.loc[i, "Polyphen2_HDIV_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'Polyphen2_HDIV_score'] = np.NaN + patient.loc[i, "Polyphen2_HDIV_score"] = np.NaN else: - patient.loc[i, 'Polyphen2_HDIV_score'] = max(score_list) - patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') - patient.loc[patient['Polyphen2_HVAR_score'] == -100, 'Polyphen2_HVAR_score'] = np.NaN - - #Polyphen2_HVAR_score - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'] #.str.split(',') - patient.loc[patient['Polyphen2_HVAR_score'] == '-','Polyphen2_HVAR_score'] = -100.0 - for i in patient[patient['Polyphen2_HVAR_score'] != -100.0].index: - score_list = patient.loc[i, 'Polyphen2_HVAR_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient.loc[i, "Polyphen2_HDIV_score"] = max(score_list) + patient["Polyphen2_HDIV_score"] = patient["Polyphen2_HDIV_score"].astype("float64") + patient.loc[patient["Polyphen2_HVAR_score"] == -100, "Polyphen2_HVAR_score"] = ( + np.NaN + ) + + # Polyphen2_HVAR_score + patient["Polyphen2_HVAR_score"] = patient["Polyphen2_HVAR_score"] # .str.split(',') + patient.loc[patient["Polyphen2_HVAR_score"] == "-", "Polyphen2_HVAR_score"] = -100.0 + for i in patient[patient["Polyphen2_HVAR_score"] != -100.0].index: + score_list = patient.loc[i, "Polyphen2_HVAR_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'Polyphen2_HVAR_score'] = np.NaN + patient.loc[i, "Polyphen2_HVAR_score"] = np.NaN else: - patient.loc[i, 'Polyphen2_HVAR_score'] = max(score_list) - patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') - patient.loc[patient['Polyphen2_HVAR_score'] == -100, 'Polyphen2_HVAR_score'] = np.NaN - - - #SIFT_score - patient['SIFT_score'] = patient['SIFT_score'] #.str.split(',') - patient.loc[patient['SIFT_score'] == '-','SIFT_score'] = -100.0 - for i in patient[patient['SIFT_score'] != -100.0].index: - score_list = patient.loc[i, 'SIFT_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient.loc[i, "Polyphen2_HVAR_score"] = max(score_list) + patient["Polyphen2_HVAR_score"] = patient["Polyphen2_HVAR_score"].astype("float64") + patient.loc[patient["Polyphen2_HVAR_score"] == -100, "Polyphen2_HVAR_score"] = ( + np.NaN + ) + + # SIFT_score + patient["SIFT_score"] = patient["SIFT_score"] # .str.split(',') + patient.loc[patient["SIFT_score"] == "-", "SIFT_score"] = -100.0 + for i in patient[patient["SIFT_score"] != -100.0].index: + score_list = patient.loc[i, "SIFT_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'SIFT_score'] = np.NaN + patient.loc[i, "SIFT_score"] = np.NaN else: - patient.loc[i, 'SIFT_score'] = min(score_list) - patient['SIFT_score'] = patient['SIFT_score'].astype('float64') - patient.loc[patient['SIFT_score'] == -100, 'SIFT_score'] = np.NaN - + patient.loc[i, "SIFT_score"] = min(score_list) + patient["SIFT_score"] = patient["SIFT_score"].astype("float64") + patient.loc[patient["SIFT_score"] == -100, "SIFT_score"] = np.NaN - patient.loc[patient['zyg'] == 'HET','zyg'] = 1 - patient.loc[patient['zyg'] == 'HOM','zyg'] = 2 - patient.loc[patient['zyg'] == '-','zyg'] = np.NaN + patient.loc[patient["zyg"] == "HET", "zyg"] = 1 + patient.loc[patient["zyg"] == "HOM", "zyg"] = 2 + patient.loc[patient["zyg"] == "-", "zyg"] = np.NaN - patient['zyg'] = patient['zyg'].astype('float64') + patient["zyg"] = patient["zyg"].astype("float64") + # patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN + # patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') - #patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN - #patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') - - - #FATHMM_score - patient['FATHMM_score'] = patient['FATHMM_score'] #.str.split(',') - patient.loc[patient['FATHMM_score'] == '-','FATHMM_score'] = -100.0 - for i in patient[patient['FATHMM_score'] != -100.0].index: - score_list = patient.loc[i, 'FATHMM_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + # FATHMM_score + patient["FATHMM_score"] = patient["FATHMM_score"] # .str.split(',') + patient.loc[patient["FATHMM_score"] == "-", "FATHMM_score"] = -100.0 + for i in patient[patient["FATHMM_score"] != -100.0].index: + score_list = patient.loc[i, "FATHMM_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'FATHMM_score'] = np.NaN + patient.loc[i, "FATHMM_score"] = np.NaN else: - patient.loc[i, 'FATHMM_score'] = min(score_list) - patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') - patient.loc[patient['FATHMM_score'] == -100, 'FATHMM_score'] = np.NaN - - - #M_CAP_score - patient.loc[patient['M_CAP_score'] == '-', 'M_CAP_score'] = np.NaN - patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') - - - #MutationAssessor_score - patient['MutationAssessor_score'] = patient['MutationAssessor_score'] #.str.split(',') - patient.loc[patient['MutationAssessor_score'] == '-','MutationAssessor_score'] = -100.0 - for i in patient[patient['MutationAssessor_score'] != -100.0].index: - score_list = patient.loc[i, 'MutationAssessor_score'].split(',') - score_list = [float(i) for i in score_list if i!='-' and i!='.'] + patient.loc[i, "FATHMM_score"] = min(score_list) + patient["FATHMM_score"] = patient["FATHMM_score"].astype("float64") + patient.loc[patient["FATHMM_score"] == -100, "FATHMM_score"] = np.NaN + + # M_CAP_score + patient.loc[patient["M_CAP_score"] == "-", "M_CAP_score"] = np.NaN + patient["M_CAP_score"] = patient["M_CAP_score"].astype("float64") + + # MutationAssessor_score + patient["MutationAssessor_score"] = patient[ + "MutationAssessor_score" + ] # .str.split(',') + patient.loc[patient["MutationAssessor_score"] == "-", "MutationAssessor_score"] = ( + -100.0 + ) + for i in patient[patient["MutationAssessor_score"] != -100.0].index: + score_list = patient.loc[i, "MutationAssessor_score"].split(",") + score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: - patient.loc[i, 'MutationAssessor_score'] = np.NaN + patient.loc[i, "MutationAssessor_score"] = np.NaN else: - patient.loc[i, 'MutationAssessor_score'] = max(score_list) - patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') - patient.loc[patient['MutationAssessor_score'] == -100, 'MutationAssessor_score'] = np.NaN - - - - #ESP6500_AA_AF - patient.loc[patient['ESP6500_AA_AF'] == '-', 'ESP6500_AA_AF'] = np.NaN - patient['ESP6500_AA_AF'] = patient['ESP6500_AA_AF'].astype('float64') - - - #ESP6500_AA_AF - patient.loc[patient['ESP6500_EA_AF'] == '-', 'ESP6500_EA_AF'] = np.NaN - patient['ESP6500_EA_AF'] = patient['ESP6500_EA_AF'].astype('float64') - - - #hom - patient.loc[patient['hom'] == '-', 'hom'] = np.NaN - patient['hom'] = patient['hom'].astype('float64') - - patient['hgmd_rs'] = patient['hgmd_rs'].apply(lambda x: x.split(",")[0]) - patient.loc[patient['hgmd_rs'] == '-','hgmd_rs'] = np.NaN - patient['hgmd_rs'] = patient['hgmd_rs'].astype('float64') - - - #spliceAImax - patient.loc[patient['spliceAImax'] == '-', 'spliceAImax'] = np.NaN - patient['spliceAImax'] = patient['spliceAImax'].astype('float64') - patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = patient.loc[~pd.isna(patient['spliceAImax']), 'spliceAImax'].describe()['min'] - - - #Consequence - consequence = ['transcript_ablation', 'splice_acceptor_variant', 'splice_donor_variant', 'stop_gained', - 'frameshift_variant', 'stop_lost', 'start_lost', 'transcript_amplification', 'inframe_insertion', - 'inframe_deletion', 'missense_variant', 'protein_altering_variant', 'splice_region_variant', - 'splice_donor_5th_base_variant', 'splice_donor_region_variant'] + patient.loc[i, "MutationAssessor_score"] = max(score_list) + patient["MutationAssessor_score"] = patient["MutationAssessor_score"].astype( + "float64" + ) + patient.loc[patient["MutationAssessor_score"] == -100, "MutationAssessor_score"] = ( + np.NaN + ) + + # ESP6500_AA_AF + patient.loc[patient["ESP6500_AA_AF"] == "-", "ESP6500_AA_AF"] = np.NaN + patient["ESP6500_AA_AF"] = patient["ESP6500_AA_AF"].astype("float64") + + # ESP6500_AA_AF + patient.loc[patient["ESP6500_EA_AF"] == "-", "ESP6500_EA_AF"] = np.NaN + patient["ESP6500_EA_AF"] = patient["ESP6500_EA_AF"].astype("float64") + + # hom + patient.loc[patient["hom"] == "-", "hom"] = np.NaN + patient["hom"] = patient["hom"].astype("float64") + + patient["hgmd_rs"] = patient["hgmd_rs"].apply(lambda x: x.split(",")[0]) + patient.loc[patient["hgmd_rs"] == "-", "hgmd_rs"] = np.NaN + patient["hgmd_rs"] = patient["hgmd_rs"].astype("float64") + + # spliceAImax + patient.loc[patient["spliceAImax"] == "-", "spliceAImax"] = np.NaN + patient["spliceAImax"] = patient["spliceAImax"].astype("float64") + patient.loc[pd.isna(patient["spliceAImax"]), "spliceAImax"] = patient.loc[ + ~pd.isna(patient["spliceAImax"]), "spliceAImax" + ].describe()["min"] + + # Consequence + consequence = [ + "transcript_ablation", + "splice_acceptor_variant", + "splice_donor_variant", + "stop_gained", + "frameshift_variant", + "stop_lost", + "start_lost", + "transcript_amplification", + "inframe_insertion", + "inframe_deletion", + "missense_variant", + "protein_altering_variant", + "splice_region_variant", + "splice_donor_5th_base_variant", + "splice_donor_region_variant", + ] for cons in consequence: - patient['cons_%s'%cons] = patient['Consequence'].str.contains(cons).astype('int') - variable_name.append('cons_%s'%cons) - - variable_name.remove('Consequence') - - - #nc_isPLP - patient.loc[patient['nc_isPLP']!=True, 'nc_isPLP'] = 0 - patient.loc[patient['nc_isPLP']==True, 'nc_isPLP'] = 1 - patient['nc_isPLP'] = patient['nc_isPLP'].astype('float64') - - #nc_isBLB - patient.loc[patient['nc_isBLB']!=True, 'nc_isBLB'] = 0 - patient.loc[patient['nc_isBLB']==True, 'nc_isBLB'] = 1 - patient['nc_isBLB'] = patient['nc_isBLB'].astype('float64') - - #c_isPLP - patient.loc[patient['c_isPLP']!=True, 'c_isPLP'] = 0 - patient.loc[patient['c_isPLP']==True, 'c_isPLP'] = 1 - patient['c_isPLP'] = patient['c_isPLP'].astype('float64') - - #c_isBLB - patient.loc[patient['c_isBLB']!=True, 'c_isBLB'] = 0 - patient.loc[patient['c_isBLB']==True, 'c_isBLB'] = 1 - patient['c_isBLB'] = patient['c_isBLB'].astype('float64') - - - #nc_ClinVar_Exp - patient.loc[patient['nc_ClinVar_Exp']!='nonCoding','nc_ClinVar_Exp'] = 0 - patient.loc[patient['nc_ClinVar_Exp']=='nonCoding','nc_ClinVar_Exp'] = 1 - patient['nc_ClinVar_Exp'] = patient['nc_ClinVar_Exp'].astype('float64') - - #nc_HGMD_Exp - patient.loc[patient['nc_HGMD_Exp']!='nonCoding','nc_HGMD_Exp'] = 0 - patient.loc[patient['nc_HGMD_Exp']=='nonCoding','nc_HGMD_Exp'] = 1 - patient['nc_HGMD_Exp'] = patient['nc_HGMD_Exp'].astype('float64') - - #c_ClinVar_Exp - c_clin_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange'] + patient["cons_%s" % cons] = ( + patient["Consequence"].str.contains(cons).astype("int") + ) + variable_name.append("cons_%s" % cons) + + variable_name.remove("Consequence") + + # nc_isPLP + patient.loc[patient["nc_isPLP"] != True, "nc_isPLP"] = 0 + patient.loc[patient["nc_isPLP"] == True, "nc_isPLP"] = 1 + patient["nc_isPLP"] = patient["nc_isPLP"].astype("float64") + + # nc_isBLB + patient.loc[patient["nc_isBLB"] != True, "nc_isBLB"] = 0 + patient.loc[patient["nc_isBLB"] == True, "nc_isBLB"] = 1 + patient["nc_isBLB"] = patient["nc_isBLB"].astype("float64") + + # c_isPLP + patient.loc[patient["c_isPLP"] != True, "c_isPLP"] = 0 + patient.loc[patient["c_isPLP"] == True, "c_isPLP"] = 1 + patient["c_isPLP"] = patient["c_isPLP"].astype("float64") + + # c_isBLB + patient.loc[patient["c_isBLB"] != True, "c_isBLB"] = 0 + patient.loc[patient["c_isBLB"] == True, "c_isBLB"] = 1 + patient["c_isBLB"] = patient["c_isBLB"].astype("float64") + + # nc_ClinVar_Exp + patient.loc[patient["nc_ClinVar_Exp"] != "nonCoding", "nc_ClinVar_Exp"] = 0 + patient.loc[patient["nc_ClinVar_Exp"] == "nonCoding", "nc_ClinVar_Exp"] = 1 + patient["nc_ClinVar_Exp"] = patient["nc_ClinVar_Exp"].astype("float64") + + # nc_HGMD_Exp + patient.loc[patient["nc_HGMD_Exp"] != "nonCoding", "nc_HGMD_Exp"] = 0 + patient.loc[patient["nc_HGMD_Exp"] == "nonCoding", "nc_HGMD_Exp"] = 1 + patient["nc_HGMD_Exp"] = patient["nc_HGMD_Exp"].astype("float64") + + # c_ClinVar_Exp + c_clin_exp = ["Del_to_Missense", "Different_pChange", "Same_pChange"] for exp in c_clin_exp: - patient['c_ClinVar_Exp_%s'%exp] = patient['c_ClinVar_Exp'].str.contains(exp).astype('int') - variable_name.append('c_ClinVar_Exp_%s'%exp) + patient["c_ClinVar_Exp_%s" % exp] = ( + patient["c_ClinVar_Exp"].str.contains(exp).astype("int") + ) + variable_name.append("c_ClinVar_Exp_%s" % exp) - variable_name.remove('c_ClinVar_Exp') + variable_name.remove("c_ClinVar_Exp") - #c_HGMD_Exp - c_hgmd_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange', 'Stop_Loss', 'Start_Loss'] + # c_HGMD_Exp + c_hgmd_exp = [ + "Del_to_Missense", + "Different_pChange", + "Same_pChange", + "Stop_Loss", + "Start_Loss", + ] for exp in c_hgmd_exp: - patient['c_HGMD_Exp_%s'%exp] = patient['c_HGMD_Exp'].str.contains(exp).astype('int') - variable_name.append('c_HGMD_Exp_%s'%exp) - - variable_name.remove('c_HGMD_Exp') - - - #nc_CLNREVSTAT - CLN_STAT = ['no_assertion_provided', 'no_assertion_criteria_provided','no_assertion_for_the_individual_variant', - 'criteria_provided,_single_submitter', 'criteria_provided,_conflicting_interpretations', - 'criteria_provided,_multiple_submitters,_no_conflicts', 'reviewed_by_expert_panel', 'practice_guideline'] - - CLN_STAT_SCORE = [0,0,0,1,1,2,3,4] - patient.loc[patient['nc_CLNREVSTAT'] == '-', 'nc_CLNREVSTAT'] = np.NaN - patient.loc[patient['c_CLNREVSTAT'] == '-', 'c_CLNREVSTAT'] = np.NaN + patient["c_HGMD_Exp_%s" % exp] = ( + patient["c_HGMD_Exp"].str.contains(exp).astype("int") + ) + variable_name.append("c_HGMD_Exp_%s" % exp) + + variable_name.remove("c_HGMD_Exp") + + # nc_CLNREVSTAT + CLN_STAT = [ + "no_assertion_provided", + "no_assertion_criteria_provided", + "no_assertion_for_the_individual_variant", + "criteria_provided,_single_submitter", + "criteria_provided,_conflicting_interpretations", + "criteria_provided,_multiple_submitters,_no_conflicts", + "reviewed_by_expert_panel", + "practice_guideline", + ] + + CLN_STAT_SCORE = [0, 0, 0, 1, 1, 2, 3, 4] + patient.loc[patient["nc_CLNREVSTAT"] == "-", "nc_CLNREVSTAT"] = np.NaN + patient.loc[patient["c_CLNREVSTAT"] == "-", "c_CLNREVSTAT"] = np.NaN for i in range(len(CLN_STAT)): - patient.loc[patient['nc_CLNREVSTAT'] == CLN_STAT[i], 'nc_CLNREVSTAT'] = CLN_STAT_SCORE[i] - patient.loc[patient['c_CLNREVSTAT'] == CLN_STAT[i], 'c_CLNREVSTAT'] = CLN_STAT_SCORE[i] - - patient['nc_CLNREVSTAT'] = patient['nc_CLNREVSTAT'].astype('float64') - patient['c_CLNREVSTAT'] = patient['c_CLNREVSTAT'].astype('float64') - - - #nc_RANKSCORE - patient.loc[patient['nc_RANKSCORE'] == '-', 'nc_RANKSCORE'] = np.NaN - patient['nc_RANKSCORE'] = patient['nc_RANKSCORE'].astype('float64') - - #nc_RANKSCORE - patient.loc[patient['c_RANKSCORE'] == '-', 'c_RANKSCORE'] = np.NaN - patient['c_RANKSCORE'] = patient['c_RANKSCORE'].astype('float64') - - #CLASS - patient.loc[patient['CLASS'] == '-', 'CLASS'] = np.NaN - patient.loc[patient['CLASS'] == 'DM?', 'CLASS'] = 1 - patient.loc[patient['CLASS'] == 'DM', 'CLASS'] = 2 - patient['CLASS'] = patient['CLASS'].astype('float64') - - - patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', - 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] - patient = patient.groupby(['varId_dash'], sort=False)[variable_name[1:]].max() - patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', - 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] - - - #adding in tier features - #tier = pd.read_csv(tier_file, sep='\t') - tier=tier_file - tier_vars = ['IMPACT.from.Tier','TierAD','TierAR','TierAR.adj', - 'No.Var.HM','No.Var.H','No.Var.M','No.Var.L', - 'AD.matched','AR.matched', - 'OMIM.Inheritance.recessive', 'OMIM.Inheritance.dominant', - 'OMIM.Inheritance.both', 'OMIM.Inheritance.others'] - - tier['OMIM.Inheritance.recessive'] = 0 - tier['OMIM.Inheritance.dominant'] = 0 - tier['OMIM.Inheritance.both'] = 0 - tier['OMIM.Inheritance.others'] = 0 - tier.loc[tier['OMIM.Inheritance'] == 'recessive', 'OMIM.Inheritance.recessive'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'dominant', 'OMIM.Inheritance.dominant'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'both', 'OMIM.Inheritance.both'] = 1 - tier.loc[tier['OMIM.Inheritance'] == 'others', 'OMIM.Inheritance.others'] = 1 - - tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] - tier = tier.groupby(['Uploaded_variation'], sort=False)[tier_vars].max() - tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] - - patient = pd.concat([patient, tier], axis=1) + patient.loc[patient["nc_CLNREVSTAT"] == CLN_STAT[i], "nc_CLNREVSTAT"] = ( + CLN_STAT_SCORE[i] + ) + patient.loc[patient["c_CLNREVSTAT"] == CLN_STAT[i], "c_CLNREVSTAT"] = ( + CLN_STAT_SCORE[i] + ) + + patient["nc_CLNREVSTAT"] = patient["nc_CLNREVSTAT"].astype("float64") + patient["c_CLNREVSTAT"] = patient["c_CLNREVSTAT"].astype("float64") + + # nc_RANKSCORE + patient.loc[patient["nc_RANKSCORE"] == "-", "nc_RANKSCORE"] = np.NaN + patient["nc_RANKSCORE"] = patient["nc_RANKSCORE"].astype("float64") + + # nc_RANKSCORE + patient.loc[patient["c_RANKSCORE"] == "-", "c_RANKSCORE"] = np.NaN + patient["c_RANKSCORE"] = patient["c_RANKSCORE"].astype("float64") + + # CLASS + patient.loc[patient["CLASS"] == "-", "CLASS"] = np.NaN + patient.loc[patient["CLASS"] == "DM?", "CLASS"] = 1 + patient.loc[patient["CLASS"] == "DM", "CLASS"] = 2 + patient["CLASS"] = patient["CLASS"].astype("float64") + + patient.loc[ + :, + [ + "gnomadAF", + "gnomadAFg", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "SIFT_score", + "FATHMM_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + ], + ] = -patient.loc[ + :, + [ + "gnomadAF", + "gnomadAFg", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "SIFT_score", + "FATHMM_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + ], + ] + patient = patient.groupby(["varId_dash"], sort=False)[variable_name[1:]].max() + patient.loc[ + :, + [ + "gnomadAF", + "gnomadAFg", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "SIFT_score", + "FATHMM_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + ], + ] = -patient.loc[ + :, + [ + "gnomadAF", + "gnomadAFg", + "gnomadGeneOELof", + "gnomadGeneOELofUpper", + "SIFT_score", + "FATHMM_score", + "ESP6500_AA_AF", + "ESP6500_EA_AF", + ], + ] + + # adding in tier features + # tier = pd.read_csv(tier_file, sep='\t') + tier = tier_file + tier_vars = [ + "IMPACT.from.Tier", + "TierAD", + "TierAR", + "TierAR.adj", + "No.Var.HM", + "No.Var.H", + "No.Var.M", + "No.Var.L", + "AD.matched", + "AR.matched", + "OMIM.Inheritance.recessive", + "OMIM.Inheritance.dominant", + "OMIM.Inheritance.both", + "OMIM.Inheritance.others", + ] + + tier["OMIM.Inheritance.recessive"] = 0 + tier["OMIM.Inheritance.dominant"] = 0 + tier["OMIM.Inheritance.both"] = 0 + tier["OMIM.Inheritance.others"] = 0 + tier.loc[tier["OMIM.Inheritance"] == "recessive", "OMIM.Inheritance.recessive"] = 1 + tier.loc[tier["OMIM.Inheritance"] == "dominant", "OMIM.Inheritance.dominant"] = 1 + tier.loc[tier["OMIM.Inheritance"] == "both", "OMIM.Inheritance.both"] = 1 + tier.loc[tier["OMIM.Inheritance"] == "others", "OMIM.Inheritance.others"] = 1 + + tier.loc[:, ["TierAD", "TierAR", "TierAR.adj"]] = -tier.loc[ + :, ["TierAD", "TierAR", "TierAR.adj"] + ] + tier = tier.groupby(["Uploaded_variation"], sort=False)[tier_vars].max() + tier.loc[:, ["TierAD", "TierAR", "TierAR.adj"]] = -tier.loc[ + :, ["TierAD", "TierAR", "TierAR.adj"] + ] + patient = pd.concat([patient, tier], axis=1) return patient - - - - - - diff --git a/bin/location_to_gene.py b/bin/location_to_gene.py index dc6a461..b7dec00 100755 --- a/bin/location_to_gene.py +++ b/bin/location_to_gene.py @@ -8,81 +8,92 @@ def load_loc_maps(locmap): - by_start = defaultdict(list) - by_end = defaultdict(list) - by_symbol = defaultdict(lambda: defaultdict(lambda: None)) - for line in open(locmap): - lineData = line.strip().split("\t") - symbol = lineData[0] - if "." in symbol: continue - chrom = lineData[1] - start = int(lineData[2]) - end = int(lineData[3]) - if not by_symbol[chrom][symbol]: by_symbol[chrom][symbol] = [start, end] - else: - if by_symbol[chrom][symbol][0] > start: by_symbol[chrom][symbol][0] = start - if by_symbol[chrom][symbol][1] < end: by_symbol[chrom][symbol][1] = end - by_start[chrom].append((start, end, symbol)) - by_end[chrom].append((end, start, symbol)) - for chrom in by_start.keys(): - by_start[chrom].sort() - by_end[chrom].sort() - return by_start, by_end, by_symbol + by_start = defaultdict(list) + by_end = defaultdict(list) + by_symbol = defaultdict(lambda: defaultdict(lambda: None)) + for line in open(locmap): + lineData = line.strip().split("\t") + symbol = lineData[0] + if "." in symbol: + continue + chrom = lineData[1] + start = int(lineData[2]) + end = int(lineData[3]) + if not by_symbol[chrom][symbol]: + by_symbol[chrom][symbol] = [start, end] + else: + if by_symbol[chrom][symbol][0] > start: + by_symbol[chrom][symbol][0] = start + if by_symbol[chrom][symbol][1] < end: + by_symbol[chrom][symbol][1] = end + by_start[chrom].append((start, end, symbol)) + by_end[chrom].append((end, start, symbol)) + for chrom in by_start.keys(): + by_start[chrom].sort() + by_end[chrom].sort() + return by_start, by_end, by_symbol + def binary_search(loc_to_symbol, start, end): - minimum = 0 #Inclusive - maximum = len(loc_to_symbol) #Exclusive - returnSet = set() - index = None - while minimum < maximum: - index = (minimum + maximum) // 2 - location = loc_to_symbol[index] - if location[0] < start: - minimum = index + 1 - continue - if location[0] > end: - maximum = index - continue - break - if index is None: return returnSet - returnSet.add(loc_to_symbol[index][2]) - i = index - 1 - while i >= 0: - location = loc_to_symbol[i] - i -= 1 - if location[0] < start: break - returnSet.add(location[2]) - i = index + 1 - while i < len(loc_to_symbol): - location = loc_to_symbol[i] - i += 1 - if location[0] > end: break - returnSet.add(location[2]) - return returnSet + minimum = 0 # Inclusive + maximum = len(loc_to_symbol) # Exclusive + returnSet = set() + index = None + while minimum < maximum: + index = (minimum + maximum) // 2 + location = loc_to_symbol[index] + if location[0] < start: + minimum = index + 1 + continue + if location[0] > end: + maximum = index + continue + break + if index is None: + return returnSet + returnSet.add(loc_to_symbol[index][2]) + i = index - 1 + while i >= 0: + location = loc_to_symbol[i] + i -= 1 + if location[0] < start: + break + returnSet.add(location[2]) + i = index + 1 + while i < len(loc_to_symbol): + location = loc_to_symbol[i] + i += 1 + if location[0] > end: + break + returnSet.add(location[2]) + return returnSet + def find_overlapping_genes(by_symbol, by_start, by_end): - gene_overlaps = defaultdict(set) - for chrom in by_symbol.keys(): - for symbol in by_symbol[chrom].keys(): - start, end = by_symbol[chrom][symbol] - start_overlaps = binary_search(by_start[chrom], start, end) - end_overlaps = binary_search(by_end[chrom], start, end) - gene_overlaps[symbol] = (start_overlaps | end_overlaps) - set([symbol]) - return gene_overlaps + gene_overlaps = defaultdict(set) + for chrom in by_symbol.keys(): + for symbol in by_symbol[chrom].keys(): + start, end = by_symbol[chrom][symbol] + start_overlaps = binary_search(by_start[chrom], start, end) + end_overlaps = binary_search(by_end[chrom], start, end) + gene_overlaps[symbol] = (start_overlaps | end_overlaps) - set([symbol]) + return gene_overlaps + def main(variants): - by_start, by_end, by_symbol = load_loc_maps(GENE_LOCS) - #atxn1start = by_symbol["6"]["ENSG00000124788"][0] - #atxn1end = by_symbol["6"]["ENSG00000124788"][1] - for line in open(variants): - lineData = line.strip().split(":") - chrom = lineData[0] - pos = int(lineData[1]) - start_overlaps = binary_search(by_start[chrom], pos, pos) - end_overlaps = binary_search(by_end[chrom], pos, pos) - genes = start_overlaps | end_overlaps - for gene in genes: print(line.strip() + "\t" + gene) + by_start, by_end, by_symbol = load_loc_maps(GENE_LOCS) + # atxn1start = by_symbol["6"]["ENSG00000124788"][0] + # atxn1end = by_symbol["6"]["ENSG00000124788"][1] + for line in open(variants): + lineData = line.strip().split(":") + chrom = lineData[0] + pos = int(lineData[1]) + start_overlaps = binary_search(by_start[chrom], pos, pos) + end_overlaps = binary_search(by_end[chrom], pos, pos) + genes = start_overlaps | end_overlaps + for gene in genes: + print(line.strip() + "\t" + gene) -if __name__ == "__main__": - main(sys.argv[1]) +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/bin/merge_rm.py b/bin/merge_rm.py index 6df6b37..ad3089d 100755 --- a/bin/merge_rm.py +++ b/bin/merge_rm.py @@ -2,24 +2,45 @@ import pandas as pd import sys -###merge score files with final rank matrix - -#read rank matrix and score file -rm=pd.read_csv(sys.argv[1]+".csv") -annot=pd.read_csv("scores.txt.gz", sep="\t", compression="gzip") - - -#get columns from score file -annot=annot[["varId","varId_dash", "geneSymbol", "geneEnsId", "rsId", "HGVSc", "HGVSp", "IMPACT", "Consequence", "phenoList", "phenoInhList", "clin_code", "clinvarCondition"]] - -#get original coordinates -annot["varId"]=annot["varId"].apply(lambda x: x.split("_E")[0]) - -#rename for final output -annot=annot.rename(columns={"varId":"origId", "IMPACT": "IMPACT_text", "clin_code": "clinvarSignDesc"}) - -#merge -test=rm.merge(annot, right_on="varId_dash", left_on="Unnamed: 0", how="left") - -test.to_csv("final_matrix_expanded/"+sys.argv[1]+".expanded.csv.gz", index=False, compression="gzip") - +###merge score files with final rank matrix + +# read rank matrix and score file +rm = pd.read_csv(sys.argv[1] + ".csv") +annot = pd.read_csv("scores.txt.gz", sep="\t", compression="gzip") + + +# get columns from score file +annot = annot[ + [ + "varId", + "varId_dash", + "geneSymbol", + "geneEnsId", + "rsId", + "HGVSc", + "HGVSp", + "IMPACT", + "Consequence", + "phenoList", + "phenoInhList", + "clin_code", + "clinvarCondition", + ] +] + +# get original coordinates +annot["varId"] = annot["varId"].apply(lambda x: x.split("_E")[0]) + +# rename for final output +annot = annot.rename( + columns={"varId": "origId", "IMPACT": "IMPACT_text", "clin_code": "clinvarSignDesc"} +) + +# merge +test = rm.merge(annot, right_on="varId_dash", left_on="Unnamed: 0", how="left") + +test.to_csv( + "final_matrix_expanded/" + sys.argv[1] + ".expanded.csv.gz", + index=False, + compression="gzip", +) diff --git a/bin/mod5_diffusion.py b/bin/mod5_diffusion.py index 4c6fb5f..0f5b6b9 100755 --- a/bin/mod5_diffusion.py +++ b/bin/mod5_diffusion.py @@ -1,5 +1,5 @@ from os.path import exists -from scipy import stats +from scipy import stats from os import mkdir import numpy as np import pandas as pd @@ -8,9 +8,10 @@ __email__ = "dmao@bcm.edu; linhuaw@bcm.edu" -#runs diffusion module +# runs diffusion module -def diffusion(nn, y, alpha = 0.5, max_iter = 100): + +def diffusion(nn, y, alpha=0.5, max_iter=100): """Helper function that runs the network diffusion. Parameters: @@ -31,11 +32,12 @@ def diffusion(nn, y, alpha = 0.5, max_iter = 100): ## initialize an empty array for the heat Fs = np.empty((nn.shape[0], 0), float) ## restart - fY = (1-alpha) * y + fY = (1 - alpha) * y for i in range(0, max_iter): Fs = np.append(Fs, F, axis=1) - F = alpha * nn @ Fs[:,[i]] + fY - return(F) + F = alpha * nn @ Fs[:, [i]] + fY + return F + def diffuseSample(ID, Anno_df, Phrank_folder): """Method that runs diffusion network for a specific sample. @@ -48,45 +50,50 @@ def diffuseSample(ID, Anno_df, Phrank_folder): Return: ------- - Pandas Data Frame, index indicates variant ID, + Pandas Data Frame, index indicates variant ID, columnns contatins diffused phrank scores normalized as percentile. """ ## Get the PPI network for diffusion cor_path = "mod5_diffusion/combined_score.hdf5" - cor_df = pd.read_hdf(cor_path, mode='r') + cor_df = pd.read_hdf(cor_path, mode="r") cor_df = cor_df[(cor_df.T != 0).sum() > 1] cor_df = cor_df.loc[cor_df.index, cor_df.index] cor = cor_df.values - cor_GeneID = pd.DataFrame({"ID":cor_df.columns.tolist()}) + cor_GeneID = pd.DataFrame({"ID": cor_df.columns.tolist()}) # Phrank_path = Phrank_folder + ID.split('.')[0] + ".txt" Phrank_path = ID + ".phrank.txt" ## Validate Phrank file's existence if not exists(Phrank_path): - print("File %s not exists, skipping..." %Phrank_path) + print("File %s not exists, skipping..." % Phrank_path) return - Phrank = pd.read_csv(Phrank_path, sep = "\t", header = None) - Phrank = Phrank.rename({0:"Ensembl_Gene_ID",1:"Score"}, axis = "columns") + Phrank = pd.read_csv(Phrank_path, sep="\t", header=None) + Phrank = Phrank.rename({0: "Ensembl_Gene_ID", 1: "Score"}, axis="columns") ## remove rows without Ensembl_Gene_ID - Phrank["Ensembl_Gene_ID"].replace('', np.nan, inplace=True) - Phrank.dropna(subset=["Ensembl_Gene_ID"], inplace= True) + Phrank["Ensembl_Gene_ID"].replace("", np.nan, inplace=True) + Phrank.dropna(subset=["Ensembl_Gene_ID"], inplace=True) ## remove duplicated genes and keep the highest similarity score - Phrank = Phrank.sort_values('Score', ascending=False).drop_duplicates('Ensembl_Gene_ID').sort_index() - simi = Phrank.rename({0:"Ensembl_Gene_ID","Score":"Similarity_Score"}, axis = "columns") - + Phrank = ( + Phrank.sort_values("Score", ascending=False) + .drop_duplicates("Ensembl_Gene_ID") + .sort_index() + ) + simi = Phrank.rename( + {0: "Ensembl_Gene_ID", "Score": "Similarity_Score"}, axis="columns" + ) ## load module 1 & 2 file as reference - #geneFiltFn = "%s/%s.txt.gz" %(Anno_folder, ID) + # geneFiltFn = "%s/%s.txt.gz" %(Anno_folder, ID) ## Validate annotation file's existence - #if not exists(geneFiltFn): - #print("Module 1-2 file not exists for %s, skipping..." %ID) - #return + # if not exists(geneFiltFn): + # print("Module 1-2 file not exists for %s, skipping..." %ID) + # return - #m12_df = pd.read_csv(geneFiltFn, sep="\t", compression="gzip") - m12_df=Anno_df + # m12_df = pd.read_csv(geneFiltFn, sep="\t", compression="gzip") + m12_df = Anno_df m12_genes = [g for g in m12_df.loc[:, "geneEnsId"].tolist() if "ENSG" in g] m12_gc = len(set(m12_genes)) @@ -97,17 +104,17 @@ def diffuseSample(ID, Anno_df, Phrank_folder): m12_wSimi_woCor = simi[simi["Ensembl_Gene_ID"].isin(m12_wSimi_woCor)] ## normalized the cor matrix net = abs(cor) - D = 1/np.sqrt(net.sum(axis = 1)) + D = 1 / np.sqrt(net.sum(axis=1)) D2 = np.diag(D) net_norm = D2 @ net @ D2 ## Set Y as similarity score - Y = cor_GeneID.merge(simi, left_on = "ID", right_on = "Ensembl_Gene_ID", how = "left") - Y = Y[["ID","Similarity_Score"]].fillna(0) - Y.set_index("ID", inplace = True) + Y = cor_GeneID.merge(simi, left_on="ID", right_on="Ensembl_Gene_ID", how="left") + Y = Y[["ID", "Similarity_Score"]].fillna(0) + Y.set_index("ID", inplace=True) ## Start diffusion - diff_res = diffusion(net_norm,Y,0.5,100) + diff_res = diffusion(net_norm, Y, 0.5, 100) ## Combine diffusion result with GeneID diff_wGeneID = pd.DataFrame(diff_res, columns=["Final_Heat"]) @@ -115,34 +122,42 @@ def diffuseSample(ID, Anno_df, Phrank_folder): ## remove the genes with zero heat diff_wGeneID = diff_wGeneID[diff_wGeneID["Final_Heat"] != 0] - diff_wGeneID = diff_wGeneID[["GeneID","Final_Heat"]] + diff_wGeneID = diff_wGeneID[["GeneID", "Final_Heat"]] ## combine diffusion result with similarity scores - FinalHeat_wSimi = pd.concat([m12_wSimi_woCor,diff_wGeneID],ignore_index=True, sort=True) + FinalHeat_wSimi = pd.concat( + [m12_wSimi_woCor, diff_wGeneID], ignore_index=True, sort=True + ) ## remove diffusion result with non-vep genes - diff_wGeneID_in_m12 = diff_wGeneID[diff_wGeneID['GeneID'].isin(m12_genes)] - m12_wSimi_woCor = m12_wSimi_woCor.rename(columns = {"Similarity_Score":"Final_Heat"}) - diff_wGeneID_in_m12 = diff_wGeneID_in_m12.rename(columns = {"GeneID":"Ensembl_Gene_ID"}) - FinalHeat_wSimi = pd.concat([m12_wSimi_woCor,diff_wGeneID_in_m12],ignore_index=True, sort=True) - FinalHeat_wSimi = FinalHeat_wSimi.sort_values(by = 'Final_Heat', ascending = False) + diff_wGeneID_in_m12 = diff_wGeneID[diff_wGeneID["GeneID"].isin(m12_genes)] + m12_wSimi_woCor = m12_wSimi_woCor.rename(columns={"Similarity_Score": "Final_Heat"}) + diff_wGeneID_in_m12 = diff_wGeneID_in_m12.rename( + columns={"GeneID": "Ensembl_Gene_ID"} + ) + FinalHeat_wSimi = pd.concat( + [m12_wSimi_woCor, diff_wGeneID_in_m12], ignore_index=True, sort=True + ) + FinalHeat_wSimi = FinalHeat_wSimi.sort_values(by="Final_Heat", ascending=False) ## Get the diffusion scores for genes in the annotation data, 0 if not existed score_ordered = [] gene_ordered = m12_df.loc[:, "geneEnsId"].tolist() for gene in gene_ordered: if gene in FinalHeat_wSimi.Ensembl_Gene_ID.tolist(): - score_ordered.append(FinalHeat_wSimi.loc[FinalHeat_wSimi.Ensembl_Gene_ID == gene, 'Final_Heat'].to_numpy()[0]) + score_ordered.append( + FinalHeat_wSimi.loc[ + FinalHeat_wSimi.Ensembl_Gene_ID == gene, "Final_Heat" + ].to_numpy()[0] + ) else: score_ordered.append(0) ## Normalize to have a z-score (max 1, min 0) - scores = stats.rankdata(score_ordered, 'max')/len(score_ordered) + scores = stats.rankdata(score_ordered, "max") / len(score_ordered) - m12_df['diffuse_Phrank_STRING'] = scores - m12_df = m12_df.drop_duplicates(subset = ["varId_dash"]) + m12_df["diffuse_Phrank_STRING"] = scores + m12_df = m12_df.drop_duplicates(subset=["varId_dash"]) m12_df = m12_df[["varId_dash", "diffuse_Phrank_STRING"]] m12_df = m12_df.set_index("varId_dash", drop=True) return m12_df - - diff --git a/bin/predict_new/confidence.py b/bin/predict_new/confidence.py index 3ebf265..c3a42f1 100755 --- a/bin/predict_new/confidence.py +++ b/bin/predict_new/confidence.py @@ -9,26 +9,32 @@ import numpy as np import scipy -def get_causal_distribution(pred_fd, label_col='is_strong'): + +def get_causal_distribution(pred_fd, label_col="is_strong"): # pred_fd: folder for prediction files only fns = os.listdir(pred_fd) # get top20 predictions top20_preds = [] for fn in fns: - top20_preds.append(pd.read_csv(f'{pred_fd}/{fn}', index_col=0).iloc[:20,:]) + top20_preds.append(pd.read_csv(f"{pred_fd}/{fn}", index_col=0).iloc[:20, :]) top20_preds = pd.concat(top20_preds) # get scores of causal variants - top20_causals = np.ravel(top20_preds.loc[top20_preds[label_col]==1, 'predict'].to_numpy()) + top20_causals = np.ravel( + top20_preds.loc[top20_preds[label_col] == 1, "predict"].to_numpy() + ) return top20_causals -def assign_confidence_score(causal_distr, pred_df, label_col='is_strong'): + +def assign_confidence_score(causal_distr, pred_df, label_col="is_strong"): # pred_fd: folder for prediction files only # pred_df: prediction data frame of the sample of interest - + prediction_scores = pred_df.predict.to_numpy() # assign confidence score using percentile - conf_scores = [scipy.stats.percentileofscore(causal_distr, i) for i in prediction_scores] + conf_scores = [ + scipy.stats.percentileofscore(causal_distr, i) for i in prediction_scores + ] # assign confidence category by discretizing conf_categories = [] for conf_score in conf_scores: @@ -40,11 +46,12 @@ def assign_confidence_score(causal_distr, pred_df, label_col='is_strong'): conf_categories.append("Solved (Medium)") else: conf_categories.append("Solved (High)") - pred_df['confidence'] = conf_scores - pred_df['confidence level'] = conf_categories + pred_df["confidence"] = conf_scores + pred_df["confidence level"] = conf_categories pred_df.sort_values("confidence", ascending=False) return pred_df + # def determine_case_confidence(causal_distr, pred_df, label_col='is_strong'): # pred_df = assign_confidence_score(causal_distr, pred_df, label_col) -# return pred_df['confidence level'].tolist()[0] \ No newline at end of file +# return pred_df['confidence level'].tolist()[0] diff --git a/bin/predict_new/main.py b/bin/predict_new/main.py index 7edc4c8..f7bcd7a 100755 --- a/bin/predict_new/main.py +++ b/bin/predict_new/main.py @@ -7,20 +7,39 @@ import joblib from confidence import * -def test(final_model, val_fns, features, test_label, out_folder, mode, train_causals, train_pred_folder): + +def test( + final_model, + val_fns, + features, + test_label, + out_folder, + mode, + train_causals, + train_pred_folder, +): if train_pred_folder is not None: causal_distr = get_causal_distribution(train_pred_folder, label_col=test_label) else: causal_distr = None - - causal_rank_raw = rank_test_patients(final_model, val_fns, features, test_label, causal_distr = causal_distr, - out_folder=f"{out_folder}/{mode}_pred_raw_rank", train_causals=None) + + causal_rank_raw = rank_test_patients( + final_model, + val_fns, + features, + test_label, + causal_distr=causal_distr, + out_folder=f"{out_folder}/{mode}_pred_raw_rank", + train_causals=None, + ) f_raw, eval_df_raw = plot_topK_performance(causal_rank_raw) f_raw.savefig(f"{out_folder}/{mode}_test_perf_raw.png", dpi=100) eval_df_raw.to_csv(f"{out_folder}/{mode}_test_perf_raw.csv") ## evaluate only on non-seen variants + + # causal_rank_exclude = rank_test_patients(final_model, val_fns, features, test_label,train_pred_folder, # out_folder=f"{out_folder}/{mode}_pred_exclude_rank", # train_causals=train_causals) @@ -29,9 +48,16 @@ def test(final_model, val_fns, features, test_label, out_folder, mode, train_cau # f_exclude.savefig(f"{out_folder}/{mode}_test_perf_exclude.png", dpi=100) # eval_df_exclude.to_csv(f"{out_folder}/{mode}_test_perf_exclude.csv") -def main(feature_fillna=None, feature_tier_with_clinvar=None, - train_label=None, test_label=None, out_folder=None, - tune=True, train_test=None): + +def main( + feature_fillna=None, + feature_tier_with_clinvar=None, + train_label=None, + test_label=None, + out_folder=None, + tune=True, + train_test=None, +): """ feature_fillna: bool, fill na feature or not feature_tier_with_clinvar: bool, tier feature use clinvar/hgmd info or not @@ -39,7 +65,7 @@ def main(feature_fillna=None, feature_tier_with_clinvar=None, test_label: str, label to be used in training (is_causal, is_strong) out_folder: if not specified, will be created automatically tune: bool, whether or not tuning is needed - train_test: train/test split strategy, + train_test: train/test split strategy, if str, must be one of ['BG_val', 'version1', 'leave_one_out'] if int/float, represent number/fraction of test samples to be randomly used. """ @@ -47,9 +73,9 @@ def main(feature_fillna=None, feature_tier_with_clinvar=None, f = open("meta.json") meta = json.load(f) data_folder = meta["data_folder"] - classifier = meta['classifier'] + classifier = meta["classifier"] if train_test is None: - train_test = meta['train_test'] + train_test = meta["train_test"] if feature_fillna is None: feature_fillna = meta["feature_fillna"] if feature_tier_with_clinvar is None: @@ -60,16 +86,18 @@ def main(feature_fillna=None, feature_tier_with_clinvar=None, test_label = meta["test_label"] if out_folder is None: fillna = "fillNA" if feature_fillna else "withNA" - tier = 'tierDB' if feature_tier_with_clinvar else 'tierNDB' - dateid = datetime.now().strftime('%m-%d-%Y') - fd = '_'.join([classifier, fillna, tier, train_label, train_test, f'tune{tune}', dateid]) + tier = "tierDB" if feature_tier_with_clinvar else "tierNDB" + dateid = datetime.now().strftime("%m-%d-%Y") + fd = "_".join( + [classifier, fillna, tier, train_label, train_test, f"tune{tune}", dateid] + ) out_folder = f"./results/{fd}" if not os.path.exists("./results/"): os.mkdir("./results/") if not os.path.exists(out_folder): os.mkdir(out_folder) - + print(f"### Classifier: {classifier}.") print(f"### DataHub: {data_folder}.") print(f"### TrainTestMode: {train_test}.") @@ -94,15 +122,17 @@ def main(feature_fillna=None, feature_tier_with_clinvar=None, ### [Start] ---> Get training and testing files ## Files BG provided for additional validation are taken out of training - fns = [os.path.abspath(fn) for fn in fns] # take absolute paths + fns = [os.path.abspath(fn) for fn in fns] # take absolute paths sample_meta = pd.read_csv(meta["sample_meta_fn"], sep=" ") - bg_samples = sample_meta.loc[sample_meta.val==1,'BG_ID'].tolist() + bg_samples = sample_meta.loc[sample_meta.val == 1, "BG_ID"].tolist() print(len(bg_samples)) ## Testing files bg_val_fns = [os.path.abspath(f"{data_folder}/{vid}_{ext}") for vid in bg_samples] - udn_val_fns = [fn for fn in fns if 'UDN' in fn] - ddd_val_fns = [fn for fn in fns if 'DDD' in fn] - print(f'#fns={len(fns)}, #BG validation={len(bg_val_fns)}, #UDN validation={len(udn_val_fns)}, #DDD validation={len(ddd_val_fns)}') + udn_val_fns = [fn for fn in fns if "UDN" in fn] + ddd_val_fns = [fn for fn in fns if "DDD" in fn] + print( + f"#fns={len(fns)}, #BG validation={len(bg_val_fns)}, #UDN validation={len(udn_val_fns)}, #DDD validation={len(ddd_val_fns)}" + ) ## Training files train_fns = list(set(fns) - set(udn_val_fns) - set(bg_val_fns) - set(ddd_val_fns)) ### <- Get training and testing files [End] @@ -110,53 +140,87 @@ def main(feature_fillna=None, feature_tier_with_clinvar=None, # if isinstance(train_sample_num, int) and (len(train_fns) > train_sample_num): # train_fns = np.random.choice(train_fns, train_sample_num, replace=False) if tune: - validation_performances = tune_params(train_fns, classifier=classifier) + validation_performances = tune_params(train_fns, classifier=classifier) validation_performances.to_csv(f"{out_folder}/param_tuning_perfs.csv") val_best_params = validation_performances.iloc[0, :].tolist() - if classifier == 'xgb': - n_est, md, classifier = int(val_best_params[0]), int(val_best_params[1]), float(val_best_params[2]) + if classifier == "xgb": + n_est, md, classifier = ( + int(val_best_params[0]), + int(val_best_params[1]), + float(val_best_params[2]), + ) else: n_est, md = int(val_best_params[0]), int(val_best_params[1]) else: - ### Fix parameter tuned, TO BE REMOVED --> - if classifier == 'xgb': + ### Fix parameter tuned, TO BE REMOVED --> + if classifier == "xgb": n_est, lr, md = 300, 0.1, None print(f"n_est={n_est}, lr={lr}, md={md}") - elif classifier == 'rf': + elif classifier == "rf": n_est, md = 300, None ### <-- Fix parameter tuned, TO BE REMOVED - if classifier == 'xgb': + if classifier == "xgb": trainX, trainY, neg_pos_ratio = get_training_data(train_fns, train_label) features = trainX.columns.tolist() print(f"[Final XGB] Training with {len(train_fns)} samples.") final_model = train_XGBoost(trainX, trainY, n_est, lr, md, neg_pos_ratio) causal_idx = np.where(trainY == 1)[0] - train_causals = trainX.iloc[causal_idx,:] + train_causals = trainX.iloc[causal_idx, :] train_causals.to_csv(f"{out_folder}/train_causal_df.csv") train_causals = train_causals.index.tolist() - elif classifier == 'rf': - final_model, features, train_causals = train_minibatch_RF(train_fns, n_estimator=n_est, max_depth=md, - label_col=train_label, batch=2000) + elif classifier == "rf": + final_model, features, train_causals = train_minibatch_RF( + train_fns, + n_estimator=n_est, + max_depth=md, + label_col=train_label, + batch=2000, + ) print(f"[Final RF] Trained, #trees = {final_model.get_params('n_estimators')}") joblib.dump(final_model, f"{out_folder}/final_model_wo_bg_val.job") - + with open(f"{out_folder}/features.csv", "w") as feature_f: feature_f.write(",".join(features)) - + # ### predict on training samples # test(final_model, train_fns, features, test_label, out_folder, 'Training', train_causals, train_pred_folder=None) ### BG validation - test(final_model, bg_val_fns, features, test_label, out_folder, 'BG', train_causals, train_pred_folder=None) + test( + final_model, + bg_val_fns, + features, + test_label, + out_folder, + "BG", + train_causals, + train_pred_folder=None, + ) ### UDN validation - test(final_model, udn_val_fns, features, test_label, out_folder, 'UDN', train_causals, train_pred_folder=f"{out_folder}/BG_pred_raw_rank") + test( + final_model, + udn_val_fns, + features, + test_label, + out_folder, + "UDN", + train_causals, + train_pred_folder=f"{out_folder}/BG_pred_raw_rank", + ) ### DDD validation - test(final_model, ddd_val_fns, features, test_label, out_folder, 'DDD', train_causals, train_pred_folder=f"{out_folder}/BG_pred_raw_rank") - + test( + final_model, + ddd_val_fns, + features, + test_label, + out_folder, + "DDD", + train_causals, + train_pred_folder=f"{out_folder}/BG_pred_raw_rank", + ) if __name__ == "__main__": - warnings.filterwarnings('ignore') + warnings.filterwarnings("ignore") main(tune=False) - \ No newline at end of file diff --git a/bin/predict_new/utilities.py b/bin/predict_new/utilities.py index a1174c3..da293ce 100755 --- a/bin/predict_new/utilities.py +++ b/bin/predict_new/utilities.py @@ -13,16 +13,17 @@ import itertools from predict_new.confidence import * + def get_training_data(fns, label_col): """Read in training data, drop features with specified mode in the meta['feature_mode'] json. - Allowed modes are ['all', 'raw', 'var_disase_db_off', 'gene_disase_db_off'] + Allowed modes are ['all', 'raw', 'var_disase_db_off', 'gene_disase_db_off'] """ np.random.seed(0) ### READ PARAMETERS f = open("meta.json") meta = json.load(f) feature_mode = meta["feature_mode"] - feature_meta = pd.read_csv(meta["feature_meta_fn"], sep=',') + feature_meta = pd.read_csv(meta["feature_meta_fn"], sep=",") ### READ training samples t0 = time() print("[TrainVal-Train] Start reading training data.") @@ -36,19 +37,24 @@ def get_training_data(fns, label_col): t1 = time() print(f"[TrainVal-Train] Completed reading training data in {(t1-t0):.2f} seconds.") train_Y = dfs[label_col].to_numpy().astype(int) - neg_pos_ratios = [(train_Y == 0).sum()/(train_Y == 1).sum()] + neg_pos_ratios = [(train_Y == 0).sum() / (train_Y == 1).sum()] ## Drop features based on feature mode if feature_mode == "all": features = feature_meta.Feature.tolist() elif feature_mode == "raw": - features = feature_meta.loc[feature_meta.Engineered != 1,:].Feature.tolist() + features = feature_meta.loc[feature_meta.Engineered != 1, :].Feature.tolist() elif feature_mode == "var_disase_db_off": - features = feature_meta.loc[feature_meta.DiseaseDB_VariantLevel != 1,:].Feature.tolist() + features = feature_meta.loc[ + feature_meta.DiseaseDB_VariantLevel != 1, : + ].Feature.tolist() elif feature_mode == "gene_disase_db_off": - features = feature_meta.loc[feature_meta.DiseaseDB_GeneLevel != 1,:].Feature.tolist() + features = feature_meta.loc[ + feature_meta.DiseaseDB_GeneLevel != 1, : + ].Feature.tolist() else: - print(f"Wrong feature mode entered, should be one of\ + print( + f"Wrong feature mode entered, should be one of\ ['all', 'raw', 'var_disease_db_off', 'gene_disease_db_off'],\ your input is {feature_mode}." ) @@ -57,97 +63,152 @@ def get_training_data(fns, label_col): print("[TrainVal-Train] Training data obtained.") return train_X, train_Y, np.mean(neg_pos_ratios) -def tune_params(fns, classifier='xgb'): + +def tune_params(fns, classifier="xgb"): ### READ PARAMETERS f = open("meta.json") meta = json.load(f) train_label = meta["train_label"] test_label = meta["test_label"] train_val_frac = meta["train_val_frac"] - sep2 = "\t" + sep2 = "\t" train_fns_sampled = list(np.random.choice(fns, int(train_val_frac * len(fns)))) val_fns_sampled = list(set(fns) - set(train_fns_sampled)) - print(f"[TrainVal-Train] train {len(train_fns_sampled)} samples with label {train_label},\ - validate {len(val_fns_sampled)} samples with label {test_label}.") + print( + f"[TrainVal-Train] train {len(train_fns_sampled)} samples with label {train_label},\ + validate {len(val_fns_sampled)} samples with label {test_label}." + ) - para_train_dfs = [pd.read_csv(train_fn, usecols=[train_label], sep=sep2) for train_fn in train_fns_sampled] + para_train_dfs = [ + pd.read_csv(train_fn, usecols=[train_label], sep=sep2) + for train_fn in train_fns_sampled + ] para_train_dfs = pd.concat(para_train_dfs) - if classifier == 'xgb': + if classifier == "xgb": ## tune parameters n_estimators = [200, 300] learning_rates = list(np.logspace(0.001, 0.2, num=10, endpoint=True)) max_depths = [None] params = list(itertools.product(n_estimators, learning_rates, max_depths)) - elif classifier == 'rf': + elif classifier == "rf": n_estimators = [100, 200, 300, 400] max_depths = [None] params = list(itertools.product(n_estimators, max_depths)) - + train_X, train_Y, neg_pos_ratio = get_training_data(train_fns_sampled, train_label) perf_dfs = [] for param in params: t0 = time() - if classifier == 'xgb': + if classifier == "xgb": n_est, lr, md = param - print(f"[TrainVal-Train] n_trees: {n_est}, lr: {lr}, max_depth: {md}, n/p ratio: {neg_pos_ratio}.") + print( + f"[TrainVal-Train] n_trees: {n_est}, lr: {lr}, max_depth: {md}, n/p ratio: {neg_pos_ratio}." + ) clf = train_XGBoost(train_X, train_Y, n_est, lr, md, neg_pos_ratio) - - elif classifier == 'rf': + + elif classifier == "rf": n_est, md = param - print(f"[TrainVal-Train] n_trees: {n_est}, max_depth: {md}, n/p ratio: {neg_pos_ratio}.") - clf, _, _ = train_minibatch_RF(train_X,train_Y, n_est, md, neg_pos_ratio) + print( + f"[TrainVal-Train] n_trees: {n_est}, max_depth: {md}, n/p ratio: {neg_pos_ratio}." + ) + clf, _, _ = train_minibatch_RF(train_X, train_Y, n_est, md, neg_pos_ratio) t1 = time() print(f"[TrainVal-Train] done in {(t1-t0):.2f} seconds.") - rank_dfs = rank_test_patients(clf, val_fns_sampled, train_X.columns.tolist(), test_label) + rank_dfs = rank_test_patients( + clf, val_fns_sampled, train_X.columns.tolist(), test_label + ) obj_df = objective(rank_dfs) - if classifier == 'xgb': - perf_df = pd.DataFrame(data=[[n_est, md,lr, obj_df['objective'], - obj_df['var_top1_acc'], obj_df['var_top5_acc'], obj_df['var_top10_acc'] ]], - columns=['n_estimator', 'max_depth', 'learning_rate', - 'objective', 'var_top1', 'var_top5', 'var_top10']) + if classifier == "xgb": + perf_df = pd.DataFrame( + data=[ + [ + n_est, + md, + lr, + obj_df["objective"], + obj_df["var_top1_acc"], + obj_df["var_top5_acc"], + obj_df["var_top10_acc"], + ] + ], + columns=[ + "n_estimator", + "max_depth", + "learning_rate", + "objective", + "var_top1", + "var_top5", + "var_top10", + ], + ) else: - perf_df = pd.DataFrame(data=[[n_est, md, obj_df['objective'], - obj_df['var_top1_acc'], obj_df['var_top5_acc'], obj_df['var_top10_acc'] ]], - columns=['n_estimator', 'max_depth', - 'objective', 'var_top1', 'var_top5', 'var_top10']) + perf_df = pd.DataFrame( + data=[ + [ + n_est, + md, + obj_df["objective"], + obj_df["var_top1_acc"], + obj_df["var_top5_acc"], + obj_df["var_top10_acc"], + ] + ], + columns=[ + "n_estimator", + "max_depth", + "objective", + "var_top1", + "var_top5", + "var_top10", + ], + ) perf_dfs.append(perf_df) t2 = time() - print(f"[TrainVal-Validate] done in {(t2-t1):.2f} seconds, objective = {obj_df['objective']}.") - perf_dfs = pd.concat(perf_dfs).sort_values(["objective", "var_top1", "var_top5", "var_top10"], ascending=False) + print( + f"[TrainVal-Validate] done in {(t2-t1):.2f} seconds, objective = {obj_df['objective']}." + ) + perf_dfs = pd.concat(perf_dfs).sort_values( + ["objective", "var_top1", "var_top5", "var_top10"], ascending=False + ) return perf_dfs -def quantify_perf(test_perfs, topK=5, mode='variant_level'): - if mode == 'sample_level': - sample_perfs = test_perfs.groupby('identifier')[['ranking']].min() + +def quantify_perf(test_perfs, topK=5, mode="variant_level"): + if mode == "sample_level": + sample_perfs = test_perfs.groupby("identifier")[["ranking"]].min() else: sample_perfs = test_perfs.copy() - - topK_acc = (sample_perfs['ranking'] <= topK).sum() / sample_perfs.shape[0] + + topK_acc = (sample_perfs["ranking"] <= topK).sum() / sample_perfs.shape[0] return topK_acc + def plot_topK_performance(rank_dfs): - f = plt.figure(figsize=(10,4)) + f = plt.figure(figsize=(10, 4)) topks = range(1, 101) var_accs = [] sample_accs = [] for i in topks: var_accs.append(quantify_perf(rank_dfs, topK=i)) - sample_accs.append(quantify_perf(rank_dfs, topK=i, mode='sample_level')) - df = pd.DataFrame({"top_k": topks, "var_accuracy": var_accs, "sample_accuracy": sample_accs}) - ax1 = plt.subplot(1,2,1) - ax1.plot(topks, var_accs, linestyle='--', marker='o', color='blue') + sample_accs.append(quantify_perf(rank_dfs, topK=i, mode="sample_level")) + df = pd.DataFrame( + {"top_k": topks, "var_accuracy": var_accs, "sample_accuracy": sample_accs} + ) + ax1 = plt.subplot(1, 2, 1) + ax1.plot(topks, var_accs, linestyle="--", marker="o", color="blue") ax1.set_xlabel("Top K", fontsize=14) ax1.set_ylabel("Variant Accuracy", fontsize=14) - ax2 = plt.subplot(1,2,2) - ax2.plot(topks, sample_accs, linestyle='--', marker='o', color='green') + ax2 = plt.subplot(1, 2, 2) + ax2.plot(topks, sample_accs, linestyle="--", marker="o", color="green") ax2.set_xlabel("Top K", fontsize=14) ax2.set_ylabel("Sample Accuracy", fontsize=14) plt.subplots_adjust(wspace=0.3) return f, df + def objective(rank_dfs): ### READ PARAMETERS f = open("meta.json") @@ -160,59 +221,82 @@ def objective(rank_dfs): vl1 = quantify_perf(rank_dfs, topK=1) vl5 = quantify_perf(rank_dfs, topK=5) vl10 = quantify_perf(rank_dfs, topK=10) - sl1 = quantify_perf(rank_dfs, topK=1, mode='sample_level') - sl5 = quantify_perf(rank_dfs, topK=5, mode='sample_level') - sl10 = quantify_perf(rank_dfs, topK=10, mode='sample_level') + sl1 = quantify_perf(rank_dfs, topK=1, mode="sample_level") + sl5 = quantify_perf(rank_dfs, topK=5, mode="sample_level") + sl10 = quantify_perf(rank_dfs, topK=10, mode="sample_level") obj_score = alpha * vl1 + beta * (vl5 - vl1) + gamma * (vl10 - vl5) - result = {"var_top1_acc": vl1, - "var_top5_acc": vl5, - "var_top10_acc": vl10, - "sample_top1_acc": sl1, - "sample_top5_acc": sl5, - "sample_top10_acc": sl10, - "objective": obj_score} + result = { + "var_top1_acc": vl1, + "var_top5_acc": vl5, + "var_top10_acc": vl10, + "sample_top1_acc": sl1, + "sample_top5_acc": sl5, + "sample_top10_acc": sl10, + "objective": obj_score, + } return result + def train_XGBoost(X, Y, n_estimator, lr, max_depth, ratio): xgb.config_context(verbosity=0) - bst = xgb.XGBClassifier(max_depth = max_depth, - n_estimators=n_estimator, - learning_rate= lr, - scale_pos_weight = ratio, - verbosity=0, n_jobs=-1) + bst = xgb.XGBClassifier( + max_depth=max_depth, + n_estimators=n_estimator, + learning_rate=lr, + scale_pos_weight=ratio, + verbosity=0, + n_jobs=-1, + ) bst.fit(X, Y) return bst + def train_RF(X, Y, n_estimator=None, max_depth=None, ratio=None, model=None): if model is None: - model = RF(class_weight = {0:1, 1:ratio}, random_state=2021, n_jobs=-1, - n_estimators=n_estimator, max_depth=max_depth, warm_start=True) + model = RF( + class_weight={0: 1, 1: ratio}, + random_state=2021, + n_jobs=-1, + n_estimators=n_estimator, + max_depth=max_depth, + warm_start=True, + ) model.fit(X, Y) return model + def train_minibatch_RF(fns, n_estimator, max_depth, label_col, batch=500): k = ceil(len(fns) / batch) - model = RF(random_state=2021, n_jobs=-1, - n_estimators=n_estimator, - max_depth=max_depth, warm_start=True) + model = RF( + random_state=2021, + n_jobs=-1, + n_estimators=n_estimator, + max_depth=max_depth, + warm_start=True, + ) train_causals = [] for i in range(k): - if i < k-1: - batch_fns = fns[i*batch:(i+1)*batch] + if i < k - 1: + batch_fns = fns[i * batch : (i + 1) * batch] else: - batch_fns = fns[i*batch:] - trainX, trainY, neg_pos_ratio = get_training_data(batch_fns, label_col=label_col) + batch_fns = fns[i * batch :] + trainX, trainY, neg_pos_ratio = get_training_data( + batch_fns, label_col=label_col + ) features = trainX.columns.tolist() - model.set_params(**{'class_weight': {0:1, 1:neg_pos_ratio}, - 'n_estimators': n_estimator}) + model.set_params( + **{"class_weight": {0: 1, 1: neg_pos_ratio}, "n_estimators": n_estimator} + ) model.fit(trainX, trainY) n_estimator += 10 - train_causals += trainX.index[np.where(trainY==1)[0]].tolist() + train_causals += trainX.index[np.where(trainY == 1)[0]].tolist() return model, features, train_causals def rank_patient(model, fn, features, train_causals=None): - df = pd.read_csv(fn, sep="\t", index_col=0) ## File name format can be a parameter in the future + df = pd.read_csv( + fn, sep="\t", index_col=0 + ) ## File name format can be a parameter in the future identifier = fn.split("/")[-1].split(".")[0] patient_features = df.loc[:, features] patient_preds = model.predict_proba(patient_features)[:, 1] @@ -220,18 +304,26 @@ def rank_patient(model, fn, features, train_causals=None): pred_df["predict"] = patient_preds if train_causals is not None: - pred_df = pred_df.loc[~pred_df.index.isin(train_causals),:] + pred_df = pred_df.loc[~pred_df.index.isin(train_causals), :] pred_df = pred_df.sort_values("predict", ascending=False) - min_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='min').astype(int) - max_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method='max').astype(int) - pred_df['min_ranking'] = min_rankings - pred_df['max_ranking'] = max_rankings - pred_df['ranking'] = max_rankings - pred_df['identifier'] = identifier + min_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method="min").astype(int) + max_rankings = rankdata(1 - pred_df["predict"].to_numpy(), method="max").astype(int) + pred_df["min_ranking"] = min_rankings + pred_df["max_ranking"] = max_rankings + pred_df["ranking"] = max_rankings + pred_df["identifier"] = identifier return pred_df -def rank_test_patients(model, fns, features, label_col, causal_distr = None, - out_folder=None, train_causals=None): + +def rank_test_patients( + model, + fns, + features, + label_col, + causal_distr=None, + out_folder=None, + train_causals=None, +): print(out_folder) causal_rank_dfs = [] for i in trange(len(fns)): @@ -242,25 +334,28 @@ def rank_test_patients(model, fns, features, label_col, causal_distr = None, identifier = fn.split("/")[-1].split("_")[0] rank_df = rank_patient(model, fn, features, train_causals) - + if causal_distr is not None: - rank_df = assign_confidence_score(causal_distr, rank_df, label_col='is_strong') - + rank_df = assign_confidence_score( + causal_distr, rank_df, label_col="is_strong" + ) + if out_folder is not None: if not os.path.exists(out_folder): os.mkdir(out_folder) rank_df.to_csv(f"{out_folder}/{identifier}_variant_rank.csv") - + causal_rank = get_causal_rank(rank_df, label_col) - + if causal_rank is not None: causal_rank_dfs.append(causal_rank) - + causal_rank_dfs = pd.concat(causal_rank_dfs) return causal_rank_dfs + def get_causal_rank(pred_df, label_col): if (pred_df[label_col] == 1).sum() == 0: return None - return pred_df.loc[pred_df[label_col] == 1,:] \ No newline at end of file + return pred_df.loc[pred_df[label_col] == 1, :] diff --git a/bin/run_final.py b/bin/run_final.py index fc835f9..835aca0 100755 --- a/bin/run_final.py +++ b/bin/run_final.py @@ -9,14 +9,14 @@ from predict_new.confidence import * import sys -#read in precalulated model -model=joblib.load("predict_new/hg19/final_model_wo_bg_val.job") +# read in precalulated model +model = joblib.load("predict_new/hg19/final_model_wo_bg_val.job") -#read in features to use -features=list(pd.read_csv("predict_new/hg19/features.csv")) +# read in features to use +features = list(pd.read_csv("predict_new/hg19/features.csv")) -#predict with Linhua's code -out=rank_patient(model, sys.argv[1]+".matrix.txt", features, train_causals=None) +# predict with Linhua's code +out = rank_patient(model, sys.argv[1] + ".matrix.txt", features, train_causals=None) -#write out -out.to_csv(sys.argv[1]+".csv") \ No newline at end of file +# write out +out.to_csv(sys.argv[1] + ".csv") diff --git a/bin/run_phrank.py b/bin/run_phrank.py index bb9809d..d817e2b 100755 --- a/bin/run_phrank.py +++ b/bin/run_phrank.py @@ -11,24 +11,27 @@ def load_list(listfile): - returnList = [] - for line in open(listfile): - returnList.append(line.strip()) - return returnList + returnList = [] + for line in open(listfile): + returnList.append(line.strip()) + return returnList + def load_set(listfile): - returnSet = set() - for line in open(listfile): - returnSet.add(line.strip().split("\t")[0]) - return returnSet + returnSet = set() + for line in open(listfile): + returnSet.add(line.strip().split("\t")[0]) + return returnSet + def main(genelist, phenotypelist): - p = Phrank(DAGFILE, DISEASEANNOTATIONS, DISEASEGENE, GENEANNOTATIONS) - genes = load_set(genelist) - phenos = load_set(phenotypelist) - ranking = p.rank_genes(genes, phenos) - for item in ranking: print(str(item[1]) + "\t" + str(item[0])) + p = Phrank(DAGFILE, DISEASEANNOTATIONS, DISEASEGENE, GENEANNOTATIONS) + genes = load_set(genelist) + phenos = load_set(phenotypelist) + ranking = p.rank_genes(genes, phenos) + for item in ranking: + print(str(item[1]) + "\t" + str(item[0])) -if __name__ == "__main__": - main(sys.argv[1], sys.argv[2]) +if __name__ == "__main__": + main(sys.argv[1], sys.argv[2]) diff --git a/bin/simple_repeat_anno.py b/bin/simple_repeat_anno.py index a695f46..52b68b7 100755 --- a/bin/simple_repeat_anno.py +++ b/bin/simple_repeat_anno.py @@ -2,28 +2,43 @@ import numpy as np import os -#annotates a feature matrix with a simple repeat column +# annotates a feature matrix with a simple repeat column + def simple_repeat_anno(sample_id, feature_file, sp_bed_file): sample = feature_file - sample_bed = pd.DataFrame({'chr':sample.index.str.split('-').str[0], - 'start':sample.index.str.split('-').str[1], - 'end':sample.index.str.split('-').str[1], - 'ref':sample.index.str.split('-').str[2], - 'alt':sample.index.str.split('-').str[3]}) - sample_bed.to_csv('%s.bed'%(sample_id),index=False,header=False,sep='\t') + sample_bed = pd.DataFrame( + { + "chr": sample.index.str.split("-").str[0], + "start": sample.index.str.split("-").str[1], + "end": sample.index.str.split("-").str[1], + "ref": sample.index.str.split("-").str[2], + "alt": sample.index.str.split("-").str[3], + } + ) + sample_bed.to_csv("%s.bed" % (sample_id), index=False, header=False, sep="\t") # bedtools needed - os.system('/run/bedtools intersect -a %s.bed -b %s -wa > %s.sp.bed'%(sample_id, sp_bed_file, sample_id)) + os.system( + "/run/bedtools intersect -a %s.bed -b %s -wa > %s.sp.bed" + % (sample_id, sp_bed_file, sample_id) + ) - if os.stat('%s.sp.bed'%(sample_id)).st_size == 0: - sample['simple_repeat'] = 0 + if os.stat("%s.sp.bed" % (sample_id)).st_size == 0: + sample["simple_repeat"] = 0 else: - sample_sp = pd.read_csv('%s.sp.bed'%(sample_id),sep='\t',header=None) - sample_sp['var_dash'] = sample_sp[0].astype(str) + '-' + sample_sp[2].astype(str) + '-' + sample_sp[3].astype(str) + '-' + sample_sp[4].astype(str) - sample['simple_repeat'] = sample.index.isin(sample_sp['var_dash'].to_numpy()).astype(int) + sample_sp = pd.read_csv("%s.sp.bed" % (sample_id), sep="\t", header=None) + sample_sp["var_dash"] = ( + sample_sp[0].astype(str) + + "-" + + sample_sp[2].astype(str) + + "-" + + sample_sp[3].astype(str) + + "-" + + sample_sp[4].astype(str) + ) + sample["simple_repeat"] = sample.index.isin( + sample_sp["var_dash"].to_numpy() + ).astype(int) return sample - - - diff --git a/bin/split_vep_chunks.py b/bin/split_vep_chunks.py index 25d296e..1223193 100755 --- a/bin/split_vep_chunks.py +++ b/bin/split_vep_chunks.py @@ -8,39 +8,45 @@ VEP_FILE = sys.argv[1] RAMlimit = float(sys.argv[2]) -#numHeaderSkip=354#this depends on the type of annotation used -numHeaderSkip=0 -with open(VEP_FILE,'r') as F: +# numHeaderSkip=354#this depends on the type of annotation used +numHeaderSkip = 0 +with open(VEP_FILE, "r") as F: for line in F: - if line.startswith('##'): + if line.startswith("##"): numHeaderSkip += 1 else: break # 06/30/2023: Process the vep annotation result chunk by chunk command = f'wc -l {VEP_FILE} | cut -d" " -f1' -nlines = int(subprocess.check_output(command, shell=True, text=True).strip()) - numHeaderSkip - 1 +nlines = ( + int(subprocess.check_output(command, shell=True, text=True).strip()) + - numHeaderSkip + - 1 +) command = f"du {VEP_FILE}" -filesize = int(subprocess.check_output(command, shell=True, text=True).strip().split('\t')[0]) +filesize = int( + subprocess.check_output(command, shell=True, text=True).strip().split("\t")[0] +) -num_chunk = math.ceil( (filesize/1024) / (RAMlimit * 1024 / 23) ) +num_chunk = math.ceil((filesize / 1024) / (RAMlimit * 1024 / 23)) -chunk_lines = math.ceil(nlines/num_chunk) +chunk_lines = math.ceil(nlines / num_chunk) lines = [] if num_chunk > 1: - for i in range(num_chunk-1): - lines.append(f"{i+1}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + i*chunk_lines)}\t{int(numHeaderSkip+1 + (i+1)*chunk_lines)}") + for i in range(num_chunk - 1): + lines.append( + f"{i+1}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + i*chunk_lines)}\t{int(numHeaderSkip+1 + (i+1)*chunk_lines)}" + ) -lines.append(f"{num_chunk}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + (num_chunk-1)*chunk_lines)}\t{int(nlines+numHeaderSkip+1)}") +lines.append( + f"{num_chunk}\t{int(numHeaderSkip+1)}\t{int(numHeaderSkip+2 + (num_chunk-1)*chunk_lines)}\t{int(nlines+numHeaderSkip+1)}" +) -vep_split = open("vep_split.txt", 'w') -vep_split.write('\n'.join(lines)) -vep_split.write('\n') +vep_split = open("vep_split.txt", "w") +vep_split.write("\n".join(lines)) +vep_split.write("\n") vep_split.close() - - - - From 365775ba535935336789a159a8ae5bef3fbf5a24 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 30 May 2024 13:54:31 -0500 Subject: [PATCH 31/71] lint the R files. --- bin/VarTierDiseaseDBFalse.R | 364 ++++++++++++++++++++++-------------- bin/phenoSim.R | 88 ++++----- 2 files changed, 266 insertions(+), 186 deletions(-) diff --git a/bin/VarTierDiseaseDBFalse.R b/bin/VarTierDiseaseDBFalse.R index eb8b7d2..a70b2dc 100755 --- a/bin/VarTierDiseaseDBFalse.R +++ b/bin/VarTierDiseaseDBFalse.R @@ -5,16 +5,16 @@ # Update on 2021.Sep13 #' Updates: #' 1. corrected chromosome number 23->X, 24-> Y -#' 2. add interaction term of phenotype and Tier. +#' 2. add interaction term of phenotype and Tier. #' 3. for non-coding var, if ClinVarClinSig == conflict -> push the Tier higher (if IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict, Tier = Tier + 1) #' 4. Keep all variant that in-trans (sudo) with a strong Var in the same gene, adjust the Tier to 3.5? # Updates on 2022.Apr5 #' 1. remove the pre-processing of OMIM inheritance to VarTier_preprocessOMIM.R #' 2. Add the number of variants (H/M/L impact) per gene: "No.Var.HM","No.Var.H","No.Var.M","No.Var.L" -#' 3. Add the option if IncludeClinVarHGMD -#' +#' 3. Add the option if IncludeClinVarHGMD +#' #' Updates on 2022.Sep29 -#' ClinVar clinvarSignDesc is in dif format after optimizing the pipeline, update the code to be compatible for that +#' ClinVar clinvarSignDesc is in dif format after optimizing the pipeline, update the code to be compatible for that #' gnomadAF, gnomadAFg is "-" not "NA", update the code for that #' add IncludeClinVarHGMD for line 101 @@ -22,37 +22,27 @@ #' change genemap2.Inh into 2 features, AD, AR, save to tsv file #' keep GeneID == "-" assinge Tier value #' input output /out/explain/ "ID" + "_Tier" -#' +#' # parameter settings #### + # set adj.k value to adjust Tier adj.k <- 1.05 # set if diseases DB will be included IncludeClinVarHGMD <- FALSE -# load database: OMIM inheritance #### -# genemap2.Inh.F <- readRDS(file.path("/run/genemap2.Inh.F.rds")) -#genemap2.Inh.F <- read.delim2(file.path("var_tier/hg19/genemap2.Inh.F.txt")) -# genemap2.Inh.F <- read.delim2("/houston_10t/dongxue/MARRVEL_AI/resource/OMIM/out/genemap2.Inh.F.txt") -args = commandArgs(trailingOnly=TRUE) +args <- commandArgs(trailingOnly = TRUE) refg <- args[1] -# load database: OMIM inheritance #### -# genemap2.Inh.F <- readRDS(file.path("/run/genemap2.Inh.F.rds")) -genemap2.Inh.F <- read.delim2(file.path(paste0('var_tier/',refg,'/genemap2.Inh.F.txt'))) - -# set the input output file / directory #### - - -# in.f.path <- "/out/input/" - -# When test the code without docker -#in.f.path <- out.f.path <- "/out/" +# load database: OMIM inheritance #### +genemap2.Inh.F <- read.delim2( + file.path(paste0("var_tier/", refg, "/genemap2.Inh.F.txt")) + ) in.f <- "scores.csv" -out.f <-"Tier.v2.tsv" +out.f <- "Tier.v2.tsv" # load libraries #### library("data.table") @@ -61,61 +51,97 @@ library("dplyr") # load the input #### -anno.columns <- c('varId_dash','zyg','geneSymbol','geneEnsId','gnomadAF','gnomadAFg','omimSymptomSimScore','hgmdSymptomSimScore', - 'IMPACT','Consequence','hgmdVarFound','clinvarSignDesc','spliceAImax') - -anno.orig <- read.table(in.f, stringsAsFactors = F, sep = ",", header = T) -# To test: -# score.path <- "/houston_20t/chaozhong/MARRVEL_AI_2022/explain/906246_raw.csv" -# score.path <- "/houston_20t/chaozhong/MARRVEL_AI_2022/opt_09302022/output/rami-test/906246_scores.csv" -# score.path <- "/houston_30t/chaozhong/NDV_model/fake_VCF_test/out/rami-test/NDVG_causal_scores.csv" -# anno.orig <- read.csv(score.path) -anno <- anno.orig[,anno.columns] +anno.columns <- c( + "varId_dash", + "zyg", + "geneSymbol", + "geneEnsId", + "gnomadAF", + "gnomadAFg", + "omimSymptomSimScore", + "hgmdSymptomSimScore", + "IMPACT", + "Consequence", + "hgmdVarFound", + "clinvarSignDesc", + "spliceAImax" +) + +anno.orig <- read.table(in.f, + stringsAsFactors = F, + sep = ",", + header = T) +anno <- anno.orig[, anno.columns] # rename the col -colnames(anno) <- c("Uploaded_variation","GT","SYMBOL","Gene","gnomadAF","gnomadAFg", - "omimSymptomSimScore", "hgmdSymptomSimScore","IMPACT", - "Consequence","hgmdVarFound","clinvarSignDesc","spliceAImax") +colnames(anno) <- c( + "Uploaded_variation", + "GT", + "SYMBOL", + "Gene", + "gnomadAF", + "gnomadAFg", + "omimSymptomSimScore", + "hgmdSymptomSimScore", + "IMPACT", + "Consequence", + "hgmdVarFound", + "clinvarSignDesc", + "spliceAImax" +) if ("-" %in% anno$Gene) { # for rows without gene ID -> Assign Tier now: - anno_noGeneID <- anno[anno$Gene == "-",c("Uploaded_variation","Gene","IMPACT")] - # convert IMPACT to numeric value - anno_noGeneID$IMPACT.max <- ifelse(anno_noGeneID$IMPACT == "MODIFIER", 1, ifelse(anno_noGeneID$IMPACT == "LOW", 2, ifelse(anno_noGeneID$IMPACT == "MODERATE",3,4)) ) + anno_noGeneID <- anno[anno$Gene == "-", c("Uploaded_variation", "Gene", "IMPACT")] + anno_noGeneID$IMPACT.max <- ifelse( + anno_noGeneID$IMPACT == "MODIFIER", + 1, + ifelse( + anno_noGeneID$IMPACT == "LOW", + 2, + ifelse(anno_noGeneID$IMPACT == "MODERATE", 3, 4) + ) + ) anno_noGeneID$IMPACT <- NULL - # "TierAD","TierAR","TierAR.adj" anno_noGeneID$TierAD <- 5 - anno_noGeneID$IMPACT.max - anno_noGeneID[,c("TierAR","TierAR.adj")] <- ifelse(anno_noGeneID$IMPACT.max >= 3, 3,4) + anno_noGeneID[, c("TierAR", "TierAR.adj")] <- ifelse(anno_noGeneID$IMPACT.max >= 3, 3, 4) anno_noGeneID$No.Var.HM <- as.numeric(anno_noGeneID$IMPACT.max >= 3) anno_noGeneID$No.Var.H <- as.numeric(anno_noGeneID$IMPACT.max == 4) anno_noGeneID$No.Var.M <- as.numeric(anno_noGeneID$IMPACT.max == 3) - anno_noGeneID$No.Var.L <- as.numeric(anno_noGeneID$IMPACT.max <=2) + anno_noGeneID$No.Var.L <- as.numeric(anno_noGeneID$IMPACT.max <= 2) } # For rows with Gene ID -> Tier analysis -anno <- anno[anno$Gene != "-",] +anno <- anno[anno$Gene != "-", ] anno <- unique(anno) - + # Tier #### # STEP 0. Adjust variant IMPACT for ClinVar, HGMD, SpliceAImax >= 0.8 #### -if (IncludeClinVarHGMD){ - anno$IMPACT <- ifelse(grepl("Pathogenic|Likely_pathogenic",anno$clinvarSignDesc) | anno$hgmdVarFound == 1 , "HIGH",anno$IMPACT) +if (IncludeClinVarHGMD) { + anno$IMPACT <- ifelse( + grepl("Pathogenic|Likely_pathogenic", anno$clinvarSignDesc) | + anno$hgmdVarFound == 1, + "HIGH", + anno$IMPACT + ) } # label Splice AI > 0.8 as HIGH -anno$IMPACT <- ifelse(anno$spliceAImax >=0.8, "HIGH",anno$IMPACT) +anno$IMPACT <- ifelse(anno$spliceAImax >= 0.8, "HIGH", anno$IMPACT) # convert IMPACT to numeric value -anno$IMPACT.no <- ifelse(anno$IMPACT == "MODIFIER", 1, ifelse(anno$IMPACT == "LOW", 2, ifelse(anno$IMPACT == "MODERATE",3,4)) ) - +anno$IMPACT.no <- ifelse(anno$IMPACT == "MODIFIER", + 1, + ifelse(anno$IMPACT == "LOW", 2, ifelse(anno$IMPACT == "MODERATE", 3, 4))) + # Implement the rule based Tier classification #### - + # take IMPACT HIGH, MODERATE, LOW, or MODIFIER as H,M,L,L # Tier at Gene level @@ -123,30 +149,35 @@ anno$IMPACT.no <- ifelse(anno$IMPACT == "MODIFIER", 1, ifelse(anno$IMPACT == "LO # STEP 1.1 Keep Genes with IMPACT H or M variants Gene.all <- unique(anno$Gene) -Gene.IMPACT <- unique(anno[,c("Gene","IMPACT")]) -Gene.IMPACT.HM <- Gene.IMPACT[Gene.IMPACT$IMPACT %in% c("HIGH","MODERATE"),] - +Gene.IMPACT <- unique(anno[, c("Gene", "IMPACT")]) +Gene.IMPACT.HM <- Gene.IMPACT[Gene.IMPACT$IMPACT %in% c("HIGH", "MODERATE"), ] + # GeneTier4 will be removed for remaining Tier analysis GeneTier4 <- Gene.all[!(Gene.all %in% Gene.IMPACT.HM$Gene)] -anno$TierAD[anno$Gene %in% GeneTier4] = anno$TierAR[anno$Gene %in% GeneTier4] <- 4 +anno$TierAD[anno$Gene %in% GeneTier4] <- anno$TierAR[anno$Gene %in% GeneTier4] <- 4 -# STEP 1.2 Keep Var with max IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict +# STEP 1.2 Keep Var with max IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict # note: many variants with “Conflicting” are non-coding and coding for different transcripts, only keep the var with max impact as MODIFIER # find the non-Coding Var to keep nonCodingVar2keep <- c() -if(IncludeClinVarHGMD){ - nonCodingModifier.idx <- which(grepl("Conflicting_interpretations_of_pathogenicity", anno$clinvarSignDesc) & (anno$IMPACT %in% c("MODIFIER"))) +if (IncludeClinVarHGMD) { + nonCodingModifier.idx <- which( + grepl( + "Conflicting_interpretations_of_pathogenicity", + anno$clinvarSignDesc + ) & (anno$IMPACT %in% c("MODIFIER")) + ) nonCodingModifier <- anno$Uploaded_variation[nonCodingModifier.idx] - nonCodingModifier.df <- anno[anno$Uploaded_variation %in% nonCodingModifier,] + nonCodingModifier.df <- anno[anno$Uploaded_variation %in% nonCodingModifier, ] - for(var in unique(nonCodingModifier.df$Uploaded_variation)){ - var.df <- nonCodingModifier.df[nonCodingModifier.df$Uploaded_variation == var,] + for (var in unique(nonCodingModifier.df$Uploaded_variation)) { + var.df <- nonCodingModifier.df[nonCodingModifier.df$Uploaded_variation == var, ] var.impact <- unique(var.df$IMPACT) - if(length(var.impact) == 1 ){ - if(var.impact == "MODIFIER"){ - nonCodingVar2keep <- c(nonCodingVar2keep,var) + if (length(var.impact) == 1) { + if (var.impact == "MODIFIER") { + nonCodingVar2keep <- c(nonCodingVar2keep, var) } } } @@ -157,19 +188,20 @@ if(IncludeClinVarHGMD){ # Run Full Tier analysis: # non - GeneTier4 and nonCodingVar2keep -idx <- is.na(anno$TierAD) | (anno$Uploaded_variation %in% nonCodingVar2keep) +idx <- is.na(anno$TierAD) | + (anno$Uploaded_variation %in% nonCodingVar2keep) Var.RunTier <- unique(anno$Uploaded_variation[idx]) # Only Count No.Var.L： # Others: # check if there are variants without Tier ParseVar.NoTier <- F -if(F %in% idx){ +if (F %in% idx) { ParseVar.NoTier <- T Var.NoTier <- unique(anno$Uploaded_variation[!idx]) } -anno.f1 <- anno[idx,] +anno.f1 <- anno[idx, ] # set nonCodingVar2keep IMPACT as 3 anno.f1$IMPACT.no[anno.f1$Uploaded_variation %in% nonCodingVar2keep] <- 3 @@ -181,44 +213,61 @@ VEP.f1 <- anno.f1 # for Gene-Var pair with multiple IMPACT, take the MAX VEP.f1.maxIMAPCT <- data.frame(matrix(ncol = ncol(VEP.f1) + 1, nrow = 0)) -colnames(VEP.f1.maxIMAPCT) <- c(colnames(VEP.f1),"IMPACT.max") -for (var in Var.RunTier){ - var.df <- VEP.f1[VEP.f1$Uploaded_variation == var,] +colnames(VEP.f1.maxIMAPCT) <- c(colnames(VEP.f1), "IMPACT.max") +for (var in Var.RunTier) { + var.df <- VEP.f1[VEP.f1$Uploaded_variation == var, ] # check no. of gene for this variant - if(length(unique(var.df$Gene)) == 1){ + if (length(unique(var.df$Gene)) == 1) { var.df$IMPACT.max <- max(var.df$IMPACT.no) - VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT,var.df) + VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT, var.df) } else { - for (gene in unique(var.df$Gene)){ - var.g.df <- var.df[var.df$Gene == gene,] + for (gene in unique(var.df$Gene)) { + var.g.df <- var.df[var.df$Gene == gene, ] var.g.df$IMPACT.max <- max(var.g.df$IMPACT.no) - VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT,var.g.df) + VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT, var.g.df) } } } - + # find all intronic Var with AF == 0, will adjust the Tier if in trans with a high impact variant. #### -rareVar <- VEP.f1.maxIMAPCT$gnomadAF == "-" & VEP.f1.maxIMAPCT$gnomadAFg == "-" -intronicVar <- grepl("intron_variant",VEP.f1.maxIMAPCT$Consequence) & VEP.f1.maxIMAPCT$IMPACT.max == 1 -rareIntronicVar <- VEP.f1.maxIMAPCT[rareVar & intronicVar, c("Uploaded_variation","Gene")] +rareVar <- VEP.f1.maxIMAPCT$gnomadAF == "-" & + VEP.f1.maxIMAPCT$gnomadAFg == "-" +intronicVar <- grepl("intron_variant", VEP.f1.maxIMAPCT$Consequence) & + VEP.f1.maxIMAPCT$IMPACT.max == 1 +rareIntronicVar <- VEP.f1.maxIMAPCT[rareVar & + intronicVar, c("Uploaded_variation", "Gene")] rareIntronicVar <- unique(rareIntronicVar) # for each Variant keep the most severe IMPACT only -VEP.f2 <- unique(VEP.f1.maxIMAPCT[,c("Uploaded_variation", "SYMBOL","Gene","GT","IMPACT.max","omimSymptomSimScore","hgmdSymptomSimScore")]) +VEP.f2 <- unique( + VEP.f1.maxIMAPCT[, c("Uploaded_variation", + "SYMBOL", + "Gene", + "GT", + "IMPACT.max", + "omimSymptomSimScore", + "hgmdSymptomSimScore")]) # STEP 3: assign Tiers #### - + # VEP.f2$GT <- ifelse(VEP.f2$GT=="HET", "0/1","1/1") -# though Tier classification is Gene based, Tier should be assigned to each Variant - -VEP.Tier.part1 <- data.frame(matrix(ncol = ncol(VEP.f2) + 6, nrow = 0)) -colnames(VEP.Tier.part1) <- c(colnames(VEP.f2),"TierAR","TierAD","No.Var.HM","No.Var.H","No.Var.M","No.Var.L") +# though Tier classification is Gene based, Tier should be assigned to each Variant -for (g in unique(VEP.f2$Gene)){ - - g.df <- VEP.f2[VEP.f2$Gene == g,] +VEP.Tier.part1 <- data.frame(matrix(ncol = ncol(VEP.f2) + 6, nrow = 0)) +colnames(VEP.Tier.part1) <- c( + colnames(VEP.f2), + "TierAR", + "TierAD", + "No.Var.HM", + "No.Var.H", + "No.Var.M", + "No.Var.L" +) + +for (g in unique(VEP.f2$Gene)) { + g.df <- VEP.f2[VEP.f2$Gene == g, ] # X linked, seudo autosomal region -> treat as autosomal # if (sum(grepl("X",g.df$Uploaded_variation)) >0) { @@ -226,60 +275,61 @@ for (g in unique(VEP.f2$Gene)){ # } # AD regardless of the GT - g.df$TierAD <- ifelse(g.df$IMPACT.max <3, 4, 5-g.df$IMPACT.max) + g.df$TierAD <- ifelse(g.df$IMPACT.max < 3, 4, 5 - g.df$IMPACT.max) # AR g.df2 <- g.df # duplicate rows with HOM var - if ("HOM" %in% g.df$GT){ - g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1",]) + if ("HOM" %in% g.df$GT) { + g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1", ]) } IMPACT.max <- g.df2$IMPACT.max - if(sum(IMPACT.max==4) >=2){ + if (sum(IMPACT.max == 4) >= 2) { # >=2 strong alleles - g.df$TierAR <- 5-g.df$IMPACT.max + g.df$TierAR <- 5 - g.df$IMPACT.max # update 202221005: H+H: M-> change Tie from 2 to 1.5 - g.df$TierAR[g.df$IMPACT.max ==3] <- 1.5 - } else if (sum(IMPACT.max==4) ==1 & sum(IMPACT.max==3) >=1) { + g.df$TierAR[g.df$IMPACT.max == 3] <- 1.5 + } else if (sum(IMPACT.max == 4) == 1 & + sum(IMPACT.max == 3) >= 1) { # H + M - g.df$TierAR[g.df$IMPACT.max >=3] <- 1.5 # (need more thought, 2 or 1.5 ???) - g.df$TierAR[g.df$IMPACT.max <3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max <3] - } else if (sum(IMPACT.max==4) ==1 ) { + g.df$TierAR[g.df$IMPACT.max >= 3] <- 1.5 # (need more thought, 2 or 1.5 ???) + g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] + } else if (sum(IMPACT.max == 4) == 1) { # H only - g.df$TierAR[g.df$IMPACT.max ==4] <- 3 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max <=3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max <=3] - } else if (sum(IMPACT.max==3) >=2 ) { + g.df$TierAR[g.df$IMPACT.max == 4] <- 3 # (need more thought, 2 or 3 ???) + g.df$TierAR[g.df$IMPACT.max <= 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max <= 3] + } else if (sum(IMPACT.max == 3) >= 2) { # M + M - g.df$TierAR[g.df$IMPACT.max ==3] <- 2 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max < 3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max < 3] - } else if (sum(IMPACT.max==3) == 1 ) { + g.df$TierAR[g.df$IMPACT.max == 3] <- 2 # (need more thought, 2 or 3 ???) + g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] + } else if (sum(IMPACT.max == 3) == 1) { # M only - g.df$TierAR[g.df$IMPACT.max ==3] <- 3 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max < 3] <- 5-g.df$IMPACT.max[g.df$IMPACT.max < 3] + g.df$TierAR[g.df$IMPACT.max == 3] <- 3 # (need more thought, 2 or 3 ???) + g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] } - # count the number of high IMPACT var per gene. - No.Var.HM <- sum(IMPACT.max >=3) + # count the number of high IMPACT var per gene. + No.Var.HM <- sum(IMPACT.max >= 3) No.Var.H <- sum(IMPACT.max == 4) - No.Var.M <- sum(IMPACT.max ==3) + No.Var.M <- sum(IMPACT.max == 3) No.Var.L <- sum(IMPACT.max == 2) g.df$No.Var.HM <- No.Var.HM g.df$No.Var.H <- No.Var.H g.df$No.Var.M <- No.Var.M g.df$No.Var.L <- No.Var.L - VEP.Tier.part1 <- rbind(VEP.Tier.part1,g.df) + VEP.Tier.part1 <- rbind(VEP.Tier.part1, g.df) } - + # STEP 4: Find all genes with No.Var.H == 1, adjust the Tier for rareIntronicVar #### -if (IncludeClinVarHGMD){ +if (IncludeClinVarHGMD) { gene.w1HighVar <- VEP.Tier.part1$Gene[VEP.Tier.part1$No.Var.H == 1] - rareIntronicVar.2adj <- rareIntronicVar[rareIntronicVar$Gene %in% gene.w1HighVar,] + rareIntronicVar.2adj <- rareIntronicVar[rareIntronicVar$Gene %in% gene.w1HighVar, ] # adjust the Tier for rareIntronicVar based on similarity - phenoSimi <- unique(VEP.f1.maxIMAPCT[,c("Gene","omimSymptomSimScore","hgmdSymptomSimScore")]) + phenoSimi <- unique(VEP.f1.maxIMAPCT[, c("Gene", "omimSymptomSimScore", "hgmdSymptomSimScore")]) # check for non numeric values and convert to 0 omim_NA_idx <- which(is.na(as.numeric(phenoSimi$omimSymptomSimScore))) @@ -291,31 +341,50 @@ if (IncludeClinVarHGMD){ phenoSimi$omimSymptomSimScore <- as.numeric(phenoSimi$omimSymptomSimScore) phenoSimi$hgmdSymptomSimScore <- as.numeric(phenoSimi$hgmdSymptomSimScore) - phenoSimi <- phenoSimi[phenoSimi$omimSymptomSimScore != 0 | phenoSimi$hgmdSymptomSimScore != 0,] + phenoSimi <- phenoSimi[phenoSimi$omimSymptomSimScore != 0 | + phenoSimi$hgmdSymptomSimScore != 0, ] - rareIntronicVar.2adj.wPhenoSimi <- merge(rareIntronicVar.2adj, phenoSimi, by.x = "Gene", by.y = "Gene") + rareIntronicVar.2adj.wPhenoSimi <- merge(rareIntronicVar.2adj, + phenoSimi, + by.x = "Gene", + by.y = "Gene") rareIntronicVar.2adj.wPhenoSimi$simi <- apply(rareIntronicVar.2adj.wPhenoSimi[, 3:4], 1, max) - rareIntronicVar.2adj.wPhenoSimi$TierAR.adj <- 5-(5-4)*exp(adj.k*rareIntronicVar.2adj.wPhenoSimi$simi) + rareIntronicVar.2adj.wPhenoSimi$TierAR.adj <- 5 - (5 - 4) * exp(adj.k * rareIntronicVar.2adj.wPhenoSimi$simi) - rareIntronicVar.df <- rareIntronicVar.2adj.wPhenoSimi[,c("Uploaded_variation","Gene","TierAR.adj")] - VEP.Tier.part1 <- merge(VEP.Tier.part1,rareIntronicVar.df, - by.x <- c("Uploaded_variation","Gene"), - by.y = c("Uploaded_variation","Gene"), - all.x = T) + rareIntronicVar.df <- rareIntronicVar.2adj.wPhenoSimi[, c("Uploaded_variation", "Gene", "TierAR.adj")] + VEP.Tier.part1 <- merge( + VEP.Tier.part1, + rareIntronicVar.df, + by.x <- c("Uploaded_variation", "Gene"), + by.y = c("Uploaded_variation", "Gene"), + all.x = T + ) } else { VEP.Tier.part1$TierAR.adj <- VEP.Tier.part1$TierAR } # add the TierAR.adj to the VEP.Tier df -VEP.Tier.part1 <- VEP.Tier.part1[,c("Uploaded_variation","Gene","GT","IMPACT.max","TierAD","TierAR","TierAR.adj","No.Var.HM","No.Var.H","No.Var.M","No.Var.L")] +VEP.Tier.part1 <- VEP.Tier.part1[, c( + "Uploaded_variation", + "Gene", + "GT", + "IMPACT.max", + "TierAD", + "TierAR", + "TierAR.adj", + "No.Var.HM", + "No.Var.H", + "No.Var.M", + "No.Var.L" +)] # STEP 5: add the No.Var for Var.NoTier #### -if (ParseVar.NoTier){ - VEP.Tier.part2 <- anno[anno$Uploaded_variation %in% Var.NoTier,c("Uploaded_variation","Gene","GT")] +if (ParseVar.NoTier) { + VEP.Tier.part2 <- anno[anno$Uploaded_variation %in% Var.NoTier, c("Uploaded_variation", "Gene", "GT")] VEP.Tier.part2$IMPACT.max <- 1 - VEP.Tier.part2[,c("TierAD","TierAR","TierAR.adj")] <- 4 - VEP.Tier.part2[,c("No.Var.HM","No.Var.H","No.Var.M","No.Var.L")] <- NA + VEP.Tier.part2[, c("TierAD", "TierAR", "TierAR.adj")] <- 4 + VEP.Tier.part2[, c("No.Var.HM", "No.Var.H", "No.Var.M", "No.Var.L")] <- NA VEP.Tier <- rbind(VEP.Tier.part1, VEP.Tier.part2) } else { @@ -323,28 +392,27 @@ if (ParseVar.NoTier){ } VEP.Tier.wGene <- data.frame(matrix(ncol = ncol(VEP.Tier), nrow = 0)) -for (g in unique(VEP.Tier$Gene)){ - - g.df <- VEP.Tier[VEP.Tier$Gene == g,] +for (g in unique(VEP.Tier$Gene)) { + g.df <- VEP.Tier[VEP.Tier$Gene == g, ] # AR g.df2 <- g.df # duplicate rows with HOM var - if ("HOM" %in% g.df$GT){ - g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1",]) + if ("HOM" %in% g.df$GT) { + g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1", ]) } IMPACT.max <- g.df2$IMPACT.max - No.Var.HM <- sum(IMPACT.max >=3) + No.Var.HM <- sum(IMPACT.max >= 3) No.Var.H <- sum(IMPACT.max == 4) - No.Var.M <- sum(IMPACT.max ==3) + No.Var.M <- sum(IMPACT.max == 3) No.Var.L <- sum(IMPACT.max == 2) g.df$No.Var.HM <- No.Var.HM g.df$No.Var.H <- No.Var.H g.df$No.Var.M <- No.Var.M g.df$No.Var.L <- No.Var.L - VEP.Tier.wGene <- rbind(VEP.Tier.wGene,g.df) + VEP.Tier.wGene <- rbind(VEP.Tier.wGene, g.df) } # remove the GT column VEP.Tier.wGene$GT <- NULL @@ -358,21 +426,33 @@ VEP.Tier.final <- VEP.Tier.wGene colnames(VEP.Tier.final)[colnames(VEP.Tier.final) == "IMPACT.max"] <- "IMPACT.from.Tier" VEP.Tier.final$TierAR.adj[is.na(VEP.Tier.final$TierAR.adj)] <- VEP.Tier.final$TierAR[is.na(VEP.Tier.final$TierAR.adj)] - + # add inheritance information colnames(genemap2.Inh.F)[1] <- "Gene" -VEP.Tier.wInh <- merge(VEP.Tier.final, genemap2.Inh.F, by.x = "Gene", by.y = "Gene", all.x = T) +VEP.Tier.wInh <- merge( + VEP.Tier.final, + genemap2.Inh.F, + by.x = "Gene", + by.y = "Gene", + all.x = T +) VEP.Tier.wInh$dominant[is.na(VEP.Tier.wInh$dominant)] <- 0 VEP.Tier.wInh$recessive[is.na(VEP.Tier.wInh$recessive)] <- 0 -VEP.Tier.wInh$AD.matched <- ifelse(VEP.Tier.wInh$TierAD <=2 & (VEP.Tier.wInh$dominant == 1),1,0) -VEP.Tier.wInh$AR.matched <- ifelse(VEP.Tier.wInh$TierAR <=2 & (VEP.Tier.wInh$recessive == 1),1,0) - -write.table(VEP.Tier.wInh,out.f, sep = "\t", quote = F, col.names = T, row.names = F) +VEP.Tier.wInh$AD.matched <- ifelse(VEP.Tier.wInh$TierAD <= 2 & + (VEP.Tier.wInh$dominant == 1), 1, 0) +VEP.Tier.wInh$AR.matched <- ifelse(VEP.Tier.wInh$TierAR <= 2 & + (VEP.Tier.wInh$recessive == 1), 1, 0) + +write.table( + VEP.Tier.wInh, + out.f, + sep = "\t", + quote = F, + col.names = T, + row.names = F +) # write.table(VEP.Tier.wInh,"/houston_20t/dongxue/MARRVEL_AI/V2/FeatureEng/TestOut/906246_Tier.tsv", sep = "\t", quote = F, col.names = T, row.names = F) # write.table(VEP.Tier.wInh,"/houston_30t/dongxue/AI/UDN_Sample_2023/ModifiedAIMCode/NDVG_causal_Tier.v2.tsv", sep = "\t", quote = F, col.names = T, row.names = F) ### Plot for Tier adjustment #### - - - diff --git a/bin/phenoSim.R b/bin/phenoSim.R index b04f099..d284229 100755 --- a/bin/phenoSim.R +++ b/bin/phenoSim.R @@ -1,25 +1,26 @@ #!/usr/bin/env Rscript -rm(list=ls()) +rm(list = ls()) library(ontologyIndex) library(ontologySimilarity) -args = commandArgs(trailingOnly=TRUE) +args <- commandArgs(trailingOnly = TRUE) -PATIENT_HPO=args[1] -OMIM_HGMD=args[2] -OMIM_OBO=args[3] -OMIM_GENEMAP2=args[4] -OMIM_PHENO=args[5] -OUTFILE_CZ_NAME=args[6] -OUTFILE_DX_NAME=args[7] +PATIENT_HPO <- args[1] +OMIM_HGMD <- args[2] +OMIM_OBO <- args[3] +OMIM_GENEMAP2 <- args[4] +OMIM_PHENO <- args[5] +OUTFILE_CZ_NAME <- args[6] +OUTFILE_DX_NAME <- args[7] -dat <- read.csv(OMIM_HGMD,sep='\t') +dat <- read.csv(OMIM_HGMD, sep = "\t") library(dplyr) -get_HPO_list <- function(df1){ +get_HPO_list <- function(df1) { df2 <- df1 %>% dplyr::group_by(acc_num, phen_id, gene_sym) %>% dplyr::summarise(HPO = paste0(hpo_id, collapse = " ")) %>% - dplyr::ungroup() %>% as.data.frame() + dplyr::ungroup() %>% + as.data.frame() df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) return(df2) @@ -27,7 +28,7 @@ get_HPO_list <- function(df1){ # Load HPO_obo -HPO_obo <- get_OBO(OMIM_OBO,propagate_relationships = c('is_a','part_of'), extract_tags = "minimal") +HPO_obo <- get_OBO(OMIM_OBO, propagate_relationships = c("is_a", "part_of"), extract_tags = "minimal") # set simi_thresh simi_thresh <- 0 @@ -35,27 +36,26 @@ simi_thresh <- 0 # In public release, there might be empty HGMD phenotype file if (dim(dat)[1] == 0) { - col_names <- c('acc_num', 'phen_id', 'gene_sym', 'HPO', 'HPO_list', 'Similarity_Score') + col_names <- c("acc_num", "phen_id", "gene_sym", "HPO", "HPO_list", "Similarity_Score") dat2 <- data.frame(matrix(nrow = 0, ncol = length(col_names))) - colnames(dat2)<- col_names - + colnames(dat2) <- col_names } else { - dat <- dat[!is.na(dat$hpo_id),] + dat <- dat[!is.na(dat$hpo_id), ] dat2_ori <- get_HPO_list(dat) dat2 <- dat2_ori - + # Load patient HPO - HPO.orig <- read.table(PATIENT_HPO, sep = "\t", fill=T, header=F, stringsAsFactors=F) + HPO.orig <- read.table(PATIENT_HPO, sep = "\t", fill = T, header = F, stringsAsFactors = F) HPO <- HPO.orig$V1 # remove terms without a HPO ID HPO <- HPO[grepl("HP:", HPO)] HPO <- list(HPO) - sim_mat <- get_asym_sim_grid(HPO, dat2$HPO_list, ontology=HPO_obo) + sim_mat <- get_asym_sim_grid(HPO, dat2$HPO_list, ontology = HPO_obo) dat2$Similarity_Score <- as.vector(sim_mat) dat2$HPO_list <- unlist(lapply(dat2$HPO_list, function(x) paste0(unlist(x), collapse = "|"))) - dat2 <- dat2[order(dat2$Similarity_Score, decreasing = T),] + dat2 <- dat2[order(dat2$Similarity_Score, decreasing = T), ] } @@ -65,37 +65,38 @@ write.table(dat2, OUTFILE_CZ_NAME, sep = "\t", quote = F, row.names = F) # Load OMIM gene-disease data -genemap2 <-readRDS(OMIM_GENEMAP2) +genemap2 <- readRDS(OMIM_GENEMAP2) ## ---- Load OMIM Phenotype ---- -HPO_orig <- read.table(OMIM_PHENO, sep="\t", header= T, stringsAsFactors = FALSE, comment.char = "", fill = TRUE, quote = "\"") -OMIM_HPO <- HPO_orig[,c('OMIM_ID', 'DiseaseName', 'HPO_ID')] +HPO_orig <- read.table(OMIM_PHENO, sep = "\t", header = T, stringsAsFactors = FALSE, comment.char = "", fill = TRUE, quote = "\"") +OMIM_HPO <- HPO_orig[, c("OMIM_ID", "DiseaseName", "HPO_ID")] # rename colnames -colnames(OMIM_HPO) = c("OMIM_ID", "Disease_Name", "HPO_ID") +colnames(OMIM_HPO) <- c("OMIM_ID", "Disease_Name", "HPO_ID") OMIM_HPO_cl <- unique(OMIM_HPO) -# Function to get_HPO_list -get_HPO_list <- function(df1){ - df2 <- df1 %>% - dplyr::group_by(OMIM_ID, Disease_Name) %>% - dplyr::summarise(HPO = paste0(HPO_ID, collapse = " ")) %>% - dplyr::ungroup() %>% as.data.frame() +# Function to get_HPO_list +get_HPO_list <- function(df1) { + df2 <- df1 %>% + dplyr::group_by(OMIM_ID, Disease_Name) %>% + dplyr::summarise(HPO = paste0(HPO_ID, collapse = " ")) %>% + dplyr::ungroup() %>% + as.data.frame() df2$HPO_list <- lapply(df2$HPO, function(i) unlist(strsplit(i, " "))) - #rownames(df2) <- ifelse(is.na(df2$OMIM_ID), df2$Disease_Name, df2$OMIM_ID) + # rownames(df2) <- ifelse(is.na(df2$OMIM_ID), df2$Disease_Name, df2$OMIM_ID) return(df2) } # Prepare OMIM HPO list for ontology similarity comparision -OMIM_HPO_all <- get_HPO_list(OMIM_HPO_cl[, c("OMIM_ID", "Disease_Name","HPO_ID")]) +OMIM_HPO_all <- get_HPO_list(OMIM_HPO_cl[, c("OMIM_ID", "Disease_Name", "HPO_ID")]) -HPO.orig <- read.table(PATIENT_HPO, sep = "\t", fill=T, header=F, stringsAsFactors=F) +HPO.orig <- read.table(PATIENT_HPO, sep = "\t", fill = T, header = F, stringsAsFactors = F) HPO <- HPO.orig$V1 # remove terms without a HPO ID HPO <- HPO[grepl("HP:", HPO)] HPO <- list(HPO) - -sim_mat <- get_asym_sim_grid(HPO, OMIM_HPO_all$HPO_list, ontology=HPO_obo) + +sim_mat <- get_asym_sim_grid(HPO, OMIM_HPO_all$HPO_list, ontology = HPO_obo) OMIM_HPO_all$Similarity_Score <- as.vector(sim_mat) @@ -104,17 +105,16 @@ OMIM_HPO_all$Similarity_Score <- as.vector(sim_mat) OMIM_HPO_all$HPO_term <- unlist(lapply(OMIM_HPO_all$HPO_list, function(x) paste0(HPO_obo$name[unlist(x)], collapse = "|"))) ## Add gene - disease relationship -OMIM_HPO_all_wGene <- merge(unique(genemap2[,c("Pheno_ID","Approved_Gene_Symbol","Ensembl_Gene_ID","Entrez_Gene_ID")]), - OMIM_HPO_all[,c("OMIM_ID","Disease_Name","Similarity_Score","HPO_term")], by.y = "OMIM_ID", by.x = "Pheno_ID") +OMIM_HPO_all_wGene <- merge(unique(genemap2[, c("Pheno_ID", "Approved_Gene_Symbol", "Ensembl_Gene_ID", "Entrez_Gene_ID")]), + OMIM_HPO_all[, c("OMIM_ID", "Disease_Name", "Similarity_Score", "HPO_term")], + by.y = "OMIM_ID", by.x = "Pheno_ID" +) -colnames(OMIM_HPO_all_wGene)[2] <- 'Gene_Symbol' -OMIM_HPO_all_order <-OMIM_HPO_all_wGene[order(OMIM_HPO_all_wGene$Similarity_Score, decreasing = T),] +colnames(OMIM_HPO_all_wGene)[2] <- "Gene_Symbol" +OMIM_HPO_all_order <- OMIM_HPO_all_wGene[order(OMIM_HPO_all_wGene$Similarity_Score, decreasing = T), ] # write.table(OMIM_HPO_all_order, paste0(unknown_disease_path,"HPOsimi_all_",output_file_name), sep = "\t", quote = F, row.names = F) # OMIM_HPO_all_filt <- head(OMIM_HPO_all_order, n = No_candidate) -OMIM_HPO_all_filt <- OMIM_HPO_all_order[OMIM_HPO_all_order$Similarity_Score >= simi_thresh,] +OMIM_HPO_all_filt <- OMIM_HPO_all_order[OMIM_HPO_all_order$Similarity_Score >= simi_thresh, ] write.table(OMIM_HPO_all_filt, OUTFILE_DX_NAME, sep = "\t", quote = F, row.names = F) - - - From 3005db398e983a9f31e23b30e91d68845712da3f Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 30 May 2024 14:05:03 -0500 Subject: [PATCH 32/71] stop tracking .gitignore. --- .gitignore | 11 ----------- 1 file changed, 11 deletions(-) delete mode 100644 .gitignore diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 3d081c6..0000000 --- a/.gitignore +++ /dev/null @@ -1,11 +0,0 @@ -.gitignore -bcftools-1.9.tar.bz2 -run/bedtools -.vscode/ -.DS_Store -*/.DS_Store -.nextflow* -.nextflow/ -work/ -test_data/ -test_ref/ From 64dada687487e4c30e927f1862382111c187025a Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 30 May 2024 14:07:09 -0500 Subject: [PATCH 33/71] update .gitignore. --- .gitignore | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c664424 --- /dev/null +++ b/.gitignore @@ -0,0 +1,17 @@ +.gitignore +bcftools-1.9.tar.bz2 +run/bedtools +.vscode/ +.DS_Store +*/.DS_Store +.nextflow* +.nextflow/ +work/ +test_data/ +test_ref/ +test_output/ +trace* +**/__pycache__ +report-*html +trace-*txt + From 8b7b975be632dcd21c7603c4317d83cef682a97f Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 30 May 2024 14:07:53 -0500 Subject: [PATCH 34/71] update .gitignore. --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index c664424..c7c2aaa 100644 --- a/.gitignore +++ b/.gitignore @@ -14,4 +14,6 @@ trace* **/__pycache__ report-*html trace-*txt +.nextflow/ +work/ From 304b82f2777dd942ceb934df519dd1c76249547e Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 6 Jun 2024 22:56:42 -0500 Subject: [PATCH 35/71] edit to preserve the selected variants. --- bin/extraModel/generate_bivar_data.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index 78cb9c1..300f589 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -91,7 +91,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False, n_threa else: print("No recessive variant pair found. End and remove temp files") - shutil.rmtree(f"{tmp_folder}") + # shutil.rmtree(f"{tmp_folder}") def process_gene(param): @@ -116,9 +116,12 @@ def process_gene(param): if len(varIDs) > 6: defaultPred = param["default_pred"].copy() - defaultPred = defaultPred.loc[varIDs, :].sort_values("predict", ascending=False) + + varIDs = sorted(varIDs) + defaultPred = defaultPred.loc[varIDs, :].sort_values(["predict", "IMPACT.from.Tier"], ascending=[False,False]) varIDs = defaultPred.index.tolist()[:6] + gene_feats = feature_df.loc[varIDs, feature_names].copy() gene_feats = gene_feats.values assert gene_feats.shape[0] == len(varIDs) From cda752fb837d5a0ce5aae84083a970a36d84e0e4 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 6 Jun 2024 22:57:07 -0500 Subject: [PATCH 36/71] add TODO --- bin/feature.py | 1 + 1 file changed, 1 insertion(+) diff --git a/bin/feature.py b/bin/feature.py index 7206dd3..f3cc14a 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -370,6 +370,7 @@ def main(): # print('head:', varDf.head) # print('column names:', varDf.columns) # update the GERp column names since cant use ++ in the varaible name + # TODO: change with `.rename` if "GERP++_RS" in varDf.columns: print("found GERP++RS") varDf["GERPpp_RS"] = varDf["GERP++_RS"] From 6cb5d7611faf41a05d5b07c282af4a09760e53c5 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 6 Jun 2024 22:57:29 -0500 Subject: [PATCH 37/71] change output names to have run_id. --- main.nf | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/main.nf b/main.nf index abdd8fb..22fd422 100644 --- a/main.nf +++ b/main.nf @@ -53,10 +53,10 @@ process VCF_PRE_PROCESS { bcftools annotate --rename-chrs $chrmap -x ID $vcf -Oz -o ${params.run_id}-annot # Annotate with new IDs based on CHROM, POS, REF, ALT - bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o add-id.vcf.gz + bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o ${params.run_id}-add-id.vcf.gz #run quality filters - bcftools filter add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o ${params.run_id}.filt.vcf.gz + bcftools filter ${params.run_id}-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o ${params.run_id}.filt.vcf.gz #check number of variants left variant_count=\$(bcftools view -H ${params.run_id}.filt.vcf.gz | wc -l) @@ -65,7 +65,7 @@ process VCF_PRE_PROCESS { else echo "The VCF file doesn't have FILTER annotation, or all variants filtered." echo "Pipeline will proceed with unfiltered VCF file." - cp add-id.vcf.gz ${params.run_id}.filt.vcf.gz + cp ${params.run_id}-add-id.vcf.gz ${params.run_id}.filt.vcf.gz fi """ } @@ -101,8 +101,8 @@ process ANNOT_PHRANK { script: """ - zcat $vcf | awk 'substr(\$0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > var.txt - cat var.txt | sed 's/chr//g' | sort -u > ${params.run_id}-var-filt.txt + zcat $vcf | awk 'substr(\$0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > ${params.run_id}-var.txt + cat ${params.run_id}-var.txt | sed 's/chr//g' | sort -u > ${params.run_id}-var-filt.txt """ } @@ -270,8 +270,8 @@ process FEATURE_ENGINEERING_PART1 { // not sure why projectDir is not working output: - path "scores.csv" - path "r1_scores.txt" + path "${params.run_id}_scores.csv" + path "r1_${params.run_id}_scores.txt" script: """ @@ -307,19 +307,19 @@ process FEATURE_ENGINEERING_PART1 { for INDEX in \$(cut -d\$'\\t' -f1 vep_split.txt) do cat scores_\${INDEX}.csv - done > scores.csv + done > ${params.run_id}_scores.csv for INDEX in \$(cut -d\$'\\t' -f1 vep_split.txt) do cat r1_scores_\${INDEX}.txt - done > r1_scores.txt + done > r1_${params.run_id}_scores.txt """ } process FEATURE_ENGINEERING_PART2 { input: - path scores - path r1_scores + path scores, stageAs: "scores.csv" + path r1_scores, stageAs: "r1_scores.txt" path phrank path ref_annot_dir path ref_var_tier_dir @@ -338,7 +338,7 @@ process FEATURE_ENGINEERING_PART2 { } process PREDICTION { - debug true + publishDir "${params.outdir}/prediction/", mode: "copy" input: path matrix @@ -346,9 +346,12 @@ process PREDICTION { path ref_predict_new_dir path ref_model_inputs_dir + output: + path "conf_4Model/*.csv" + path "conf_4Model/integrated/*.csv" + script: """ - mkdir recessive_matrix mkdir final_matrix_expanded mkdir conf_4Model run_final.py ${params.run_id} From a7d28dc0ff443cd3616cbdd74e591801fa485865 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 6 Jun 2024 22:57:40 -0500 Subject: [PATCH 38/71] change nextflow.config --- nextflow.config | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/nextflow.config b/nextflow.config index 9c8b3db..342aafa 100644 --- a/nextflow.config +++ b/nextflow.config @@ -8,7 +8,7 @@ params { input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" input_hpo = "/home/hwan/AI_MARRVEL/test_data/hpos.txt" - outdir = "/home/hwan/AI_MARRVEL/test_output/" + outdir = "/home/hwan/AI_MARRVEL/test_output_060624_2/" // for data dependency chrmap = "${ref_dir}/data_public/bcf_annotate/chrmap.txt" @@ -61,6 +61,7 @@ params { ref_mod5_diffusion_dir = "${ref_dir}/mod5_diffusion" ref_predict_new_dir = "${ref_dir}/predict_new" ref_model_inputs_dir = "${ref_dir}/model_inputs" + } docker.enabled = true @@ -68,4 +69,4 @@ docker.enabled = true process { container = "chaozhongliu/aim-lite:latest" cache = "lenient" -} \ No newline at end of file +} From 0ce5e0b7585585a783439cd98014978ecb22ac08 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Thu, 6 Jun 2024 22:59:20 -0500 Subject: [PATCH 39/71] add run_diff to compare outputs. --- utils/run_diff.py | 89 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 utils/run_diff.py diff --git a/utils/run_diff.py b/utils/run_diff.py new file mode 100644 index 0000000..e06c9e8 --- /dev/null +++ b/utils/run_diff.py @@ -0,0 +1,89 @@ +import pandas as pd +from pandas.api.types import is_numeric_dtype +import numpy as np + +def comp(file_a, file_b, sort = False, skipped_col = []): + print(file_a) + print(file_b) + df_a = pd.read_csv(file_a) if "csv" in file_a else pd.read_csv(file_a, sep="\t") + df_b = pd.read_csv(file_b) if "csv" in file_b else pd.read_csv(file_b, sep="\t") + + if sort: + def sort_df(df): + return df.sort_values(by=df.columns.tolist()).reset_index(drop=True) + + df_a = sort_df(df_a) + df_b = sort_df(df_b) + + assert df_a.shape == df_b.shape, f"The dimensions has to be the same. {df_a.shape} {df_b.shape}" + assert all(df_a.columns == df_b.columns), "The columns have to be identical each other." + + for i, (c, t) in enumerate(zip(df_a.columns, df_a.dtypes)): + if c in skipped_col: continue + + va = df_a[c].values + vb = df_b[c].values + + if is_numeric_dtype(t): + diff = np.isclose(va, vb) + else: + diff = va == vb + + if not all(diff): + if all(pd.isnull(va[~diff])) and all(pd.isnull(vb[~diff])): + diff[~diff] = True + + idx = np.where(~diff) + + assert all(diff), f"The {c} column ({'numeric' if is_numeric_dtype(t) else 'non-numeric'}) has {sum(~diff)} mismatches.\n{idx}\n{va[~diff]}\n{vb[~diff]}" + + print("Two files are identical!") + return None + + + + +file_a = "work/ee/14e106eee477c2b81ca0179455e845/Tier.v2.tsv" +file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker/tier-test-false/12345_Tier.v2.tsv" +comp(file_a, file_b, False) + +file_a = "work/ee/14e106eee477c2b81ca0179455e845/1.matrix.txt" +file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345.matrix.txt" + +comp(file_a, file_b, False) + +file_a = "work/b7/98771d7a080c1794613a028c5e8a3d/final_matrix_expanded/1.expanded.csv.gz" +file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/final_matrix_expanded/12345.expanded.csv.gz" + +comp(file_a, file_b, False, ["identifier"]) + + + +file_a = "work/b7/98771d7a080c1794613a028c5e8a3d/1.csv" +file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/final_matrix/12345.csv" + +comp(file_a, file_b, False, ["identifier"]) + + +#file_a = "work/a5/d5a4ba2afe072b2d4b560b566ec341/conf_4Model/1_default_predictions.csv" +#file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_default_predictions.csv" + +#comp(file_a, file_b, False, ["identifier"]) + + +file_a = "work/fc/cf9599ea4aa75d44af34fdff403f7a/conf_4Model/1_nd_predictions.csv" +file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_nd_predictions.csv" + +comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) + +file_a = "work/fc/cf9599ea4aa75d44af34fdff403f7a/conf_4Model/1_recessive_predictions.csv" +file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_recessive_predictions.csv" + +comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "identifier"]) + + +#file_a = "work/fc/cf9599ea4aa75d44af34fdff403f7a/conf_4Model/integrated/1_integrated.csv" +#file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_integrated.csv" + +#comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) + From c0abdd8cb9236ba1d9708bdeb983eb73d98edfd4 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Mon, 10 Jun 2024 11:46:48 -0500 Subject: [PATCH 40/71] fix the order. --- bin/extraModel/generate_bivar_data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index 300f589..d51d5d3 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -114,11 +114,11 @@ def process_gene(param): if len(varIDs) == 0: return + varIDs = sorted(varIDs) if len(varIDs) > 6: defaultPred = param["default_pred"].copy() - varIDs = sorted(varIDs) - defaultPred = defaultPred.loc[varIDs, :].sort_values(["predict", "IMPACT.from.Tier"], ascending=[False,False]) + defaultPred = defaultPred.loc[varIDs, :].sort_values(["predict", "IMPACT.from.Tier"], ascending=[False,False], kind="stable") varIDs = defaultPred.index.tolist()[:6] From ef362e637f918954281a927be3184eadd546f9c7 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Mon, 10 Jun 2024 11:49:16 -0500 Subject: [PATCH 41/71] clean-up --- utils/run_diff.py | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/utils/run_diff.py b/utils/run_diff.py index e06c9e8..15afbfe 100644 --- a/utils/run_diff.py +++ b/utils/run_diff.py @@ -19,7 +19,8 @@ def sort_df(df): assert all(df_a.columns == df_b.columns), "The columns have to be identical each other." for i, (c, t) in enumerate(zip(df_a.columns, df_a.dtypes)): - if c in skipped_col: continue + if c in skipped_col: + continue va = df_a[c].values vb = df_b[c].values @@ -50,40 +51,38 @@ def sort_df(df): file_a = "work/ee/14e106eee477c2b81ca0179455e845/1.matrix.txt" file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345.matrix.txt" -comp(file_a, file_b, False) +comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) file_a = "work/b7/98771d7a080c1794613a028c5e8a3d/final_matrix_expanded/1.expanded.csv.gz" file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/final_matrix_expanded/12345.expanded.csv.gz" -comp(file_a, file_b, False, ["identifier"]) +comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) file_a = "work/b7/98771d7a080c1794613a028c5e8a3d/1.csv" file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/final_matrix/12345.csv" -comp(file_a, file_b, False, ["identifier"]) - - -#file_a = "work/a5/d5a4ba2afe072b2d4b560b566ec341/conf_4Model/1_default_predictions.csv" -#file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_default_predictions.csv" +comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) -#comp(file_a, file_b, False, ["identifier"]) -file_a = "work/fc/cf9599ea4aa75d44af34fdff403f7a/conf_4Model/1_nd_predictions.csv" +file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/1_nd_predictions.csv" file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_nd_predictions.csv" -comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) +comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) -file_a = "work/fc/cf9599ea4aa75d44af34fdff403f7a/conf_4Model/1_recessive_predictions.csv" +file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/1_recessive_predictions.csv" file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_recessive_predictions.csv" -comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "identifier"]) +comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) +file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/1_nd_recessive_predictions.csv" +file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_nd_recessive_predictions.csv" -#file_a = "work/fc/cf9599ea4aa75d44af34fdff403f7a/conf_4Model/integrated/1_integrated.csv" -#file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_integrated.csv" +comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) -#comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) +file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/integrated/1_integrated.csv" +file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_integrated.csv" +comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) From f846fb35d098d032dc9406788dfb005cb779cdd2 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Mon, 10 Jun 2024 12:11:17 -0500 Subject: [PATCH 42/71] add errorStrategy. --- nextflow.config | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/nextflow.config b/nextflow.config index 342aafa..1988975 100644 --- a/nextflow.config +++ b/nextflow.config @@ -67,6 +67,11 @@ params { docker.enabled = true process { + cpus 16 + memory { 128.GB * task.attempt } + errorStrategy { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } + maxRetries 3 + container = "chaozhongliu/aim-lite:latest" cache = "lenient" } From 0461fbff110c3082729db35b0d358e83ef254189 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Mon, 10 Jun 2024 12:56:42 -0500 Subject: [PATCH 43/71] fix paraemter typos. --- nextflow.config | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/nextflow.config b/nextflow.config index 1988975..9c49183 100644 --- a/nextflow.config +++ b/nextflow.config @@ -67,10 +67,10 @@ params { docker.enabled = true process { - cpus 16 - memory { 128.GB * task.attempt } - errorStrategy { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } - maxRetries 3 + cpus = 16 + memory = { 128.GB * task.attempt } + errorStrategy = { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } + maxRetries = 3 container = "chaozhongliu/aim-lite:latest" cache = "lenient" From b416d009c744342af4f60987b4f80aa0bc6d1f08 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Mon, 10 Jun 2024 12:57:18 -0500 Subject: [PATCH 44/71] clean-up. --- utils/run_diff.py | 43 ++----------------------------------------- 1 file changed, 2 insertions(+), 41 deletions(-) diff --git a/utils/run_diff.py b/utils/run_diff.py index 15afbfe..abe0dd1 100644 --- a/utils/run_diff.py +++ b/utils/run_diff.py @@ -42,47 +42,8 @@ def sort_df(df): return None - - -file_a = "work/ee/14e106eee477c2b81ca0179455e845/Tier.v2.tsv" -file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker/tier-test-false/12345_Tier.v2.tsv" -comp(file_a, file_b, False) - -file_a = "work/ee/14e106eee477c2b81ca0179455e845/1.matrix.txt" -file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345.matrix.txt" - -comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) - -file_a = "work/b7/98771d7a080c1794613a028c5e8a3d/final_matrix_expanded/1.expanded.csv.gz" -file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/final_matrix_expanded/12345.expanded.csv.gz" - -comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) - - - -file_a = "work/b7/98771d7a080c1794613a028c5e8a3d/1.csv" -file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/final_matrix/12345.csv" - -comp(file_a, file_b, False, ["diffuse_Phrank_STRING", "identifier"]) - - - -file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/1_nd_predictions.csv" -file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_nd_predictions.csv" - -comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) - -file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/1_recessive_predictions.csv" -file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_recessive_predictions.csv" - -comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) - -file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/1_nd_recessive_predictions.csv" -file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_nd_recessive_predictions.csv" - -comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) - +""" file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/integrated/1_integrated.csv" file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_integrated.csv" - comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) +""" From 8acac3edcf7b26305f9a459d3281031540592e7c Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 14 Jun 2024 12:59:43 -0500 Subject: [PATCH 45/71] change the process setup for the Feature Engineering Part 1. --- main.nf | 2 -- 1 file changed, 2 deletions(-) diff --git a/main.nf b/main.nf index 22fd422..c15f1a1 100644 --- a/main.nf +++ b/main.nf @@ -260,8 +260,6 @@ process VEP_ANNOTATE { } process FEATURE_ENGINEERING_PART1 { - cpus 10 - memory '64 GB' input: path vep path hgmd_sim From 43ad5d44d5997c26bdf856d3a07217015abc904f Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 14 Jun 2024 13:02:19 -0500 Subject: [PATCH 46/71] change cpu configure for some processes --- main.nf | 1 - 1 file changed, 1 deletion(-) diff --git a/main.nf b/main.nf index c15f1a1..3b8ae75 100644 --- a/main.nf +++ b/main.nf @@ -219,7 +219,6 @@ process FILTER_PROBAND { } process VEP_ANNOTATE { - cpus 10 publishDir "${params.outdir}/vep/", mode: "copy" input: From fc161b3f813550e53defbd6cb70be7ea52d68b5f Mon Sep 17 00:00:00 2001 From: hyunhwan-bcm <166857764+hyunhwan-bcm@users.noreply.github.com> Date: Fri, 14 Jun 2024 13:03:23 -0500 Subject: [PATCH 47/71] Update main.nf --- main.nf | 3 --- 1 file changed, 3 deletions(-) diff --git a/main.nf b/main.nf index 22fd422..3b8ae75 100644 --- a/main.nf +++ b/main.nf @@ -219,7 +219,6 @@ process FILTER_PROBAND { } process VEP_ANNOTATE { - cpus 10 publishDir "${params.outdir}/vep/", mode: "copy" input: @@ -260,8 +259,6 @@ process VEP_ANNOTATE { } process FEATURE_ENGINEERING_PART1 { - cpus 10 - memory '64 GB' input: path vep path hgmd_sim From a706fb165c5b5efca4f2c921f9f5564db13efd0a Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Tue, 18 Jun 2024 13:24:27 -0500 Subject: [PATCH 48/71] Handle MT values as -1 to avoid string comparison (#1) This change ensures that MT (empty) values are treated consistently as -1, improving the speed by eliminating string comparisons. --- bin/add_c_nc.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/bin/add_c_nc.py b/bin/add_c_nc.py index 8c4b74d..e8b42f5 100755 --- a/bin/add_c_nc.py +++ b/bin/add_c_nc.py @@ -26,6 +26,9 @@ def add_c_nc(score, ref): bl = clin_nc.new_start.values bc = clin_nc.new_chr.values + bc[bc=='MT'] = -1 # Treat MT chromosome as NaN values to avoid string comparison + bc = bc.astype(int) + i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) & (ac[:, None] == bc)) cln = pd.concat( From ed361df0dbd318fbf6368325ae07885c671b5826 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Tue, 18 Jun 2024 19:20:19 -0500 Subject: [PATCH 49/71] Refactor prediction step to eliminate multiprocessing (#2) Previously, multiprocessing was utilized in the prediction step, resulting in increased memory consumption proportional to the number of worker processes. By refactoring the code to replace multiprocessing with a simple for loop, memory usage has been significantly reduced. This change not only enhances efficiency by eliminating the overhead of virtual memory but also accelerates runtime performance. --- bin/extraModel/generate_bivar_data.py | 14 +++++--------- bin/extraModel_main.py | 13 ++----------- 2 files changed, 7 insertions(+), 20 deletions(-) diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index d51d5d3..b529ba9 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -4,12 +4,11 @@ from glob import glob from tqdm import tqdm, trange import logging -from multiprocessing import Pool from os.path import exists import shutil -def process_sample(data_folder, sample_id, default_pred, labeling=False, n_thread=10): +def process_sample(data_folder, sample_id, default_pred, labeling=False): # recessive_folder = f'{data_folder}/recessive_matrix' # if not os.path.exists(recessive_folder): @@ -67,15 +66,12 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False, n_threa for gene in gene_dict ] print( - f"Now starting to generate recessive feature matrix for {len(gene_dict)} genes, {feature_df.shape[0]} variants using {n_thread} threads." + f"Now starting to generate recessive feature matrix for {len(gene_dict)} genes, {feature_df.shape[0]} variants." ) - p = Pool(processes=n_thread) - with tqdm(total=len(params)) as pbar: - for result in p.imap_unordered(process_gene, params): - pbar.update() - p.close() - p.join() + for param in tqdm(params): + process_gene(param) + print("Recessive features for each gene finished, now putting together...") bivar_feature_mats = [] diff --git a/bin/extraModel_main.py b/bin/extraModel_main.py index e489951..2e9f0c8 100755 --- a/bin/extraModel_main.py +++ b/bin/extraModel_main.py @@ -16,19 +16,11 @@ parser.add_argument("-id", metavar="I", type=str, help="sample ID") -parser.add_argument( - "-n_cpu", - type=int, - default=10, - help="folders containing all extended final matrices", -) - args = parser.parse_args() # st_time = time() # prj_name = args.project sample_id = args.id -n_cpu = args.n_cpu out_folder = "conf_4Model" @@ -65,7 +57,7 @@ def assign_ranking(df): return pred_df -def AIM(data_folder, sample_id, n_thread): +def AIM(data_folder, sample_id): feature_fn = f"{sample_id}.csv" if not os.path.exists(feature_fn): @@ -98,7 +90,6 @@ def AIM(data_folder, sample_id, n_thread): sample_id=sample_id, default_pred=default_pred, labeling=False, - n_thread=n_thread, ) recessive_feature_file = f"{out_folder}/recessive_matrix/{sample_id}.csv" @@ -131,4 +122,4 @@ def AIM(data_folder, sample_id, n_thread): # for sample_id in tqdm(sample_folders): -AIM(out_folder, sample_id, n_thread=n_cpu) +AIM(out_folder, sample_id) From 9d56554dbcc042344975e94465d8a830cc790b01 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 19 Jun 2024 22:17:56 -0500 Subject: [PATCH 50/71] Optimize duplication checking to index-based comparison (#3) --- bin/extraModel/generate_bivar_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index b529ba9..9843b22 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -184,7 +184,7 @@ def generate_feature_vector(i, j): if len(bivar_feature_mats) > 0: bivar_feature_mats = pd.concat(bivar_feature_mats, axis=0) - bivar_feature_mats = bivar_feature_mats.drop_duplicates() + bivar_feature_mats = bivar_feature_mats[~bivar_feature_mats.index.duplicated()] bivar_feature_mats.to_csv(f"{out_folder}/{gene}.csv") return From 1ac1a55563c0e3e04a64249fd905b90a7dc2a7ed Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Thu, 27 Jun 2024 13:11:39 -0500 Subject: [PATCH 51/71] Optimize process_sample() Function Using Pandas DataFrame (#5) * Optimize process_sample() Function Using Pandas DataFrame The previous implementation of process_sample() relied on Python built-in for-loops, which were less efficient. This update replaces the for-loops with optimized Pandas DataFrame operations to enhance performance. * Remove unused old codes related to process_sample() * Remove an unnecessary line --- bin/extraModel/generate_bivar_data.py | 226 ++++++++++---------------- 1 file changed, 84 insertions(+), 142 deletions(-) diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index 9843b22..b65845a 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -1,5 +1,6 @@ -import pandas as pd import os +import itertools +import pandas as pd import numpy as np from glob import glob from tqdm import tqdm, trange @@ -7,12 +8,9 @@ from os.path import exists import shutil - def process_sample(data_folder, sample_id, default_pred, labeling=False): - - # recessive_folder = f'{data_folder}/recessive_matrix' - # if not os.path.exists(recessive_folder): - # os.mkdir(recessive_folder) + if labeling: + raise 'The below code was not tested with real data with labeling=True' recessive_folder = f"{data_folder}/recessive_matrix/" if not os.path.exists(recessive_folder): @@ -22,7 +20,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): if not os.path.exists(tmp_folder): os.mkdir(tmp_folder) - ## read feature matrix for single var + # read feature matrix for single var feature_fn = f"{sample_id}.csv" # feature_df = [] # for feature_fn in feature_fns: @@ -34,9 +32,8 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): # feature_df = pd.concat(feature_df) feature_df = feature_df.loc[~feature_df.index.duplicated(keep="first")] - ## Group