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10x_sACC-n5_step03_markerDetxn_MNT.R
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10x_sACC-n5_step03_markerDetxn_MNT.R
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### MNT 10x snRNA-seq workflow: step 03 - marker detection
### **Region-specific analyses**
### - (2x) sACC samples from: Br5161 & Br5212
### Initiated MNT 12Feb2020
### MNT 24May2021: add expansion samples (n=3, incl'g 2 female)
#####################################################################
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_sACC-n5_cleaned-combined_SCE_MNT2021.rda",
verbose=T)
## sce.sacc, chosen.hvgs.sacc, pc.choice.sacc, clusterRefTab.sacc, ref.sampleInfo
table(sce.sacc$cellType)
# Astro_A Astro_B drop.doublet drop.lowNTx Excit_A Excit_B
# 747 160 28 298 856 575
# Excit_C Excit_D Excit_E Excit_F Excit_G Inhib_A
# 1735 311 428 228 30 842
# Inhib_B Inhib_C Inhib_D Inhib_E Inhib_F Inhib_G
# 912 465 384 330 521 206
# Inhib_H Inhib_I Inhib_J Inhib_K Micro Neu_FAT2.CDH15
# 208 39 42 25 784 20
# Oligo_A Oligo_B OPC
# 4389 195 911
# First drop "drop." lowNTx & doublet clusters (298; 28, respectively)
sce.sacc <- sce.sacc[ ,-grep("drop.", sce.sacc$cellType)]
sce.sacc$cellType <- droplevels(sce.sacc$cellType)
# Remove 0 genes across all nuclei
sce.sacc <- sce.sacc[!rowSums(assay(sce.sacc, "counts"))==0, ] # keeps same 29583 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.sacc
assay(sce.sacc, "logcounts") <- NULL
sizeFactors(sce.sacc) <- NULL
sce.sacc <- logNormCounts(sce.sacc)
### First make a list of Boolean param / cell subtype ===
# Will use this to assess more 'valid', non-noise-driving markers
cellSubtype.idx <- splitit(sce.sacc$cellType)
medianNon0.sacc <- lapply(cellSubtype.idx, function(x){
apply(as.matrix(assay(sce.sacc, "logcounts")), 1, function(y){
median(y[x]) > 0
})
})
sapply(medianNon0.sacc, table)
## Traditional t-test implementation ===
mod <- with(colData(sce.sacc), model.matrix(~ donor))
mod <- mod[ , -1, drop=F] # intercept otherwise automatically dropped by `findMarkers()`
# Run pairwise t-tests
markers.sacc.t.pw <- findMarkers(sce.sacc, groups=sce.sacc$cellType,
assay.type="logcounts", design=mod, test="t",
direction="up", pval.type="all", full.stats=T)
sapply(markers.sacc.t.pw, function(x){table(x$FDR<0.05)})
# Astro Excit.1 Excit.2 Excit.3 Excit.4 Inhib.1 Inhib.2 Micro Oligo OPC
# FALSE 27821 28246 28493 28378 27967 28455 28282 27319 28059 28272
# TRUE 953 528 281 396 807 319 492 1455 715 502
## WMW: Blocking on donor (this test doesn't take 'design=' argument) ===
markers.sacc.wilcox.block <- findMarkers(sce.sacc, groups=sce.sacc$cellType,
assay.type="logcounts", block=sce.sacc$donor, test="wilcox",
direction="up", pval.type="all", full.stats=T)
sapply(markers.sacc.wilcox.block, function(x){table(x$FDR<0.05)["TRUE"]})
# Astro_A.TRUE Astro_B.NA Excit_A.TRUE Excit_B.TRUE Excit_C.TRUE
# 76 NA 7 18 27
# Excit_D.TRUE Excit_E.TRUE Excit_F.TRUE Excit_G.TRUE Inhib_A.NA
# 11 2 22 5 NA
# Inhib_B.TRUE Inhib_C.TRUE Inhib_D.TRUE Inhib_E.NA Inhib_F.TRUE
# 10 3 8 NA 9
# Inhib_G.TRUE Inhib_H.TRUE Inhib_I.NA Inhib_J.NA Inhib_K.TRUE
# 6 33 NA NA 1
# Micro.TRUE Neu_FAT2.CDH15.NA Oligo_A.TRUE Oligo_B.NA OPC.TRUE
# 86 NA 46 NA 39
## Binomial ===
markers.sacc.binom.block <- findMarkers(sce.sacc, groups=sce.sacc$cellType,
assay.type="logcounts", block=sce.sacc$donor, test="binom",
direction="up", pval.type="all", full.stats=T)
