Hello, everyone: I have the following experiment that I want to conduct, but I am not sure which is the right way to use design matrix and contrasts. Here is the experiment: say I have 3 different strains that are genetically different, A, B and C where A is the control. I also have two different treatments, T1 and T2. For each strain, I have 4 arrays for each treatment, so in total, I have 24 arrays. What I want to find out is the significantly differentially expressed genes for the following comparison: 1) for control strain A: T1 vs T2 2)under T1, B vs. A (control) 3) under T1, C vs. A 4) for B, T1 vs T2 5) for C, T1 vs T2 6) interaction term of A and B , T1 and T2 7) interaction term of A and C, T1 and T2. There are two ways I could use lmFit One is: for the design matrix, I use the following code: A_T1, A_T2, B_T1, B_T2, C_T1, C_T2 sample1: 1 ,0 ,0, 0, 0 , 0 sample2 : Then make a contrast matrix and follow the code below: fitGene<-lmFit(gene,design=design,weights=arrayWt); fitGene2<-contrasts.fit(fitGene,cont.matrix) fitGene2<-eBayes(fitGene2,proportion=p); Two: Instead of using all samples at one time to fit into a lmFit function, I use two design matrix only involves A and B, T1 and T2, and second design matrix that involves A and C, T1 and T2, and make contrast matrix and fit separately. The question I have is: which one is the right one? For the first method, I will have larege DOF , and much lower p-values, but it was the same test, am I correct? Thanks for your help! Sabrina -- Sabrina [[alternative HTML version deleted]]