After analysis of microarray data, I am confriming differential gene expression by qRT-PCR, using delta Ct method. I can analyze data manually, Is there any easy method or package to analyze these data? I have looked at Prada package there is no clear instruction manual. Any help will be appreciated? Thanks in advance. Narendra

Dear list, I have three groups of samples (Tto, NoTto, C) and have made the three possible pairwise contrasts as follows: design.tto <- model.matrix(~-1+factor(c (1,1,2,2,2,3,2,1,3,1,2,2,3,1,1,2,1,1,3,1))) colnames(design.tto) <- c("C","Tto", "NoTto") fit.tto <- lmFit(eset,design.tto) contrast.matrix.tto <- makeContrasts(Tto-C,NoTto-C, Tto-NoTto, levels = design.tto) fit2.tto <- contrasts.fit(fit.tto, contrast.matrix.tto) fit3.tto <-eBayes(fit2.tto) If I select all d.e (p<0.05) genes from each contrast of interest I get the same number of genes, 463, although different topTable(fit3.tto, coef = X(where X =1,2 or 3)#, adjust="none", sort.by="P", number=5506); NoTto_vs_C_y <- NoTto_vs_C[x$P.Value < 0.05,] dim(Tto_vs_C_y) [1] 463 7 > dim(NoTto_vs_C_y) [1] 463 7 > dim(Tto_vs_NoTto_y) [1] 463 7 Is this normal? Thanks in advance David