sapply(markers.sacc.binom.block, function(x){table(x$FDR<0.05)["TRUE"]})
# no results for any subpops
# Add respective 'non0median' column to the stats for each set of markers
for(i in names(markers.sacc.t.pw)){
markers.sacc.t.pw[[i]] <- cbind(markers.sacc.t.pw[[i]],
medianNon0.sacc[[i]][match(rownames(markers.sacc.t.pw[[i]]),
names(medianNon0.sacc[[i]]))])
colnames(markers.sacc.t.pw[[i]])[28] <- "non0median"
}
sapply(markers.sacc.t.pw, function(x){table(x$FDR<0.05 & x$non0median == TRUE)["TRUE"]})
# Astro_A Astro_B Excit_A Excit_B Excit_C Excit_D Excit_E Excit_F Excit_G Inhib_A
# FALSE 29348 29541 29567 29554 29537 29558 29551 29522 29520 29573
# TRUE 235 42 16 29 46 25 32 61 63 10
# Inhib_B Inhib_C Inhib_D Inhib_E Inhib_F Inhib_G Inhib_H Inhib_I Inhib_J Inhib_K
# FALSE 29547 29565 29562 29575 29569 29562 29515 29563 29555 29558
# TRUE 36 18 21 8 14 21 68 20 28 25
# Micro Neu_FAT2.CDH15 Oligo_A Oligo_B OPC
# FALSE 29276 29471 29312 29579 29450
# TRUE 307 112 271 4 133
## Save all these for future reference
save(markers.sacc.t.pw, markers.sacc.wilcox.block, medianNon0.sacc, #markers.sacc.binom.block,
file="rdas/revision/markers-stats_sACC-n5_findMarkers-SN-LEVEL_MNT2021.rda")
# # As needed:
# load("rdas/revision/markers-stats_sACC-n5_findMarkers-SN-LEVEL_MNT2021.rda", verbose=T)
# # markers.sacc.t.pw, markers.sacc.wilcox.block, medianNon0.sacc
# Print these to pngs
markerList.t.pw <- lapply(markers.sacc.t.pw, function(x){
rownames(x)[x$FDR < 0.05 & x$non0median == TRUE]
}
)
genes.top40.t <- lapply(markerList.t.pw, function(x){head(x, n=40)})
#dir.create("pdfs/revision/sACC/")
smaller.set <- names(genes.top40.t)[lengths(genes.top40.t) <= 20]
left.set <- setdiff(names(genes.top40.t), smaller.set)
# Smaller graphical window
for(i in smaller.set){
png(paste0("pdfs/revision/sACC/sACC_t_pairwise_top40markers-", i, "_logExprs_MNT2021.png"), height=950, width=1200)
print(
plotExpressionCustom(sce = sce.hold,
features = genes.top40.t[[i]],
features_name = i,
anno_name = "cellType",
ncol=5, point_alpha=0.4) +
scale_color_manual(values = cell_colors.sacc) +
ggtitle(label=paste0("sACC ", i, " top markers: single-nucleus-level p.w. t-tests (FDR<0.05)")) +
theme(plot.title = element_text(size = 20))
)
dev.off()
}
# 20-40 markers
for(i in left.set){
png(paste0("pdfs/revision/sACC/sACC_t_pairwise_top40markers-", i, "_logExprs_MNT2021.png"), height=1900, width=1200)
print(
plotExpressionCustom(sce = sce.hold,
features = genes.top40.t[[i]],
features_name = i,
anno_name = "cellType",
ncol=5, point_alpha=0.4) +
scale_color_manual(values = cell_colors.sacc) +
ggtitle(label=paste0("sACC ", i, " top markers: single-nucleus-level p.w. t-tests (FDR<0.05)")) +
theme(plot.title = element_text(size = 20))
)
dev.off()
}
### Cluster-vs-all single-nucleus-level iteration ======
## Load SCE with new info
load("/dcs04/lieber/marmaypag/Tran_LIBD001/Matt/MNT_thesis/snRNAseq/10x_pilot_FINAL/rdas/revision/regionSpecific_sACC-n5_cleaned-combined_SCE_MNT2021.rda",
verbose=T)
## sce.sacc, chosen.hvgs.sacc, pc.choice.sacc, clusterRefTab.sacc, ref.sampleInfo
table(sce.sacc$cellType)
# First drop "drop." lowNTx & doublet clusters (298; 28, respectively)
sce.sacc <- sce.sacc[ ,-grep("drop.", sce.sacc$cellType)]
sce.sacc$cellType <- droplevels(sce.sacc$cellType)
# Remove 0 genes across all nuclei
sce.sacc <- sce.sacc[!rowSums(assay(sce.sacc, "counts"))==0, ] # keeps same 29583 genes
