--- /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 "<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" && $OPTIONS{d} eq "tag") {
+	print HTML "<li><a href=Tagwise_Dispersion_vs_Abundance.pdf>Tagwise_Dispersion_vs_Abundance.pdf</a></li>\n";
+} elsif($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);
+