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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,64 +1,99 @@ | ||
| library('reshape') | ||
| library('ggplot2') | ||
| library('edgeR') | ||
| library('DESeq2') | ||
| library('tidyverse') | ||
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||
| writegzfile <- function(m,f) { | ||
| m=as.data.frame(m) | ||
| m$id=rownames(m) | ||
| m=separate(data=m,col=id,into=c('ensID','geneName'),sep="\\|",remove=TRUE) | ||
| m=m %>% select('ensID','geneName',everything()) | ||
| write.table(m,file=gzfile(f),sep="\t",row.names = FALSE,quote=F) | ||
| } | ||
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| args <- commandArgs(trailingOnly = TRUE) | ||
| DIR <- args[1] | ||
| FILES <- args[2] | ||
| MINCOUNT <- args[3] | ||
| MINSAMPLES <- args[4] | ||
| ANNOTATE <- args[5] | ||
| ANNOTATE <- args[3] | ||
| SAMPLETABLE <- args[4] | ||
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| #SAMPLETABLE="fullsampletable.txt" | ||
| #DIR="~/Desktop/Temp/ccbr842/RNASeq/RSEM_filtering" | ||
| #FILES=c("iv_4T1C1_1.RSEM.genes.results iv_4T1C1_2.RSEM.genes.results iv_NF1_1.RSEM.genes.results iv_NF1_2.RSEM.genes.results iv_TSC1_1.RSEM.genes.results iv_TSC1_2.RSEM.genes.results iv_Tgfbr_2_1.RSEM.genes.results iv_Tgfbr_2_2.RSEM.genes.results") | ||
| #ANNOTATE="annotate.genes.txt" | ||
|
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||
| MINCOUNT=0.5 | ||
| MINSAMPLES=0.5 | ||
|
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|
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||
| setwd(DIR) | ||
| x=read.table(SAMPLETABLE,header = T,sep="\t") | ||
| myfiles=as.character(unlist(strsplit(FILES, split=" "))) | ||
| res=read.delim(myfiles[1],header=T)[,c(1,5)] | ||
| # colnames(res)[1]="gene" | ||
| res=read.delim(myfiles[1],header=T)[,c(1,5)] | ||
| colnames(res)[2]=as.character(myfiles[1]) | ||
| # remove the last 5 statistics lines ... | ||
| # nr=dim(res)[1] | ||
| # res=res[-c((nr-4):nr),] | ||
| # | ||
| for(i in seq(2, length(myfiles), by = 1)) | ||
| {{ | ||
| temp=read.delim(myfiles[i],header=T)[,c(1,5)] | ||
| #colnames(temp)[1]="gene" | ||
| colnames(temp)[2]=as.character(myfiles[i]) | ||
| res=merge(res,temp) | ||
| }} | ||
|
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||
| gene_name=read.delim(ANNOTATE,header=F,sep=" ") | ||
| res2=merge(gene_name,res,by.x=1,by.y=1) | ||
| res3=cbind(symbol=paste(res2[,1],"|",res2[,3],sep=""),res2[,-c(1,2,3,4,5)]) | ||
| write.table(as.data.frame(res3),file="RawCountFile_RSEM_genes.txt",sep="\t",row.names=F) | ||
| # | ||
| mydata=read.delim("RawCountFile_RSEM_genes.txt",row.names=1) | ||
| # rounding | ||
| mydata=round(mydata) | ||
| val1=as.numeric(MINCOUNT) | ||
| val2=as.numeric(MINSAMPLES) | ||
| # cat(val1," ", val2, "checking..\n",file="check.txt") | ||
| ## filter <- apply(mydata, 1, function(x) length(x[x>val1])>=val2) | ||
| ## res=mydata[filter,] | ||
| tot=colSums(mydata) | ||
| val1=(val1/max(tot))*1e6 | ||
| # cat(val1," ", val2, "checking..\n",file="check2.txt") | ||
| filter <- apply(cpm(mydata), 1, function(x) length(x[x>val1])>=val2) | ||