## Re-create 'logcounts' (don't want to use 'multiBatchNorm's down-scaling across donor 'batches')
# First 'hold' the MBN 'logcounts' for printing
sce.hold <- sce.sacc
assay(sce.sacc, "logcounts") <- NULL
sizeFactors(sce.sacc) <- NULL
sce.sacc <- logNormCounts(sce.sacc)
# Traditional t-test ===
mod <- with(colData(sce.sacc), model.matrix(~ donor))
mod <- mod[ , -1, drop=F] # intercept otherwise automatically dropped by `findMarkers()`
markers.sacc.t.1vAll <- list()
for(i in levels(sce.sacc$cellType)){
# Make temporary contrast
sce.sacc$contrast <- ifelse(sce.sacc$cellType==i, 1, 0)
# Test cluster vs. all others
markers.sacc.t.1vAll[[i]] <- findMarkers(sce.sacc, groups=sce.sacc$contrast,
assay.type="logcounts", design=mod, test="t",
std.lfc=TRUE,
direction="up", pval.type="all", full.stats=T)
}
## Since all other stats are the same, and don't really use the non-standardized
# logFC, just generate one object, unlike before
class(markers.sacc.t.1vAll[["Micro"]])
# a SimpleList of length 2, named "0" and "1" (from the temporary 'contrast')
# -> we want the second entry, named "1"
# (for other purposes, might be interesting to look into that "0" entry, which
# is basically what genes are depleted in the cell type of interest)
sapply(markers.sacc.t.1vAll, function(x){
table(x[["1"]]$stats.0$log.FDR < log(.001))
})
# Do some reorganizing
markers.sacc.t.1vAll <- lapply(markers.sacc.t.1vAll, function(x){
# Basically take the 'stats.[1 or 0]' since is redundant with the 'summary'-level stats
lapply(x, function(y){ y[ ,4] })
})
# Re-name std.lfc column and the entries; add non-0-median info
for(i in names(markers.sacc.t.1vAll)){
colnames(markers.sacc.t.1vAll[[i]][["0"]])[1] <- "std.logFC"
colnames(markers.sacc.t.1vAll[[i]][["1"]])[1] <- "std.logFC"
# Add non0median Boolean - might be informative for both sets of stats
markers.sacc.t.1vAll[[i]][["0"]] <- cbind(markers.sacc.t.1vAll[[i]][["0"]],
medianNon0.sacc[[i]][match(rownames(markers.sacc.t.1vAll[[i]][["0"]]),
names(medianNon0.sacc[[i]]))])
colnames(markers.sacc.t.1vAll[[i]][["0"]])[4] <- "non0median"
# "1" aka 'enriched'
markers.sacc.t.1vAll[[i]][["1"]] <- cbind(markers.sacc.t.1vAll[[i]][["1"]],
medianNon0.sacc[[i]][match(rownames(markers.sacc.t.1vAll[[i]][["1"]]),
names(medianNon0.sacc[[i]]))])
colnames(markers.sacc.t.1vAll[[i]][["1"]])[4] <- "non0median"
# Then re-name the entries to more interpretable, because we'll keeping both contrasts
names(markers.sacc.t.1vAll[[i]]) <- paste0(i,c("_depleted", "_enriched"))
}
## Let's save this along with the previous pairwise results
save(markers.sacc.t.pw, markers.sacc.wilcox.block, markers.sacc.t.1vAll, medianNon0.sacc,
file="rdas/revision/markers-stats_sACC-n5_findMarkers-SN-LEVEL_MNT2021.rda")
## Print these to pngs
markerList.t.1vAll <- lapply(markers.sacc.t.1vAll, function(x){
rownames(x[[2]])[ x[[2]]$log.FDR < log(0.05) & x[[2]]$non0median==TRUE ]
}
)
genes.top40.t <- lapply(markerList.t.1vAll, function(x){head(x, n=40)})
for(i in names(genes.top40.t)){
png(paste0("pdfs/revision/sACC/sACC_t_1vALL_top40markers-",i,"_logExprs_MNT2021.png"), height=1900, width=1200)
print(
plotExpressionCustom(sce = sce.hold,
features = genes.top40.t[[i]],
features_name = i,
anno_name = "cellType",
ncol=5, point_alpha=0.4) +
scale_color_manual(values = cell_colors.sacc) +
ggtitle(label=paste0("sACC ", i, " top markers: 'cluster-vs-all-others' t-tests (FDR<0.05)")) +
theme(plot.title = element_text(size = 20))
)
dev.off()
