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DESeq2.pl
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#!/usr/bin/perl -w
use strict;
#use File::Slurp;
#use Cwd;
use Data::Dumper;
use Data::Dump;
my $largest = 0;
my $contig = '';
if (@ARGV < 1) {
print "\n\nUsage: DESeq2.pl merged.reads my.design <included columns> <prefix> <design> <file with genes> \n\n" ;
print " mz3 script for taking eXpress_read_counts and turning them into a DESeq2 matrix \n\n";
print "Suitable for interaction terms\n\n";
print "Verify the re-leveling strategy!!!\n";
exit ;
}
push(@ARGV, " ");
my $file = shift;
my $des = shift;
my $incl = shift;
my $pre = shift;
my $de=shift;
my $genes = shift;
# open the file
open (IN, "$file") || die "I can't open $file\n";
open (IN2, "$des") || die "I can't open $des\n";
my @des = <IN2>;
# make a choice of subset rows and columns
my @inc = split(/\,/, $incl);
unshift @inc, "0";
my @design;
my $first=0;
=pod
# Fix the first line of the input
foreach my $ne (@des) {
chomp $ne;
if ($first<1) {
$ne=~s/ID\t//;
#$ne=$ne . "\tADDED";
$first++;
#print ":$ne:\n";
}
$ne = $ne . "\n";
}
=cut
# Remove the rows not used from the design
foreach my $ele (@inc) {
chomp $ele;
my $ne = $des[$ele];
$ne=~s/^\t//;
$ne=~s/^\s+//g;
push (@design, "$ne" );
print "$ele\t$ne\n";
}
# make the same choice for genes
my @submat;
while (<IN>) {
chomp;
my @arr = split(/\s+/, $_);
my @new_arr;
#print "@new_arr\n";
foreach my $ele (@inc) {
#$ele--;
if (exists $arr[$ele]) {
push (@new_arr, $arr[$ele] );
}
}
if (scalar(@new_arr)>0) {
my $new = join("\t", @new_arr);
#print "$new\n";
push(@submat, "$new\n");
}
}
# write outfiles
open (OUT, ">$pre.tab") || die "I can't open $pre.tab\n";
open (OUT2, ">$pre.design") || die "I can't open $pre.design\n";
#print "@submat\n";
foreach my $l (@design) {
print OUT2 "$l";
}
foreach my $l (@submat) {
print OUT "$l";
}
close(OUT2);
close(OUT);
=pod
# check if there are any genes we should check
my @genes;
if (-s "$genes") {
open (GEN, "<$genes") || die "I can't open $genes\n";
while (<GEN>) {
chomp;
push(@genes, $_);
}
}
=cut
# check the experimental design
my @desi1 = split(/[\*,\+,\:]/, $de);
$desi1[0]=~s/~//;
# now do DESeq2 commandlines
# this bit is same for all
open (R, ">$pre.R") || die "I can't open $pre.R\n";
print R "\n";
print R "\n";
print R "library(DESeq2)\n";
print R "CountTable = read.table( \"$pre.tab\", header=TRUE, row.names=1 )\n";
print R "design = read.table( \"$pre.design\", header=TRUE, row.names=1 )\ndesign\n";
print R "dds<-DESeqDataSetFromMatrix(countData= CountTable,colData= design,design=$de)\n";
print R "## releveling\n";
print R "try(dds\$Trans <- relevel(dds\$Trans, \"normal\"))\n";
print R "try(dds\$Cell <- relevel(dds\$Cell, \"LSK\"))\n";
print R "try(dds\$Hox <- relevel(dds\$Hox, \"hasHox\"))\n";
print R "try(dds\$BCat <- relevel(dds\$BCat, \"WT\"))\n";
print R "try(dds\$Biol <- relevel(dds\$Biol, \"A\"))\n";
print R "try(dds\$Virus <- relevel(dds\$Virus, \"Puro\"))\n";
print R "try(dds\$Treatment <- relevel(dds\$Treatment, \"WT\"))\n";
print R "try(dds\$Treatment <- relevel(dds\$Treatment, \"shKDM5B\"))\n";
print R "try(dds\$Treatment <- relevel(dds\$Treatment, \"shKDM5A\"))\n";
print R "try(dds\$Treatment <- relevel(dds\$Treatment, \"pLKO1\"))\n";
print R "try(dds\$BMI1 <- relevel(dds\$BMI1, \"WT\"))\n";
