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check_clinical_gene_sets.R
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###
library('readxl')
library('limma')
library('sessioninfo')
library('parallel')
library('jaffelab')
library('janitor')
library('lattice')
library('org.Hs.eg.db')
library('GenomicFeatures')
library('scran')
library('here')
library('RColorBrewer')
library('ggplot2')
library('fields')
## load sce object
sce_layer_file <-
here('Analysis', 'Layer_Guesses', 'rda', 'sce_layer.Rdata')
if (file.exists(sce_layer_file))
load(sce_layer_file, verbose = TRUE)
###################
## load modeling outputs
load("rda/eb_contrasts.Rdata")
load("rda/eb0_list.Rdata")
## Extract the p-values
logFC0_contrasts <- sapply(eb0_list, function(x) {
x$coef[, 2, drop = FALSE]
})
rownames(logFC0_contrasts) = rownames(eb_contrasts)
## Extract the p-values
pvals0_contrasts <- sapply(eb0_list, function(x) {
x$p.value[, 2, drop = FALSE]
})
rownames(pvals0_contrasts) = rownames(eb_contrasts)
fdrs0_contrasts = apply(pvals0_contrasts, 2, p.adjust, "fdr")
## Extract the t-stats
t0_contrasts <- sapply(eb0_list, function(x) {
x$t[, 2, drop = FALSE]
})
rownames(t0_contrasts) = rownames(eb_contrasts)
## Expand https://github.com/LieberInstitute/HumanPilot/blob/master/Analysis/Layer_Guesses/layer_specificity.R#L1445-L1457
do.call(rbind, lapply(seq_len(ncol(fdrs0_contrasts)), function(i) {
data.frame(
Layer = colnames(fdrs0_contrasts)[i],
FDR5_anyT = sum(fdrs0_contrasts[, i] < 0.05),
FDR5_positiveT = sum(t0_contrasts[, i] > 0 & fdrs0_contrasts[, i] < 0.05),
FDR10_positiveT = sum(t0_contrasts[, i] > 0 & fdrs0_contrasts[, i] < 0.1)
)
}))
# Layer FDR5_anyT FDR5_positiveT FDR10_positiveT
# 1 WM 9124 4406 5010
# 2 Layer1 3033 1404 1876
# 3 Layer2 1562 1093 1512
# 4 Layer3 183 139 270
# 5 Layer4 740 348 610
# 6 Layer5 643 537 794
# 7 Layer6 379 264 432
## Total genes: 22331
#########################
## load in gene sets ####
#########################
##################################
## Satterstrom et al, Cell 2020 ##
##################################
asd_exome = read_excel("gene_sets/1-s2.0-S0092867419313984-mmc2.xlsx",
sheet = 2)
asd_exome = as.data.frame(asd_exome)
## get ensembl IDs
asd_exome_geneList = apply(asd_exome[,
c("ASC33_2014",
"SSC27_2014",
"ASC65_2015",
"ASC102_2018",
"ASD53",
"DDID49")], 2,
function(x)
asd_exome$ensembl_gene_id[x == 1])
names(asd_exome_geneList) = gsub("_", ".", names(asd_exome_geneList))
names(asd_exome_geneList) = paste0("Gene_Satterstrom_",
names(asd_exome_geneList))
###############
### SFARI #####
###############
asd_sfari = read.csv("gene_sets/SFARI-Gene_genes_01-03-2020release_02-04-2020export.csv",
as.is = TRUE)
asd_sfari_geneList = list(
Gene_SFARI_all = asd_sfari$ensembl.id,
Gene_SFARI_high = asd_sfari$ensembl.id[asd_sfari$gene.score < 3],
Gene_SFARI_syndromic = asd_sfari$ensembl.id[asd_sfari$syndromic == 1]
)
