Actually the limma guide is right and I am wrong.  For a moment I forgot
the precise meaning of ":" in context of R's formula system.

You still need to be careful with this, as the limma guide also states
quite clearly.

Kasper


On Tue, Jul 9, 2013 at 9:01 PM, Steve Lianoglou <lianoglou.steve@gene.com>wrote:

> Hi Kasper,
>
> On Tue, Jul 9, 2013 at 5:51 PM, Kasper Daniel Hansen
> <kasperdanielhansen@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
>

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