Thanks for the fast response, Kasper.  No, thats not confusing, we were
thinking the same thing early on... its often a question of figuring out how
to express the model in the given software package.

How would you recommend testing these main effect terms within DESeq, in
that case?

On Wed, Jul 13, 2011 at 3:17 PM, Kasper Daniel Hansen <
kasperdanielhansen@gmail.com> wrote:

> On Wed, Jul 13, 2011 at 3:56 PM, Eli Meyer <EliMeyer@mail.utexas.edu>
> wrote:
> > 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)
>
> The smaller model makes no sense.  You are (in words) asking for an
> interaction between condition and type without including a condition
> (main) effect.  If you are confused about this (and I agree it might
> be confusing at first depending on your training) you should read any
> introduction to linear models.
>
> Kasper
>



-- 
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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