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Sebastien Gerega
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370

@sebastien-gerega-2229
Last seen 8.1 years ago

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?
So far, from looking at expression profiles, I don't seem to be
picking
out interesting genes....
Any help would be greatly appreciated.
thanks,
Sebastien