[BioC] RMA normalization when using subsets of samples

[Larry.Lapointe@csiro.au]

Dear Martin,

We have run up to 550 chips achieving a reasonable processing time -- not more than an hour or so.  The practical limits seem to be more related to machine RAM and R memory management.  RMA normalization of 550 chips occupies about 12 GB or so on our quad processor Opteron-based system.

Larry


Lawrence LaPointe
CSIRO Bioinformatics for Human Health
Sydney, Australia




-----Original Message-----
From:	bioconductor-bounces at stat.math.ethz.ch on behalf of martin.schumacher at novartis.com
Sent:	Wed 2/15/2006 7:43 PM
To:	bioconductor at stat.math.ethz.ch
Cc:	
Subject:	Re: [BioC] RMA normalization when using subsets of samples

Dear Colleagues,

Greetings from Switzerland !
I agree with the statements of Wolfgang and Adai. Using all chips will 
certainly put you on the safe side. 
I wonder what you feel would be the minimal number of chips for a "stable" 
(meaning that a larger set would give essentially the same results) RMA 
processing? People from GeneLogic told me that about 20 chips are 
sufficient.
Is it possible to run RMA using Bioconductor with 200 chips and get the 
results back within a reasonable time?

Best regards,
Martin






Adaikalavan Ramasamy <ramasamy at cancer.org.uk>
Sent by: bioconductor-bounces at stat.math.ethz.ch
15.02.2006 01:01
Please respond to ramasamy

 
        To:     Wolfgang Huber <huber at ebi.ac.uk>
        cc:     Sylvia.Merk at ukmuenster.de, bioconductor at stat.math.ethz.ch, (bcc: Martin 
Schumacher/PH/Novartis)
        Subject:        Re: [BioC] RMA normalization when using subsets of samples
        Category: 



This would be a problem if one or more of the resulting subsets is small
and contains outliers.

My preference is to preprocess all arrays together. My reasoning is that
doing this will give RMA median polish (and to a lesser extent with the
quantile normalisation) steps much more information to work with.

Regards, Adai




On Wed, 2006-02-15 at 17:16 +0000, Wolfgang Huber wrote:
> Dear Sylvia,
> 
> this might not be the answer that you want to hear, but for the end 
> result it should not matter (substantially) which of the two 
> possibilities you take, and I would be worried if it did.
> 
> Best wishes
>   Wolfgang
> 
> -------------------------------------
> Wolfgang Huber
> European Bioinformatics Institute
> European Molecular Biology Laboratory
> Cambridge CB10 1SD
> England
> Phone: +44 1223 494642
> Fax:   +44 1223 494486
> Http:  www.ebi.ac.uk/huber
> -------------------------------------
> 
> Sylvia.Merk at ukmuenster.de wrote:
> > Dear bioconductor list,
> > 
> > I have a question concerning RMA-normalization:
> > 
> > There are for example 200 CEL-Files and the clinicians have several
> > research questions - each concernig only a subset of the 200 samples
> > including the possibility that some samples are included in more than
> > one question.
> > 
> > There are two possibilities to normalize the CEL-Files: 
> > 
> > 1.: Read all 200 chips in an affybatch-object and normalize all 200
> > chips together and further analyze the required subset. 
> > 
> > 2.: Read only the required chips in an affybatch-object, normalize 
these
> > chips and then perform further analysis 
> > I think that this approach is the better one but it has the 
disadvantage
> > that some samples are included in several normalizations ending in
> > different gene expression levels for a single sample.
> > 
> > What is (from a statisticians view) the appropriate approach to
> > normalize CEL-Files in this case?
> > 
> > Thank you in advance
> > Sylvia 
> >
> 
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> Bioconductor at stat.math.ethz.ch
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