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RSAT.R
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# detailed analysis in 11_autoantigen.R
library(seqinr)
library(dplyr)
library(tidyverse)
library(data.table)
library(Biostrings)
library(foreach)
library(doParallel)
library(data.table)
library(grid)
library(gridExtra)
library(effsize)
library(ggpubr)
library(ggplot2)
library(caret)
plot_cumulative_dist_immunogenicity <- function(data, feature = 'LD1', title = NA, remove_legends = F){
data <- data %>% dplyr::select(Peptide, Immunogenicity, !!sym(feature)) %>% filter(!is.na(!!sym(feature))) %>%
mutate_if(is.factor, as.character) %>% as.data.frame() %>%
filter(!is.infinite(!!sym(feature)))
d <- data %>% pull(!!sym(feature))
f <- data %>% pull(Immunogenicity)
if(length(unique(f)) == 0){
p <- NULL
}else{
if(unique(f) %in% c('Positive', 'Negative')){
data <- data %>% mutate(Immunogenicity = factor(Immunogenicity, levels = c('Positive', 'Negative')))
npeptides <- data %>% plyr::count(vars = 'Immunogenicity')
npos <- npeptides$freq[which(npeptides$Immunogenicity == 'Positive')]
nneg <- npeptides$freq[which(npeptides$Immunogenicity == 'Negative')]
count_peptides <- paste0('# Pos = ', npos, ' # Neg = ', nneg)
}else{
npeptides <- data %>% plyr::count(vars = 'Immunogenicity')
count_peptides <- c()
for(row in 1:nrow(npeptides)){
peptide <- paste0(npeptides$Immunogenicity[row], ': ', npeptides$freq[row])
count_peptides <- c(count_peptides, peptide)
}; count_peptides <- paste(count_peptides, collapse = ', ')
}
if(length(unique(f)) == 2){
result <- cohen.d(d ~ f)
# t_test <- t.test(d~f)
t_test <- tryCatch( t.test(d~f), error=function(err) NA)
if(!is.na(t_test)){
if(t_test$p.value < 0.01){
t_test_p_values <- formatC(t_test$p.value, format = "e", digits = 2)
}else{
t_test_p_values <- round(t_test$p.value, 3)
}
}else{
t_test_p_values <- NA
}
p <- data %>% ggplot( aes_string(feature, colour = 'Immunogenicity')) + stat_ecdf(size=2) + theme_bw() +
scale_color_brewer(palette="Dark2") +ylab('Cumulative % of peptides') +
labs(subtitle = paste0('Cohen d = ', round(result$estimate, 3), ', t-test p = ', t_test_p_values,
'\n', count_peptides),
title = title) +
theme(plot.subtitle = element_text(hjust = 0.5, size = 10.5), plot.title = element_text(hjust = 0.5))
}else{
p <- data %>% ggplot( aes_string(feature, colour = 'Immunogenicity')) + stat_ecdf(size=2) + theme_bw() +
scale_color_brewer(palette="Dark2") +ylab('Cumulative % of peptides') +
labs(subtitle = count_peptides, title = title) +
theme(plot.subtitle = element_text(hjust = 0.5, size = 10.5), plot.title = element_text(hjust = 0.5))
}
if(remove_legends == T){
p <- p + theme(legend.position = "none")
}
}
return(p)
}
plot_violin_immunogenicity <- function(data, feature = 'LD1', title = NA, remove_legends = F){
data <- data %>% dplyr::select(Peptide, Immunogenicity, !!sym(feature)) %>% filter(!is.na(!!sym(feature))) %>%
mutate_if(is.factor, as.character) %>% as.data.frame() %>%
filter(!is.infinite(!!sym(feature)))
d <- data %>% pull(!!sym(feature))
f <- data %>% pull(Immunogenicity)
if(length(unique(f)) == 0){
p <- NULL
}else{
if(sort(unique(f)) == c('Negative', 'Positive')){
data <- data %>% mutate(Immunogenicity = factor(Immunogenicity, levels = c('Positive', 'Negative')))
