Mercurial > repos > bgruening > deseq2
changeset 17:a05999fd6e26 draft
Uploaded
| author | bgruening |
|---|---|
| date | Mon, 30 Sep 2013 10:51:41 -0400 |
| parents | 1d2a02bc2208 |
| children | 72613128bd65 |
| files | deseq2.R deseq2.py deseq2.xml matrix_helper.py tool_dependencies.xml |
| diffstat | 5 files changed, 276 insertions(+), 212 deletions(-) [+] |
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--- a/deseq2.R Mon Sep 09 07:09:38 2013 -0400 +++ b/deseq2.R Mon Sep 30 10:51:41 2013 -0400 @@ -10,23 +10,24 @@ #get options, using the spec as defined by the enclosed list. #we read the options from the default: commandArgs(TRUE). spec = matrix(c( - 'verbose', 'v', 2, "integer", - 'help' , 'h', 0, "logical", - 'outputfile' , 'o', 1, "character", - 'plots' , 'p', 2, "character", - 'input' , 'i', 1, "character", - 'samples', 's', 1, "character", - 'factors', 'f', 2, "character", - 'fittype', 't', 2, "character" + 'verbose', 'v', 2, "integer", + 'help' , 'h', 0, "logical", + 'outfile' , 'o', 1, "character", + 'outfilefiltered' , 'f', 1, "character", + 'plots' , 'p', 2, "character", + 'input' , 'i', 1, "character", + 'factors', 'm', 2, "character", + 'fittype', 't', 2, "character", + 'threshold', 'c', 2, "double" +# 'organism', 'g', 2, "character" ), byrow=TRUE, ncol=4); opt = getopt(spec); - # if help was asked for print a friendly message # and exit with a non-zero error code if ( !is.null(opt$help) ) { - cat(getopt(spec, usage=TRUE)); - q(status=1); + cat(getopt(spec, usage=TRUE)); + q(status=1); } if( is.null(opt$fittype)) @@ -42,148 +43,146 @@ names(htseqCountTable)<-colnames(htseqCountTable) conditions <- htseqCountTable[1,] conditions <- unlist(conditions[,-1]) -tagnames <- htseqCountTable[-(1:2), 1] +featurenames <- htseqCountTable[-(1:2), 1] htseqCountTable <- htseqCountTable[-(1:2),-1] htseqCountTable[,1] <- gsub(",", ".", htseqCountTable[,1]) l <- unique(c(conditions)) -library( "DESeq2" ) +library('rjson') +library('DESeq2') +#library('biomaRt') if ( !is.null(opt$plots) ) { - pdf(opt$plots) + pdf(opt$plots) } -if (opt$samples=="all_vs_all"){ - # all versus all - for (i in seq(1, length(l), by=1)) { - k=i+1 - if(k<=length(l)){ - for (j in seq(k, length(l), by=1)) { - currentColmuns <- which(conditions %in% c(l[[i]], l[[j]])) - sampleNames <- names(htseqCountTable[currentColmuns]) - - currentPairTable <- htseqCountTable[currentColmuns] - ncondition1cols <- length(which(conditions %in% c(l[[i]]))) - ncondition2cols <- length(which(conditions %in% c(l[[i]]))) - - ntabcols <- length(currentPairTable) - currentPairTable <- as.integer(unlist(currentPairTable)) - currentPairTable <- matrix(unlist(currentPairTable), ncol = ntabcols, byrow = FALSE) - colnames(currentPairTable) <- sampleNames - - pdata = data.frame(condition=factor(conditions[currentColmuns]),row.names=colnames(currentPairTable)) - print(pdata) - - dds = DESeqDataSetFromMatrix(countData = currentPairTable, colData = pdata, design = ~ condition) - - dds <- DESeq(dds, fitType= opt$fittype) - sizeFactors(dds) - res <- results(dds) - resCols <- colnames(res) - - cond <- c(rep(paste(unique(conditions[currentColmuns]),collapse="_"), nrow(res))) - res[, "condition"] <- cond - res[, "geneIds"] <- tagnames - - resSorted <- res[order(res$padj),] - write.table(as.data.frame(resSorted[,c("condition", "geneIds", resCols)]), file = opt$outputfile, sep="\t", quote = FALSE, append=TRUE, row.names = FALSE, col.names = FALSE) - - if ( !is.null(opt$plots) ) { - plotDispEsts(dds) - plotMA(dds) - - library("RColorBrewer") - library("gplots") - select <- order(rowMeans(counts(dds,normalized=TRUE)),decreasing=TRUE)[1:30] - hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100) - - rld <- rlogTransformation(dds, blind=TRUE) - vsd <- varianceStabilizingTransformation(dds, blind=TRUE) - distsRL <- dist(t(assay(rld))) - mat <- as.matrix(distsRL) - heatmap.2(mat, trace="none", col = rev(hmcol), main = "Sample-to-sample distances") - } - } - } - } -}else{ - sampleSets <- unlist(strsplit(opt$samples, " ")) - samplesControl <- {} - samplesExperiment <- {} - samplesControl <- unlist(strsplit(sampleSets[1], ",")) - samplesExperiment <- unlist(strsplit(sampleSets[2], ",")) - - # the minus one is needed because we get column indizes including the first column - sampleColumns <- c() - for (i in samplesControl) sampleColumns[[length(sampleColumns) + 1]] <- as.integer(i) - 1 - for (i in samplesExperiment) sampleColumns[[length(sampleColumns) + 1]] <- as.integer(i) - 1 - htseqSubCountTable <- htseqCountTable[,sampleColumns] - - ntabcols <- length(htseqSubCountTable) - htseqSubCountTable <- as.integer(unlist(htseqSubCountTable)) - htseqSubCountTable <- matrix(unlist(htseqSubCountTable), ncol = ntabcols, byrow = FALSE) - colnames(htseqSubCountTable) <- names(htseqCountTable)[sampleColumns] - - pdata = data.frame(row.names=colnames(htseqSubCountTable)) +## The following function takes deseq data object, computes, writes and plots the results. +computeAndWriteResults <- function(dds, sampleCols, outputcsv, featurenames_filtered) { + dds <- DESeq(dds, fitType= opt$fittype) + sizeFactors(dds) + res <- results(dds) + resCols <- colnames(res) + cond <- c(rep(paste(unique(conditions[sampleCols]),collapse="_"), nrow(res))) + if(missing(featurenames_filtered)){ + res[, "geneIds"] <- featurenames +# rownames(res) <- featurenames + title_prefix = "Complete: " + }else{ + res[, "geneIds"] <- featurenames_filtered +# rownames(res) <- featurenames + title_prefix = "Filtered: " + } + print(sum(res$padj < .1, na.rm=TRUE)) + print(opt$organism) +# if(opt$organism != "other"){ +# dataset = "" +# if(opt$organism == "mouse") +# dataset = "mmusculus_gene_ensembl" +# if(opt$organism == "human") +# dataset = "hsapiens_gene_ensembl" +# if(opt$organism == "fly") +# dataset = "dmelanogaster_gene_ensembl" +# ensembldb = useMart("ensembl",dataset=dataset) +# +# annot <- getBM(attributes = c("ensembl_gene_id", "external_gene_id","description"), +# filters = "ensembl_gene_id", +# values=res[, "geneIds"], +# mart=ensembldb) - if(!is.null(opt$factors)){ - factorSets <- unlist(strsplit(opt$factors, " ")) - for (factorSet in factorSets) { - currentFactor <- unlist(strsplit(factorSet, ":")) - for (factor in currentFactor[2]) { - factoredCols <- unlist(strsplit(factor, ",")) - factoredCols <- as.numeric(factoredCols) - factors <- c() - for (i in sampleColumns){ - j=i+1 - if(j %in% factoredCols) - factors[[length(factors) + 1]] <- "yes" - else - factors[[length(factors) + 1]] <- "no" - } - # only add factor if factor is applied to the selected samples - if((length(unique(factors))) >= 2){ - pdata[[currentFactor[1]]]<-factor(factors) - }else{ - write("Input-Error 01: You can only apply factors to selected samples.", stderr()) - } - } - } - } - pdata$condition<-factor(conditions[sampleColumns]) - print(pdata) - - designFormula <- as.formula(paste("", paste(names(pdata), collapse=" + "), sep=" ~ ")) - dds = DESeqDataSetFromMatrix(countData = htseqSubCountTable, colData = pdata, design = designFormula) - - dds <- DESeq(dds, fitType= opt$fittype) - sizeFactors(dds) - res <- results(dds) - resCols <- colnames(res) - - cond <- c(rep(paste(unique(conditions[sampleColumns]),collapse="_"), nrow(res))) - res[, "condition"] <- cond - res[, "geneIds"] <- tagnames - - resSorted <- res[order(res$padj),] - write.table(as.data.frame(resSorted[,c("condition", "geneIds", resCols)]), file = opt$outputfile, sep="\t", quote = FALSE, row.names = FALSE, col.names = FALSE) - - if ( !is.null(opt$plots) ) { - plotDispEsts(dds) - plotMA(dds) +# res <- merge(res, annot, +# by.x = "geneIds", +# by.y = "ensembl_gene_id", +# all.x=TRUE) + +# resCols <- colnames(res) +# resSorted <- res[order(res$padj),] +# write.table(as.data.frame(resSorted[,c(resCols)]), file = outputcsv, sep="\t", quote = FALSE, append=TRUE, row.names = FALSE, col.names = FALSE) +# }else{ + resSorted <- res[order(res$padj),] + write.table(as.data.frame(resSorted[,c("geneIds", resCols)]), file = outputcsv, sep="\t", quote = FALSE, append=TRUE, row.names = FALSE, col.names = FALSE) +# } + + if ( !is.null(opt$plots) ) { + plotDispEsts(dds, main= paste(title_prefix, "Dispersion estimate plot")) + plotMA(dds, main= paste(title_prefix, "MA-plot")) library("RColorBrewer") library("gplots") select <- order(rowMeans(counts(dds,normalized=TRUE)),decreasing=TRUE)[1:30] hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100) - + rld <- rlogTransformation(dds, blind=TRUE) vsd <- varianceStabilizingTransformation(dds, blind=TRUE) distsRL <- dist(t(assay(rld))) mat <- as.matrix(distsRL) - heatmap.2(mat, trace="none", col = rev(hmcol), main = "Sample-to-sample distances") - } + heatmap.2(mat, trace="none", col = rev(hmcol), main = paste(title_prefix, "Sample-to-sample distances")) + } + return(res) +} + +## This functions filters out the genes with low counts and plots p-value distribution +independentFilter <- function(deseqRes, countTable, sampleColumns, designFormula){ +# use = (deseqRes$baseMean > quantile(deseqRes$baseMean, probs = opt$threshold) & (!is.na(deseqRes$pvalue))) + use = (deseqRes$baseMean > opt$threshold & !is.na(deseqRes$pvalue)) + ddsFilt <- DESeqDataSetFromMatrix(countData = countTable[use, ], colData = sampleTable, design = designFormula) + computeAndWriteResults(ddsFilt, sampleColumns, opt$outfilefiltered, featurenames[use]) + + ## p-value distribution + h1 <- hist(deseqRes$pvalue[!use], breaks=50, plot=FALSE) + h2 <- hist(deseqRes$pvalue[use], breaks=50, plot=FALSE) + colori <- c(`filtered out`="khaki", `complete`="powderblue") + barplot(height = rbind(h1$counts, h2$counts), beside = FALSE, + col = colori, space = 0, main = "Distribution of p-values", ylab="frequency") + text(x = c(0, length(h1$counts)), y = 0, label = paste(c(0,1)), adj = c(0.5,1.7), xpd=NA) + legend("topright", fill=rev(colori), legend=rev(names(colori))) +} + +parser <- newJSONParser() +parser$addData( opt$factors ) +factorsList <- parser$getObject() + +sampleColumns <- c() + +for (j in 2:length(factorsList[[1]])){ + for (k in 1:length(names(factorsList[[1]][[j]]))){ + for (l in 1:length(factorsList[[1]][[j]][[k]])){ + sampleColumns[[length(sampleColumns) + 1]] <- as.integer(factorsList[[1]][[j]][[k]][l]) - 1 + } + } } + +sampleColumns<-sort(unique(sampleColumns)) +htseqSubCountTable <- htseqCountTable[,sampleColumns] +ntabcols <- length(htseqSubCountTable) +htseqSubCountTable <- as.integer(unlist(htseqSubCountTable)) +htseqSubCountTable <- matrix(unlist(htseqSubCountTable), ncol = ntabcols, byrow = FALSE) +colnames(htseqSubCountTable) <- names(htseqCountTable)[sampleColumns] +sampleTable = data.frame(row.names=colnames(htseqSubCountTable)) + +for(i in 1:length(factorsList)){ + factorName<-factorsList[[i]][[1]] + for (j in 2:length(factorsList[[i]])){ + effected_cols <- c() + for (k in 1:length(names(factorsList[[i]][[j]]))){ + for (l in 1:length(factorsList[[i]][[j]][[k]])){ + if(colnames(htseqCountTable)[factorsList[[i]][[j]][[k]][l]-1] %in% rownames(sampleTable)){ + sampleTable[colnames(htseqCountTable)[factorsList[[i]][[j]][[k]][l]-1],factorName] <- names(factorsList[[i]][[j]])[k] + } + } + } + } +} +sampleTable[is.na(sampleTable)] <- "undefined" +sampleTable + +factorNames <- c(names(sampleTable)[-1], names(sampleTable)[1]) +designFormula <- as.formula(paste("", paste(factorNames, collapse=" + "), sep=" ~ ")) +dds = DESeqDataSetFromMatrix(countData = htseqSubCountTable, colData = sampleTable, design = designFormula) +deseqres <- computeAndWriteResults(dds, sampleColumns, opt$outfile) + +## independent filtering +independentFilter(deseqres, htseqSubCountTable, sampleColumns, designFormula) + dev.off() - +sessionInfo()
