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extract_utmost_results.R
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extract_utmost_results.R
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options(stringsAsFactors=F)
suppressMessages(library("qqman"))
suppressMessages(library("data.table"))
suppressMessages(library("GWASTools"))
args = commandArgs(trailingOnly=TRUE)
utmost_results = args[1] ## path to utmost joint test output
utmost_single_tissue_results = args[2] ## path to utmost joint test output
out_prefix = args[3] ## prefix to gene-level Manhattan plot/qq plot/significant gene table
aname = args[4] ## trait name
gene_table = args[5] ## gene position info
## joint GBJ results summary
a = as.data.frame(fread(paste0(utmost_results), header=T, sep="\t"))
#gene test_score p_value
a = unique(a)
a = a[complete.cases(a), ]
thres = 0.05/nrow(a)
write.csv(a[a$p_value<thres,], paste0(out_prefix,'/', aname, '_utmost_joint_sig_genes.csv'), row.names=F)
##### plot #####
#gene_table = "/ysm-gpfs/pi/zhao/from_louise/bl537/GWAS/VA/metaxcan/GeneList.txt"
g = read.table(gene_table, header=T, sep="\t")
d = a[,c(1,3)]
colnames(d) = c("gene_name", "pvalue")
dd = merge(d, g, all.x=T, by.x="gene_name", by.y="GENE")
dd = dd[order(dd$CHR, dd$POS),]
a = dd
m = a[complete.cases(a),]
colnames(m) = c("Gene", "P", "CHR", "BP")
m$P = as.numeric(m$P)
mdir = paste0(out_prefix, "/", aname, "_utmost_joint_manhattan.png")
png(mdir, width=1000, height=600, res=100)
manhattan(m, chr = "CHR", bp = "BP", p = "P",
col = c("#4682B4", "#B4464B"), chrlabs = as.character(c(1:22)), main=aname,
suggestiveline = FALSE, genomewideline = -log10(thres),
ylim = c(0, max(-log10(m$P), -log10(thres))+2),
logp = TRUE,
las = 2,
cex = 0.5)
dev.off()
qdir = paste0(out_prefix, "/", aname, "_utmost_joint_qq.png")
png(qdir, width=1000, height=800, res=120)
qqPlot(m$P, truncate = FALSE, ylim = NULL, ci=TRUE, main=aname)
dev.off()
# by tissue
Files.csv = list.files(utmost_single_tissue_results)
Files.csv = Files.csv[grepl(".csv", Files.csv)]
thres = 1.277782e-07
pb = txtProgressBar(0, length(Files.csv), style=3)
for(j in 1:length(Files.csv)){
d = as.data.frame(fread(paste0(utmost_single_tissue_results, '/',Files.csv[j]), header=T, sep=","))
#colnames(d) = c("gene_name", "pvalue")
d = na.omit(d)
d = unique(d)
d = d[d$pvalue<thres, ]
if(nrow(d)>0){
d['Tissue'] = gsub(".csv", "", Files.csv[j])
}
if (j==1) {a=d} else{
a = rbind(a, d)
}
setTxtProgressBar(pb, j)
}
write.table(a, paste0(out_prefix,'/',aname,'_utmost_single_sig_genes.txt'), quote=F, sep='\t', row.names=F, col.names=T)
##### plot #####
g = read.table(gene_table, header=T, sep="\t")
pb = txtProgressBar(0, length(Files.csv), style=3)
for(j in 1:length(Files.csv)){
d = as.data.frame(fread(paste0(utmost_single_tissue_results, '/',Files.csv[j]), header=T, sep=","))
d = na.omit(d)
d = unique(d)
d = d[, c("gene_name", "pvalue")]
dd = merge(d, g, all.x=T, by.x="gene_name", by.y="GENE")
if (j==1) {a=dd} else{
a = rbind(a, dd)
}
setTxtProgressBar(pb, j)
}
aa = a[order(a$CHR, a$POS),]
a = aa
m = a[complete.cases(a),]
colnames(m) = c("Gene", "P", "CHR", "BP")
mdir = paste0(outfolder[i], "/",tt,aname, "_by-tissue_manhattan.png")
png(mdir, width=1000, height=600, res=100)
manhattan(m, chr = "CHR", bp = "BP", p = "P",
col = c("#4682B4", "#B4464B"), chrlabs = as.character(c(1:22)), main=aname,
suggestiveline = FALSE, genomewideline = -log10(thres),
ylim = c(0, max(-log10(m$P), -log10(thres))+2),
logp = TRUE,
las = 2,
cex = 0.5)
dev.off()
tissue_helper <- function(tis){
tmp = strsplit(tis, '\\.')[[1]][1]
tmp = unlist(strsplit(tmp, '_'))
tmp = tmp[-1]
paste(tmp, collapse = '_')
}
setwd(outfolder[i])
aname = traits[i]
df = read.table(paste0(outfolder[i],'/',tt,'.',aname,'_by-tissue_sig_genes.txt'), header=T)
df = read.table(paste0(outfolder[i],'/',aname,'_by-tissue_sig_genes.txt'), header=T)
if(nrow(df)==0){
print(paste0(tt, " empty!"))
