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10x_all-FACS-n10_2021rev_step01_processing-QC_MNT.R
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################################################################################
### LIBD 10x snRNA-seq [pilot] revision (n=10)
### STEP 01: Read in SCEs and perform nuclei calling and QC
### Initiated: MNT 25Feb2021
################################################################################
library(SingleCellExperiment)
library(scRNAseq)
library(batchelor)
library(EnsDb.Hsapiens.v86)
library(scater)
library(scran)
library(uwot)
library(DropletUtils)
library(jaffelab)
library(Rtsne)
library(gridExtra)
library(rtracklayer)
### Palette taken from `scater`
tableau10medium = c("#729ECE", "#FF9E4A", "#67BF5C", "#ED665D",
"#AD8BC9", "#A8786E", "#ED97CA", "#A2A2A2",
"#CDCC5D", "#6DCCDA")
tableau20 = c("#1F77B4", "#AEC7E8", "#FF7F0E", "#FFBB78", "#2CA02C",
"#98DF8A", "#D62728", "#FF9896", "#9467BD", "#C5B0D5",
"#8C564B", "#C49C94", "#E377C2", "#F7B6D2", "#7F7F7F",
"#C7C7C7", "#BCBD22", "#DBDB8D", "#17BECF", "#9EDAE5")
### =======
#### THIS CHUNK DONE IN SUBMITTED JOB ====
### Read in (2021) 'samples.manifest' for streamlining
samples.revision <- read.table("/dcs04/lieber/lcolladotor/rawDataTDSC_LIBD001/raw-data/2021-10-15_Tran2021_published/Feb2021/samples.manifest",
sep="\t", header=F)$V1
# Make list of paths
paths.rawCounts <- c(paste0("/dcl01/ajaffe/data/lab/singleCell/10x_pilot/premRNA/Feb2021/",
samples.revision,"/outs/raw_feature_bc_matrix"))
# Make sure works
sapply(paths.rawCounts, list.files) # good
# Make names for individual SCEs
names(paths.rawCounts) <- gsub("_",".", tolower(samples.revision))
## Read in raw UMI x barcode matrix - **use pre-mRNA-aligned reads
pilot.data.2 <- lapply(paths.rawCounts, function(x){ read10xCounts(x, col.names=TRUE) })
names(pilot.data.2) <- names(paths.rawCounts)
### Gene annotation (from scater) ===
# Pull in GTF information
gtf = import("/dcl01/ajaffe/data/lab/singleCell/refdata-cellranger-GRCh38-3.0.0/genes/genes.gtf")
gtf = gtf[gtf$type == "gene"]
length(gtf)
names(gtf) = gtf$gene_id
table(names(gtf) == rowData(pilot.data.2[[1]])$ID)
seqlevels(gtf)[1:25] = paste0("chr", seqlevels(gtf)[1:25])
mcols(gtf) = mcols(gtf)[,c(5:9)]
for(i in 1:length(pilot.data.2)){
rowRanges(pilot.data.2[[i]]) <- gtf
# Because some gene names are the same:
rownames(pilot.data.2[[i]]) <- uniquifyFeatureNames(rowData(pilot.data.2[[i]])$gene_id, rowData(pilot.data.2[[i]])$gene_name)
rowData(pilot.data.2[[i]])$Symbol.uniq <- rownames(pilot.data.2[[i]])
}
# Preliminary save
save(pilot.data.2, file="rdas/revision/all-FACS-n10_2021rev_SCEs_processing-QC_MNTFeb2021.rda")