# #################
# ## harmonizome ##
# #################
# harmonizome = read.delim(
# "gene_sets/Harmonizome_CTD Gene-Disease Associations Dataset.txt",
# as.is = TRUE,
# skip = 1
# )
# ## add ensembl
# ens = select(org.Hs.eg.db,
# columns = c("ENSEMBL", "ENTREZID"),
# keys = as.character(unique(harmonizome$GeneID)))
# harmonizome$ensemblID = ens$ENSEMBL[match(harmonizome$GeneID, ens$ENTREZID)]
# ## split by dx
# harmonizome_geneList = split(harmonizome$ensemblID, harmonizome$Disease)
# ## filter by set size
# harmonizome_geneList = harmonizome_geneList[lengths(harmonizome_geneList) >= 100]
# names(harmonizome_geneList) = gsub(" ", ".", names(harmonizome_geneList))
# names(harmonizome_geneList) = paste0("Harmonizome_",
# names(harmonizome_geneList))
####################
### birnbaum sets ##
####################
birnbaum = read_excel("gene_sets/Supplementary Tables for paper.Birnbaum November 2013.AJP.xlsx",
sheet = 1)
ens2 = select(org.Hs.eg.db,
columns = c("ENSEMBL", "ENTREZID"),
keys = as.character(unique(birnbaum$`EntrezGene ID`)))
birnbaum$ensemblID = ens2$ENSEMBL[match(birnbaum$`EntrezGene ID`, ens2$ENTREZID)]
birnbaum_geneList = split(birnbaum$ensemblID, birnbaum$`Gene Set`)
names(birnbaum_geneList) = gsub(" ", ".", names(birnbaum_geneList))
names(birnbaum_geneList) = gsub("-", ".", names(birnbaum_geneList))
names(birnbaum_geneList) = paste0("Gene_Birnbaum_",
names(birnbaum_geneList))
birnbaum_geneList = birnbaum_geneList[rev(seq(along=birnbaum_geneList))]
######################
## psychENCODE DEGs ##
######################
psychENCODE = as.data.frame(read_excel("gene_sets/aat8127_Table_S1.xlsx", sheet = "DGE"))
pe_geneList = with(
psychENCODE,
list(
DE_PE_ASD.Up = ensembl_gene_id[ASD.t.value > 0 & ASD.fdr < 0.05],
DE_PE_ASD.Down = ensembl_gene_id[ASD.t.value < 0 & ASD.fdr < 0.05],
DE_PE_BD.Up = ensembl_gene_id[BD.t.value > 0 & BD.fdr < 0.05],
DE_PE_BD.Down = ensembl_gene_id[BD.t.value < 0 & BD.fdr < 0.05],
DE_PE_SCZ.Up = ensembl_gene_id[SCZ.t.value > 0 & SCZ.fdr < 0.05],
DE_PE_SCZ.Down = ensembl_gene_id[SCZ.t.value < 0 & SCZ.fdr < 0.05]
)
)
#################
## brainseq ####
#################
## DLPFC RiboZero
load(
"/dcl01/ajaffe/data/lab/qsva_brain/brainseq_phase2_qsv/rdas/dxStats_dlpfc_filtered_qSVA_noHGoldQSV_matchDLPFC.rda"
)
bs2_geneList = with(outGene,
list(DE_BS2_SCZ.Up = ensemblID[logFC > 0 & adj.P.Val < 0.05],
DE_BS2_SCZ.Down = ensemblID[logFC < 0 & adj.P.Val < 0.05]))
##############################
### Sestan DS Neuron 2017? ###
ds = read_excel("gene_sets/1-s2.0-S0896627316000891-mmc4.xlsx",skip=2)
ds = clean_names(ds)
ds = as.data.frame(ds)
ens3 = select(org.Hs.eg.db,
columns = c("ENSEMBL", "ENTREZID"),
keys = as.character(unique(ds$geneid)))
ds$ensemblID = ens3$ENSEMBL[match(ds$geneid, ens3$ENTREZID)]
ds$fold_difference_log2 = as.numeric(ds$fold_difference_log2)
ds$p_value = readr::parse_number(ds$p_value)
ds$qval = readr::parse_number(ds$qval)
ds_geneList = list(DE_DS_DS.Up = ds$ensemblID[ds$fold_difference_log2 > 0 & ds$qval < 0.05],
DE_DS_DS.Down = ds$ensemblID[ds$fold_difference_log2 < 0 & ds$qval < 0.05])
#############################
## various TWAS sets ########
#############################
## brainseq 2
load("/dcl01/ajaffe/data/lab/dg_hippo_paper/rdas/tt_objects_gene.Rdata")
tt_dlpfc= as.data.frame(tt[tt$region == "DLPFC",])
tt_dlpfc$ensemblID = ss(tt_dlpfc$geneid, "\\.")
