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10x_Amyg-n5_step03_markerDetxn_MNT.R
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### MNT 10x snRNA-seq workflow: step 03 - marker detection
### **Region-specific analyses**
### Initiated MNT 12Feb2020
### For revision 2021: (5x) amygdala samples
#####################################################################
library(SingleCellExperiment)
library(EnsDb.Hsapiens.v86)
library(scater)
library(scran)
library(batchelor)
library(DropletUtils)
library(jaffelab)
library(limma)
source("plotExpressionCustom.R")
### 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")
# ===
## Load SCE with new info
load("/dcs04/lieber/marmaypag/Tran_LIBD001/Matt/MNT_thesis/snRNAseq/10x_pilot_FINAL/rdas/revision/regionSpecific_Amyg-n5_cleaned-combined_SCE_MNT2021.rda",
verbose=T)
# sce.amy, chosen.hvgs.amy, pc.choice.amy, clusterRefTab.amy, ref.sampleInfo, annotationTab.amy, cell_colors.amy
table(sce.amy$cellType)
# Astro_A Astro_B drop.lowNTx_A drop.lowNTx_B Endo Excit_A
# 1555 83 1067 71 31 344
# Excit_B Excit_C Inhib_A Inhib_B Inhib_C Inhib_D
# 44 55 728 541 525 555
# Inhib_E Inhib_F Inhib_G Inhib_H Micro Mural
# 414 216 86 52 1201 39
# Oligo OPC Tcell
# 6080 1459 31
# First drop "drop.lowNTx_" (1138 nuclei)
sce.amy <- sce.amy[ ,-grep("drop.",sce.amy$cellType)]
sce.amy$cellType <- droplevels(sce.amy$cellType)
# Remove 0 genes across all nuclei
sce.amy <- sce.amy[!rowSums(assay(sce.amy, "counts"))==0, ] # keeps same 29371 genes
## Re-create 'logcounts' (don't want to use 'multiBatchNorm's down-scaling across donor 'batches')
# First 'hold' the MBN 'logcounts' for printing
sce.hold <- sce.amy
assay(sce.amy, "logcounts") <- NULL
sizeFactors(sce.amy) <- NULL
sce.amy <- logNormCounts(sce.amy)
### First make a list of Boolean param / cell subtype ===
# Will use this to assess more 'valid', non-noise-driving markers
cellSubtype.idx <- splitit(sce.amy$cellType)
medianNon0.amy <- lapply(cellSubtype.idx, function(x){
apply(as.matrix(assay(sce.amy, "logcounts")), 1, function(y){
median(y[x]) > 0
})
})
sapply(medianNon0.amy, table)
## Traditional t-test implementation ===
mod <- with(colData(sce.amy), model.matrix(~ donor))
mod <- mod[ , -1, drop=F] # intercept otherwise automatically dropped by `findMarkers()`
# Run pairwise t-tests
markers.amy.t.pw <- findMarkers(sce.amy, groups=sce.amy$cellType,
assay.type="logcounts", design=mod, test="t",
direction="up", pval.type="all", full.stats=T)
sapply(markers.amy.t.pw, function(x){table(x$FDR<0.05)})
# Astro_A Astro_B Endo Excit_A Excit_B Excit_C Inhib_A Inhib_B Inhib_C Inhib_D
# FALSE 28862 29311 28824 29228 28801 29061 29338 29202 29338 29187
# TRUE 509 60 547 143 570 310 33 169 33 184
# Inhib_E Inhib_F Inhib_G Inhib_H Micro Mural Oligo OPC Tcell
# FALSE 29095 29265 29283 29223 28703 28807 28987 29109 28764
# TRUE 276 106 88 148 668 564 384 262 607
## WMW: Blocking on donor (this test doesn't take 'design=' argument) ===
markers.amy.wilcox.block <- findMarkers(sce.amy, groups=sce.amy$cellType,
assay.type="logcounts", block=sce.amy$donor, test="wilcox",