print R "try(dds\$Ezh2 <- relevel(dds\$Ezh2, \"WT\"))\n";
print R "\n";
#write output
print R "rld<-rlog(dds) # do blind=TRUE # not informed by design\n";
print R "vsd<-varianceStabilizingTransformation(dds) # do blind=TRUE # not informed by design\n";
print R "rlogMat<-assay(rld)\n";
print R "vstMat<-assay(vsd)\n";
print R "write.table(as.data.frame(rlogMat),file=\"$pre.rlogMat.txt\", sep=\"\\t\")\n";
print R "write.table(as.data.frame(vstMat),file=\"$pre.vstMat.txt\", sep=\"\\t\")\n";
print R "pdf(file=\"$pre.all.PCA.pdf\", useDingbats=FALSE)\n";
$de=~s/\~//g;
my @de2 = split(/[\*\+]/,$de);
print R "plotPCA(vsd,intgroup=c(\"$de2[0]\"))\n";
print R "dev.off()\n\n";
if ( scalar(@desi1) <2 ) {
# this bit needs to be repeated for all interactions
print R "# Starting to test interactions\n";
print R "dds <-DESeq(dds)\n";
print R "read.counts <- counts(dds, normalized=TRUE)\n";
print R "write.table(as.data.frame(read.counts),file=\"$pre.normread.txt\", sep=\"\t\")\n";
print R "resultsNames(dds)\n";
#print R "res <-results(dds, addMLE=TRUE )\n";
print R "res <-results(dds)\n";
print R "mcols(res, use.names=TRUE)\n";
print R "summary(res)\n";
print R "resOrdered<-res[order(res\$padj),]\n";
print R "write.table(as.data.frame(resOrdered),file=\"$pre.dat\", sep=\"\\t\")\n";
#&pathway("$pre");
print R "pdf(file=\"$pre.MA.pdf\", useDingbats=FALSE)\n";
print R "plotMA(res,main=\"DESeq2\",ylim=c(-2,2))\n";
print R "\ndev.off()\n";
print R "resMLE<-results(dds,addMLE=TRUE)\n";
# check specific genes
#print R "pdf(file=\"$pre.43630.pdf\", useDingbats=FALSE)\n";
#print R "plotCounts(dds,gene=43630,intgroup=\"Trans\")\n";
#print R "dev.off()\n\n";
#print R "res[362,]\n";
print R "matrix1 <- model.matrix(~$de, colData(dds))\n";
print R "matrix1\n";
print R "write.table(as.data.frame(matrix1),file=\"$pre.matrix.txt\", sep=\"\\t\")\n";
# do plots
print R "pdf(file=\"$pre.normalisation.pdf\", useDingbats=FALSE)\n";
print R '
par( mfrow = c( 1, 2 ) )
plot( log2( 1 + counts(dds, normalized=TRUE)[ , 1:2] ), col=rgb(0,0,0,.2), pch=16, cex=0.3 )
plot( assay(rld)[ , 1:2], col=rgb(0,0,0,.2), pch=16, cex=0.3 )
';
print R "dev.off()\n\n";
print R "attr(dds,\"betaPriorVar\")\n";
print R "dispersionFunction(dds)\n";
print R "attr(dispersionFunction(dds),\"dispPriorVar\")\n";
print "Now starting R... \nR CMD BATCH $pre.R $pre.Rout\n\n";
system "R CMD BATCH $pre.R $pre.Rout"; wait;
}
else {
my @desi2= ("$desi1[0]","$desi1[1]","$desi1[0]:$desi1[1]");
push(@desi1, "$desi1[0]:$desi1[1]" );
my $i1=2;
my $i2=2;
foreach my $el (@desi1) {
#print "DESI1 $el\n";
}
foreach my $el (@desi2) {
#print "DESI2 $el\n";
}
print R "dds <-DESeq(dds, betaPrior=FALSE)\n";
print R "read.counts <- counts(dds, normalized=TRUE)\n";
print R "write.table(as.data.frame(read.counts),file=\"$pre.normread.txt\", sep=\"\\t\")\n";
print R "# Starting to test interactions\n";
foreach my $el (@desi1) {
$i2=2;
foreach my $el2 (@desi2) {
# this bit needs to be repeated for all interactions
if ($i1==$i2) {
$i2++;
next;
}
print R "\n\n# Starting to test interaction $el $el2\n";
print R "resultsNames(dds)\n";
print R "\n";
print R "res <- results(dds, contrast=list(c(resultsNames(dds)[$i1],resultsNames(dds)[$i2])), )\n";
print R "mcols(res, use.names=TRUE)\n";
print R "summary(res)\n";