npeptides <- data %>% plyr::count(vars = 'Immunogenicity')
npos <- npeptides$freq[which(npeptides$Immunogenicity == 'Positive')]
nneg <- npeptides$freq[which(npeptides$Immunogenicity == 'Negative')]
count_peptides <- paste0('# Pos = ', npos, ' # Neg = ', nneg)
}else{
npeptides <- data %>% plyr::count(vars = 'Immunogenicity')
count_peptides <- c()
for(row in 1:nrow(npeptides)){
peptide <- paste0(npeptides$Immunogenicity[row], ': ', npeptides$freq[row])
count_peptides <- c(count_peptides, peptide)
}; count_peptides <- paste(count_peptides, collapse = ', ')
}
max_y <- max(d, rm.na = T)
if(length(unique(f)) == 2){
result <- cohen.d(d ~ f)
t_test <- t.test(d~f)
if(t_test$p.value < 0.01){
t_test_p_values <- formatC(t_test$p.value, format = "e", digits = 2)
}else{
t_test_p_values <- round(t_test$p.value, 3)
}
my_comparisons <- list(c('Positive', 'Negative'))
p <- data %>% ggviolin(x = 'Immunogenicity', y = feature, fill = "Immunogenicity",
palette = c("#006400", "#FC4E07"), legend = 'none',
add = "boxplot", add.params = list(fill = "white")) +
stat_compare_means(comparisons = my_comparisons, label = "p.signif") + theme_bw() +
labs(subtitle = paste0('Cohen d = ', round(result$estimate, 3), ', t-test p = ', t_test_p_values,
'\n', count_peptides),
title = title) +
theme(plot.subtitle = element_text(hjust = 0.5, size = 10.5), plot.title = element_text(hjust = 0.5)) +
ylim(NA, max_y*1.25)
}else{
p <- data %>% ggviolin(x = 'Immunogenicity', y = feature, fill = "Immunogenicity",
palette = c("#006400", "#FC4E07"), legend = 'none',
add = "boxplot", add.params = list(fill = "white")) +
stat_compare_means(comparisons = my_comparisons, label = "p.signif") + theme_bw() +
labs(subtitle = count_peptides, title = title) +
theme(plot.subtitle = element_text(hjust = 0.5, size = 10.5), plot.title = element_text(hjust = 0.5)) +
ylim(NA, max_y*1.25)
}
if(remove_legends == T){
p <- p + theme(legend.position = "none")
}
}
return(p)
}
plot_boxplot_immunogenicity <- function(data, feature = 'LD1', title = NA, remove_legends = F){
data <- data %>% dplyr::select(Peptide, Immunogenicity, !!sym(feature)) %>% filter(!is.na(!!sym(feature))) %>%
mutate_if(is.factor, as.character) %>% as.data.frame() %>%
filter(!is.infinite(!!sym(feature)))
d <- data %>% pull(!!sym(feature))
f <- data %>% pull(Immunogenicity)
if(length(unique(f)) == 0){
p <- NULL
}else{
if(unique(f) %in% c('Positive', 'Negative')){
data <- data %>% mutate(Immunogenicity = factor(Immunogenicity, levels = c('Positive', 'Negative')))
npeptides <- data %>% plyr::count(vars = 'Immunogenicity')
npos <- npeptides$freq[which(npeptides$Immunogenicity == 'Positive')]
nneg <- npeptides$freq[which(npeptides$Immunogenicity == 'Negative')]
count_peptides <- paste0('# Pos = ', npos, ' # Neg = ', nneg)
}else{
npeptides <- data %>% plyr::count(vars = 'Immunogenicity')
count_peptides <- c()
for(row in 1:nrow(npeptides)){
peptide <- paste0(npeptides$Immunogenicity[row], ': ', npeptides$freq[row])
count_peptides <- c(count_peptides, peptide)
}; count_peptides <- paste(count_peptides, collapse = ', ')
}
max_y <- max(d, rm.na = T)
if(length(unique(f)) == 2){
result <- cohen.d(d ~ f)
t_test <- t.test(d~f)
if(t_test$p.value < 0.01){
t_test_p_values <- formatC(t_test$p.value, format = "e", digits = 2)
