Question: Multifactorial analysis with RMA and LIMMA of Affymetrix microarrays
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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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