## PE
twas_sczd = as.data.frame(read_excel("gene_sets/aat8127_Table_S4.xlsx", sheet = "SCZ.TWAS"))
twas_sczd$TWAS.FDR = p.adjust(twas_sczd$TWAS.P, "fdr")
twas_asd = as.data.frame(read_excel("gene_sets/aat8127_Table_S4.xlsx", sheet = "ASD.TWAS"))
twas_asd$TWAS.FDR = p.adjust(twas_asd$TWAS.P, "fdr")
twas_bpdscz = as.data.frame(read_excel("gene_sets/aat8127_Table_S4.xlsx", sheet = "BD.SCZ"))
twas_bpdscz$TWAS.FDR = p.adjust(twas_bpdscz$TWAS.P, "fdr")
twas_geneList = list(TWAS_BS2_SCZ.Up = tt_dlpfc$ensemblID[tt_dlpfc$TWAS.Z > 0 & tt_dlpfc$TWAS.FDR < 0.05],
TWAS_BS2_SCZ.Down = tt_dlpfc$ensemblID[tt_dlpfc$TWAS.Z < 0 & tt_dlpfc$TWAS.FDR < 0.05],
TWAS_PE_SCZ.Up = twas_sczd$GeneID[twas_sczd$TWAS.Z > 0 & twas_sczd$TWAS.FDR < 0.05],
TWAS_PE_SCZ.Down = twas_sczd$GeneID[twas_sczd$TWAS.Z < 0 & twas_sczd$TWAS.FDR < 0.05],
TWAS_PE_ASD.Up = twas_asd$GeneID[twas_asd$TWAS.Z > 0 & twas_asd$TWAS.FDR < 0.05],
TWAS_PE_ASD.Down = twas_asd$GeneID[twas_asd$TWAS.Z < 0 & twas_asd$TWAS.FDR < 0.05],
TWAS_PE_SCZBD.Up = twas_bpdscz$ID[twas_bpdscz$TWAS.Z > 0 & twas_bpdscz$TWAS.FDR < 0.05],
TWAS_PE_SCZBD.Down = twas_bpdscz$ID[twas_bpdscz$TWAS.Z < 0 & twas_bpdscz$TWAS.FDR < 0.05])
###############
### combine ###
###############
## gene list ##
geneList = c(
birnbaum_geneList,
asd_sfari_geneList,
asd_exome_geneList,
pe_geneList,
bs2_geneList,
ds_geneList,
twas_geneList
)
## filter for those present in spatial data
geneList_present = lapply(geneList, function(x) {
x = x[!is.na(x)]
x[x %in% rownames(t0_contrasts)]
})
## do enrichment
enrich_stat_list = eb0_list
for (i in seq(along = eb0_list)) {
layer = t0_contrasts[, i] > 0 & fdrs0_contrasts[, i] < 0.1
tabList = mclapply(geneList_present, function(g) {
tt = table(Set = factor(names(layer) %in% g, c(FALSE, TRUE)),
Layer = factor(layer, c(FALSE, TRUE)))
}, mc.cores = 8)
enrichList = lapply(tabList,fisher.test)
o = data.frame(
OR = sapply(enrichList, "[[", "estimate"),
Pval = sapply(enrichList, "[[", "p.value"),
NumSig = sapply(tabList, function(x) x[2,2])
)
rownames(o) = gsub(".odds ratio", "", rownames(o))
enrich_stat_list[[i]] = o
}
enrichTab = do.call("cbind", enrich_stat_list)
# name
enrichTab$Type = ss(rownames(enrichTab), "_", 1)
enrichTab$Type[enrichTab$Group == "Birnbaum"] = "Birnbaum"
enrichTab$Type[enrichTab$Type == "Gene"] = "ASD"
enrichTab$Group = ss(rownames(enrichTab), "_", 2)
enrichTab$Set = ss(rownames(enrichTab), "_", 3)
enrichTab$ID = rownames(enrichTab)
enrichTab$SetSize = sapply(geneList_present, length)
### save a copy as a supp table
enrichTabOut = enrichTab[,c(25, 22:24,26, 1:21)]
write.csv(enrichTabOut, file = "SupplementaryTableXX_clinical_enrichment.csv",row.names=FALSE)
## look at enrichment
pMat = enrichTab[, grep("Pval", colnames(enrichTab))]
orMat = enrichTab[, grep("OR", colnames(enrichTab))]
colnames(pMat) = ss(colnames(pMat), "\\.")