| res=mydata[filter,] | ||
| write.table(as.data.frame(res),file="RawCountFile_RSEM_genes_filtered.txt",sep="\t",col.names=NA,quote=F) | ||
| png("RSEM_HistBeforenormFilter.png") | ||
| df.m <- melt(as.data.frame(res)) | ||
| print(ggplot(df.m) + geom_density(aes(x = value, colour = variable)) + labs(x = NULL) + theme(legend.position='top') + scale_x_log10()) | ||
| dev.off() | ||
| write.table(as.data.frame(res3),file="RawCountFile_RSEM_genes.txt",sep="\t",row.names=F,quote = F) | ||
| mydata=read.delim("RawCountFile_RSEM_genes.txt",row.names=1,check.names=FALSE,header=T) | ||
| print(colnames(mydata)) | ||
| colnames(mydata)=gsub('\\..*$','',colnames(mydata)) | ||
| print(colnames(mydata)) | ||
| colnames(mydata)=gsub('.*/','',colnames(mydata)) | ||
| print(colnames(mydata)) | ||
| mydata=ceiling(mydata) | ||
| writegzfile(cpm(mydata),"RSEM_CPM_counts.txt.gz") | ||
|
|
||
| groups=levels(x$condition) | ||
| G1=groups[1] | ||
| g1_samples=(x$condition==G1) | ||
| ng1=max(1,floor(length(g1_samples[g1_samples==TRUE])*MINSAMPLES)) | ||
| CPM_CUTOFF=MINCOUNT | ||
| mydata1=mydata[,g1_samples] | ||
| k_g1=rowSums(cpm(mydata1)>CPM_CUTOFF)>=ng1 | ||
| k=k_g1 | ||
| table(k) | ||
|
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||
| for(i in seq(2,length(levels(x$condition)))){ | ||
| Gi=groups[i] | ||
| gi_samples=(x$condition==Gi) | ||
| ngi=max(1,floor(length(gi_samples[gi_samples==TRUE])*MINSAMPLES)) | ||
| mydatai=mydata[,gi_samples] | ||
| k_gi=rowSums(cpm(mydatai)>CPM_CUTOFF)>=ngi | ||
| k=k|k_gi | ||
| print(table(k)) | ||
| } | ||
|
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| res=mydata[k,] | ||
| res2=res | ||
| res2$symbol=rownames(res2) | ||
| res2=res2 %>% select('symbol',everything()) | ||
| write.table(res2,file="RawCountFile_RSEM_genes_filtered.txt",row.names = F,quote = F,sep="\t") | ||
| # png("RSEM_HistBeforenormFilter.png") | ||
| # df.m <- melt(as.data.frame(res)) | ||
| # print(ggplot(df.m) + geom_density(aes(x = value, colour = variable)) + labs(x = NULL) + theme(legend.position='top') + scale_x_log10()) | ||
| # dev.off() | ||
| y = DGEList(counts=res) | ||
| ## Normalization TMM ------------------------------------------------------------ | ||
| ## method = =c("TMM","RLE","upperquartile","none") | ||
| y <- calcNormFactors(y,method="TMM") | ||
| ndata= cpm(y,log=FALSE,normalized.lib.sizes=TRUE) | ||
| ## save it | ||
| write.table(ndata,file="RSEM_CPM_TMM_counts.txt",sep="\t",col.names=NA,quote=F) | ||
| writegzfile(ndata,"RSEM_CPM_TMM_counts.txt.gz") | ||
| ## unfiltered normalization | ||
| y2 = DGEList(counts=mydata) | ||
| y2 <- calcNormFactors(y2,method="TMM") | ||
| ndata2= cpm(y2,log=FALSE,normalized.lib.sizes=TRUE) | ||
| ## save it | ||
| write.table(ndata2,file="RSEM_CPM_TMM_unfiltered_counts.txt",sep="\t",col.names=NA) | ||
|
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| writegzfile(ndata2,"RSEM_CPM_TMM_unfiltered_counts.txt.gz") | ||
| rlogres=rlog(as.matrix(res),blind=TRUE) | ||
| rownames(rlogres)=rownames(res) | ||
| writegzfile(rlogres,"RSEM_rlog_counts.txt.gz") |
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