# HG changeset patch # User amawla # Date 1440456649 14400 # Node ID a8a56766694ec8ab59a489d1529a7505d2bb7111 # Parent 3fb55f96f065248ffb7212132447eaf1dd4e5d5a Uploaded diff -r 3fb55f96f065 -r a8a56766694e edgeR.pl --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/edgeR.pl Mon Aug 24 18:50:49 2015 -0400 @@ -0,0 +1,390 @@ +#!/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) +} +tab <- cbind(Feature=rownames(tab), tab) +"; +} +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 "EdgeR: Empirical analysis of digital gene expression data

EdgeR Additional Files:

\n"; +close(HTML); +