# HG changeset patch # User iuc # Date 1517229018 18000 # Node ID bd06df00180ad2bd6a4782b427c9808876070dcd # Parent 25204a860b79723ee1066c720b036f7d805936c2 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/deseq2 commit 62e9101c1e7b8467e395f31ccbd9321de01a6418 diff -r 25204a860b79 -r bd06df00180a deseq2.R --- a/deseq2.R Sun Jan 28 04:04:12 2018 -0500 +++ b/deseq2.R Mon Jan 29 07:30:18 2018 -0500 @@ -52,6 +52,7 @@ "outfile", "o", 1, "character", "countsfile", "n", 1, "character", "factors", "f", 1, "character", + "files_to_labels", "l", 1, "character", "plots" , "p", 1, "character", "sample_table", "s", 1, "character", "tximport", "i", 0, "logical", @@ -118,9 +119,11 @@ parser <- newJSONParser() parser$addData(opt$factors) factorList <- parser$getObject() + filenames_to_labels <- fromJSON(opt$files_to_labels) factors <- sapply(factorList, function(x) x[[1]]) primaryFactor <- factors[1] filenamesIn <- unname(unlist(factorList[[1]][[2]])) + labs = unname(unlist(filenames_to_labels[basename(filenamesIn)])) sampleTable <- data.frame(sample=basename(filenamesIn), filename=filenamesIn, row.names=filenamesIn, @@ -135,7 +138,7 @@ } sampleTable[[factorName]] <- factor(sampleTable[[factorName]], levels=lvls) } - rownames(sampleTable) <- sampleTable$sample + rownames(sampleTable) <- labs } else { # read the sample_table argument # this table is described in ?DESeqDataSet @@ -163,19 +166,22 @@ # these are plots which are made once for each analysis generateGenericPlots <- function(dds, factors) { + library("ggplot2") + library("ggrepel") + library("pheatmap") + rld <- rlog(dds) - d=plotPCA(rld, intgroup=rev(factors), returnData=TRUE) - labs <- paste0(seq_len(ncol(dds)), ": ", do.call(paste, as.list(colData(dds)[factors]))) - library(ggplot2) - print(ggplot(d, aes(x=PC1,y=PC2, col=group,label=factor(labs)), environment = environment()) + geom_point() + geom_text(size=3)) + p <- plotPCA(rld, intgroup=rev(factors)) + print(p + geom_text_repel(aes_string(x = "PC1", y = "PC2", label = factor(colnames(dds))), size=3) + geom_point()) dat <- assay(rld) - colnames(dat) <- labs distsRL <- dist(t(dat)) mat <- as.matrix(distsRL) - hc <- hclust(distsRL) - hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100) - heatmap.2(mat, Rowv=as.dendrogram(hc), symm=TRUE, trace="none", col = rev(hmcol), - main="Sample-to-sample distances", margin=c(13,13)) + colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255) + pheatmap(mat, + clustering_distance_rows=distsRL, + clustering_distance_cols=distsRL, + col=colors, + main="Sample-to-sample distances") plotDispEsts(dds, main="Dispersion estimates") } @@ -223,6 +229,8 @@ dds <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = dir, design = designFormula) + labs <- unname(unlist(filenames_to_labels[colnames(dds)])) + colnames(dds) <- labs } else { # construct the object using tximport # first need to make the tx2gene table @@ -238,7 +246,8 @@ } library("tximport") txiFiles <- as.character(sampleTable[,2]) - names(txiFiles) <- as.character(sampleTable[,1]) + labs <- unname(unlist(filenames_to_labels[sampleTable[,1]])) + names(txiFiles) <- labs txi <- tximport(txiFiles, type=opt$txtype, tx2gene=tx2gene) dds <- DESeqDataSetFromTximport(txi, sampleTable[,3:ncol(sampleTable),drop=FALSE], @@ -299,9 +308,7 @@ allLevels <- levels(colData(dds)[[primaryFactor]]) if (!is.null(opt$countsfile)) { - labs <- paste0(seq_len(ncol(dds)), ": ", do.call(paste, as.list(colData(dds)[factors]))) normalizedCounts<-counts(dds,normalized=TRUE) - colnames(normalizedCounts)<-labs write.table(normalizedCounts, file=opt$countsfile, sep="\t", col.names=NA, quote=FALSE) } diff -r 25204a860b79 -r bd06df00180a deseq2.xml --- a/deseq2.xml Sun Jan 28 04:04:12 2018 -0500 +++ b/deseq2.xml Mon Jan 29 07:30:18 2018 -0500 @@ -1,8 +1,10 @@ - + Determines differentially expressed features from count tables bioconductor-deseq2 bioconductor-tximport + r-ggrepel + r-pheatmap