variants by Ens IDs + # Group variants by Ens IDs expanded_fn = f"final_matrix_expanded/{sample_id}.expanded.csv.gz" - gene_dict = {} # for expanded_fn in expanded_fns: if "csv" in expanded_fn: df = pd.read_csv(expanded_fn, sep=",", index_col=0, compression="infer") @@ -46,145 +43,90 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): if df.shape[0] > 100000: df = df.loc[df["IMPACT.from.Tier"] > 1] - for varId, gene in zip(df.index.tolist(), df.geneEnsId.tolist()): - if gene.startswith("ENSG"): - if gene not in gene_dict: - gene_dict[gene] = {varId} - else: - gene_dict[gene].add(varId) - - ## create parameters and run bivar feature generation in parallel - params = [ - { - "feature_df": feature_df, - "default_pred": default_pred, - "labeling": labeling, - "out_folder": f"{tmp_folder}", - "gene": gene, - "varIDs": list(gene_dict[gene]), - } - for gene in gene_dict - ] - print( - f"Now starting to generate recessive feature matrix for {len(gene_dict)} genes, {feature_df.shape[0]} variants." + varId_geneEnsId_df = df[['geneEnsId']] + varId_geneEnsId_df.index.name = 'varId' + + geneEnsId_varId_df = varId_geneEnsId_df[varId_geneEnsId_df.geneEnsId.str.startswith("ENSG")] \ + .reset_index() \ + .sort_values(['geneEnsId', 'varId']) \ + .drop_duplicates() + + # Generate variant pairs for each gene + gene_var_pairs = [] + for geneEnsId, grouped_df in geneEnsId_varId_df.groupby('geneEnsId'): + # Choose upto 6 the most meaningful variants. + if grouped_df.shape[0] > 6: + grouped_df = grouped_df \ + .join(default_pred, on='varId') \ + .sort_values(["predict", "IMPACT.from.Tier"], ascending=[False, False], kind="stable") \ + .head(6) + + gene_var_pairs += [ + {"geneEnsId": geneEnsId, "varId1": pair[0][1].varId, "varId2": pair[1][1].varId} + for pair in itertools.product(grouped_df.iterrows(), grouped_df.iterrows()) + ] + + # Remove all the duplicated variant pairs. + gene_var_pairs_df = pd.DataFrame(gene_var_pairs) + gene_var_pairs_df = gene_var_pairs_df.drop_duplicates(['varId1', 'varId2']) + + # Use only subset columns of features + subset_feature_names = "diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat".split(',') + if labeling: + subset_feature_names.append('is_strong') + subset_feature_df = feature_df[subset_feature_names] + + # Join feature matrix for each variant pairs. + recessive_feature_df = gene_var_pairs_df.join( + subset_feature_df.add_suffix('_1'), + on="varId1" + ).join( + subset_feature_df.add_suffix('_2'), + on="varId2" ) - for param in tqdm(params): - process_gene(param) - - print("Recessive features for each gene finished, now putting together...") - - bivar_feature_mats = [] - fns = glob(tmp_folder + "/*.csv") - for fn in tqdm(fns, total=len(fns)): - df = pd.read_csv(fn, index_col=0) - bivar_feature_mats.append(df) - if bivar_feature_mats: - bivar_feature_mats = pd.concat(bivar_feature_mats) - bivar_feature_mats.to_csv(f"{recessive_folder}/{sample_id}.csv") - print("Recessive features saved, now removing tmp files...") - print("Recessive features generation finished...") - else: - print("No recessive variant pair found. End and remove temp files") - - # shutil.rmtree(f"{tmp_folder}") - - -def process_gene(param): - - feature_names = "diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat" - feature_names = feature_names.split(",") - nFeatures = len(feature_names) - bivar_feature_mats = [] - - gene = param["gene"] - varIDs, labeling = param["varIDs"], param["labeling"] - - feature_df, out_folder = param["feature_df"], param["out_folder"] - - seen_pairs = set() - - allVars = feature_df.index.tolist() - varIDs = [v for v in varIDs if v in allVars] - - if len(varIDs) == 0: - return - - varIDs = sorted(varIDs) - if len(varIDs) > 6: - defaultPred = param["default_pred"].copy() - - defaultPred = defaultPred.loc[varIDs, :].sort_values(["predict", "IMPACT.from.Tier"], ascending=[False,False], kind="stable") - varIDs = defaultPred.index.tolist()[:6] - - - gene_feats = feature_df.loc[varIDs, feature_names].copy() - gene_feats = gene_feats.values - assert gene_feats.shape[0] == len(varIDs) - # print(gene_feats, varIDs) - - def generate_feature_vector(i, j): - fmat = np.zeros((2, nFeatures * 2 + 1)) - dist = np.absolute(int(varIDs[i].split("-")[1]) - int(varIDs[j].split("-")[1])) - - fmat[0] = np.append(np.append(gene_feats[i], gene_feats[j]), [dist]) - fmat[1] = np.append(np.append(gene_feats[j], gene_feats[i]), [dist]) - - f1_features = [f"{f}_1" for f in feature_names] - f2_features = [f"{f}_2" for f in feature_names] + # Remove unnecessary var_i == var_j cases because of zygosity + recessive_feature_df = recessive_feature_df[ + ~((recessive_feature_df.varId1 == recessive_feature_df.varId2) & + (recessive_feature_df.zyg_1 != 2)) + ] - return pd.DataFrame( - data=fmat, - columns=f1_features + f2_features + ["var_dist"], - index=[f"{varIDs[i]}_{varIDs[j]}", f"{varIDs[j]}_{varIDs[i]}"], + # Calculate variant distance + recessive_feature_df['var_dist'] = ( + recessive_feature_df.varId1.str.split('-').apply(lambda x: x[1]).astype(float) + - recessive_feature_df.varId2.str.split('-').apply(lambda x: x[1]).astype(float) + ).abs() + + # Calculate label + if labeling: + recessive_feature_df['is_causal'] = ( + (recessive_feature_df.varId1 == recessive_feature_df.varId2) & + (recessive_feature_df.zyg_1 == 2) & + (recessive_feature_df.is_strong_1 == 1) + ) | ( + (recessive_feature_df.varId1 != recessive_feature_df.varId2) & + (recessive_feature_df.zyg_1 == 1) & + (recessive_feature_df.zyg_2 == 1) & + (recessive_feature_df.is_strong_1 == 1) & + (recessive_feature_df.is_strong_2 == 1) ) - for i in range(len(varIDs)): - varii_feature_matrix = generate_feature_vector(i, i) - var1 = varIDs[i] - - if f"{var1}_{var1}" not in seen_pairs: - if feature_df.loc[var1, "zyg"] == 2: + # Create pair id as the legacy did + recessive_feature_df = recessive_feature_df.set_index(recessive_feature_df.varId1 + '_' + recessive_feature_df.varId2) - if labeling: - if feature_df.loc[var1, "is_strong"] == 1: - is_causal_wo_OMIM = 1 - else: - is_causal_wo_OMIM = 0 - varii_feature_matrix["is_causal"] = is_causal_wo_OMIM - - bivar_feature_mats.append(varii_feature_matrix) - seen_pairs.add(f"{var1}_{var1}") - - if i < len(varIDs) - 1: - for j in range(i + 1, len(varIDs)): - var2 = varIDs[j] - if ( - (f"{var1}_{var2}" not in seen_pairs) - and (f"{var2}_{var1}" not in seen_pairs) - and (var1 != var2) - ): - varij_feature_matrix = generate_feature_vector(i, j) - - if labeling: - is_causal_wo_OMIM = 0 - - if (feature_df.loc[[var1, var2], "is_strong"] == 1).all(): - if (feature_df.loc[[var1, var2], "zyg"] == 1).all(): - is_causal_wo_OMIM = 1 - - varij_feature_matrix["is_causal"] = is_causal_wo_OMIM - - bivar_feature_mats.append(varij_feature_matrix) + # Drop the intermediate columns + recessive_feature_df = recessive_feature_df.drop( + columns=['geneEnsId', 'varId1', 'varId2'] + ) + if labeling: + recessive_feature_df = recessive_feature_df.drop( + columns=['is_strong_1', 'is_strong_2'] + ) - seen_pairs.add(f"{var1}_{var2}") - seen_pairs.add(f"{var2}_{var1}") - else: - pass + # Cast all the values to float as the legacy did + recessive_feature_df = recessive_feature_df.astype(float) - if len(bivar_feature_mats) > 0: - bivar_feature_mats = pd.concat(bivar_feature_mats, axis=0) - bivar_feature_mats = bivar_feature_mats[~bivar_feature_mats.index.duplicated()] - bivar_feature_mats.to_csv(f"{out_folder}/{gene}.csv") + # Sort before saving + recessive_feature_df = recessive_feature_df.sort_index() - return + recessive_feature_df.to_csv(f"{recessive_folder}/{sample_id}.csv") From df6b6b328ee5fe55124a8f168ea353608cd6f512 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Thu, 27 Jun 2024 13:16:42 -0500 Subject: [PATCH 52/71] Removed Multiprocessing from feature.py (#6) The use of multiprocessing in feature.py caused high memory consumption due to the forking mechanism, making it a bottleneck in the project. To maintain performance, indexing techniques have been applied to optimize query speed as a trade-off. --- bin/annotation/utils_for_marrvel_flatfile.py | 714 +++++++++++-------- bin/feature.py | 180 +++-- main.nf | 2 +- 3 files changed, 515 insertions(+), 381 deletions(-) diff --git a/bin/annotation/utils_for_marrvel_flatfile.py b/bin/annotation/utils_for_marrvel_flatfile.py index e8ee648..09e3e99 100755 --- a/bin/annotation/utils_for_marrvel_flatfile.py +++ b/bin/annotation/utils_for_marrvel_flatfile.py @@ -715,312 +715,426 @@ def getVarObjListFromDict(varDictList, numVars): return varObjList -def getAnnotateInfo_2( - rowList, +def getAnnotateInfoRow_2( + row, genomeRef, clinvarGeneDf, clinvarAlleleDf, - omimGeneList, + omimGeneSortedDf, omimAlleleList, hgmdDf, moduleList, - dgvDf, - decipherDf, - gnomadMetricsGeneDf, + decipherSortedDf, + gnomadMetricsGeneSortedDf, ): - """ - Function to read the input annoatated varaint file and call the marrvel flat files to get annotations such as OMIM, DGV, Decipher - Parameters: - rowList: the input variants as a lits - other inputs are the flat file objects read locally instaed of an API - Returns: - A dict of annoatated varaints - """ - debugFlag = 0 - # print('\nlen numVars:', len(rowList), 'typerow00:', type(rowList[0][0]), 'typerow[0]:', - # type(rowList[0]) ) - # print('len(row[0][0]):', len(rowList[0][0])) - # print('moduleList:', moduleList) - varObjList = [] - varDictList = [] - i = 0 - # loop thru the rows - for row in rowList: - # CL 03-14-2023: commented all printing lines - # print('type of row:', type(row)) - varObj = Variant() - transcriptId = row.Feature - # s=row.Uploaded_variation.split('_') '1_10204_-/T' 1_1588250_T_A - ####row[0]: 21_11039079_C/A - ####s: ['21', '11039079', 'C/A'] - # print('row[0]:', row[0]) - - # two ways of input of first column either 1_1588250_T_A OR 21_11039079_C/A, so use the option flag - optFlag = 0 - if row[0].find("/") != -1: - optFlag = 1 - - if optFlag == 0: - s = row[0].split("_") - # print('s:', s) - chrom = s[0] - pos = int(s[1]) - ref = s[2] - alt = s[3] - elif optFlag == 1: - s = row[0].split("_") - # print('s:', s) - chrom = s[0] - pos = int(s[1]) - s = s[2].split("/") - ref = s[0] - alt = s[1] + # CL 03-14-2023: commented all printing lines + # print('type of row:', type(row)) + varObj = Variant() + transcriptId = row.Feature + # s=row.Uploaded_variation.split('_') '1_10204_-/T' 1_1588250_T_A + ####row[0]: 21_11039079_C/A + ####s: ['21', '11039079', 'C/A'] + # print('row[0]:', row[0]) + + # two ways of input of first column either 1_1588250_T_A OR 21_11039079_C/A, so use the option flag + optFlag = 0 + if row[0].find("/") != -1: + optFlag = 1 + + if optFlag == 0: + s = row[0].split("_") + # print('s:', s) + chrom = s[0] + pos = int(s[1]) + ref = s[2] + alt = s[3] + elif optFlag == 1: + s = row[0].split("_") + # print('s:', s) + chrom = s[0] + pos = int(s[1]) + s = s[2].split("/") + ref = s[0] + alt = s[1] - # get the start and stop from second column like '1:10203-10204' - if "-" in row[1]: - s = row[1].split(":") - tmp = s[1] - s = tmp.split("-") - # print('s:',s) - start = int(s[0]) - stop = int(s[1]) + # get the start and stop from second column like '1:10203-10204' + if "-" in row[1]: + s = row[1].split(":") + tmp = s[1] + s = tmp.split("-") + # print('s:',s) + start = int(s[0]) + stop = int(s[1]) + else: + # start and stop the same + s = row[1].split(":") + start = int(s[1]) + stop = int(s[1]) + + # print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) + # change chrom X and Y and MT to numbers + if chrom == "X": + chrom = 23 + elif chrom == "Y": + chrom = 24 + elif chrom == "MT": + chrom = 25 + elif re.search(r"GL", chrom): + chrom = 26 + chrom = int(chrom) + + # if it is hg38 get its hg19 coordinates + # CL 03-14-2023: we have separate database for hg19 and hg38, + # we don't need to use LiftOver which is inaccurate + # related codes commented and modified + if genomeRef == "hg38": + varObj.hg38Chrom = chrom + varObj.hg38Pos = pos + varObj.chrom = chrom + varObj.pos = pos + varObj.start = start + varObj.stop = stop + + """ + retList=gethg19LocFromHg38(chrom, pos)#called from the utils_1.py + # retList=[newChrom, newPos] + varObj.hg19Chrom=retList[0] + varObj.hg19Pos=retList[1] + varObj.chrom=retList[0] + varObj.pos=retList[1] + #get the start + retList=gethg19LocFromHg38(chrom, start) + varObj.start=int(retList[1]) + #get the stop + retList=gethg19LocFromHg38(chrom, stop) + varObj.stop=int(retList[1]) + """ + else: + varObj.hg19Chrom = chrom + varObj.hg19Pos = pos + varObj.chrom = chrom + varObj.pos = pos + varObj.start = start + varObj.stop = stop + + geneSymbol = row.SYMBOL + # print('gene:', geneSymbol) + varObj.geneSymbol = geneSymbol + varObj.CADD_phred = row.CADD_phred + varObj.CADD_PHRED = row.CADD_PHRED + + # assign + varObj.ref = ref + varObj.alt = alt + varObj.varId_dash = "-".join([str(chrom), str(start), ref, alt]) + # print('varId dash:', varObj.varId_dash) + varId = "_".join([str(chrom), str(pos), ref, alt, transcriptId]) + varObj.varId = varId + if "ZYG" in row: + varObj.zyg = row.ZYG + varObj.geneEnsId = row.Gene + varObj.rsId = row.Existing_variation + varObj.GERPpp_RS = row.GERPpp_RS + varObj.featureType = row.Feature_type + varObj.gnomadAF = row.gnomAD_AF + varObj.gnomadAFg = row.gnomADg_AF + varObj.CLIN_SIG = row.CLIN_SIG # CL: useless but kept for now + varObj.LRT_Omega = row.LRT_Omega + varObj.LRT_score = row.LRT_score + varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate + varObj.IMPACT = row.IMPACT + varObj.Consequence = row.Consequence + varObj.HGVSc = row.HGVSc + varObj.HGVSp = row.HGVSp + # dbnsfp attributes + varObj.GERPpp_NR = row.GERPpp_NR + varObj.DANN_score = row.DANN_score + varObj.FATHMM_pred = row.FATHMM_pred + varObj.FATHMM_score = row.FATHMM_score + varObj.GTEx_V8_gene = row.GTEx_V8_gene + varObj.GTEx_V8_tissue = row.GTEx_V8_tissue + varObj.Polyphen2_HDIV_score = row.Polyphen2_HDIV_score + varObj.Polyphen2_HVAR_score = row.Polyphen2_HVAR_score + varObj.REVEL_score = row.REVEL_score + varObj.SIFT_score = row.SIFT_score + + varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz + varObj.clinvar_clnsig = ( + row.clinvar_CLNSIG + ) # CL: Clinvar SIG from clinvar.vcf.gz + # varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted + varObj.clinvar_CLNREVSTAT = ( + row.clinvar_CLNREVSTAT + ) # CL: Clinvar STAT from clinvar.vcf.gz, for interface only + varObj.clinvar_CLNSIGCONF = ( + row.clinvar_CLNSIGCONF + ) # CL: Clinvar SIGCONF from clinvar.vcf.gz + varObj.clin_code = row.clinvar_CLNSIG # CL: feature name for ai + + varObj.fathmm_MKL_coding_score = row.fathmm_MKL_coding_score + varObj.LRT_score = row.LRT_score + varObj.LRT_Omega = row.LRT_Omega + varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate + varObj.M_CAP_score = row.M_CAP_score + varObj.MutationAssessor_score = row.MutationAssessor_score + varObj.MutationTaster_score = row.MutationTaster_score + varObj.ESP6500_AA_AC = row.ESP6500_AA_AC + varObj.ESP6500_AA_AF = row.ESP6500_AA_AF + varObj.ESP6500_EA_AC = row.ESP6500_EA_AC + varObj.ESP6500_EA_AF = row.ESP6500_EA_AF + + varObj.VARIANT_CLASS = row.VARIANT_CLASS + varObj.Feature = row.Feature + varObj.hom = row.gnomADg_controls_nhomalt + varObj.hgmd_id = row.hgmd # CL added + varObj.hgmd_symbol = row.hgmd_GENE # CL added + varObj.hgmd_rs = row.hgmd_RANKSCORE + varObj.hgmd_PHEN = row.hgmd_PHEN # CL added + varObj.hgmd_CLASS = row.hgmd_CLASS # CL added + + if row.clinvar_CLNSIGCONF != "-": + clin_dict = dict() + for ro in row.clinvar_CLNSIGCONF.split("|_"): + temp = ro.split("(") + clin_dict[temp[0]] = int(temp[1][0]) + PLP_sum = clin_dict.get("Pathogenic", 0) + clin_dict.get( + "Likely_pathogenic", 0 + ) + varObj.clin_dict = clin_dict + varObj.clin_PLP = PLP_sum + varObj.clin_PLP_perc = PLP_sum / sum(clin_dict.values()) + else: + if "benign" in row.clinvar_clnsig.lower(): + varObj.clin_PLP_perc = 0 + elif "pathogenic" in row.clinvar_clnsig.lower(): + varObj.clin_PLP_perc = 1 else: - # start and stop the same - s = row[1].split(":") - start = int(s[1]) - stop = int(s[1]) - - # print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop) - # change chrom X and Y and MT to numbers - if chrom == "X": - chrom = 23 - elif chrom == "Y": - chrom = 24 - elif chrom == "MT": - chrom = 25 - elif re.search(r"GL", chrom): - chrom = 26 - chrom = int(chrom) - - # if it is hg38 get its hg19 coordinates - # CL 03-14-2023: we have separate database for hg19 and hg38, - # we don't need to use LiftOver which is inaccurate - # related codes commented and modified - if genomeRef == "hg38": - varObj.hg38Chrom = chrom - varObj.hg38Pos = pos - varObj.chrom = chrom - varObj.pos = pos - varObj.start = start - varObj.stop = stop - - """ - retList=gethg19LocFromHg38(chrom, pos)#called from the utils_1.py - # retList=[newChrom, newPos] - varObj.hg19Chrom=retList[0] - varObj.hg19Pos=retList[1] - varObj.chrom=retList[0] - varObj.pos=retList[1] - #get the start - retList=gethg19LocFromHg38(chrom, start) - varObj.start=int(retList[1]) - #get the stop - retList=gethg19LocFromHg38(chrom, stop) - varObj.stop=int(retList[1]) - """ + varObj.clin_PLP_perc = "-" + varObj.clin_PLP = "-" + varObj.clin_dict = "-" + + if row.SpliceAI_pred != "-": + varObj.spliceAI = row.SpliceAI_pred + temp = row.SpliceAI_pred.split("|") + varObj.spliceAImax = max( + float(temp[1]), float(temp[2]), float(temp[3]), float(temp[4]) + ) + else: + varObj.spliceAI = "-" + varObj.spliceAImax = "-" + + if "conserve" in moduleList: + # # get dgv: 1.3s + # # print('\nGetting DGV') + # dgvDictList = [] + # typeList = [] + # subtypeList = [] + # dgvVarFound = 0 + # dgvType = "-" + # dgvSubtype = "-" + + # chromVal = int(varObj.chrom) + # posVal = int(varObj.pos) + # startVal = int(varObj.start) + # stopVal = int(varObj.stop) + + # # CL 03-14-2023: changed column names to be compatible with hg38 + # # vals=dgvDf[ ( dgvDf['hg19Chr'] == chromVal ) & ( dgvDf['hg19Start']<=startVal ) & (dgvDf['hg19Stop']>=stopVal) ] + # vals = dgvSortedDf.loc[chromVal].loc[:(startVal+1)].loc[:stopVal] + # numRows = len(vals.index) + + # if numRows > 0: + # dgvVarFound = 1 + # # print('\tnumrows:',numRows) + # # print('\t type of vals:', type(vals)) + # # print('\tvals:', vals) + # dgvType = vals.iloc[0]["type"] + # dgvSubtype = vals.iloc[0]["subType"] + + # # print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) + # # print('\tdgvVarFound:',dgvVarFound,'dgvType:', dgvType, 'dgvsubtype:', dgvSubtype) + # typeList.append(dgvType) + # subtypeList.append(dgvSubtype) + # retList = [dgvDictList, typeList, subtypeList, dgvVarFound] + + # varObj.dgvDictList = retList[0] + # varObj.dgvTypeList = retList[1] + # varObj.dgvSubtypeList = retList[2] + # varObj.dgvVarFound = retList[3] + + # get decipher: 0.6s + decipherDictList = [] + decipherDeletionObsList = [] + decipherStudyList = [] + decipherVarFound = 0 + deletionObs = "-" + # get the varaint object info from varObj + chromVal = int(varObj.chrom) + posVal = int(varObj.pos) + startVal = int(varObj.start) + stopVal = int(varObj.stop) + + # CL 03-14-2023: changed column names to be compatible with hg38 + # vals=decipherDf[ ( decipherDf['hg19Chr'] == chromVal ) & ( decipherDf['hg19Start']==startVal ) & (decipherDf['hg19Stop']==stopVal) ] + if (chromVal, startVal, stopVal) in decipherSortedDf: + vals = decipherSortedDf.loc[(chromVal, startVal, stopVal)] + + decipherVarFound = 1 + deletionObs = vals.iloc[0]["deletion.obs"] + decipherDeletionObsList.append(deletionObs) + + # print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal) + # print('\tdecipherVarFound:',decipherVarFound,'decipherDeletionObs:', deletionObs) + retList = [ + decipherDictList, + decipherDeletionObsList, + decipherStudyList, + decipherVarFound, + ] + + # [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] + varObj.decipherDictList = retList[0] + varObj.decipherDeletionObsList = retList[1] + varObj.decipherStudyList = retList[2] + varObj.decipherVarFound = retList[3] + + # get gnomad gene metrics from gnomad file: 3.1s + if varObj.geneSymbol in gnomadMetricsGeneSortedDf.index: # pLI, oe_lof, oe_lof_upper,mis_z + val = gnomadMetricsGeneSortedDf.loc[varObj.geneSymbol] + gnomadGeneZscore = val["mis_z"] + gnomadGenePLI = val["pLI"] + gnomadGeneOELof = val["oe_lof"] + gnomadGeneOELofUpper = val["oe_lof_upper"] else: - varObj.hg19Chrom = chrom - varObj.hg19Pos = pos - varObj.chrom = chrom - varObj.pos = pos - varObj.start = start - varObj.stop = stop - - geneSymbol = row.SYMBOL - # print('gene:', geneSymbol) - varObj.geneSymbol = geneSymbol - varObj.CADD_phred = row.CADD_phred - varObj.CADD_PHRED = row.CADD_PHRED - - # assign - varObj.ref = ref - varObj.alt = alt - varObj.varId_dash = "-".join([str(chrom), str(start), ref, alt]) - # print('varId dash:', varObj.varId_dash) - varId = "_".join([str(chrom), str(pos), ref, alt, transcriptId]) - varObj.varId = varId - if "ZYG" in row: - varObj.zyg = row.ZYG - varObj.geneEnsId = row.Gene - varObj.rsId = row.Existing_variation - varObj.GERPpp_RS = row.GERPpp_RS - varObj.featureType = row.Feature_type - varObj.gnomadAF = row.gnomAD_AF - varObj.gnomadAFg = row.gnomADg_AF - varObj.CLIN_SIG = row.CLIN_SIG # CL: useless but kept for now - varObj.LRT_Omega = row.LRT_Omega - varObj.LRT_score = row.LRT_score - varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate - varObj.IMPACT = row.IMPACT - varObj.Consequence = row.Consequence - varObj.HGVSc = row.HGVSc - varObj.HGVSp = row.HGVSp - # dbnsfp attributes - varObj.GERPpp_NR = row.GERPpp_NR - varObj.DANN_score = row.DANN_score - varObj.FATHMM_pred = row.FATHMM_pred - varObj.FATHMM_score = row.FATHMM_score - varObj.GTEx_V8_gene = row.GTEx_V8_gene - varObj.GTEx_V8_tissue = row.GTEx_V8_tissue - varObj.Polyphen2_HDIV_score = row.Polyphen2_HDIV_score - varObj.Polyphen2_HVAR_score = row.Polyphen2_HVAR_score - varObj.REVEL_score = row.REVEL_score - varObj.SIFT_score = row.SIFT_score - - varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz - varObj.clinvar_clnsig = ( - row.clinvar_CLNSIG - ) # CL: Clinvar SIG from clinvar.vcf.gz - # varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted - varObj.clinvar_CLNREVSTAT = ( - row.clinvar_CLNREVSTAT - ) # CL: Clinvar STAT from clinvar.vcf.gz, for interface only - varObj.clinvar_CLNSIGCONF = ( - row.clinvar_CLNSIGCONF - ) # CL: Clinvar SIGCONF from clinvar.vcf.gz - varObj.clin_code = row.clinvar_CLNSIG # CL: feature name for ai - - varObj.fathmm_MKL_coding_score = row.fathmm_MKL_coding_score - varObj.LRT_score = row.LRT_score - varObj.LRT_Omega = row.LRT_Omega - varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate - varObj.M_CAP_score = row.M_CAP_score - varObj.MutationAssessor_score = row.MutationAssessor_score - varObj.MutationTaster_score = row.MutationTaster_score - varObj.ESP6500_AA_AC = row.ESP6500_AA_AC - varObj.ESP6500_AA_AF = row.ESP6500_AA_AF - varObj.ESP6500_EA_AC = row.ESP6500_EA_AC - varObj.ESP6500_EA_AF = row.ESP6500_EA_AF - - varObj.VARIANT_CLASS = row.VARIANT_CLASS - varObj.Feature = row.Feature - varObj.hom = row.gnomADg_controls_nhomalt - varObj.hgmd_id = row.hgmd # CL added - varObj.hgmd_symbol = row.hgmd_GENE # CL added - varObj.hgmd_rs = row.hgmd_RANKSCORE - varObj.hgmd_PHEN = row.hgmd_PHEN # CL added - varObj.hgmd_CLASS = row.hgmd_CLASS # CL added - - if row.clinvar_CLNSIGCONF != "-": - clin_dict = dict() - for ro in row.clinvar_CLNSIGCONF.split("|_"): - temp = ro.split("(") - clin_dict[temp[0]] = int(temp[1][0]) - PLP_sum = clin_dict.get("Pathogenic", 0) + clin_dict.get( - "Likely_pathogenic", 0 - ) - varObj.clin_dict = clin_dict - varObj.clin_PLP = PLP_sum - varObj.clin_PLP_perc = PLP_sum / sum(clin_dict.values()) + # get the values + gnomadGeneZscore = "-" + gnomadGenePLI = "-" + gnomadGeneOELof = "-" + gnomadGeneOELofUpper = "-" + + retList = [gnomadGeneZscore, gnomadGenePLI, gnomadGeneOELof, gnomadGeneOELofUpper] + + # [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] + varObj.gnomadGeneZscore = retList[0] + varObj.gnomadGenePLI = retList[1] + varObj.gnomadGeneOELof = retList[2] # O/E lof + varObj.gnomadGeneOELofUpper = retList[3] # O/E lof upper + + if "curate" in moduleList: + # get OMIM: 2s + # print('\nGetting OMIM') + # varObj.omimList=jsonDict['omim'] + # retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] + inputSnpList = [] + if "," in varObj.rsId: + inputSnpList = varObj.rsId.split(",") else: - if "benign" in row.clinvar_clnsig.lower(): - varObj.clin_PLP_perc = 0 - elif "pathogenic" in row.clinvar_clnsig.lower(): - varObj.clin_PLP_perc = 1 + inputSnpList = varObj.rsId + # print('\tinputSnpList:', inputSnpList) + varFound = 0 + geneFound = 0 + omimDict = {} + omimGeneDict = {} + omimAlleleDict = {} + phenoList = [] + phenoInhList = [] + phenoMimList = [] + # check gene + # keys: dict_keys(['phenotypes', 'allelicVariants', 'mimNumber', 'status', 'title', 'description', 'geneEntrezId', 'geneSymbol']) + if varObj.geneSymbol in omimGeneSortedDf.index: + # print('\tgene:', varObj.geneSymbol, 'found') + geneFound = 1 + omimGeneDict = omimGeneSortedDf.loc[varObj.geneSymbol] + snpList = [] + for a in omimGeneDict["allelicVariants"]: + # print('a:', a) + # print('type:', type(a)) + if "dbSnps" in a: + snpList.append(a["dbSnps"]) + # print('\tsnpList:', snpList) + # print('\tlen snpList:', len(snpList)) + # check if input snpID matches the OMIM one + set1 = set(inputSnpList) + set2 = set(snpList) + if set1.intersection(set2): + varFound = 1 else: - varObj.clin_PLP_perc = "-" - varObj.clin_PLP = "-" - varObj.clin_dict = "-" - - if row.SpliceAI_pred != "-": - varObj.spliceAI = row.SpliceAI_pred - temp = row.SpliceAI_pred.split("|") - varObj.spliceAImax = max( - float(temp[1]), float(temp[2]), float(temp[3]), float(temp[4]) - ) - else: - varObj.spliceAI = "-" - varObj.spliceAImax = "-" - - # get dgv - if "conserve" in moduleList: - # print('\nGetting DGV') - dgvRet = getDGVUsingMarrvelFlatFile(varObj, dgvDf) - varObj.dgvDictList = dgvRet[0] - varObj.dgvTypeList = dgvRet[1] - varObj.dgvSubtypeList = dgvRet[2] - varObj.dgvVarFound = dgvRet[3] - - # get decipher - if "conserve" in moduleList: - # print('\nGetting DECIPHER') - decipherRet = getDecipherUsingMarrvelFlatFile(varObj, decipherDf) - # [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] - varObj.decipherDictList = decipherRet[0] - varObj.decipherDeletionObsList = decipherRet[1] - varObj.decipherStudyList = decipherRet[2] - varObj.decipherVarFound = decipherRet[3] - - # get gnomad gene metrics from gnomad file - if "conserve" in moduleList: - # print('\nGetting gnomad gene metrics') - gnomadGeneMetricRet = getGnomadGeneMetricFlatFile( - varObj, gnomadMetricsGeneDf - ) - # [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound] - varObj.gnomadGeneZscore = gnomadGeneMetricRet[0] - varObj.gnomadGenePLI = gnomadGeneMetricRet[1] - varObj.gnomadGeneOELof = gnomadGeneMetricRet[2] # O/E lof - varObj.gnomadGeneOELofUpper = gnomadGeneMetricRet[3] # O/E lof upper - - # for module one will need OMIM, HGMD, clinvar - # get OMIM - if "curate" in moduleList: - # print('\nGetting OMIM') - # varObj.omimList=jsonDict['omim'] - # retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict] - omimRet = getOMIMUsingMarrvelFlatFile(varObj, omimGeneList, omimAlleleList) - varObj.omimVarFound = omimRet[0] - varObj.omimGeneFound = omimRet[1] - varObj.omimDict = omimRet[2] - varObj.omimGeneDict = omimRet[3] - varObj.omimAlleleDict = omimRet[4] - varObj.phenoList = omimRet[5] - varObj.phenoInhList = omimRet[6] - varObj.phenoMimList = omimRet[7] - # print('OMIM res:') - # print('\tgeneFound:',varObj.omimGeneFound,'varFound:',varObj.omimVarFound ) - - # get clinvar - if "curate" in moduleList: - # print('\nReading clinVar') - clinVarRet = getClinVarUsingMarrvelFlatFile( - varObj, clinvarAlleleDf, clinvarGeneDf - ) - varObj.clinVarVarFound = clinVarRet[0] - varObj.clinVarVarDict = clinVarRet[1] - varObj.clinVarGeneFound = clinVarRet[2] - varObj.clinVarGeneDict = clinVarRet[3] - varObj.clinvarTotalNumVars = clinVarRet[4] - varObj.clinvarNumP = clinVarRet[5] - varObj.clinvarNumLP = clinVarRet[6] - varObj.clinvarNumLB = clinVarRet[7] - varObj.clinvarNumB = clinVarRet[8] - varObj.clinvarTitle = clinVarRet[9] - varObj.clinvarSignDesc = ( - row.clinvar_CLNSIG - ) # clinVarRet[10] #CL: changed to clinvar.vcf.gz annotation - varObj.clinvarCondition = clinVarRet[11] - # print('clinVar res:') - """ - if debugFlag==1: - print('\tgeneFound::',varObj.clinVarGeneFound,'varFound:',varObj.clinVarVarFound) - print('\tnumVars:',varObj.clinvarTotalNumVars,'numPathologic:',varObj.clinvarNumP,'numBenign:',varObj.clinvarNumB) - print('\tsignDesc:', varObj.clinvarSignDesc) - """ - - # get HGMD + varFound = 0 + + # get disease info from OMIM + # print('\tphenotypes:', type(omimGeneDict['phenotypes']), ' len:', len(omimGeneDict['phenotypes']) ) + for a in omimGeneDict["phenotypes"]: + # print('type:', type(a)) + pheno = a["phenotype"] + if "phenotypeMimNumber" in a: + phenoMim = a["phenotypeMimNumber"] + else: + phenoMim = "-" + if "phenotypeInheritance" in a: + phenoInh = a["phenotypeInheritance"] + else: + phenoInh = "-" + phenoList.append(pheno) + phenoInhList.append(phenoInh) + phenoMimList.append(str(phenoMim)) + # print('phenotype:', pheno,phenoMim,phenoInh) + + # print('\tvarFound:', varFound) + # print('\tphenoList:', phenoList) + # print('\tphenoInhList:', phenoInhList) + # print('\tphenoMimList:', phenoMimList) + + omimRet = [ + varFound, + geneFound, + omimDict, + omimGeneDict, + omimAlleleDict, + phenoList, + phenoInhList, + phenoMimList, + ] + + varObj.omimVarFound = omimRet[0] + varObj.omimGeneFound = omimRet[1] + varObj.omimDict = omimRet[2] + varObj.omimGeneDict = omimRet[3] + varObj.omimAlleleDict = omimRet[4] + varObj.phenoList = omimRet[5] + varObj.phenoInhList = omimRet[6] + varObj.phenoMimList = omimRet[7] + # print('OMIM res:') + # print('\tgeneFound:',varObj.omimGeneFound,'varFound:',varObj.omimVarFound ) + + # get clinvar: 0.1s + # print('\nReading clinVar') + clinVarRet = getClinVarUsingMarrvelFlatFile( + varObj, clinvarAlleleDf, clinvarGeneDf + ) + varObj.clinVarVarFound = clinVarRet[0] + varObj.clinVarVarDict = clinVarRet[1] + varObj.clinVarGeneFound = clinVarRet[2] + varObj.clinVarGeneDict = clinVarRet[3] + varObj.clinvarTotalNumVars = clinVarRet[4] + varObj.clinvarNumP = clinVarRet[5] + varObj.clinvarNumLP = clinVarRet[6] + varObj.clinvarNumLB = clinVarRet[7] + varObj.clinvarNumB = clinVarRet[8] + varObj.clinvarTitle = clinVarRet[9] + varObj.clinvarSignDesc = ( + row.clinvar_CLNSIG + ) # clinVarRet[10] #CL: changed to clinvar.vcf.gz annotation + varObj.clinvarCondition = clinVarRet[11] + # print('clinVar res:') + """ + if debugFlag==1: + print('\tgeneFound::',varObj.clinVarGeneFound,'varFound:',varObj.clinVarVarFound) + print('\tnumVars:',varObj.clinvarTotalNumVars,'numPathologic:',varObj.clinvarNumP,'numBenign:',varObj.clinvarNumB) + print('\tsignDesc:', varObj.clinvarSignDesc) + """ + + # get HGMD: 0.3s if "curate" in moduleList: # print('\nReading HGMD') hgmdRet = getHGMDUsingFlatFile(varObj, hgmdDf) @@ -1036,8 +1150,7 @@ def getAnnotateInfo_2( # varObj.hgmdVarHPOIdList, # 'hgmdVarHPOStrList:',varObj.hgmdVarHPOStrList) - # make a dict from the varObj. the varObj has one element for each row of the input VEP annotated file - varDict = { + return { "hg19Chrom": varObj.hg19Chrom, "hg19Pos": varObj.hg19Pos, "chrom": varObj.chrom, @@ -1158,11 +1271,8 @@ def getAnnotateInfo_2( "clin_PLP_perc": varObj.clin_PLP_perc, "spliceAI": varObj.spliceAI, "spliceAImax": varObj.spliceAImax, - } - varDictList.append(varDict) - # print('finsihed making dict') - # increment index - i = i + 1 - # return object - return varDictList + "zyg": varObj.zyg, + 'geneEnsId': varObj.geneEnsId, + 'rsId': varObj.rsId + } diff --git a/bin/feature.py b/bin/feature.py index f3cc14a..1bb55be 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -24,7 +24,6 @@ from annotation.utils_for_marrvel_flatfile import * # from utils_for_marrvel_flatfile_module_3 import * -import multiprocessing from functools import partial from annotation.marrvel_score_recalc import * @@ -367,9 +366,6 @@ def main(): inputReadTime = t2 - t1 inputNumRows = len(varDf.index) - # print('head:', varDf.head) - # print('column names:', varDf.columns) - # update the GERp column names since cant use ++ in the varaible name # TODO: change with `.rename` if "GERP++_RS" in varDf.columns: print("found GERP++RS") @@ -383,63 +379,74 @@ def main(): if "M-CAP_score" in varDf.columns: varDf["M_CAP_score"] = varDf["M-CAP_score"] - print("start making variant objects") - numCores = 8 - p1 = time.time() - # store rows into list for parallel function - inputList = [] - for index, rows in varDf.iterrows(): - inputList.append(rows) - p2 = time.time() - makeListTime = p2 - p1 - print("makeListTime :", makeListTime) - print("type inputList:", type(inputList[0]), "len inputList:", len(inputList)) - # group or chunks for parallel function - groupSize = min( - (len(inputList) // numCores) + (len(inputList) % numCores), 1000 - ) - print("groupSize:", groupSize) - groupList = list(getVarGroups(inputList, groupSize)) - print("type groupList:", type(groupList), "len:", len(groupList)) - - # call parallel function, pass it the main function "getAnnotateInfo_2" from utils_for_marrvel_flatfile.py and any needed files. - # this function will get the marrvel annoatations from marrvel flat files - parallelFlag = 1 - if parallelFlag == 1: - pool = multiprocessing.Pool(processes=numCores) - # CL: hgmdDf changed to hgmdHPOScoreDf - func = partial( - getAnnotateInfo_2, - genomeRef=args.genomeRef, - clinvarGeneDf=clinvarGeneDf, - clinvarAlleleDf=clinvarAlleleDf, - omimGeneList=omimGeneList, - omimAlleleList=omimAlleleList, - hgmdDf=hgmdHPOScoreDf, - moduleList=moduleList, - dgvDf=dgvDf, - decipherDf=decipherDf, - gnomadMetricsGeneDf=gnomadMetricsGeneDf, - ) - varDictList = pool.map(func, groupList) - pool.close() - else: - pass + if "conserve" in moduleList: + decipherSortedDf = decipherDf.set_index(['Chr', 'Start', 'Stop']).sort_index() + gnomadMetricsGeneSortedDf = gnomadMetricsGeneDf.groupby('gene').first().sort_index() - print("finsihed getAnnotateInfo_2") - # make a var obj list after paralle processing for each group, need a better more efficient way for doing this !! - t1 = time.time() - varObjList = getVarObjListFromDict(varDictList, len(varDf.index)) - t2 = time.time() - makeVarObjListTime = t2 - t1 - # print('len varObjList:', len(varObjList)) - print("makeVarObjListTime 1:", makeVarObjListTime) + if "curate" in moduleList: + omimGeneDf = pd.DataFrame(omimGeneList) + omimGeneSortedDf = omimGeneDf.set_index('geneSymbol').sort_index() + + def f(row): + return getAnnotateInfoRow_2( + row, + args.genomeRef, + clinvarGeneDf, + clinvarAlleleDf, + omimGeneSortedDf, + omimAlleleList, + hgmdHPOScoreDf, + moduleList, + decipherSortedDf, + gnomadMetricsGeneSortedDf, + ) + annotateInfoDf = varDf.apply(f, axis=1, result_type='expand') + if "conserve" in moduleList: + dgvDfV = dgvDf[['Chr', 'Start', 'Stop']].values.astype('int32') + def f(varObj): + # print('\nGetting DGV') + dgvDictList = [] + dgvTypeList = [] + dgvSubtypeList = [] + dgvVarFound = 0 + dgvType = "-" + dgvSubtype = "-" + + chromVal = int(varObj.chrom) + posVal = int(varObj.pos) + startVal = int(varObj.start) + stopVal = int(varObj.stop) + + # CL 03-14-2023: changed column names to be compatible with hg38 + # vals=dgvDf[ ( dgvDf['hg19Chr'] == chromVal ) & ( dgvDf['hg19Start']<=startVal ) & (dgvDf['hg19Stop']>=stopVal) ] + + vals = dgvDf[(dgvDfV[:, 0] == chromVal) & (dgvDfV[:, 1] <= startVal) & (dgvDfV[:, 2] >= stopVal)] + numRows = len(vals.index) + + if numRows > 0: + dgvVarFound = 1 + dgvType = vals.iloc[0]["type"] + dgvSubtype = vals.iloc[0]["subType"] + + dgvTypeList.append(dgvType) + dgvSubtypeList.append(dgvSubtype) + return { + "dgvDictList": dgvDictList, + "dgvTypeList": dgvTypeList, + "dgvSubtypeList": dgvSubtypeList, + "dgvVarFound": dgvVarFound, + } + + + resDf = annotateInfoDf.apply(f, axis=1, result_type='expand') + annotateInfoDf[resDf.columns] = resDf # we now have the variant object list "varObjList" with annotations; loop thru it and get the scores - for varObj in varObjList: - # print('var:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol, 'var-gene-pLI:', varObj.gnomadGenePLI) - # the curate score is under the utils_1.py file - if "curate" in moduleList: + if "curate" in moduleList: + df = [] + for i, varObj in annotateInfoDf.iterrows(): + # print('var:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol, 'var-gene-pLI:', varObj.gnomadGenePLI) + # the curate score is under the utils_1.py file # print('finding symptom matching') omimSymMatch(varObj, omimHPOScoreDf, args.inFileType) hgmdSymMatch(varObj, hgmdHPOScoreDf) @@ -448,29 +455,47 @@ def main(): retList = getCurationScore( varObj ) # retList=[omimScore, hgmdScore, clinVarScore, totalScore] - varObj.curationScoreOMIM = retList[0] - varObj.curationScoreHGMD = retList[1] - varObj.curationScoreClinVar = retList[2] - varObj.curationScoreTotal = retList[3] - # print('curation score total:', varObj.curationScoreTotal) - # conserve score is under - if "conserve" in moduleList: + df.append({ + "curationScoreOMIM": retList[0], + "curationScoreHGMD": retList[1], + "curationScoreClinVar": retList[2], + "curationScoreTotal": retList[3], + + # Due to direct update inside the functions, here we additionally copy the columns + "symptomScore": varObj.symptomScore, + "symptomName": varObj.symptomName, + "omimSymMatchFlag": varObj.omimSymMatchFlag, + "omimSymptomSimScore": varObj.omimSymptomSimScore, + "hgmdSymptomScore": varObj.hgmdSymptomScore, + "hgmdSymMatchFlag": varObj.hgmdSymMatchFlag, + "hgmdSymptomSimScore": varObj.hgmdSymptomSimScore, + }) + + df = pd.DataFrame(df) + annotateInfoDf[df.columns] = df + + # conserve score is under + if "conserve" in moduleList: + df = [] + for i, varObj in annotateInfoDf.iterrows(): retList = getConservationScore(varObj, args.diseaseInh) - # retList=[conservationScoreGnomad, conservationScoreDGV, conservationScoreOELof] - varObj.conservationScoreGnomad = retList[0] - varObj.conservationScoreDGV = retList[1] - varObj.conservationScoreOELof = retList[2] - # print('conservation score GERP++:',varObj.conservationScoreGerpPP,'DGV:',varObj.conservationScoreDGV,) - # print('conserve score DGV:',varObj.conservationScoreDGV,'conserve score gnomad:', varObj.conservationScoreGnomad) + + df.append({ + "conservationScoreGnomad": retList[0], + "conservationScoreDGV": retList[1], + "conservationScoreOELof": retList[2], + }) + + df = pd.DataFrame(df) + annotateInfoDf[df.columns] = df # write the objects info scores to a file print("writing results") - df = pd.DataFrame([t.