Hi All, I have two color microarray data (genepix format) from patients with cancer. I have a variable number of replicates per patient and in some cases a dye-swap. On each slide we compared the RNA from tumor tissue to RNA from healthy tissue in the same patient. Going through the limma documentation I used the approach below but I am not sure its correct and makes sense. I am looking for genes differentially expressed between healthy and tumor tissues. The approach below resulted in 12,000 significant genes after multiple testing correction. I am wondering if anyone could suggest an approach that makes most sense. Finally, can someone tell me how to export data for making a heatmap? With Affy its always easy to just export the expression values of significant genes, but what do I export here to see expression level of each significant gene on each slide? Thanks for the help, Yair #dataset was background corrected (rma), normalized within arrays and normalized between arrays (quantile) #setup design matrix with replicate information pateint1<-c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) pateint2<-c(0, 0, 1, 1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0) pateint3<-c(0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0) pateint4<-c(0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0) pateint5<-c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, -1) design<-cbind(pateint1, patient2, patient3, patient4, patient5) #setup contrast matrix to find difference between control and tumor cont.matrix<-makeContrasts(CONTROLvsTUMOR=(pateint1+patient2+patient3+ patien t4+patient5)/5, levels=design) #fit linear model fit<-lmFit(MA, design) fit2 <- contrasts.fit(fit, cont.matrix) fit2<-eBayes(fit2) topTable(fit2, adjust="BH")