colnames(orMat) = ss(colnames(orMat), "\\.")
pMat < 0.05 / nrow(pMat)
pMat < 0.001
round(-log10(pMat),1)
# #######################
# # FDR < 0.05 version ##
# #######################
# # do enrichment
# enrich_stat_list_05 = eb0_list
# for (i in seq(along = eb0_list)) {
# layer = t0_contrasts[, i] > 0 & fdrs0_contrasts[, i] < 0.05
# tabList = mclapply(geneList_present, function(g) {
# tt = table(Set = factor(names(layer) %in% g, c(FALSE, TRUE)),
# Layer = factor(layer, c(FALSE, TRUE)))
# }, mc.cores = 8)
# enrichList = lapply(tabList,fisher.test)
# o = data.frame(
# OR = sapply(enrichList, "[[", "estimate"),
# Pval = sapply(enrichList, "[[", "p.value"),
# NumSig = sapply(tabList, function(x) x[2,2])
# )
# rownames(o) = gsub(".odds ratio", "", rownames(o))
# enrich_stat_list_05[[i]] = o
# }
# enrichTab_05 = do.call("cbind", enrich_stat_list_05)
# name
# enrichTab_05$Type = ss(rownames(enrichTab_05), "_", 1)
# enrichTab_05$Type[enrichTab_05$Group == "Birnbaum"] = "Birnbaum"
# enrichTab_05$Type[enrichTab_05$Type == "Gene"] = "ASD"
# enrichTab_05$Group = ss(rownames(enrichTab_05), "_", 2)
# enrichTab_05$Set = ss(rownames(enrichTab_05), "_", 3)
# enrichTab_05$ID = rownames(enrichTab_05)
# enrichTab_05$SetSize = sapply(geneList_present, length)
######################
## pull out results ##
######################
## summary stats from genes
enrichTab["Gene_SFARI_all",]
enrichTab["Gene_Satterstrom_ASC102.2018",]
enrichTab["Gene_Satterstrom_ASD53",]
enrichTab["Gene_Satterstrom_DDID49",]
## Satterstrom deep dive
sat_102_l2= which(t0_contrasts[,"Layer2"] > 0 & fdrs0_contrasts[,"Layer2"] < 0.1 &
rownames(t0_contrasts) %in% geneList_present$Gene_Satterstrom_ASC102.2018)
rowData(sce_layer)$gene_name[sat_102_l2]
sat_102_l5= which(t0_contrasts[,"Layer5"] > 0 & fdrs0_contrasts[,"Layer5"] < 0.1 &
rownames(t0_contrasts) %in% geneList_present$Gene_Satterstrom_ASC102.2018)
rowData(sce_layer)$gene_name[sat_102_l5]
sat_49_l2= which(t0_contrasts[,"Layer2"] > 0 & fdrs0_contrasts[,"Layer2"] < 0.1 &
rownames(t0_contrasts) %in% geneList_present$Gene_Satterstrom_DDID49)
cat(rowData(sce_layer)$gene_name[sat_49_l2], sep=", ")
sat_53_l5= which(t0_contrasts[,"Layer5"] > 0 & fdrs0_contrasts[,"Layer5"] < 0.1 &
rownames(t0_contrasts) %in% geneList_present$Gene_Satterstrom_ASD53)
cat(rowData(sce_layer)$gene_name[sat_53_l5], sep=", ")
## case control - asd
enrichTab["DE_PE_ASD.Up",]
enrichTab["DE_PE_ASD.Down",]
## case control - sczd
enrichTab[c("DE_PE_SCZ.Up","DE_BS2_SCZ.Up"),]
enrichTab[c("DE_PE_SCZ.Down","DE_BS2_SCZ.Down"),]
################
## make plots ##
################
## make long
enrichLong = reshape2::melt(enrichTab[,c(seq(1,19,by=3),22:26)],id.vars = 8:12)
colnames(enrichLong)[6:7] = c("Layer", "OR")
enrichLong_P = reshape2::melt(enrichTab[,c(seq(2,20,by=3),22:26)],id.vars = 8:12)
identical(enrichLong$ID, enrichLong_P$ID)
enrichLong$P = enrichLong_P$value
enrichLong$Layer = ss(as.character(enrichLong$Layer), "\\.")
