I am using affymetrix arrays to study primary and metastic tumors from the same patients (five patients, so five primary and five metastatic tumors). As we have two classes and paired experiments I have been using a paired t-test. However, I would like to use limma with my arrays. It seems that one can fit patient as a fixed effect in order to limma compute paired t-tests with moderated denominators. How to fit patient as a fixed effect? It would be by adding it in the phenotable. tmp<-pData(eset) tmp<-cbind(tmp,TumorMetastasis=factor(c(0,1,0,1,0,1,0,1,0,1))) tmp<-cbind(tmp,BLOCKpaireddata=factor(c(1,1,2,2,3,3,4,4,5,5))) pData(eset)<-tmp and then design<-model.matrix(~TumorMetastasis*BLOCKpaireddata, data=pData(eset)) fit<-lmFit(eset,design) contrast.matrix<-makeContrasts(Tumor_Metastasis,levels=design) fit2<-contrasts.fit(fit,contrast.matrix) Thank you very much for your help, Best wishes, Pedro Pedro Jares, Ph.D. Genomics Unit, IDIBAPS University of Barcelona C/ Villarroel 170 08036, Barcelona, Spain Telf 93 2275400 Ext. 2184 o 2129 Fax 93 2275717

> Date: Wed, 9 Mar 2005 16:36:40 +0100 > From: <pjares@clinic.ub.es> > Subject: [BioC] RE: How to fit patient as a fixed effect/paired > samples > To: <bioconductor@stat.math.ethz.ch> > > I am using affymetrix arrays to study primary and > metastic tumors from the same patients (five patients, > so five primary and five metastatic tumors). As we > have two classes and paired experiments I have been > using a paired t-test. However, I would like to use > limma with my arrays. > > It seems that one can fit patient as a fixed effect in order to limma > compute paired t-tests with moderated denominators. A paired t-test is the same as a two-way additive anova, in limma and also in traditional statistics. > How to fit patient as a fixed effect? It would be by adding it in the > phenotable. Have you tried the code below and found it didn't work? Trying it out and looking at the results may help you to understand what's going on. > tmp<-pData(eset) > tmp<-cbind(tmp,TumorMetastasis=factor(c(0,1,0,1,0,1,0,1,0,1))) > tmp<-cbind(tmp,BLOCKpaireddata=factor(c(1,1,2,2,3,3,4,4,5,5))) > pData(eset)<-tmp > > and then > > design<-model.matrix(~TumorMetastasis*BLOCKpaireddata, data=pData(eset)) You need "+" not "*". You would see that you need something different when you fit the model and find that there are no degrees of freedom for residuals. > fit<-lmFit(eset,design) > contrast.matrix<-makeContrasts(Tumor_Metastasis,levels=design) What is "Tumor_Metastasis", i.e., why the underscore "_"? This function will give an error, so you know it must be wrong. Actually you don't need to use constrasts as all. Just use fit <- eBayes(fit) and pick out the coefficient corresponding to the tumour effect. Type colnames(design) to see what the name of this coefficient is. The corresponding t-statistic is the paired t-statistic. Gordon > fit2<-contrasts.fit(fit,contrast.matrix) > > Thank you very much for your help, > > Best wishes, > > Pedro > > Pedro Jares, Ph.D. > Genomics Unit, IDIBAPS > University of Barcelona > C/ Villarroel 170 > 08036, Barcelona, Spain > Telf 93 2275400 > Ext. 2184 o 2129 > Fax 93 2275717