[BioC] DEXSeq - identical p-values for all exons in a gene

[Steve Lianoglou]

Hi Katherine,

You mention running into "the same problem" no matter how the formulas
are encoded, but I do not see anywhere in your email what this problem
actually is.

Can you please explain and show explicitly what the problem actually is?

Is an error being thrown somewhere, or?

Thanks,
-steve

On Thu, Jan 16, 2014 at 2:48 PM, KP [guest] <guest at bioconductor.org> wrote:
>
> Hi All,
>
> I have been running DEXSeq successfully on my dataset using the default, in-built linear models, but have been repeatedly running into problems when attempting to add an extra 'blocking' term to the model.
>
> I have checked the vignette and I thought my usage was correct:
>
> formulaFullModel <- ~ sample + exon + libType:exon + condition:exon
> formulaReducedModel <- ~ sample + exon + libType:exon
>
> ecs <- read.HTSeqCounts(countfiles=countFiles, design=sampleTable, flattenedfile=annotationfile)
> ecs <- estimateSizeFactors(ecs)
> ecs <- estimateDispersions(ecs, formula=formulaFullModel, nCores=cores, quiet=FALSE, file='dexseq_progress_report.txt')
> ecs<- fitDispersionFunction(ecs)
>
> ecs <- testForDEU(ecs, formula0=formulaReducedModel, formula1=formulaFullModel, nCores=cores, quiet=FALSE, file='testforDEU_progress_report.txt')
>
> To check whether my problem was related to the extra term or just manually specifying the model, I ran DEXSeq using only a basic model (no additional terms: formulaFullModel <- ~ sample + exon + condition:exon
> formulaReducedModel <- ~ sample + exon ), and had the same problem. This suggests it must be something to do with how I am specifying the model or providing it to the functions...?
>
> This is the sample table setup for the basic model:
>
>       X     countFile condition
> 1    H1   H.r1.counts   control
> 2    M1   M.r1.counts   treated
> 3  H2ac H.r2ac.counts   control
> 4  M2ac M.r2ac.counts   treated
> 5 Hshap   H.NA.counts   control
> 6 Mshap   M.NA.counts   treated
>
>
> I assume the cause is something silly on my part but I am stumped.  Any help would be greatly appreciated!
>
> Thanks!
>
> P.S. I also tried running the pasilla dataset in the same way but that time it worked, normal p-values.
>
>  -- output of sessionInfo():
>
> Sorry but this session info might not be quite right - the original run was done on a server and the workspace was saved and opened in RStudio. This is the session info from RStudio.
>
> R version 3.0.2 (2013-09-25)
> Platform: x86_64-pc-linux-gnu (64-bit)
>
> locale:
>  [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8
>  [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8    LC_PAPER=en_AU.UTF-8       LC_NAME=C
>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
>
> attached base packages:
> [1] parallel  stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] BiocInstaller_1.12.0 DEXSeq_1.8.0         Biobase_2.22.0       BiocGenerics_0.8.0
>
> loaded via a namespace (and not attached):
>  [1] biomaRt_2.18.0       Biostrings_2.30.1    bitops_1.0-6         GenomicRanges_1.14.4 hwriter_1.3
>  [6] IRanges_1.20.6       RCurl_1.95-4.1       Rsamtools_1.14.2     statmod_1.4.18       stats4_3.0.2
> [11] stringr_0.6.2        tools_3.0.2          XML_3.98-1.1         XVector_0.2.0        zlibbioc_1.8.0
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
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-- 
Steve Lianoglou
Computational Biologist
Genentech