Hi all,

Several questions concerning intricacies of limma's decideTests() have
emerged from a discussion with some colleagues here at the EBI. Perhaps
someone can enlighten us.

1. The docs (and previously on this list) say that nestedF is especially
powerful in identifying genes that are diff. expressed in many contrasts,
and less powerful for ones diff. expressed in only one contrast. On the
other hand we also read that with nestedF "at least one contrast will be
classified as significant if and only if the overall F-statistic is
significant" - meaning that it should pick up genes diff. expressed in only
one contrast, shouldn't it? Why less powerful?

2. Say we have 5 contrasts adn we run decideTests() both with "separate" and
with "nestedF" methods and are comparing the results. Suppose some gene is
marked as differentially expressed in 2 out of 5 "separate" contrasts, but
is significant in only 1 out of 5 "nestedF" ones. What's the best way to
interpret such a result? Shall we say this gene is not sufficiently variable
overall? And vice versa, if it's marked significant in 2 columns in
"nestedF" but only in 1 in "separate" results, does "nestedF" overestimate
its significance, or is it that "separate" failed to pick it up in some
contrast?

3. Does it make sense to rank genes in order of significance of differential
expression by looking at how many columns of "nestedF" results have non-zero
values for each gene? If a gene is classified by "nestedF" as diff.
expressed in only 1 contrast, is it still one of the most variable genes
across all contrasts?

4. Say we have an experiment with treatment A, treatment B and a compound
treatment A+B (not time course), is it legitimate to apply "nestedF" to all
pair-wise contrasts to identify the most responsive overall genes, but then
to look at the results matrix separately to say which treatments contributed
most to this overall significance? Or is it more sensible to do it with the
method "separate"?

Many thanks in advance for any answers to these questions!

--Misha K. and colleagues
Microarray Informatics Team, EBI

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