}else{
df = df[,c(2,3,5,13)]
for(i in 1:nrow(df)){
df[i,4] = tissue_helper(df[i,4])
}
df = df[order(df[,1]),]
write.table(df, paste0(aname,'.full.txt'), sep='\t', col.names=T, row.names=F, quote=F)
write.table(df, paste0(tt,'.',aname,'.full.txt'), sep='\t', col.names=T, row.names=F, quote=F)
gset = unique(df[,1])
most_sig = data.frame()
for(g in gset){
chunk = df[df[,1]==g, ]
insert = chunk[which.min(chunk[,3]),]
most_sig = rbind(most_sig, insert)
}
write.table(most_sig, paste0(aname,'.most.sig.txt'), sep='\t', col.names=T, row.names=F, quote=F)
write.table(most_sig, paste0(tt,'.',aname,'.most.sig.txt'), sep='\t', col.names=T, row.names=F, quote=F)
}
##xxx = c("alcamt", "pclc", "slprs")
##ttV = list()
##ttV[[1]] = c("alcamt.eu", "alcamt.g1", "alcamt.g2")
##ttV[[2]] = c("pclc.eu", "pclc.eu_cmbt", "pclc.eu_noncmbt")
##ttV[[3]] = c("slprs.13_eu", "slprs.64_eu")
##setwd("/ysm-gpfs/pi/zhao/from_louise/yh367/VA/workflow/gene_level_analysis/MetaXcan/summary/new_ones")
##tt = "new_ones.CasePclcEA"
##tt = "new_ones.slprEA"
##setwd("/ysm-gpfs/pi/zhao/from_louise/yh367/VA/workflow/gene_level_analysis/MetaXcan/summary/ptsd-exp-ea113017")
##tt = "ptsd-exp-ea113017"
#
##traits = c("e-c-ea", "une-b-ea", "une-c-ea")
##outfolder = paste0("/ysm-gpfs/pi/zhao/from_louise/yh367/VA/workflow/gene_level_analysis/MetaXcan/summary/",traits, '/')
##tt = "ptsd"
#
#traits = c("csp572_bipol_eu", "csp572_cogni_eu", "csp572_schiz_eu")
#outfolder = paste0("/ysm-gpfs/pi/zhao/from_louise/yh367/VA/workflow/gene_level_analysis/MetaXcan/summary/",traits, '/')
#
#for(i in 1:length(outfolder)){
# setwd(outfolder[i])
# aname = traits[i]
## df = read.table(paste0(outfolder[i],'/',tt,'.',aname,'_by-tissue_sig_genes.txt'), header=T)
# df = read.table(paste0(outfolder[i],'/',aname,'_by-tissue_sig_genes.txt'), header=T)
# if(nrow(df)==0){
# print(paste0(tt, " empty!"))
# }else{
# df = df[,c(2,3,5,13)]
# for(i in 1:nrow(df)){
# df[i,4] = tissue_helper(df[i,4])
# }
# df = df[order(df[,1]),]
# write.table(df, paste0(aname,'.full.txt'), sep='\t', col.names=T, row.names=F, quote=F)
## write.table(df, paste0(tt,'.',aname,'.full.txt'), sep='\t', col.names=T, row.names=F, quote=F)
# gset = unique(df[,1])
# most_sig = data.frame()
# for(g in gset){
# chunk = df[df[,1]==g, ]
# insert = chunk[which.min(chunk[,3]),]
# most_sig = rbind(most_sig, insert)
# }
# write.table(most_sig, paste0(aname,'.most.sig.txt'), sep='\t', col.names=T, row.names=F, quote=F)
## write.table(most_sig, paste0(tt,'.',aname,'.most.sig.txt'), sep='\t', col.names=T, row.names=F, quote=F)
# }
#}
#
#