}else{
t_test_p_values <- round(t_test$p.value, 3)
}
my_comparisons <- list(c('Positive', 'Negative'))
p <- data %>% ggboxplot(x = 'Immunogenicity', y = feature, fill = "Immunogenicity",
palette = c("#006400", "#FC4E07"), legend = 'none') +
stat_compare_means(comparisons = my_comparisons, label = "p.signif") + theme_bw() +
labs(subtitle = paste0('Cohen d = ', round(result$estimate, 3), ', t-test p = ', t_test_p_values,
'\n', count_peptides),
title = title) +
theme(plot.subtitle = element_text(hjust = 0.5, size = 10.5), plot.title = element_text(hjust = 0.5)) +
ylim(NA, max_y*1.25)
}else{
p <- data %>% ggboxplot(x = 'Immunogenicity', y = feature, fill = "Immunogenicity",
palette = c("#006400", "#FC4E07"), legend = 'none') +
stat_compare_means(comparisons = my_comparisons, label = "p.signif") + theme_bw() +
labs(subtitle = count_peptides, title = title) +
theme(plot.subtitle = element_text(hjust = 0.5, size = 10.5), plot.title = element_text(hjust = 0.5)) +
ylim(NA, max_y*1.25)
}
if(remove_legends == T){
p <- p + theme(legend.position = "none")
}
}
return(p)
}
################ overview of autoantigen dataset ##########################################
autoantigen_data <- read.csv(file = 'autoantigen_5data.csv', stringsAsFactors=F)
autoantigen_peptides <- autoantigen_data %>%
group_by(Peptide) %>%
dplyr::summarise(MHC_Restriction = paste0(sort(unique(MHC_Restriction)), collapse="|"),
wtPeptide = paste0(sort(unique(wtPeptide)), collapse="|"),
Antigen_Name = paste0(sort(unique(Antigen_Name)), collapse="|"),
Type = paste0(sort(unique(Type)), collapse="|"),
data = paste0(sort(unique(data)), collapse="|")) %>%
filter(!grepl('\\+', Peptide))
autoantigen_data %>% distinct(Peptide, data) %>% plyr::count( vars=c("data")) %>%
ggplot(aes(x= reorder(data, -freq), y=freq)) +
geom_bar(stat="identity", fill = jcolors('pal7')[1], color = 'black', width = 0.7) +
theme_classic() + labs(y = '# of self-peptides', x = 'Database') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
peptide_overlap <- autoantigen_data %>% distinct(Peptide, data) %>%
plyr::count( vars=c("data", 'Peptide')) %>%
reshape2::dcast(Peptide ~ data, value.var="freq") %>% rename_all(toupper) %>%
replace(is.na(.), 0) %>%
dplyr::rename(CANCER_PEPTIDE_DB = CANCER_PEPTIDE_DATABASE)
library(UpSetR)
upset(peptide_overlap, nsets = ncol(peptide_overlap),
nintersects = NA, order.by = "freq", line.size = 1.2, point.size = 2.5, text.scale = 1.4,
# scale.intersections = "log10",
scale.sets = "log10")
# histogram of lengths
autoantigen_peptides %>% mutate(length = nchar(as.character(Peptide))) %>%
plyr::count(vars = c('length') ) %>%
ggplot(aes(x= length, y=freq)) + geom_bar(stat="identity", position = "dodge2") +
# scale_x_continuous(breaks=0:7) +
theme_classic() + labs(y = '# of autoantigenic peptides', x= 'Length of peptides (aa)')
########################## compute RSAT ###################################
allsubstr <- function(x, n) unique(substring(x, 1:(nchar(x) - n + 1), n:nchar(x)))
iedbData <- read.csv(file = '1_pathogenic_db.csv') %>% filter(!Antigen_Organism %in% c('Homo sapiens', NA, '')) %>%
select(Peptide, Immunogenicity, Supertype, Antigen_Organism)
PEPTIDES <- iedbData %>% pull(Peptide) %>% unique %>% as.data.table() %>% dplyr::rename(Peptide=".")
