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sQTL_test.R
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sQTL_test.R
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#### splicing QTL
load("/mnt/data5/BGI/UCB/tangchao/DSU/RData/psi_list_left_right_table.RData")
require(data.table) # v1.6.6
require(gdata)
FastRemoveMissingValues = function(DT) {
# either of the following for loops
# by name :
for (j in names(DT))
set(DT,which(is.na(DT[[j]])),j,0)
# or by number (slightly faster than by name) :
for (j in seq_len(ncol(DT)))
set(DT,which(is.na(DT[[j]])),j,0)
}
te <- as.data.frame(psi_sj_same_start_table)
FastRemoveMissingValues(te)
FastRemoveMissingValues = function(DT) {
# either of the following for loops
# by name :
for (j in names(DT))
set(DT,which(DT[[j]]>0),j,1)
# or by number (slightly faster than by name) :
for (j in seq_len(ncol(DT)))
set(DT,which(DT[[j]]>0),j,1)
}
FastRemoveMissingValues(te)
TPM <- read.table("/mnt/data5/BGI/UCB/ExpMat_NewID/UCB_rsem_TPM_mat.txt", header = T, row.names = 1, stringsAsFactors = F)
TPM <- TPM[,colnames(te)]
f0 <- apply(te,1,function(x) sum(x==0)/length(x))
sum(f0>0.1 & f0<0.9)
[1] 7214
summary(f0[f0>0.1 & f0<0.9])
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1004 0.7278 0.8281 0.7634 0.8724 0.8998
te_tu <- te[f0>0.1 & f0<0.9,]
f1 <- apply(te_tu,1,function(x) sum(x==0)/length(x))
summary(f1)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1004 0.7278 0.8281 0.7634 0.8724 0.8998
dim(te_tu)
[1] 7214 3574
dim(TPM)
[1] 31907 3574
library(MatrixEQTL)
snps = SlicedData$new()
snps$CreateFromMatrix(as.matrix(te_tu))
gene = SlicedData$new()
gene$CreateFromMatrix(as.matrix(TPM))
for( sl in 1:length(gene) ) {
mat = gene[[sl]];
mat = t(apply(mat, 1, rank, ties.method = "average"));
mat = qnorm(mat / (ncol(gene)+1));
gene[[sl]] = mat;
}
rm(sl, mat);
me <- Matrix_eQTL_engine(snps = snps,
gene = gene,
cvrt = SlicedData$new(),
output_file_name = " ",
pvOutputThreshold = 1e-6,
useModel = modelANOVA,
verbose = TRUE,
pvalue.hist = "qqplot",
min.pv.by.genesnp = FALSE,
noFDRsaveMemory = FALSE)
save(me, file = "/mnt/data5/BGI/UCB/tangchao/QTL/me_anova.RData")
me <- Matrix_eQTL_engine(snps = snps,
gene = gene,
cvrt = SlicedData$new(),
output_file_name = " ",
pvOutputThreshold = 1e-6,
useModel = modelLINEAR,
verbose = TRUE,
pvalue.hist = "qqplot",
min.pv.by.genesnp = FALSE,
noFDRsaveMemory = FALSE)
save(me, file = "/mnt/data5/BGI/UCB/tangchao/QTL/me_linear.RData")
dim(me$all$eqtls)
[1] 17669932 5
my_eqtls <- me$all$eqtls
my_eqtls[which.max(my_eqtls$beta),]
head(my_eqtls)
test_1 <- data.frame(AS = as.numeric(te_tu["7:134442771-134445237",]), GE = as.numeric(TPM["ENSG00000085662", ]))
test_1$AS <- as.factor(test_1$AS)
aggregate(test_1[,2],by=list(test_1$AS),mean)
aggregate(test_1[,2],by=list(test_1$AS),median)
library(dplyr)
group_by(test_1, AS) %>% summarize_each(funs(mean), GE)
group_by(test_1, AS) %>% summarize(mean(GE))
for(i in 1:10){
test_1 <- data.frame(AS = as.numeric(te_tu[my_eqtls$snps[i],]), GE = as.numeric(TPM[my_eqtls$gene[i],]))
test_1$AS <- as.factor(test_1$AS)
pdf(paste("/mnt/data5/BGI/UCB/tangchao/QTL/Figure/", my_eqtls$snps[i], "_", my_eqtls$gene[i], ".pdf", sep = ""))
print(ggplot(test_1, aes(x=AS, y=GE)) +
geom_boxplot(fill="cornflowerblue",color="black",outlier.size = 0, outlier.alpha = 0)+
geom_point(position="jitter", color="blue", alpha=.5)+
geom_rug(color="black")+
xlab(label = my_eqtls$snps[2])+
ylab(label = my_eqtls$gene[2])+
ggtitle(paste("FDR =",signif(my_eqtls$FDR[i],3),
