Dear Stephen,

I have a similar situation and my approach has been to use avereps() from the limma package to average repeated probes. Then I use the biomaRt package to retrieve Ensembl's mapping for the probes since this is being updated with every new release of Ensembl. That should ensure a more up-to-date mapping to gene level. If you need one expression value for each gene you could then take the median or mean of repeated genes. If you want to convert the MA values back to RG values you can use the RG.MA() function from the limma package. That depends on how you are going to use your data. However, I think the most common is to use the MA values for downstream analysis, but I would also like to hear other opinions on this.

Best,
Daniel



On Jan 11, 2012, at 10:45 PM, Stephen Turner wrote:

> Hello.
> 
> I have 4 Agilent two-channel arrays that I read in using read.maimages().
> I've done normalization and background subtraction. How do I now summarize
> the probe information (62976 probes) to gene-level expression values (39430
> entrez RNAs, 16251 lincRNAs). I normally did this using rma() or gcrma()
> from the affy package when I have Affymetrix data.
> 
> Thanks,
> 
> Stephen
> 
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