--- a/edgeR.pl	Thu Dec 26 05:36:07 2013 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,391 +0,0 @@
-#/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", 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, 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
-			-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}\")
-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)))
-";
-	}
-	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\")
-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) >= 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 "<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 "glm" && ($OPTIONS{d} eq "trend" || $OPTIONS{d} eq "common")) {
-	print HTML "<li><a href=Smear_Plots.pdf>Smear_Plots.pdf</a></li>\n";
-} elsif($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);
-