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deseq2.r
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deseq2.r
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#!/usr/bin/env Rscript
#######################################################
# Differential expression analysis module using DESeq2#
#######################################################
args=commandArgs(TRUE)
library("getopt")
spec <- matrix(c(
'rawreads', 'r', 1, "character", "gene raw reads table (required)
\ngene\tSamples1 Sample2\nGene1\t23\t234\nGene2\t565\t23\n",
'factorlist' , 'f', 1, "character", "factor list, no header required, at least 3 reaplicates for each condition, place the reference condition to the top of list (required)
\nSample1\tNormal <--- reference condition\nSample2\tNormal\nSample3\tTumor\nSample4\tTumor\n",
'core' , 'c', 2, "numeric","Number of cores for computation",
'annotation' , 'a', 2, "character", "-a gencodev25, -a gencodev19 (required)",
'circmatrix' , 'i', 2, "character","circRNA raw reads table",
'mode' , 'm', 2, "character", "lnc for lncRNA correlation, circ for circRNA correlation (required)"
),ncol=5,byrow=T)
opt = getopt(spec);
if ( is.null(opt$mode)) {
cat(paste(getopt(spec, usage=T),"\nExample ./deseq2.r -r count_matrix.txt -f count_matrix_conditon.txt -m lnc -a gencodev25
Example ./deseq2.r -r count_matrix_circ.txt -f factor_OSCC_Rseq_circrna.txt -m lnc -a gencodev19
Example ./deseq2.r -r OSCC_circRNA_raw2.txt -f factor_OSCC_Rseq_circrna.txt -m circ -a gencodev19
DEMO DATA CMD CIRCRNA ./deseq2.r -r encode_example_Gene_raw_read_count_casted.txt -i encode_example_circRNA_raw_read_count_casted.txt -f encode_example_circRNA_condition.txt -m circ -a gencodev25
DEMO DATA CMD LNCRNA ./deseq2.r -r TCGA_COADREAD_GENCODEV25_raw_read_count.txt -f TCGA_COADREAD_GENCODEV25_condition.txt -m lnc -a gencodev25 -c 6
DEMO DATA CMD TCGA ./deseq2.r -m TCGA-COAD \n"));
q();
}
# define default value
if ( is.null(opt$core ) ) { opt$core = 4 }
library("data.table")
#library("DESeq2")
library("S4Vectors")
library("factoextra")
#library("BiocParallel")
#library("reshape2")
#read table and factor
ann<-opt$annotation[1]
mode<-opt$mode[1]
TCGA_folder<-"tcga"
TCGA_df<-data.frame(rds=system(paste0("ls ",TCGA_folder,"/deseq_list*TCGA*rds"),intern = T))
TCGA_df$cancer<-gsub(".*(TCGA.*)\\.rds","\\1",TCGA_df$rds)
if (!(mode %like% "TCGA-")){
if (ann=="gencodev25" ) {
gencodev25_sym_id<-readRDS("gencodev25_sym_id.rds")
symbol_id<-gencodev25_sym_id[["symbol_id"]]
lncRNA_symbol_id<-gencodev25_sym_id[["lncRNA_symbol_id"]]
coding_symbol_id<-gencodev25_sym_id[["coding_symbol_id"]]
}
if (ann=="gencodev19" ) {
gencodev25_sym_id<-readRDS("gencodev19_sym_id.rds")
symbol_id<-gencodev25_sym_id[["symbol_id"]]
lncRNA_symbol_id<-gencodev25_sym_id[["lncRNA_symbol_id"]]
coding_symbol_id<-gencodev25_sym_id[["coding_symbol_id"]]
}
}
# pca analysis
pca<-function(input,condition,output){
gene_profile=data.table(input)
cols<-colnames(gene_profile)[2:ncol(gene_profile)]
gene_profile[,(cols):=lapply(.SD, function(x) {log2(as.numeric(x)+1)}), .SDcols = cols]
gene_profile=dcast.data.table(melt(gene_profile, id.vars = "gene",variable.name = "sample"), sample ~ gene)
sample_condition=condition
colnames(sample_condition)=c("sample","group")
gene_profile=merge(gene_profile,sample_condition,all.x=T,by="sample")
gene_profile_pca= prcomp(gene_profile[,-c(1,ncol(gene_profile)),with=F],scale=TRUE)