}
## How do they intersect?
markerList.t.pw <- lapply(markers.sacc.t.pw, function(x){
rownames(x)[ x$FDR < 0.05 & x$non0median==TRUE ]
}
)
# From pairwise t-tests, FDR < 0.05
lengths(markerList.t.pw)
# Astro_A Astro_B Excit_A Excit_B Excit_C
# 235 42 16 29 46
# Excit_D Excit_E Excit_F Excit_G Inhib_A
# 25 32 61 63 10
# Inhib_B Inhib_C Inhib_D Inhib_E Inhib_F
# 36 18 21 8 14
# Inhib_G Inhib_H Inhib_I Inhib_J Inhib_K
# 21 68 20 28 25
# Micro Neu_FAT2.CDH15 Oligo_A Oligo_B OPC
# 307 112 271 4 133
# From cluster-vs-all others, FDR < 0.05
lengths(markerList.t.1vAll)
# Astro_A Astro_B Excit_A Excit_B Excit_C
# 1050 373 4523 4226 4613
# Excit_D Excit_E Excit_F Excit_G Inhib_A
# 4087 3969 3835 966 2507
# Inhib_B Inhib_C Inhib_D Inhib_E Inhib_F
# 3707 2268 2590 1490 1784
# Inhib_G Inhib_H Inhib_I Inhib_J Inhib_K
# 2035 2476 929 612 645
# Micro Neu_FAT2.CDH15 Oligo_A Oligo_B OPC
# 719 472 797 152 1214
# Intersection
sapply(names(markerList.t.pw), function(c){
length(intersect(markerList.t.pw[[c]],
markerList.t.1vAll[[c]]))
})
# Astro_A Astro_B Excit_A Excit_B Excit_C
# 235 42 16 29 46
# Excit_D Excit_E Excit_F Excit_G Inhib_A
# 25 32 61 63 10
# Inhib_B Inhib_C Inhib_D Inhib_E Inhib_F
# 36 18 21 8 14
# Inhib_G Inhib_H Inhib_I Inhib_J Inhib_K
# 21 68 20 28 25
# Micro Neu_FAT2.CDH15 Oligo_A Oligo_B OPC
# 307 112 271 4 133
# 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 Excit_A Excit_B Excit_C
# 28 26 11 18 24
# Excit_D Excit_E Excit_F Excit_G Inhib_A
# 10 16 29 25 9
# Inhib_B Inhib_C Inhib_D Inhib_E Inhib_F
# 17 14 14 8 11
# Inhib_G Inhib_H Inhib_I Inhib_J Inhib_K
# 13 27 16 23 15
# Micro Neu_FAT2.CDH15 Oligo_A Oligo_B OPC
# 36 31 25 4 27
## 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_sACC-n5_cellType_SN-LEVEL-tests_MNT2021.csv",
row.names=FALSE)
# ## Aside: add in 't.stat' as in 'step04' analyses to save for LoHu/LeCo ===
# for(s in names(markers.sacc.t.1vAll)){
# markers.sacc.t.1vAll[[s]]$t.stat <- markers.sacc.t.1vAll[[s]]$std.logFC * sqrt(ncol(sce.sacc))
# }
#
# save(markers.sacc.t.1vAll, markers.sacc.t.pw, sce.sacc,
# file="rdas/markerStats-and-SCE_sACC-n2_sn-level_cleaned_MNTNov2020.rda")
### Session info for 07Jun2021 ============
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
