Mercurial > repos > mvdbeek > deseq2
comparison deseq2.xml @ 0:a903407e3ca0 draft default tip
planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/deseq2 commit f95b47ed1a09ce14d3b565e8ea56d8bf12c35814-dirty
author | mvdbeek |
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date | Sat, 05 Mar 2016 07:05:06 -0500 |
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-1:000000000000 | 0:a903407e3ca0 |
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1 <tool id="DESeq2" name="DESeq2 Profiling" version="1.0.2" hidden="true"> | |
2 <description>of readcount lists</description> | |
3 <requirements> | |
4 <requirement type="package" version="3.1.2">R</requirement> | |
5 <requirement type="package" version="2.14">biocbasics</requirement> | |
6 </requirements> | |
7 <command>Rscript $DESeq2 </command> | |
8 <inputs> | |
9 <param name="input" type="data" format="tabular" label="miR hit lists, more thant 2 samples"/> | |
10 <param name="expPlan" type="text" label="experimental plan" help="Use a string of Cs and Ts. exemple: CCCTTT means 3 control samples versus 3 test samples"/> | |
11 </inputs> | |
12 <outputs> | |
13 <data name="output" format="tabular" label="DESeq2 differential calling" /> | |
14 </outputs> | |
15 <tests> | |
16 <test> | |
17 <param name="input" value="counts.tab" ftype="tabular"/> | |
18 <param name="expPlan" value="CCTTT"/> | |
19 <output name="output" file="dge.tab.re_match.modified" ftype="tabular" compare="re_match" lines_diff="50"/> | |
20 </test> | |
21 </tests> | |
22 <configfiles> | |
23 <configfile name="DESeq2"> | |
24 ## Setup R error handling to go to stderr | |
25 options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) | |
26 suppressMessages(require(DESeq2)) | |
27 ## suppressMessages(require(ReportingTools)) | |
28 countData = read.delim("${input}", header=TRUE, check.names=FALSE) | |
29 rownames( countData )= countData[,1] | |
30 countData= countData[ , -1 ] | |
31 stringconds = "${expPlan}" | |
32 conds = unlist(strsplit(stringconds, split="")) | |
33 colData=data.frame(row.names=colnames(countData), condition=conds) | |
34 dds = DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~ condition) | |
35 colData(dds)\$condition = factor(colData(dds)\$condition, levels=c("C","T")) | |
36 dds = DESeq(dds, quiet=TRUE) | |
37 res = results(dds) | |
38 res = res[order(res\$padj),] | |
39 baseMeanA = rowMeans(counts(dds, normalized=TRUE)[rownames(res),colData(dds)\$condition== "C"]) | |
40 baseMeanB = rowMeans(counts(dds, normalized=TRUE)[rownames(res),colData(dds)\$condition== "T"]) | |
41 res2 = data.frame (gene=rownames(res), baseMeanA=baseMeanA, baseMeanB=baseMeanB, res) | |
42 ## resNA = res[-which(is.na(res[,8])),] ## omit the NA lignes | |
43 write.table ( res2, file = "${output}", row.names=FALSE, col.names=TRUE, quote= FALSE, dec = ".", sep = "\t", eol = "\n") | |
44 ## write.csv(as.data.frame(res), file="${output}") | |
45 </configfile> | |
46 </configfiles> | |
47 <help> | |
48 | |
49 **What it does** | |
50 | |
51 DESeq2 differential calling (order by padj, ascending). | |
52 Still in development and testing for replicates/no replicates | |
53 | |
54 | |
55 </help> | |
56 </tool> |