__dict__ for t in varObjList]) - print("shape of output file:", df.shape) + print("shape of output file:", annotateInfoDf.shape) # write fileName = args.outPrefix + "_scores.txt" print("out file name:", fileName) - df.to_csv(fileName, sep="\t", index=False) + annotateInfoDf.to_csv(fileName, sep="\t", index=False) end_time = time.time() process_time = end_time - start_time @@ -489,12 +514,10 @@ def main(): print("log file name:", fileName) print("input read time:", inputReadTime) print("input num rows:", inputNumRows) - print("makeListTime from pd:", makeListTime) - print("makeVarObjListTime 1:", makeVarObjListTime) print("m:", moduleList) print("Score re-calculation:") - score = load_raw_matrix(df) + score = load_raw_matrix(annotateInfoDf) score = omimCurate(score) score = hgmdCurate(score) score = clinvarCurate(score) @@ -506,4 +529,5 @@ def main(): exit() -main() +if __name__ == '__main__': + main() diff --git a/main.nf b/main.nf index 3b8ae75..f18fe46 100644 --- a/main.nf +++ b/main.nf @@ -393,7 +393,7 @@ workflow { params.ref_gnomad_exome_idx ) - VEP_ANNOTATE( + VEP_ANNOTATE( FILTER_PROBAND.out, params.vep_dir_cache, params.vep_dir_plugins, From 867c884ec4199a683182ac4a6dc40616cb8e45ce Mon Sep 17 00:00:00 2001 From: hyunhwan-bcm <166857764+hyunhwan-bcm@users.noreply.github.com> Date: Thu, 27 Jun 2024 18:11:43 -0500 Subject: [PATCH 53/71] Fix vep split (#7) * change the process setup for the Feature Engineering Part 1. * change cpu configure for some processes * split was not working by symlink, and it worked with readlink -f --- main.nf | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main.nf b/main.nf index f18fe46..df27ec8 100644 --- a/main.nf +++ b/main.nf @@ -274,7 +274,7 @@ process FEATURE_ENGINEERING_PART1 { """ AIM_FREE_RAM=\$(free -g | awk 'NR==2{printf \$7}') - split_vep_chunks.py $vep \$AIM_FREE_RAM + split_vep_chunks.py \$(readlink -f $vep) \$AIM_FREE_RAM while read -r INDEX LINEH LINEA LINEB do From d21702cc72eb42384b710be79efddb8a0c3d523b Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 3 Jul 2024 15:05:26 -0500 Subject: [PATCH 54/71] Stop generating unused intermediate file (#10) r1_scores.txt was unused but unnecessarily generated and adds the complexity on the overall code. --- bin/annotation/marrvel_score_recalc.py | 3 - bin/feature.py | 18 +- main.nf | 11 - scripts/proc.sh | 544 ++++++++++++------------- 4 files changed, 275 insertions(+), 301 deletions(-) diff --git a/bin/annotation/marrvel_score_recalc.py b/bin/annotation/marrvel_score_recalc.py index 9c224a8..216b67c 100755 --- a/bin/annotation/marrvel_score_recalc.py +++ b/bin/annotation/marrvel_score_recalc.py @@ -5,9 +5,6 @@ def load_raw_matrix(score): - # score_file = '/out/rami-test/r1_%s_scores.txt'%(ID) #'/out/rami-test/r1_%s_scores.txt'%(ID) - # score = pd.read_csv(score_file, sep='\t') - raw_features = [ "chrom", "pos", diff --git a/bin/feature.py b/bin/feature.py index 1bb55be..d32572f 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -47,7 +47,7 @@ def checkUserArgs(inArgs): def main(): # example of running the script: - # python3 main.py -outPrefix ./test/r1 \ + # python3 main.py \ # -patientID UDN630665 -patientHPOsimiOMIM /houston_10t/dongxue/MARRVEL_AI/BG/HPO2Gene/out_May12021/UDN/HPOsimi_UDN630665_simi_0.tsv \ # -patientHPOsimiHGMD /six_tera/chaozhong/module_1/out/UDN/HPOsimi_UDNUDN630665_simi_0.tsv \ # -varFile /houston_20t/rami/vep_PassVarQual_filt_UDN_Apr2521/540200-UDN630665-P_HGYTFBCXY-2-ID01-vep_gnomADg_gnomADe_splice_clin_hgmd_filt.txt \ @@ -87,15 +87,11 @@ def main(): parser.add_argument( "-genomeRef", "--genomeRef", help="Proivde genome ref: hg19, hg38" ) - parser.add_argument( - "-outPrefix", "--outPrefix", help="Proivde prefix for output files" - ) args = parser.parse_args() # check the user args checkUserArgs(args) print("input file:", args.varFile) print("type of input file:", args.inFileType) - print("outPrefix:", args.outPrefix) print("modules:", args.modules) moduleList = args.modules.split(",") print("modules list:", moduleList) @@ -489,19 +485,12 @@ def f(varObj): df = pd.DataFrame(df) annotateInfoDf[df.columns] = df - # write the objects info scores to a file - print("writing results") - print("shape of output file:", annotateInfoDf.shape) - # write - fileName = args.outPrefix + "_scores.txt" - print("out file name:", fileName) - annotateInfoDf.to_csv(fileName, sep="\t", index=False) - end_time = time.time() process_time = end_time - start_time + print("pipeline time:", process_time) # log file for times - fileName = args.outPrefix + "_log.txt" + fileName = "log.txt" f = open(fileName, "w") f.write( "Process time:" @@ -523,7 +512,6 @@ def f(varObj): score = clinvarCurate(score) score = conservationCurate(score) - # score.to_csv('/out/rami-test/%s_scores.csv'%(args.patientID), index=False) score.to_csv("scores.csv", index=False) exit() diff --git a/main.nf b/main.nf index df27ec8..8ea43d3 100644 --- a/main.nf +++ b/main.nf @@ -268,7 +268,6 @@ process FEATURE_ENGINEERING_PART1 { output: path "${params.run_id}_scores.csv" - path "r1_${params.run_id}_scores.txt" script: """ @@ -281,7 +280,6 @@ process FEATURE_ENGINEERING_PART1 { sed -n -e "\${LINEH}p" -e "\${LINEA},\${LINEB}p" $vep > vep-\${INDEX}.txt feature.py \\ - -outPrefix r1 \\ -patientHPOsimiOMIM $omim_sim \\ -patientHPOsimiHGMD $hgmd_sim \\ -varFile vep-\${INDEX}.txt \\ @@ -293,10 +291,8 @@ process FEATURE_ENGINEERING_PART1 { if [ \${INDEX} -gt 1 ]; then sed -n "2,\$p" scores.csv > scores_\${INDEX}.csv - sed -n "2,\$p" r1_scores.txt > r1_scores_\${INDEX}.txt else mv scores.csv scores_\${INDEX}.csv - mv r1_scores.txt r1_scores_\${INDEX}.txt fi done < vep_split.txt @@ -305,18 +301,12 @@ process FEATURE_ENGINEERING_PART1 { do cat scores_\${INDEX}.csv done > ${params.run_id}_scores.csv - - for INDEX in \$(cut -d\$'\\t' -f1 vep_split.txt) - do - cat r1_scores_\${INDEX}.txt - done > r1_${params.run_id}_scores.txt """ } process FEATURE_ENGINEERING_PART2 { input: path scores, stageAs: "scores.csv" - path r1_scores, stageAs: "r1_scores.txt" path phrank path ref_annot_dir path ref_var_tier_dir @@ -417,7 +407,6 @@ workflow { FEATURE_ENGINEERING_PART2 ( FEATURE_ENGINEERING_PART1.out[0], - FEATURE_ENGINEERING_PART1.out[1], PHRANK_SCORING.out, file(params.ref_annot_dir), file(params.ref_var_tier_dir), diff --git a/scripts/proc.sh b/scripts/proc.sh index 4e535ac..857618f 100755 --- a/scripts/proc.sh +++ b/scripts/proc.sh @@ -1,284 +1,284 @@ -#!/bin/bash -#$1 submission id -#$2 ref genome -#$3 memory limitation - -#make output subdirectories -echo $(date +"%T") > /out/whole_log.txt -mkdir -m777 /out/phrank -mkdir -m777 /out/input -mkdir -m777 /out/tier-test-false -mkdir -m777 /out/rami-test -mkdir -m777 /out/scores -mkdir -m777 /out/final_matrix -mkdir -m777 /out/final_matrix_expanded - -REF_DIR=$2 -CHUNK_RAM=$3 -KEEP_INTERMEDIATE=${4:-True} - -if [ "$REF_DIR" = "hg19" ] -then - REF_GEN="GRCh37" -else - REF_GEN="GRCh38" -fi - -#Check if hpo.txt is empty or has no valid HPO ID -if [[ -z $(egrep 'HP:[0-9]{7}' /input/hpo.txt) ]] ; then - #Replace it with HP:0000001 - echo "HP:0000001" > /input/hpo.txt -fi - - -cp /input/vcf.gz /out/vcf.gz ### New Step: Copying file to output directory - - -# Initial path of the VCF file -INPUT_VCF_PATH="/out/vcf.gz" ### Changed: Adjust path to new output location - -#Check if the file exists -if [ -e "$INPUT_VCF_PATH" ]; then - #Use file command to determine the file type - INPUT_VCF_TYPE=$(file -b "$INPUT_VCF_PATH") - - # Determine if the file is gzip or bgzip format - if echo "$INPUT_VCF_TYPE" | grep -q 'gzip compressed data'; then - # Check if it's bgzip format for tabix compatibility - if echo "$INPUT_VCF_TYPE" | grep -q 'BGZF'; then - echo "The file is in BGZF format, ready for tabix." - tabix -p vcf "$INPUT_VCF_PATH" ### Adjusted Step: Only index if BGZF - else - echo "GZIP format detected, converting to BGZF." - gunzip -c "$INPUT_VCF_PATH" | bgzip > "${INPUT_VCF_PATH%.gz}" - mv "${INPUT_VCF_PATH%.gz}" "$INPUT_VCF_PATH" ### Ensuring the correct .gz extension - tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file - fi - elif echo "$INPUT_VCF_TYPE" | grep -q 'ASCII text'; then - echo "Plain VCF file detected, compressing and indexing." - bgzip -c "$INPUT_VCF_PATH" > "${INPUT_VCF_PATH}.gz" - mv "${INPUT_VCF_PATH}.gz" "$INPUT_VCF_PATH" ### Replace uncompressed with compressed - tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file - fi -else - echo "The file $INPUT_VCF_PATH does not exist." -fi - - - -echo "VCF pre-processing" -#annotate with new chromosomes and preserve original coordinates in ID -bcftools annotate --rename-chrs /run/data_dependencies/bcf_annotate/chrmap.txt -x ID /out/vcf.gz -Oz -o /out/$1-annot.txt -bcftools annotate --set-id +'%CHROM\_%POS\_%REF\_%FIRST_ALT' /out/$1-annot.txt -Oz -o /out/$1-add-id.vcf.gz - - -echo "VCF quality filtering" -#run quality filters #removed 27th line -bcftools filter /out/$1-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o /out/$1.filt.vcf.gz - -#check number of variants left -variant_count=$(bcftools view -H /out/$1.filt.vcf.gz | wc -l) -if [ "$variant_count" -gt 0 ]; then - echo "Quality filtering completed successfully. Variants passing the filters: $variant_count" -else - echo "The VCF file doesn't have FILTER annotation, or all variants filtered." - echo "Pipeline will proceed with unfiltered VCF file." - cp /out/$1-add-id.vcf.gz /out/$1.filt.vcf.gz -fi - -echo "Remove mitochondrial and unknown chromosome variants" -#gunzip /out/$1.filt.vcf.gz -#grep -vE "chrM" /out/$1.filt.vcf > /out/$1.filt.rmMT.vcf -tabix -p vcf /out/$1.filt.vcf.gz -bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y /out/$1.filt.vcf.gz -o /out/$1.filt.rmMT.vcf - -#Phrank annotation -echo "Phrank scoring" -zcat /out/vcf.gz | awk 'substr($0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > /out/$1-var.txt -cat /out/$1-var.txt | sed 's/chr//g' | sort -u > /out/$1-var-filt.txt - - -#annotate with ENSG -#echo "ensg annotates" -python2.7 /run/phrank/src/location_to_gene.py /out/$1-var-filt.txt ${REF_DIR} | sed 's/:/\t/g' | sed 's/X\t/23\t/g' | sed 's/Y\t/24\t/g' | sed 's/MT\t/25\t/g' > /out/$1-ensmbl.txt - - -#echo "gene-sym" -cat /out/$1-ensmbl.txt | sort -k5,5 | join -1 5 -2 1 - /run/data_dependencies/phrank/hg19/ensembl_to_symbol.txt | sed 's/ /\t/g' | cut -f2- > /out/$1-genesym.txt -cat /out/$1-genesym.txt | cut -f5 | sort -u | join -t$'\t' -1 1 -2 2 - /run/data_dependencies/phrank/hg19/gene_to_symbol_sorted.txt | cut -f2 | sort -u > /out/$1-gene.txt - - -# #run phrank scoring -#echo "phrank scoring" -python3.8 /run/phrank/src/run_phrank.py /out/$1-gene.txt /input/hpo.txt ${REF_DIR} > /out/phrank/$1.txt +# #!/bin/bash +# #$1 submission id +# #$2 ref genome +# #$3 memory limitation + +# #make output subdirectories +# echo $(date +"%T") > /out/whole_log.txt +# mkdir -m777 /out/phrank +# mkdir -m777 /out/input +# mkdir -m777 /out/tier-test-false +# mkdir -m777 /out/rami-test +# mkdir -m777 /out/scores +# mkdir -m777 /out/final_matrix +# mkdir -m777 /out/final_matrix_expanded + +# REF_DIR=$2 +# CHUNK_RAM=$3 +# KEEP_INTERMEDIATE=${4:-True} + +# if [ "$REF_DIR" = "hg19" ] +# then +# REF_GEN="GRCh37" +# else +# REF_GEN="GRCh38" +# fi + +# #Check if hpo.txt is empty or has no valid HPO ID +# if [[ -z $(egrep 'HP:[0-9]{7}' /input/hpo.txt) ]] ; then +# #Replace it with HP:0000001 +# echo "HP:0000001" > /input/hpo.txt +# fi + + +# cp /input/vcf.gz /out/vcf.gz ### New Step: Copying file to output directory + + +# # Initial path of the VCF file +# INPUT_VCF_PATH="/out/vcf.gz" ### Changed: Adjust path to new output location + +# #Check if the file exists +# if [ -e "$INPUT_VCF_PATH" ]; then +# #Use file command to determine the file type +# INPUT_VCF_TYPE=$(file -b "$INPUT_VCF_PATH") + +# # Determine if the file is gzip or bgzip format +# if echo "$INPUT_VCF_TYPE" | grep -q 'gzip compressed data'; then +# # Check if it's bgzip format for tabix compatibility +# if echo "$INPUT_VCF_TYPE" | grep -q 'BGZF'; then +# echo "The file is in BGZF format, ready for tabix." +# tabix -p vcf "$INPUT_VCF_PATH" ### Adjusted Step: Only index if BGZF +# else +# echo "GZIP format detected, converting to BGZF." +# gunzip -c "$INPUT_VCF_PATH" | bgzip > "${INPUT_VCF_PATH%.gz}" +# mv "${INPUT_VCF_PATH%.gz}" "$INPUT_VCF_PATH" ### Ensuring the correct .gz extension +# tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file +# fi +# elif echo "$INPUT_VCF_TYPE" | grep -q 'ASCII text'; then +# echo "Plain VCF file detected, compressing and indexing." +# bgzip -c "$INPUT_VCF_PATH" > "${INPUT_VCF_PATH}.gz" +# mv "${INPUT_VCF_PATH}.gz" "$INPUT_VCF_PATH" ### Replace uncompressed with compressed +# tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file +# fi +# else +# echo "The file $INPUT_VCF_PATH does not exist." +# fi + + + +# echo "VCF pre-processing" +# #annotate with new chromosomes and preserve original coordinates in ID +# bcftools annotate --rename-chrs /run/data_dependencies/bcf_annotate/chrmap.txt -x ID /out/vcf.gz -Oz -o /out/$1-annot.txt +# bcftools annotate --set-id +'%CHROM\_%POS\_%REF\_%FIRST_ALT' /out/$1-annot.txt -Oz -o /out/$1-add-id.vcf.gz + + +# echo "VCF quality filtering" +# #run quality filters #removed 27th line +# bcftools filter /out/$1-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o /out/$1.filt.vcf.gz + +# #check number of variants left +# variant_count=$(bcftools view -H /out/$1.filt.vcf.gz | wc -l) +# if [ "$variant_count" -gt 0 ]; then +# echo "Quality filtering completed successfully. Variants passing the filters: $variant_count" +# else +# echo "The VCF file doesn't have FILTER annotation, or all variants filtered." +# echo "Pipeline will proceed with unfiltered VCF file." +# cp /out/$1-add-id.vcf.gz /out/$1.filt.vcf.gz +# fi + +# echo "Remove mitochondrial and unknown chromosome variants" +# #gunzip /out/$1.filt.vcf.gz +# #grep -vE "chrM" /out/$1.filt.vcf > /out/$1.filt.rmMT.vcf +# tabix -p vcf /out/$1.filt.vcf.gz +# bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y /out/$1.filt.vcf.gz -o /out/$1.filt.rmMT.vcf + +# #Phrank annotation +# echo "Phrank scoring" +# zcat /out/vcf.gz | awk 'substr($0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > /out/$1-var.txt +# cat /out/$1-var.txt | sed 's/chr//g' | sort -u > /out/$1-var-filt.txt + + +# #annotate with ENSG +# #echo "ensg annotates" +# python2.7 /run/phrank/src/location_to_gene.py /out/$1-var-filt.txt ${REF_DIR} | sed 's/:/\t/g' | sed 's/X\t/23\t/g' | sed 's/Y\t/24\t/g' | sed 's/MT\t/25\t/g' > /out/$1-ensmbl.txt + + +# #echo "gene-sym" +# cat /out/$1-ensmbl.txt | sort -k5,5 | join -1 5 -2 1 - /run/data_dependencies/phrank/hg19/ensembl_to_symbol.txt | sed 's/ /\t/g' | cut -f2- > /out/$1-genesym.txt +# cat /out/$1-genesym.txt | cut -f5 | sort -u | join -t$'\t' -1 1 -2 2 - /run/data_dependencies/phrank/hg19/gene_to_symbol_sorted.txt | cut -f2 | sort -u > /out/$1-gene.txt + + +# # #run phrank scoring +# #echo "phrank scoring" +# python3.8 /run/phrank/src/run_phrank.py /out/$1-gene.txt /input/hpo.txt ${REF_DIR} > /out/phrank/$1.txt -# #annotate with OMIM and HPO path -echo "Calculating phenotype similarities" -Rscript /run/phenoSim.R /input/hpo.txt $1 ${REF_DIR} > /dev/null +# # #annotate with OMIM and HPO path +# echo "Calculating phenotype similarities" +# Rscript /run/phenoSim.R /input/hpo.txt $1 ${REF_DIR} > /dev/null -#Filter proband vcf with blacklist -echo "Filtering VCF with blacklist" -bgzip /out/$1.filt.rmMT.vcf +# #Filter proband vcf with blacklist +# echo "Filtering VCF with blacklist" +# bgzip /out/$1.filt.rmMT.vcf -tabix /out/$1.filt.rmMT.vcf.gz -mkdir -m777 /out/isec_tmp1 -bcftools isec -p /out/isec_tmp1 -w 1 -Oz \ -/out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.genomes.vcf.gz +# tabix /out/$1.filt.rmMT.vcf.gz +# mkdir -m777 /out/isec_tmp1 +# bcftools isec -p /out/isec_tmp1 -w 1 -Oz \ +# /out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.genomes.vcf.gz -tabix /out/isec_tmp1/0000.vcf.gz -mkdir -m777 /out/isec_tmp2 -bcftools isec -p /out/isec_tmp2 -w 1 -Oz \ -/out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.exomes.vcf.gz +# tabix /out/isec_tmp1/0000.vcf.gz +# mkdir -m777 /out/isec_tmp2 +# bcftools isec -p /out/isec_tmp2 -w 1 -Oz \ +# /out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.exomes.vcf.gz -tabix /out/isec_tmp2/0000.vcf.gz -mkdir -m777 /out/isec_tmp3 -bcftools isec -p /out/isec_tmp3 -Ov \ -/out/isec_tmp1/0000.vcf.gz /out/isec_tmp2/0000.vcf.gz - -mv /out/isec_tmp3/0002.vcf /out/$1.filt.rmBL.vcf -rm -rf /out/isec_tmp1 -rm -rf /out/isec_tmp2 -rm -rf /out/isec_tmp3 +# tabix /out/isec_tmp2/0000.vcf.gz +# mkdir -m777 /out/isec_tmp3 +# bcftools isec -p /out/isec_tmp3 -Ov \ +# /out/isec_tmp1/0000.vcf.gz /out/isec_tmp2/0000.vcf.gz + +# mv /out/isec_tmp3/0002.vcf /out/$1.filt.rmBL.vcf +# rm -rf /out/isec_tmp1 +# rm -rf /out/isec_tmp2 +# rm -rf /out/isec_tmp3 -#annotate with vep -echo "VEP annotation" - -if [ "$REF_DIR" = "hg38" ] -then +# #annotate with vep +# echo "VEP annotation" + +# if [ "$REF_DIR" = "hg38" ] +# then - /opt/vep/src/ensembl-vep/vep \ - --dir_cache /run/data_dependencies/vep/hg38/ \ - --dir_plugins /run/data_dependencies/vep/hg38/Plugins/ \ - --fork 10 --everything --format vcf \ - --cache --offline --tab --force_overwrite \ - --species homo_sapiens --assembly GRCh38 \ - --custom /run/data_dependencies/vep/hg38/gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ - --custom /run/data_dependencies/vep/hg38/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ - --custom /run/data_dependencies/vep/hg38/HGMD_Pro_2022.2_hg38.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ - --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg38/new_tabbed_revel_grch38.tsv.gz,ALL \ - --plugin SpliceAI,snv=/run/data_dependencies/vep/hg38/spliceai_scores.masked.snv.hg38.vcf.gz,indel=/run/data_dependencies/vep/hg38/spliceai_scores.masked.indel.hg38.vcf.gz,cutoff=0.5 \ - --plugin CADD,/run/data_dependencies/vep/hg38/hg38_whole_genome_SNV.tsv.gz,ALL \ - --plugin dbNSFP,/run/data_dependencies/vep/hg38/dbNSFP4.1a_grch38.gz,ALL \ - --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf -else - - /opt/vep/src/ensembl-vep/vep \ - --dir_cache /run/data_dependencies/vep/hg19/ \ - --dir_plugins /run/data_dependencies/vep/hg19/Plugins/ \ - --fork 10 --everything --format vcf \ - --cache --offline --tab --force_overwrite \ - --species homo_sapiens --assembly GRCh37 \ - --custom /run/data_dependencies/vep/hg19/gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ - --custom /run/data_dependencies/vep/hg19/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ - --custom /run/data_dependencies/vep/hg19/HGMD_Pro_2022.2_hg19.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ - --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg19/new_tabbed_revel.tsv.gz,ALL \ - --plugin SpliceAI,snv=/run/data_dependencies/vep/hg19/spliceai_scores.masked.snv.hg19.vcf.gz,indel=/run/data_dependencies/vep/hg19/spliceai_scores.masked.indel.hg19.vcf.gz,cutoff=0.5 \ - --plugin CADD,/run/data_dependencies/vep/hg19/hg19_whole_genome_SNVs.tsv.gz,ALL \ - --plugin dbNSFP,/run/data_dependencies/vep/hg19/dbNSFP4.3a_grch37.gz,ALL \ - --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf -fi - -echo "Feature engineering" -#annotate w/ marrvel flatfiles -AIM_FREE_RAM=$(free -g | awk 'NR==2{printf $7}') - -if (( AIM_FREE_RAM > CHUNK_RAM )); then - RAM_MAX=${CHUNK_RAM} -else - RAM_MAX=${AIM_FREE_RAM} -fi - -python3.8 /run/split_chunks.py $1 $RAM_MAX - -while read -r INDEX LINEH LINEA LINEB -do - sed -n -e "${LINEH}p" -e "${LINEA},${LINEB}p" /out/$1-vep.txt > /out/$1-vep-${INDEX}.txt - - python3.8 /run/annotation/main.py \ - -outPrefix /out/rami-test/r1 \ - -patientID $1 \ - -patientHPOsimiOMIM /out/$1-dx \ - -patientHPOsimiHGMD /out/$1-cz \ - -varFile /out/$1-vep-$INDEX.txt \ - -inFileType vepAnnotTab \ - -patientFileType one \ - -genomeRef ${REF_DIR} \ - -diseaseInh AD \ - -modules curate,conserve +# /opt/vep/src/ensembl-vep/vep \ +# --dir_cache /run/data_dependencies/vep/hg38/ \ +# --dir_plugins /run/data_dependencies/vep/hg38/Plugins/ \ +# --fork 10 --everything --format vcf \ +# --cache --offline --tab --force_overwrite \ +# --species homo_sapiens --assembly GRCh38 \ +# --custom /run/data_dependencies/vep/hg38/gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ +# --custom /run/data_dependencies/vep/hg38/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ +# --custom /run/data_dependencies/vep/hg38/HGMD_Pro_2022.2_hg38.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ +# --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg38/new_tabbed_revel_grch38.tsv.gz,ALL \ +# --plugin SpliceAI,snv=/run/data_dependencies/vep/hg38/spliceai_scores.masked.snv.hg38.vcf.gz,indel=/run/data_dependencies/vep/hg38/spliceai_scores.masked.indel.hg38.vcf.gz,cutoff=0.5 \ +# --plugin CADD,/run/data_dependencies/vep/hg38/hg38_whole_genome_SNV.tsv.gz,ALL \ +# --plugin dbNSFP,/run/data_dependencies/vep/hg38/dbNSFP4.1a_grch38.gz,ALL \ +# --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf +# else + +# /opt/vep/src/ensembl-vep/vep \ +# --dir_cache /run/data_dependencies/vep/hg19/ \ +# --dir_plugins /run/data_dependencies/vep/hg19/Plugins/ \ +# --fork 10 --everything --format vcf \ +# --cache --offline --tab --force_overwrite \ +# --species homo_sapiens --assembly GRCh37 \ +# --custom /run/data_dependencies/vep/hg19/gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ +# --custom /run/data_dependencies/vep/hg19/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ +# --custom /run/data_dependencies/vep/hg19/HGMD_Pro_2022.2_hg19.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ +# --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg19/new_tabbed_revel.tsv.gz,ALL \ +# --plugin SpliceAI,snv=/run/data_dependencies/vep/hg19/spliceai_scores.masked.snv.hg19.vcf.gz,indel=/run/data_dependencies/vep/hg19/spliceai_scores.masked.indel.hg19.vcf.gz,cutoff=0.5 \ +# --plugin CADD,/run/data_dependencies/vep/hg19/hg19_whole_genome_SNVs.tsv.gz,ALL \ +# --plugin dbNSFP,/run/data_dependencies/vep/hg19/dbNSFP4.3a_grch37.gz,ALL \ +# --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf +# fi + +# echo "Feature engineering" +# #annotate w/ marrvel flatfiles +# AIM_FREE_RAM=$(free -g | awk 'NR==2{printf $7}') + +# if (( AIM_FREE_RAM > CHUNK_RAM )); then +# RAM_MAX=${CHUNK_RAM} +# else +# RAM_MAX=${AIM_FREE_RAM} +# fi + +# python3.8 /run/split_chunks.py $1 $RAM_MAX + +# while read -r INDEX LINEH LINEA LINEB +# do +# sed -n -e "${LINEH}p" -e "${LINEA},${LINEB}p" /out/$1-vep.txt > /out/$1-vep-${INDEX}.txt + +# python3.8 /run/annotation/main.py \ +# -outPrefix /out/rami-test/r1 \ +# -patientID $1 \ +# -patientHPOsimiOMIM /out/$1-dx \ +# -patientHPOsimiHGMD /out/$1-cz \ +# -varFile /out/$1-vep-$INDEX.txt \ +# -inFileType vepAnnotTab \ +# -patientFileType one \ +# -genomeRef ${REF_DIR} \ +# -diseaseInh AD \ +# -modules curate,conserve - if [ $INDEX -gt 1 ]; then - sed -n "2,\$p" /out/rami-test/$1_scores.csv > /out/rami-test/$1_scores_$INDEX.csv - rm /out/rami-test/$1_scores.csv - sed -n "2,\$p" /out/rami-test/r1_$1_scores.txt > /out/rami-test/r1_$1_scores_$INDEX.txt - rm /out/rami-test/r1_$1_scores.txt - else - mv /out/rami-test/$1_scores.csv /out/rami-test/$1_scores_$INDEX.csv - mv /out/rami-test/r1_$1_scores.txt /out/rami-test/r1_$1_scores_$INDEX.txt - fi -done < /out/vep_split.txt - - -# Combine all $1_scores_$INDEX.csv -for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) -do - cat /out/rami-test/$1_scores_$INDEX.csv -done > /out/rami-test/$1_scores.csv - -for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) -do - cat /out/rami-test/r1_$1_scores_$INDEX.txt -done > /out/rami-test/r1_$1_scores.txt - -rm /out/$1-vep-*.txt -rm /out/rami-test/r1_$1_scores_*.txt -rm /out/rami-test/$1_scores_*.csv - -Rscript /run/VarTierDiseaseDBFalse.R $1 ${REF_DIR} - -python3.8 /run/generate_new_matrix_2.py $1 ${REF_DIR} - -echo "Making prediction" -python3.8 /run/predict_new/run_final.py $1 - -python3.8 /run/merge_rm.py $1 - -python3.8 /run/extraModel/main.py -id $1 - - -echo $(date +"%T") >> /out/whole_log.txt - -# delete intermediate files -if [ "$KEEP_INTERMEDIATE" = "False" ]; then - rm /out/$1-add-id.vcf.gz - rm /out/$1-annot.txt - rm /out/$1-cz - rm /out/$1-dx - rm /out/$1-ensmbl.txt - rm /out/$1.filt.rmBL.vcf - rm /out/$1.filt.rmMT.vcf.gz - rm /out/$1.filt.rmMT.vcf.gz.tbi - rm /out/$1.filt.vcf - rm /out/$1-genesym.txt - rm /out/$1-gene.txt - rm /out/$1.matrix.txt - rm /out/$1-var-filt.txt - rm /out/$1-var.txt - rm /out/$1-vep.txt - rm /out/$1-vep.txt* - rm /out/vep_split.txt - rm -r /out/final_matrix - rm -r /out/final_matrix_expanded - rm -r /out/input - rm -r /out/phrank - rm -r /out/rami-test - rm -r /out/scores - rm -r /out/tier-test-false -fi - -mv /out/conf_4Model/*.csv /out/ -mv /out/conf_4Model/integrated/*.csv /out/ -rm -r /out/conf_4Model +# if [ $INDEX -gt 1 ]; then +# sed -n "2,\$p" /out/rami-test/$1_scores.csv > /out/rami-test/$1_scores_$INDEX.csv +# rm /out/rami-test/$1_scores.csv +# sed -n "2,\$p" /out/rami-test/r1_$1_scores.txt > /out/rami-test/r1_$1_scores_$INDEX.txt +# rm /out/rami-test/r1_$1_scores.txt +# else +# mv /out/rami-test/$1_scores.csv /out/rami-test/$1_scores_$INDEX.csv +# mv /out/rami-test/r1_$1_scores.txt /out/rami-test/r1_$1_scores_$INDEX.txt +# fi +# done < /out/vep_split.txt + + +# # Combine all $1_scores_$INDEX.csv +# for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) +# do +# cat /out/rami-test/$1_scores_$INDEX.csv +# done > /out/rami-test/$1_scores.csv + +# for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) +# do +# cat /out/rami-test/r1_$1_scores_$INDEX.txt +# done > /out/rami-test/r1_$1_scores.txt + +# rm /out/$1-vep-*.txt +# rm /out/rami-test/r1_$1_scores_*.txt +# rm /out/rami-test/$1_scores_*.csv + +# Rscript /run/VarTierDiseaseDBFalse.R $1 ${REF_DIR} + +# python3.8 /run/generate_new_matrix_2.py $1 ${REF_DIR} + +# echo "Making prediction" +# python3.8 /run/predict_new/run_final.py $1 + +# python3.8 /run/merge_rm.py $1 + +# python3.8 /run/extraModel/main.py -id $1 + + +# echo $(date +"%T") >> /out/whole_log.txt + +# # delete intermediate files +# if [ "$KEEP_INTERMEDIATE" = "False" ]; then +# rm /out/$1-add-id.vcf.gz +# rm /out/$1-annot.txt +# rm /out/$1-cz +# rm /out/$1-dx +# rm /out/$1-ensmbl.txt +# rm /out/$1.filt.rmBL.vcf +# rm /out/$1.filt.rmMT.vcf.gz +# rm /out/$1.filt.rmMT.vcf.gz.tbi +# rm /out/$1.filt.vcf +# rm /out/$1-genesym.txt +# rm /out/$1-gene.txt +# rm /out/$1.matrix.txt +# rm /out/$1-var-filt.txt +# rm /out/$1-var.txt +# rm /out/$1-vep.txt +# rm /out/$1-vep.txt* +# rm /out/vep_split.txt +# rm -r /out/final_matrix +# rm -r /out/final_matrix_expanded +# rm -r /out/input +# rm -r /out/phrank +# rm -r /out/rami-test +# rm -r /out/scores +# rm -r /out/tier-test-false +# fi + +# mv /out/conf_4Model/*.csv /out/ +# mv /out/conf_4Model/integrated/*.csv /out/ +# rm -r /out/conf_4Model From 246503e27016c18078e235b4f94ba96a1acd5a33 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 3 Jul 2024 15:06:42 -0500 Subject: [PATCH 55/71] Optimize Feature Engineering Part 2 (#9) * Applied scalar multiplication to vector * Used indexing technique * Transformed from float64 to float32 --- bin/mod5_diffusion.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/bin/mod5_diffusion.py b/bin/mod5_diffusion.py index 0f5b6b9..6795f7d 100755 --- a/bin/mod5_diffusion.py +++ b/bin/mod5_diffusion.py @@ -27,15 +27,13 @@ def diffusion(nn, y, alpha=0.5, max_iter=100): """ alpha = 0.5 max_iter = 100 - y = y.to_numpy() + y = y.to_numpy().astype('float32') F = y ## initialize an empty array for the heat - Fs = np.empty((nn.shape[0], 0), float) - ## restart - fY = (1 - alpha) * y + Fs = np.empty((nn.shape[0], 0), 'float32') for i in range(0, max_iter): Fs = np.append(Fs, F, axis=1) - F = alpha * nn @ Fs[:, [i]] + fY + F = nn @ (alpha * Fs[:, [i]]) + (1 - alpha) * y return F @@ -103,7 +101,7 @@ def diffuseSample(ID, Anno_df, Phrank_folder): m12_wSimi_woCor = list(set(m12_wSimi) - (set(cor_GeneID["ID"]))) m12_wSimi_woCor = simi[simi["Ensembl_Gene_ID"].isin(m12_wSimi_woCor)] ## normalized the cor matrix - net = abs(cor) + net = abs(cor).astype('float32') D = 1 / np.sqrt(net.sum(axis=1)) D2 = np.diag(D) net_norm = D2 @ net @ D2 @@ -141,14 +139,15 @@ def diffuseSample(ID, Anno_df, Phrank_folder): FinalHeat_wSimi = FinalHeat_wSimi.sort_values(by="Final_Heat", ascending=False) ## Get the diffusion scores for genes in the annotation data, 0 if not existed + FinalHeat_wSimi_indexed = FinalHeat_wSimi.set_index('Ensembl_Gene_ID') score_ordered = [] gene_ordered = m12_df.loc[:, "geneEnsId"].tolist() for gene in gene_ordered: - if gene in FinalHeat_wSimi.Ensembl_Gene_ID.tolist(): + if gene in FinalHeat_wSimi_indexed.index: score_ordered.append( - FinalHeat_wSimi.loc[ - FinalHeat_wSimi.Ensembl_Gene_ID == gene, "Final_Heat" - ].to_numpy()[0] + FinalHeat_wSimi_indexed.loc[ + gene, "Final_Heat" + ] ) else: score_ordered.append(0) From b0ccfa2eaec66eb694e117e65e4db067d0fbc317 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 3 Jul 2024 15:07:08 -0500 Subject: [PATCH 56/71] Fix a bug that vep splitter didn't work under nextflow env (#8) * Fix a bug that vep splitter didn't work under nextflow env * Revert "Fix vep split (#7)" This reverts commit 867c884ec4199a683182ac4a6dc40616cb8e45ce. --- bin/split_vep_chunks.py | 7 ++----- main.nf | 2 +- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/bin/split_vep_chunks.py b/bin/split_vep_chunks.py index 1223193..8939a37 100755 --- a/bin/split_vep_chunks.py +++ b/bin/split_vep_chunks.py @@ -25,12 +25,9 @@ - 1 ) -command = f"du {VEP_FILE}" -filesize = int( - subprocess.check_output(command, shell=True, text=True).strip().split("\t")[0] -) +filesize = os.path.getsize(VEP_FILE) -num_chunk = math.ceil((filesize / 1024) / (RAMlimit * 1024 / 23)) +num_chunk = math.ceil((filesize / 1024 / 1024) / (RAMlimit * 1024 / 23)) chunk_lines = math.ceil(nlines / num_chunk) diff --git a/main.nf b/main.nf index 8ea43d3..c08957f 100644 --- a/main.nf +++ b/main.nf @@ -273,7 +273,7 @@ process FEATURE_ENGINEERING_PART1 { """ AIM_FREE_RAM=\$(free -g | awk 'NR==2{printf \$7}') - split_vep_chunks.py \$(readlink -f $vep) \$AIM_FREE_RAM + split_vep_chunks.py $vep \$AIM_FREE_RAM while read -r INDEX LINEH LINEA LINEB do From f9888e1215f7289e2dd19a0134b86883edf18f8f Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Tue, 9 Jul 2024 18:19:17 -0500 Subject: [PATCH 57/71] Untrack Nextflow Config (#11) * Untrack Nextflow Config * Keep tracking nextflow config and Use params.yaml * Update README.md * Update README.md --- .gitignore | 19 ++++++++++--------- README.md | 17 ++++++++++++----- nextflow.config | 11 +++-------- params.yaml.example | 9 +++++++++ 4 files changed, 34 insertions(+), 22 deletions(-) create mode 100644 params.yaml.example diff --git a/.gitignore b/.gitignore index c7c2aaa..9ba53d2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,19 +1,20 @@ -.gitignore +# General +**/__pycache__ bcftools-1.9.tar.bz2 run/bedtools .vscode/ .DS_Store */.DS_Store -.nextflow* -.nextflow/ -work/ test_data/ test_ref/ test_output/ -trace* -**/__pycache__ -report-*html -trace-*txt + +# Nextflow Related +.nextflow* .nextflow/ work/ - +out/ +trace* +trace-*txt +report-*html +params.yaml diff --git a/README.md b/README.md index e32832f..7324817 100644 --- a/README.md +++ b/README.md @@ -48,13 +48,20 @@ docker pull chaozhongliu/aim-lite:latest ### Run with your sample ``` -docker run -v :/input/vcf.gz \ - -v :/input/hpo.txt \ - -v :/run/data_dependencies \ - -v :/out \ - chaozhongliu/aim-lite /run/proc.sh [Sample ID] [Reference genome: hg19/hg38] [Memory Limit (G)] +nextflow run . --ref_dir + --input_vcf + --input_hpo + --outdir + --run_id [Sample Id] # Optional, default: 1 + --ref_ver [Reference genome: hg19/hg38] # Optional, default: hg19 ``` +Alternatively, the pipeline can be executed with a parameter file (yaml) +``` +nextflow run . -params-file params.yaml +``` +NOTE: You need to create `params.yaml` by copying [params.yaml.example](params.yaml.example) file and follow the instruction. + ## License AI-MARRVEL is licensed under GPL-3.0. You are welcomed to use it for research purpose. For business purpose, please contact us for licensing. diff --git a/nextflow.config b/nextflow.config index 9c49183..f950b88 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,17 +1,11 @@ params { run_id = 1 - ref_ver = "hg19" - ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" - ref_dir = "/home/hwan/aim_data_dependencies_04_24_2024_sasi" - input_vcf = "/home/hwan/AI_MARRVEL/test_data/demo.vcf.gz" - input_hpo = "/home/hwan/AI_MARRVEL/test_data/hpos.txt" + ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" - outdir = "/home/hwan/AI_MARRVEL/test_output_060624_2/" - // for data dependency - chrmap = "${ref_dir}/data_public/bcf_annotate/chrmap.txt" + chrmap = "${ref_dir}/bcf_annotate/chrmap.txt" ref_loc = "${ref_dir}/phrank/${ref_ver}/${ref_assembly}_symbol_to_location.txt" ref_to_sym = "${ref_dir}/phrank/${ref_ver}/ensembl_to_symbol.txt" @@ -75,3 +69,4 @@ process { container = "chaozhongliu/aim-lite:latest" cache = "lenient" } + diff --git a/params.yaml.example b/params.yaml.example new file mode 100644 index 0000000..bf3f876 --- /dev/null +++ b/params.yaml.example @@ -0,0 +1,9 @@ +# Please Copy This File to `params.yaml` +# And Complete Placeholder Marked With , +# Examples Are In Comment +# NOTE: It's intended that params.yaml file is not tracked by git. + +ref_dir: # Example: "/home/jaeyeon/Data/aim_data_dependencies/" +input_vcf: # Example: "/home/jaeyeon/Data/aim_test/demo.vcf.gz" +input_hpo: # Example: "/home/jaeyeon/Data/aim_test/demo.hpos.txt" +outdir: # Example: "./out" From 2525837501dc15069ed669c6f4768c17b36cdd3d Mon Sep 17 00:00:00 2001 From: hyunhwan-bcm <166857764+hyunhwan-bcm@users.noreply.github.com> Date: Wed, 10 Jul 2024 13:43:38 -0500 Subject: [PATCH 58/71] Update VarTierDiseaseDBFalse.R To Improve Speed - replace the file reader - Optimization by unnest gene/variant level operation --- bin/VarTierDiseaseDBFalse.R | 422 +++++++++++++----------------------- 1 file changed, 155 insertions(+), 267 deletions(-) diff --git a/bin/VarTierDiseaseDBFalse.R b/bin/VarTierDiseaseDBFalse.R index a70b2dc..90e8513 100755 --- a/bin/VarTierDiseaseDBFalse.R +++ b/bin/VarTierDiseaseDBFalse.R @@ -1,54 +1,22 @@ #!/usr/bin/env Rscript -# Run Tier - -# Updates history #### -# Update on 2021.Sep13 -#' Updates: -#' 1. corrected chromosome number 23->X, 24-> Y -#' 2. add interaction term of phenotype and Tier. -#' 3. for non-coding var, if ClinVarClinSig == conflict -> push the Tier higher (if IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict, Tier = Tier + 1) -#' 4. Keep all variant that in-trans (sudo) with a strong Var in the same gene, adjust the Tier to 3.5? -# Updates on 2022.Apr5 -#' 1. remove the pre-processing of OMIM inheritance to VarTier_preprocessOMIM.R -#' 2. Add the number of variants (H/M/L impact) per gene: "No.Var.HM","No.Var.H","No.Var.M","No.Var.L" -#' 3. Add the option if IncludeClinVarHGMD -#' -#' Updates on 2022.Sep29 -#' ClinVar clinvarSignDesc is in dif format after optimizing the pipeline, update the code to be compatible for that -#' gnomadAF, gnomadAFg is "-" not "NA", update the code for that -#' add IncludeClinVarHGMD for line 101 - - -#' change genemap2.Inh into 2 features, AD, AR, save to tsv file -#' keep GeneID == "-" assinge Tier value -#' input output /out/explain/ "ID" + "_Tier" -#' - -# parameter settings #### - +library(tidyverse) +library(data.table) # set adj.k value to adjust Tier adj.k <- 1.05 # set if diseases DB will be included -IncludeClinVarHGMD <- FALSE args <- commandArgs(trailingOnly = TRUE) refg <- args[1] +refg<-"hg19" -# load database: OMIM inheritance #### -genemap2.Inh.F <- read.delim2( - file.path(paste0("var_tier/", refg, "/genemap2.Inh.F.txt")) - ) - +cat("load database: OMIM inheritance ####\n") +omim_inheritance <- file.path(paste0("var_tier/", refg, "/genemap2.Inh.F.txt")) in.f <- "scores.csv" out.f <- "Tier.v2.tsv" -# load libraries #### -library("data.table") -library("dplyr") - - +genemap2.Inh.F <- readr::read_tsv(omim_inheritance) # load the input #### anno.columns <- c( @@ -67,10 +35,7 @@ anno.columns <- c( "spliceAImax" ) -anno.orig <- read.table(in.f, - stringsAsFactors = F, - sep = ",", - header = T) +anno.orig <- readr::read_csv(in.f) anno <- anno.orig[, anno.columns] # rename the col @@ -90,54 +55,38 @@ colnames(anno) <- c( "spliceAImax" ) -if ("-" %in% anno$Gene) { - # for rows without gene ID -> Assign Tier now: - anno_noGeneID <- anno[anno$Gene == "-", c("Uploaded_variation", "Gene", "IMPACT")] - anno_noGeneID$IMPACT.max <- ifelse( - anno_noGeneID$IMPACT == "MODIFIER", - 1, - ifelse( - anno_noGeneID$IMPACT == "LOW", - 2, - ifelse(anno_noGeneID$IMPACT == "MODERATE", 3, 4) +anno_noGeneID <- anno |> + mutate( + IMPACT.max = case_when( + IMPACT == "MODIFIER" ~ 1, + IMPACT == "LOW" ~ 2, + IMPACT == "MODERATE" ~ 3, + T ~ 4 ) - ) - anno_noGeneID$IMPACT <- NULL - anno_noGeneID$TierAD <- 5 - anno_noGeneID$IMPACT.max - anno_noGeneID[, c("TierAR", "TierAR.adj")] <- ifelse(anno_noGeneID$IMPACT.max >= 3, 3, 4) - anno_noGeneID$No.Var.HM <- as.numeric(anno_noGeneID$IMPACT.max >= 3) - anno_noGeneID$No.Var.H <- as.numeric(anno_noGeneID$IMPACT.max == 4) - anno_noGeneID$No.Var.M <- as.numeric(anno_noGeneID$IMPACT.max == 3) - anno_noGeneID$No.Var.L <- as.numeric(anno_noGeneID$IMPACT.max <= 2) -} - - + ) |> + mutate( + IMPACT = NULL, + TierAD = 5 - IMPACT.max, + TierAR = ifelse(IMPACT.max >= 3, 3, 4), + TierAR.adj = ifelse(IMPACT.max >= 3, 3, 4), + No.Var.HM = as.numeric(IMPACT.max >= 3), + No.Var.H = as.numeric(IMPACT.max == 4), + No.Var.M = as.numeric(IMPACT.max == 3), + No.Var.L = as.numeric(IMPACT.max <= 2) + ) + # For rows with Gene ID -> Tier analysis -anno <- anno[anno$Gene != "-", ] -anno <- unique(anno) - - -# Tier #### - - -# STEP 0. Adjust variant IMPACT for ClinVar, HGMD, SpliceAImax >= 0.8 #### - -if (IncludeClinVarHGMD) { - anno$IMPACT <- ifelse( - grepl("Pathogenic|Likely_pathogenic", anno$clinvarSignDesc) | - anno$hgmdVarFound == 1, - "HIGH", - anno$IMPACT - ) -} - -# label Splice AI > 0.8 as HIGH -anno$IMPACT <- ifelse(anno$spliceAImax >= 0.8, "HIGH", anno$IMPACT) -# convert IMPACT to numeric value -anno$IMPACT.no <- ifelse(anno$IMPACT == "MODIFIER", - 1, - ifelse(anno$IMPACT == "LOW", 2, ifelse(anno$IMPACT == "MODERATE", 3, 4))) +anno <- anno |> filter(Gene != "-") |> distinct() |> + mutate( + IMPACT = ifelse(spliceAImax >= 0.8, "HIGH", IMPACT), + IMPACT.no = case_when( + IMPACT == "MODIFIER" ~ 1, + IMPACT == "LOW" ~ 2, + IMPACT == "MODERATE" ~ 3, + T ~ 4) + ) + # Implement the rule based Tier classification #### @@ -145,16 +94,22 @@ anno$IMPACT.no <- ifelse(anno$IMPACT == "MODIFIER", # take IMPACT HIGH, MODERATE, LOW, or MODIFIER as H,M,L,L # Tier at Gene level -# STEP 1. classify T4: genes with only L variants, nonCodingVar2keep #### +cat("STEP 1. classify T4: genes with only L variants, nonCodingVar2keep\n") # STEP 1.1 Keep Genes with IMPACT H or M variants Gene.all <- unique(anno$Gene) Gene.IMPACT <- unique(anno[, c("Gene", "IMPACT")]) -Gene.IMPACT.HM <- Gene.IMPACT[Gene.IMPACT$IMPACT %in% c("HIGH", "MODERATE"), ] + +Gene.IMPACT <- anno |> select(Gene, IMPACT) |> distinct() +Gene.IMPACT.HM <- Gene.IMPACT |> filter(IMPACT %in% c("HIGH", "MODERATE")) # GeneTier4 will be removed for remaining Tier analysis GeneTier4 <- Gene.all[!