# [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 utils datasets
# [8] methods base
#
# other attached packages:
# [1] limma_3.46.0 jaffelab_0.99.30
# [3] rafalib_1.0.0 DropletUtils_1.10.3
# [5] batchelor_1.6.2 scran_1.18.5
# [7] scater_1.18.6 ggplot2_3.3.3
# [9] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.14.1
# [11] AnnotationFilter_1.14.0 GenomicFeatures_1.42.3
# [13] AnnotationDbi_1.52.0 SingleCellExperiment_1.12.0
# [15] SummarizedExperiment_1.20.0 Biobase_2.50.0
# [17] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
# [19] IRanges_2.24.1 S4Vectors_0.28.1
# [21] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
# [23] matrixStats_0.58.0
#
# loaded via a namespace (and not attached):
# [1] googledrive_1.0.1 ggbeeswarm_0.6.0
# [3] colorspace_2.0-0 ellipsis_0.3.2
# [5] scuttle_1.0.4 bluster_1.0.0
# [7] XVector_0.30.0 BiocNeighbors_1.8.2
# [9] rstudioapi_0.13 farver_2.1.0
# [11] bit64_4.0.5 fansi_0.4.2
# [13] xml2_1.3.2 splines_4.0.4
# [15] R.methodsS3_1.8.1 sparseMatrixStats_1.2.1
# [17] cachem_1.0.4 Rsamtools_2.6.0
# [19] ResidualMatrix_1.0.0 dbplyr_2.1.1
# [21] R.oo_1.24.0 HDF5Array_1.18.1
# [23] compiler_4.0.4 httr_1.4.2
# [25] dqrng_0.2.1 assertthat_0.2.1
# [27] Matrix_1.3-2 fastmap_1.1.0
# [29] lazyeval_0.2.2 BiocSingular_1.6.0
# [31] prettyunits_1.1.1 tools_4.0.4
# [33] rsvd_1.0.3 igraph_1.2.6
# [35] gtable_0.3.0 glue_1.4.2
# [37] GenomeInfoDbData_1.2.4 dplyr_1.0.5
# [39] rappdirs_0.3.3 Rcpp_1.0.6
# [41] vctrs_0.3.6 Biostrings_2.58.0
# [43] rhdf5filters_1.2.0 rtracklayer_1.50.0
# [45] DelayedMatrixStats_1.12.3 stringr_1.4.0
# [47] beachmat_2.6.4 lifecycle_1.0.0
# [49] irlba_2.3.3 statmod_1.4.35
# [51] XML_3.99-0.6 edgeR_3.32.1
# [53] zlibbioc_1.36.0 scales_1.1.1
# [55] hms_1.0.0 ProtGenerics_1.22.0
# [57] rhdf5_2.34.0 RColorBrewer_1.1-2
# [59] curl_4.3 memoise_2.0.0
# [61] gridExtra_2.3 segmented_1.3-3
# [63] biomaRt_2.46.3 stringi_1.5.3
# [65] RSQLite_2.2.7 BiocParallel_1.24.1
# [67] rlang_0.4.10 pkgconfig_2.0.3
# [69] bitops_1.0-7 lattice_0.20-41
# [71] purrr_0.3.4 Rhdf5lib_1.12.1
# [73] labeling_0.4.2 GenomicAlignments_1.26.0
# [75] cowplot_1.1.1 bit_4.0.4
# [77] tidyselect_1.1.1 magrittr_2.0.1
# [79] R6_2.5.0 generics_0.1.0
# [81] DelayedArray_0.16.3 DBI_1.1.1
# [83] pillar_1.6.0 withr_2.4.2
# [85] RCurl_1.98-1.3 tibble_3.1.1
# [87] crayon_1.4.1 utf8_1.2.1
# [89] BiocFileCache_1.14.0 viridis_0.6.0
# [91] progress_1.2.2 locfit_1.5-9.4
# [93] grid_4.0.4 blob_1.2.1
# [95] digest_0.6.27 R.utils_2.10.1
# [97] openssl_1.4.3 munsell_0.5.0
# [99] beeswarm_0.3.1 viridisLite_0.4.0
# [101] vipor_0.4.5 askpass_1.1