### Quality control ============================================================
## - Going to ignore the adaptive NMAD-approach to outlier detection for UMI/feature count
# because this hasn't been as straightforward in past experience (might throw away neurons)
## - Vignette for the 10x PBMC dataset (OSCA Ch.24) only does mito & droplet QC anyhow
# - (mention that for a sample with very heterogeneous cell comp., don't want
# to drop potential cells with low RNA content)
## Cell detection (droplet exclusion, rather)
# Can use UMI count vs barcode rank (knee/inflection plot) to decide threshold, but
# "this unnecessarily discards libraries derived from cell types with low RNA content" (OSCA, Ch. 6)
# -> Instead should prefer this Monte Carlo-simulation-based empty droplet test:
# Additionally:
# For any Sig==FALSE & Limited==TRUE, may need to increase n iterations (default = 10000) with 'niters='
# - this field = whether "the computed p-value for a...barcode is bounded by the number of iterations"
# -> In exploratory phase (preprint), not all samples passed with niters=15000 (default 10,000), so use 20,000
e.out.2 <- list()
for(i in 1:length(pilot.data.2)){
cat(paste0("Simulating empty drops for: ",names(pilot.data.2)[i],"... \n"))
set.seed(109)
e.out.2[[i]] <- emptyDrops(counts(pilot.data.2[[i]]), niters=20000)
cat(paste0("\n\t...Simulations complete. \n\t", date(), "\n\n\n"))
date()
}
names(e.out.2) <- names(pilot.data.2)
save(pilot.data.2, e.out.2, file="rdas/revision/all-FACS-n10_2021rev_SCEs_processing-QC_MNTFeb2021.rda")
#### ** END JOB - pick up interactive assessment, below ** ====
### (Interactive:) Read in data with `emptyDrops` stats =====
load("rdas/revision/all-FACS-n10_2021rev_SCEs_processing-QC_MNTMar2021.rda", verbose=T)
# pilot.data.2, e.out.2
for(i in 1:length(e.out.2)){
print(names(e.out.2)[[i]])
print(table(Signif = e.out.2[[i]]$FDR <= 0.001, Limited = e.out.2[[i]]$Limited))
}
# [1] "br5276.sacc.neun"
# Limited
# Signif FALSE TRUE
# FALSE 6649 0
# TRUE 26 900
# [1] "br5400.nac"
# Limited
# Signif FALSE TRUE
# FALSE 66248 0
# TRUE 0 4859
# [1] "br5276.nac"
# Limited
# Signif FALSE TRUE
# FALSE 42164 0
# TRUE 0 3012
# [1] "br5701.nac.neun"
# Limited
# Signif FALSE TRUE
# FALSE 8280 0
# TRUE 0 814
# [1] "br5701.sacc.neun"
# Limited
# Signif FALSE TRUE
# FALSE 73689 0
# TRUE 0 4518
# [1] "br5207.dlpfc"
# Limited
# Signif FALSE TRUE
# FALSE 74753 0
# TRUE 0 6453
# [1] "br5276.amy.neun"
# Limited
# Signif FALSE TRUE
# FALSE 68568 1918 <- needs more iters, just do interactively (below)
# TRUE 1 816
# [1] "br5400.amy.neun"
# Limited
# Signif FALSE TRUE
# FALSE 55624 0
# TRUE 0 3185
# [1] "br5400.sacc"
# Limited
# Signif FALSE TRUE
# FALSE 67139 0
# TRUE 0 4364
# [1] "br5701.amy"
# Limited
# Signif FALSE TRUE
# FALSE 66271 0
# TRUE 25 4174
# - all are good and not lower-p-value-bound-limited? All but 'br5276.amy.neun'
# Increase 'niters' for br5276.amy.neun
e.out.5276amy <- emptyDrops(counts(pilot.data.2[["br5276.amy.neun"]]), niters=30000)
table(Signif = e.out.5276amy$FDR <= 0.001, Limited = e.out.5276amy$Limited)
