Dear All, When we use limma, we apply the same linear model to all genes. However, at least some times, we can think of different genes requiring slightly different models (e.g., geneA ~ class + age, geneC ~ class). When having a set of prespecified, and nested, set of models, and only one dependent var., I'd use F-tests to compare among models. Sure, I can still do the same for thousands of genes, but I'd be working gene-by-gene (i.e., not taking advantage of the ebayes approaches available in limma) and incurring in massive multiple testing and sequential testing problems. Has anybody thought about a more principled and rigorous framework? I haven't seen this issue dealt with in lists or the literature. Best, R. P.S. I am not looking for any stepwise-like solution. In the particular case I am working on now, we are considering three pre-specified models (age*trt, age + trt, trt). And, also in this case, my collaborators do not like at all the idea of fitting an age term when it is obviously not needed for at least some genes that differ strongly in non-age-adjusted intercept. -- Ram?n D?az-Uriarte Bioinformatics Unit Centro Nacional de Investigaciones Oncol?gicas (CNIO) (Spanish National Cancer Center) Melchor Fern?ndez Almagro, 3 28029 Madrid (Spain) Fax: +-34-91-224-6972 Phone: +-34-91-224-6900 http://bioinfo.cnio.es/~rdiaz PGP KeyID: 0xE89B3462 (http://bioinfo.cnio.es/~rdiaz/0xE89B3462.asc)