enrichLong$ID = factor(enrichLong$ID, levels=rev(rownames(enrichTab)))
enrichLong$Set = factor(enrichLong$Set, levels=unique(rev(enrichTab$Set)))
enrichLong$FDR = p.adjust(enrichLong$P, "fdr")
## what p-value controls FDR?
enrichLongSort = enrichLong[order(enrichLong$P),]
max(enrichLongSort$P[enrichLongSort$FDR < 0.05] )
# 0.01009034
## overall ##
enrichLong$P_thresh = enrichLong$P
enrichLong$P_thresh[enrichLong$P_thresh < 2.2e-16] = 2.2e-16
### ASD focus
enrichLong_ASD = enrichLong[enrichLong$ID %in%
c("Gene_SFARI_all", "Gene_Satterstrom_ASC102.2018",
"Gene_Satterstrom_ASD53", "Gene_Satterstrom_DDID49",
"DE_PE_ASD.Down", "DE_PE_ASD.Up",
"TWAS_PE_ASD.Up", "TWAS_PE_ASD.Down"),]
enrichLong_ASD$ID2 = as.character(droplevels(enrichLong_ASD$Set))
enrichLong_ASD$ID2[enrichLong_ASD$ID2 == "all"] = "SFARI"
enrichLong_ASD$ID2[enrichLong_ASD$ID2 == "ASC102.2018"] = "ASC102"
enrichLong_ASD$ID2[enrichLong_ASD$ID == "DE_PE_ASD.Up"] = "DE.Up"
enrichLong_ASD$ID2[enrichLong_ASD$ID == "DE_PE_ASD.Down"] = "DE.Down"
enrichLong_ASD$ID2[enrichLong_ASD$ID == "TWAS_PE_ASD.Up"] = "TWAS.Up"
enrichLong_ASD$ID2[enrichLong_ASD$ID == "TWAS_PE_ASD.Down"] = "TWAS.Down"
enrichLong_ASD$ID2 = factor(enrichLong_ASD$ID2, unique(enrichLong_ASD$ID2))
enrichLong_ASD$LayerFac = factor(as.character(enrichLong_ASD$Layer),
c("WM", paste0("Layer", 6:1)))
enrichLong_ASD = enrichLong_ASD[order(enrichLong_ASD$ID2, enrichLong_ASD$LayerFac),]
### custom heatmap
midpoint = function(x) x[-length(x)] + diff(x)/2
customLayerEnrichment = function(enrichTab , groups, xlabs,
Pthresh = 12, ORcut = 3, enrichOnly = FALSE,
layerHeights = c(0,40,55,75,85,110,120,135),
mypal = c("white", colorRampPalette(brewer.pal(9,"YlOrRd"))(50)), ...) {
wide_p = -log10( enrichTab[groups,grep("Pval", colnames(enrichTab))])
wide_p[wide_p > Pthresh] = Pthresh
wide_p = t(round(wide_p[,
c("WM.Pval", "Layer6.Pval", "Layer5.Pval", "Layer4.Pval", "Layer3.Pval","Layer2.Pval", "Layer1.Pval")],2))
wide_or = enrichTab[groups,grep("OR", colnames(enrichTab))]
wide_or= round(t(wide_or[,
c("WM.OR", "Layer6.OR", "Layer5.OR", "Layer4.OR", "Layer3.OR", "Layer2.OR", "Layer1.OR")]),1)
if(enrichOnly) wide_p[wide_or < 1] = 0
wide_or[wide_p < ORcut] = ""
image.plot(x = seq(0,ncol(wide_p),by=1), y = layerHeights, z = as.matrix(t(wide_p)),
col = mypal,xaxt="n", yaxt="n",xlab = "", ylab="", ...)
