Summarize per-exon p-values into per-gene q-values.
perGeneQValue.RdThe use case for this function is the following analysis: given per-exon p-values for null hypothesis H0, we can determine the number of genes in which at least for one exon H0 is rejected. What is the associated false disovery rate?
Examples
data(pasillaDEXSeqDataSet, package="pasilla")
dxd <- estimateSizeFactors( dxd )
dxd <- estimateDispersions( dxd )
dxd <- testForDEU( dxd )
dxr <- DEXSeqResults( dxd )
perGeneQValue(dxr)
#> FBgn0000256 FBgn0000578 FBgn0002921 FBgn0003089 FBgn0010226 FBgn0010280
#> 3.983813e-12 2.252844e-08 2.259578e-02 1.000000e+00 1.000000e+00 5.973451e-01
#> FBgn0010638 FBgn0010808 FBgn0010909 FBgn0011300 FBgn0013811 FBgn0015278
#> 1.000000e+00 9.938660e-01 0.000000e+00 5.204589e-01 1.000000e+00 1.000000e+00
#> FBgn0020309 FBgn0020372 FBgn0026777 FBgn0027084 FBgn0027579 FBgn0028341
#> 1.613712e-03 1.000000e+00 1.000000e+00 1.000000e+00 3.968176e-03 1.000000e+00
#> FBgn0030170 FBgn0030302 FBgn0031108 FBgn0032003 FBgn0032785 FBgn0032979
#> 1.000000e+00 9.703793e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> FBgn0034158 FBgn0034180 FBgn0034420 FBgn0037050 FBgn0037683 FBgn0037926
#> 3.720118e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> FBgn0038725 FBgn0039776 FBgn0039914 FBgn0040297 FBgn0050460 FBgn0051021
#> 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.891757e-02 9.908623e-01
#> FBgn0051352 FBgn0051790 FBgn0051847 FBgn0085442 FBgn0250871 FBgn0259735
#> 1.000000e+00 1.000000e+00 1.000000e+00 1.104926e-09 1.000000e+00 1.000000e+00
#> FBgn0261573
#> 3.617344e-03