[BioC] Conservative results using DEXSeq

[Gu [guest]]

Dear All:

I am using DEXSeq for comparing two groups: control and treatment, and I think the signal in the treatment is strong enough to get many significant results on differential exon usage. However, among the ~15,000 genes (after filtering low counts) considered, only ~100 genes are associated with DEU by a FDR control of 0.1. The number of raw p-values < 0.1 is ~5,800. This is obtained by

table ( tapply( res2$pvalue < 0.1, geneIDs(ExonCountSet), any ) )
# FALSE   TRUE 
# 1364    5802

By checking the histogram of raw p-values of exons (NOT genes), I find that it is monotonically increasing from 0 to 1, with relatively few counting bins falling into the bins from 0 to 0.2.

My question is: does this result make sense in terms of DE testing? Among 240,000 counting bins, only 14,000 are with p-value < 0.1. If I randomly sample, then there would be ~240,000*0.1 = 24,000 counting bins called DE, so the result is really conservative (I think the signal of treatment is pretty strong so the result is weird). Can I know if there is any explanation for this situation?

Thank you so much!

Best,
Gu

 -- output of sessionInfo(): 

R version 3.0.1 (2013-05-16)
Platform: x86_64-unknown-linux-gnu (64-bit)

locale:
  [1] LC_CTYPE=en_US       LC_NUMERIC=C         LC_TIME=en_US
  [4] LC_COLLATE=en_US     LC_MONETARY=en_US    LC_MESSAGES=en_US
  [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C
[10] LC_TELEPHONE=C       LC_MEASUREMENT=en_US LC_IDENTIFICATION=C

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
[1] DEXSeq_1.6.0       Biobase_2.20.0     BiocGenerics_0.6.0

loaded via a namespace (and not attached):
  [1] biomaRt_2.16.0       Biostrings_2.28.0    bitops_1.0-5
  [4] GenomicRanges_1.12.4 hwriter_1.3          IRanges_1.18.1
  [7] RCurl_1.95-4.1       Rsamtools_1.12.3     statmod_1.4.17
[10] stats4_3.0.1         stringr_0.6.2        tools_3.0.1
[13] XML_3.96-1.1         zlibbioc_1.6.0

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