Question: Multifactorial analysis with RMA and LIMMA of Affymetrix microarrays

0

15.7 years ago by

Gordon Smyth ♦

**39k**Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

Gordon Smyth ♦

**39k**wrote:At 02:48 AM 18/03/2004, Jordi Altirriba Guti?rrez wrote:
>Thank you very much Gordon for your quick answer!
>My phenoData is:
>>pData(eset)
> DIABETES TREATMENT
>DNT1 TRUE FALSE
>DNT2 TRUE FALSE
>DNT3 TRUE FALSE
>DT1 TRUE TRUE
>DT2 TRUE TRUE
>DT3 TRUE TRUE
>SNT1 FALSE FALSE
>SNT2 FALSE FALSE
>SNT3 FALSE FALSE
>ST1 FALSE TRUE
>ST2 FALSE TRUE
>ST3 FALSE TRUE
>
>(DNT=Diabetic untreated, DT=Diabetic treated, SNT=Health treated,
>ST=Health untreated)
>
>I want to know the genes characteristics of the diabetes, the
treatment
>and the treatment + diabetes. Moreover when I analyse my data with
SAM and
>I compare Health treated vs the Health untreated I don't see many
>differences, but when I compare the Diabetic treated vs the Diabetic
>treated I see a lot of differences, so is correct to apply a 2 x 2
>factorial design?
You simply need to fit a model which contains four coefficient which
distinguish your four groups. The classical 2x2 model is just one
particular parametrization you can use:
design <- model.matrix( ~ DIABETES*TREATMENT, data=pData(eset))
fit <- lmFit(eset, design)
>Is LIMMA the correct tool to answer my questions? If it is the
correct
>tool, how can I do a factorial design matrix (if to do a factorial
design
>is correct)? (Robert Gentleman has suggested me to use the
factDesign).
You're just fitting a linear model, so the above calculation is
exactly
equivalent to what factDesign does, although probably a bit faster. I
would
use limma myself because it allows you go on to do empirical Bayes
moderation of the residual standard deviations etc, which I think it
important, but Robert may be able to make a further case for
factDesign.
Cheers
Gordon
>Thank you very much for your time, patience and your suggestions.
>Yours sincerely,