Dear List, I need to analyze an experiment with a design more complex than usual for our facility, and I am not sure about the best way to analyze this dataset. I would really appreciate your advice on whether the method I am planning to use is correct, or if there is a better way to analyze this data. The experiment design is the following: We have 10 patients, and we are using one-color Agilent arrays. Each patient performed a physical test twice: once without anything added, and once taking a drug during the test. Samples are collected before and after the physical test, for a total of 4 samples by patients. The drug was administered randomly during the first or second test. Here is the top of my target file: Patient.ID TimePoint Drug TestOrder Drug.Included.In.Test Pt1 Before no test1 control Pt1 After no test1 control Pt1 Before no test2 test Pt1 After yes test2 test Pt2 Before no test2 control Pt2 After no test2 control Pt2 Before no test1 test Pt2 After yes test1 test We are interested in finding: - DE genes related to physical test only - DE genes related to the addition of the drug only - Genes differentially regulated by the drug during the physical test I usually use limma for differential analysis, so following the limma user?s guide, I was planning to use a design with blocks of size 4 for patients, and a variable with 4 levels combining Drug.Included.In.Test and TimePoint. Is this approach correct? I read in the user?s guide patient information could also be modeled as random effect using the duplicateCorrelation function. Would this method be more appropriate? Is there a better way to model the data, that would estimate the physical test effect and the drug effect directly? Thank you very much for your help, Agnes