direction="up", pval.type="all", full.stats=T)
# WMW FDR<0.05
sapply(markers.amy.wilcox.block, function(x){table(x$FDR<0.05)["TRUE"]})
#Astro_A.TRUE Astro_B.NA Endo.NA Excit_A.NA Excit_B.NA Excit_C.NA
# 199 NA NA NA NA NA
# Inhib_A.NA Inhib_B.TRUE Inhib_C.TRUE Inhib_D.TRUE Inhib_E.NA Inhib_F.TRUE
# NA 31 2 60 NA 4
# Inhib_G.NA Inhib_H.NA Micro.TRUE Mural.NA Oligo.TRUE OPC.TRUE
# NA NA 65 NA 245 125
# Tcell.TRUE
# 4
## Binomial ===
markers.amy.binom.block <- findMarkers(sce.amy, groups=sce.amy$cellType,
assay.type="logcounts", block=sce.amy$donor, test="binom",
direction="up", pval.type="all", full.stats=T)
sapply(markers.amy.binom.block, function(x){table(x$FDR<0.05)["TRUE"]})
# only a few hits for 'Astro_A'- disregard these
#Astro_A.TRUE Astro_B.NA Endo.NA Excit_A.NA Excit_B.NA Excit_C.NA
# 6 NA NA NA NA NA
# Inhib_A.NA Inhib_B.NA Inhib_C.NA Inhib_D.NA Inhib_E.NA Inhib_F.NA
# NA NA NA NA NA NA
# Inhib_G.NA Inhib_H.NA Micro.NA Mural.NA Oligo.NA OPC.NA
# NA NA NA NA NA NA
# Tcell.NA
# NA
# Add respective 'non0median' column to the stats for each set of markers
for(i in names(markers.amy.t.pw)){
markers.amy.t.pw[[i]] <- cbind(markers.amy.t.pw[[i]],
medianNon0.amy[[i]][match(rownames(markers.amy.t.pw[[i]]),
names(medianNon0.amy[[i]]))])
colnames(markers.amy.t.pw[[i]])[22] <- "non0median"
}
sapply(markers.amy.t.pw, function(x){table(x$FDR<0.05 & x$non0median == TRUE)["TRUE"]})
# Astro_A.TRUE Astro_B.TRUE Endo.TRUE Excit_A.TRUE Excit_B.TRUE Excit_C.TRUE
# 326 3 180 59 173 142
# Inhib_A.TRUE Inhib_B.TRUE Inhib_C.TRUE Inhib_D.TRUE Inhib_E.TRUE Inhib_F.TRUE
# 26 88 14 131 147 43
# Inhib_G.TRUE Inhib_H.TRUE Micro.TRUE Mural.TRUE Oligo.TRUE OPC.TRUE
# 4 79 284 172 306 172
# Tcell.TRUE
# 112
## Save all these for future reference ===
save(markers.amy.t.pw, markers.amy.wilcox.block, medianNon0.amy, #markers.amy.binom.block,
file="rdas/revision/markers-stats_Amyg-n5_findMarkers-SN-LEVEL_MNT2021.rda")
# As needed:
#load("rdas/revision/markers-stats_Amyg-n5_findMarkers-SN-LEVEL_MNT2021.rda", verbose=T)
# markers.amy.t.pw, markers.amy.wilcox.block, medianNon0.amy
# Print these to pngs
markerList.t.pw <- lapply(markers.amy.t.pw, function(x){
rownames(x)[x$FDR < 0.05 & x$non0median == TRUE]
}
)
genes.top40.t <- lapply(markerList.t.pw, function(x){head(x, n=40)})
#dir.create("pdfs/revision/Amyg/")
smaller.set <- names(genes.top40.t)[lengths(genes.top40.t) <= 20]
left.set <- setdiff(names(genes.top40.t), smaller.set)
# Smaller graphical window
for(i in smaller.set){
png(paste0("pdfs/revision/Amyg/Amyg_t_pairwise_top40markers-", i, "_logExprs_MNT2021.png"), height=950, width=1200)
print(
plotExpressionCustom(sce = sce.hold,
features = genes.top40.t[[i]],
features_name = i,
anno_name = "cellType",
ncol=5, point_alpha=0.4) +
scale_color_manual(values = cell_colors.amy) +
ggtitle(label=paste0("Amyg ", i, " top markers: single-nucleus-level p.w. t-tests (FDR<0.05)")) +
theme(plot.title = element_text(size = 20))
)
dev.off()
}
# 20-40 markers
for(i in left.set){
png(paste0("pdfs/revision/Amyg/Amyg_t_pairwise_top40markers-", i, "_logExprs_MNT2021.png"), height=1900, width=1200)