print R "resOrdered<-res[order(res\$padj),]\n";
print R "write.table(as.data.frame(resOrdered),file=\"$pre.$el.$el2.dat\", sep=\"\\t\")\n";
#print "$i1 $i2 $el $el2\n";
# &pathway("$pre.$el.$el2");
# print R "write.table(as.data.frame(resOrdered),file=\"$pre.$i.$2.txt\", sep=\"\\t\")\n";
# #print R "head(resOrdered)\n";
print R "pdf(file=\"$pre.$el.$el2.MA.pdf\", useDingbats=FALSE)\n";
print R "plotMA(res,main=\"$pre.$el.$el2\",ylim=c(-2,2))\n";
print R "dev.off()\n\n";
print R "attr(dds,\"betaPriorVar\")\n";
print R "dispersionFunction(dds)\n";
print R "attr(dispersionFunction(dds),\"dispPriorVar\")\n";
$i2++;
}
# Now do the factor separately
print "name=resultsNames(dds)[$i1] \n";
print R "res <- results(dds, name=resultsNames(dds)[$i1] )\n";
print R "mcols(res, use.names=TRUE)\n";
print R "summary(res)\n";
print R "resOrdered<-res[order(res\$padj),]\n";
print R "write.table(as.data.frame(resOrdered),file=\"$pre.$el.int.dat\", sep=\"\\t\")\n";
# &pathway("$pre.$el.$el2");
# print R "write.table(as.data.frame(resOrdered),file=\"$pre.$i.$2.txt\", sep=\"\\t\")\n";
# #print R "head(resOrdered)\n";
print R "pdf(file=\"$pre.$el.MA.pdf\", useDingbats=FALSE)\n";
print R "plotMA(res,main=\"$pre.$el\",ylim=c(-2,2))\n";
print R "\ndev.off()\n";
print R "top500<-head(rownames(resOrdered), 500)\n";
print R "chose <- vsd[top500,]\n";
print R "pdf(file=\"$pre.$key.$key2.PCA.pdf\", useDingbats=FALSE)\n";
print R "plotPCA(chose,intgroup=c($desi))\n";
print R "dev.off()\n\n";
print R "resOrdered <- na.omit(resOrdered)\n";
print R "# Remove downregulated\n";
print R "resOrderedUp <- resOrdered[resOrdered\$log2FoldChange<0,]\n";
print R "lab <- design\$Sample\n";
print R "pdf(file=\"$pre.$key.$key2.barplot.up.pdf\", useDingbats=FALSE)\n";
print R "barplot(assay(vsd)[rownames(head(resOrderedUp,10)),], names.arg=lab, cex.names=0.8, las=2)\n";
print R "dev.off()\n\n";
print R "# Remove upregulated\n";
print R "resOrderedDown <- resOrdered[resOrdered\$log2FoldChange>0,]\n";
print R "lab <- design\$Sample\n";
print R "pdf(file=\"$pre.$key.$key2.barplot.down.pdf\", useDingbats=FALSE)\n";
print R "barplot(assay(vsd)[rownames(head(resOrderedDown,10)),], names.arg=lab, cex.names=0.8, las=2)\n";
print R "dev.off()\n\n";
print R "attr(dds,\"betaPriorVar\")\n";
print R "dispersionFunction(dds)\n";
print R "attr(dispersionFunction(dds),\"dispPriorVar\")\n\n";
$i1++;
}
#write output
print R "rld<-rlog(dds) # do blind=TRUE # not informed by design\n";
print R "vsd<-varianceStabilizingTransformation(dds) # do blind=TRUE # not informed by design\n";
print R "rlogMat<-assay(rld)\n";
print R "vstMat<-assay(vsd)\n";
print R "write.table(as.data.frame(rlogMat),file=\"$pre.rlogMat.txt\", sep=\"\\t\")\n";
print R "write.table(as.data.frame(vstMat),file=\"$pre.vstMat.txt\", sep=\"\\t\")\n";
# do plots
print R "\npdf(file=\"$pre.normalisation.pdf\", useDingbats=FALSE)\n";
print R '
par( mfrow = c( 1, 2 ) )
plot(log2( 1 + counts(dds, normalized=TRUE)[ , 1:2] ),
col=rgb(0,0,0,.2), pch=16, cex=0.3 )
plot( assay(rld)[ , 1:2],
col=rgb(0,0,0,.2), pch=16, cex=0.3 )
';
print R "dev.off()\n\n";
print R "\n";
print R "matrix1 <- model.matrix($de, colData(dds))\n";
print R "matrix1\n";
print R "write.table(as.data.frame(matrix1),file=\"$pre.matrix.txt\", sep=\"\\t\")\n";
}
close(R);
