On Thu, Jul 17, 2014 at 11:21 AM, James W. MacDonald <jmacdon@uw.edu> wrote:

> On the other hand, you are increasing your degrees of freedom markedly by
> including all the other groups, so your variance estimates will be much
> more accurate (and you are then borrowing information from all groups to
> estimate the intra-group variance, which is how ANOVA works, and isn't
> something specific to limma). I would think this has the largest effect on
> your results.
>

The dark side of this notion is that if your groupB happens to have higher
true variance than the other groups (i.e. heteroscedastic), then by
including the other groups you've shrunken the variance too much, and
inflated significance.  ANOVA (and limma) assumes all groups have the same
intra-group variance.  We see this when we (mistakenly) co-model data from
different groups for genes that are "off" in many groups (and thus have
much smaller variance driven by measurement noise) but "on" and more
biologically variable in a subset of groups.  You can use your two fits to
compare how much the estimated variance has decreased with the complete
dataset, especially for your newly significant genes, and whether the mean
expression of those genes is also much lower in the complete dataset.

-Aaron

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