load('hsProteomeDF.RData')
hsProteomeDF <- str_replace_all(hsProteomeDF, 'U', 'C')
proteome <- AAString(hsProteomeDF)
load(file = 'autoantigen_peptide_vector.RData')
aa1 <- AAString(autoantigen_peptide_vector)
# Run analysis
registerDoParallel(cores=4)
iedb_cognate_epitope = foreach(peptide.i=1:nrow(PEPTIDES),.combine = rbind,.packages = c("dplyr","data.table","magrittr","Biostrings","doParallel")) %dopar% {
dts = foreach(proteome_nrow = 1.,combine = rbind,.packages = c("dplyr","data.table","magrittr","Biostrings")) %do% {
if(peptide.i == 1){
print(c(" Processing Peptide Number: ",peptide.i))
}
aa2 <- AAString(PEPTIDES[peptide.i,]$Peptide)
align <- Biostrings::pairwiseAlignment(aa1, aa2, type = "local-global", substitutionMatrix = 'BLOSUM62')
autoantigen_pattern <- AAString(as.character(align@pattern))
peptid_pattern <- AAString(as.character(align@subject))
autoantigen_score <- Biostrings::pairwiseAlignment(autoantigen_pattern, autoantigen_pattern, type = "global", substitutionMatrix = 'BLOSUM62', scoreOnly = T)
peptide_score <- Biostrings::pairwiseAlignment(peptid_pattern, peptid_pattern, type = "global", substitutionMatrix = 'BLOSUM62', scoreOnly = T)
proteome_align <- Biostrings::pairwiseAlignment(proteome, autoantigen_pattern, type = "local-global", substitutionMatrix = 'BLOSUM62')
proteome_pattern <- AAString(as.character(proteome_align@pattern))
proteome_score <- Biostrings::pairwiseAlignment(proteome_pattern, proteome_pattern, type = "global", substitutionMatrix = 'BLOSUM62', scoreOnly = T)
bl_proteoem_iedb_align <- Biostrings::pairwiseAlignment(peptid_pattern, proteome_pattern, type = "global", substitutionMatrix = 'BLOSUM62', scoreOnly = T)
DT = data.frame(Peptide = PEPTIDES[peptide.i,]$Peptide, Peptide_pattern = as.character(align@subject),
autoantigen_pattern = as.character(align@pattern), hsproteome_pattern = as.character(proteome_align@pattern),
peptide_score = as.numeric(peptide_score), autoantigen_score = as.numeric(autoantigen_score),
hsproteome_score = as.numeric(proteome_score), BL_iedb_autoantigen = as.numeric(align@score),
BL_iedb_hsproteome = as.numeric(bl_proteoem_iedb_align))
DT
}
write.table(dts,
file = paste0("rsat/PEP_",peptide.i,".txt"),quote=F,row.names = FALSE,sep="\t") # make rsat folder
}
setwd('rsat')
temp = list.files(pattern="*.txt")
dt = lapply(temp, read.table, sep="\t", row.names=NULL, colClasses = "character", header = TRUE)
blosum62_autoantigen_iedb <- rbindlist(dt)
blosum62_autoantigen_iedb <- blosum62_autoantigen_iedb %>%
mutate(BL_iedb_autoantigen= as.numeric(BL_iedb_autoantigen), peptide_score = as.numeric(peptide_score),
autoantigen_score = as.numeric(autoantigen_score), hsproteome_score = as.numeric(hsproteome_score),
BL_iedb_hsproteome = as.numeric(BL_iedb_hsproteome)) %>%
mutate(Matchscore_iedb_autoantigen = BL_iedb_autoantigen/sqrt(peptide_score*autoantigen_score),
Matchscore_iedb_hsproteome = BL_iedb_hsproteome/sqrt(peptide_score*hsproteome_score)) %>%
