[BioC] necessity of moderated t statistic and false discoveries for small predefined gene list?

Moshe Olshansky olshansky at wehi.EDU.AU
Thu May 17 06:35:28 CEST 2012

Hi Rich,

I think that Gordon Smyth (the author of limma) has explained at this list
what moderated t-statistic is.
The brief explanation is that when there are few samples the estimate of
the variance which is used in a standard t-test is quite noisy and because
one must account for this noise the standard t-test has a low statistical
power. The Empirical Bayes model used in the moderated t-tests allows to
estimate the variance with more confidence and therefore has a better
power. So it can be used even if you are interested in just a few genes.
It has (almost) nothing to do with the multiple testing adjustment. Well,
one may ask whether moderated p-values satisfy the assumptions of multiple
testing adjustment procedures (in particular the BH), but this is another
story. May be Gordon will comment on this.

Best regards,

> Moshe and List,
>  	Thanks for yoru reply. The method you describe retains
> the raw p-value based on the moderated t-statistic and adjusts
> it to give an adjusted p-value (usually a false discovery rate).
> However, as I understand it, the moderated
> t-statistic given by Limma based on
> all of the genes in the array, pools variance information
> to moderate the standard deviation to prevent fortuitously
> low p-values stemming from fortuitously low standard deviations
> encountered in thousands of multiple tests.I am wondering
> that if the experimentalist asks me to look up just 10 genes
> I should use the unmoderated frequentist t-statistic which
> will differ from the one in Limma and may imply significance
> where Limma does not. I guess another way to phrase it is
> "How many simulataneous tests does one need before one
> should prefer the moderated statistic to the empirical
> Bayesian one". Or should I fit just those 10 genes
> (~30 affy probes) with Limma?
> Best wishes,
> Rich
> On Thu, 17 May 2012, Moshe Olshansky wrote:
>> Hi Rich,
>> Whether to use the moderated t-statistic or not does not depend on
>> whether
>> you are interested in the 10 particular genes or in all differentially
>> expressed ones. This will affect your multiple testing adjustment.
>> The simplest way for you to proceed is to use limma as usual, get the
>> topTable but then take the UNADJUSTED p-values for your 10 genes of
>> interest and use the p.adjust function to adjust for multiple testing if
>> you wish. In any case you should also look at (log)Fold Changes.
>> Best regards,
>> Moshe.
>>> Dear Bioconductor  List.
>>> 	I am using Limma to analyze differential expression between 2
>>> conditions on an Affy chip.
>>> My experimental collaborator asks for the differential  expression of
>>> 10 predefined genes.
>>> A, Should I correct for false discoveries based upon all of the genes
>>> on the chip?
>>> B. If not, should I correct for false discoveries just for the
>>> probeids for the 10 predefined
>>> genes?
>>> C. Should I use the moderated t-statistic or just use an unmoderated t-
>>> test for those 10
>>> genes.
>>> Thanks and best wishes,
>>> Rich
>>> ------------------------------------------------------------
>>> Richard A. Friedman, PhD
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> --
> ------------------------------------------------------------
> Richard A. Friedman, PhD
> Associate Research Scientist
> Herbert Irving Comprehensive Cancer Center
> Biomedical Informatics Shared Resource
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