Entering edit mode
Edmund Chang wrote:
> Hi Jim,
> Thank you very much for the many tips! I really do appreciate it. I
do
> have another question about p-values associated with F-statistics.
> results<- decideTests(fit.all, method="nestedF", adj="none"
>
> In the limma user guide, it says the following about F-statistic
(Sec 18):
>
> "... In a complex experiment with many contrasts, it may be
desirable to
> select genes firstly on the basis of their moderated F-statistics,
and
> subsequently to decide which of the individual contrasts are
significant
> for the selected genes. This cuts down on the number of tests which
> need to be conducted and therefore on the amount of adjustment for
> multiple testing. The function decideTests() with method="nestedF"
is
> able to conduct such tests."
>
> If I do
> results<- decideTests(fit.all, method="nestedF", adj="none")
>
> I take this to mean that I can select the genes with either +1 or -1
in
> results$Res.contrastcoefficient
I think if you do something like that R will return NULL because your
results object isn't a list(), nor does it have anything in it called
Res.contrastcoefficient. Maybe an earlier version of limma used a list
for the TestResults object, but the version I have uses an S4 object
with only one slot.
Anyway, I think what is meant by the quote from the limma user's guide
is this; if you do decideTests() with method = "separate" or
"hierarchical" or "global", you are from the outset deciding to do a
fixed bunch of contrasts and will have to adjust for all the resulting
comparisons.
In contrast, "nestedF" looks at each individual significant
F-statistic
and then tries to decide which contrast(s) contributed to that
significant result. As an example, say geneX has a significant
F-statistic, and it is due to only one contrast (from a total of say,
four). If you select 'nestedF', it will find that first contrast and
see
that it is significant, but then won't make any more comparisons
because
it will be able to tell that the other contrasts didn't contribute to
the overall significance. Because 'nestedF' stops when the remaining
contrasts are not significant, you do less comparisons than the other
methods, and thus have to adjust less for multiplicity.
So, long story short, 'nestedF' doesn't allow you to make any choices
as
to what genes to test, it simply does fewer tests (well, technically I
think the upper bound is the same as 'hierarchical', but I don't think
it is likely that this would ever happen).
HTH,
Jim
and then do multiple-testing correction
> (using say q-value?) on the unadjusted p-values my contrast-of-
interest
> (which I would then choose some cutoff). I am wondering if
application
> of multiple-testing correction in this fashion would underestimate
the
> true FDR (rather than running q-value of on the entire set of genes
> regardless of how they contribute to the F-statistic?)
>
> Thank you for your time,
> Edmund
>
--
James W. MacDonald
University of Michigan
Affymetrix and cDNA Microarray Core
1500 E Medical Center Drive
Ann Arbor MI 48109
734-647-5623
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