(Gene.all %in% Gene.IMPACT.HM$Gene)] -anno$TierAD[anno$Gene %in% GeneTier4] <- anno$TierAR[anno$Gene %in% GeneTier4] <- 4 + +anno <- anno |> + mutate(TierAD = ifelse(Gene %in% GeneTier4, 4, NA)) |> + mutate(TierAR = ifelse(Gene %in% GeneTier4, 4, NA)) + # STEP 1.2 Keep Var with max IMPACT == MODIFIER/LOW and ClinVarClinSig == conflict # note: many variants with “Conflicting” are non-coding and coding for different transcripts, only keep the var with max impact as MODIFIER @@ -162,34 +117,11 @@ anno$TierAD[anno$Gene %in% GeneTier4] <- anno$TierAR[anno$Gene %in% GeneTier4] < # find the non-Coding Var to keep nonCodingVar2keep <- c() -if (IncludeClinVarHGMD) { - nonCodingModifier.idx <- which( - grepl( - "Conflicting_interpretations_of_pathogenicity", - anno$clinvarSignDesc - ) & (anno$IMPACT %in% c("MODIFIER")) - ) - nonCodingModifier <- anno$Uploaded_variation[nonCodingModifier.idx] - nonCodingModifier.df <- anno[anno$Uploaded_variation %in% nonCodingModifier, ] - - for (var in unique(nonCodingModifier.df$Uploaded_variation)) { - var.df <- nonCodingModifier.df[nonCodingModifier.df$Uploaded_variation == var, ] - var.impact <- unique(var.df$IMPACT) - if (length(var.impact) == 1) { - if (var.impact == "MODIFIER") { - nonCodingVar2keep <- c(nonCodingVar2keep, var) - } - } - } -} - - # split var to two groups: #### # Run Full Tier analysis: # non - GeneTier4 and nonCodingVar2keep -idx <- is.na(anno$TierAD) | - (anno$Uploaded_variation %in% nonCodingVar2keep) +idx <- is.na(anno$TierAD) Var.RunTier <- unique(anno$Uploaded_variation[idx]) # Only Count No.Var.L： @@ -203,31 +135,17 @@ if (F %in% idx) { anno.f1 <- anno[idx, ] -# set nonCodingVar2keep IMPACT as 3 -anno.f1$IMPACT.no[anno.f1$Uploaded_variation %in% nonCodingVar2keep] <- 3 - - -# STEP 2: Get the most severe IMPACT for each variant #### - +cat("STEP 2: Get the most severe IMPACT for each variant ####\n") VEP.f1 <- anno.f1 -# for Gene-Var pair with multiple IMPACT, take the MAX -VEP.f1.maxIMAPCT <- data.frame(matrix(ncol = ncol(VEP.f1) + 1, nrow = 0)) -colnames(VEP.f1.maxIMAPCT) <- c(colnames(VEP.f1), "IMPACT.max") -for (var in Var.RunTier) { - var.df <- VEP.f1[VEP.f1$Uploaded_variation == var, ] - # check no. of gene for this variant - if (length(unique(var.df$Gene)) == 1) { - var.df$IMPACT.max <- max(var.df$IMPACT.no) - VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT, var.df) - } else { - for (gene in unique(var.df$Gene)) { - var.g.df <- var.df[var.df$Gene == gene, ] - var.g.df$IMPACT.max <- max(var.g.df$IMPACT.no) - VEP.f1.maxIMAPCT <- rbind(VEP.f1.maxIMAPCT, var.g.df) - } - } -} +# it won't be the same order but will work +VEP.f1.maxIMAPCT <- VEP.f1 |> + filter(is.na(TierAD)) |> + group_by(Uploaded_variation, Gene) |> + mutate( + IMPACT.max = max(IMPACT.no) + ) |> + ungroup() # find all intronic Var with AF == 0, will adjust the Tier if in trans with a high impact variant. #### @@ -240,7 +158,7 @@ rareIntronicVar <- VEP.f1.maxIMAPCT[rareVar & rareIntronicVar <- unique(rareIntronicVar) # for each Variant keep the most severe IMPACT only -VEP.f2 <- unique( +VEP.f2 <- distinct( VEP.f1.maxIMAPCT[, c("Uploaded_variation", "SYMBOL", "Gene", @@ -249,10 +167,9 @@ VEP.f2 <- unique( "omimSymptomSimScore", "hgmdSymptomSimScore")]) -# STEP 3: assign Tiers #### +cat("STEP 3: assign Tiers\n") # VEP.f2$GT <- ifelse(VEP.f2$GT=="HET", "0/1","1/1") - # though Tier classification is Gene based, Tier should be assigned to each Variant VEP.Tier.part1 <- data.frame(matrix(ncol = ncol(VEP.f2) + 6, nrow = 0)) @@ -266,102 +183,73 @@ colnames(VEP.Tier.part1) <- c( "No.Var.L" ) -for (g in unique(VEP.f2$Gene)) { - g.df <- VEP.f2[VEP.f2$Gene == g, ] - - # X linked, seudo autosomal region -> treat as autosomal - # if (sum(grepl("X",g.df$Uploaded_variation)) >0) { - # g.df$TierAD = g.df$TierAR = ifelse(g.df$IMPACT.max <3, 4, 5-g.df$IMPACT.max) - # } - - # AD regardless of the GT - g.df$TierAD <- ifelse(g.df$IMPACT.max < 3, 4, 5 - g.df$IMPACT.max) - - # AR - g.df2 <- g.df - - # duplicate rows with HOM var - if ("HOM" %in% g.df$GT) { - g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1", ]) - } - IMPACT.max <- g.df2$IMPACT.max - if (sum(IMPACT.max == 4) >= 2) { - # >=2 strong alleles - g.df$TierAR <- 5 - g.df$IMPACT.max - # update 202221005: H+H: M-> change Tie from 2 to 1.5 - g.df$TierAR[g.df$IMPACT.max == 3] <- 1.5 - } else if (sum(IMPACT.max == 4) == 1 & - sum(IMPACT.max == 3) >= 1) { - # H + M - g.df$TierAR[g.df$IMPACT.max >= 3] <- 1.5 # (need more thought, 2 or 1.5 ???) - g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] - } else if (sum(IMPACT.max == 4) == 1) { - # H only - g.df$TierAR[g.df$IMPACT.max == 4] <- 3 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max <= 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max <= 3] - } else if (sum(IMPACT.max == 3) >= 2) { - # M + M - g.df$TierAR[g.df$IMPACT.max == 3] <- 2 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] - } else if (sum(IMPACT.max == 3) == 1) { - # M only - g.df$TierAR[g.df$IMPACT.max == 3] <- 3 # (need more thought, 2 or 3 ???) - g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] - } - - # count the number of high IMPACT var per gene. - No.Var.HM <- sum(IMPACT.max >= 3) - No.Var.H <- sum(IMPACT.max == 4) - No.Var.M <- sum(IMPACT.max == 3) - No.Var.L <- sum(IMPACT.max == 2) - g.df$No.Var.HM <- No.Var.HM - g.df$No.Var.H <- No.Var.H - g.df$No.Var.M <- No.Var.M - g.df$No.Var.L <- No.Var.L - - VEP.Tier.part1 <- rbind(VEP.Tier.part1, g.df) -} +VEP.Tier.part1 <- data.frame(matrix(ncol = ncol(VEP.f2) + 6, nrow = 0)) +colnames(VEP.Tier.part1) <- c( + colnames(VEP.f2), + "TierAR", + "TierAD", + "No.Var.HM", + "No.Var.H", + "No.Var.M", + "No.Var.L" +) -# STEP 4: Find all genes with No.Var.H == 1, adjust the Tier for rareIntronicVar #### +cat("STEP 4: Find all genes with No.Var.H == 1, adjust the Tier for rareIntronicVar\n") + +VEP.Tier.part1 <- VEP.f2 |> + group_by(Gene) |> + group_modify(~{ + g.df <- .x + + # AD regardless of the GT + g.df <- g.df |> mutate(TierAD = if_else(IMPACT.max < 3, 4, 5 - IMPACT.max)) + + # AR + g.df2 <- g.df + + # duplicate rows with HOM var + if ("HOM" %in% g.df$GT) { + g.df2 <- bind_rows(g.df2, filter(g.df, GT == "1/1")) + } + + IMPACT.max <- g.df2$IMPACT.max + # Calculate TierAR + if (sum(IMPACT.max == 4) >= 2) { + g.df$TierAR <- 5 - g.df$IMPACT.max + g.df$TierAR[g.df$IMPACT.max == 3] <- 1.5 + } else if (sum(IMPACT.max == 4) == 1 & sum(IMPACT.max == 3) >= 1) { + g.df$TierAR[g.df$IMPACT.max >= 3] <- 1.5 + g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] + } else if (sum(IMPACT.max == 4) == 1) { + g.df$TierAR[g.df$IMPACT.max == 4] <- 3 + g.df$TierAR[g.df$IMPACT.max <= 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max <= 3] + } else if (sum(IMPACT.max == 3) >= 2) { + g.df$TierAR[g.df$IMPACT.max == 3] <- 2 + g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] + } else if (sum(IMPACT.max == 3) == 1) { + g.df$TierAR[g.df$IMPACT.max == 3] <- 3 + g.df$TierAR[g.df$IMPACT.max < 3] <- 5 - g.df$IMPACT.max[g.df$IMPACT.max < 3] + } else { + g.df$TierAR <- 5 - g.df$IMPACT.max + } + # Calculate the count columns + g.df$No.Var.HM <- sum(IMPACT.max >= 3) + g.df$No.Var.H <- sum(IMPACT.max == 4) + g.df$No.Var.M <- sum(IMPACT.max == 3) + g.df$No.Var.L <- sum(IMPACT.max == 2) + + g.df + + }) |> + ungroup() + +# If TierAR.adj is needed and not calculated earlier, you can add it here +# For example, if it's the same as TierAR: + VEP.Tier.part1 <- VEP.Tier.part1 |> + mutate(TierAR.adj = TierAR) + +VEP.Tier.part1$TierAR.adj <- VEP.Tier.part1$TierAR -if (IncludeClinVarHGMD) { - gene.w1HighVar <- VEP.Tier.part1$Gene[VEP.Tier.part1$No.Var.H == 1] - rareIntronicVar.2adj <- rareIntronicVar[rareIntronicVar$Gene %in% gene.w1HighVar, ] - - # adjust the Tier for rareIntronicVar based on similarity - phenoSimi <- unique(VEP.f1.maxIMAPCT[, c("Gene", "omimSymptomSimScore", "hgmdSymptomSimScore")]) - - # check for non numeric values and convert to 0 - omim_NA_idx <- which(is.na(as.numeric(phenoSimi$omimSymptomSimScore))) - hgmd_NA_idx <- which(is.na(as.numeric(phenoSimi$hgmdSymptomSimScore))) - # print(hgmd_NA_idx) - phenoSimi$omimSymptomSimScore[omim_NA_idx] <- 0 - phenoSimi$hgmdSymptomSimScore[hgmd_NA_idx] <- 0 - - phenoSimi$omimSymptomSimScore <- as.numeric(phenoSimi$omimSymptomSimScore) - phenoSimi$hgmdSymptomSimScore <- as.numeric(phenoSimi$hgmdSymptomSimScore) - - phenoSimi <- phenoSimi[phenoSimi$omimSymptomSimScore != 0 | - phenoSimi$hgmdSymptomSimScore != 0, ] - - rareIntronicVar.2adj.wPhenoSimi <- merge(rareIntronicVar.2adj, - phenoSimi, - by.x = "Gene", - by.y = "Gene") - rareIntronicVar.2adj.wPhenoSimi$simi <- apply(rareIntronicVar.2adj.wPhenoSimi[, 3:4], 1, max) - rareIntronicVar.2adj.wPhenoSimi$TierAR.adj <- 5 - (5 - 4) * exp(adj.k * rareIntronicVar.2adj.wPhenoSimi$simi) - - rareIntronicVar.df <- rareIntronicVar.2adj.wPhenoSimi[, c("Uploaded_variation", "Gene", "TierAR.adj")] - VEP.Tier.part1 <- merge( - VEP.Tier.part1, - rareIntronicVar.df, - by.x <- c("Uploaded_variation", "Gene"), - by.y = c("Uploaded_variation", "Gene"), - all.x = T - ) -} else { - VEP.Tier.part1$TierAR.adj <- VEP.Tier.part1$TierAR -} # add the TierAR.adj to the VEP.Tier df @@ -379,7 +267,7 @@ VEP.Tier.part1 <- VEP.Tier.part1[, c( "No.Var.L" )] -# STEP 5: add the No.Var for Var.NoTier #### +cat("STEP 5: add the No.Var for Var.NoTier\n") if (ParseVar.NoTier) { VEP.Tier.part2 <- anno[anno$Uploaded_variation %in% Var.NoTier, c("Uploaded_variation", "Gene", "GT")] VEP.Tier.part2$IMPACT.max <- 1 @@ -391,34 +279,39 @@ if (ParseVar.NoTier) { VEP.Tier <- VEP.Tier.part1 } -VEP.Tier.wGene <- data.frame(matrix(ncol = ncol(VEP.Tier), nrow = 0)) -for (g in unique(VEP.Tier$Gene)) { - g.df <- VEP.Tier[VEP.Tier$Gene == g, ] - # AR - g.df2 <- g.df - - # duplicate rows with HOM var - if ("HOM" %in% g.df$GT) { - g.df2 <- rbind(g.df, g.df[g.df$GT == "1/1", ]) - } - IMPACT.max <- g.df2$IMPACT.max - - No.Var.HM <- sum(IMPACT.max >= 3) - No.Var.H <- sum(IMPACT.max == 4) - No.Var.M <- sum(IMPACT.max == 3) - No.Var.L <- sum(IMPACT.max == 2) - g.df$No.Var.HM <- No.Var.HM - g.df$No.Var.H <- No.Var.H - g.df$No.Var.M <- No.Var.M - g.df$No.Var.L <- No.Var.L - - VEP.Tier.wGene <- rbind(VEP.Tier.wGene, g.df) -} -# remove the GT column -VEP.Tier.wGene$GT <- NULL - -# STEP 6: add the Tiers from anno_noGeneID +VEP.Tier.wGene <- VEP.Tier |> + group_by(Gene) |> + group_modify(~{ + g.df <- .x + + # Duplicate rows with HOM var + g.df2 <- if ("HOM" %in% g.df$GT) { + bind_rows(g.df, filter(g.df, GT == "1/1")) + } else { + g.df + } + + IMPACT.max <- g.df2$IMPACT.max + + g.df |> + mutate( + No.Var.HM = sum(IMPACT.max >= 3), + No.Var.H = sum(IMPACT.max == 4), + No.Var.M = sum(IMPACT.max == 3), + No.Var.L = sum(IMPACT.max == 2) + ) + }) |> + ungroup() |> + select(-GT) # Remove the GT column + +# If you need to ensure the same column order as the original VEP.Tier +VEP.Tier.wGene <- VEP.Tier.wGene |> + select(names(VEP.Tier)[names(VEP.Tier) != "GT"]) + +VEP.Tier.wGene <- as_tibble(VEP.Tier.wGene) + +print("STEP 6: add the Tiers from anno_noGeneID") if ("-" %in% anno$Gene) { VEP.Tier.final <- rbind(VEP.Tier.wGene, anno_noGeneID) } @@ -427,7 +320,6 @@ VEP.Tier.final <- VEP.Tier.wGene colnames(VEP.Tier.final)[colnames(VEP.Tier.final) == "IMPACT.max"] <- "IMPACT.from.Tier" VEP.Tier.final$TierAR.adj[is.na(VEP.Tier.final$TierAR.adj)] <- VEP.Tier.final$TierAR[is.na(VEP.Tier.final$TierAR.adj)] - # add inheritance information colnames(genemap2.Inh.F)[1] <- "Gene" VEP.Tier.wInh <- merge( @@ -452,7 +344,3 @@ write.table( col.names = T, row.names = F ) - -# write.table(VEP.Tier.wInh,"/houston_20t/dongxue/MARRVEL_AI/V2/FeatureEng/TestOut/906246_Tier.tsv", sep = "\t", quote = F, col.names = T, row.names = F) -# write.table(VEP.Tier.wInh,"/houston_30t/dongxue/AI/UDN_Sample_2023/ModifiedAIMCode/NDVG_causal_Tier.v2.tsv", sep = "\t", quote = F, col.names = T, row.names = F) -### Plot for Tier adjustment #### From 4b4b7b98e5632eb36fda30824cda65b5a2e592bc Mon Sep 17 00:00:00 2001 From: hyunhwan-bcm <166857764+hyunhwan-bcm@users.noreply.github.com> Date: Wed, 10 Jul 2024 13:56:18 -0500 Subject: [PATCH 59/71] remove a code-line for debug a value was overridden for testing. --- bin/VarTierDiseaseDBFalse.R | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/bin/VarTierDiseaseDBFalse.R b/bin/VarTierDiseaseDBFalse.R index 90e8513..d60f832 100755 --- a/bin/VarTierDiseaseDBFalse.R +++ b/bin/VarTierDiseaseDBFalse.R @@ -9,9 +9,8 @@ adj.k <- 1.05 args <- commandArgs(trailingOnly = TRUE) refg <- args[1] -refg<-"hg19" -cat("load database: OMIM inheritance ####\n") +cat("STEP0. load database: OMIM inheritance ####\n") omim_inheritance <- file.path(paste0("var_tier/", refg, "/genemap2.Inh.F.txt")) in.f <- "scores.csv" out.f <- "Tier.v2.tsv" @@ -311,7 +310,7 @@ VEP.Tier.wGene <- VEP.Tier.wGene |> VEP.Tier.wGene <- as_tibble(VEP.Tier.wGene) -print("STEP 6: add the Tiers from anno_noGeneID") +cat("STEP 6: add the Tiers from anno_noGeneID\n") if ("-" %in% anno$Gene) { VEP.Tier.final <- rbind(VEP.Tier.wGene, anno_noGeneID) } From 2da37a6cd4cb1d0374e8ba2fb0c4ee5230663942 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Fri, 12 Jul 2024 12:16:18 -0500 Subject: [PATCH 60/71] Remove unused rundiff.py (#12) --- utils/run_diff.py | 49 ----------------------------------------------- 1 file changed, 49 deletions(-) delete mode 100644 utils/run_diff.py diff --git a/utils/run_diff.py b/utils/run_diff.py deleted file mode 100644 index abe0dd1..0000000 --- a/utils/run_diff.py +++ /dev/null @@ -1,49 +0,0 @@ -import pandas as pd -from pandas.api.types import is_numeric_dtype -import numpy as np - -def comp(file_a, file_b, sort = False, skipped_col = []): - print(file_a) - print(file_b) - df_a = pd.read_csv(file_a) if "csv" in file_a else pd.read_csv(file_a, sep="\t") - df_b = pd.read_csv(file_b) if "csv" in file_b else pd.read_csv(file_b, sep="\t") - - if sort: - def sort_df(df): - return df.sort_values(by=df.columns.tolist()).reset_index(drop=True) - - df_a = sort_df(df_a) - df_b = sort_df(df_b) - - assert df_a.shape == df_b.shape, f"The dimensions has to be the same. {df_a.shape} {df_b.shape}" - assert all(df_a.columns == df_b.columns), "The columns have to be identical each other." - - for i, (c, t) in enumerate(zip(df_a.columns, df_a.dtypes)): - if c in skipped_col: - continue - - va = df_a[c].values - vb = df_b[c].values - - if is_numeric_dtype(t): - diff = np.isclose(va, vb) - else: - diff = va == vb - - if not all(diff): - if all(pd.isnull(va[~diff])) and all(pd.isnull(vb[~diff])): - diff[~diff] = True - - idx = np.where(~diff) - - assert all(diff), f"The {c} column ({'numeric' if is_numeric_dtype(t) else 'non-numeric'}) has {sum(~diff)} mismatches.\n{idx}\n{va[~diff]}\n{vb[~diff]}" - - print("Two files are identical!") - return None - - -""" -file_a = "work/2d/f671cde44671f7989140c3772a841f/conf_4Model/integrated/1_integrated.csv" -file_b = "/home/hwan/AI_MARRVEL/test_data/output_all_latest_docker_check_simple_repeat/12345_integrated.csv" -comp(file_a, file_b, True, ["diffuse_Phrank_STRING", "diffuse_Phrank_STRING_1", "diffuse_Phrank_STRING_2", "identifier"]) -""" From b930322eba2b8fa552b9dd039b64dec2e8c88213 Mon Sep 17 00:00:00 2001 From: hyunhwan-bcm <166857764+hyunhwan-bcm@users.noreply.github.com> Date: Fri, 12 Jul 2024 12:39:40 -0500 Subject: [PATCH 61/71] Change library in VarTierDiseaseDBFalse.R (#13) * remove scripts/ * change imported library; tidyverse is not working - why? --- bin/VarTierDiseaseDBFalse.R | 5 +- scripts/.gitignore | 43 ------ scripts/README.md | 1 - scripts/proc.sh | 284 ------------------------------------ 4 files changed, 4 insertions(+), 329 deletions(-) delete mode 100644 scripts/.gitignore delete mode 100644 scripts/README.md delete mode 100755 scripts/proc.sh diff --git a/bin/VarTierDiseaseDBFalse.R b/bin/VarTierDiseaseDBFalse.R index d60f832..1e0c613 100755 --- a/bin/VarTierDiseaseDBFalse.R +++ b/bin/VarTierDiseaseDBFalse.R @@ -1,6 +1,9 @@ #!/usr/bin/env Rscript -library(tidyverse) +library(tidyr) +library(readr) +library(tibble) +library(dplyr) library(data.table) # set adj.k value to adjust Tier adj.k <- 1.05 diff --git a/scripts/.gitignore b/scripts/.gitignore deleted file mode 100644 index 801baac..0000000 --- a/scripts/.gitignore +++ /dev/null @@ -1,43 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - - -# Mac/OSX -.DS_Store - -# Windows -Thumbs.db - -# Data dependencies -predict_new/*.job -data_dependencies/ -model_inputs/ - -# Misc. -old_bak/ - diff --git a/scripts/README.md b/scripts/README.md deleted file mode 100644 index 826d7c2..0000000 --- a/scripts/README.md +++ /dev/null @@ -1 +0,0 @@ -# aim_one_pipeline diff --git a/scripts/proc.sh b/scripts/proc.sh deleted file mode 100755 index 857618f..0000000 --- a/scripts/proc.sh +++ /dev/null @@ -1,284 +0,0 @@ -# #!/bin/bash -# #$1 submission id -# #$2 ref genome -# #$3 memory limitation - -# #make output subdirectories -# echo $(date +"%T") > /out/whole_log.txt -# mkdir -m777 /out/phrank -# mkdir -m777 /out/input -# mkdir -m777 /out/tier-test-false -# mkdir -m777 /out/rami-test -# mkdir -m777 /out/scores -# mkdir -m777 /out/final_matrix -# mkdir -m777 /out/final_matrix_expanded - -# REF_DIR=$2 -# CHUNK_RAM=$3 -# KEEP_INTERMEDIATE=${4:-True} - -# if [ "$REF_DIR" = "hg19" ] -# then -# REF_GEN="GRCh37" -# else -# REF_GEN="GRCh38" -# fi - -# #Check if hpo.txt is empty or has no valid HPO ID -# if [[ -z $(egrep 'HP:[0-9]{7}' /input/hpo.txt) ]] ; then -# #Replace it with HP:0000001 -# echo "HP:0000001" > /input/hpo.txt -# fi - - -# cp /input/vcf.gz /out/vcf.gz ### New Step: Copying file to output directory - - -# # Initial path of the VCF file -# INPUT_VCF_PATH="/out/vcf.gz" ### Changed: Adjust path to new output location - -# #Check if the file exists -# if [ -e "$INPUT_VCF_PATH" ]; then -# #Use file command to determine the file type -# INPUT_VCF_TYPE=$(file -b "$INPUT_VCF_PATH") - -# # Determine if the file is gzip or bgzip format -# if echo "$INPUT_VCF_TYPE" | grep -q 'gzip compressed data'; then -# # Check if it's bgzip format for tabix compatibility -# if echo "$INPUT_VCF_TYPE" | grep -q 'BGZF'; then -# echo "The file is in BGZF format, ready for tabix." -# tabix -p vcf "$INPUT_VCF_PATH" ### Adjusted Step: Only index if BGZF -# else -# echo "GZIP format detected, converting to BGZF." -# gunzip -c "$INPUT_VCF_PATH" | bgzip > "${INPUT_VCF_PATH%.gz}" -# mv "${INPUT_VCF_PATH%.gz}" "$INPUT_VCF_PATH" ### Ensuring the correct .gz extension -# tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file -# fi -# elif echo "$INPUT_VCF_TYPE" | grep -q 'ASCII text'; then -# echo "Plain VCF file detected, compressing and indexing." -# bgzip -c "$INPUT_VCF_PATH" > "${INPUT_VCF_PATH}.gz" -# mv "${INPUT_VCF_PATH}.gz" "$INPUT_VCF_PATH" ### Replace uncompressed with compressed -# tabix -p vcf "$INPUT_VCF_PATH" ### Index the newly bgzipped file -# fi -# else -# echo "The file $INPUT_VCF_PATH does not exist." -# fi - - - -# echo "VCF pre-processing" -# #annotate with new chromosomes and preserve original coordinates in ID -# bcftools annotate --rename-chrs /run/data_dependencies/bcf_annotate/chrmap.txt -x ID /out/vcf.gz -Oz -o /out/$1-annot.txt -# bcftools annotate --set-id +'%CHROM\_%POS\_%REF\_%FIRST_ALT' /out/$1-annot.txt -Oz -o /out/$1-add-id.vcf.gz - - -# echo "VCF quality filtering" -# #run quality filters #removed 27th line -# bcftools filter /out/$1-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o /out/$1.filt.vcf.gz - -# #check number of variants left -# variant_count=$(bcftools view -H /out/$1.filt.vcf.gz | wc -l) -# if [ "$variant_count" -gt 0 ]; then -# echo "Quality filtering completed successfully. Variants passing the filters: $variant_count" -# else -# echo "The VCF file doesn't have FILTER annotation, or all variants filtered." -# echo "Pipeline will proceed with unfiltered VCF file." -# cp /out/$1-add-id.vcf.gz /out/$1.filt.vcf.gz -# fi - -# echo "Remove mitochondrial and unknown chromosome variants" -# #gunzip /out/$1.filt.vcf.gz -# #grep -vE "chrM" /out/$1.filt.vcf > /out/$1.filt.rmMT.vcf -# tabix -p vcf /out/$1.filt.vcf.gz -# bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y /out/$1.filt.vcf.gz -o /out/$1.filt.rmMT.vcf - -# #Phrank annotation -# echo "Phrank scoring" -# zcat /out/vcf.gz | awk 'substr($0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > /out/$1-var.txt -# cat /out/$1-var.txt | sed 's/chr//g' | sort -u > /out/$1-var-filt.txt - - -# #annotate with ENSG -# #echo "ensg annotates" -# python2.7 /run/phrank/src/location_to_gene.py /out/$1-var-filt.txt ${REF_DIR} | sed 's/:/\t/g' | sed 's/X\t/23\t/g' | sed 's/Y\t/24\t/g' | sed 's/MT\t/25\t/g' > /out/$1-ensmbl.txt - - -# #echo "gene-sym" -# cat /out/$1-ensmbl.txt | sort -k5,5 | join -1 5 -2 1 - /run/data_dependencies/phrank/hg19/ensembl_to_symbol.txt | sed 's/ /\t/g' | cut -f2- > /out/$1-genesym.txt -# cat /out/$1-genesym.txt | cut -f5 | sort -u | join -t$'\t' -1 1 -2 2 - /run/data_dependencies/phrank/hg19/gene_to_symbol_sorted.txt | cut -f2 | sort -u > /out/$1-gene.txt - - -# # #run phrank scoring -# #echo "phrank scoring" -# python3.8 /run/phrank/src/run_phrank.py /out/$1-gene.txt /input/hpo.txt ${REF_DIR} > /out/phrank/$1.txt - - -# # #annotate with OMIM and HPO path -# echo "Calculating phenotype similarities" -# Rscript /run/phenoSim.R /input/hpo.txt $1 ${REF_DIR} > /dev/null - - -# #Filter proband vcf with blacklist -# echo "Filtering VCF with blacklist" -# bgzip /out/$1.filt.rmMT.vcf - -# tabix /out/$1.filt.rmMT.vcf.gz -# mkdir -m777 /out/isec_tmp1 -# bcftools isec -p /out/isec_tmp1 -w 1 -Oz \ -# /out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.genomes.vcf.gz - -# tabix /out/isec_tmp1/0000.vcf.gz -# mkdir -m777 /out/isec_tmp2 -# bcftools isec -p /out/isec_tmp2 -w 1 -Oz \ -# /out/$1.filt.rmMT.vcf.gz /run/data_dependencies/filter_vep/${REF_DIR}/gnomad.${REF_DIR}.blacklist.exomes.vcf.gz - -# tabix /out/isec_tmp2/0000.vcf.gz -# mkdir -m777 /out/isec_tmp3 -# bcftools isec -p /out/isec_tmp3 -Ov \ -# /out/isec_tmp1/0000.vcf.gz /out/isec_tmp2/0000.vcf.gz - -# mv /out/isec_tmp3/0002.vcf /out/$1.filt.rmBL.vcf -# rm -rf /out/isec_tmp1 -# rm -rf /out/isec_tmp2 -# rm -rf /out/isec_tmp3 - - -# #annotate with vep -# echo "VEP annotation" - -# if [ "$REF_DIR" = "hg38" ] -# then - -# /opt/vep/src/ensembl-vep/vep \ -# --dir_cache /run/data_dependencies/vep/hg38/ \ -# --dir_plugins /run/data_dependencies/vep/hg38/Plugins/ \ -# --fork 10 --everything --format vcf \ -# --cache --offline --tab --force_overwrite \ -# --species homo_sapiens --assembly GRCh38 \ -# --custom /run/data_dependencies/vep/hg38/gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ -# --custom /run/data_dependencies/vep/hg38/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ -# --custom /run/data_dependencies/vep/hg38/HGMD_Pro_2022.2_hg38.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ -# --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg38/new_tabbed_revel_grch38.tsv.gz,ALL \ -# --plugin SpliceAI,snv=/run/data_dependencies/vep/hg38/spliceai_scores.masked.snv.hg38.vcf.gz,indel=/run/data_dependencies/vep/hg38/spliceai_scores.masked.indel.hg38.vcf.gz,cutoff=0.5 \ -# --plugin CADD,/run/data_dependencies/vep/hg38/hg38_whole_genome_SNV.tsv.gz,ALL \ -# --plugin dbNSFP,/run/data_dependencies/vep/hg38/dbNSFP4.1a_grch38.gz,ALL \ -# --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf -# else - -# /opt/vep/src/ensembl-vep/vep \ -# --dir_cache /run/data_dependencies/vep/hg19/ \ -# --dir_plugins /run/data_dependencies/vep/hg19/Plugins/ \ -# --fork 10 --everything --format vcf \ -# --cache --offline --tab --force_overwrite \ -# --species homo_sapiens --assembly GRCh37 \ -# --custom /run/data_dependencies/vep/hg19/gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz,gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \ -# --custom /run/data_dependencies/vep/hg19/clinvar_20220730.vcf.gz,clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \ -# --custom /run/data_dependencies/vep/hg19/HGMD_Pro_2022.2_hg19.vcf.gz,hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \ -# --af_gnomad --plugin REVEL,/run/data_dependencies/vep/hg19/new_tabbed_revel.tsv.gz,ALL \ -# --plugin SpliceAI,snv=/run/data_dependencies/vep/hg19/spliceai_scores.masked.snv.hg19.vcf.gz,indel=/run/data_dependencies/vep/hg19/spliceai_scores.masked.indel.hg19.vcf.gz,cutoff=0.5 \ -# --plugin CADD,/run/data_dependencies/vep/hg19/hg19_whole_genome_SNVs.tsv.gz,ALL \ -# --plugin dbNSFP,/run/data_dependencies/vep/hg19/dbNSFP4.3a_grch37.gz,ALL \ -# --individual all --output_file /out/$1-vep.txt --input_file /out/$1.filt.rmBL.vcf -# fi - -# echo "Feature engineering" -# #annotate w/ marrvel flatfiles -# AIM_FREE_RAM=$(free -g | awk 'NR==2{printf $7}') - -# if (( AIM_FREE_RAM > CHUNK_RAM )); then -# RAM_MAX=${CHUNK_RAM} -# else -# RAM_MAX=${AIM_FREE_RAM} -# fi - -# python3.8 /run/split_chunks.py $1 $RAM_MAX - -# while read -r INDEX LINEH LINEA LINEB -# do -# sed -n -e "${LINEH}p" -e "${LINEA},${LINEB}p" /out/$1-vep.txt > /out/$1-vep-${INDEX}.txt - -# python3.8 /run/annotation/main.py \ -# -outPrefix /out/rami-test/r1 \ -# -patientID $1 \ -# -patientHPOsimiOMIM /out/$1-dx \ -# -patientHPOsimiHGMD /out/$1-cz \ -# -varFile /out/$1-vep-$INDEX.txt \ -# -inFileType vepAnnotTab \ -# -patientFileType one \ -# -genomeRef ${REF_DIR} \ -# -diseaseInh AD \ -# -modules curate,conserve - -# if [ $INDEX -gt 1 ]; then -# sed -n "2,\$p" /out/rami-test/$1_scores.csv > /out/rami-test/$1_scores_$INDEX.csv -# rm /out/rami-test/$1_scores.csv -# sed -n "2,\$p" /out/rami-test/r1_$1_scores.txt > /out/rami-test/r1_$1_scores_$INDEX.txt -# rm /out/rami-test/r1_$1_scores.txt -# else -# mv /out/rami-test/$1_scores.csv /out/rami-test/$1_scores_$INDEX.csv -# mv /out/rami-test/r1_$1_scores.txt /out/rami-test/r1_$1_scores_$INDEX.txt -# fi -# done < /out/vep_split.txt - - -# # Combine all $1_scores_$INDEX.csv -# for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) -# do -# cat /out/rami-test/$1_scores_$INDEX.csv -# done > /out/rami-test/$1_scores.csv - -# for INDEX in $(cut -d$'\t' -f1 /out/vep_split.txt) -# do -# cat /out/rami-test/r1_$1_scores_$INDEX.txt -# done > /out/rami-test/r1_$1_scores.txt - -# rm /out/$1-vep-*.txt -# rm /out/rami-test/r1_$1_scores_*.txt -# rm /out/rami-test/$1_scores_*.csv - -# Rscript /run/VarTierDiseaseDBFalse.R $1 ${REF_DIR} - -# python3.8 /run/generate_new_matrix_2.py $1 ${REF_DIR} - -# echo "Making prediction" -# python3.8 /run/predict_new/run_final.py $1 - -# python3.8 /run/merge_rm.py $1 - -# python3.8 /run/extraModel/main.py -id $1 - - -# echo $(date +"%T") >> /out/whole_log.txt - -# # delete intermediate files -# if [ "$KEEP_INTERMEDIATE" = "False" ]; then -# rm /out/$1-add-id.vcf.gz -# rm /out/$1-annot.txt -# rm /out/$1-cz -# rm /out/$1-dx -# rm /out/$1-ensmbl.txt -# rm /out/$1.filt.rmBL.vcf -# rm /out/$1.filt.rmMT.vcf.gz -# rm /out/$1.filt.rmMT.vcf.gz.tbi -# rm /out/$1.filt.vcf -# rm /out/$1-genesym.txt -# rm /out/$1-gene.txt -# rm /out/$1.matrix.txt -# rm /out/$1-var-filt.txt -# rm /out/$1-var.txt -# rm /out/$1-vep.txt -# rm /out/$1-vep.txt* -# rm /out/vep_split.txt -# rm -r /out/final_matrix -# rm -r /out/final_matrix_expanded -# rm -r /out/input -# rm -r /out/phrank -# rm -r /out/rami-test -# rm -r /out/scores -# rm -r /out/tier-test-false -# fi - -# mv /out/conf_4Model/*.csv /out/ -# mv /out/conf_4Model/integrated/*.csv /out/ -# rm -r /out/conf_4Model - From b7e3c6d3aea7c4bbc7ee07d5534175698400621c Mon Sep 17 00:00:00 2001 From: hyunhwan-bcm <166857764+hyunhwan-bcm@users.noreply.github.com> Date: Fri, 12 Jul 2024 13:43:39 -0500 Subject: [PATCH 62/71] remove commented lines. (#14) * rebase commit https://github.com/LiuzLab/AI_MARRVEL/commit/16b72dde8d1f72fc4949b2b3919c72c8e2342c5a * docs: Update installation.rst * edit dockerfile for automatical bedtools/bcftools installation. * remove bcftools/bedtools installation. * fix Dockerfile for R installation. * ci: add GitHub Actions for automated testing * paper link updated * remove `dplyr` `tidyverse` has `dplyr`, so no need to install twice. * remove commented lines. --------- Co-authored-by: ZaahidShaik Co-authored-by: SasidharPasupuleti <34906695+sasidharPasupuleti@users.noreply.github.com> Co-authored-by: Sun-Young Kim Co-authored-by: ChaozhongLiu --- .github/workflows/test.yml | 93 ++++++++++++++++++++++++++++++ Dockerfile | 29 ++++++---- README.md | 27 +++++++-- bin/feature.py | 107 ++++------------------------------- docs/source/index.rst | 4 +- docs/source/installation.rst | 30 ++++++---- 6 files changed, 166 insertions(+), 124 deletions(-) create mode 100644 .github/workflows/test.yml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000..b2d4f39 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,93 @@ +name: Create SQS Queue and Monitor Message + +on: [push, pull_request] + +permissions: + id-token: write + contents: read + +jobs: + smoke-test: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Configure AWS Credentials + uses: aws-actions/configure-aws-credentials@v1 + with: + aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} + aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} + aws-region: ${{ secrets.AWS_REGION }} + + + - name: Install jq + run: sudo apt-get install jq + + - name: Generate Queue Name + id: step_qn + run: echo "queue_name=aim_pipeline_test_$(uuidgen)_res" >> $GITHUB_OUTPUT + + - name: Create SQS Queue + id: step_queue + run: | + QUEUE_URL=$(aws sqs create-queue --queue-name ${{ steps.step_qn.outputs.queue_name }} --output text --query 'QueueUrl') + echo "queue_url=$QUEUE_URL" >> $GITHUB_OUTPUT + + - name: Send Message to SQS + run: | + BRANCH_NAME="${{ github.head_ref || github.ref }}" + aws sqs send-message --queue-url "https://sqs.us-west-1.amazonaws.com/769006734761/aim_pipeline_test_req" \ + --message-body "${{ steps.step_qn.outputs.queue_name }}" \ + --message-attributes '{ + "branchName": { "DataType": "String", "StringValue": "'"$BRANCH_NAME"'" }, + "commitId": { "DataType": "String", "StringValue": "${{ github.sha }}" } + }' + + - name: Wait for Queue Message and Check Result + run: | + while true; do + # Poll the queue for messages + RESPONSE=$(aws sqs receive-message --queue-url ${{ steps.step_queue.outputs.queue_url }} --attribute-names All --message-attribute-names All --wait-time-seconds 20 --max-number-of-messages 1) + MESSAGE_BODY=$(echo $RESPONSE | jq -r '.Messages[0].Body') + + if [ -n "$MESSAGE_BODY" ]; then + echo "Message received: $MESSAGE_BODY" + RESULT=$(echo $MESSAGE_BODY | jq -r '.result') + + # Process the message as needed + if [ "$RESULT" == "PASS" ]; then + echo "Operation was successful." + break + else + echo "Operation failed." + exit 1 + fi + + # Get the receipt handle to delete the message + RECEIPT_HANDLE=$(echo $RESPONSE | jq -r '.Messages[0].ReceiptHandle') + + # Delete the message from the queue to prevent it from being read again + aws sqs delete-message --queue-url ${{ steps.step_queue.outputs.queue_url }} --receipt-handle "$RECEIPT_HANDLE" + echo "Message deleted from the queue." + else + echo "No message received, continuing to poll..." + fi + done + + - name: Delete SQS Queue + if: always() + run: | + aws sqs delete-queue --queue-url ${{ steps.step_queue.outputs.queue_url }} + echo "Queue deleted: ${{ steps.step_queue.outputs.queue_url }}" + + + - name: Close Pull Request on Failure + if: ${{ failure() }} && github.event_name == 'pull_request' + run: | + curl \ + -X PATCH \ + -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \ + -H "Accept: application/vnd.github.v3+json" \ + -d '{"state":"closed"}' \ + "https://api.github.com/repos/${{ github.repository }}/pulls/${{ github.event.pull_request.number }}" diff --git a/Dockerfile b/Dockerfile index 3f0f085..3affd64 100644 --- a/Dockerfile +++ b/Dockerfile @@ -46,26 +46,31 @@ RUN apt install wget RUN wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc # add the R 4.0 repo from CRAN -- adjust 'focal' to 'groovy' or 'bionic' as needed -RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ - apt-key adv --keyserver ha.pool.sks-keyservers.net --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ - apt-key adv --keyserver pgp.mit.edu --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ - apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ - apt-key adv --keyserver keyserver.pgp.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 +#RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ +# apt-key adv --keyserver ha.pool.sks-keyservers.net --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ +# apt-key adv --keyserver pgp.mit.edu --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ +# apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ +# apt-key adv --keyserver keyserver.pgp.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 +#RUN add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" + RUN add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" +RUN add-apt-repository universe +RUN apt-get update -RUN apt install -y r-base r-base-core r-recommended r-base-dev +RUN apt install -y r-base r-base-core # Install R libs RUN R -e "install.packages('data.table',dependencies=TRUE, repos='http://cran.rstudio.com/')" -RUN R -e "install.packages('dplyr',dependencies=TRUE, repos='http://cran.rstudio.com/')" +RUN R -e "install.packages('tidyverse',dependencies=TRUE, repos='http://cran.rstudio.com/')" RUN R -e "install.packages('ontologyIndex',dependencies=TRUE, repos='http://cran.rstudio.com/')" RUN R -e "install.packages('ontologySimilarity',dependencies=TRUE, repos='http://cran.rstudio.com/')" -RUN R -e "install.packages('tidyverse',dependencies=TRUE, repos='http://cran.rstudio.com/')" # Install bcftools -COPY bcftools-1.9.tar.bz2 /opt/bcftools-1.9.tar.bz2 +RUN wget https://github.com/samtools/bcftools/releases/download/1.9/bcftools-1.9.tar.bz2 +RUN mv bcftools-1.9.tar.bz2 /opt/bcftools-1.9.tar.bz2 + RUN tar -xf /opt/bcftools-1.9.tar.bz2 -C /opt/ && \ rm /opt/bcftools-1.9.tar.bz2 && \ cd /opt/bcftools-1.9 && \ @@ -74,10 +79,14 @@ RUN tar -xf /opt/bcftools-1.9.tar.bz2 -C /opt/ && \ make install && \ rm -rf /opt/bcftools-1.9 - # Copy the pipeline into Docker image COPY run /run/ RUN chmod +x /run/proc.sh +# Install bedtools +RUN wget https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary +RUN mv bedtools.static.binary /run/bedtools +RUN chmod a+x /run/bedtools + diff --git a/README.md b/README.md index 7324817..400f981 100644 --- a/README.md +++ b/README.md @@ -12,9 +12,6 @@ Documentation Status - - -

**AI-MARRVEL (AIM)** is an AI system for rare genetic disease diagnosis. @@ -25,8 +22,10 @@ and generates **prediction score** + **confidence score** as output. You can use AI-MARRVEL from our [website](https://ai.marrvel.org/) or follow the [documentation](https://ai-marrvel.readthedocs.io/en/latest/) to run locally. + - +:new: Our paper is now published in [NEJM AI](https://ai.nejm.org/doi/full/10.1056/AIoa2300009)! + ## Quick Start @@ -70,3 +69,23 @@ For business purpose, please contact us for licensing. ## Disclaimer - Some of the data and software included in the distribution may be subject to third-party constraints. Users of the data and software are solely responsible for establishing the nature of and complying with any such restrictions. - AI-MARRVEL provides this data and software in good faith, but make no warranty, express or implied, nor assume any legal liability or responsibility for any purpose for which they are used. + +## Citing AI-MARRVEL + +```bibtex +@article{doi:10.1056/AIoa2300009, +author = {Dongxue Mao and Chaozhong Liu and Linhua Wang and Rami AI-Ouran and Cole Deisseroth and Sasidhar Pasupuleti and Seon Young Kim and Lucian Li and Jill A. Rosenfeld and Linyan Meng and Lindsay C. Burrage and Michael F. Wangler and Shinya Yamamoto and Michael Santana and Victor Perez and Priyank Shukla and Christine M. Eng and Brendan Lee and Bo Yuan and Fan Xia and Hugo J. Bellen and Pengfei Liu and Zhandong Liu }, +title = {AI-MARRVEL — A Knowledge-Driven AI System for Diagnosing Mendelian Disorders}, +journal = {NEJM AI}, +volume = {1}, +number = {5}, +pages = {AIoa2300009}, +year = {2024}, +doi = {10.1056/AIoa2300009}, + +URL = {https://ai.nejm.org/doi/abs/10.1056/AIoa2300009}, +eprint = {https://ai.nejm.org/doi/pdf/10.1056/AIoa2300009} +, + abstract = { AI-MARRVEL is an AI system for genetic diagnosis that improves diagnostic accuracy, surpassing state-of-the-art benchmarked methods. } +} +``` diff --git a/bin/feature.py b/bin/feature.py index d32572f..59443da 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -129,65 +129,6 @@ def main(): clinvarAlleleDf = [] omimAlleleList = [] - ## CL: It's no longer needed to extract info from HGMD_allmut.txt - """ - #read HGMD files - if 'curate' in moduleList: - #hgmd dir - fileName='/run/annotation/HGMD_allmut.txt' - hgmdDf=pd.read_csv(fileName, sep='\t') - print('hgmdDf shape:', hgmdDf.shape) - hgmdDf=hgmdDf.fillna(0) - hgmdDf['startCoord']=hgmdDf['startCoord'].astype(int) - hgmdDf['endCoord']=hgmdDf['endCoord'].astype(int) - hgmdDf['chromosome']=hgmdDf['chromosome'].replace('X',23 ) - hgmdDf['chromosome']=hgmdDf['chromosome'].replace('Y',24 ) - hgmdDf['chromosome']=hgmdDf['chromosome'].replace('MT',25 ) - hgmdDf['chromosome']=hgmdDf['chromosome'].replace('GL.*','26',regex=True ) - #make chr as int - hgmdDf['chromosome']=hgmdDf['chromosome'].astype(int) - #sort by gene name - hgmdDf.sort_values("gene", inplace=True) - #setting gene as index column - hgmdDf.set_index("gene", inplace = True,drop=False) - - #test/check HGMD - if debugFlag==1: - print('in HGMD') - #print('\tvar:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol) - geneSymbol='SAMD11' - pos=865595 - chrom='1' - print('\tchrom:', chrom, 'pos:',pos,'geneSymbol:', geneSymbol) - HGMDDict={} - hgmdGeneFound=0 - hgmdVarFound=0 - hgmdVarPhenIdList=[] - hgmdVarHPOIdList=[] - hgmdVarHPOStrList=[] - #check variant - print('\ttype pos:', type(hgmdVarDf['pos'][0]), 'type chrom:', type(hgmdVarDf['chrom'][0])) - vals=hgmdVarDf[(hgmdVarDf['pos']==pos) & (hgmdVarDf['chrom'] == int(chrom))] - numRows=len(vals.index) - print('\tvar numRows:', numRows) - if numRows>0: - hgmdVarFound=1 - hgmdVarPhenIdList.extend(vals['phen_id'].tolist()) - hgmdVarHPOIdList.extend(vals['hpo_id'].tolist()) - hgmdVarHPOStrList.extend(vals['hpo_str'].tolist()) - - #check gene - vals=hgmdGeneDf[(hgmdGeneDf['gene']==geneSymbol)] - numRows=len(vals.index) - print('\tgene numRows:', numRows) - if numRows>0: - hgmdGeneFound=1 - print('\thgmdVarFound:',hgmdVarFound,'hgmdGeneFound:',hgmdGeneFound, - 'hgmdVarPhenIdList:',hgmdVarPhenIdList,'hgmdVarHPOIdList:',hgmdVarHPOIdList, - 'hgmdVarHPOStrList:',hgmdVarHPOStrList) - - """ - # read HPO files if "curate" in moduleList: fileName = args.patientHPOsimiOMIM @@ -206,26 +147,21 @@ def main(): if args.genomeRef == "hg38": fileName = "annotate/anno_hg19/gene_clinvar.csv" else: - fileName = "annotate/anno_hg19/gene_clinvar.csv" - # fileName='/database_test/gene_clinvar.csv' - # print('\nfileName:', fileName) + fileName = "annotate/anno_hg19/gene_clinvar.csv" + clinvarGeneDf = pd.read_csv(fileName, sep=",") # sort by gene name clinvarGeneDf.sort_values("symbol", inplace=True) clinvarGeneDf.set_index(["symbol"], inplace=True, drop=False) - # print('clinvarGenes dim:', clinvarGeneDf.shape)#41754 x 10,, entrezId,symbol,chr,hg19Start,hg19Stop,totalClinvarVars,P,LP,LB,B - # print(clinvarGeneDf.head()) # read OMIM if "curate" in moduleList: # read the OMIM gene file fileName = "annotate/anno_hg19/gene_omim.json" - # fileName='/database_test/gene_omim.json' - # print('\nfileName:', fileName) + with open(fileName) as f: omimGeneList = json.load(f) - # print('type omimGeneList:', type(omimGeneList), 'len:', len(omimGeneList)) - # check the type of data and keys + if debugFlag == 1: for omimGeneDict in omimGeneList: print("type of omimGeneDict:", type(omimGeneDict)) @@ -236,16 +172,12 @@ def main(): if isinstance(omimGeneDict[keyVal], list): print("\n\t\tfound list") print("\t\t type:", type(omimGeneDict[keyVal])) - # print('\t\t type:',omimGeneDict[keyVal]) - # for row in omimGeneDict[keyVal]: - # print('row keys:', row.keys()) break # read the OMIM allele file fileName = "annotate/anno_hg19/omim_alleric_variants.json" - # fileName='/database_test/omim_alleric_variants.json' - # print('\nfileName:', fileName) + with open(fileName) as f: omimAlleleList = json.load(f) if debugFlag == 1: @@ -271,10 +203,7 @@ def main(): fileName = "annotate/anno_hg38/dgv.csv" else: fileName = "annotate/anno_hg19/dgv.csv" - # fileName='/database_test/dgv.csv' dgvDf = pd.read_csv(fileName, sep=",") - # print('dgvDf shape:', dgvDf.shape) - # print('dgvHead:', dgvDf.head()) # hg19Chr hg19Start hg19Stop dgvDf = dgvDf.fillna(0) @@ -289,19 +218,8 @@ def main(): dgvDf["Chr"] = dgvDf["Chr"].replace("GL.*", 26, regex=True) # make chr as int - # dgvDf = dgvDf.loc[dgvDf['Chr'].isin(list(range(27))),:].copy() dgvDf["Chr"] = dgvDf["Chr"].astype(int) - """ CL: change column name to be compatible with hg38 - dgvDf['hg19Start']=dgvDf['hg19Start'].astype(int) - dgvDf['hg19Stop']=dgvDf['hg19Stop'].astype(int) - dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('X',23 ) - dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('Y',24 ) - dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('MT',25 ) - dgvDf['hg19Chr']=dgvDf['hg19Chr'].replace('GL.