# HG changeset patch # User fcaramia # Date 1347507885 14400 # Node ID 674c75219f15fdb1696ba52fd1cf8f42bdfa0027 # Parent 457a02a69f4d2c966dd9cd2f39b85caa9554ee14 Uploaded diff -r 457a02a69f4d -r 674c75219f15 edgeR.pl --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/edgeR.pl Wed Sep 12 23:44:45 2012 -0400 @@ -0,0 +1,364 @@ +#/bin/perl + +use strict; +use warnings; +use Getopt::Std; +use File::Basename; +use File::Path qw(make_path remove_tree); +$| = 1; + +# Grab and set all options +my %OPTIONS = (a => "glm", d => "tag", f => "BH", p => 0.3, r => 5, u => "movingave"); + +getopts('a:d:e:f:h:lmn:o:p: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, trend, common] (default: $OPTIONS{d}) + -e STR Path to place additional output files + -f STR False discovery rate adjustment method [BH, holm, hochberg, hommel, BY, none] (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 + -p FLT The proportion of all tags/genes to be used for the locally weighted estimation of the tagwise dispersion, ony applicable when 1 factor analysis selected (default: $OPTIONS{p}) + -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", "tricube", "none"] (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) + + # read in matrix and groups + 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 $OPTIONS{t}) { + print Rcmd " + disp <- estimateTagwiseDisp(disp, trend=\"$OPTIONS{u}\", prop.used=$OPTIONS{p}) + pdf(file=\"$OPTIONS{e}/Tagwise_Dispersion_vs_Abundance.pdf\") + plot(log2(1e06*disp\$conc\$conc.common), disp\$tagwise.dispersion, xlab=\"Counts per million (log2 scale)\", ylab=\"Tagwise dispersion\") + 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=\"$OPTIONS{e}/Smear_Plots.pdf\") + for(i in 1:length(pw_tests)) { + if(nrow(decideTestsDGE(tested[[i]] , p.value=0.05)) > 0) { + de_tags <- rownames(decideTestsDGE(tested[[i]] , p.value=0.05, adjust.method=\"$OPTIONS{f}\")) + ttl <- \"(Diff. Exp. Genes With adj. Pvalue < 0.05 highlighted)\" + } else { + de_tags <- rownames(topTags(tested[[i]], n=100)\$table) + ttl <- \"(Top 100 tags highlighted)\" + } + + plotSmear(disp, pair=pw_tests[[i]], de.tags = de_tags, main = paste(\"FC plot\", ttl)) + abline(h = c(-2, 2), col = \"dodgerblue\") + } + 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))) + "; + } + print Rcmd " + disp <- estimateGLMCommonDisp(DGE, design) + "; + if($OPTIONS{d} eq "tag" || $OPTIONS{d} eq "trend") { + print Rcmd " + disp <- estimateGLMTrendedDisp(disp, design) + "; + } + if($OPTIONS{d} eq "tag") { + print Rcmd " + disp <- estimateGLMTagwiseDisp(disp, design) + fit <- glmFit(disp, design) + pdf(file=\"$OPTIONS{e}/Tagwise_Dispersion_vs_Abundance.pdf\") + plot(fit\$abund+log(1e06), sqrt(disp\$tagwise.dispersion), xlab=\"Counts per million (log2 scale)\", ylab=\"Tagwise dispersion\") + oo <- order(disp\$abundance) + lines(fit\$abundance[oo]+log(1e06), sqrt(disp\$trended.dispersion[oo]), col=\"dodgerblue\", lwd=3) + abline(h=sqrt(disp\$common.dispersion), col=\"firebrick\", lwd=3) + 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(disp, fit, contrast=cont[,i]) + "; + 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) >= 2 + 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 "EdgeR: Empirical analysis of digital gene expression data

EdgeR Additional Files:

\n"; +close(HTML); +