I have a reasonably large RNA-Seq dataset for a non-model plant, and want to
fit a GLM with interaction terms:
expression ~ treatment + time + treatment:time

In theory, I should be able to test the significance of each term by
comparing a reduced model (dropping that term) with the full model above.

It seems DESeq can handle this for main effect terms: it produces a vector
of p-values with a reasonable distribution. See this example, using data
from the pasilla package:
data(pasillaGenes)
design <- pData(pasillaGenes)[, c("condition", "type")]
fullCountsTable <- counts(pasillaGenes)
cdsFull <- newCountDataSet(fullCountsTable[1:1000,], design)
cdsFull <- estimateSizeFactors(cdsFull)
cdsFull <- estimateDispersions(cdsFull, method="pooled")
fit1 <- fitNbinomGLMs(cdsFull, count ~ type + condition)
fit0 <- fitNbinomGLMs(cdsFull, count ~ type)
pvalsGLM <- nbinomGLMTest(fit1, fit0)


However, when I try this with an interaction term included it gives clearly
wrong results: every p value is either 0 or 1.
fit1 <- fitNbinomGLMs(cdsFull, count ~ type + condition + type:condition)
fit0 <- fitNbinomGLMs(cdsFull, count ~ type + type:condition)
pvalsGLM <- nbinomGLMTest(fit1, fit0)

So my questions:
(a) Is this even possible in DESeq?
(b) If so, can anyone spot the error in how I coded this?

Thanks for any advice!

-- 
Eli Meyer
Postdoctoral Fellow
Department of Integrative Biology
University of Texas at Austin
Austin, TX 78712
office: (512) 471-3278
cell: (310) 618-4483

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