Hello all, I do need some help on analyzing such unorganized data. Please help me out. Thank you so much! I basically followed the analysis of multi-level experiments in limma user guide. But I do not feel right about the code below. Please give me some suggestions. # I want to compare Normal vs. Tumor negative, and Normal vs Tumor positive. There are partial pairing (subject) and batch effect (chip). Treat <- factor(paste(targets$chip,targets$type,sep=".")) design <- model.matrix(~0+Treat) colnames(design) <- levels(Treat) corfit <- duplicateCorrelation(y,design,block=targets$subject) corfit$consensus fit <- lmFit(y,design,block=targets$subject,correlation=corfit$consensus) cm <- makeContrasts(TposvsN=(a1.Tpos+a2.Tpos+a3.Tpos)/3-(a1.N+a2.N)/2, TnegvsN=(a1.Tneg+a3.Tneg)/2-(a1.N+a2.N)/2, levels=design) ???? fit2 <- contrasts.fit(fit, cm) fit2 <- eBayes(fit2) topTable(fit2, coef=1, sort.by="p") sample type subject chip s1 Tneg 1 a1 s2 N 1 a1 s3 Tpos 2 a1 s4 N 2 a1 s5 Tneg 3 a1 s6 N 3 a1 s7 Tpos 4 a1 s8 N 4 a1 s9 Tpos 5 a2 s10 N 5 a2 s11 N 6 a2 s12 Tpos 7 a2 s13 N 7 a2 s14 Tpos 8 a2 s15 N 8 a2 s16 Tneg 9 a3 s17 Tneg 10 a3 s18 Tneg 11 a3 s19 Tpos 6 a3 s20 Tpos 12 a3 s21 Tneg 13 a3 s22 Tpos 14 a3 Thanks, Xiayu [[alternative HTML version deleted]]