[BioC] Array Set - Multiple Testing Problem
c.mayer at abdn.ac.uk
Fri Sep 11 16:46:26 CEST 2009
I might have misunderstood something, but assuming such an array set consists of k arrays, wouldn't it be the easiest to perform k normalizations and analyses which give you k lists of p-values for k (non-overlapping (I assume!?)) sets of genes.
To adjust for multiple testing you need to bind those k lists together to one long vector of p-values and apply p.adjust or whatever function is your favourite one.
This saves you from normalizing between arrays that have different genes on them etc. and seems very easy to do.
> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-
> bounces at stat.math.ethz.ch] On Behalf Of Tefina Paloma
> Sent: 11 September 2009 14:48
> To: bioconductor at stat.math.ethz.ch
> Subject: Re: [BioC] Array Set - Multiple Testing Problem
> To be able to fit the same model to all arrays, an additional between-
> normalization would be necessary, so to make all the arrays really
> and I don't want to over-normalize the data either.....
> therefore I just thought of an sensible p value adjustment
> 2009/9/11 Sean Davis <seandavi at gmail.com>
> > On Fri, Sep 11, 2009 at 8:58 AM, Tefina Paloma
> <tefina.paloma at gmail.com>wrote:
> >> Dear all
> >> unfortunately I did not get any reply on my post, so thats why I am
> >> again,
> >> assuming that lots of people already came across that problem.
> >> Working with an array set ( cDNA or any single color platform) just
> >> that the probes you are interested in, are spread out over more than
> >> array
> >> (usually due to space limitations),
> >> So sample samples, but different features.
> >> But actually that kind of separation of the probes is rather random.
> >> The question arises at which level of the analysis the arrays should be
> >> aggregated.
> >> I think the normalization and also the model fitting should be done
> >> separately.
> >> But as we do not only consider contrasts within each array of the array
> >> set,
> >> but at the contrast,
> >> we want to look at the results of all arrays at the same time, the
> >> p-values
> >> must be adjusted somehow for
> >> this array-effect.
> >> To do this in a "global" manner similar to the "global method" of
> >> decide.tests will probably result in being overly
> >> conservative.
> >> Any suggestions?
> > Why not just normalize each array in the set separately and then combine
> > the normalized data for analysis? I'm not sure I see why the arrays
> > need to be treated independently for analysis, assuming the technology
> > the same for each array in the set.
> > Sean
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