[BioC] interaction effect (4x2)

[James W. MacDonald]

Hi Sebastien,

Sebastien Gerega wrote:
> Hi,
> I am having trouble setting up the design for a microarray analysis.
> It involves 40 samples that are split into 4 groups and are treated in 
> one of 2 ways.
> What I want to do is identify genes with an interaction effect between 
> group and treatment.
> What would the best way to go about this? I have attempted the following:
> 
> interDesign = model.matrix(~factor(sDrug) * factor(sGroup))
> interFit = lmFit(lumi.N.P, interDesign)
> interCont = cbind(c(0,0,0,0,0,1,0,0),c(0,0,0,0,0,0,1,0),c(0,0,0,0,0,0,0,1))
> interFit = contrasts.fit(interFit, interCont)
> interFit = eBayes(interFit)
> interDTest = decideTests(interFit, method="nestedF", 
> adjust.method="fdr", p.value=0.05)
> which(abs(interDTest[,1]) == 1 | abs(interDTest[,2]) == 1 | 
> abs(interDTest[,3]) == 1)
> 
> Is this a suitable way to identify the genes with an interaction effect?

Well, with 4 groups and 2 treatments I get 6 total interactions. Are the 
three you are testing here the interesting interactions?

> So far, from looking at expression profiles, I don't seem to be picking 
> out interesting genes....

Interesting defined how? The genes you get aren't a priori genes you 
want to see? Or you aren't getting any significant genes?

Best,

Jim


> Any help would be greatly appreciated.
> thanks,
> Sebastien
> 
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-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
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