# Limited
#Signif FALSE TRUE
# FALSE 68565 0
# TRUE 1 2737 ok good.
#
# Replace that entry in e.out.2
e.out.2[["br5276.amy.neun"]] <- e.out.5276amy
# Subset in for-loop:
for(i in 1:length(pilot.data.2)){
pilot.data.2[[i]] <- pilot.data.2[[i]][ ,which(e.out.2[[i]]$FDR <= 0.001)]
}
# Check
sapply(pilot.data.2, dim)
## Save this
save(pilot.data.2, e.out.2, file="rdas/revision/all-FACS-n10_2021rev_SCEs_processing-QC_MNTMar2021.rda")
### Mito rate QC ==================
table(rownames(pilot.data.2[[7]])==rownames(pilot.data.2[[10]])) # and checked various other pairs
location <- mapIds(EnsDb.Hsapiens.v86, keys=rowData(pilot.data.2[[1]])$gene_id,
column="SEQNAME", keytype="GENEID")
#Warning message:
#Unable to map 312 of 33538 requested IDs. - ok bc none of these are MT genes (13 pt-coding; `table(location)`)
# ID those mito genes
stats <- list()
for(i in 1:length(pilot.data.2)){
stats[[i]] <- perCellQCMetrics(pilot.data.2[[i]], subsets=list(Mito=which(location=="MT")))
}
names(stats) <- names(pilot.data.2)
### Trick: Add a pseudo-count==1 for a 'MT transcript' ===
# Note: This was implemented because we realized samples with mito rate distributions that
# were 'clean' and tightly distributed about 0 would yield a 3x MAD = 0, thus over-penalizing
# nuclei even if they had a single MT transcript (throwing out upwards of 50% of the sample)
# First check computation of mito percent:
table(stats[[8]]$subsets_Mito_percent == (stats[[8]]$subsets_Mito_sum/stats[[8]]$sum)*100)
# All TRUE
test.stats <- stats
for(i in 1:length(test.stats)){
test.stats[[i]]$pseudo_subsets_Mito_sum <- test.stats[[i]]$subsets_Mito_sum + 1
test.stats[[i]]$pseudo_subsets_Mito_percent <- test.stats[[i]]$pseudo_subsets_Mito_sum / (test.stats[[i]]$sum+1) * 100
}
## Lapply: MAD approach for mito rate thresholding
pseudo.high.mito <- lapply(test.stats, function(x) isOutlier(x$pseudo_subsets_Mito_percent, nmads=3, type="higher"))
pseudo.high.mito.table <- lapply(pseudo.high.mito, table)
# Percept dropped
sapply(pseudo.high.mito.table, function(x) round(x[2]/sum(x), 3))
#br5276.sacc.neun.TRUE br5400.nac.TRUE br5276.nac.TRUE
# 0.081 0.155 0.128
# br5701.nac.neun.TRUE br5701.sacc.neun.TRUE br5207.dlpfc.TRUE
# 0.205 0.158 0.180
# br5276.amy.neun.TRUE br5400.amy.neun.TRUE br5400.sacc.TRUE
# 0.100 0.173 0.093
# br5701.amy.TRUE
# 0.161
# Thresholds
sapply(pseudo.high.mito, function(x){round(attributes(x)[["thresholds"]]["higher"], 4)})
#br5276.sacc.neun.higher br5400.nac.higher br5276.nac.higher
# 5.6623 0.0921 1.3381
# br5701.nac.neun.higher br5701.sacc.neun.higher br5207.dlpfc.higher
# 3.5073 0.0981 0.0591
# br5276.amy.neun.higher br5400.amy.neun.higher br5400.sacc.higher
# 5.2953 0.6188 0.0712
# br5701.amy.higher
# 0.2177
## Bind [true] stats to colData
for(i in 1:length(pilot.data.2)){
colData(pilot.data.2[[i]]) <- cbind(colData(pilot.data.2[[i]]), stats[[i]],
#high.mito[[i]]
pseudo.high.mito[[i]]
)
colnames(colData(pilot.data.2[[i]]))[9] <- "high.mito"
