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comparison deseq2.R @ 6:4939397c4706 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/deseq2 commit 3bc8d91ee546682ef8e9303bd1044bb14cf21b07
author iuc
date Wed, 09 Nov 2016 17:00:31 -0500
parents
children 6f91b8ce6e07
comparison
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5:d38fd393402e 6:4939397c4706
1 #!/usr/bin/env Rscript
2
3 # A command-line interface to DESeq2 for use with Galaxy
4 # written by Bjoern Gruening and modified by Michael Love 2016.03.30
5 #
6 # one of these arguments is required:
7 #
8 # 'factors' a JSON list object from Galaxy
9 #
10 # 'sample_table' is a sample table as described in ?DESeqDataSetFromHTSeq
11 # with columns: sample name, filename, then factors (variables)
12 #
13 # the output file has columns:
14 #
15 # baseMean (mean normalized count)
16 # log2FoldChange (by default a moderated LFC estimate)
17 # lfcSE (the standard error)
18 # stat (the Wald statistic)
19 # pvalue (p-value from comparison of Wald statistic to a standard Normal)
20 # padj (adjusted p-value, Benjamini Hochberg correction on genes which pass the mean count filter)
21 #
22 # the first variable in 'factors' and first column in 'sample_table' will be the primary factor.
23 # the levels of the primary factor are used in the order of appearance in factors or in sample_table.
24 #
25 # by default, levels in the order A,B,C produces a single comparison of B vs A, to a single file 'outfile'
26 #
27 # for the 'many_contrasts' flag, levels in the order A,B,C produces comparisons C vs A, B vs A, C vs B,
28 # to a number of files using the 'outfile' prefix: 'outfile.condition_C_vs_A' etc.
29 # all plots will still be sent to a single PDF, named by the arg 'plots', with extra pages.
30 #
31 # fit_type is an integer valued argument, with the options from ?estimateDisperions
32 # 1 "parametric"
33 # 2 "local"
34 # 3 "mean"
35
36 # setup R error handling to go to stderr
37 options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
38
39 # we need that to not crash galaxy with an UTF8 error on German LC settings.
40 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
41
42 library("getopt")
43 library("tools")
44 options(stringAsFactors = FALSE, useFancyQuotes = FALSE)
45 args <- commandArgs(trailingOnly = TRUE)
46
47 # get options, using the spec as defined by the enclosed list.
48 # we read the options from the default: commandArgs(TRUE).
49 spec <- matrix(c(
50 "quiet", "q", 0, "logical",
51 "help", "h", 0, "logical",
52 "outfile", "o", 1, "character",
53 "countsfile", "n", 1, "character",
54 "factors", "f", 1, "character",
55 "plots" , "p", 1, "character",
56 "sample_table", "s", 1, "character",
57 "tximport", "i", 0, "logical",
58 "tx2gene", "x", 1, "character", # a space-sep tx-to-gene map or GTF file (auto detect .gtf/.GTF)
59 "fit_type", "t", 1, "integer",
60 "many_contrasts", "m", 0, "logical",
61 "outlier_replace_off" , "a", 0, "logical",
62 "outlier_filter_off" , "b", 0, "logical",
63 "auto_mean_filter_off", "c", 0, "logical",
64 "beta_prior_off", "d", 0, "logical"),
65 byrow=TRUE, ncol=4)
66 opt <- getopt(spec)
67
68 # if help was asked for print a friendly message
69 # and exit with a non-zero error code
70 if (!is.null(opt$help)) {
71 cat(getopt(spec, usage=TRUE))
72 q(status=1)
73 }
74
75 # enforce the following required arguments
76 if (is.null(opt$outfile)) {
77 cat("'outfile' is required\n")
78 q(status=1)
79 }
