[BioC] question about RMA normalized gene expression values

Bjoern Usadel usadel at mpimp-golm.mpg.de
Mon Jan 25 19:59:20 CET 2010


generally in RMA the individual probe distributions are identical, the 
probe-set distributions are usually only similar.

You can verify this by sampling (w/o replacement) twice n/l  vectors 
(i.e. probe sets) of length l from the same population of n values (i.e 
probes) and then using average/median/median polish whatever you like on 
the vectors and compare the distributions you get.

Best Wishes,

James Anderson wrote:
> Hi James,
> Thanks for your reply. In the example you provided, the distribution is ALMOST identical, except in the region with large intensities. So I think the correct way should be that the distribution in probe set level from different arrays should be ALMOST identical, but theoretically, it could deviate from strictly identical, due to the median polish. The reason I asked this question is because I actually downloaded one dataset online which says it has been RMA normalized, but the distribution deviates from identical more than what I expect, that's why I am suspicious of the question: do different arrays have identical distribution in probe set level after RMA? 
> Thanks again,
> -Jim
> --- On Wed, 1/20/10, James W. MacDonald <jmacdon at med.umich.edu> wrote:
> From: James W. MacDonald <jmacdon at med.umich.edu>
> Subject: Re: [BioC] question about RMA normalized gene expression values
> To: "James Anderson" <janderson_net at yahoo.com>
> Cc: bioconductor at stat.math.ethz.ch
> Date: Wednesday, January 20, 2010, 1:04 PM
> Hi James,
> James Anderson wrote:
>> Hi, 
>> Do different arrays have identical distribution in probe set level after RMA normalization? Since RMA does quantile normalization in probe level, the distribution in probe level should be identical. Does the median polish summarization (which summarizes the expression value from probe level to probe set level) make the distribution different from each other? 
> You can test this for yourself.
> biocLite("affydata")
> library(affydata)
> data(Dilution)
> hist(Dilution)
> a <- normalize(Dilution)
> hist(a)
> eset <- rma(Dilution)
> plotDensity(exprs(eset))
> Best,
> Jim
>> Thanks,
>> -James
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> -- James W. MacDonald, M.S.
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Björn Usadel, PhD
Max Planck Institute of Molecular Plant Physiology
AG Integrative Carbon Biology
Am Muehlenberg 1
14476 Potsdam-Golm
Tel.: +49 331 5678153
email usadel at mpimp-golm.mpg.de

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