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Old 07-13-2011, 11:50 AM   #1
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Location: Austin TX

Join Date: Jul 2008
Posts: 7
Default DESeq: GLMs for RNA-Seq with interaction terms

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:
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!
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