mutate(Ratio_BL_autoantigen_hsproteome = BL_iedb_autoantigen/BL_iedb_hsproteome,
Ratio_Matchscore_autoantigen_hsproteome = Matchscore_iedb_autoantigen/Matchscore_iedb_hsproteome) %>%
left_join(iedbData, by = c('Peptide'))
##################### analysis for similarity with autoantigens ###################################
library(stringdist)
blosum62_autoantigen_iedb <- blosum62_autoantigen_iedb %>%
filter(!Antigen_Organism %in% c('', 'Homo sapiens') ) %>%
mutate(length = nchar(as.character(Peptide))) %>%
mutate(Matchscore_iedb_autoantigen_binary = ifelse(Matchscore_iedb_autoantigen >= 0.6, 1, 0)) %>%
mutate(Ratio_BL_iedb_autoantigen_binary = ifelse(Ratio_BL_autoantigen_hsproteome > 1, 1, 0)) %>%
mutate(Ratio_Matchscore_iedb_autoantigen_binary = ifelse(Ratio_Matchscore_autoantigen_hsproteome > 1, 1, 0)) %>%
mutate(Ratio_BL_iedb_autoantigen_incomplete = ifelse(Ratio_BL_autoantigen_hsproteome > 1, Ratio_BL_autoantigen_hsproteome, NA)) %>%
mutate(Ratio_Matchscore_iedb_autoantigen_incomplete = ifelse(Ratio_Matchscore_autoantigen_hsproteome > 1, Ratio_Matchscore_autoantigen_hsproteome, NA)) %>%
mutate(Ratio_BL_iedb_autoantigen_incomplete_byMatchscore = ifelse(Matchscore_iedb_autoantigen >= 0.6 , Ratio_BL_autoantigen_hsproteome, NA)) %>%
mutate(Ratio_Matchscore_iedb_autoantigen_incomplete_byMatchscore = ifelse(Matchscore_iedb_autoantigen >= 0.6, Ratio_Matchscore_autoantigen_hsproteome, NA)) %>%
mutate(hamming_dist = stringdist(Peptide_pattern, autoantigen_pattern, method = 'hamming')) %>% filter(hamming_dist != 0)
#filter out 18 peptides overlapping both dataset
9856 -9838
blosum62_autoantigen_iedb %>% filter(Matchscore_iedb_autoantigen >= 0.6) %>% plyr::count(vars = c('Immunogenicity'))
blosum62_autoantigen_iedb %>% plyr::count(vars = c('Immunogenicity'))
####### all peptides - FIG
blosum62_autoantigen_iedb %>% plot_violin_immunogenicity(feature = 'Matchscore_iedb_autoantigen',
title = 'Match score', remove_legends = T) +
ylim(c(NA, 1.15)) + labs(y = 'Match score\n[pathogenic peptides, autoantigens]') + theme_classic()
# BLOSUM - FIG
blosum62_autoantigen_iedb %>%
plot_violin_immunogenicity(feature = 'Ratio_BL_iedb_autoantigen_incomplete_byMatchscore',
title = 'Ratio BL score (homologous)', remove_legends =T)+
ylim(c(NA, 2)) +
labs(y = 'Relative similarity to self antigens') + theme_classic()
blosum62_autoantigen_iedb %>%
plot_cumulative_dist_immunogenicity(feature = 'Ratio_BL_iedb_autoantigen_incomplete_byMatchscore',
title = 'Ratio BL score (homologous)', remove_legends =T) +
labs(x = 'Relative similarity to self antigens') + theme_classic()
# Match score
blosum62_autoantigen_iedb %>%
plot_violin_immunogenicity(feature = 'Ratio_Matchscore_iedb_autoantigen_incomplete_byMatchscore',
title = 'Ratio Match score (homologous)', remove_legends =T) + theme_classic()
blosum62_autoantigen_iedb %>%
plot_cumulative_dist_immunogenicity(feature = 'Ratio_Matchscore_iedb_autoantigen_incomplete_byMatchscore',
