# HG changeset patch # User fcaramia # Date 1377137583 14400 # Node ID e5fcbabbdea7a2a0163332f537a468d7c920a5d5 # Parent 86292c2b0ba9c0b190b02fa2229fce465d0950f2 Uploaded diff -r 86292c2b0ba9 -r e5fcbabbdea7 edgeR.pl --- a/edgeR.pl Tue May 14 21:40:04 2013 -0400 +++ b/edgeR.pl Wed Aug 21 22:13:03 2013 -0400 @@ -8,9 +8,9 @@ $| = 1; # Grab and set all options -my %OPTIONS = (a => "glm", d => "tag", f => "BH", p => 0.3, r => 5, u => "movingave"); +my %OPTIONS = (a => "glm", d => "tag", f => "BH", r => 5, u => "movingave"); -getopts('a:d:e:f:h:lmn:o:p:r:tu:', \%OPTIONS); +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 @@ -24,7 +24,6 @@ -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}) @@ -34,46 +33,46 @@ my $matrix = pop @ARGV; make_path($OPTIONS{e}); -open(Rcmd,">$OPTIONS{e}/r_script.R") or die "Cannot open $OPTIONS{e}/r_script.R\n\n"; +open(Rcmd,">$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) +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)) +# 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=\"$OPTIONS{e}/MDSplot.pdf\") - plotMDS(DGE, main=\"MDS Plot\", col=as.numeric(factor(names(groups)))+1, xlim=c(-3,3)) - dev.off() - tested <- list() +pw_tests <- list() +uniq_groups <- unique(names(groups)) +for(i in 1:(length(uniq_groups)-1)) for(j in (i+1):length(uniq_groups)) pw_tests[[length(pw_tests)+1]] <- c(uniq_groups[i], uniq_groups[j]) +DGE <- DGEList(toc, lib.size=norm_factors*colSums(toc), group=names(groups)) +pdf(\"$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 $all_cont; @@ -106,47 +105,54 @@ if($OPTIONS{a} eq "pw") { print Rcmd " - disp <- estimateCommonDisp(DGE, rowsum.filter=$OPTIONS{r}) - "; +disp <- estimateCommonDisp(DGE, rowsum.filter=$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() - " +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=\"$OPTIONS{e}/Smear_Plots.pdf\") - for(i in 1:length(pw_tests)) { - if(nrow(decideTestsDGE(tested[[i]] , p.value=0.05)) > 0) { - de_tags <- rownames(decideTestsDGE(tested[[i]] , p.value=0.05, adjust.method=\"$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() - "; +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]) - "; +$fact_names[$fct] <- c($fact[$fct]) +"; } for(my $fct = 0; $fct <= $#cp_names; $fct++) { print Rcmd " - $cp_names[$fct] <- c($cp[$fct]) - "; +$cp_names[$fct] <- c($cp[$fct]) +"; } my $all_fact = ""; if(@fact_names) { @@ -159,76 +165,94 @@ $all_cp = " + ".join(" + ", @cp_names); } print Rcmd " - group_fact <- factor(names(groups)) - design <- model.matrix(~ -1 + group_fact${all_fact}${all_cp}) - colnames(design) <- sub(\"group_fact\", \"\", colnames(design)) - "; +group_fact <- factor(names(groups)) +design <- model.matrix(~ -1 + group_fact${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))) - "; +colnames(design) <- make.names(sub(\"factor.$fct.\", \"\", colnames(design))) +"; } print Rcmd " - disp <- estimateGLMCommonDisp(DGE, design) - "; +disp <- estimateGLMCommonDisp(DGE, design) +"; if($OPTIONS{d} eq "tag" || $OPTIONS{d} eq "trend") { print Rcmd " - disp <- estimateGLMTrendedDisp(disp, design) - "; +disp <- estimateGLMTrendedDisp(disp, design) +"; } if($OPTIONS{d} eq "tag") { print Rcmd " - disp <- estimateGLMTagwiseDisp(disp, design) - fit <- glmFit(disp, design) - pdf(file=\"$OPTIONS{e}/Tagwise_Dispersion_vs_Abundance.pdf\") - plot(fit\$abund+log(1e06), sqrt(disp\$tagwise.dispersion), xlab=\"Counts per million (log2 scale)\", ylab=\"Tagwise dispersion\") - oo <- order(disp\$abundance) - lines(fit\$abundance[oo]+log(1e06), sqrt(disp\$trended.dispersion[oo]), col=\"dodgerblue\", lwd=3) - abline(h=sqrt(disp\$common.dispersion), col=\"firebrick\", lwd=3) - dev.off() - "; +disp <- estimateGLMTagwiseDisp(disp, design) +fit <- glmFit(disp, design) +pdf(file=\"$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) - "; +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 - "; +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=\"\")) - "; +for(i in 1:length(pw_tests)) cont <- c(cont, paste(pw_tests[[i]][2], \"-\", pw_tests[[i]][1], sep=\"\")) +"; } if(!defined $OPTIONS{m} && !