print(
plotExpressionCustom(sce = sce.hold,
features = genes.top40.t[[i]],
features_name = i,
anno_name = "cellType",
ncol=5, point_alpha=0.4) +
scale_color_manual(values = cell_colors.amy) +
ggtitle(label=paste0("Amyg ", i, " top markers: single-nucleus-level p.w. t-tests (FDR<0.05)")) +
theme(plot.title = element_text(size = 20))
)
dev.off()
}
### Cluster-vs-all single-nucleus-level iteration ========================
## Load SCE with new info
load("/dcs04/lieber/marmaypag/Tran_LIBD001/Matt/MNT_thesis/snRNAseq/10x_pilot_FINAL/rdas/revision/regionSpecific_Amyg-n5_cleaned-combined_SCE_MNT2021.rda",
verbose=T)
# sce.amy, chosen.hvgs.amy, pc.choice.amy, clusterRefTab.amy, ref.sampleInfo, annotationTab.amy, cell_colors.amy
# First drop "drop.lowNTx_" (1138 nuclei)
sce.amy <- sce.amy[ ,-grep("drop.",sce.amy$cellType)]
sce.amy$cellType <- droplevels(sce.amy$cellType)
# Remove 0 genes across all nuclei
sce.amy <- sce.amy[!rowSums(assay(sce.amy, "counts"))==0, ] # keeps same 29371 genes
## Re-create 'logcounts' (don't want to use 'multiBatchNorm's down-scaling across donor 'batches')
# First 'hold' the MBN 'logcounts' for printing
sce.hold <- sce.amy
assay(sce.amy, "logcounts") <- NULL
sizeFactors(sce.amy) <- NULL
sce.amy <- logNormCounts(sce.amy)
## Traditional t-test with design as in PB'd/limma approach ===
mod <- with(colData(sce.amy), model.matrix(~ donor))
mod <- mod[ , -1, drop=F] # intercept otherwise automatically dropped by `findMarkers()`
markers.amy.t.1vAll <- list()
for(i in levels(sce.amy$cellType)){
# Make temporary contrast
sce.amy$contrast <- ifelse(sce.amy$cellType==i, 1, 0)
# Test cluster vs. all others
markers.amy.t.1vAll[[i]] <- findMarkers(sce.amy, groups=sce.amy$contrast,
assay.type="logcounts", design=mod, test="t",
std.lfc=TRUE,
direction="up", pval.type="all", full.stats=T)
}
## Since all other stats are the same, and don't really use the non-standardized
# logFC, just generate one object, unlike before
class(markers.amy.t.1vAll[["Oligo"]])
# a SimpleList of length 2, named "0" and "1" (from the temporary 'contrast')
# -> we want the second entry, named "1"
# (for other purposes, might be interesting to look into that "0" entry, which
# is basically what genes are depleted in the cell type of interest)
sapply(markers.amy.t.1vAll, function(x){
table(x[["1"]]$stats.0$log.FDR < log(.001))
})
# Astro_A Astro_B Endo Excit_A Excit_B Excit_C Inhib_A Inhib_B Inhib_C
# FALSE 23522 28810 28028 21690 27214 26898 23342 23325 24620
# TRUE 5849 561 1343 7681 2157 2473 6029 6046 4751
# Inhib_D Inhib_E Inhib_F Inhib_G Inhib_H Micro Mural Oligo OPC Tcell
# FALSE 23992 25883 25815 28348 28189 24995 27850 26614 25021 27921
# TRUE 5379 3488 3556 1023 1182 4376 1521 2757 4350 1450
# Do some reorganizing
markers.amy.t.1vAll <- lapply(markers.amy.t.1vAll, function(x){
# Basically take the 'stats.[1 or 0]' since is redundant with the 'summary'-level stats
lapply(x, function(y){ y[ ,4] })
})
# Re-name std.lfc column and the entries; add non-0-median info
for(i in names(markers.amy.t.1vAll)){