80 if (is.null(opt$sample_table) & is.null(opt$factors)) {
81 cat("'factors' or 'sample_table' is required\n")
82 q(status=1)
83 }
84
85 verbose <- if (is.null(opt$quiet)) {
86 TRUE
87 } else {
88 FALSE
89 }
90
91 if (!is.null(opt$tximport)) {
92 if (is.null(opt$tx2gene)) stop("A transcript-to-gene map or a GTF file is required for tximport")
93 if (tolower(file_ext(opt$tx2gene)) == "gtf") {
94 gtfFile <- opt$tx2gene
95 } else {
96 gtfFile <- NULL
97 tx2gene <- read.table(opt$tx2gene, header=FALSE)
98 }
99 useTXI <- TRUE
100 } else {
101 useTXI <- FALSE
102 }
103
104 suppressPackageStartupMessages({
105 library("DESeq2")
106 library("RColorBrewer")
107 library("gplots")
108 })
109
110 # build or read sample table
111
112 trim <- function (x) gsub("^\\s+|\\s+$", "", x)
113
114 # switch on if 'factors' was provided:
115 if (!is.null(opt$factors)) {
116 library("rjson")
117 parser <- newJSONParser()
118 parser$addData(opt$factors)
119 factorList <- parser$getObject()
120 factors <- sapply(factorList, function(x) x[[1]])
121 primaryFactor <- factors[1]
122 filenamesIn <- unname(unlist(factorList[[1]][[2]]))
123 sampleTable <- data.frame(sample=basename(filenamesIn),
124 filename=filenamesIn,
125 row.names=filenamesIn,
126 stringsAsFactors=FALSE)
127 for (factor in factorList) {
128 factorName <- trim(factor[[1]])
129 sampleTable[[factorName]] <- character(nrow(sampleTable))
130 lvls <- sapply(factor[[2]], function(x) names(x))
131 for (i in seq_along(factor[[2]])) {
132 files <- factor[[2]][[i]][[1]]
133 sampleTable[files,factorName] <- trim(lvls[i])
134 }
135 sampleTable[[factorName]] <- factor(sampleTable[[factorName]], levels=lvls)
136 }
137 rownames(sampleTable) <- sampleTable$sample
138 } else {
139 # read the sample_table argument
140 # this table is described in ?DESeqDataSet
141 # one column for the sample name, one for the filename, and
142 # the remaining columns for factors in the analysis
143 sampleTable <- read.delim(opt$sample_table, stringsAsFactors=FALSE)
144 factors <- colnames(sampleTable)[-c(1:2)]
145 for (factor in factors) {
146 lvls <- unique(as.character(sampleTable[[factor]]))
147 sampleTable[[factor]] <- factor(sampleTable[[factor]], levels=lvls)
148 }
149 }
150
151 primaryFactor <- factors[1]
152 designFormula <- as.formula(paste("~", paste(rev(factors), collapse=" + ")))
153
154 if (verbose) {
155 cat("DESeq2 run information\n\n")
156 cat("sample table:\n")
157 print(sampleTable[,-c(1:2),drop=FALSE])
158 cat("\ndesign formula:\n")
159 print(designFormula)
160 cat("\n\n")
161 }
162
163 # these are plots which are made once for each analysis
164 generateGenericPlots <- function(dds, factors) {
165 rld <- rlog(dds)
166 d=plotPCA(rld, intgroup=rev(factors), returnData=TRUE)
167 labs <- paste0(seq_len(ncol(dds)), ": ", do.call(paste, as.list(colData(dds)[factors])))
168 library(ggplot2)
169 print(ggplot(d, aes(x=PC1,y=PC2, col=group,label=factor(labs)), environment = environment()) + geom_point() + geom_text(size=3))
170 dat <- assay(rld)
171 colnames(dat) <- labs
172 distsRL <- dist(t(dat))
173 mat <- as.matrix(distsRL)
174 hc <- hclust(distsRL)
175 hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100)
176 heatmap.2(mat, Rowv=as.dendrogram(hc), symm=TRUE, trace="none", col = rev(hmcol),
177 main="Sample-to-sample distances", margin=c(13,13))
178 plotDispEsts(dds, main="Dispersion estimates")
179 }
180
181 # these are plots which can be made for each comparison, e.g.