}
# $sum vs. $total ??
for(i in 1:length(pilot.data.2)){
print(table(pilot.data.2[[i]]$sum == pilot.data.2[[i]]$total))
}
## all TRUE so can remove this 'duplicate' column:
for(i in 1:length(pilot.data.2)){
pilot.data.2[[i]]$total <- NULL
}
# Store original for comparison/plotting
pilot.data.2.unfiltered <- pilot.data.2
## Subset - remove those indexed as high.mito
for(i in 1:length(pilot.data.2)){
pilot.data.2[[i]] <- pilot.data.2[[i]][ ,!pilot.data.2[[i]]$high.mito]
}
sapply(pilot.data.2, dim)
## Plot metrics === ===
mitoCutoffs <- unlist(lapply(high.mito, function(x){attributes(x)$thresholds["higher"]}))
#mitoCutoffs <- unlist(lapply(pseudo.high.mito, function(x){attributes(x)$thresholds["higher"]}))
mean(mitoCutoffs)
# [1] 1.453033;; 0.3903217 for first batch (n=12)
## with pseudo-MT count:
# [1] 1.696016
median(mitoCutoffs)
# [1] 0.1657229;; 0.138046 for first batch (n=12)
## with pseudo-MT count:
# [1] 0.4182892
mitoCutoffs <- round(mitoCutoffs, 3)
#dir.create("pdfs/revision")
pdf("pdfs/revision/all-FACS-n10_2021rev_QCmetrics_high-mitoColored_MNT.pdf", height=4)
#pdf("pdfs/revision/all-FACS-n10_2021rev_QCmetrics_high-mitoColored_wPseudoMTcount_MNT.pdf", height=4)
for(i in 1:length(pilot.data.2.unfiltered)){
grid.arrange(
plotColData(pilot.data.2.unfiltered[[i]], y="sum", colour_by="high.mito") +
scale_y_log10() + ggtitle(paste0("Total count: ", names(pilot.data.2.unfiltered)[[i]])),
plotColData(pilot.data.2.unfiltered[[i]], y="detected", colour_by="high.mito") +
scale_y_log10() + ggtitle("Detected features"),
plotColData(pilot.data.2.unfiltered[[i]], y="subsets_Mito_percent",
colour_by="high.mito") + ggtitle(paste0("Mito % (cutoff = ", mitoCutoffs[i],")")),
ncol=3
)
# Mito rate vs n detected features
print(
plotColData(pilot.data.2.unfiltered[[i]], x="detected", y="subsets_Mito_percent",
colour_by="high.mito", point_size=2.5, point_alpha=0.5) +
ggtitle(paste0("Sample: ", names(pilot.data.2.unfiltered)[[i]],
"; pre-QC nNuclei: ", ncol(pilot.data.2.unfiltered[[i]]),"; ",
"nNuclei kept: ", ncol(pilot.data.2[[i]])," (",
round(ncol(pilot.data.2[[i]]) / ncol(pilot.data.2.unfiltered[[i]]), 2), "%)"
))
)
# Detected features vs total count
print(
plotColData(pilot.data.2.unfiltered[[i]], x="sum", y="detected",
colour_by="high.mito", point_size=2.5, point_alpha=0.5) +
ggtitle(paste0("Sample: ", names(pilot.data.2.unfiltered)[[i]],
"; pre-QC nNuclei: ", ncol(pilot.data.2.unfiltered[[i]]),"; ",
"nNuclei kept: ", ncol(pilot.data.2[[i]])," (",
round(ncol(pilot.data.2[[i]]) / ncol(pilot.data.2.unfiltered[[i]]), 2), "%)"
))
)
}
dev.off()
