Hello, I've been presented with the following design: Tissue strain Cy5 Cy3 1 tis1 KO d0 d01 2 tis1 KO d01 d0 3 tis1 KO d0 d03 4 tis1 WT d03 d0 5 tis1 WT d0 d01 6 tis1 KO d03 d0 7 tis1 WT d01 d0 8 tis1 WT d0 d03 9 tis2 KO d0 d01 10 tis2 KO d01 d0 11 tis2 KO d0 d02 12 tis2 KO d02 d0 13 tis2 WT d0 d01 14 tis2 WT d01 d0 15 tis2 WT d0 d02 16 tis2 WT d02 d0 17 tis3 WT d0 d03 18 tis3 WT d03 d0 19 tis3 KO d03 d0 20 tis3 KO d0 d03 In summary: there are three different tissues. There are two strains and four measurement points for the experiment (d0, d1, d2, d3), with d0 as reference. The two-channel arrays have been loaded with RNA samples such that the "day 0" from the respective tissue is the reference. There is a dye swap present. I am looking for differences in the strains -- which genes are regulated in a different manner between WT and KO. How should I do it best? obviously, simply comparing WT with KO is *not* the way to go, since change in d04 (as compared with d0) can be very different from response in d02, and also response is very variable between tissues. However, I am looking for genes that are, for example, differentially expressed in the KO, but not changing in the WT. One way of solving that would be splitting the data sets, fitting the model separately to the subsets, creating lists for subsets (e.g. list of genes diff. expressed in KO, list of DE genes in WT) and comparing the subsets. It works, but I don't like it. Best regards, j. -- -------- Dr. January Weiner 3 -------------------------------------- Max Planck Institute for Infection Biology Charit?platz 1 D-10117 Berlin, Germany Web?? : www.mpiib-berlin.mpg.de Tel? ?? : +49-30-28460514