182 # once for C vs A and once for B vs A
183 generateSpecificPlots <- function(res, threshold, title_suffix) {
184 use <- res$baseMean > threshold
185 if (sum(!use) == 0) {
186 h <- hist(res$pvalue, breaks=0:50/50, plot=FALSE)
187 barplot(height = h$counts,
188 col = "powderblue", space = 0, xlab="p-values", ylab="frequency",
189 main=paste("Histogram of p-values for",title_suffix))
190 text(x = c(0, length(h$counts)), y = 0, label=paste(c(0,1)), adj=c(0.5,1.7), xpd=NA)
191 } else {
192 h1 <- hist(res$pvalue[!use], breaks=0:50/50, plot=FALSE)
193 h2 <- hist(res$pvalue[use], breaks=0:50/50, plot=FALSE)
194 colori <- c("filtered (low count)"="khaki", "not filtered"="powderblue")
195 barplot(height = rbind(h1$counts, h2$counts), beside = FALSE,
196 col = colori, space = 0, xlab="p-values", ylab="frequency",
197 main=paste("Histogram of p-values for",title_suffix))
198 text(x = c(0, length(h1$counts)), y = 0, label=paste(c(0,1)), adj=c(0.5,1.7), xpd=NA)
199 legend("topright", fill=rev(colori), legend=rev(names(colori)), bg="white")
200 }
201 plotMA(res, main= paste("MA-plot for",title_suffix), ylim=range(res$log2FoldChange, na.rm=TRUE))
202 }
203
204 if (verbose) {
205 cat(paste("primary factor:",primaryFactor,"\n"))
206 if (length(factors) > 1) {
207 cat(paste("other factors in design:",paste(factors[-length(factors)],collapse=","),"\n"))
208 }
209 cat("\n---------------------\n")
210 }
211
212 # if JSON input from Galaxy, path is absolute
213 # otherwise, from sample_table, assume it is relative
214 dir <- if (is.null(opt$factors)) {
215 "."
216 } else {
217 ""
218 }
219
220 if (!useTXI) {
221 # construct the object from HTSeq files
222 dds <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
223 directory = dir,
224 design = designFormula)
225 } else {
226 # construct the object using tximport
227 # first need to make the tx2gene table
228 # this takes ~2-3 minutes using Bioconductor functions
229 if (!is.null(gtfFile)) {
230 suppressPackageStartupMessages({
231 library("GenomicFeatures")
232 })
233 txdb <- makeTxDbFromGFF(gtfFile, format="gtf")
234 k <- keys(txdb, keytype = "GENEID")
235 df <- select(txdb, keys = k, keytype = "GENEID", columns = "TXNAME")
236 tx2gene <- df[, 2:1] # tx ID, then gene ID
237 }
238 library("tximport")
239 txiFiles <- as.character(sampleTable[,2])
240 names(txiFiles) <- as.character(sampleTable[,1])
241 txi <- tximport(txiFiles, type="sailfish", tx2gene=tx2gene)
242 dds <- DESeqDataSetFromTximport(txi,
243 sampleTable[,3:ncol(sampleTable),drop=FALSE],
244 designFormula)
245 }
246
247 if (verbose) cat(paste(ncol(dds), "samples with counts over", nrow(dds), "genes\n"))
248 # optional outlier behavior
249 if (is.null(opt$outlier_replace_off)) {
250 minRep <- 7
251 } else {
252 minRep <- Inf
253 if (verbose) cat("outlier replacement off\n")
254 }
255 if (is.null(opt$outlier_filter_off)) {
256 cooksCutoff <- TRUE
257 } else {
258 cooksCutoff <- FALSE
259 if (verbose) cat("outlier filtering off\n")
260 }
261
262 # optional automatic mean filtering
263 if (is.null(opt$auto_mean_filter_off)) {
264 independentFiltering <- TRUE
265 } else {
266 independentFiltering <- FALSE
267 if (verbose) cat("automatic filtering on the mean off\n")
268 }
269
270 # shrinkage of LFCs
271 if (is.null(opt$beta_prior_off)) {
272 betaPrior <- TRUE
273 } else {
274 betaPrior <- FALSE
