Hello,

first of all, thanks for all the answers.

Unfortunately I do not have information about the exact probes-behaviour.
The library was in-house selected and surely does not represent an
uniform-behaving set of genes (like in the example of the hgu133a and b
chip)

In my special case we are talking of cDNA arrays, unfortunately the quality
is not very consistent and the arrays behave very different.
So, I do have doubts about combining the data after normalization, the
quality is just not good enough.

Another issue is that in 2 arrays of the array set about a quarter of the
spots are the same. But only in these 2 arrays.
I still have to sort out how to deal with this.

I think the "safest approach" would be adjusting all p values from all lists
together,
I am curious if this will work..(so if this approach will leave me with some
significant p values, (assuming that there is an effect), or if the number
of tests will be just too large)

Best,
Tefina

2009/9/11 Mayer, Claus-Dieter <c.mayer@abdn.ac.uk>

> Hello,
>
> 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.
>
> Claus
>
> > -----Original Message-----
> > From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-
> > bounces@stat.math.ethz.ch] On Behalf Of Tefina Paloma
> > Sent: 11 September 2009 14:48
> > To: bioconductor@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-
> > array
> > normalization would be necessary, so to make all the arrays really
> > comparable
> > 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@gmail.com>
> >
> > >
> > >
> > > On Fri, Sep 11, 2009 at 8:58 AM, Tefina Paloma
> > <tefina.paloma@gmail.com>wrote:
> > >
> > >> Dear all
> > >>
> > >> unfortunately I did not get any reply on my post, so thats why I am
> > asking
> > >> again,
> > >> assuming that lots of people already came across that problem.
> > >>
> > >> Working with an array set ( cDNA or any single color platform) just
> > means
> > >> that the probes you are interested in, are spread out over more than
> > one
> > >> 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
> > would
> > > need to be treated independently for analysis, assuming the technology
> > was
> > > the same for each array in the set.
> > >
> > > Sean
> > >
> > >
> >
> >         [[alternative HTML version deleted]]
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