[BioC] Very low P-values in limma
Paul Geeleher
paulgeeleher at gmail.com
Wed Oct 28 14:23:33 CET 2009
Dear list,
The following are the words of a professor in my department:
I still don't get why the 'real' p-values could be better than
p-values you get with the assumption of zero measurement error. By
averaging over within array replicates you are not ignoring the within
array replicates, instead you are acting as though there were
infinitely many of them, so that the standard error of the expression
level within array is zero. Stats is about making inferences about
populations from finite samples. The population you are making
inferences about is the population of all late-stage breast cancers.
The data are from 7 individuals. The within-array replicates give an
indication of measurement error of the expression levels but don't
give you a handle on the variability of the quantity of interest in
the population.
Paul
On Sat, Oct 24, 2009 at 2:44 AM, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>
>
> On Sat, 24 Oct 2009, Gordon K Smyth wrote:
>
>> Dear Paul,
>>
>> Give your consensus correlation value, limma is treating your within-array
>> replicates as worth about 1/3 as much as replicates on independent arrays
>> (because 1-0.81^2 is about 1/3).
>
> Sorry, my maths is wrong. The effective weight of the within-array
> replicates is quite a bit less than 1/3, given ndups=4 and cor=0.81.
>
> Best wishes
> Gordon
>
--
Paul Geeleher
School of Mathematics, Statistics and Applied Mathematics
National University of Ireland
Galway
Ireland
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