colnames(markers.amy.t.1vAll[[i]][["0"]])[1] <- "std.logFC"
colnames(markers.amy.t.1vAll[[i]][["1"]])[1] <- "std.logFC"
# Add non0median Boolean - might be informative for both sets of stats
markers.amy.t.1vAll[[i]][["0"]] <- cbind(markers.amy.t.1vAll[[i]][["0"]],
medianNon0.amy[[i]][match(rownames(markers.amy.t.1vAll[[i]][["0"]]),
names(medianNon0.amy[[i]]))])
colnames(markers.amy.t.1vAll[[i]][["0"]])[4] <- "non0median"
# "1" aka 'enriched'
markers.amy.t.1vAll[[i]][["1"]] <- cbind(markers.amy.t.1vAll[[i]][["1"]],
medianNon0.amy[[i]][match(rownames(markers.amy.t.1vAll[[i]][["1"]]),
names(medianNon0.amy[[i]]))])
colnames(markers.amy.t.1vAll[[i]][["1"]])[4] <- "non0median"
# Then re-name the entries to more interpretable, because we'll keeping both contrasts
names(markers.amy.t.1vAll[[i]]) <- paste0(i,c("_depleted", "_enriched"))
}
## Let's save this along with the previous pairwise results
save(markers.amy.t.pw, markers.amy.wilcox.block, markers.amy.t.1vAll, medianNon0.amy,
file="rdas/revision/markers-stats_Amyg-n5_findMarkers-SN-LEVEL_MNT2021.rda")
## Print these to pngs
markerList.t.1vAll <- lapply(markers.amy.t.1vAll, function(x){
rownames(x[[2]])[ x[[2]]$log.FDR < log(0.05) & x[[2]]$non0median==TRUE ]
}
)
genes.top40.t <- lapply(markerList.t.1vAll, function(x){head(x, n=40)})
for(i in names(genes.top40.t)){
png(paste0("pdfs/revision/Amyg/Amyg_t_1vALL_top40markers-",i,"_logExprs_MNT2021.png"), height=1900, width=1200)
print(
plotExpressionCustom(sce = sce.hold,
features = genes.top40.t[[i]],
features_name = i,
anno_name = "cellType",
ncol=5, point_alpha=0.4) +
scale_color_manual(values = cell_colors.amy) +
ggtitle(label=paste0("Amyg ", i, " top markers: 'cluster-vs-all-others' t-tests (FDR<0.05)")) +
theme(plot.title = element_text(size = 20))
)
dev.off()
}
## How do they intersect?
markerList.t.pw <- lapply(markers.amy.t.pw, function(x){
rownames(x)[ x$FDR < 0.05 & x$non0median==TRUE ]
}
)
# From pairwise t-tests, FDR < 0.05
lengths(markerList.t.pw)
# From cluster-vs-all others, FDR < 1e6
lengths(markerList.t.1vAll)
# Intersection
sapply(names(markerList.t.pw), function(c){
length(intersect(markerList.t.pw[[c]],
markerList.t.1vAll[[c]]))
})
# Of top 40's:
sapply(names(markerList.t.pw), function(c){
length(intersect(lapply(markerList.t.pw, function(l){head(l,n=40)})[[c]],
lapply(markerList.t.1vAll, function(l){head(l,n=40)})[[c]]
))
})
# Astro_A Astro_B Endo Excit_A Excit_B Excit_C Inhib_A Inhib_B Inhib_C Inhib_D
# 26 3 34 7 33 28 9 28 12 27
# Inhib_E Inhib_F Inhib_G Inhib_H Micro Mural Oligo OPC Tcell
# 25 20 4 24 24 32 30 28 33
## Write these top 40 lists to a csv
names(markerList.t.pw) <- paste0(names(markerList.t.pw),"_pw")
names(markerList.t.1vAll) <- paste0(names(markerList.t.1vAll),"_1vAll")
# Many of the PW results don't have 40 markers:
extend.idx <- names(which(lengths(markerList.t.pw) < 40))
for(i in extend.idx){
markerList.t.pw[[i]] <- c(markerList.t.pw[[i]], rep("", 40-length(markerList.t.pw[[i]])))
}
top40genes <- cbind(sapply(markerList.t.pw, function(x) head(x, n=40)),
sapply(markerList.t.1vAll, function(y) head(y, n=40)))
