Dear Valeria, You should always at least cc emails like this to the Bioconductor list. I do not have time to go through your code in detail. I agree that you have a split plot design. My understanding of limma is that it handles 1 random effect. In the split plot design you describe, you should test disease status against subject(disease) and growth and disease x growth against the error, so that actually you have 2 random effects. All of the contrasts you are doing are for individual treatments and so will be tested against the "pure error". So, I think that you should be able to do this. However, you will need to add "subject" to the design matrix for the 3 healthy subjects. --Naomi At 08:23 AM 3/26/2007, you wrote: >Dear Dr. Altman >I saw you are a very active member of the Biocond mailinglist, >especially for questions on the use of limma for any kind of >experimental designs. >I apologize I'm writing to you directly but I posted a question two >months ago and I didn't receive any feedback from the list. > From an extensive analysis of all the previous emails, I realized >you could be the right person for my question and that's why I'm >writing to you. >If you could help me out of this, it would really be great! >I'm analyzing an experiment using 10 Affymetrix chips. >I have 5 subjects: 3 of them are different healthy persons, 2 have >two different illnesses, TTD or CS. >Each subject is considered twice, at different levels of growth, P or >D, of his cells. >Pairs are A and G, B and H and so on: > > >targets > > GROWTHLEVEL ILLNESS >A P H >B P H >C P H >D P TTD >F P CS >G D H >H D H >I D H >L D TTD >N D CS > >I adopted a factorial design with a blocking variable, blocco, for >identifying the five subjects. >The code is the following: > >SS<-paste(targets$GROWTHLEVEL, targets$ILLNESS, sep=".") >SS<-factor(SS, levels=c("P.H","P.TTD","P.CS","D.H","D.TTD","D.CS")) >design.ch<-model.matrix(~0+SS) >colnamesdesign.ch)<-levels(SS) >rownamesdesign.ch)<-rownames(targets) >blocco<-c(1,2,3,4,5,1,2,3,4,5) >corfit<-duplicateCorrelation(MA, design=design.ch,block=blocco) >fit<-lmFit(MA, design.ch, block=blocco, cor=corfit$consensus) > >The biologists I'm working with are interested in the following >contrasts: > >contrast.matrix<-makeContrasts(H.PvsD=P.H-D.H, TTD.PvsD=P.TTD-D.TTD, >CS.PvsD=P.CS-D.CS, P.TTDvsH=P.TTD-P.H, P.CSvsH=P.CS-P.H, >D.TTDvsH=D.TTD-D.H, D.CSvsH=D.CS-D.H,levels=design.ch) > >fit2<-contrasts.fit(fit, contrast.matrix) >fit3<-eBayes(fit2) > >Could a two treatment split-plot factorial design be slightly better >for my case? >I see a whole plot variable completely counfounded with factor >"illness": >level 1: Healthy >level2: TTD >level3: CS >Is it a good idea? > >Is there a way to perform such an analysis with limma? >If not, can I still stick to my current solution? >I know I can use lme() or aov(), but I should modify them to moderate >the random effects across genes. > >Thank you very much for your help. >Best wishes >Valeria Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111