--- a/deseq2.py Mon Sep 09 07:09:38 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,8 +0,0 @@ -#!/usr/bin/env python -import os, sys -import rpy2.robjects as robjects -from rpy2.robjects.packages import importr -deseq2 = importr("DESeq2") - -sys.stdout.write('Works!') -
--- a/deseq2.xml Mon Sep 09 07:09:38 2013 -0400 +++ b/deseq2.xml Mon Sep 30 10:51:41 2013 -0400 @@ -1,5 +1,5 @@ <tool id="deseq2" name="DESeq2" version="2.0.1"> - <description>Determines differentially expressed transcripts from read alignments</description> + <description>Determines differentially expressed features from count data</description> <requirements> <requirement type="binary">Rscript</requirement> <requirement type="R-module">DESeq2</requirement> @@ -7,33 +7,33 @@ <requirement type="set_environment">DESEQ2_SCRIPT_PATH</requirement> </requirements> <command interpreter="Rscript"> + #import simplejson deseq2.R -o "$deseq_out" + --outfilefiltered "$deseq_out_filtered" #if $pdf: - -p "$plots" + -p "$plots" #end if - + -i "$input_matrix" - #if $filter_sel.filter_sel_opts == 'all_vs_all': - -s 'all_vs_all' - #else: - -s ## build a string like '1,2 5,6' - "${filter_sel.control_cols} ${filter_sel.experiement_cols}" + #set $temp_factor_name = list() + #for $factor in $rep_factorName: + #set $temp_factor = dict() + #for $level in $factor.rep_factorLevel: + ##$temp_factor_list.append( '%s::%s:%s' % ($factor.factorName.replace(' ','_'), $level.factorLevel, $level.factorIndex) ) + $temp_factor.update({str($level.factorLevel): map(int, str($level.factorIndex).split(','))}) + #end for + $temp_factor_name.append([str($factor.factorName), $temp_factor]) - #set $temp_factor_list = list() - #set $is_multi_factor_analysis = False - #for $factor in $filter_sel.factor: - #set $is_multi_factor_analysis = True - $temp_factor_list.append( '%s:%s' % ($factor.factor_name.replace(' ','_'), $factor.factor_index) ) #end for - #if $is_multi_factor_analysis: - -f "#echo ' '.join( $temp_factor_list )#" - #end if - #end if - -t $fittype + ##-m "#echo ' '.join( $temp_factor_list )#" + -m '#echo simplejson.dumps(temp_factor_name)#' + ##--organism "$organism" + ##-t "$fittype" + -c $countthreshold </command> <stdio> <regex match="Execution halted" @@ -51,56 +51,64 @@ </stdio> <inputs> <param format="tabular" name="input_matrix" type="data" label="Countmatrix" help="You can create a count matrix with the tool - 'Count reads in features with htseq-count'"/> + 'Count reads in features with htseq-count'"/> + + <repeat name="rep_factorName" title="Factor/Condition" min="1"> + <param name="factorName" type="text" value="FactorName" label="Specify a factor name" help=""/> + <repeat name="rep_factorLevel" title="Factor level" min="1"> + <param name="factorLevel" type="text" value="FactorLevel" label="Specify a factor level" help=""/> - <conditional name="filter_sel"> - <param name="filter_sel_opts" type="select" label="Select mode of analysis" help="All against all: analyses all possible pairs of conditions, - <br> Specific: select your samples of interest"> - <option value="all_vs_all">All against all</option> - <option value="specific">specific</option> - </param> - <when value="specific"> - - <param name="control_cols" label="Select columns containing first condition" type="data_column" data_ref="input_matrix" - numerical="True" multiple="true" use_header_names="true" size="120" display="checkboxes"> - <validator type="no_options" message="Please select at least one column."/> - </param> - - <param name="experiement_cols" label="Select columns containing second condition" type="data_column" data_ref="input_matrix" + <param name="factorIndex" label="Select columns that are associated with this factor level" type="data_column" data_ref="input_matrix" numerical="True" multiple="true" use_header_names="true" size="120" display="checkboxes"> <validator type="no_options" message="Please select at least one column."