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module load plink
for CHR in `seq 22`
do
# convert bfiles to .raw files
plink \
--bfile /project2/xuanyao/data/DGN/genotype/chr${CHR}_QCed \
--recodeA \
--out /project2/xuanyao/data/DGN/txt_file_of_DGN/chr${CHR}
# extract genotype meta
awk \
'BEGIN{print "id\tchr\tpos"}{printf("%s\tchr%s\t%s\n",$2,$1,$4)}' \
/project2/xuanyao/data/DGN/genotype/chr${CHR}_QCed.bim > \
/project2/xuanyao/data/DGN/txt_file_of_DGN/chr${CHR}_genotype_meta.txt
# convert genotype to txt file
Rscript --no-restore --no-save /project2/xuanyao/data/DGN/make_geno.R $CHR
donemake_geno.R
rm(list = ls())
args = commandArgs(trailing = T)
chr = args[1]
dat = read.table(paste("/project2/xuanyao/data/DGN/txt_file_of_DGN/chr",chr,".raw",sep=""),header=T,check.names=F)
geno = as.matrix(dat[,7:ncol(dat)])
rownames(geno) = as.character(dat$IID)
colnames(geno) = unlist(lapply(strsplit(colnames(dat)[7:ncol(dat)],"_"),"[",1))
write.table(t(geno),
paste("./project2/xuanyao/data/DGN/txt_file_of_DGN/chr",chr,".genotype.matrix.eqtl.txt",sep = ""),
sep = "\t", quote = FALSE)rm(list = ls())
# import expression and covariates data
load("./data/dgn.rdata")
covariates_tec = read.table("./data/Technical_factors.txt",
sep = '\t',
header = T, row.names = 1,
stringsAsFactors = F)
covariates_bio = read.table("./data/Biological_and_hidden_factors.txt",
sep = '\t',
header = T, row.names = 1,
stringsAsFactors = F)
sample_names = read.table("data/samples.txt",
header = T,
stringsAsFactors = F)
extract_residual <- function(y, x){
return(lm(y ~ x)$residuals)
}
rownames(ex) = sample_names$sample; colnames(ex) = gnames
covariates_tec_used = as.matrix(covariates_tec[rownames(ex), ])
covariates_bio_used = as.matrix(covariates_bio[rownames(ex), ])
# regress out technical covariates
ex_tec_regressed = apply(ex, 2, function(y) extract_residual(y, covariates_tec_used))
ex_tec_bio_regressed = apply(ex_tec_regressed, 2, function(e) extract_residual(e, covariates_bio_used))
saveRDS(ex_tec_bio_regressed, "./data/ex_var_regressed.rds")
print("Done!")Remove seudogenes, genes with mappability\(<0.9\), and gene pairs with cross-mappability higher than 1.
rm(list = ls())
library(WGCNA)
source("functions_for_additional_analysis.R")
options(stringsAsFactors = FALSE)
# expression input
datExpr = readRDS("./data/ex_var_regressed.rds")
# remove pseudogenes
pseudogenes = read.table("./result/pseudogenes.txt", sep ="\t", stringsAsFactors = F)
ind_pseudo = colnames(datExpr) %in% unique(pseudogenes$V1)
# remove low mappability genes
mappability = read.table("./result/mappability.txt", sep ="\t", stringsAsFactors = F, header = T)
ind_map = colnames(datExpr) %in% unique(mappability$gene_name)
# remove cross-mappable genes
cross_mappability = readRDS("./result/cross_mappable_genes.rds")
ind_cross_map = unique(names(cross_mappability[!is.na(cross_mappability)]))
ind_cross_map = colnames(datExpr) %in% ind_cross_map
# all removed genes
ind_remove = ind_pseudo | ind_map | ind_cross_mapCluster the co-expressed genes by WGCNA.
