Hi Kasper, On Tue, Jul 9, 2013 at 5:51 PM, Kasper Daniel Hansen <kasperdanielhan...@gmail.com> wrote: > This is side-stepping the question, but I am not aware that it ever makes > sense to include the "A:B" term in a design matrix without also including > the main effects of A and B (here I include obvious extensions such as A + > A:B + C where B is a coarser factor than C, so here the main effects of B > are essentially included). Of course, the A+A:B+C example seems to also > fail in the DESeq2 code you quote, but these examples are rare in comp bio. > > If you're just fitting a model like > A + A:B > you're almost certainly doing something wrong from a statistical point of > view.
Thanks for the input/guidance -- I'm always happy to get some linear-modeling-schooling. If that's the case, does that mean that I'm interpreting the advice from the limma user's guide incorrectly? Page 44 in the "Nested Interaction Formula" section: http://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf Aren't the coefficients extracted for cellA:treatment1, cellA:treatment2, ..., cellD:treatment4 that you get when modeling this way exactly the fold changes for the effect of the treatment within each cell type? Thanks, -steve -- Steve Lianoglou Computational Biologist Bioinformatics and Computational Biology Genentech _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