system "R CMD BATCH $pre.R $pre.Rout"; wait;
#print "With more complext interaction you have to run this script in interactive mode\nyour script is $pre.R\nR CMD BATCH $pre.R $pre.Rout\n\n";
exit;
#############################################################
sub pathway
{
my $PRE = $_[0];
print "PRE $PRE\n";
# print "Not a very interesting routine\n";
# print "This does the same thing every time\n";
print R " \n";
print R "####### PATHWAY ANALYSIS #######\n";
print R "require(gage)\n";
print R "require(pathview)\n";
# add pathway data
#print R "featureData = read.table( \"mart_export.Mouse_Ensembl2Entrez.txt\", header=TRUE, row.names=1 )\n";
#print R "mcols(dds)<-DataFrame(mcols(dds), featureData)\n";
#print R "resOrd <-data.frame(resOrdered)\n";
#print R "m3 <- merge(resOrd,featureData, by=0 )\n";
#print R "deseq2.fc=m3\$log2FoldChange\n";
#print R "names(deseq2.fc)=m3\$Entrez\n";
# get results
#print R "deseq2.res <- resOrdered\n";
print R "deseq2.fc=resOrdered\$log2FoldChange\n";
print R "names(deseq2.fc)=rownames(resOrdered)\n";
print R "exp.fc=deseq2.fc\n";
print R "out.suffix=\"$PRE\"\n";
print R "kegg.mouse <- kegg.gsets(species=\"mmu\", id.type=\"entrez\")\n";
print R "fc.kegg.p <- gage(exp.fc, gsets = kegg.mouse , ref = NULL, samp = NULL)\n";
print R "write.table(as.data.frame(fc.kegg.p),file=\"$PRE\.gage.txt\", sep=\"\\t\")\n";
print R 'sel.h <- fc.kegg.p$greater[, "q.val"] < 0.2 & !is.na(fc.kegg.p$greater[, "q.val"])';
print R " \n";
print R "path.ids.h <- rownames(fc.kegg.p\$greater)[sel.h]\n";
print R 'sel.l <- fc.kegg.p$less[, "q.val"] < 0.2 & !is.na(fc.kegg.p$less[,"q.val"])';
print R " \n";
print R 'path.ids.l <- rownames(fc.kegg.p$less)[sel.l]';
print R " \n";
print R "path.ids.all <- c(substr(c(path.ids.h, path.ids.l),1,5),\"05202\",\"04550\",\"04310\")\n";
print R "path.ids.all \n";
print R "pathpv.out.list <- sapply(path.ids.all, function(pid) pathview(gene.data = exp.fc, pathway.id = pid, out.suffix=\"$PRE.gage\", ";
print R 'species = "mmu", low = list(gene = "seagreen3", cpd = "blue"), mid = list(gene = "lightgoldenrod", cpd = "lightgoldenrod"), high = list(gene = "indianred1", cpd ="yellow"), limit=list(gene=20, cpd=20) ))';
print R " \n\n#SPIA\n";
print R "require(SPIA)\n";
print R "sig_genes1 <- subset(resOrdered, padj<0.01)\n";
print R "sig_genes <-sig_genes1\$log2FoldChange\n";
print R "names(sig_genes) <- rownames(sig_genes1)\n";
print R "all_genes <- rownames(dds)\n";
#print R "all_genes <- mcols(dds)\$Entrez\n";
#print R "all_genes <- as.vector(na.exclude(mcols(dds)\$Entrez))\n";
print R "spia_result <- spia(de=sig_genes, all=all_genes, organism=\"mmu\", plots=FALSE)\n";
print R "spia_result\n";
print R "write.table(as.data.frame(spia_result),file=\"$PRE.spia.txt\", sep=\"\\t\")\n";
print R "try(plotP(spia_result, threshold=0.01)) \n";
print R "sign.spia <- subset(spia_result, pGFdr<0.01) \n";
#print R "sign.spia$ID \n";
print R "pathpv.out.list <- sapply(sign.spia\$ID , function(pid) pathview(gene.data = exp.fc, pathway.id = pid, out.suffix=\"$PRE.spia\", ";
print R 'species = "mmu", low = list(gene = "seagreen3", cpd = "blue"), mid = list(gene = "lightgoldenrod", cpd = "lightgoldenrod"), high = list(gene = "indianred1", cpd ="yellow"), limit=list(gene=20, cpd=20) ))';
print R "\n####### END PATHWAY ANALYSIS #######\n\n";
}