*','26',regex=True ) - #make chr as int - dgvDf['hg19Chr']=dgvDf['hg19Chr'].astype(int) - """ print("finsihed reading DGV") # read DECIPHER database @@ -311,13 +229,10 @@ def main(): fileName = "annotate/anno_hg38/decipher.csv" else: fileName = "annotate/anno_hg19/decipher.csv" - # fileName='/database_test/decipher.csv' decipherDf = pd.read_csv(fileName, sep=",") - # print('decipherDf shape:', decipherDf.shape) # hg19Chr,hg19Start,hg19Stop decipherDf = decipherDf.fillna(0) - # if args.genomeRef == 'hg19': decipherDf.columns = ["Chr", "Start", "Stop"] + decipherDf.columns.tolist()[3:] decipherDf["Start"] = decipherDf["Start"].astype(int) @@ -343,7 +258,8 @@ def main(): # read the input variant file if args.inFileType == "vepAnnotTab" and flatFileFlag == 1: - # numHeaderSkip=354#this depends on the type of annotation used + # numHeaderSkip=354 + # this depends on the type of annotation used numHeaderSkip = 0 with open(args.varFile, "r") as F: for line in F: @@ -356,7 +272,8 @@ def main(): varDf = pd.read_csv( args.varFile, sep="\t", skiprows=numHeaderSkip, error_bad_lines=False ) - # varDf=varDf[0:10]#do this if need to have a small test + # varDf=varDf[0:10] + # #do this if need to have a small test print("shape:", varDf.shape) t2 = time.time() inputReadTime = t2 - t1 @@ -414,8 +331,6 @@ def f(varObj): stopVal = int(varObj.stop) # CL 03-14-2023: changed column names to be compatible with hg38 - # vals=dgvDf[ ( dgvDf['hg19Chr'] == chromVal ) & ( dgvDf['hg19Start']<=startVal ) & (dgvDf['hg19Stop']>=stopVal) ] - vals = dgvDf[(dgvDfV[:, 0] == chromVal) & (dgvDfV[:, 1] <= startVal) & (dgvDfV[:, 2] >= stopVal)] numRows = len(vals.index) @@ -441,16 +356,14 @@ def f(varObj): if "curate" in moduleList: df = [] for i, varObj in annotateInfoDf.iterrows(): - # print('var:', varObj.varId_dash, 'var-gene:', varObj.geneSymbol, 'var-gene-pLI:', varObj.gnomadGenePLI) # the curate score is under the utils_1.py file - # print('finding symptom matching') omimSymMatch(varObj, omimHPOScoreDf, args.inFileType) hgmdSymMatch(varObj, hgmdHPOScoreDf) clinVarSymMatch(varObj, args.inFileType) # OMIM and clinvar info retList = getCurationScore( varObj - ) # retList=[omimScore, hgmdScore, clinVarScore, totalScore] + ) df.append({ "curationScoreOMIM": retList[0], "curationScoreHGMD": retList[1], diff --git a/docs/source/index.rst b/docs/source/index.rst index 6b977a4..2e0eab2 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -34,8 +34,8 @@ Prerequisites Citation ===================== -**Enhancing Molecular Diagnostics of Mendelian Disorders with AI-MARRVEL: A Knowledge-Driven Artificial Intelligence Approach.** -Click `here `_ for details. +**AI-MARRVEL — A Knowledge-Driven AI System for Diagnosing Mendelian Disorders.** +Click `here `_ for details. License diff --git a/docs/source/installation.rst b/docs/source/installation.rst index b157646..18b658c 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -32,23 +32,31 @@ If you want to keep a stable local version of AIM, follow the instruction below. git clone https://github.com/LiuzLab/AI_MARRVEL.git cd AI_MARRVEL - # Download necessary software - # bcftools - wget https://github.com/samtools/bcftools/releases/download/1.9/bcftools-1.9.tar.bz2 - # bedtools - wget https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary - mv bedtools.static.binary run/bedtools - chmod a+x run/bedtools - # Build docker image (takes some time) docker build -t aim-lite . Install Required Data Dependencies -=================================== -AIM utilizes various databases for variant annotation, all of which have been compiled and made available for user download. +=================================== +AIM utilizes various databases for variant annotation, all of which have been compiled and are available for download. We use AWS S3 for data access, and the data can be downloaded by following these steps: + +1. **Install the AWS CLI**: + Follow the instructions provided in the `AWS CLI Installation Guide `_ + + +2. **Navigate to Your Desired Directory**: + Change to the directory where you want your data dependencies downloaded. For example, in Ubuntu, use: + + .. code-block:: bash + + cd + +3. **Use the following command to sync the S3 bucket to your local directory**: + + .. code-block:: bash + + aws s3 sync s3://aim-data-dependencies-public . --no-sign-request -We use Globus for data accessing `here `_ .. warning:: From 716bcd5c69b229528ee2a2e817c3a0b2a86ec312 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Mon, 15 Jul 2024 13:03:42 -0500 Subject: [PATCH 63/71] Filter VCFs to Exonic Regions Only (#17) To prevent it from running more than days with the input vcf of WGS (Whole Genome Sequence), this changes filter to exonic regions only, based on the BED file generated with UCSC genome browser, by merging all exome probeset tracks (union function) (Mohammed provided) This change ensures the pipeline can process within 30 minutes with the user's input of WGS. --- README.md | 1 + main.nf | 26 +++++++++++++++++++++++++- nextflow.config | 4 ++++ 3 files changed, 30 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 400f981..9623184 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,7 @@ nextflow run . --ref_dir --outdir --run_id [Sample Id] # Optional, default: 1 --ref_ver [Reference genome: hg19/hg38] # Optional, default: hg19 + --no_filter_exonic # Optional, default: false ``` Alternatively, the pipeline can be executed with a parameter file (yaml) diff --git a/main.nf b/main.nf index c08957f..aa7a635 100644 --- a/main.nf +++ b/main.nf @@ -38,6 +38,28 @@ process INDEX_VCF { """ } +process FILTER_EXONIC { + input: + path vcf + path tbi + path ref_exonic_filter_bed + + output: + path "${params.run_id}.recode.vcf.gz" + path "${params.run_id}.recode.vcf.gz.tbi" + + script: + """ + if [ !${params.no_filter_exonic} ]; then + bcftools filter --regions-file ${ref_exonic_filter_bed} ${vcf} -Oz -o "${params.run_id}.recode.vcf.gz" + tabix -p vcf "${params.run_id}.recode.vcf.gz" + else + cp ${vcf} "${params.run_id}.recode.vcf.gz" + cp ${tbi} "${params.run_id}.recode.vcf.gz.tbi" + fi + """ +} + process VCF_PRE_PROCESS { input: path vcf @@ -351,7 +373,9 @@ process PREDICTION { workflow { INDEX_VCF(params.input_vcf) - VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) + FILTER_EXONIC(INDEX_VCF.out, params.ref_exonic_filter_bed) + + VCF_PRE_PROCESS(FILTER_EXONIC.out, params.chrmap) REMOVE_MITO_AND_UNKOWN_CHR(VCF_PRE_PROCESS.out) diff --git a/nextflow.config b/nextflow.config index f950b88..d74ed25 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,6 +1,7 @@ params { run_id = 1 ref_ver = "hg19" + no_filter_exonic = false ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" @@ -23,6 +24,9 @@ params { omim_genemap2 = "${ref_dir}/omim_annotate/${ref_ver}/genemap2_v2022.rds" omim_pheno = "${ref_dir}/omim_annotate/${ref_ver}/HPO_OMIM.tsv" + // EXONIC FILTER BED + ref_exonic_filter_bed = "${ref_dir}/filter_exonic/${ref_ver}.bed" + // GNOMAD VCF ref_gnomad_genome = "${ref_dir}/filter_vep/${ref_ver}/gnomad.${ref_ver}.blacklist.genomes.vcf.gz" ref_gnomad_genome_idx = "${ref_gnomad_genome}.tbi" From 357939b73fd80e3d6a4000c9af44be29e03f1a80 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Mon, 15 Jul 2024 13:32:02 -0500 Subject: [PATCH 64/71] Preprocess for normalizing mod5 network (#16) * Separate process for normalizing mod5 network * Adjust memory and cpus setting for nextflow processes * Preprocess Mod5 Diffusion * Remove an empty line --- bin/mod5_diffusion.py | 16 ++++------------ utils/normalize_network.py | 22 ++++++++++++++++++++++ 2 files changed, 26 insertions(+), 12 deletions(-) create mode 100755 utils/normalize_network.py diff --git a/bin/mod5_diffusion.py b/bin/mod5_diffusion.py index 6795f7d..ea4fe07 100755 --- a/bin/mod5_diffusion.py +++ b/bin/mod5_diffusion.py @@ -52,13 +52,10 @@ def diffuseSample(ID, Anno_df, Phrank_folder): columnns contatins diffused phrank scores normalized as percentile. """ - ## Get the PPI network for diffusion - cor_path = "mod5_diffusion/combined_score.hdf5" - cor_df = pd.read_hdf(cor_path, mode="r") - cor_df = cor_df[(cor_df.T != 0).sum() > 1] - cor_df = cor_df.loc[cor_df.index, cor_df.index] - cor = cor_df.values - cor_GeneID = pd.DataFrame({"ID": cor_df.columns.tolist()}) + ## Load PPI Network for Diffusion + net_norm_cor_GeneID = np.load('./mod5_diffusion/net_norm_cor_GeneID.npz', allow_pickle=True) + net_norm = net_norm_cor_GeneID['net_norm'] + cor_GeneID = pd.DataFrame(net_norm_cor_GeneID['cor_GeneID_arr'], columns=["ID"]) # Phrank_path = Phrank_folder + ID.split('.')[0] + ".txt" Phrank_path = ID + ".phrank.txt" @@ -100,11 +97,6 @@ def diffuseSample(ID, Anno_df, Phrank_folder): m12_wSimi = list(set(Phrank["Ensembl_Gene_ID"]) & set(m12_genes)) m12_wSimi_woCor = list(set(m12_wSimi) - (set(cor_GeneID["ID"]))) m12_wSimi_woCor = simi[simi["Ensembl_Gene_ID"].isin(m12_wSimi_woCor)] - ## normalized the cor matrix - net = abs(cor).astype('float32') - D = 1 / np.sqrt(net.sum(axis=1)) - D2 = np.diag(D) - net_norm = D2 @ net @ D2 ## Set Y as similarity score Y = cor_GeneID.merge(simi, left_on="ID", right_on="Ensembl_Gene_ID", how="left") diff --git a/utils/normalize_network.py b/utils/normalize_network.py new file mode 100755 index 0000000..bd1603f --- /dev/null +++ b/utils/normalize_network.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3.8 +import numpy as np +import pandas as pd + +def main(): + ## Get the PPI network for diffusion + cor_path = "mod5_diffusion/combined_score.hdf5" + cor_df = pd.read_hdf(cor_path, mode="r") + cor_df = cor_df[(cor_df.T != 0).sum() > 1] + cor_df = cor_df.loc[cor_df.index, cor_df.index] + + cor = cor_df.values + cor_GeneID_arr = pd.DataFrame({"ID": cor_df.columns.tolist()}).values + + net = abs(cor).astype('float32') + D = 1 / np.sqrt(net.sum(axis=1)) + D2 = np.diag(D) + net_norm = D2 @ net @ D2 + np.savez("net_norm_cor_GeneID.npz", net_norm=net_norm, cor_GeneID_arr=cor_GeneID_arr) + +if __name__ == "__main__": + main() From 5f19be6baee934b04eb21ae28e50c2b8f4d9b0b6 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Mon, 15 Jul 2024 15:23:47 -0500 Subject: [PATCH 65/71] Revert "Filter VCFs to Exonic Regions Only (#17)" (#18) This reverts commit 716bcd5c69b229528ee2a2e817c3a0b2a86ec312. --- README.md | 1 - main.nf | 26 +------------------------- nextflow.config | 4 ---- 3 files changed, 1 insertion(+), 30 deletions(-) diff --git a/README.md b/README.md index 9623184..400f981 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,6 @@ nextflow run . --ref_dir --outdir --run_id [Sample Id] # Optional, default: 1 --ref_ver [Reference genome: hg19/hg38] # Optional, default: hg19 - --no_filter_exonic # Optional, default: false ``` Alternatively, the pipeline can be executed with a parameter file (yaml) diff --git a/main.nf b/main.nf index aa7a635..c08957f 100644 --- a/main.nf +++ b/main.nf @@ -38,28 +38,6 @@ process INDEX_VCF { """ } -process FILTER_EXONIC { - input: - path vcf - path tbi - path ref_exonic_filter_bed - - output: - path "${params.run_id}.recode.vcf.gz" - path "${params.run_id}.recode.vcf.gz.tbi" - - script: - """ - if [ !${params.no_filter_exonic} ]; then - bcftools filter --regions-file ${ref_exonic_filter_bed} ${vcf} -Oz -o "${params.run_id}.recode.vcf.gz" - tabix -p vcf "${params.run_id}.recode.vcf.gz" - else - cp ${vcf} "${params.run_id}.recode.vcf.gz" - cp ${tbi} "${params.run_id}.recode.vcf.gz.tbi" - fi - """ -} - process VCF_PRE_PROCESS { input: path vcf @@ -373,9 +351,7 @@ process PREDICTION { workflow { INDEX_VCF(params.input_vcf) - FILTER_EXONIC(INDEX_VCF.out, params.ref_exonic_filter_bed) - - VCF_PRE_PROCESS(FILTER_EXONIC.out, params.chrmap) + VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) REMOVE_MITO_AND_UNKOWN_CHR(VCF_PRE_PROCESS.out) diff --git a/nextflow.config b/nextflow.config index d74ed25..f950b88 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,7 +1,6 @@ params { run_id = 1 ref_ver = "hg19" - no_filter_exonic = false ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" @@ -24,9 +23,6 @@ params { omim_genemap2 = "${ref_dir}/omim_annotate/${ref_ver}/genemap2_v2022.rds" omim_pheno = "${ref_dir}/omim_annotate/${ref_ver}/HPO_OMIM.tsv" - // EXONIC FILTER BED - ref_exonic_filter_bed = "${ref_dir}/filter_exonic/${ref_ver}.bed" - // GNOMAD VCF ref_gnomad_genome = "${ref_dir}/filter_vep/${ref_ver}/gnomad.${ref_ver}.blacklist.genomes.vcf.gz" ref_gnomad_genome_idx = "${ref_gnomad_genome}.tbi" From 09af85c8c9d2942387a87193ffca8ec90c6cda9e Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Mon, 15 Jul 2024 17:06:55 -0500 Subject: [PATCH 66/71] Revert "Revert "Filter VCFs to Exonic Regions Only (#17)" (#18)" (#20) This reverts commit 5f19be6baee934b04eb21ae28e50c2b8f4d9b0b6. --- README.md | 1 + main.nf | 26 +++++++++++++++++++++++++- nextflow.config | 4 ++++ 3 files changed, 30 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 400f981..9623184 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,7 @@ nextflow run . --ref_dir --outdir --run_id [Sample Id] # Optional, default: 1 --ref_ver [Reference genome: hg19/hg38] # Optional, default: hg19 + --no_filter_exonic # Optional, default: false ``` Alternatively, the pipeline can be executed with a parameter file (yaml) diff --git a/main.nf b/main.nf index c08957f..aa7a635 100644 --- a/main.nf +++ b/main.nf @@ -38,6 +38,28 @@ process INDEX_VCF { """ } +process FILTER_EXONIC { + input: + path vcf + path tbi + path ref_exonic_filter_bed + + output: + path "${params.run_id}.recode.vcf.gz" + path "${params.run_id}.recode.vcf.gz.tbi" + + script: + """ + if [ !${params.no_filter_exonic} ]; then + bcftools filter --regions-file ${ref_exonic_filter_bed} ${vcf} -Oz -o "${params.run_id}.recode.vcf.gz" + tabix -p vcf "${params.run_id}.recode.vcf.gz" + else + cp ${vcf} "${params.run_id}.recode.vcf.gz" + cp ${tbi} "${params.run_id}.recode.vcf.gz.tbi" + fi + """ +} + process VCF_PRE_PROCESS { input: path vcf @@ -351,7 +373,9 @@ process PREDICTION { workflow { INDEX_VCF(params.input_vcf) - VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) + FILTER_EXONIC(INDEX_VCF.out, params.ref_exonic_filter_bed) + + VCF_PRE_PROCESS(FILTER_EXONIC.out, params.chrmap) REMOVE_MITO_AND_UNKOWN_CHR(VCF_PRE_PROCESS.out) diff --git a/nextflow.config b/nextflow.config index f950b88..d74ed25 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,6 +1,7 @@ params { run_id = 1 ref_ver = "hg19" + no_filter_exonic = false ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" @@ -23,6 +24,9 @@ params { omim_genemap2 = "${ref_dir}/omim_annotate/${ref_ver}/genemap2_v2022.rds" omim_pheno = "${ref_dir}/omim_annotate/${ref_ver}/HPO_OMIM.tsv" + // EXONIC FILTER BED + ref_exonic_filter_bed = "${ref_dir}/filter_exonic/${ref_ver}.bed" + // GNOMAD VCF ref_gnomad_genome = "${ref_dir}/filter_vep/${ref_ver}/gnomad.${ref_ver}.blacklist.genomes.vcf.gz" ref_gnomad_genome_idx = "${ref_gnomad_genome}.tbi" From 3190492075b0e67982c487d11679c52d510ff13f Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 17 Jul 2024 14:59:30 -0500 Subject: [PATCH 67/71] Reorder exonic filtering step to after mitochondria removal (#21) * Quick Fix * Reorder --- main.nf | 22 ++++++++-------------- 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/main.nf b/main.nf index aa7a635..224b209 100644 --- a/main.nf +++ b/main.nf @@ -50,8 +50,9 @@ process FILTER_EXONIC { script: """ - if [ !${params.no_filter_exonic} ]; then - bcftools filter --regions-file ${ref_exonic_filter_bed} ${vcf} -Oz -o "${params.run_id}.recode.vcf.gz" + if [ ${params.no_filter_exonic} == false ]; then + awk '{gsub(/^chr/, ""); print}' ${ref_exonic_filter_bed} > bed + bcftools filter --regions-file bed ${vcf} -Oz -o "${params.run_id}.recode.vcf.gz" tabix -p vcf "${params.run_id}.recode.vcf.gz" else cp ${vcf} "${params.run_id}.recode.vcf.gz" @@ -372,19 +373,11 @@ process PREDICTION { workflow { INDEX_VCF(params.input_vcf) - - FILTER_EXONIC(INDEX_VCF.out, params.ref_exonic_filter_bed) - - VCF_PRE_PROCESS(FILTER_EXONIC.out, params.chrmap) - - REMOVE_MITO_AND_UNKOWN_CHR(VCF_PRE_PROCESS.out) + VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) ANNOT_PHRANK(VCF_PRE_PROCESS.out) - ANNOT_ENSMBLE(ANNOT_PHRANK.out, params.ref_loc) - TO_GENE_SYM(ANNOT_ENSMBLE.out, params.ref_to_sym, params.ref_sorted_sym) - PHRANK_SCORING( TO_GENE_SYM.out, params.input_hpo, params.phrank_dagfile, @@ -398,15 +391,16 @@ workflow { params.omim_genemap2, params.omim_pheno) + REMOVE_MITO_AND_UNKOWN_CHR(VCF_PRE_PROCESS.out) + FILTER_EXONIC(REMOVE_MITO_AND_UNKOWN_CHR.out, params.ref_exonic_filter_bed) FILTER_PROBAND( - REMOVE_MITO_AND_UNKOWN_CHR.out[0], - REMOVE_MITO_AND_UNKOWN_CHR.out[1], + FILTER_EXONIC.out[0], + FILTER_EXONIC.out[1], params.ref_gnomad_genome, params.ref_gnomad_genome_idx, params.ref_gnomad_exome, params.ref_gnomad_exome_idx ) - VEP_ANNOTATE( FILTER_PROBAND.out, params.vep_dir_cache, From 159d7f9b19f5cfea89a0e8af0f6b60c0e75c1122 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Thu, 18 Jul 2024 09:40:14 -0500 Subject: [PATCH 68/71] Update bcftools to 1.20 (#22) --- Dockerfile | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/Dockerfile b/Dockerfile index 3affd64..05ae4e9 100644 --- a/Dockerfile +++ b/Dockerfile @@ -68,20 +68,16 @@ RUN R -e "install.packages('ontologySimilarity',dependencies=TRUE, repos='http:/ # Install bcftools -RUN wget https://github.com/samtools/bcftools/releases/download/1.9/bcftools-1.9.tar.bz2 -RUN mv bcftools-1.9.tar.bz2 /opt/bcftools-1.9.tar.bz2 +RUN wget https://github.com/samtools/bcftools/releases/download/1.20/bcftools-1.20.tar.bz2 +RUN mv bcftools-1.20.tar.bz2 /opt/bcftools-1.20.tar.bz2 -RUN tar -xf /opt/bcftools-1.9.tar.bz2 -C /opt/ && \ - rm /opt/bcftools-1.9.tar.bz2 && \ - cd /opt/bcftools-1.9 && \ +RUN tar -xf /opt/bcftools-1.20.tar.bz2 -C /opt/ && \ + rm /opt/bcftools-1.20.tar.bz2 && \ + cd /opt/bcftools-1.20 && \ ./configure && \ make && \ make install && \ - rm -rf /opt/bcftools-1.9 - -# Copy the pipeline into Docker image -COPY run /run/ -RUN chmod +x /run/proc.sh + rm -rf /opt/bcftools-1.20 # Install bedtools RUN wget https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary From 95af06491f12b25b83692edf955c767beb9e30a0 Mon Sep 17 00:00:00 2001 From: hyunhwan-bcm <166857764+hyunhwan-bcm@users.noreply.github.com> Date: Thu, 1 Aug 2024 13:40:54 -0500 Subject: [PATCH 69/71] Hj/nxf comp/update bivar (#27) * revert some files to main. * fix the known sorting issue. * make process_gene same as main_fix_sort branch * conf4 are now the same. * force var_grp to be sorted by the index. * add indexing sorting for the var index. * temporal off of the dedup. * change the sorting for index of each group df. * make confidence sorting stable. --- bin/add_c_nc.py | 3 - bin/extraModel/generate_bivar_data.py | 3 +- bin/extraModel/integrate_output.py | 4 +- bin/extraModel_main.py | 3 +- bin/fillna_tier.py | 110 +++++++++++++------------- bin/generate_new_matrix_2.py | 2 +- main.nf | 2 +- 7 files changed, 63 insertions(+), 64 deletions(-) mode change 100755 => 100644 bin/fillna_tier.py diff --git a/bin/add_c_nc.py b/bin/add_c_nc.py index e8b42f5..8c4b74d 100755 --- a/bin/add_c_nc.py +++ b/bin/add_c_nc.py @@ -26,9 +26,6 @@ def add_c_nc(score, ref): bl = clin_nc.new_start.values bc = clin_nc.new_chr.values - bc[bc=='MT'] = -1 # Treat MT chromosome as NaN values to avoid string comparison - bc = bc.astype(int) - i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) & (ac[:, None] == bc)) cln = pd.concat( diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index b65845a..06a7c2c 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -55,6 +55,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): gene_var_pairs = [] for geneEnsId, grouped_df in geneEnsId_varId_df.groupby('geneEnsId'): # Choose upto 6 the most meaningful variants. + grouped_df.sort_index() if grouped_df.shape[0] > 6: grouped_df = grouped_df \ .join(default_pred, on='varId') \ @@ -68,7 +69,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): # Remove all the duplicated variant pairs. gene_var_pairs_df = pd.DataFrame(gene_var_pairs) - gene_var_pairs_df = gene_var_pairs_df.drop_duplicates(['varId1', 'varId2']) + # gene_var_pairs_df = gene_var_pairs_df.drop_duplicates(['varId1', 'varId2']) # Use only subset columns of features subset_feature_names = "diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat".split(',') diff --git a/bin/extraModel/integrate_output.py b/bin/extraModel/integrate_output.py index ca5b5ce..81aac12 100755 --- a/bin/extraModel/integrate_output.py +++ b/bin/extraModel/integrate_output.py @@ -11,7 +11,8 @@ def process_recessive_matrix(df): result = [] for _, var_grp in var_grps: - var_grp = var_grp.sort_values("predict", ascending=False) + var_grp = var_grp.sort_values("var2", kind="stable") + var_grp = var_grp.sort_values("predict", ascending=False, kind="stable") result.append(var_grp.iloc[[0], :]) result = pd.concat(result) result.set_index("var1", inplace=True) @@ -31,6 +32,7 @@ def integrate_output(prj_folder, data_folder, sample_id): expanded_df = pd.read_csv( expanded_fn, sep="\t", index_col=0, compression="infer" ) + expanded_df = expanded_df.sort_index() # expanded_df.append(df) # expanded_df = pd.concat(expanded_df) # expanded_df = expanded_df.loc[~expanded_df.index.duplicated(keep='first')] diff --git a/bin/extraModel_main.py b/bin/extraModel_main.py index 2e9f0c8..57c1f50 100755 --- a/bin/extraModel_main.py +++ b/bin/extraModel_main.py @@ -104,8 +104,9 @@ def AIM(data_folder, sample_id): df_pred.loc[:, model_dict[mn]["features"]] )[:, 1] df_pred.insert(loc=df_pred.shape[1] - 1, column="predict", value=predict) + df_pred = df_pred.sort_index() df_pred = assign_confidence_score(model_dict[mn]["ref"], df_pred) - df_pred = df_pred.sort_values("confidence", ascending=False) + df_pred = df_pred.sort_values("confidence", ascending=False, kind="stable") df_pred = assign_ranking(df_pred) df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") else: diff --git a/bin/fillna_tier.py b/bin/fillna_tier.py old mode 100755 new mode 100644 index de88306..4c5f78c --- a/bin/fillna_tier.py +++ b/bin/fillna_tier.py @@ -98,6 +98,7 @@ def feature_engineering(score_file, tier_file): # patient = pd.read_csv(score_file,sep='\t') feature_stat = pd.read_csv("annotate/feature_stats.csv", index_col=0) + patient = score_file.copy() patient = patient[variable_name] @@ -199,7 +200,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["GERPpp_RS"])) & np.array(indel_index), "GERPpp_RS" ] = feature_stat.loc["GERPpp_RS", "mean"] patient.loc[pd.isna(patient["GERPpp_RS"]), "GERPpp_RS"] = feature_stat.loc[ - "GERPpp_RS", "mean" + "GERPpp_RS", "min" ] patient["GERPpp_RS"] = patient["GERPpp_RS"].astype("float64") else: @@ -210,7 +211,7 @@ def feature_engineering(score_file, tier_file): ] = patient["GERPpp_RS"].describe()["mean"] patient.loc[pd.isna(patient["GERPpp_RS"]), "GERPpp_RS"] = patient[ "GERPpp_RS" - ].describe()["mean"] + ].describe()["min"] gnomadAFVal = patient["gnomadAF"].copy() gnomadAFVal = np.array([getValFromStr(str(i), "min") for i in gnomadAFVal]) @@ -249,7 +250,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["LRT_score"])) & np.array(indel_index), "LRT_score" ] = feature_stat.loc["LRT_score", "mean"] patient.loc[pd.isna(patient["LRT_score"]), "LRT_score"] = feature_stat.loc[ - "LRT_score", "mean" + "LRT_score", "min" ] patient["LRT_score"] = patient["LRT_score"].astype("float64") else: @@ -260,7 +261,7 @@ def feature_engineering(score_file, tier_file): ] = patient["LRT_score"].describe()["mean"] patient.loc[pd.isna(patient["LRT_score"]), "LRT_score"] = patient[ "LRT_score" - ].describe()["mean"] + ].describe()["min"] patient.loc[patient["LRT_Omega"] == "-", "LRT_Omega"] = np.NaN if np.all(pd.isna(patient["LRT_Omega"])): @@ -269,7 +270,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["LRT_Omega"])) & np.array(indel_index), "LRT_Omega" ] = feature_stat.loc["LRT_Omega", "mean"] patient.loc[pd.isna(patient["LRT_Omega"]), "LRT_Omega"] = feature_stat.loc[ - "LRT_Omega", "mean" + "LRT_Omega", "min" ] patient["LRT_Omega"] = patient["LRT_Omega"].astype("float64") else: @@ -280,7 +281,7 @@ def feature_engineering(score_file, tier_file): ] = patient["LRT_Omega"].describe()["mean"] patient.loc[pd.isna(patient["LRT_Omega"]), "LRT_Omega"] = patient[ "LRT_Omega" - ].describe()["mean"] + ].describe()["min"] patient.loc[ patient["phyloP100way_vertebrate"] == "-", "phyloP100way_vertebrate" @@ -293,7 +294,7 @@ def feature_engineering(score_file, tier_file): ] = feature_stat.loc["phyloP100way_vertebrate", "mean"] patient.loc[ pd.isna(patient["phyloP100way_vertebrate"]), "phyloP100way_vertebrate" - ] = feature_stat.loc["phyloP100way_vertebrate", "mean"] + ] = feature_stat.loc["phyloP100way_vertebrate", "min"] patient["phyloP100way_vertebrate"] = patient["phyloP100way_vertebrate"].astype( "float64" ) @@ -308,13 +309,13 @@ def feature_engineering(score_file, tier_file): ] = patient["phyloP100way_vertebrate"].describe()["mean"] patient.loc[ pd.isna(patient["phyloP100way_vertebrate"]), "phyloP100way_vertebrate" - ] = patient["phyloP100way_vertebrate"].describe()["mean"] + ] = patient["phyloP100way_vertebrate"].describe()["min"] patient.loc[patient["gnomadGeneZscore"] == "-", "gnomadGeneZscore"] = np.NaN if np.all(pd.isna(patient["gnomadGeneZscore"])): # Single variant query patient.loc[pd.isna(patient["gnomadGeneZscore"]), "gnomadGeneZscore"] = ( - feature_stat.loc["gnomadGeneZscore", "mean"] + feature_stat.loc["gnomadGeneZscore", "min"] ) patient["gnomadGeneZscore"] = patient["gnomadGeneZscore"].astype("float64") else: @@ -322,13 +323,13 @@ def feature_engineering(score_file, tier_file): patient["gnomadGeneZscore"] = patient["gnomadGeneZscore"].astype("float64") patient.loc[pd.isna(patient["gnomadGeneZscore"]), "gnomadGeneZscore"] = patient[ "gnomadGeneZscore" - ].describe()["mean"] + ].describe()["min"] patient.loc[patient["gnomadGenePLI"] == "-", "gnomadGenePLI"] = np.NaN if np.all(pd.isna(patient["gnomadGenePLI"])): # Single variant query patient.loc[pd.isna(patient["gnomadGenePLI"]), "gnomadGenePLI"] = ( - feature_stat.loc["gnomadGenePLI", "mean"] + feature_stat.loc["gnomadGenePLI", "min"] ) patient["gnomadGenePLI"] = patient["gnomadGenePLI"].astype("float64") else: @@ -336,13 +337,13 @@ def feature_engineering(score_file, tier_file): patient["gnomadGenePLI"] = patient["gnomadGenePLI"].astype("float64") patient.loc[pd.isna(patient["gnomadGenePLI"]), "gnomadGenePLI"] = patient[ "gnomadGenePLI" - ].describe()["mean"] + ].describe()["min"] patient.loc[patient["gnomadGeneOELof"] == "-", "gnomadGeneOELof"] = np.NaN if np.all(pd.isna(patient["gnomadGeneOELof"])): # Single variant query patient.loc[pd.isna(patient["gnomadGeneOELof"]), "gnomadGeneOELof"] = ( - feature_stat.loc["gnomadGeneOELof", "mean"] + feature_stat.loc["gnomadGeneOELof", "max"] ) patient["gnomadGeneOELof"] = patient["gnomadGeneOELof"].astype("float64") else: @@ -350,14 +351,14 @@ def feature_engineering(score_file, tier_file): patient["gnomadGeneOELof"] = patient["gnomadGeneOELof"].astype("float64") patient.loc[pd.isna(patient["gnomadGeneOELof"]), "gnomadGeneOELof"] = patient[ "gnomadGeneOELof" - ].describe()["mean"] + ].describe()["max"] patient.loc[patient["gnomadGeneOELofUpper"] == "-", "gnomadGeneOELofUpper"] = np.NaN if np.all(pd.isna(patient["gnomadGeneOELofUpper"])): # Single variant query patient.loc[ pd.isna(patient["gnomadGeneOELofUpper"]), "gnomadGeneOELofUpper" - ] = feature_stat.loc["gnomadGeneOELofUpper", "mean"] + ] = feature_stat.loc["gnomadGeneOELofUpper", "max"] patient["gnomadGeneOELofUpper"] = patient["gnomadGeneOELofUpper"].astype( "float64" ) @@ -368,7 +369,7 @@ def feature_engineering(score_file, tier_file): ) patient.loc[ pd.isna(patient["gnomadGeneOELofUpper"]), "gnomadGeneOELofUpper" - ] = patient["gnomadGeneOELofUpper"].describe()["mean"] + ] = patient["gnomadGeneOELofUpper"].describe()["max"] patient.loc[patient["IMPACT"] == "-", "IMPACT"] = 0 patient.loc[patient["IMPACT"] == "MODIFIER", "IMPACT"] = 1 @@ -385,7 +386,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["CADD_phred"])) & np.array(indel_index), "CADD_phred" ] = feature_stat.loc["CADD_phred", "mean"] patient.loc[pd.isna(patient["CADD_phred"]), "CADD_phred"] = feature_stat.loc[ - "CADD_phred", "mean" + "CADD_phred", "min" ] patient["CADD_phred"] = patient["CADD_phred"].astype("float64") else: @@ -396,7 +397,7 @@ def feature_engineering(score_file, tier_file): ] = patient["CADD_phred"].describe()["mean"] patient.loc[pd.isna(patient["CADD_phred"]), "CADD_phred"] = patient[ "CADD_phred" - ].describe()["mean"] + ].describe()["min"] patient.loc[patient["CADD_PHRED"] == "-", "CADD_PHRED"] = np.NaN if np.all(pd.isna(patient["CADD_PHRED"])): @@ -405,7 +406,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["CADD_PHRED"])) & np.array(indel_index), "CADD_PHRED" ] = feature_stat.loc["CADD_PHRED", "mean"] patient.loc[pd.isna(patient["CADD_PHRED"]), "CADD_PHRED"] = feature_stat.loc[ - "CADD_PHRED", "mean" + "CADD_PHRED", "min" ] patient["CADD_PHRED"] = patient["CADD_PHRED"].astype("float64") else: @@ -416,7 +417,7 @@ def feature_engineering(score_file, tier_file): ] = patient["CADD_PHRED"].describe()["mean"] patient.loc[pd.isna(patient["CADD_PHRED"]), "CADD_PHRED"] = patient[ "CADD_PHRED" - ].describe()["mean"] + ].describe()["min"] # DANN_score patient.loc[patient["DANN_score"] == "-", "DANN_score"] = np.NaN @@ -426,7 +427,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["DANN_score"])) & np.array(indel_index), "DANN_score" ] = feature_stat.loc["DANN_score", "mean"] patient.loc[pd.isna(patient["DANN_score"]), "DANN_score"] = feature_stat.loc[ - "DANN_score", "mean" + "DANN_score", "min" ] patient["DANN_score"] = patient["DANN_score"].astype("float64") else: @@ -437,13 +438,12 @@ def feature_engineering(score_file, tier_file): ] = patient["DANN_score"].describe()["mean"] patient.loc[pd.isna(patient["DANN_score"]), "DANN_score"] = patient[ "DANN_score" - ].describe()["mean"] + ].describe()["min"] # REVEL_score - # patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') patient.loc[patient["REVEL_score"] == "-", "REVEL_score"] = np.NaN for i in patient[~patient["REVEL_score"].isna()].index: - score_list = patient.loc[i, "REVEL_score"].split(",") + score_list = str(patient.loc[i, "REVEL_score"]).split(",") score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: patient.loc[i, "REVEL_score"] = np.NaN @@ -456,7 +456,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["REVEL_score"])) & np.array(indel_index), "REVEL_score" ] = feature_stat.loc["REVEL_score", "mean"] patient.loc[pd.isna(patient["REVEL_score"]), "REVEL_score"] = feature_stat.loc[ - "REVEL_score", "mean" + "REVEL_score", "min" ] patient["REVEL_score"] = patient["REVEL_score"].astype("float64") else: @@ -467,7 +467,7 @@ def feature_engineering(score_file, tier_file): ] = patient["REVEL_score"].describe()["mean"] patient.loc[pd.isna(patient["REVEL_score"]), "REVEL_score"] = patient[ "REVEL_score" - ].describe()["mean"] + ].describe()["min"] # fathmm_MKL_coding_score patient.loc[ @@ -481,7 +481,7 @@ def feature_engineering(score_file, tier_file): ] = feature_stat.loc["fathmm_MKL_coding_score", "mean"] patient.loc[ pd.isna(patient["fathmm_MKL_coding_score"]), "fathmm_MKL_coding_score" - ] = feature_stat.loc["fathmm_MKL_coding_score", "mean"] + ] = feature_stat.loc["fathmm_MKL_coding_score", "min"] patient["fathmm_MKL_coding_score"] = patient["fathmm_MKL_coding_score"].astype( "float64" ) @@ -496,7 +496,7 @@ def feature_engineering(score_file, tier_file): ] = patient["fathmm_MKL_coding_score"].describe()["mean"] patient.loc[ pd.isna(patient["fathmm_MKL_coding_score"]), "fathmm_MKL_coding_score" - ] = patient["fathmm_MKL_coding_score"].describe()["mean"] + ] = patient["fathmm_MKL_coding_score"].describe()["min"] # conservationScoreGnomad patient.loc[ @@ -525,10 +525,10 @@ def feature_engineering(score_file, tier_file): ) # Polyphen2_HDIV_score - patient["Polyphen2_HDIV_score"] = patient["Polyphen2_HDIV_score"] # .str.split(',') + patient["Polyphen2_HDIV_score"] = patient["Polyphen2_HDIV_score"] patient.loc[patient["Polyphen2_HDIV_score"] == "-", "Polyphen2_HDIV_score"] = np.NaN for i in patient[~patient["Polyphen2_HDIV_score"].isna()].index: - score_list = patient.loc[i, "Polyphen2_HDIV_score"].split(",") + score_list = str(patient.loc[i, "Polyphen2_HDIV_score"]).split(",") score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: patient.loc[i, "Polyphen2_HDIV_score"] = np.NaN @@ -543,7 +543,7 @@ def feature_engineering(score_file, tier_file): ] = feature_stat.loc["Polyphen2_HDIV_score", "50%"] patient.loc[ pd.isna(patient["Polyphen2_HDIV_score"]), "Polyphen2_HDIV_score" - ] = feature_stat.loc["Polyphen2_HDIV_score", "50%"] + ] = feature_stat.loc["Polyphen2_HDIV_score", "min"] patient["Polyphen2_HDIV_score"] = patient["Polyphen2_HDIV_score"].astype( "float64" ) @@ -558,13 +558,13 @@ def feature_engineering(score_file, tier_file): ] = patient["Polyphen2_HDIV_score"].describe()["50%"] patient.loc[ pd.isna(patient["Polyphen2_HDIV_score"]), "Polyphen2_HDIV_score" - ] = patient["Polyphen2_HDIV_score"].describe()["50%"] + ] = patient["Polyphen2_HDIV_score"].describe()["min"] # Polyphen2_HVAR_score - patient["Polyphen2_HVAR_score"] = patient["Polyphen2_HVAR_score"] # .str.split(',') + patient["Polyphen2_HVAR_score"] = patient["Polyphen2_HVAR_score"] patient.loc[patient["Polyphen2_HVAR_score"] == "-", "Polyphen2_HVAR_score"] = np.NaN for i in patient[~patient["Polyphen2_HVAR_score"].isna()].index: - score_list = patient.loc[i, "Polyphen2_HVAR_score"].split(",") + score_list = str(patient.loc[i, "Polyphen2_HVAR_score"]).split(",") score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: patient.loc[i, "Polyphen2_HVAR_score"] = np.NaN @@ -579,7 +579,7 @@ def feature_engineering(score_file, tier_file): ] = feature_stat.loc["Polyphen2_HVAR_score", "50%"] patient.loc[ pd.isna(patient["Polyphen2_HVAR_score"]), "Polyphen2_HVAR_score" - ] = feature_stat.loc["Polyphen2_HVAR_score", "50%"] + ] = feature_stat.loc["Polyphen2_HVAR_score", "min"] patient["Polyphen2_HVAR_score"] = patient["Polyphen2_HVAR_score"].astype( "float64" ) @@ -594,13 +594,13 @@ def feature_engineering(score_file, tier_file): ] = patient["Polyphen2_HVAR_score"].describe()["50%"] patient.loc[ pd.isna(patient["Polyphen2_HVAR_score"]), "Polyphen2_HVAR_score" - ] = patient["Polyphen2_HVAR_score"].describe()["50%"] + ] = patient["Polyphen2_HVAR_score"].describe()["min"] # SIFT_score - patient["SIFT_score"] = patient["SIFT_score"] # .str.split(',') + patient["SIFT_score"] = patient["SIFT_score"] patient.loc[patient["SIFT_score"] == "-", "SIFT_score"] = np.NaN for i in patient[~patient["SIFT_score"].isna()].index: - score_list = patient.loc[i, "SIFT_score"].split(",") + score_list = str(patient.loc[i, "SIFT_score"]).split(",") score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: patient.loc[i, "SIFT_score"] = np.NaN @@ -613,7 +613,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["SIFT_score"])) & np.array(indel_index), "SIFT_score" ] = feature_stat.loc["SIFT_score", "50%"] patient.loc[pd.isna(patient["SIFT_score"]), "SIFT_score"] = feature_stat.loc[ - "SIFT_score", "50%" + "SIFT_score", "max" ] patient["SIFT_score"] = patient["SIFT_score"].astype("float64") else: @@ -624,7 +624,7 @@ def feature_engineering(score_file, tier_file): ] = patient["SIFT_score"].describe()["50%"] patient.loc[pd.isna(patient["SIFT_score"]), "SIFT_score"] = patient[ "SIFT_score" - ].describe()["50%"] + ].describe()["max"] patient.loc[patient["zyg"] == "HET", "zyg"] = 1 patient.loc[patient["zyg"] == "HOM", "zyg"] = 2 @@ -638,10 +638,10 @@ def feature_engineering(score_file, tier_file): # patient.loc[pd.isna(patient['GERPpp_NR']), 'GERPpp_NR'] = patient['GERPpp_NR'].describe()['min'] # FATHMM_score - patient["FATHMM_score"] = patient["FATHMM_score"] # .str.split(',') + patient["FATHMM_score"] = patient["FATHMM_score"] patient.loc[patient["FATHMM_score"] == "-", "FATHMM_score"] = np.NaN for i in patient[~patient["FATHMM_score"].isna()].index: - score_list = patient.loc[i, "FATHMM_score"].split(",") + score_list = str(patient.loc[i, "FATHMM_score"]).split(",") score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: patient.loc[i, "FATHMM_score"] = np.NaN @@ -654,7 +654,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["FATHMM_score"])) & np.array(indel_index), "FATHMM_score" ] = feature_stat.loc["FATHMM_score", "50%"] patient.loc[pd.isna(patient["FATHMM_score"]), "FATHMM_score"] = ( - feature_stat.loc["FATHMM_score", "50%"] + feature_stat.loc["FATHMM_score", "max"] ) patient["FATHMM_score"] = patient["FATHMM_score"].astype("float64") else: @@ -665,7 +665,7 @@ def feature_engineering(score_file, tier_file): ] = patient["FATHMM_score"].describe()["50%"] patient.loc[pd.isna(patient["FATHMM_score"]), "FATHMM_score"] = patient[ "FATHMM_score" - ].describe()["50%"] + ].describe()["max"] # M_CAP_score patient.loc[patient["M_CAP_score"] == "-", "M_CAP_score"] = np.NaN @@ -675,7 +675,7 @@ def feature_engineering(score_file, tier_file): (pd.isna(patient["M_CAP_score"])) & np.array(indel_index), "M_CAP_score" ] = feature_stat.loc["M_CAP_score", "mean"] patient.loc[pd.isna(patient["M_CAP_score"]), "M_CAP_score"] = feature_stat.loc[ - "M_CAP_score", "mean" + "M_CAP_score", "min" ] patient["M_CAP_score"] = patient["M_CAP_score"].astype("float64") else: @@ -686,17 +686,15 @@ def feature_engineering(score_file, tier_file): ] = patient["M_CAP_score"].describe()["mean"] patient.loc[pd.isna(patient["M_CAP_score"]), "M_CAP_score"] = patient[ "M_CAP_score" - ].describe()["mean"] + ].describe()["min"] # MutationAssessor_score - patient["MutationAssessor_score"] = patient[ - "MutationAssessor_score" - ] # .str.split(',') + patient["MutationAssessor_score"] = patient["MutationAssessor_score"] patient.loc[patient["MutationAssessor_score"] == "-", "MutationAssessor_score"] = ( np.NaN ) for i in patient[~patient["MutationAssessor_score"].isna()].index: - score_list = patient.loc[i, "MutationAssessor_score"].split(",") + score_list = str(patient.loc[i, "MutationAssessor_score"]).split(",") score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: patient.loc[i, "MutationAssessor_score"] = np.NaN @@ -711,7 +709,7 @@ def feature_engineering(score_file, tier_file): ] = feature_stat.loc["MutationAssessor_score", "50%"] patient.loc[ pd.isna(patient["MutationAssessor_score"]), "MutationAssessor_score" - ] = feature_stat.loc["MutationAssessor_score", "50%"] + ] = feature_stat.loc["MutationAssessor_score", "min"] patient["MutationAssessor_score"] = patient["MutationAssessor_score"].astype( "float64" ) @@ -726,20 +724,20 @@ def feature_engineering(score_file, tier_file): ] = patient["MutationAssessor_score"].describe()["50%"] patient.loc[ pd.isna(patient["MutationAssessor_score"]), "MutationAssessor_score" - ] = patient["MutationAssessor_score"].describe()["50%"] + ] = patient["MutationAssessor_score"].describe()["min"] # ESP6500_AA_AF patient.loc[patient["ESP6500_AA_AF"] == "-", "ESP6500_AA_AF"] = 0.0 patient["ESP6500_AA_AF"] = patient["ESP6500_AA_AF"].astype("float64") - # ESP6500_AA_AF + # ESP6500_EA_AF patient.loc[patient["ESP6500_EA_AF"] == "-", "ESP6500_EA_AF"] = 0.0 patient["ESP6500_EA_AF"] = patient["ESP6500_EA_AF"].astype("float64") # hom patient.loc[patient["hom"] == "-", "hom"] = np.NaN for i in patient[~patient["hom"].isna()].index: - score_list = patient.loc[i, "hom"].split(",") + score_list = str(patient.loc[i, "hom"]).split(",") score_list = [float(i) for i in score_list if i != "-" and i != "."] if score_list == []: patient.loc[i, "hom"] = np.NaN @@ -761,7 +759,7 @@ def feature_engineering(score_file, tier_file): if np.all(pd.isna(patient["spliceAImax"])): # Single variant query patient.loc[pd.isna(patient["spliceAImax"]), "spliceAImax"] = feature_stat.loc[ - "spliceAImax", "mean" + "spliceAImax", "min" ] patient["spliceAImax"] = patient["spliceAImax"].astype("float64") else: @@ -769,7 +767,7 @@ def feature_engineering(score_file, tier_file): patient["spliceAImax"] = patient["spliceAImax"].astype("float64") patient.loc[pd.isna(patient["spliceAImax"]), "spliceAImax"] = patient.loc[ ~pd.isna(patient["spliceAImax"]), "spliceAImax" - ].describe()["mean"] + ].describe()["min"] # Consequence consequence = [ diff --git a/bin/generate_new_matrix_2.py b/bin/generate_new_matrix_2.py index 436fa0f..9d0b6a7 100755 --- a/bin/generate_new_matrix_2.py +++ b/bin/generate_new_matrix_2.py @@ -32,7 +32,7 @@ merged["varId"] = merged["varId"].apply(lambda x: x.split("_E")[0]) ### read tier ### -tier_f = pd.read_csv("Tier.v2.tsv", sep="\t") +tier_f = pd.read_csv("Tier.v2.tsv", sep="\t").sort_index() ### add phrank calculated elsewhere for final combination ### if phrank_empty: diff --git a/main.nf b/main.nf index 224b209..3392006 100644 --- a/main.nf +++ b/main.nf @@ -375,7 +375,7 @@ workflow { INDEX_VCF(params.input_vcf) VCF_PRE_PROCESS(INDEX_VCF.out, params.chrmap) - ANNOT_PHRANK(VCF_PRE_PROCESS.out) + ANNOT_PHRANK(INDEX_VCF.out[0]) ANNOT_ENSMBLE(ANNOT_PHRANK.out, params.ref_loc) TO_GENE_SYM(ANNOT_ENSMBLE.out, params.ref_to_sym, params.ref_sorted_sym) PHRANK_SCORING( TO_GENE_SYM.out, From c9990ad161717450ca0c42a9bb3ffb1f261aebe2 Mon Sep 17 00:00:00 2001 From: hyunhwan-bcm <166857764+hyunhwan-bcm@users.noreply.github.com> Date: Thu, 1 Aug 2024 16:01:25 -0500 Subject: [PATCH 70/71] Check completed - nextflow comparison with main using hg38 (#25) * revert some files to main. * fix the known sorting issue. * make process_gene same as main_fix_sort branch * conf4 are now the same. * force var_grp to be sorted by the index. * fetching bin/generate_new_matrix_2.py from nextflow_coversion, checked output. (#26) * Revert 27 hj/nxf comp/update bivar (#28) * Hj/nxf comp/update bivar (#27) * revert some files to main. * fix the known sorting issue. * make process_gene same as main_fix_sort branch * conf4 are now the same. * force var_grp to be sorted by the index. * add indexing sorting for the var index. * temporal off of the dedup. * change the sorting for index of each group df. * make confidence sorting stable. * Revert "Hj/nxf comp/update bivar (#27)" This reverts commit 95af06491f12b25b83692edf955c767beb9e30a0. * pulling nextflow_conversion branch files. From 891e7f4e70674d42b16fbec291b26aa41bfaa046 Mon Sep 17 00:00:00 2001 From: Hyun-Hwan Jeong Date: Fri, 2 Aug 2024 10:26:41 -0500 Subject: [PATCH 71/71] checkout confict files from main --- Dockerfile | 39 ++- run/fillna_tier.py | 682 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 699 insertions(+), 22 deletions(-) create mode 100755 run/fillna_tier.py diff --git a/Dockerfile b/Dockerfile index 05ae4e9..3f0f085 100644 --- a/Dockerfile +++ b/Dockerfile @@ -46,43 +46,38 @@ RUN apt install wget RUN wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc # add the R 4.0 repo from CRAN -- adjust 'focal' to 'groovy' or 'bionic' as needed -#RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ -# apt-key adv --keyserver ha.pool.sks-keyservers.net --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ -# apt-key adv --keyserver pgp.mit.edu --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ -# apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ -# apt-key adv --keyserver keyserver.pgp.