top40genes <- top40genes[ ,sort(colnames(top40genes))]
write.csv(top40genes, file="tables/revision/top40genesLists_Amyg-n5_cellType_SN-LEVEL-tests_MNT2021.csv",
row.names=FALSE)
### For fig: Plot some top markers in vlnplot array (12Jun2020) === === === ===
load("rdas/regionSpecific_Amyg-n2_cleaned-combined_SCE_MNTFeb2020.rda", verbose=T)
load("rdas/markers-stats_Amyg-n2_findMarkers-SN-LEVEL_MNTMay2020.rda", verbose=T)
# markers.amy.t.1vAll, markers.amy.t.pw, markers.amy.wilcox.block
# Focus on the pairwise result (".pw") bc more specific
rm(markers.amy.t.1vAll, markers.amy.wilcox.block)
# First drop "ambig.lowNtrxts" (50 nuclei)
sce.amy <- sce.amy[ ,sce.amy$cellType != "Ambig.lowNtrxts"]
sce.amy$cellType <- droplevels(sce.amy$cellType)
# Take top two for broad glia
topToPrint <- as.data.frame(sapply(markers.amy.t.pw, function(x) {head(rownames(x),n=2)}))
topToPrint <- c(topToPrint$Astro, c("NPTX1", "SLC30A3", # Excit.1
"SLC17A6", "SOX4", "SOX11", #Excit.2
"MCHR2", "CDH22", # Excit.3
"CCK", "CALB2", "KIT", # Inhib.1/2/4
"CNTNAP3", "CNTNAP3B", "CALB1", # Inhib.3
"NPFFR2", "TLL1"), # Inhib.5
topToPrint$Micro, topToPrint$Oligo, topToPrint$OPC)
table(topToPrint %in% rownames(sce.amy)) # good
# With top 2 per glial
pdf("pdfs/pubFigures/Amyg_topMarkers-ARRAY_logExprs_Jun2020_v1.pdf", height=17, width=4)
print(
plotExpression(sce.amy, exprs_values = "logcounts", features=topToPrint,
x="cellType", colour_by="cellType", point_alpha=0.5, point_size=.7, ncol=1,
add_legend=F) + stat_summary(fun = median, fun.min = median, fun.max = median,
geom = "crossbar", width = 0.3,
colour=rep(tableau20[1:12], length(topToPrint))) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 12), plot.title = element_text(size = 25)) +
ggtitle(label="AMY top marker array (with glial)")
)
dev.off()
# Neuronal markers only (highlighted in paper)
topToPrint <- c("NRN1", "NPTX1", "SLC30A3", # Excit_A
"SLC17A6", "VCAN", #Excit_B
"MCHR2", "CDH22", # Excit_C
"PENK", "ACTN2", # Inhib_A (& Inhib_H)
"CCK", "CALB2", "KIT", # Inhib_B/D
"CRH", # Inhib_B
"NPFFR2", "TLL1", # Inhib_C
"SYT2", "ONECUT2", # Inhib_E
"LHX6", "ELAVL2", # Inhib_F
"DIO2", # Inhib_G
"NPY", "PRLR") # Inhib_H
#pdf("pdfs/revision/pubFigures/regionSpecific_Amyg-n5_topMarkers-ARRAY_MNT2021_1stHalf.pdf", height=11, width=3.8)
pdf("pdfs/revision/pubFigures/regionSpecific_Amyg-n5_topMarkers-ARRAY_MNT2021_2ndHalf.pdf", height=11, width=3.8)
print(
# plotExpressionCustom(sce.amy, features=topToPrint[1:11], features_name="manually-selected",
plotExpressionCustom(sce.amy, features=topToPrint[12:22], features_name="manually-selected",
point_alpha=0.5, point_size=.7, ncol=1,
scales="free_y") +
scale_color_manual(values = cell_colors.amy) +
xlab("") +
ylab("") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 10),
axis.text.y = element_text(size = 8),
axis.title.y = element_text(angle = 90, size = 14),
plot.title = element_text(size = 12),
panel.grid.major=element_line(colour="grey95", size=0.8),
panel.grid.minor=element_line(colour="grey95", size=0.4)) +
ggtitle(label="AMY top neuronal markers array")
)
dev.off()
