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. Somewhere, I found that I could introduce a block in order to get the lmFit. I have been doing that for a while and getting pretty nice results. However, recently in the new limma manual appears that the block option should be used for technical replicates, and you need to calculate duplicateCorrelation. As far as I understood using block argument alone in lmFit you assume a default correlation of 0.75. However, if I do duplicateCorrelation, the correlation is 0.5. When I compare the top 50 genes that I got doing block without dupCorr and with dupCorr, I see that the p values and B values are worst for block with dupCorr, moreover if I do a hierarchical cluster of the samples using the two list of genes the one obtained by doing block without dupCorr seems to performs much better. I would like to know what to do in order to analyze paired experiments (this will allow us to remove inter-person variability!!!,) to compare two classes with single color arrays in limma. I have the impression that paired experiments are clearly different to technical replicates. So, I would appreciate if anybody can tell if I should use duplicateCorrelation or something else. The commands I have been using are: fit<-lmFit(eset,design,block=c(1,1,2,2)) or corfit<-duplicateCorrelation(eset,design,block=c(1,1,2,2)) fit<-lmFit(eset,design,block=c(1,1,2,2),correlation=corfit$consensus) Thank you very much for your help and your time, Best wishes, Pedro