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 -#RUN add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" - +RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ + apt-key adv --keyserver ha.pool.sks-keyservers.net --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ + apt-key adv --keyserver pgp.mit.edu --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ + apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ + apt-key adv --keyserver keyserver.pgp.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 RUN add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" -RUN add-apt-repository universe -RUN apt-get update -RUN apt install -y r-base r-base-core +RUN apt install -y r-base r-base-core r-recommended r-base-dev # Install R libs RUN R -e "install.packages('data.table',dependencies=TRUE, repos='http://cran.rstudio.com/')" -RUN R -e "install.packages('tidyverse',dependencies=TRUE, repos='http://cran.rstudio.com/')" +RUN R -e "install.packages('dplyr',dependencies=TRUE, repos='http://cran.rstudio.com/')" RUN R -e "install.packages('ontologyIndex',dependencies=TRUE, repos='http://cran.rstudio.com/')" RUN R -e "install.packages('ontologySimilarity',dependencies=TRUE, repos='http://cran.rstudio.com/')" +RUN R -e "install.packages('tidyverse',dependencies=TRUE, repos='http://cran.rstudio.com/')" # Install bcftools -RUN wget https://github.com/samtools/bcftools/releases/download/1.20/bcftools-1.20.tar.bz2 -RUN mv bcftools-1.20.tar.bz2 /opt/bcftools-1.20.tar.bz2 - -RUN tar -xf /opt/bcftools-1.20.tar.bz2 -C /opt/ && \ - rm /opt/bcftools-1.20.tar.bz2 && \ - cd /opt/bcftools-1.20 && \ +COPY bcftools-1.9.tar.bz2 /opt/bcftools-1.9.tar.bz2 +RUN tar -xf /opt/bcftools-1.9.tar.bz2 -C /opt/ && \ + rm /opt/bcftools-1.9.tar.bz2 && \ + cd /opt/bcftools-1.9 && \ ./configure && \ make && \ make install && \ - rm -rf /opt/bcftools-1.20 + rm -rf /opt/bcftools-1.9 + -# Install bedtools -RUN wget https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary -RUN mv bedtools.static.binary /run/bedtools -RUN chmod a+x /run/bedtools +# Copy the pipeline into Docker image +COPY run /run/ +RUN chmod +x /run/proc.sh diff --git a/run/fillna_tier.py b/run/fillna_tier.py new file mode 100755 index 0000000..d0e2148 --- /dev/null +++ b/run/fillna_tier.py @@ -0,0 +1,682 @@ +from random import sample +import numpy as np +import pandas as pd + +__author__ = "Chaozhong Liu" +__email__ = "chaozhol@bcm.edu" + +###Feature engineering and incorporation of tier module outputs. +###fills NA w/ 0 + +def getValFromStr(valStr: str, select: str = 'min'): + select_method = {'min':min, 'max':max} + vals = valStr.split(',') + if '-' in vals or '.' in vals: + return np.NaN + else: + vals = [float(i) for i in vals] + return select_method[select](vals) + +def feature_engineering(score_file, tier_file): + variable_name = ['varId_dash', 'hgmdSymptomScore', + 'omimSymMatchFlag', 'hgmdSymMatchFlag', 'clinVarSymMatchFlag', + 'omimGeneFound', 'omimVarFound', 'hgmdGeneFound', 'hgmdVarFound', + 'clinVarVarFound', 'clinVarGeneFound', + 'clinvarTotalNumVars','clinvarNumP', 'clinvarNumLP', 'clinvarNumLB', 'clinvarNumB', + 'dgvVarFound','decipherVarFound', + 'curationScoreHGMD', 'curationScoreOMIM', 'curationScoreClinVar', + 'conservationScoreDGV', 'omimSymptomSimScore', 'hgmdSymptomSimScore', 'clin_code', + 'GERPpp_RS', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'phyloP100way_vertebrate', + 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELof', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_phred', + 'CADD_PHRED', 'DANN_score', 'REVEL_score', 'fathmm_MKL_coding_score', 'conservationScoreGnomad', + 'conservationScoreOELof', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'SIFT_score', 'zyg', + 'FATHMM_score', 'M_CAP_score', 'MutationAssessor_score', 'ESP6500_AA_AF', + 'ESP6500_EA_AF', 'hom', 'hgmd_rs', 'spliceAImax', 'clin_PLP_perc', 'Consequence', + 'nc_ClinVar_Exp', 'c_ClinVar_Exp', 'c_HGMD_Exp', 'nc_HGMD_Exp', + 'nc_isPLP', 'nc_isBLB', 'c_isPLP', 'c_isBLB', + 'nc_CLNREVSTAT', 'c_CLNREVSTAT', + 'nc_RANKSCORE', 'c_RANKSCORE', 'CLASS', "phrank"] + + + + #sample_name = score_file.split('/')[-1].split('_')[1] + #patient = pd.read_csv(score_file,sep='\t') + feature_stat = pd.read_csv('/run/data_dependencies/annotate/feature_stats.csv', index_col=0) + + patient = score_file.copy() + + patient = patient[variable_name] + patient = patient.fillna('-') + indel_index = [len(i.split('-')[-1]) != 1 or len(i.split('-')[-2]) != 1 for i in patient['varId_dash'].to_list()] + + patient.loc[patient['phrank'] == '-','phrank'] = 0 + patient['phrank'] = patient['phrank'].astype('float64') + + patient.loc[patient['hgmdSymptomScore'] == '-','hgmdSymptomScore'] = 0 + patient['hgmdSymptomScore'] = patient['hgmdSymptomScore'].astype('float64') + + patient.loc[patient['omimSymMatchFlag'] == '-','omimSymMatchFlag'] = 0 + patient['omimSymMatchFlag'] = patient['omimSymMatchFlag'].astype('float64') + + patient.loc[patient['hgmdSymMatchFlag'] == '-','hgmdSymMatchFlag'] = 0 + patient['hgmdSymMatchFlag'] = patient['hgmdSymMatchFlag'].astype('float64') + + patient.loc[patient['clinVarSymMatchFlag'] == '-','clinVarSymMatchFlag'] = 0 + patient['clinVarSymMatchFlag'] = patient['clinVarSymMatchFlag'].astype('float64') + + + patient['clinvarNumLP'] = (patient['clinvarNumLP']+patient['clinvarNumP']) / patient['clinvarTotalNumVars'] + patient['clinvarNumLP'] = patient['clinvarNumLP'].fillna(0.0) + patient['clinvarNumP'] = patient['clinvarNumP'] / patient['clinvarTotalNumVars'] + patient['clinvarNumP'] = patient['clinvarNumP'].fillna(0.0) + patient['clinvarNumLB'] = (patient['clinvarNumLB']+patient['clinvarNumB']) / patient['clinvarTotalNumVars'] + patient['clinvarNumLB'] = patient['clinvarNumLB'].fillna(1.0) + patient['clinvarNumB'] = patient['clinvarNumB'] / patient['clinvarTotalNumVars'] + patient['clinvarNumB'] = patient['clinvarNumB'].fillna(1.0) + patient = patient.drop(columns=['clinvarTotalNumVars']) + variable_name.remove('clinvarTotalNumVars') + + + patient.loc[patient['curationScoreHGMD'] == 'Low','curationScoreHGMD'] = 1 + patient.loc[patient['curationScoreHGMD'] == 'Medium','curationScoreHGMD'] = 2 + patient.loc[patient['curationScoreHGMD'] == 'High','curationScoreHGMD'] = 3 + patient['curationScoreHGMD'] = patient['curationScoreHGMD'].astype('float64') + + patient.loc[patient['curationScoreOMIM'] == 'Low','curationScoreOMIM'] = 1 + patient.loc[patient['curationScoreOMIM'] == 'Medium','curationScoreOMIM'] = 2 + patient.loc[patient['curationScoreOMIM'] == 'High','curationScoreOMIM'] = 3 + patient['curationScoreOMIM'] = patient['curationScoreOMIM'].astype('float64') + + patient.loc[patient['curationScoreClinVar'] == 'Low','curationScoreClinVar'] = 1 + patient.loc[patient['curationScoreClinVar'] == 'Medium','curationScoreClinVar'] = 2 + patient.loc[patient['curationScoreClinVar'] == 'High','curationScoreClinVar'] = 3 + patient['curationScoreClinVar'] = patient['curationScoreClinVar'].astype('float64') + + patient.loc[patient['conservationScoreDGV'] == 'Low','conservationScoreDGV'] = 1 + patient.loc[patient['conservationScoreDGV'] == 'Medium','conservationScoreDGV'] = 2 + patient.loc[patient['conservationScoreDGV'] == 'High','conservationScoreDGV'] = 3 + patient['conservationScoreDGV'] = patient['conservationScoreDGV'].astype('float64') + + + patient.loc[patient['omimSymptomSimScore'] == '-','omimSymptomSimScore'] = 0.0 + patient['omimSymptomSimScore'] = patient['omimSymptomSimScore'].astype('float64') + + patient.loc[patient['hgmdSymptomSimScore'] == '-','hgmdSymptomSimScore'] = 0.0 + patient['hgmdSymptomSimScore'] = patient['hgmdSymptomSimScore'].astype('float64') + + + patient['isB/LB'] = 0 + patient['isP/LP'] = 0 + patient.loc[patient['clin_code'].str.contains('Benign'),'isB/LB'] = 1 + patient.loc[patient['clin_code'].str.contains('Likely_benign'),'isB/LB'] = 1 + patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isB/LB'] = 0 + + patient.loc[patient['clin_code'].str.contains('Likely_pathogenic'),'isP/LP'] = 1 + patient.loc[patient['clin_code'].str.contains('Pathogenic'),'isP/LP'] = 1 + patient.loc[patient['clin_code'].str.contains('Conflicting_interpretations_of_pathogenicity'),'isP/LP'] = 0 + + patient.loc[patient['clin_PLP_perc']!='-','isP/LP'] = patient.loc[patient['clin_PLP_perc']!='-','clin_PLP_perc'].astype('float64') + + variable_name.append('isB/LB') + variable_name.append('isP/LP') + variable_name.remove('clin_PLP_perc') + variable_name.remove('clin_code') + + + patient.loc[patient['GERPpp_RS'] == '-', 'GERPpp_RS'] = np.NaN + if np.all(pd.isna(patient['GERPpp_RS'])): + # Single variant query + patient.loc[(pd.isna(patient['GERPpp_RS'])) & np.array(indel_index), 'GERPpp_RS'] = feature_stat.loc['GERPpp_RS', 'mean'] + patient.loc[pd.isna(patient['GERPpp_RS']), 'GERPpp_RS'] = feature_stat.loc['GERPpp_RS', 'mean'] + patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') + else: + # Patient whole VCF + patient['GERPpp_RS'] = patient['GERPpp_RS'].astype('float64') + patient.loc[(pd.isna(patient['GERPpp_RS'])) & np.array(indel_index), 'GERPpp_RS'] = patient['GERPpp_RS'].describe()['mean'] + patient.loc[pd.isna(patient['GERPpp_RS']), 'GERPpp_RS'] = patient['GERPpp_RS'].describe()['mean'] + + + gnomadAFVal = patient['gnomadAF'].copy() + gnomadAFVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFVal ]) + gnomadAFVal = gnomadAFVal.astype(float) + patient['gnomadAF'] = gnomadAFVal + + gnomadAFgVal = patient['gnomadAFg'].copy() + gnomadAFgVal = np.array([ getValFromStr(str(i), 'min') for i in gnomadAFgVal ]) + gnomadAFgVal = gnomadAFgVal.astype(float) + patient['gnomadAFg'] = gnomadAFgVal + patient.loc[patient['gnomadAFg'].isna(), 'gnomadAFg'] = patient.loc[patient['gnomadAFg'].isna(), 'gnomadAF'] + patient.loc[patient['gnomadAF'].isna(), 'gnomadAF'] = patient.loc[patient['gnomadAF'].isna(), 'gnomadAFg'] + patient['gnomadAF'] = patient['gnomadAF'].fillna(0.0) + patient['gnomadAFg'] = patient['gnomadAFg'].fillna(0.0) + + #patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '-', 'gnomadAF'] #0.0 # + #patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = patient.loc[patient['gnomadAFg'] == '.', 'gnomadAF'] # + #patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '-', 'gnomadAFg'] #0.0 # + #patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = patient.loc[patient['gnomadAF'] == '.', 'gnomadAFg'] + #patient.loc[patient['gnomadAFg'] == '-', 'gnomadAFg'] = 0.0 + #patient.loc[patient['gnomadAFg'] == '.', 'gnomadAFg'] = 0.0 + #patient.loc[patient['gnomadAF'] == '-', 'gnomadAF'] = 0.0 + #patient.loc[patient['gnomadAF'] == '.', 'gnomadAF'] = 0.0 + #patient['gnomadAFg'] = patient['gnomadAFg'].astype('float64') + #patient['gnomadAF'] = patient['gnomadAF'].astype('float64') + #patient.loc[patient['gnomadAFg'] == -100, 'gnomadAFg'] = patient['gnomadAFg'].describe()['max'] + + + patient.loc[patient['LRT_score'] == '-', 'LRT_score'] = np.NaN + if np.all(pd.isna(patient['LRT_score'])): + # Single variant query + patient.loc[(pd.isna(patient['LRT_score'])) & np.array(indel_index), 'LRT_score'] = feature_stat.loc['LRT_score', 'mean'] + patient.loc[pd.isna(patient['LRT_score']), 'LRT_score'] = feature_stat.loc['LRT_score', 'mean'] + patient['LRT_score'] = patient['LRT_score'].astype('float64') + else: + # Patient whole VCF + patient['LRT_score'] = patient['LRT_score'].astype('float64') + patient.loc[(pd.isna(patient['LRT_score'])) & np.array(indel_index), 'LRT_score'] = patient['LRT_score'].describe()['mean'] + patient.loc[pd.isna(patient['LRT_score']), 'LRT_score'] = patient['LRT_score'].describe()['mean'] + + + patient.loc[patient['LRT_Omega'] == '-', 'LRT_Omega'] = np.NaN + if np.all(pd.isna(patient['LRT_Omega'])): + # Single variant query + patient.loc[(pd.isna(patient['LRT_Omega'])) & np.array(indel_index), 'LRT_Omega'] = feature_stat.loc['LRT_Omega', 'mean'] + patient.loc[pd.isna(patient['LRT_Omega']), 'LRT_Omega'] = feature_stat.loc['LRT_Omega', 'mean'] + patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') + else: + # Patient whole VCF + patient['LRT_Omega'] = patient['LRT_Omega'].astype('float64') + patient.loc[(pd.isna(patient['LRT_Omega'])) & np.array(indel_index), 'LRT_Omega'] = patient['LRT_Omega'].describe()['mean'] + patient.loc[pd.isna(patient['LRT_Omega']), 'LRT_Omega'] = patient['LRT_Omega'].describe()['mean'] + + + patient.loc[patient['phyloP100way_vertebrate'] == '-', 'phyloP100way_vertebrate'] = np.NaN + if np.all(pd.isna(patient['phyloP100way_vertebrate'])): + # Single variant query + patient.loc[(pd.isna(patient['phyloP100way_vertebrate'])) & np.array(indel_index), 'phyloP100way_vertebrate'] = feature_stat.loc['phyloP100way_vertebrate', 'mean'] + patient.loc[pd.isna(patient['phyloP100way_vertebrate']), 'phyloP100way_vertebrate'] = feature_stat.loc['phyloP100way_vertebrate', 'mean'] + patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') + else: + # Patient whole VCF + patient['phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].astype('float64') + patient.loc[(pd.isna(patient['phyloP100way_vertebrate'])) & np.array(indel_index), 'phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].describe()['mean'] + patient.loc[pd.isna(patient['phyloP100way_vertebrate']), 'phyloP100way_vertebrate'] = patient['phyloP100way_vertebrate'].describe()['mean'] + + + patient.loc[patient['gnomadGeneZscore'] == '-', 'gnomadGeneZscore'] = np.NaN + if np.all(pd.isna(patient['gnomadGeneZscore'])): + # Single variant query + patient.loc[pd.isna(patient['gnomadGeneZscore']), 'gnomadGeneZscore'] = feature_stat.loc['gnomadGeneZscore', 'mean'] + patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') + else: + # Patient whole VCF + patient['gnomadGeneZscore'] = patient['gnomadGeneZscore'].astype('float64') + patient.loc[pd.isna(patient['gnomadGeneZscore']), 'gnomadGeneZscore'] = patient['gnomadGeneZscore'].describe()['mean'] + + + patient.loc[patient['gnomadGenePLI'] == '-', 'gnomadGenePLI'] = np.NaN + if np.all(pd.isna(patient['gnomadGenePLI'])): + # Single variant query + patient.loc[pd.isna(patient['gnomadGenePLI']), 'gnomadGenePLI'] = feature_stat.loc['gnomadGenePLI', 'mean'] + patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') + else: + # Patient whole VCF + patient['gnomadGenePLI'] = patient['gnomadGenePLI'].astype('float64') + patient.loc[pd.isna(patient['gnomadGenePLI']), 'gnomadGenePLI'] = patient['gnomadGenePLI'].describe()['mean'] + + + patient.loc[patient['gnomadGeneOELof'] == '-', 'gnomadGeneOELof'] = np.NaN + if np.all(pd.isna(patient['gnomadGeneOELof'])): + # Single variant query + patient.loc[pd.isna(patient['gnomadGeneOELof']), 'gnomadGeneOELof'] = feature_stat.loc['gnomadGeneOELof', 'mean'] + patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') + else: + # Patient whole VCF + patient['gnomadGeneOELof'] = patient['gnomadGeneOELof'].astype('float64') + patient.loc[pd.isna(patient['gnomadGeneOELof']), 'gnomadGeneOELof'] = patient['gnomadGeneOELof'].describe()['mean'] + + patient.loc[patient['gnomadGeneOELofUpper'] == '-', 'gnomadGeneOELofUpper'] = np.NaN + if np.all(pd.isna(patient['gnomadGeneOELofUpper'])): + # Single variant query + patient.loc[pd.isna(patient['gnomadGeneOELofUpper']), 'gnomadGeneOELofUpper'] = feature_stat.loc['gnomadGeneOELofUpper', 'mean'] + patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') + else: + # Patient whole VCF + patient['gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].astype('float64') + patient.loc[pd.isna(patient['gnomadGeneOELofUpper']), 'gnomadGeneOELofUpper'] = patient['gnomadGeneOELofUpper'].describe()['mean'] + + + patient.loc[patient['IMPACT'] == '-','IMPACT'] = 0 + patient.loc[patient['IMPACT'] == 'MODIFIER','IMPACT'] = 1 + patient.loc[patient['IMPACT'] == 'LOW','IMPACT'] = 2 + patient.loc[patient['IMPACT'] == 'MODERATE','IMPACT'] = 3 + patient.loc[patient['IMPACT'] == 'HIGH','IMPACT'] = 4 + patient['IMPACT'] = patient['IMPACT'].astype('float64') + + + #"CADD_phred" + patient.loc[patient['CADD_phred'] == '-', 'CADD_phred'] = np.NaN + if np.all(pd.isna(patient['CADD_phred'])): + # Single variant query + patient.loc[(pd.isna(patient['CADD_phred'])) & np.array(indel_index), 'CADD_phred'] = feature_stat.loc['CADD_phred', 'mean'] + patient.loc[pd.isna(patient['CADD_phred']), 'CADD_phred'] = feature_stat.loc['CADD_phred', 'mean'] + patient['CADD_phred'] = patient['CADD_phred'].astype('float64') + else: + # Patient whole VCF + patient['CADD_phred'] = patient['CADD_phred'].astype('float64') + patient.loc[(pd.isna(patient['CADD_phred'])) & np.array(indel_index), 'CADD_phred'] = patient['CADD_phred'].describe()['mean'] + patient.loc[pd.isna(patient['CADD_phred']), 'CADD_phred'] = patient['CADD_phred'].describe()['mean'] + + + patient.loc[patient['CADD_PHRED'] == '-', 'CADD_PHRED'] = np.NaN + if np.all(pd.isna(patient['CADD_PHRED'])): + # Single variant query + patient.loc[(pd.isna(patient['CADD_PHRED'])) & np.array(indel_index), 'CADD_PHRED'] = feature_stat.loc['CADD_PHRED', 'mean'] + patient.loc[pd.isna(patient['CADD_PHRED']), 'CADD_PHRED'] = feature_stat.loc['CADD_PHRED', 'mean'] + patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') + else: + # Patient whole VCF + patient['CADD_PHRED'] = patient['CADD_PHRED'].astype('float64') + patient.loc[(pd.isna(patient['CADD_PHRED'])) & np.array(indel_index), 'CADD_PHRED'] = patient['CADD_PHRED'].describe()['mean'] + patient.loc[pd.isna(patient['CADD_PHRED']), 'CADD_PHRED'] = patient['CADD_PHRED'].describe()['mean'] + + + #DANN_score + patient.loc[patient['DANN_score'] == '-', 'DANN_score'] = np.NaN + if np.all(pd.isna(patient['DANN_score'])): + # Single variant query + patient.loc[(pd.isna(patient['DANN_score'])) & np.array(indel_index), 'DANN_score'] = feature_stat.loc['DANN_score', 'mean'] + patient.loc[pd.isna(patient['DANN_score']), 'DANN_score'] = feature_stat.loc['DANN_score', 'mean'] + patient['DANN_score'] = patient['DANN_score'].astype('float64') + else: + # Patient whole VCF + patient['DANN_score'] = patient['DANN_score'].astype('float64') + patient.loc[(pd.isna(patient['DANN_score'])) & np.array(indel_index), 'DANN_score'] = patient['DANN_score'].describe()['mean'] + patient.loc[pd.isna(patient['DANN_score']), 'DANN_score'] = patient['DANN_score'].describe()['mean'] + + + #REVEL_score + #patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') + patient.loc[patient['REVEL_score'] == '-', 'REVEL_score'] = np.NaN + for i in patient[~patient['REVEL_score'].isna()].index: + score_list = patient.loc[i, 'REVEL_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'REVEL_score'] = np.NaN + else: + patient.loc[i, 'REVEL_score'] = max(score_list) + + if np.all(pd.isna(patient['REVEL_score'])): + # Single variant query + patient.loc[(pd.isna(patient['REVEL_score'])) & np.array(indel_index), 'REVEL_score'] = feature_stat.loc['REVEL_score', 'mean'] + patient.loc[pd.isna(patient['REVEL_score']), 'REVEL_score'] = feature_stat.loc['REVEL_score', 'mean'] + patient['REVEL_score'] = patient['REVEL_score'].astype('float64') + else: + # Patient whole VCF + patient['REVEL_score'] = patient['REVEL_score'].astype('float64') + patient.loc[(patient['REVEL_score'].isna()) & np.array(indel_index), 'REVEL_score'] = patient['REVEL_score'].describe()['mean'] + patient.loc[pd.isna(patient['REVEL_score']), 'REVEL_score'] = patient['REVEL_score'].describe()['mean'] + + + + #fathmm_MKL_coding_score + patient.loc[patient['fathmm_MKL_coding_score'] == '-', 'fathmm_MKL_coding_score'] = np.NaN + if np.all(pd.isna(patient['fathmm_MKL_coding_score'])): + # Single variant query + patient.loc[(pd.isna(patient['fathmm_MKL_coding_score'])) & np.array(indel_index), 'fathmm_MKL_coding_score'] = feature_stat.loc['fathmm_MKL_coding_score', 'mean'] + patient.loc[pd.isna(patient['fathmm_MKL_coding_score']), 'fathmm_MKL_coding_score'] = feature_stat.loc['fathmm_MKL_coding_score', 'mean'] + patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') + else: + # Patient whole VCF + patient['fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].astype('float64') + patient.loc[(pd.isna(patient['fathmm_MKL_coding_score'])) & np.array(indel_index), 'fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].describe()['mean'] + patient.loc[pd.isna(patient['fathmm_MKL_coding_score']), 'fathmm_MKL_coding_score'] = patient['fathmm_MKL_coding_score'].describe()['mean'] + + + #conservationScoreGnomad + patient.loc[patient['conservationScoreGnomad'] == '-','conservationScoreGnomad'] = 1 + patient.loc[patient['conservationScoreGnomad'] == 'Low','conservationScoreGnomad'] = 1 + patient.loc[patient['conservationScoreGnomad'] == 'High','conservationScoreGnomad'] = 2 + patient['conservationScoreGnomad'] = patient['conservationScoreGnomad'].astype('float64') + + #conservationScoreOELof + patient.loc[patient['conservationScoreOELof'] == '-','conservationScoreOELof'] = 1 + patient.loc[patient['conservationScoreOELof'] == 'Low','conservationScoreOELof'] = 1 + patient.loc[patient['conservationScoreOELof'] == 'High','conservationScoreOELof'] = 2 + patient['conservationScoreOELof'] = patient['conservationScoreOELof'].astype('float64') + + + #Polyphen2_HDIV_score + patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'] #.str.split(',') + patient.loc[patient['Polyphen2_HDIV_score'] == '-','Polyphen2_HDIV_score'] = np.NaN + for i in patient[~patient['Polyphen2_HDIV_score'].isna()].index: + score_list = patient.loc[i, 'Polyphen2_HDIV_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'Polyphen2_HDIV_score'] = np.NaN + else: + patient.loc[i, 'Polyphen2_HDIV_score'] = max(score_list) + + if np.all(pd.isna(patient['Polyphen2_HDIV_score'])): + # Single variant query + patient.loc[(pd.isna(patient['Polyphen2_HDIV_score'])) & np.array(indel_index), 'Polyphen2_HDIV_score'] = feature_stat.loc['Polyphen2_HDIV_score', '50%'] + patient.loc[pd.isna(patient['Polyphen2_HDIV_score']), 'Polyphen2_HDIV_score'] = feature_stat.loc['Polyphen2_HDIV_score', '50%'] + patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') + else: + # Patient whole VCF + patient['Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].astype('float64') + patient.loc[(pd.isna(patient['Polyphen2_HDIV_score'])) & np.array(indel_index), 'Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].describe()['50%'] + patient.loc[pd.isna(patient['Polyphen2_HDIV_score']), 'Polyphen2_HDIV_score'] = patient['Polyphen2_HDIV_score'].describe()['50%'] + + + #Polyphen2_HVAR_score + patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'] #.str.split(',') + patient.loc[patient['Polyphen2_HVAR_score'] == '-','Polyphen2_HVAR_score'] = np.NaN + for i in patient[~patient['Polyphen2_HVAR_score'].isna()].index: + score_list = patient.loc[i, 'Polyphen2_HVAR_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'Polyphen2_HVAR_score'] = np.NaN + else: + patient.loc[i, 'Polyphen2_HVAR_score'] = max(score_list) + + if np.all(pd.isna(patient['Polyphen2_HVAR_score'])): + # Single variant query + patient.loc[(pd.isna(patient['Polyphen2_HVAR_score'])) & np.array(indel_index), 'Polyphen2_HVAR_score'] = feature_stat.loc['Polyphen2_HVAR_score', '50%'] + patient.loc[pd.isna(patient['Polyphen2_HVAR_score']), 'Polyphen2_HVAR_score'] = feature_stat.loc['Polyphen2_HVAR_score', '50%'] + patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') + else: + # Patient whole VCF + patient['Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].astype('float64') + patient.loc[(pd.isna(patient['Polyphen2_HVAR_score'])) & np.array(indel_index), 'Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].describe()['50%'] + patient.loc[pd.isna(patient['Polyphen2_HVAR_score']), 'Polyphen2_HVAR_score'] = patient['Polyphen2_HVAR_score'].describe()['50%'] + + + #SIFT_score + patient['SIFT_score'] = patient['SIFT_score'] #.str.split(',') + patient.loc[patient['SIFT_score'] == '-','SIFT_score'] = np.NaN + for i in patient[~patient['SIFT_score'].isna()].index: + score_list = patient.loc[i, 'SIFT_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'SIFT_score'] = np.NaN + else: + patient.loc[i, 'SIFT_score'] = min(score_list) + + if np.all(pd.isna(patient['SIFT_score'])): + # Single variant query + patient.loc[(pd.isna(patient['SIFT_score'])) & np.array(indel_index), 'SIFT_score'] = feature_stat.loc['SIFT_score', '50%'] + patient.loc[pd.isna(patient['SIFT_score']), 'SIFT_score'] = feature_stat.loc['SIFT_score', '50%'] + patient['SIFT_score'] = patient['SIFT_score'].astype('float64') + else: + # Patient whole VCF + patient['SIFT_score'] = patient['SIFT_score'].astype('float64') + patient.loc[(pd.isna(patient['SIFT_score'])) & np.array(indel_index), 'SIFT_score'] = patient['SIFT_score'].describe()['50%'] + patient.loc[pd.isna(patient['SIFT_score']), 'SIFT_score'] = patient['SIFT_score'].describe()['50%'] + + + patient.loc[patient['zyg'] == 'HET','zyg'] = 1 + patient.loc[patient['zyg'] == 'HOM','zyg'] = 2 + patient.loc[patient['zyg'] == '-','zyg'] = 0 + + patient['zyg'] = patient['zyg'].astype('float64') + + + #patient.loc[patient['GERPpp_NR'] == '-', 'GERPpp_NR'] = np.NaN + #patient['GERPpp_NR'] = patient['GERPpp_NR'].astype('float64') + #patient.loc[(pd.isna(patient['GERPpp_NR'])) & np.array(indel_index), 'GERPpp_NR'] = patient.loc[patient['GERPpp_NR'] != -100, 'GERPpp_NR'].describe()['mean'] + #patient.loc[pd.isna(patient['GERPpp_NR']), 'GERPpp_NR'] = patient['GERPpp_NR'].describe()['min'] + + + #FATHMM_score + patient['FATHMM_score'] = patient['FATHMM_score'] #.str.split(',') + patient.loc[patient['FATHMM_score'] == '-','FATHMM_score'] = np.NaN + for i in patient[~patient['FATHMM_score'].isna()].index: + score_list = patient.loc[i, 'FATHMM_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'FATHMM_score'] = np.NaN + else: + patient.loc[i, 'FATHMM_score'] = min(score_list) + + if np.all(pd.isna(patient['FATHMM_score'])): + # Single variant query + patient.loc[(pd.isna(patient['FATHMM_score'])) & np.array(indel_index), 'FATHMM_score'] = feature_stat.loc['FATHMM_score', '50%'] + patient.loc[pd.isna(patient['FATHMM_score']), 'FATHMM_score'] = feature_stat.loc['FATHMM_score', '50%'] + patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') + else: + # Patient whole VCF + patient['FATHMM_score'] = patient['FATHMM_score'].astype('float64') + patient.loc[(pd.isna(patient['FATHMM_score'])) & np.array(indel_index), 'FATHMM_score'] = patient['FATHMM_score'].describe()['50%'] + patient.loc[pd.isna(patient['FATHMM_score']), 'FATHMM_score'] = patient['FATHMM_score'].describe()['50%'] + + + #M_CAP_score + patient.loc[patient['M_CAP_score'] == '-', 'M_CAP_score'] = np.NaN + if np.all(pd.isna(patient['M_CAP_score'])): + # Single variant query + patient.loc[(pd.isna(patient['M_CAP_score'])) & np.array(indel_index), 'M_CAP_score'] = feature_stat.loc['M_CAP_score', 'mean'] + patient.loc[pd.isna(patient['M_CAP_score']), 'M_CAP_score'] = feature_stat.loc['M_CAP_score', 'mean'] + patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') + else: + # Patient whole VCF + patient['M_CAP_score'] = patient['M_CAP_score'].astype('float64') + patient.loc[(pd.isna(patient['M_CAP_score'])) & np.array(indel_index), 'M_CAP_score'] = patient['M_CAP_score'].describe()['mean'] + patient.loc[pd.isna(patient['M_CAP_score']), 'M_CAP_score'] = patient['M_CAP_score'].describe()['mean'] + + + #MutationAssessor_score + patient['MutationAssessor_score'] = patient['MutationAssessor_score'] #.str.split(',') + patient.loc[patient['MutationAssessor_score'] == '-','MutationAssessor_score'] = np.NaN + for i in patient[~patient['MutationAssessor_score'].isna()].index: + score_list = patient.loc[i, 'MutationAssessor_score'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'MutationAssessor_score'] = np.NaN + else: + patient.loc[i, 'MutationAssessor_score'] = max(score_list) + + if np.all(pd.isna(patient['MutationAssessor_score'])): + # Single variant query + patient.loc[(pd.isna(patient['MutationAssessor_score'])) & np.array(indel_index), 'MutationAssessor_score'] = feature_stat.loc['MutationAssessor_score', '50%'] + patient.loc[pd.isna(patient['MutationAssessor_score']), 'MutationAssessor_score'] = feature_stat.loc['MutationAssessor_score', '50%'] + patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') + else: + # Patient whole VCF + patient['MutationAssessor_score'] = patient['MutationAssessor_score'].astype('float64') + patient.loc[(pd.isna(patient['MutationAssessor_score'])) & np.array(indel_index), 'MutationAssessor_score'] = patient['MutationAssessor_score'].describe()['50%'] + patient.loc[pd.isna(patient['MutationAssessor_score']), 'MutationAssessor_score'] = patient['MutationAssessor_score'].describe()['50%'] + + + #ESP6500_AA_AF + patient.loc[patient['ESP6500_AA_AF'] == '-', 'ESP6500_AA_AF'] = 0.0 + patient['ESP6500_AA_AF'] = patient['ESP6500_AA_AF'].astype('float64') + + + #ESP6500_AA_AF + patient.loc[patient['ESP6500_EA_AF'] == '-', 'ESP6500_EA_AF'] = 0.0 + patient['ESP6500_EA_AF'] = patient['ESP6500_EA_AF'].astype('float64') + + + #hom + patient.loc[patient['hom'] == '-','hom'] = np.NaN + for i in patient[~patient['hom'].isna()].index: + score_list = patient.loc[i, 'hom'].split(',') + score_list = [float(i) for i in score_list if i!='-' and i!='.'] + if score_list == []: + patient.loc[i, 'hom'] = np.NaN + else: + patient.loc[i, 'hom'] = max(score_list) + patient['hom'] = patient['hom'].fillna(0) + patient['hom'] = patient['hom'].astype('float64') + + patient['hgmd_rs'] = patient['hgmd_rs'].apply(lambda x: x.split(",")[0] if type(x) == str else str(x)) + + patient.loc[patient['hgmd_rs'] == '-','hgmd_rs'] = 0 + + patient['hgmd_rs'] = patient['hgmd_rs'].astype('float64') + + + #spliceAImax + patient.loc[patient['spliceAImax'] == '-', 'spliceAImax'] = np.NaN + if np.all(pd.isna(patient['spliceAImax'])): + # Single variant query + patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = feature_stat.loc['spliceAImax', 'mean'] + patient['spliceAImax'] = patient['spliceAImax'].astype('float64') + else: + # Patient whole VCF + patient['spliceAImax'] = patient['spliceAImax'].astype('float64') + patient.loc[pd.isna(patient['spliceAImax']), 'spliceAImax'] = patient.loc[~pd.isna(patient['spliceAImax']), 'spliceAImax'].describe()['mean'] + + + #Consequence + consequence = ['transcript_ablation', 'splice_acceptor_variant', 'splice_donor_variant', 'stop_gained', + 'frameshift_variant', 'stop_lost', 'start_lost', 'transcript_amplification', 'inframe_insertion', + 'inframe_deletion', 'missense_variant', 'protein_altering_variant', 'splice_region_variant', + 'splice_donor_5th_base_variant', 'splice_donor_region_variant'] + + for cons in consequence: + patient['cons_%s'%cons] = patient['Consequence'].str.contains(cons).astype('int') + variable_name.append('cons_%s'%cons) + + variable_name.remove('Consequence') + + + #nc_isPLP + patient.loc[patient['nc_isPLP']!=True, 'nc_isPLP'] = 0 + patient.loc[patient['nc_isPLP']==True, 'nc_isPLP'] = 1 + patient['nc_isPLP'] = patient['nc_isPLP'].astype('float64') + + #nc_isBLB + patient.loc[patient['nc_isBLB']!=True, 'nc_isBLB'] = 0 + patient.loc[patient['nc_isBLB']==True, 'nc_isBLB'] = 1 + patient['nc_isBLB'] = patient['nc_isBLB'].astype('float64') + + #c_isPLP + patient.loc[patient['c_isPLP']!=True, 'c_isPLP'] = 0 + patient.loc[patient['c_isPLP']==True, 'c_isPLP'] = 1 + patient['c_isPLP'] = patient['c_isPLP'].astype('float64') + + #c_isBLB + patient.loc[patient['c_isBLB']!=True, 'c_isBLB'] = 0 + patient.loc[patient['c_isBLB']==True, 'c_isBLB'] = 1 + patient['c_isBLB'] = patient['c_isBLB'].astype('float64') + + + #nc_ClinVar_Exp + patient.loc[patient['nc_ClinVar_Exp']!='nonCoding','nc_ClinVar_Exp'] = 0 + patient.loc[patient['nc_ClinVar_Exp']=='nonCoding','nc_ClinVar_Exp'] = 1 + patient['nc_ClinVar_Exp'] = patient['nc_ClinVar_Exp'].astype('float64') + + #nc_HGMD_Exp + patient.loc[patient['nc_HGMD_Exp']!='nonCoding','nc_HGMD_Exp'] = 0 + patient.loc[patient['nc_HGMD_Exp']=='nonCoding','nc_HGMD_Exp'] = 1 + patient['nc_HGMD_Exp'] = patient['nc_HGMD_Exp'].astype('float64') + + #c_ClinVar_Exp + c_clin_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange'] + + for exp in c_clin_exp: + + patient['c_ClinVar_Exp_%s'%exp] = patient['c_ClinVar_Exp'].str.contains(exp).astype('int') + variable_name.append('c_ClinVar_Exp_%s'%exp) + + variable_name.remove('c_ClinVar_Exp') + + #c_HGMD_Exp + c_hgmd_exp = ['Del_to_Missense', 'Different_pChange', 'Same_pChange', 'Stop_Loss', 'Start_Loss'] + + for exp in c_hgmd_exp: + + patient['c_HGMD_Exp_%s'%exp] = patient['c_HGMD_Exp'].str.contains(exp).astype('int') + variable_name.append('c_HGMD_Exp_%s'%exp) + + variable_name.remove('c_HGMD_Exp') + + + #nc_CLNREVSTAT + CLN_STAT = ['-', 'no_assertion_provided', 'no_assertion_criteria_provided','no_assertion_for_the_individual_variant', + 'criteria_provided,_single_submitter', 'criteria_provided,_conflicting_interpretations', + 'criteria_provided,_multiple_submitters,_no_conflicts', 'reviewed_by_expert_panel', 'practice_guideline'] + + CLN_STAT_SCORE = [0,0,0,0,1,1,2,3,4] + + for i in range(len(CLN_STAT)): + patient.loc[patient['nc_CLNREVSTAT'] == CLN_STAT[i], 'nc_CLNREVSTAT'] = CLN_STAT_SCORE[i] + patient.loc[patient['c_CLNREVSTAT'] == CLN_STAT[i], 'c_CLNREVSTAT'] = CLN_STAT_SCORE[i] + + patient['nc_CLNREVSTAT'] = patient['nc_CLNREVSTAT'].astype('float64') + patient['c_CLNREVSTAT'] = patient['c_CLNREVSTAT'].astype('float64') + + + #nc_RANKSCORE + patient.loc[patient['nc_RANKSCORE'] == '-', 'nc_RANKSCORE'] = 0 + patient['nc_RANKSCORE'] = patient['nc_RANKSCORE'].astype('float64') + + #nc_RANKSCORE + patient.loc[patient['c_RANKSCORE'] == '-', 'c_RANKSCORE'] = 0 + patient['c_RANKSCORE'] = patient['c_RANKSCORE'].astype('float64') + + #CLASS + patient.loc[patient['CLASS'] == '-', 'CLASS'] = 0 + patient.loc[patient['CLASS'] == 'DM?', 'CLASS'] = 1 + patient.loc[patient['CLASS'] == 'DM', 'CLASS'] = 2 + patient['CLASS'] = patient['CLASS'].astype('float64') + + + + patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', + 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] + patient = patient.groupby(['varId_dash'], sort=False)[variable_name[1:]].max() + patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper', + 'SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] = -patient.loc[:,['gnomadAF','gnomadAFg','gnomadGeneOELof','gnomadGeneOELofUpper','SIFT_score', 'FATHMM_score', 'ESP6500_AA_AF', 'ESP6500_EA_AF']] + + + #adding in tier features + #tier = pd.read_csv(tier_file, sep='\t') + tier=tier_file.copy() + tier_vars = ['IMPACT.from.Tier','TierAD','TierAR','TierAR.adj', + 'No.Var.HM','No.Var.H','No.Var.M','No.Var.L', + 'AD.matched','AR.matched', + 'recessive', 'dominant'] #, + #'OMIM.Inheritance.both', 'OMIM.Inheritance.others'] + """ CL: deleted + # recessive and dominant is enough + tier['OMIM.Inheritance.recessive'] = 0 + tier['OMIM.Inheritance.dominant'] = 0 + tier['OMIM.Inheritance.both'] = 0 + tier['OMIM.Inheritance.others'] = 0 + tier.loc[tier['OMIM.Inheritance'] == 'recessive', 'OMIM.Inheritance.recessive'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'dominant', 'OMIM.Inheritance.dominant'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'both', 'OMIM.Inheritance.both'] = 1 + tier.loc[tier['OMIM.Inheritance'] == 'others', 'OMIM.Inheritance.others'] = 1 + """ + + tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] + tier = tier.groupby(['Uploaded_variation'], sort=False)[tier_vars].max() + tier.loc[:,['TierAD','TierAR','TierAR.adj']] = -tier.loc[:,['TierAD','TierAR','TierAR.adj']] + + patient = pd.concat([patient, tier], axis=1) + patient.loc[patient['IMPACT.from.Tier'].isna(), 'IMPACT.from.Tier'] = 1 + patient.loc[patient['TierAD'].isna(), 'TierAD'] = 4 + patient.loc[patient['TierAR'].isna(), 'TierAR'] = 4 + patient.loc[patient['TierAR.adj'].isna(), 'TierAR.adj'] = 4 + + for var in tier_vars[4:8]: + patient.loc[patient[var].isna(), var] = 0 + + patient.loc[patient['AD.matched'].isna(), 'AD.matched'] = 0 + patient.loc[patient['AR.matched'].isna(), 'AR.matched'] = 0 + + patient.loc[patient['recessive'].isna(), 'recessive'] = 0 + patient.loc[patient['dominant'].isna(), 'dominant'] = 0 + #patient.loc[patient['OMIM.Inheritance.both'].isna(), 'OMIM.Inheritance.both'] = 0 ##CL: deleted + #patient.loc[patient['OMIM.Inheritance.others'].isna(), 'OMIM.Inheritance.others'] = 0 ##CL: deleted + + return patient + + + + + + +