diff edgeR.pl @ 0:91ca33096034 draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/edgeR.pl	Tue Jan 13 21:12:26 2015 -0500
@@ -0,0 +1,389 @@
+#!/bin/perl
+
+#EdgeR.pl Version 0.0.3
+#Contributors: Monica Britton, Blythe Durbin-Johnson, Joseph Fass, Nikhil Joshi, Alex Mawla
+
+use strict;
+use warnings;
+use Getopt::Std;
+use File::Basename;
+use File::Path qw(make_path remove_tree);
+
+$| = 1;
+
+my %OPTIONS = (a => "glm",  d => "tag", f => "BH", r => 5, u => "movingave");
+
+getopts('a:d:e:f:h:lmn:o:r:tu:', \%OPTIONS);
+
+ 
+die qq(
+Usage:   edgeR.pl [OPTIONS] factor::factor1::levels [factor::factor2::levels ...] cp::cont_pred1::values [cp::cont_pred2::values ...] cnt::contrast1 [cnt::contrast2] matrix
+
+OPTIONS:	-a	STR	Type Of Analysis [glm, pw, limma] (default: $OPTIONS{a})
+			-d	STR	The dispersion estimate to use for GLM analysis [tag] (default: $OPTIONS{d})
+			-e	STR	Path to place additional output files
+			-f	STR	False discovery rate adjustment method [BH] (default: $OPTIONS{f})
+			-h	STR	Name of html file for additional files
+			-l		Output the normalised digital gene expression matrix in log2 format (only applicable when using limma and -n is also specified)
+			-m		Perform all pairwise comparisons
+			-n	STR	File name to output the normalised digital gene expression matrix (only applicable when usinf glm or limma model)
+			-o	STR	File name to output csv file with results
+			-r	INT	Common Dispersion Rowsum Filter, ony applicable when 1 factor analysis selected (default: $OPTIONS{r})
+			-t		Estimate Tagwise Disp when performing 1 factor analysis
+			-u	STR	Method for allowing the prior distribution for the dispersion to be abundance- dependent ["movingave"] (default: $OPTIONS{u})
+ 
+) if(!@ARGV);
+
+my $matrix = pop @ARGV;
+
+make_path($OPTIONS{e});
+open(Rcmd,">$OPTIONS{e}/r_script.R") or die "Cannot open $OPTIONS{e}/r_script.R\n\n";
+print Rcmd "
+zz <- file(\"$OPTIONS{e}/r_script.err\", open=\"wt\")
+sink(zz)
+sink(zz, type=\"message\")
+
+library(edgeR)
+library(limma)
+
+toc <- read.table(\"$matrix\", sep=\"\\t\", comment=\"\", as.is=T)
+groups <- sapply(toc[1, -1], strsplit, \":\")
+for(i in 1:length(groups)) { g <- make.names(groups[[i]][2]); names(groups)[i] <- g; groups[[i]] <- groups[[i]][-2] }
+colnames(toc) <- make.names(toc[2,])
+toc[,1] <- gsub(\",\", \".\", toc[,1])
+tagnames <- toc[-(1:2), 1]
+rownames(toc) <- toc[,1]
+toc <- toc[-(1:2), -1]
+for(i in colnames(toc)) toc[, i] <- as.numeric(toc[,i])
+norm_factors <- calcNormFactors(as.matrix(toc))
+
+pw_tests <- list()
+uniq_groups <- unique(names(groups))
+for(i in 1:(length(uniq_groups)-1)) for(j in (i+1):length(uniq_groups)) pw_tests[[length(pw_tests)+1]] <- c(uniq_groups[i], uniq_groups[j])