## Save!
save(pilot.data.2, pilot.data.2.unfiltered, e.out.2, file="rdas/revision/all-FACS-n10_2021rev_SCEs_processing-QC_MNTMar2021.rda")
### Doublet detection / removal ==============================
# Use default params, because this is at the single-sample-level
# (within-region normalization, PCA, etc. will be performed with corresponding samples)
library(scDblFinder)
## To speed up, run on sample-level top-HVGs - just take top 1000 ===
pilot.data.normd <- lapply(pilot.data.2, logNormCounts)
geneVar.samples <- lapply(pilot.data.normd, modelGeneVar)
topHVGs <- lapply(geneVar.samples, function(x) {getTopHVGs(x, n=1000)})
# Generate doublet density scores
set.seed(109)
dbl.dens.focused <- lapply(names(pilot.data.normd), function(x) {
computeDoubletDensity(pilot.data.normd[[x]], subset.row=topHVGs[[x]])})
names(dbl.dens.focused) <- names(pilot.data.normd)
sapply(dbl.dens.focused, function(x) round(quantile(x, probs=seq(0,1,by=0.05)),3))
# br5276.sacc.neun br5400.nac br5276.nac br5701.nac.neun br5701.sacc.neun
# 0% 0.015 0.000 0.005 0.041 0.000
# 5% 0.043 0.016 0.032 0.092 0.023
# 10% 0.071 0.025 0.053 0.135 0.046
# 15% 0.124 0.033 0.074 0.185 0.053
# 20% 0.204 0.049 0.095 0.239 0.068
# 25% 0.397 0.066 0.116 0.313 0.076
# 30% 0.645 0.090 0.142 0.399 0.084
# 35% 0.810 0.115 0.173 0.479 0.099
# 40% 0.899 0.131 0.200 0.565 0.107
# 45% 0.998 0.156 0.236 0.673 0.122
# 50% 1.091 0.181 0.268 0.735 0.137
# 55% 1.164 0.222 0.305 0.863 0.152
# 60% 1.224 0.263 0.347 1.194 0.175
# 65% 1.283 0.320 0.389 1.262 0.198
# 70% 1.336 0.394 0.441 1.351 0.228
# 75% 1.396 0.493 0.488 1.420 0.266
# 80% 1.530 0.662 0.557 1.631 0.312
# 85% 1.646 0.928 0.651 1.706 0.365
# 90% 1.777 1.364 0.817 1.799 0.479
# 95% 2.018 2.158 1.134 2.964 0.825
# 100% 3.166 17.377 25.141 4.215 23.051
# br5207.dlpfc br5276.amy.neun br5400.amy.neun br5400.sacc br5701.amy
# 0% 0.000 0.000 0.000 0.000 0.000
# 5% 0.000 0.035 0.011 0.000 0.021
# 10% 0.011 0.118 0.021 0.008 0.042
# 15% 0.011 0.163 0.053 0.008 0.063
# 20% 0.021 0.186 0.116 0.016 0.085
# 25% 0.021 0.207 0.206 0.032 0.106
# 30% 0.032 0.227 0.306 0.048 0.134
# 35% 0.042 0.242 0.411 0.063 0.169
# 40% 0.053 0.261 0.525 0.079 0.204
# 45% 0.064 0.281 0.617 0.103 0.254
# 50% 0.085 0.301 0.701 0.127 0.303
# 55% 0.106 0.320 0.764 0.158 0.352
# 60% 0.127 0.345 0.817 0.198 0.423
# 65% 0.159 0.385 0.870 0.238 0.507
# 70% 0.191 0.429 0.922 0.277 0.600
# 75% 0.244 0.493 0.980 0.325 0.726
# 80% 0.307 0.562 1.043 0.396 0.881
# 85% 0.402 0.663 1.138 0.507 1.163
# 90% 0.635 0.816 1.296 0.728 1.593
# 95% 1.539 1.241 1.544 1.211 2.515
# 100% 26.671 7.863 4.943 7.483 13.109
sapply(dbl.dens.focused, function(x) table(x >= 5))
# Percent that would be dropped at density score >= 5
round(sapply(names(dbl.dens.focused), function(x) {
table(dbl.dens.focused[[x]] >= 5)["TRUE"] / ncol(pilot.data.2[[x]]) * 100
}), 3)