# Genes left after removing low-quality genes
datExpr = datExpr[, !ind_remove]
# Parameter specification
minModuleSize = 30
MEDissThres = 0.15
if_plot_adjacency_mat_parameter_selection = F
if_plot_only_tree = F
if_plot_color_and_tree = F
if_plot_eigengene_heatmap_tree = F
if_heatmap_of_network = T
### Step1: network construction ###
# determine the paramter in adjacency function: pickSoftThreshold() OR pickHardThreshold()
powers = c(c(1:10), seq(from = 12, to=20, by=2))
sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)
softPower = sft$powerEstimate
# network construction
adjacency = adjacency(datExpr, power = softPower)
TOM = TOMsimilarity(adjacency)
dissTOM = 1-TOM
### Step2: module detection ###
# tree construction using hierarchical clustering based on TOM
geneTree = hclust(as.dist(dissTOM), method = "average")
# branch cutting using dynamic tree cut
dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM,
deepSplit = 2, pamRespectsDendro = FALSE,
minClusterSize = minModuleSize)
dynamicColors = labels2colors(dynamicMods)
# eigene genes
MEList = moduleEigengenes(datExpr, colors = dynamicColors)
MEs = MEList$eigengenes
# Call an automatic merging function
merge = mergeCloseModules(datExpr, dynamicColors, cutHeight = MEDissThres, verbose = 3)
mergedColors = merge$colors
mergedMEs = merge$newMEs
moduleLabels = match(mergedColors, c("grey", standardColors(50)))-1
names(moduleLabels) = colnames(datExpr)
# Save results
result = list(moduleColors = mergedColors,
moduleLabels = moduleLabels,
MEs = mergedMEs,
old_moduleColors = dynamicColors,
old_moduleLabels = dynamicMods,
old_MEs = MEs,
geneTree = geneTree,
ind_remove = ind_remove)
saveRDS(result, file = "./result/DGN_coexpressed_gene_network.rds")Here is the code of how I compute zscores for module \(i\) and chromosome \(j\), using randomly permuted samples. Calculating observed zscores (zscores based on the original real data) is similar, only need to remove line genotype = genotype[ind.perm, ].
rm(list = ls())
parameter = commandArgs(trailingOnly = T)
module_id = parameter[1]
chr = parameter[2]
library(foreach)
library(doParallel)
# data input
data_dir = "/project2/xuanyao/data/DGN/"
datExpr = readRDS(paste0(data_dir, "txt_file_of_DGN/ex_var_regressed.rds"))
gene_pos = read.table(paste0(data_dir, "txt_file_of_DGN/expression_meta_hg19.txt"),
sep = '\t',
header = T,
stringsAsFactors = F)
gene_result = readRDS(file = paste0(data_dir, "txt_file_of_DGN/DGN_coexpressed_gene_network.rds"))
header = ifelse(chr==1, TRUE, FALSE)
genotype = read.table(paste0(data_dir, "txt_file_of_DGN/chr", chr, ".genotype.matrix.eqtl.txt"),
header = header, row.names=NULL,
stringsAsFactors = F)
genotype = genotype[!duplicated(genotype[, 1]), ]
rownames(genotype) = genotype[, 1]
genotype = t(genotype[, -1])
# Use only the genes in the module and have postion info in the meta file
gene_in_cluster = data.frame("gene_name"=names(gene_result$moduleLabels)[gene_result$moduleLabels == module_id], stringsAsFactors = F)
gene_w_pos = merge(gene_in_cluster, gene_pos, by.x = 1, by.y = 1)
exp.data = datExpr[, gene_w_pos$gene_name]
print("Input part is done!")
# run parallel on snps
cores=(Sys.getenv("SLURM_NTASKS_PER_NODE"))
cl = makeCluster(as.numeric(cores), outfile="")
registerDoParallel(cl)
M = ncol(genotype); K = ncol(exp.data); N = nrow(exp.data); ind.perm = sample(1:N)
genotype = genotype[ind.perm, ]
chunk.size = M %/% as.numeric(cores)
system.time(
z.null <- foreach(i=1:cores, .combine='rbind') %dopar%
{
res = matrix(nrow = chunk.size, ncol = K,
dimnames = list(colnames(genotype)[((i-1)*chunk.size+1):(i*chunk.size)], colnames(exp.data)))
for(j in 1:chunk.size){
snp = genotype[, j + (i-1)*chunk.size]
res[j, ] = apply(exp.data, 2, function(x) summary(lm(x~snp))$coefficients[2, 3] )
if(j %% 100 == 0){print(j+(i-1)*chunk.size)}
}
res
}
)
stopCluster(cl)
print("Parallel part is done!")