@add_cont){ die("No Contrasts have been specified, you must at least either select multiple pairwise comparisons or specify a custom contrast\n"); } print Rcmd " - fit <- glmFit(disp, design) - cont <- makeContrasts(contrasts=cont, levels=design) - for(i in colnames(cont)) tested[[i]] <- glmLRT(disp, fit, contrast=cont[,i]) - "; +fit <- glmFit(disp, design) +cont <- makeContrasts(contrasts=cont, levels=design) +for(i in colnames(cont)) tested[[i]] <- glmLRT(fit, contrast=cont[,i]) +pdf(file=\"$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) - "; - } +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]) - "; +$fact_names[$fct] <- c($fact[$fct]) +"; } for(my $fct = 0; $fct <= $#cp_names; $fct++) { print Rcmd " - $cp_names[$fct] <- c($cp[$fct]) - "; +$cp_names[$fct] <- c($cp[$fct]) +"; } my $all_fact = ""; if(@fact_names) { @@ -241,102 +265,102 @@ $all_cp = " + ".join(" + ", @cp_names); } print Rcmd " - group_fact <- factor(names(groups)) - design <- model.matrix(~ -1 + group_fact${all_fact}${all_cp}) - colnames(design) <- sub(\"group_fact\", \"\", colnames(design)) - "; +group_fact <- factor(names(groups)) +design <- model.matrix(~ -1 + group_fact${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))) - "; +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() +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) - "; +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 $OPTIONS{l}) { print Rcmd " - tab <- data.frame(ID=rownames(y\$E), y\$E, stringsAsFactors=F) - "; +tab <- data.frame(ID=rownames(y\$E), y\$E, stringsAsFactors=F) +"; } else { print Rcmd " - tab <- data.frame(ID=rownames(y\$E), 2^y\$E, stringsAsFactors=F) - "; +tab <- data.frame(ID=rownames(y\$E), 2^y\$E, stringsAsFactors=F) +"; } print Rcmd " - write.table(tab, \"$OPTIONS{n}\", quote=F, sep=\"\\t\", row.names=F) - "; - } +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) - "; +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 - "; +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=\"\")) - "; +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) - "; +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=\"$OPTIONS{f}\")[[1]] - colnames(tab_tmp) <- paste(i, colnames(tab_tmp), sep=\":\") - tab_tmp <- tab_tmp[tagnames,] - if(is.null(tab)) { - tab <- tab_tmp - } else tab <- cbind(tab, tab_tmp) - } - tab <- cbind(Feature=rownames(tab), tab) - "; +options(digits = 6) +tab <- NULL +for(i in names(tested)) { + tab_tmp <- topTags(tested[[i]], n=Inf, adjust.method=\"$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=\"$OPTIONS{f}\") - colnames(tab_tmp)[-1] <- paste(i, colnames(tab_tmp)[-1], sep=\":\") - if(is.null(tab)) { - tab <- tab_tmp - } else tab <- cbind(tab, tab_tmp[,-1]) - } - "; +tab <- NULL +options(digits = 6) +for(i in colnames(fit2)) { + tab_tmp <- topTable(fit2, coef=i, n=Inf, sort.by=\"none\", adjust.method=\"$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, \"$OPTIONS{o}\", quote=F, sep=\"\\t\", row.names=F) - sink(type=\"message\") - sink() +write.table(tab, \"$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 > $OPTIONS{e}/r_script.out"); @@ -352,6 +376,9 @@ print HTML "
  • Smear_Plots.pdf
    • \n"; } elsif($OPTIONS{a} eq "glm" && $OPTIONS{d} eq "tag") { print HTML "
  • Tagwise_Dispersion_vs_Abundance.pdf
    • \n"; + print HTML "
  • Smear_Plots.pdf
    • \n"; +} elsif($OPTIONS{a} eq "glm" && ($OPTIONS{d} eq "trend" || $OPTIONS{d} eq "common")) { + print HTML "
  • Smear_Plots.pdf
    • \n"; } elsif($OPTIONS{a} eq "limma") { print HTML "
  • LIMMA_MDS_plot.pdf
    • \n"; print HTML "
  • LIMMA_voom.pdf
    • \n";