+DGE <- DGEList(toc, lib.size=norm_factors*colSums(toc), group=names(groups))
+pdf(\"$OPTIONS{e}/MA_plots_normalisation.pdf\", width=14)
+for(i in 1:length(pw_tests)) {
+	j <- c(which(names(groups) == pw_tests[[i]][1])[1], which(names(groups) == pw_tests[[i]][2])[1])
+	par(mfrow = c(1, 2))
+	maPlot(toc[, j[1]], toc[, j[2]], normalize = TRUE, pch = 19, cex = 0.2, ylim = c(-10, 10), main=paste(\"MA Plot\", colnames(toc)[j[1]], \"vs\", colnames(toc)[j[2]]))
+	grid(col = \"blue\")
+	abline(h = log2(norm_factors[j[2]]), col = \"red\", lwd = 4)
+	maPlot(DGE\$counts[, j[1]]/DGE\$samples\$lib.size[j[1]], DGE\$counts[, j[2]]/DGE\$samples\$lib.size[j[2]], normalize = FALSE, pch = 19, cex = 0.2, ylim = c(-8, 8), main=paste(\"MA Plot\", colnames(toc)[j[1]], \"vs\", colnames(toc)[j[2]], \"Normalised\"))
+	grid(col = \"blue\")
+}
+dev.off()
+pdf(file=\"$OPTIONS{e}/MDSplot.pdf\")
+plotMDS(DGE, main=\"MDS Plot\", col=as.numeric(factor(names(groups)))+1, xlim=c(-3,3))
+dev.off()
+tested <- list()
+";
+
+my $all_cont;
+my @add_cont;
+my @fact;
+my @fact_names;
+my @cp;
+my @cp_names;
+if(@ARGV) {
+	foreach my $input (@ARGV) {
+		my @tmp = split "::", $input;
+		if($tmp[0] eq "factor") {
+			$tmp[1] =~ s/[ \?\(\)\[\]\/\\=+<>:;\"\',\*\^\|\&-]/./g;
+			push @fact_names, $tmp[1];
+			$tmp[2] =~ s/:/\", \"/g;
+			$tmp[2] = "\"".$tmp[2]."\"";
+			push @fact, $tmp[2];
+		} elsif($tmp[0] eq "cp") {
+			$tmp[1] =~ s/[ \?\(\)\[\]\/\\=+<>:;\"\',\*\^\|\&-]/./g;
+			push @cp_names, $tmp[1];
+			$tmp[2] =~ s/:/, /g;
+			push @cp, $tmp[2];
+		} elsif($tmp[0] eq "cnt") {
+			push @add_cont, $tmp[1];
+		} else {
+			die("Unknown Input: $input\n");
+		}
+	}
+}
+
+if($OPTIONS{a} eq "pw") {
+	print Rcmd "
+disp <- estimateCommonDisp(DGE, rowsum.filter=$OPTIONS{r})
+";
+	if(defined $OPTIONS{t}) {
+		print Rcmd "
+disp <- estimateTrendedDisp (disp)
+disp <- estimateTagwiseDisp(disp, trend=\"$OPTIONS{u}\")
+pdf(file=\"$OPTIONS{e}/Tagwise_Dispersion_vs_Abundance.pdf\")
+plotBCV(disp, cex=0.4)
+abline(h=disp\$common.dispersion, col=\"firebrick\", lwd=3)
+dev.off()
+";
+	}
+	print Rcmd "
+for(i in 1:length(pw_tests)) {
+	tested[[i]] <- exactTest(disp, pair=pw_tests[[i]])
+	names(tested)[i] <- paste(pw_tests[[i]][2], \"-\", pw_tests[[i]][1], sep=\"\")
+}
+pdf(file=\"$OPTIONS{e}/Smear_Plots.pdf\")
+for(i in 1:length(pw_tests)) {
+	dt <- decideTestsDGE(tested[[i]], p.value=0.05, adjust.method=\"$OPTIONS{f}\")
+	if(sum(dt) > 0) {
+		de_tags <- rownames(disp)[which(dt != 0)]
+		ttl <- \"Diff. Exp. Genes With adj. Pvalue < 0.05\"
+	} else {
+		de_tags <- rownames(topTags(tested[[i]], n=100)\$table)
+		ttl <- \"Top 100 tags\"
+	}
+
+	if(length(dt) < 5000) {