# Run PCO on snps left in the parallel part
Nsnps_left = M %% as.numeric(cores)
Nsnps_done = (chunk.size)*as.numeric(cores)
if(Nsnps_left != 0){
res = matrix(nrow = Nsnps_left, ncol = K,
dimnames = list(colnames(genotype)[Nsnps_done+1:(Nsnps_left)], colnames(exp.data)) )
for(j in 1:Nsnps_left){
snp = genotype[, j + Nsnps_done]
res[j, ] = apply(exp.data, 2, function(x) summary(lm(x~snp))$coefficients[2, 3] )
print(Nsnps_done+j)
}
z.null = rbind(z.null, res)
}
print("All part is done!")
saveRDS(z.null, file = paste0("./NULL/z.null/z.null.module", module_id, ".chr", chr, ".rds"))Here is the code using PCO to calculate pvalues. This uses only PC’s with eigenvalue \(\lambda > 1\).
rm(list = ls())
parameter = commandArgs(trailingOnly = T)
module_id = parameter[1]
chr = parameter[2]
p.method = parameter[3] #"truncPCO" #"PCO"
script_dir = "./NULL/script/"
zscore_dir = "./NULL/z.null/"
# There files are modified PCO functions. Only use PC's with eigenvalue \lambda>1
source(paste0(script_dir, "ModifiedPCOMerged.R"))
source(paste0(script_dir, "liu.r"))
source(paste0(script_dir, "liumod.R"))
source(paste0(script_dir, "davies.R"))
source(paste0(script_dir, "SigmaMetaEstimate.R"))
library(foreach)
library(doParallel)
library(mvtnorm)
library(MPAT)
# zscore input
z.null = readRDS(file = paste0(zscore_dir, "z.null.module", module_id, ".chr", chr, ".rds"))
# input
data_dir = "/project2/xuanyao/data/DGN/txt_file_of_DGN/"
gene_all_pos = read.table(paste0(data_dir, "expression_meta_hg19.txt"),
sep = '\t',
header = T,
stringsAsFactors = F)
datExpr = readRDS(paste0(data_dir, "ex_var_regressed.rds"))
gene_module = readRDS(file = paste0(data_dir, "DGN_coexpressed_gene_network.rds"))
# Extract genes within the module that have gene meta info and are not on the chromosome for which we are calculating pvalues.
# This step makes sure the signals are due to trans genetic effects.
gene_cluster = data.frame("gene_name" = names(gene_module$moduleLabels[gene_module$moduleLabels == module_id]), stringsAsFactors = F)
gene_pos = merge(gene_cluster, gene_all_pos, by.x = 1, by.y = 1)
gene_trans = gene_pos[gene_pos$chrom != paste0("chr", chr), "gene_name"]
# inputs for PCO
Sigma = cor(datExpr[, gene_trans])
SigmaO = ModifiedSigmaOEstimate(Sigma,simNum=1000)
z.null_trans = z.null[, gene_trans]; rm(z.null)
# Do parallel across snps
cores=(Sys.getenv("SLURM_NTASKS_PER_NODE"))
cl = makeCluster(as.numeric(cores), outfile="")
registerDoParallel(cl)
Nsnps = nrow(z.null_trans); snps = rownames(z.null_trans)
chunk.size = Nsnps %/% as.numeric(cores)
Nsnps_left = Nsnps %% as.numeric(cores)
Nsnps_done = (chunk.size)*as.numeric(cores)
system.time(
p.null <- foreach(i=1:cores, .combine='c') %dopar%
{
library(MPAT)
library(mvtnorm)
res <- rep(NA, chunk.size); names(res) <- snps[((i-1)*chunk.size+1):(i*chunk.size)]
for(x in 1:chunk.size) {
res[x] <- ModifiedPCOMerged(Z.vec=z.null_trans[x + (i-1)*chunk.size, ],
Sigma=Sigma, SigmaO=SigmaO,method = "davies")
if(x %% 100 == 0){print(x + (i-1)*chunk.size)}
}
res
}
)
stopCluster(cl)
print("Parallel part is done!")
