[BioC] interaction effect (4x2)
James W. MacDonald
jmacdon at med.umich.edu
Fri Feb 29 15:02:44 CET 2008
Sebastien Gerega wrote:
> Thanks for your reply James,
> James W. MacDonald wrote:
>> Well, with 4 groups and 2 treatments I get 6 total interactions. Are
>> the three you are testing here the interesting interactions?
> I guess I am interested in all 6 interactions. How would I go about
> looking at them all?
sD <- factor(sDrug)
sG <- factor(sGroup)
design <- model.matrix(~0 + sD:sG)
Then make a contrasts matrix.
>>> 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?
> The reason I said that was initially I accidentally performed the
> analysis without applying the contrast:
> interDesign = model.matrix(~factor(sDrug) * factor(sGroup))
> interFit = lmFit(lumi.N.P, interDesign)
> interFit = eBayes(interFit)
> interDTest = decideTests(interFit, method="nestedF",
> adjust.method="fdr", p.value=0.05)
> which(abs(interDTest[,6]) == 1 | abs(interDTest[,7]) == 1 |
> abs(interDTest[,8]) == 1)
> And the genes I identified that way were interesting to me, based on a
> quick glance at expression profiles. Then I realised I should have
> applied a contrast.
> thanks again,
James W. MacDonald, M.S.
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
Ann Arbor MI 48109
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