/> </param> - - <repeat name="factor" title="Include factor"> - <param name="factor_name" type="text" value="Factor Name" label="Specify a factor name" help=""/> - - <param name="factor_index" label="Select columns that are associated with a factor." type="data_column" data_ref="input_matrix" - numerical="True" multiple="true" use_header_names="true" size="120" display="checkboxes"> - <validator type="no_options" message="Please select at least one column."/> - </param> - </repeat> - - </when> - <when value="all_vs_all" /> - </conditional> - + </repeat> + </repeat> + <!-- + <param name="control_cols" type="select" display="checkboxes" multiple="true" optional="True" label="Select columns containing first condition" + dynamic_options="get_matrix_header( input_dataset=input_matrix )" help="insert useful info here"> + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="experiement_cols" type="select" display="checkboxes" multiple="true" optional="True" label="Select columns containing second condition" + dynamic_options="get_matrix_header( input_dataset=input_matrix )" help="insert useful info here"> + <validator type="no_options" message="Please select at least one column."/> + </param> + + <repeat name="factor" title="factor"> + <param name="factor_name" type="text" value="Factor Name" label="Specify a factor name" + help="Order of factors you add effects the design formual and hence effects whole analysis. Design formual will be created as follows: ~factor1+factor2+factor3+...+factorN+condition"/> + <param name="factor_index" type="select" display="checkboxes" multiple="true" optional="True" label="Choose sample to compare with" + dynamic_options="get_matrix_header( input_dataset=input_matrix )" help="Select columns that are associated with a factor"> + <validator type="no_options" message="Please select at least one column."/> + </param> + </repeat> + --> + <!--param name="organism" size="10" type="select"> + <option value="human">human</option> + <option value="mouse">mouse</option> + <option value="fly">fly</option> + <option value="other">other</option> + </param--> + <param name="countthreshold" size="10" type="float" value="10" label="Filter out features with mean normalized counts lower than this value"/> <param name="fittype" type="select" label="Type of fitting of dispersions to the mean intensity"> - <option value="parametric">parametric</option> - <option value="local">local</option> - <option value="mean">mean</option> - </param> - <param name="pdf" type="boolean" truevalue="" falsevalue="" checked="true" label="Visualising the analysis results" - help="output an additional PDF file" /> + <option value="parametric">parametric</option> + <option value="local">local</option> + <option value="mean">mean</option> + </param> + <param name="pdf" type="boolean" truevalue="" falsevalue="" checked="true" label="Visualising the analysis results" + help="output an additional PDF files" /> </inputs> - <outputs> - <data format="tabular" name="deseq_out" label="DESeq2 result file on ${on_string}"/> - <data format="pdf" name="plots" label="DESeq2 plots on ${on_string}"> - <filter>pdf == True</filter> - </data> - </outputs> + <outputs> + <data format="tabular" name="deseq_out" label="DESeq2 result file on ${on_string}"/> + <data format="tabular" name="deseq_out_filtered" label="Independent filtering result file on ${on_string}"/> + <data format="pdf" name="plots" label="DESeq2 plots on ${on_string}"> + <filter>pdf == True</filter> + </data> + </outputs> + <code file="matrix_helper.py" /> - <help> + <help> .. class:: infomark @@ -122,13 +130,12 @@ ====== ========================================================== Column Description ------ ---------------------------------------------------------- - 1 Condition tested (corresponds to the conitions from the first line of your count matrix) - 2 Gene Identifiers - 3 mean normalised counts, averaged over all samples from both conditions - 4 the logarithm (to basis 2) of the fold