# Do PCO on snps that are left in the parallel part.
if(Nsnps_left != 0){
res_left = rep(NA, Nsnps_left); names(res_left) = snps[1:(Nsnps_left) + Nsnps_done]
for(k in 1:(Nsnps_left)){
res_left[k] = ModifiedPCOMerged(Z.vec=z.null_trans[k + Nsnps_done, ],
Sigma=Sigma, SigmaO=SigmaO,method = "davies")
print(k + Nsnps_done)
}
p.null = c(p.null, res_left)
}
print("All part is done!")
saveRDS(pT.null, file = paste0("./NULL/p.null/p.null.module", module_id, ".chr", chr, ".", p.method, ".rds"))rm(list = ls())
library(qvalue)
# estimate the p-value cut-off to control the false discovery rate at a certain level
parameter = commandArgs(trailingOnly = T)
fdr.level = parameter[1]
adjust.method = parameter[2]
# input pvalues for all 18 gene modules and 22 chromosomes
pvalue_pc = NULL
for(gene_cluster_id in 1:18){
for(chr in 1:22){
tmp_pc = readRDS(file = paste0("./TruncPCO/GeneCluster", gene_cluster_id, "/pvalue_pco_", chr, ".rds"))
names(tmp_pc) = paste0("C", gene_cluster_id, ":", names(tmp_pc))
pvalue_pc = c(pvalue_pc, tmp_pc)
cat("Gene cluster:", gene_cluster_id, ". Chr:", chr, "\n")
print(object.size(pvalue_pc))
}
}
# histogram of all pvalues
png("./FDR/Hist of all pvalues")
hist(pvalue_pc)
dev.off()
print("Histogram of pvalues saved!")
# adjust pvalues
if(adjust.method == "BH"){
p.adjusted = p.adjust(pvalue_pc, method = "BH")
print("BH correction of pvalue done!")
signal_q = p.adjusted[p.adjusted<fdr.level]
signal_name = names(signal_q)
signal_summary = cbind(pvalue_pc[signal_name], signal_q)
colnames(signal_summary) = c("pvalue.PCO", "qvalue.PCO")
write.table(signal_summary, file = "./FDR/transeQTL.Summary.BH.txt",
quote = FALSE)
print("Signals written in the file!")
saveRDS(p.adjusted, file = "./FDR/qvalueofPC.BH.rds")
print("qvalue results saved!")
}else if(adjust.method == "qvalue"){
qobj_pc = qvalue(p = pvalue_pc, fdr.level=fdr.level)
print("FDR analysis of pc has been done!")
signal_q = qobj_pc$qvalues[qobj_pc$significant]
signal_name = names(signal_q)
signal_summary = cbind(qobj_pc$pvalues[signal_name], signal_q)
colnames(signal_summary) = c("pvalue.PCO", "qvalue.PCO")
write.table(signal_summary, file = "./FDR/transeQTL.Summary.q.txt",
quote = FALSE)
print("Signals written in the file!")
saveRDS(qobj_pc, file = "./FDR/qvalueofPC.q.rds")
print("qvalue results saved!")
}Use all PC’s. Adjust pvalues using qvalue.
Use PC’s with eigenvalue \(\lambda>1\). Adjust pvalues using empirical null.
Use the first PC. (elife method)
Single-variate method. (MatrixeQTL)
Prepare files
Run MatrixeQTL
sessionInfo()