On Fri, Oct 7, 2011 at 8:15 AM, James W. MacDonald <jmacdon@med.umich.edu>wrote:

>
> First, by increasing the number of genes, you can more accurately
> estimate an overall variance, which is then used in the eBayes() step to
> 'shrink' your observed variance towards this overall variance. This is
> one of the reasons that limma is so popular - by using information from
> all genes, you can increase the power to detect differences in
> individual genes.
>

Careful though -- this is methylation data, which tends to be strongly
bimodal.  It's not clear that the assumption of a common variance across
unmethylated, partially-methylated, and methylated sites is appropriate.  I
seem to recall Gordon Smyth commenting upon this at one point -- perhaps
he'll chime in.


> Second, when you increase the number of pairs you are using, your power
> to detect differences increases as well. This has nothing to do with
> limma per se; it is just that the power of a t-test increases as you
> increase the number of observations.
>

This is of course appropriate at any time that you can get more high-quality
samples rather than fewer :-)

-- 
If people do not believe that mathematics is simple,
it is only because they do not realize how complicated life is.John von
Neumann<http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Von_Neumann.html>

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