title = 'Ratio Match score (homologous)', remove_legends =T) + theme_classic()
####### output table
output_table <- blosum62_autoantigen_iedb %>%
select(Peptide, Immunogenicity, MHC_Restriction, Supertype, netMHCpan_Rank, NetMHCstabpan_Rank, Antigen_Name, Ratio_BL_iedb_autoantigen_incomplete_byMatchscore)
# write.csv(output_table, file = 'C:/Users/Chloe/Desktop/Oxford-Projects/HashemImmunogenicity/11_final_immunogenicity_model/0_Manuscrip_figures/fig5/RSAT.csv', row.names = FALSE)
####### per length
lengths <- 9:10
length_violin_p <- list()
length_acumul_p <- list()
for(len in 1:length(lengths)){
length_violin_p[[len]] <- blosum62_autoantigen_iedb %>%
mutate(length = nchar(as.character(Peptide))) %>%
filter(length %in% lengths[len]) %>%
plot_violin_immunogenicity(feature = 'Ratio_BL_iedb_autoantigen_incomplete_byMatchscore',
title = lengths[len], remove_legends = T) +
labs(y = 'Relative similarity to self antigens') + theme_classic()
length_acumul_p[[len]] <- blosum62_autoantigen_iedb %>%
mutate(length = nchar(as.character(Peptide))) %>%
filter(length %in% lengths[len]) %>%
plot_cumulative_dist_immunogenicity(feature = 'Ratio_BL_iedb_autoantigen_incomplete_byMatchscore',
title = lengths[len], remove_legends = T) +
labs(x = 'Relative similarity to self antigens') + theme_classic()
}
do.call("grid.arrange", c(length_violin_p, ncol=2))
do.call("grid.arrange", c(length_acumul_p, ncol=2))
######## per species effect
load(file = 'iedb_dissimilar_protome_blosum62.RData')
antigen_organism_col <- blosum62_autoantigen_iedb %>% select(Peptide, Immunogenicity, Antigen_Organism) %>% distinct()
iedb_dissimilar_protome_blosum62 <- iedb_dissimilar_protome_blosum62 %>%
dplyr::select(-Antigen_Organism) %>%
left_join(antigen_organism_col, by = c('Peptide', 'Immunogenicity')) %>%
filter(!Antigen_Organism %in% c('', 'Homo sapiens') )
top_organism <- iedb_dissimilar_protome_blosum62 %>% filter(Antigen_Organism != 'Homo sapiens') %>%
filter(Antigen_Organism != "") %>%
plyr::count(vars = c('Antigen_Organism', 'Immunogenicity')) %>%
filter(freq >= 20) %>% dplyr::select(-freq) %>% plyr::count(vars = 'Antigen_Organism') %>%
filter(freq == 2) %>% pull(Antigen_Organism)
# for incomplete data
org_accum_p <- list()
for(org in 1:length(top_organism)){
org_accum_p[[org]] <- blosum62_autoantigen_iedb %>%
filter(Antigen_Organism %in% top_organism[org]) %>%
plot_cumulative_dist_immunogenicity(feature = 'Ratio_BL_iedb_autoantigen_incomplete_byMatchscore',
title = top_organism[org], remove_legends = T) +
labs(x = 'Relative similarity to self antigens') + theme_classic()
}
do.call("grid.arrange", c(org_accum_p, ncol=4))
do.call("grid.arrange", c(org_accum_p[c(1, 2, 5, 6, 7)], ncol = 3))
blosum62_autoantigen_iedb %>% filter(Antigen_Organism %in% top_organism[org]) %>%
plot_cumulative_dist_immunogenicity(feature = 'Ratio_BL_iedb_autoantigen_incomplete_byMatchscore',
title = top_organism[org], remove_legends = F)