"; p0 =", paste(round(as.numeric(f1[my_eqtls$snps[i]]),2)),
"; Median =", paste(round(aggregate(test_1[,2],by=list(test_1$AS),median)[,2],2), collapse="/", sep = ""),
"; Mean =", paste(round(aggregate(test_1[,2],by=list(test_1$AS),mean)[,2],2), collapse="/", sep = ""), sep=" ")))
dev.off()
}
testyn <- vector()
for(i in 1:1000){
test_1 <- data.frame(AS = as.numeric(te_tu[my_eqtls$snps[i],]), GE = as.numeric(TPM[my_eqtls$gene[i],]))
test_1$AS <- as.factor(test_1$AS)
testyn[i] <- diff(aggregate(test_1[,2],by=list(test_1$AS),median)[,2])>0
}
sum(testyn)
[1] 127
library(parallel)
mcmapply(function(i){
test_1 <- data.frame(AS = as.numeric(te_tu[my_eqtls$snps[i],]), GE = as.numeric(TPM[my_eqtls$gene[i],]))
test_1$AS <- as.factor(test_1$AS)
diff(aggregate(test_1[,2],by=list(test_1$AS),median)[,2])>0
},1:100) -> testyn
my_eqtls[which(testyn),] -> my_eqtls
for(i in 1:16){
test_1 <- data.frame(AS = as.numeric(te_tu[my_eqtls$snps[i],]), GE = log2(as.numeric(TPM[my_eqtls$gene[i],])+1))
test_1$AS <- as.factor(test_1$AS)
pdf(paste("/mnt/data5/BGI/UCB/tangchao/QTL/Figure/", my_eqtls$snps[i], "_", my_eqtls$gene[i], "10.pdf", sep = ""))
print(ggplot(test_1, aes(x=AS, y=GE)) +
geom_boxplot(fill="cornflowerblue",color="black",outlier.size = 0, outlier.alpha = 0)+
geom_point(position="jitter", color="blue", alpha=.5)+
geom_rug(color="black")+
xlab(label = my_eqtls$snps[2])+
ylab(label = my_eqtls$gene[2])+
ggtitle(paste("FDR =",signif(my_eqtls$FDR[i],3),
"; p0 =", paste(round(as.numeric(f1[my_eqtls$snps[i]]),2)),
"; Median =", paste(round(aggregate(test_1[,2],by=list(test_1$AS),median)[,2],2), collapse="/", sep = ""),
"; Mean =", paste(round(aggregate(test_1[,2],by=list(test_1$AS),mean)[,2],2), collapse="/", sep = ""), sep=" ")))
dev.off()
}
me <- Matrix_eQTL_engine(snps = snps,
gene = gene,
cvrt = SlicedData$new(),
output_file_name = " ",
pvOutputThreshold = 1e-6,
useModel = modelLINEAR,
verbose = TRUE,
pvalue.hist = "qqplot",
min.pv.by.genesnp = FALSE,
noFDRsaveMemory = FALSE)
save(me, file = "/mnt/data5/BGI/UCB/tangchao/QTL/me_linear.RData")
my_eqtls <- me$all$eqtls
my_eqtls <- my_eqtls[order(my_eqtls$beta,decreasing=T),]
dim(my_eqtls)
[1] 3541327 5
library(parallel)
p0 <- mcmapply(function(i){
as.numeric(f1[i])
}, my_eqtls$snps, mc.cores = 2)
my_eqtls$p0 <- p0
mcmapply(function(i){
test_1 <- data.frame(AS = as.numeric(te_tu[my_eqtls$snps[i],]), GE = as.numeric(TPM[my_eqtls$gene[i],]))
test_1$AS <- as.factor(test_1$AS)
diff(aggregate(test_1[,2],by=list(test_1$AS),median)[,2])
},1:nrow(my_eqtls), mc.cores = 4) -> diff_Median
my_eqtls[which(testyn),] -> my_eqtls
for(i in 1:10){
test_1 <- data.frame(AS = as.numeric(te_tu[my_eqtls$snps[i],]), GE = as.numeric(TPM[my_eqtls$gene[i],]))
test_1$AS <- as.factor(test_1$AS)
pdf(paste("/mnt/data5/BGI/UCB/tangchao/QTL/Figure/", my_eqtls$snps[i], "_", my_eqtls$gene[i], ".pdf", sep = ""))
print(ggplot(test_1, aes(x=AS, y=GE)) +
geom_boxplot(fill="cornflowerblue",color="black",outlier.size = 0, outlier.alpha = 0)+
geom_point(position="jitter", color="blue", alpha=.5)+
geom_rug(color="black")+
xlab(label = my_eqtls$snps[2])+
ylab(label = my_eqtls$gene[2])+
ggtitle(paste("FDR =",signif(my_eqtls$FDR[i],3),
"; p0 =", paste(round(as.numeric(f1[my_eqtls$snps[i]]),2)),
"; Median =", paste(round(aggregate(test_1[,2],by=list(test_1$AS),median)[,2],2), collapse="/", sep = ""),
"; Mean =", paste(round(aggregate(test_1[,2],by=list(test_1$AS),mean)[,2],2), collapse="/", sep = ""), sep=" ")))
dev.off()
}
library(ggplot2)
pdf("/mnt/data5/BGI/UCB/tangchao/QTL/Figure/QQplot.pdf")
plot(me)
dev.off()