# br5276.sacc.neun.NA br5400.nac.TRUE br5276.nac.TRUE
# NA 2.191 1.028
# br5701.nac.neun.NA br5701.sacc.neun.TRUE br5207.dlpfc.TRUE
# NA 1.367 1.360
# br5276.amy.neun.TRUE br5400.amy.neun.NA br5400.sacc.TRUE
# 0.243 NA 0.177
# br5701.amy.TRUE
# 2.043
# --> Thresholding (this is arbitrary!) at a score >= 5 should be fair, but acknowledging
# there is no clear cut answer and some true doublets may remain in the dataset.
# -> see http://bioconductor.org/books/release/OSCA/doublet-detection.html#doublet-simulation
# Additionally: Will be good to just check downstream if higher scores are still associated
# with any particular subcluster
# Add the doublet density scores to the colData
for(i in names(pilot.data.2)){
pilot.data.2[[i]]$doubletScore <- dbl.dens.focused[[i]]
}
# -> Will leave the thresholding at the region-specific level for flexibility
# since will save separate .rda for each of those
### Make/add some sample metadata ===
ref.sampleInfo <- data.frame(sampleID = names(pilot.data.2))
ref.sampleInfo$region <- ss(names(pilot.data.2),"\\.", 2)
ref.sampleInfo$donor <- ss(names(pilot.data.2),"\\.", 1)
ref.sampleInfo$sex <- ifelse(ref.sampleInfo$donor %in% c("br5400", "br5701"), "F", "M")
ref.sampleInfo$processBatch <- ifelse(ref.sampleInfo$sampleID %in% c("br5276.nac", "br5400.nac", "br5701.nac.neun",
"br5276.sacc.neun", "br5701.sacc.neun"),
"R6.10Feb2021", "R5.03Feb2021")
ref.sampleInfo$protocol <- "Frankenstein"
ref.sampleInfo$protocol[grep("neun", ref.sampleInfo$sampleID)] <- "Frank.NeuN.enriched"
ref.sampleInfo$sequencer <- "NovaSeq"
rownames(ref.sampleInfo) <- ref.sampleInfo$sampleID
## Add those to the colData:
for(i in names(pilot.data.2)){
pilot.data.2[[i]]$sampleID <- i
pilot.data.2[[i]]$region <- ref.sampleInfo[i, "region"]
pilot.data.2[[i]]$donor <- ref.sampleInfo[i, "donor"]
pilot.data.2[[i]]$sex <- ref.sampleInfo[i, "sex"]
pilot.data.2[[i]]$processBatch <- ref.sampleInfo[i, "processBatch"]
pilot.data.2[[i]]$protocol <- ref.sampleInfo[i, "protocol"]
pilot.data.2[[i]]$sequencer <- ref.sampleInfo[i, "sequencer"]
}
## Save:
ref.sampleInfo.rev <- ref.sampleInfo
save(pilot.data.2, pilot.data.2.unfiltered, e.out.2, ref.sampleInfo.rev,
file="rdas/revision/all-FACS-n10_2021rev_SCEs_processing-QC_MNTMar2021.rda")
# === === === === === === === === === === ===
# And end here -> proceed to 'step02' scripts
# === === === === === === === === === === ===
sessionInfo()
### session info ====================================
# R version 4.0.4 RC (2021-02-08 r79975)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: CentOS Linux 7 (Core)
#
# Matrix products: default
# BLAS: /jhpce/shared/jhpce/core/conda/miniconda3-4.6.14/envs/svnR-4.0.x/R/4.0.x/lib64/R/lib/libRblas.so
# LAPACK: /jhpce/shared/jhpce/core/conda/miniconda3-4.6.14/envs/svnR-4.0.x/R/4.0.x/lib64/R/lib/libRlapack.so
#
# locale:
# [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
# [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
# [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
# [9] LC_ADDRESS=C LC_TELEPHONE=C
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] parallel stats4 stats graphics grDevices datasets utils