png("output/pca.png")
p<-fviz_pca_ind(gene_profile_pca, geom = "point",
habillage=gene_profile$group, addEllipses=TRUE,
ellipse.level= 0.95)+scale_color_manual(values=c('#0C4B8E','#BF382A'))+scale_fill_manual(values=c("#3BBDF9","#F9713B"))+theme_minimal()
print(p)
dev.off()
pca=as.data.frame(gene_profile_pca$x)
pca$group=gene_profile$group
pca$sample=gene_profile$sample
write.table(pca,output,quote=F,row.names=F,sep="\t")
}
# for lncRNA differential expression analysis
if (mode=="lnc") {
rawreads <- as.data.frame(data.table::fread(opt$rawreads[1],stringsAsFactors = F))
factor_list <- as.data.frame(data.table::fread(opt$factorlist[1], header=FALSE))
factor_list$V2<-as.factor(factor_list$V2 )
# table check
if ( length(setdiff(colnames(rawreads[,2:ncol(rawreads)]),factor_list$V1))!=0 ){
system("echo Unmacthed sample names "); q();}
if ( length(unique(factor_list$V2)) != 2 ){
system("echo Only support 2 levels factor "); q(); }
if ( length(factor_list$V2[grep(levels(factor_list$V2)[1],factor_list$V2)]) <3 &
length(factor_list$V2[grep(levels(factor_list$V2)[2],factor_list$V2)]) <3 ) {
system("echo Not enought replicates "); q(); }
# conver gene_id to symbol
symbol_id$id<-gsub("\\..*","",symbol_id$id)
rawreads[,1]<-gsub("\\..*","",rawreads[,1])
rawreads<-merge(symbol_id,rawreads,by.x="id",by.y=colnames(rawreads)[1])
rawreads$id<-NULL
# sum the reads for each gene symbol
rawreads_melt <- data.table::data.table(reshape2::melt(rawreads, id = "symbol"))
rawreads<-data.table::dcast.data.table(rawreads_melt, symbol ~ variable, sum)
countsTable <- as.data.frame(rawreads)
names(countsTable)[1]<-"Geneid"
rownames(countsTable)<-countsTable$Geneid
countsTable$Geneid<-NULL
countsTable<-round(as.matrix(countsTable))
if (!is.numeric(countsTable)){ system("echo Data matrix contains non-numeric "); q(); }
# set the base reference
deseq_factor<-factor(factor_list[order(match(factor_list$V1,colnames(countsTable))), ]$V2)
deseq_factor<-factor(deseq_factor, levels=c(levels(deseq_factor)[grep(as.character(factor_list$V2[1]),levels(deseq_factor))], # Tvs N or NvsT
levels(deseq_factor)[-grep(as.character(factor_list$V2[1]),levels(deseq_factor))]))
colData<-data.frame(row.names=colnames(countsTable),condition=deseq_factor)
# differential expression
dds<-DESeq2::DESeqDataSetFromMatrix(countsTable,colData, ~condition)
dds <- dds[ rowSums(DESeq2::counts(dds)) > 1, ]
BiocParallel::register( BiocParallel::MulticoreParam(opt$core))
minreplicates<-min(length(factor_list[factor_list$V2 %in% levels(factor_list$V2)[1],]$V1),
length(factor_list[factor_list$V2 %in% levels(factor_list$V2)[2],]$V1))
dds<-DESeq2::DESeq(dds, parallel = T)
res <- DESeq2::results(dds, parallel = T)
res_tab<-as.data.frame(res)
# condition mean reads
system("echo condition mean reads")
deseq_norm_reads<-as.data.frame(DESeq2::counts(dds,normalized=TRUE))
level_1<-apply(deseq_norm_reads[,colnames(deseq_norm_reads) %in%
factor_list[factor_list$V2 %in% levels(factor_list$V2)[1],]$V1],1,mean)
level_2<-apply(deseq_norm_reads[,colnames(deseq_norm_reads) %in%
factor_list[factor_list$V2 %in% levels(factor_list$V2)[2],]$V1],1,mean)
mean_reads<-data.frame(level_1,level_2)
colnames(mean_reads)<-c(paste0(levels(factor_list$V2)[1]," BaseMean"),paste0(levels(factor_list$V2)[2]," BaseMean"))
res_tab<-merge(res_tab,mean_reads, by='row.names')[,c(1,3,6,7,2,8,9)]
colnames(res_tab)[1]<-"gene"
