<tool id="simpleDESeq" name="DESeq Profiling" version="1.0.2" hidden="true">
	<description>2 samples / no replicates</description>
        <requirements>
          <requirement type="package" version="3.1.2">R</requirement>
          <requirement type="package" version="2.14">biocbasics</requirement>
        </requirements>
	<command interpreter="Rscript">$simpleDESeq </command>
	<inputs>
		<param name="input" type="data" format="tabular" label="readcount lists, 2 samples"/>
	</inputs>
  <outputs>
    <data name="output" format="tabular" label="DESeq differential calling" />
  </outputs>
  <tests>
    <test>
      <param name="input" value="counts.tab" ftype="tabular"/>
      <output name="output" file="dge.tab.re_match.modified" ftype="tabular" compare="re_match" lines_diff="5"/>
    </test>
  </tests>
  <configfiles>
    <configfile name="simpleDESeq">
      ## Setup R error handling to go to stderr
      options( show.error.messages=F,  error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
      suppressMessages(require(DESeq))
      countsTable = read.delim("${input}", header=TRUE, check.names=FALSE)
      rownames( countsTable )= countsTable[,1]
      countsTable= countsTable[ , -1 ]
      conds = c("C","T")
      cds = newCountDataSet( countsTable, conds )
      cds = estimateSizeFactors( cds )
      cds = estimateDispersions( cds, method="blind", sharingMode="fit-only" ) #fitType="local" when dispersion fucks the test. Otherwise DESeq performs a parametric fit using a gamma-family GLM
      res = nbinomTest( cds, "C", "T" )
      resNA = res[-which(is.na(res[,8])),] ## omit the NA lignes
      write.table ( resNA[order(resNA[,8]), ], file = "${output}", row.names=FALSE, col.names=TRUE, quote= FALSE, dec = ".", sep = "\t", eol = "\n")
    </configfile>
  </configfiles>
 <help>

**What it does**

DESeq (version 1) differential calling (order by padj, ascending), on two samples, no replicate
  </help>
</tool>