275 if (verbose) cat("beta prior off\n")
276 }
277
278 # dispersion fit type
279 if (is.null(opt$fit_type)) {
280 fitType <- "parametric"
281 } else {
282 fitType <- c("parametric","local","mean")[opt$fit_type]
283 }
284
285 if (verbose) cat(paste("using disperion fit type:",fitType,"\n"))
286
287 # run the analysis
288 dds <- DESeq(dds, fitType=fitType, betaPrior=betaPrior, minReplicatesForReplace=minRep)
289
290 # create the generic plots and leave the device open
291 if (!is.null(opt$plots)) {
292 if (verbose) cat("creating plots\n")
293 pdf(opt$plots)
294 generateGenericPlots(dds, factors)
295 }
296
297 n <- nlevels(colData(dds)[[primaryFactor]])
298 allLevels <- levels(colData(dds)[[primaryFactor]])
299
300 if (!is.null(opt$countsfile)) {
301 labs <- paste0(seq_len(ncol(dds)), ": ", do.call(paste, as.list(colData(dds)[factors])))
302 normalizedCounts<-counts(dds,normalized=TRUE)
303 colnames(normalizedCounts)<-labs
304 write.table(normalizedCounts, file=opt$countsfile, sep="\t", col.names=NA, quote=FALSE)
305 }
306
307 if (is.null(opt$many_contrasts)) {
308 # only contrast the first and second level of the primary factor
309 ref <- allLevels[1]
310 lvl <- allLevels[2]
311 res <- results(dds, contrast=c(primaryFactor, lvl, ref),
312 cooksCutoff=cooksCutoff,
313 independentFiltering=independentFiltering)
314 if (verbose) {
315 cat("summary of results\n")
316 cat(paste0(primaryFactor,": ",lvl," vs ",ref,"\n"))
317 print(summary(res))
318 }
319 resSorted <- res[order(res$padj),]
320 outDF <- as.data.frame(resSorted)
321 outDF$geneID <- rownames(outDF)
322 outDF <- outDF[,c("geneID", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj")]
323 filename <- opt$outfile
324 write.table(outDF, file=filename, sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)
325 if (independentFiltering) {
326 threshold <- unname(attr(res, "filterThreshold"))
327 } else {
328 threshold <- 0
329 }
330 title_suffix <- paste0(primaryFactor,": ",lvl," vs ",ref)
331 if (!is.null(opt$plots)) {
332 generateSpecificPlots(res, threshold, title_suffix)
333 }
334 } else {
335 # rotate through the possible contrasts of the primary factor
336 # write out a sorted table of results with the contrast as a suffix
337 # add contrast specific plots to the device
338 for (i in seq_len(n-1)) {
339 ref <- allLevels[i]
340 contrastLevels <- allLevels[(i+1):n]
341 for (lvl in contrastLevels) {
342 res <- results(dds, contrast=c(primaryFactor, lvl, ref),
343 cooksCutoff=cooksCutoff,
344 independentFiltering=independentFiltering)
345 resSorted <- res[order(res$padj),]
346 outDF <- as.data.frame(resSorted)
347 outDF$geneID <- rownames(outDF)
348 outDF <- outDF[,c("geneID", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj")]
349 filename <- paste0(opt$outfile,".",primaryFactor,"_",lvl,"_vs_",ref)
350 write.table(outDF, file=filename, sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)
351 if (independentFiltering) {
352 threshold <- unname(attr(res, "filterThreshold"))
353 } else {
354 threshold <- 0
355 }
356 title_suffix <- paste0(primaryFactor,": ",lvl," vs ",ref)
357 if (!is.null(opt$plots)) {
358 generateSpecificPlots(res, threshold, title_suffix)
359 }
360 }
361 }
362 }
363
364 # close the plot device
365 if (!is.null(opt$plots)) {
366 cat("closing plot device\n")
367 dev.off()
368 }
369
370 cat("Session information:\n\n")
371
372 sessionInfo()
373