write.table(res_tab[res_tab$gene %in% lncRNA_symbol_id$symbol,], file="output/DEGs_lncRNA.txt",sep = '\t', row.names = F,quote = F)
write.table(res_tab, file="output/DEGs.txt",sep = '\t', row.names = F,quote = F)
deseq_norm_reads$gene<-rownames(deseq_norm_reads)
# normalized all gene table
deseq_norm_reads<-deseq_norm_reads[,c(ncol(deseq_norm_reads),1:ncol(deseq_norm_reads)-1)]
write.table(deseq_norm_reads, file="output/norm_readstable.txt",sep = '\t', row.names = F,quote = F)
# normalized lnc gene table
deseq_norm_reads_lnc<-deseq_norm_reads[rownames(deseq_norm_reads) %in% lncRNA_symbol_id$symbol,]
write.table(deseq_norm_reads_lnc, file="output/norm_readstable_lncRNA.txt",sep = '\t', row.names = F,quote = F)
# coding gene
deseq_norm_reads_coding<-deseq_norm_reads[rownames(deseq_norm_reads) %in% coding_symbol_id$symbol,]
write.table(deseq_norm_reads_coding, file="output/norm_readstable_coding_gene.txt",sep = '\t', row.names = F,quote = F)
pca(deseq_norm_reads,factor_list,"output/pca.txt")
}
# for circRNA differential expression analysis
if (mode=="circ") {
factor_list <- as.data.frame(data.table::fread(opt$factorlist[1], header=FALSE))
factor_list$V2<-as.factor(factor_list$V2 )
# loading gene reads counts
rawreads <- as.data.frame(data.table::fread(opt$rawreads[1],stringsAsFactors = F))
symbol_id$id<-gsub("\\..*","",symbol_id$id)
rawreads[,1]<-gsub("\\..*","",rawreads[,1])
rawreads<-merge(symbol_id,rawreads,by.x="id",by.y=colnames(rawreads)[1])
rawreads$id<-NULL
rawreads_melt <- data.table::data.table(reshape2::melt(rawreads, id = "symbol"))
rawreads<-as.data.frame(data.table::dcast.data.table(rawreads_melt, symbol ~ variable, sum))
rawreads<-reshape2::melt(rawreads)
# loading circRNA read counts
circmatrix <- as.data.frame(data.table::fread(opt$circmatrix[1],stringsAsFactors = F))
circname<-circmatrix[,1] # load circRNA id from first col
circmatrix<-reshape2::melt(circmatrix)
colnames(circmatrix)[1]<-colnames(rawreads)[1]
# merge for combined normalization
rawreads<-rbind(rawreads,circmatrix)
rawreads<-reshape2::dcast(rawreads,symbol~variable,sum)
# table check
if ( length(setdiff(colnames(rawreads[,2:ncol(rawreads)]),factor_list$V1))!=0 ){
system("echo Unmacthed sample names "); q();}
if ( length(unique(factor_list$V2)) != 2 ){
system("echo Only support 2 levels factor "); q(); }
if ( length(factor_list$V2[grep(levels(factor_list$V2)[1],factor_list$V2)]) <3 &
length(factor_list$V2[grep(levels(factor_list$V2)[2],factor_list$V2)]) <3 ) {
system("echo Not enought replicates "); q(); }
countsTable <- as.data.frame(rawreads)
names(countsTable)[1]<-"Geneid"
rownames(countsTable)<-countsTable$Geneid
countsTable$Geneid<-NULL
countsTable<-round(as.matrix(countsTable))
if (!is.numeric(countsTable)){ system("echo Data matrix contains non-numeric "); q(); }
deseq_factor<-factor(factor_list[order(match(factor_list$V1,colnames(countsTable))), ]$V2)
deseq_factor<-factor(deseq_factor, levels=c(levels(deseq_factor)[grep(as.character(factor_list$V2[1]),levels(deseq_factor))], # Tvs N or NvsT
levels(deseq_factor)[-grep(as.character(factor_list$V2[1]),levels(deseq_factor))]))
colData<-data.frame(row.names=colnames(countsTable),condition=deseq_factor)
dds<-DESeq2::DESeqDataSetFromMatrix(countsTable,colData, ~condition)
dds <- dds[ rowSums(DESeq2::counts(dds)) > 1, ]
BiocParallel::register(BiocParallel::MulticoreParam(opt$core))