+		pointcex = 0.5
+	} else {
+		pointcex = 0.2
+	}
+	plotSmear(disp, pair=pw_tests[[i]], de.tags = de_tags, main = paste(\"Smear Plot\", names(tested)[i]), cex=0.5)
+	abline(h = c(-1, 1), col = \"blue\")
+	legend(\"topright\", c(\"2 Fold Change\", ttl) , lty=c(1, NA), pch=c(NA, 19), pt.cex=0.5, col=c(\"blue\", \"red\"), bty=\"n\")
+}
+dev.off()
+"; 
+} 
+elsif($OPTIONS{a} eq "glm") {
+	for(my $fct = 0; $fct <= $#fact_names; $fct++) {
+		print Rcmd "
+			$fact_names[$fct] <- c($fact[$fct])
+		";
+	}
+	for(my $fct = 0; $fct <= $#cp_names; $fct++) {
+		print Rcmd "
+			$cp_names[$fct] <- c($cp[$fct])
+		";
+	}
+	my $all_fact = "";
+	if(@fact_names) {
+		foreach (@fact_names) {
+			$all_fact .= " + factor($_)";
+		}
+    	}
+	my $all_cp = "";
+	if(@cp_names) {
+		$all_cp = " + ".join(" + ", @cp_names);
+	}
+	print Rcmd "
+		group_fact <- factor(names(groups))
+		design <- model.matrix(~ -1 + group_fact${all_fact}${all_cp})
+		colnames(design) <- sub(\"group_fact\", \"\", colnames(design))
+	";
+	foreach my $fct (@fact_names) {
+		print Rcmd "
+			colnames(design) <- make.names(sub(\"factor.$fct.\", \"\", colnames(design)))
+		";
+	}
+	if($OPTIONS{d} eq "tag") {
+		print Rcmd "
+			disp <- estimateGLMCommonDisp(DGE, design)
+			disp <- estimateGLMTrendedDisp(disp, design)
+			disp <- estimateGLMTagwiseDisp(disp, design)
+			fit <- glmFit(disp, design)
+			pdf(file=\"$OPTIONS{e}/Tagwise_Dispersion_vs_Abundance.pdf\")
+			plotBCV(disp, cex=0.4)
+			dev.off()
+		";
+	}
+	if(@add_cont) {
+		$all_cont = "\"".join("\", \"", @add_cont)."\"";
+		print Rcmd "
+			cont <- c(${all_cont})
+			for(i in uniq_groups)  cont <- gsub(paste(groups[[i]], \"([^0-9])\", sep=\"\"), paste(i, \"\\\\1\", sep=\"\"), cont)
+			for(i in uniq_groups)  cont <- gsub(paste(groups[[i]], \"\$\", sep=\"\"), i, cont)
+";
+	} else {
+		print Rcmd "
+cont <- NULL
+";
+	}
+	if(defined $OPTIONS{m}) {
+		print Rcmd "
+for(i in 1:length(pw_tests)) cont <- c(cont, paste(pw_tests[[i]][2], \"-\", pw_tests[[i]][1], sep=\"\"))
+";
+	}
+	if(!defined $OPTIONS{m} && !@add_cont){
+		die("No Contrasts have been specified, you must at least either select multiple pairwise comparisons or specify a custom contrast\n");
+	}
+	print Rcmd "
+fit <- glmFit(disp, design)
+cont <- makeContrasts(contrasts=cont, levels=design)
+for(i in colnames(cont)) tested[[i]] <- glmLRT(fit, contrast=cont[,i])
+pdf(file=\"$OPTIONS{e}/Smear_Plots.pdf\")
+for(i in colnames(cont)) {
+        dt <- decideTestsDGE(tested[[i]], p.value=0.05, adjust.method=\"$OPTIONS{f}\")
+        if(sum(dt) > 0) {
+                de_tags <- rownames(disp)[which(dt != 0)]
+                ttl <- \"Diff. Exp. Genes With adj. Pvalue < 0.05\"
