[BioC] mock comparison p-value histogram in DEXSeq

[Gu [guest]]

Dear All:

In the DEXSeq paper, the authors compared DEXSeq with Cuffdiff in terms of controlling Type-I error rates. From the mock comparison results (control vs. control), we can see DEXSeq reported far fewer genes with differential exon usage (DEU), as shown in Table S2 of the DEXSeq paper (2012). However, I think this kind of mock comparison is "under the null", which means if we plot a histogram of the p-values from such comparison, it should be very close to the histogram from a uniform random variable. I am not sure if the authors from DEXSeq have checked that, or consider it inappropriate.

I use my dataset to make a control vs. control comparison, and happily find very few genes with DEU (which is good). However, when I plot the raw p-values (not the B-H adjusted p-values), the resultant histogram is not uniform-like. The height of each histogram bin is increasing monotonically, i.e. the frequencies increase as the p-values increase. In other words, there are "so few" small p-values reported for the control vs. control comparison. What can I tell from such a histogram?

The reason why I ask this question is that, even though the number of reported genes with DEU is small using DEXSeq for mock comparison, the p-values, in my thinking, should be uniform-like. I can convince myself with the small numbers, but would be more convinced from the histogram.

Thank you for your suggestions. Please correct me if I am wrong!

 -- 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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