change - 5 standard error estimate for the log2 fold change estimate - 6 p value for the statistical significance of this change - 7 p value adjusted for multiple testing with the Benjamini-Hochberg procedure + 1 Gene Identifiers + 2 mean normalised counts, averaged over all samples from both conditions + 3 the logarithm (to basis 2) of the fold change + 4 standard error estimate for the log2 fold change estimate + 5 p value for the statistical significance of this change + 6 p value adjusted for multiple testing with the Benjamini-Hochberg procedure which controls false discovery rate (FDR) ====== ========================================================== @@ -150,5 +157,5 @@ .. _DESeq2: http://master.bioconductor.org/packages/release/bioc/html/DESeq2.html - </help> + </help> </tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/matrix_helper.py Mon Sep 30 10:51:41 2013 -0400 @@ -0,0 +1,66 @@ + +from galaxy.tools.parameters import DataToolParameter + +def get_matrix_header( input_dataset ): + """ + Not used currently, because the reload of the ckeckboxes did not work. + """ + input_handle = open( input_dataset.file_name ) + first_header = input_handle.readline() + second_header = input_handle.readline() + return [('%s::%s' % (cname2,cname1), str(int(col_num) + 1), False) for col_num, (cname2, cname1) in enumerate(zip(second_header.split()[1:],first_header.split()[1:])) ] + + +def validate_input( trans, error_map, param_values, page_param_map ): + """ + Validates the user input, before execution. + """ + factors = param_values['rep_factorName'] + factor_name_list = [] + factor_duplication = False + level_duplication = False + overlapping_selection = False + + + for factor in factors: + # factor names should be unique + fn = factor['factorName'] + if fn in factor_name_list: + factor_duplication = True + break + factor_name_list.append( fn ) + + level_name_list = list() + factor_index_list = list() + + for level in factor['rep_factorLevel']: + # level names under one factor should be unique + fl = level['factorLevel'] + if fl in level_name_list: + level_duplication = True + level_name_list.append( fl ) + + fi = level['factorIndex'] + if fi: + # the checkboxes should not have an overlap + for check in fi: + if check in factor_index_list: + overlapping_selection = True + factor_index_list.append( check ) + + if level_duplication: + error_map['rep_factorName'] = [ dict() for t in factors ] + for i in range( len( factors ) ): + error_map['rep_factorName'][i]['rep_factorLevel'] = [ {'factorLevel': 'Factor levels for each factor need to be unique'} for t in factor['rep_factorLevel'] ] + break + if overlapping_selection: + error_map['rep_factorName'] = [ dict() for t in factors ] + for i in range( len( factors ) ): + error_map['rep_factorName'][i]['rep_factorLevel'] = [ {'factorIndex': 'The samples from different factors are not allowed to overlap'} for t in factor['rep_factorLevel'] ] + break + + if factor_duplication: + error_map['rep_factorName'] = [ dict() for t in factors ] + for i in range( len( factors ) ): + error_map['rep_factorName'][i]['factorName'] = 'Factor names need to be unique' +
--- a/tool_dependencies.xml Mon Sep 09 07:09:38 2013 -0400 +++ b/tool_dependencies.xml Mon Sep 30 10:51:41 2013 -0400 @@ -4,7 +4,7 @@ <repository changeset_revision="bae5c9880b71" name="package_r_3_0_1" owner="bgruening" toolshed="http://testtoolshed.g2.bx.psu.edu" /> </package> <package name="deseq2" version="1.0.17"> - <repository changeset_revision="eab36a9a70b1" name="package_deseq2_1_0_17" owner="bgruening" toolshed="http://testtoolshed.g2.bx.psu.edu" /> + <repository changeset_revision="3e3d1a50a347" name="package_deseq2_1_0_17" owner="bgruening" toolshed="http://testtoolshed.g2.bx.psu.edu" /> </package> <set_environment version="1.0"> <environment_variable action="set_to" name="DESEQ2_SCRIPT_PATH">$REPOSITORY_INSTALL_DIR/scripts</environment_variable>