[BioC] interaction effect (4x2)

[James W. MacDonald]

Hi Sebastien,

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.

Best,

Jim


> 
>>> 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,
> Sebastien

-- 
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
734-647-5623