# ## Aside: add in 't.stat' as in 'step04' analyses to save for LoHu/LeCo ===
# for(s in names(markers.amy.t.1vAll)){
# markers.amy.t.1vAll[[s]]$t.stat <- markers.amy.t.1vAll[[s]]$std.logFC * sqrt(ncol(sce.amy))
# }
#
# save(markers.amy.t.1vAll, markers.amy.t.pw, sce.amy,
# file="rdas/markerStats-and-SCE_AMY-n2_sn-level_cleaned_MNTNov2020.rda")
### Session info for 02Jun2021 ============
sessionInfo()
# 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 LC_TIME=en_US.UTF-8
# [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
# [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] parallel stats4 stats graphics grDevices datasets utils methods
# [9] base
#
# other attached packages:
# [1] ggrepel_0.9.1 dynamicTreeCut_1.63-1 dendextend_1.14.0
# [4] jaffelab_0.99.30 rafalib_1.0.0 DropletUtils_1.10.3
# [7] batchelor_1.6.2 scran_1.18.5 scater_1.18.6
# [10] ggplot2_3.3.3 EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.14.1
# [13] AnnotationFilter_1.14.0 GenomicFeatures_1.42.3 AnnotationDbi_1.52.0
# [16] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0 Biobase_2.50.0
# [19] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7 IRanges_2.24.1
# [22] S4Vectors_0.28.1 BiocGenerics_0.36.1 MatrixGenerics_1.2.1
# [25] matrixStats_0.58.0
#
# loaded via a namespace (and not attached):
# [1] googledrive_1.0.1 ggbeeswarm_0.6.0 colorspace_2.0-0
# [4] ellipsis_0.3.2 scuttle_1.0.4 bluster_1.0.0
# [7] XVector_0.30.0 BiocNeighbors_1.8.2 rstudioapi_0.13
# [10] farver_2.1.0 bit64_4.0.5 fansi_0.4.2
# [13] xml2_1.3.2 splines_4.0.4 R.methodsS3_1.8.1
# [16] sparseMatrixStats_1.2.1 cachem_1.0.4 Rsamtools_2.6.0
# [19] ResidualMatrix_1.0.0 dbplyr_2.1.1 R.oo_1.24.0
# [22] HDF5Array_1.18.1 compiler_4.0.4 httr_1.4.2
# [25] dqrng_0.2.1 assertthat_0.2.1 Matrix_1.3-2
# [28] fastmap_1.1.0 lazyeval_0.2.2 limma_3.46.0
# [31] BiocSingular_1.6.0 prettyunits_1.1.1 tools_4.0.4
# [34] rsvd_1.0.3 igraph_1.2.6 gtable_0.3.0
# [37] glue_1.4.2 GenomeInfoDbData_1.2.4 dplyr_1.0.5
# [40] rappdirs_0.3.3 Rcpp_1.0.6 vctrs_0.3.6
# [43] Biostrings_2.58.0 rhdf5filters_1.2.0 rtracklayer_1.50.0
# [46] DelayedMatrixStats_1.12.3 stringr_1.4.0 beachmat_2.6.4
# [49] lifecycle_1.0.0 irlba_2.3.3 statmod_1.4.35
# [52] XML_3.99-0.6 edgeR_3.32.1 zlibbioc_1.36.0
# [55] scales_1.1.1 hms_1.0.0 ProtGenerics_1.22.0
# [58] rhdf5_2.34.0 RColorBrewer_1.1-2 curl_4.3
# [61] memoise_2.0.0 gridExtra_2.3 segmented_1.3-3
# [64] biomaRt_2.46.3 stringi_1.5.3 RSQLite_2.2.7
# [67] BiocParallel_1.24.1 rlang_0.4.10 pkgconfig_2.0.3
# [70] bitops_1.0-7 lattice_0.20-41 purrr_0.3.4
# [73] Rhdf5lib_1.12.1 labeling_0.4.2 GenomicAlignments_1.26.0
# [76] cowplot_1.1.1 bit_4.0.4 tidyselect_1.1.1
# [79] magrittr_2.0.1 R6_2.5.0 generics_0.1.0
# [82] DelayedArray_0.16.3 DBI_1.1.1 pillar_1.6.0
# [85] withr_2.4.2 RCurl_1.98-1.3 tibble_3.1.1
# [88] crayon_1.4.1 utf8_1.2.1 BiocFileCache_1.14.0
# [91] viridis_0.6.0 progress_1.2.2 locfit_1.5-9.4
# [94] grid_4.0.4 blob_1.2.1 digest_0.6.27
# [97] R.utils_2.10.1 openssl_1.4.3 munsell_0.5.0
# [100] beeswarm_0.3.1 viridisLite_0.4.0 vipor_0.4.5
# [103] askpass_1.1