axis(2, c("WM", paste0("L", 6:1)), at = midpoint(layerHeights),las=1)
axis(1, rep("", ncol(wide_p)), at = seq(0.5,ncol(wide_p)-0.5))
text(x = seq(0.5,ncol(wide_p)-0.5), y=-1*max(nchar(xlabs))/2, xlabs,
xpd=TRUE, srt=45,cex=2,adj= 1)
abline(h=layerHeights,v=0:ncol(wide_p))
text(x = rep(seq(0.5,ncol(wide_p)-0.5),each = nrow(wide_p)),
y = rep(midpoint(layerHeights), ncol(wide_p)),
as.character(wide_or),cex=1.5,font=2)
}
pdf("pdf/asd_geneSet_heatmap.pdf",w=6)
par(mar=c(8,4.5,2.5,1), cex.axis=2,cex.lab=2)
groups = unique(as.character(enrichLong_ASD$ID))[1:6]
xlabs = as.character(enrichLong_ASD$ID2[match(groups, enrichLong_ASD$ID)])
customLayerEnrichment(enrichTab, groups,xlabs, enrichOnly=TRUE)
abline(v=4,lwd=3)
text(x = 3, y = 142, c("ASD"), xpd=TRUE,cex=2.5,font=2)
dev.off()
pdf("pdf/sczd_geneSet_heatmap.pdf",w=8)
par(mar=c(8,4.5,2.5,1), cex.axis=2,cex.lab=2)
groups =c("DE_PE_SCZ.Up", "DE_PE_SCZ.Down",
"DE_BS2_SCZ.Up", "DE_BS2_SCZ.Down",
"TWAS_BS2_SCZ.Up", "TWAS_BS2_SCZ.Down", "TWAS_PE_SCZ.Up",
"TWAS_PE_SCZ.Down")
xlabs = ss(gsub("_SCZ", "", groups), "_", 2)
customLayerEnrichment(enrichTab, groups,xlabs, enrichOnly=TRUE)
abline(v=4,lwd=3)
text(x = c(2,6), y = 142, c("SCZD-DE", "SCZD-TWAS"), xpd=TRUE,cex=2.5,font=2)
dev.off()
pdf("pdf/suppXX_birnbaum_geneSet_heatmap.pdf",w=8)
par(mar=c(12,5.5,2.5,1), cex.axis=2,cex.lab=2)
groups =grep(enrichTab$ID, pattern = "Birnbaum", value=TRUE)
xlabs = ss(groups, "_", 3)
customLayerEnrichment(enrichTab, groups,xlabs, enrichOnly=TRUE,
breaks = seq(0,12,len = 51))
dev.off()
#############################
### GSEA #####################
#############################
## do enrichment
gst_tab = apply(t0_contrasts, 2, function(tt) {
sapply(geneList_present, function(g) {
geneSetTest(index = rownames(t0_contrasts) %in% g,
statistics = tt, alternative = "up")
})
})
round(-log10(gst_tab),1)
## check densities
mypar(ncol(t0_contrasts),1)
g = rownames(t0_contrasts) %in% geneList_present$Gene_Satterstrom_ASC102.2018
g_asd = rownames(t0_contrasts) %in% geneList_present$Gene_Satterstrom_ASD53
g_dd = rownames(t0_contrasts) %in% geneList_present$Gene_Satterstrom_DDID49
for(i in 1:ncol(t0_contrasts)) {
plot(density(t0_contrasts[!g,i]), lwd=2,col="black",xlab="",
main=colnames(t0_contrasts)[i],xlim = c(-10,15))
lines(density(t0_contrasts[g,i]), lwd=2,col="red")
lines(density(t0_contrasts[g_asd,i]), lwd=2,col="red",lty=2)
lines(density(t0_contrasts[g_dd,i]), lwd=2,col="red",lty=3)
abline(v=0,lty=2)
}
pdf("pdf/ASD_genes_layer_density.pdf",h=4,useDingbats=FALSE)
par(mar=c(5,6,1,1),cex.axis= 1.4,cex.lab=1.8)
for(i in 1:ncol(t0_contrasts)) {
layer = t0_contrasts[, i] > 0 & fdrs0_contrasts[, i] < 0.1
plot(density(t0_contrasts[!g,i]), lwd=3,col="black",
xlab=paste0(colnames(t0_contrasts)[i], ": Specificity T-stats"),
sub = "", main="",xlim = c(-8,8))
lines(density(t0_contrasts[g,i]), lwd=3,col="red")
lines(density(t0_contrasts[g_asd,i]), lwd=3,col="red",lty=2)
lines(density(t0_contrasts[g_dd,i]), lwd=3,col="red",lty=3)
abline(v=0,lty=2)
abline(v= min(t0_contrasts[layer,i]))
ll = ifelse(i == 1, "topright", "topleft")
legend(ll, c("BG", "102 All", "53 ASD", "49 DDID"), bty="n",
col = c("black","red","red","red"), lty = c(1,1,2,3),cex=1.5,lwd=4)
}
dev.off()
diag(cor(t(-log10(gst_tab)),t(-log10(pMat))))