# [8] methods base
#
# other attached packages:
# [1] gridExtra_2.3 Rtsne_0.15
# [3] jaffelab_0.99.30 rafalib_1.0.0
# [5] DropletUtils_1.10.3 uwot_0.1.10
# [7] Matrix_1.3-2 scran_1.18.5
# [9] scater_1.18.6 ggplot2_3.3.3
# [11] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.14.0
# [13] AnnotationFilter_1.14.0 GenomicFeatures_1.42.3
# [15] AnnotationDbi_1.52.0 batchelor_1.6.2
# [17] scRNAseq_2.4.0 SingleCellExperiment_1.12.0
# [19] SummarizedExperiment_1.20.0 Biobase_2.50.0
# [21] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
# [23] IRanges_2.24.1 S4Vectors_0.28.1
# [25] BiocGenerics_0.36.0 MatrixGenerics_1.2.1
# [27] matrixStats_0.58.0
#
# loaded via a namespace (and not attached):
# [1] AnnotationHub_2.22.0 BiocFileCache_1.14.0
# [3] igraph_1.2.6 lazyeval_0.2.2
# [5] splines_4.0.4 BiocParallel_1.24.1
# [7] digest_0.6.27 htmltools_0.5.1.1
# [9] viridis_0.6.0 fansi_0.4.2
# [11] magrittr_2.0.1 memoise_2.0.0
# [13] limma_3.46.0 Biostrings_2.58.0
# [15] R.utils_2.10.1 askpass_1.1
# [17] prettyunits_1.1.1 colorspace_2.0-0
# [19] blob_1.2.1 rappdirs_0.3.3
# [21] dplyr_1.0.5 crayon_1.4.1
# [23] RCurl_1.98-1.3 glue_1.4.2
# [25] gtable_0.3.0 zlibbioc_1.36.0
# [27] XVector_0.30.0 DelayedArray_0.16.3
# [29] BiocSingular_1.6.0 Rhdf5lib_1.12.1
# [31] HDF5Array_1.18.1 scales_1.1.1
# [33] DBI_1.1.1 edgeR_3.32.1
# [35] Rcpp_1.0.6 viridisLite_0.4.0
# [37] xtable_1.8-4 progress_1.2.2
# [39] dqrng_0.2.1 bit_4.0.4
# [41] rsvd_1.0.3 ResidualMatrix_1.0.0
# [43] httr_1.4.2 RColorBrewer_1.1-2
# [45] ellipsis_0.3.1 pkgconfig_2.0.3
# [47] XML_3.99-0.6 R.methodsS3_1.8.1
# [49] scuttle_1.0.4 dbplyr_2.1.1
# [51] locfit_1.5-9.4 utf8_1.2.1
# [53] tidyselect_1.1.0 rlang_0.4.10
# [55] later_1.1.0.1 munsell_0.5.0
# [57] BiocVersion_3.12.0 tools_4.0.4
# [59] cachem_1.0.4 generics_0.1.0
# [61] RSQLite_2.2.6 ExperimentHub_1.16.0
# [63] stringr_1.4.0 fastmap_1.1.0
# [65] yaml_2.2.1 bit64_4.0.5
# [67] purrr_0.3.4 sparseMatrixStats_1.2.1
# [69] mime_0.10 R.oo_1.24.0
# [71] xml2_1.3.2 biomaRt_2.46.3
# [73] compiler_4.0.4 rstudioapi_0.13
# [75] beeswarm_0.3.1 curl_4.3
# [77] interactiveDisplayBase_1.28.0 tibble_3.1.0
# [79] statmod_1.4.35 stringi_1.5.3
# [81] lattice_0.20-41 bluster_1.0.0
# [83] ProtGenerics_1.22.0 vctrs_0.3.6
# [85] pillar_1.6.0 lifecycle_1.0.0
# [87] rhdf5filters_1.2.0 BiocManager_1.30.12
# [89] BiocNeighbors_1.8.2 bitops_1.0-6
# [91] irlba_2.3.3 httpuv_1.5.5
# [93] rtracklayer_1.50.0 R6_2.5.0
# [95] promises_1.2.0.1 vipor_0.4.5
# [97] assertthat_0.2.1 rhdf5_2.34.0
# [99] openssl_1.4.3 withr_2.4.1
# [101] GenomicAlignments_1.26.0 Rsamtools_2.6.0
# [103] GenomeInfoDbData_1.2.4 hms_1.0.0
# [105] grid_4.0.4 beachmat_2.6.4
# [107] DelayedMatrixStats_1.12.3 googledrive_1.0.1
# [109] segmented_1.3-3 shiny_1.6.0
# [111] ggbeeswarm_0.6.0