split-plot factorial design with limma?
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@valeria-edefonti-1998
Last seen 9.6 years ago
Hi all, I'm analyzing an experiment using 10 Affymetrix chips. I have 5 subjects, who are either healthy or having two different illnesses, TTD or CS. Each subject is considered twice, at different levels of growth, P or D, of some 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 follows: 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) 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) I guess so far so good, right? However, a two treatment split-plot factorial design may be slightly better for my case, though I'm definitely not an expert in this field. I see three groups of blocks: Healthy: 3 blocks TTD: TTD block CS: CS block, each one corresponding to a different level of factor "illness". 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
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