[BioC] Expression levels of individual affymetrix probes

[Kasper Daniel Hansen]

The summarization step of RMA (which is a core part of the algorithm)  
does not make sense unless you actually have several probes to  
summarize.

Kasper

On Oct 29, 2007, at 6:44 AM, James W. MacDonald wrote:

> Hi Alice,
>
> Johnstone, Alice wrote:
>> Hi,
>> Is it possible to apply the transformations used on the probesets to
>> individual probe expression?  Such as RMA and fitting to a lm to  
>> obtain
>> your list of probesets of interest.  Then potentially looking at
>> individual probe expression levels across samples for all 11  
>> probes of
>> that probeset?  (but normalized across samples to enable greater
>> accuracy of the eyeball-o-meter)
>> The reason behind this pondering is to investigate further transcript
>> variant effects, and also to better match qpcr investigations to the
>> location of the probe which shows the greatest change...
>
> If I understand you correctly, you want to go back to the
> background-corrected, normalized pm data for certain probesets to see
> the contribution of individual probes.
>
> This shouldn't be difficult.
>
> dat <- ReadAffy()
> dat <- bg.correct.rma(dat) ## background corrected
> dat <- normalize.AffyBatch.quantiles(dat)
>
> Now you can look at individual probesets using
>
> pm(dat, 'a.probeset.id')
>
> Best,
>
> Jim
>
>
>> Cheers,
>> Alice
>>
>>
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> -- 
> James W. MacDonald, M.S.
> Biostatistician
> Affymetrix and cDNA Microarray Core
> University of Michigan Cancer Center
> 1500 E. Medical Center Drive
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