+        } else {
+                de_tags <- rownames(topTags(tested[[i]], n=100)\$table)
+                ttl <- \"Top 100 tags\"
+        }
+
+        if(length(dt) < 5000) {
+                pointcex = 0.5
+        } else {
+                pointcex = 0.2
+        }
+        plotSmear(disp, de.tags = de_tags, main = paste(\"Smear Plot\", i), cex=pointcex)
+        abline(h = c(-1, 1), col = \"blue\")
+        legend(\"topright\", c(\"2 Fold Change\", ttl) , lty=c(1, NA), pch=c(NA, 19), pt.cex=0.5, col=c(\"blue\", \"red\"), bty=\"n\")
+}
+dev.off()
+
+	";
+	if(defined $OPTIONS{n}) {
+		print Rcmd "
+			tab <- data.frame(ID=rownames(fit\$fitted.values), fit\$fitted.values, stringsAsFactors=F)
+			write.table(tab, \"$OPTIONS{n}\", quote=F, sep=\"\\t\", row.names=F)
+		";
+	}
+} elsif($OPTIONS{a} eq "limma") {
+	for(my $fct = 0; $fct <= $#fact_names; $fct++) {
+		print Rcmd "
+$fact_names[$fct] <- c($fact[$fct])
+";
+	}
+	for(my $fct = 0; $fct <= $#cp_names; $fct++) {
+		print Rcmd "
+$cp_names[$fct] <- c($cp[$fct])
+";
+	}
+	my $all_fact = "";
+	if(@fact_names) {
+		foreach (@fact_names) {
+			$all_fact .= " + factor($_)";
+		}
+	}
+	my $all_cp = "";
+	if(@cp_names) {
+		$all_cp = " + ".join(" + ", @cp_names);
+	}
+	print Rcmd "
+
+group_fact <- factor(names(groups))
+design <- model.matrix(~ -1 + group_fact${all_fact}${all_cp})
+colnames(design) <- sub(\"group_fact\", \"\", colnames(design))
+";
+	foreach my $fct (@fact_names) {
+		print Rcmd "
+colnames(design) <- make.names(sub(\"factor.$fct.\", \"\", colnames(design)))
+";
+	}
+	print Rcmd "
+isexpr <- rowSums(cpm(toc)>1) >= 1
+toc <- toc[isexpr, ]
+pdf(file=\"$OPTIONS{e}/LIMMA_voom.pdf\")
+y <- voom(toc, design, plot=TRUE, lib.size=colSums(toc)*norm_factors)
+dev.off()
+
+pdf(file=\"$OPTIONS{e}/LIMMA_MDS_plot.pdf\")
+plotMDS(y, labels=colnames(toc), col=as.numeric(factor(names(groups)))+1, gene.selection=\"common\")
+dev.off()
+fit <- lmFit(y, design)
+";
+	if(defined $OPTIONS{n}) {
+		if(defined $OPTIONS{l}) {
+			print Rcmd "
+tab <- data.frame(ID=rownames(y\$E), y\$E, stringsAsFactors=F)
+";
+		} else {
+			print Rcmd "
+tab <- data.frame(ID=rownames(y\$E), 2^y\$E, stringsAsFactors=F)
+";
+		}
+		print Rcmd "
+write.table(tab, \"$OPTIONS{n}\", quote=F, sep=\"\\t\", row.names=F)
+";
+	}
+	if(@add_cont) {
+		$all_cont = "\"".join("\", \"", @add_cont)."\"";
+		print Rcmd "
+cont <- c(${all_cont})
+for(i in uniq_groups)  cont <- gsub(paste(groups[[i]], \"([^0-9])\", sep=\"\"), paste(i, \"\\\\1\", sep=\"\"), cont)
+for(i in uniq_groups)  cont <- gsub(paste(groups[[i]], \"\$\", sep=\"\"), i, cont)
+";
+	} else {
+		print Rcmd "
+cont <- NULL
+";
+	}
+	if(defined $OPTIONS{m}) {
+		print Rcmd "
