Question: Multifactorial analysis with RMA and LIMMA of Affymetrix microarrays
gravatar for Gordon Smyth
15.7 years ago by
Gordon Smyth39k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth39k 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,
go limma factdesign • 499 views
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