Hi! I have somewhat basic question about limma analysis design. The experimental design is as follows (using Affymetrix single channel arrays): 1. We have a set of people, a baseline sample is taken from everyone (Control) 2. People are divided in two intervention groups (group A and B) 3. After intervention, sample is taken from everyone (After) So, we basically have paired samples (before and after intervention Control/After) and two distinct groups (different types of intervention, A/B). I am interested in the following questions: 1. Differentially expressed genes in group A between Control and After 2. Differentially expressed genes in group B between Control and After 3. Differentially expressed genes between Control and After The first two cases are pretty simple; I can for example divide the expression data set to groups A and B, and then perform basic paired sample analysis inside the group between Control and After. What I am not so sure about is that with the 3rd question, how could/should I take the group in to account with the analysis? Any help would be appreciated! To provide a practical example, here is an example target frame: FileName Person Time Group 1 1.cel 1 Control A 2 2.cel 1 After A 3 3.cel 2 Control A 4 4.cel 2 After A 5 5.cel 3 Control B 6 6.cel 3 After B 7 7.cel 4 Control B 8 8.cel 4 After B And this would create a design matrix for a basic paired analysis between Control/After: > person <- factor(targets$Person) > group <- factor(targets$Group, levels=c("A","B")) > time <- factor(targets$Time, levels=c("Control","After")) > > design <- model.matrix(~person+time) > design (Intercept) person2 person3 person4 timeAfter 1 1 0 0 0 0 2 1 0 0 0 1 3 1 1 0 0 0 4 1 1 0 0 1 5 1 0 1 0 0 6 1 0 1 0 1 7 1 0 0 1 0 8 1 0 0 1 1 Thanks, Jussi -- Jussi Paananen