+for(i in 1:length(pw_tests)) cont <- c(cont, paste(pw_tests[[i]][2], \"-\", pw_tests[[i]][1], sep=\"\"))
+";
+	}
+	if(!defined $OPTIONS{m} && !@add_cont){
+		die("No Contrasts have been specified, you must at least either select multiple pairwise comparisons or specify a custom contrast\n");
+	}
+	print Rcmd "
+cont <- makeContrasts(contrasts=cont, levels=design)
+fit2 <- contrasts.fit(fit, cont)
+fit2 <- eBayes(fit2)
+";
+} else {
+	die("Anaysis type $OPTIONS{a} not found\n");
+
+}
+if($OPTIONS{a} ne "limma") {
+	print Rcmd "
+options(digits = 6)
+tab <- NULL
+for(i in names(tested)) {
+	tab_tmp <- topTags(tested[[i]], n=Inf, adjust.method=\"$OPTIONS{f}\")[[1]]
+	colnames(tab_tmp) <- paste(i, colnames(tab_tmp), sep=\":\")
+	tab_tmp <- tab_tmp[tagnames,]
+	if(is.null(tab)) {
+		tab <- tab_tmp
+	} else tab <- cbind(tab, tab_tmp)
+}
+tab <- cbind(Feature=rownames(tab), tab)
+";
+} else {
+	print Rcmd "
+tab <- NULL
+options(digits = 6)
+for(i in colnames(fit2)) {
+	tab_tmp <- topTable(fit2, coef=i, n=Inf, sort.by=\"none\", adjust.method=\"$OPTIONS{f}\")
+	colnames(tab_tmp)[-1] <- paste(i, colnames(tab_tmp)[-1], sep=\":\")
+	if(is.null(tab)) {
+		tab <- tab_tmp
+	} else tab <- cbind(tab, tab_tmp[,-1])
+}
+";
+}
+print Rcmd "
+write.table(tab, \"$OPTIONS{o}\", quote=F, sep=\"\\t\", row.names=F)
+sink(type=\"message\")
+sink()
+";
+close(Rcmd);
+system("R --no-restore --no-save --no-readline < $OPTIONS{e}/r_script.R > $OPTIONS{e}/r_script.out");
+
+open(HTML, ">$OPTIONS{h}");
+print HTML "<html><head><title>EdgeR: Empirical analysis of digital gene expression data</title></head><body><h3>EdgeR Additional Files:</h3><p><ul>\n";
+print HTML "<li><a href=MA_plots_normalisation.pdf>MA_plots_normalisation.pdf</a></li>\n";
+print HTML "<li><a href=MDSplot.pdf>MDSplot.pdf</a></li>\n";
+if($OPTIONS{a} eq "pw") {
+	if(defined $OPTIONS{t}) {
+		print HTML "<li><a href=Tagwise_Dispersion_vs_Abundance.pdf>Tagwise_Dispersion_vs_Abundance.pdf</a></li>\n";
+	}
+	print HTML "<li><a href=Smear_Plots.pdf>Smear_Plots.pdf</a></li>\n";
+} elsif($OPTIONS{a} eq "glm" && $OPTIONS{d} eq "tag") {
+	print HTML "<li><a href=Tagwise_Dispersion_vs_Abundance.pdf>Tagwise_Dispersion_vs_Abundance.pdf</a></li>\n";
+	print HTML "<li><a href=Smear_Plots.pdf>Smear_Plots.pdf</a></li>\n";
+} elsif($OPTIONS{a} eq "limma") {
+	print HTML "<li><a href=LIMMA_MDS_plot.pdf>LIMMA_MDS_plot.pdf</a></li>\n";
+	print HTML "<li><a href=LIMMA_voom.pdf>LIMMA_voom.pdf</a></li>\n";
+}
+print HTML "<li><a href=r_script.R>r_script.R</a></li>\n";
+print HTML "<li><a href=r_script.out>r_script.out</a></li>\n";
+print HTML "<li><a href=r_script.err>r_script.err</a></li>\n";
+print HTML "</ul></p>\n";
+close(HTML);
+