minreplicates<-min(length(factor_list[factor_list$V2 %in% levels(factor_list$V2)[1],]$V1),
length(factor_list[factor_list$V2 %in% levels(factor_list$V2)[2],]$V1))
dds<-DESeq2::DESeq(dds, parallel = T)
res <- DESeq2::results(dds, parallel = T)
res_tab<-as.data.frame(res)
# condition mean reads
system("echo condition mean reads")
deseq_norm_reads<-as.data.frame(DESeq2::counts(dds,normalized=TRUE))
level_1<-apply(deseq_norm_reads[,colnames(deseq_norm_reads) %in%
factor_list[factor_list$V2 %in% levels(factor_list$V2)[1],]$V1],1,mean)
level_2<-apply(deseq_norm_reads[,colnames(deseq_norm_reads) %in%
factor_list[factor_list$V2 %in% levels(factor_list$V2)[2],]$V1],1,mean)
mean_reads<-data.frame(level_1,level_2)
colnames(mean_reads)<-c(paste0(levels(factor_list$V2)[1]," BaseMean"),paste0(levels(factor_list$V2)[2]," BaseMean"))
res_tab<-merge(res_tab,mean_reads, by='row.names')[,c(1,3,6,7,2,8,9)]
colnames(res_tab)[1]<-"gene"
#write.table(res_tab[res_tab$gene %in% lncRNA_symbol_id$symbol,], file="DEGs_circRNA.txt",sep = '\t', row.names = F,quote = F)
write.table(res_tab[ res_tab$gene %in% circname,], file="output/DEGs_circRNA.txt",sep = '\t', row.names = F,quote = F)
deseq_norm_reads$gene<-rownames(deseq_norm_reads)
deseq_norm_reads<-deseq_norm_reads[,c(ncol(deseq_norm_reads),1:ncol(deseq_norm_reads)-1)]
write.table(deseq_norm_reads[deseq_norm_reads$gene %in% circname,], file="output/norm_readstable_circRNA.txt",sep = '\t', row.names = F,quote = F)
write.table(deseq_norm_reads[!deseq_norm_reads$gene %in% circname,], file="output/norm_readstable.txt",sep = '\t', row.names = F,quote = F)
# normalized lnc gene table
deseq_norm_reads_lnc<-deseq_norm_reads[rownames(deseq_norm_reads) %in% lncRNA_symbol_id$symbol,]
write.table(deseq_norm_reads_lnc, file="output/norm_readstable_lncRNA.txt",sep = '\t', row.names = F,quote = F)
# coding gene
deseq_norm_reads_coding<-deseq_norm_reads[rownames(deseq_norm_reads) %in% coding_symbol_id$symbol,]
write.table(deseq_norm_reads_coding, file="output/norm_readstable_coding_gene.txt",sep = '\t', row.names = F,quote = F)
pca(deseq_norm_reads[deseq_norm_reads$gene %in% circname,],factor_list,"output/pca.txt")
}
# TCGA datasets output
if (mode %like% "TCGA-") {
message("opening TCGA deseq2 result")
tcga.rds.path<-as.character(TCGA_df[ TCGA_df$cancer == mode,]$rds)
tcga.deseq.res<-readRDS(tcga.rds.path)
message("writing TCGA deseq2 result")
factor_list<-data.frame(V1=names(tcga.deseq.res[[ "norm_readstable" ]][2:ncol(tcga.deseq.res[[ "norm_readstable" ]])]))
factor_list$V2<-ifelse(substr(factor_list$V1,14,14) >=1,"N","T")
write.table(factor_list, file="count_matrix_condition.txt",sep = '\t', row.names = F,quote = F,col.names = F)
write.table(tcga.deseq.res[[ "DEGs_lncRNA" ]], file="output/DEGs_lncRNA.txt",sep = '\t', row.names = F,quote = F)
write.table(tcga.deseq.res[[ "DEGs" ]], file="output/DEGs.txt",sep = '\t', row.names = F,quote = F)
write.table(tcga.deseq.res[[ "norm_readstable" ]], file="output/norm_readstable.txt",sep = '\t', row.names = F,quote = F)
write.table(tcga.deseq.res[[ "norm_readstable_lncRNA" ]], file="output/norm_readstable_lncRNA.txt",sep = '\t', row.names = F,quote = F)
write.table(tcga.deseq.res[[ "norm_readstable_coding_gene" ]], file="output/norm_readstable_coding_gene.txt",sep = '\t', row.names = F,quote = F)
message("processing PCA")
pca(tcga.deseq.res[[ "norm_readstable" ]],factor_list,"output/pca.txt")
}