On Thu, Jul 16, 2009 at 12:14 AM, Anqi <dotzaq@126.com> wrote:

> To whom it may concern,
> I am a student from Peking University, China. I am currently doing some
> microarray data analysis research with Bioconductor package of R.
>
> Problem arises when I try to import into R my dataset which contains 109
> samples (total size more than 1.4 GB). The memory limit of R makes importing
> all the samples into one AffyBatch object a "mission impossible" for me.
>
> Though it will be possible to import data into several AffyBatch objects,
> and do the preprocessing respectively. Yet in this case, the results of
> background correction or normalization are not desirable, because not all
> the information known (namely 109 samples) is used to obtain a baseline or
> something like that.
>
> An alternative approach would be to pre-process the data in dChip, and then
> export it into R. Yet I am thinking about an approach that relies solely on
> R.
>
> Would you please give some suggestions on this issue, though it might be
> more a technical problem than a scientific (statistical) one? Much thanks
> for your help! look forward to your reply! All the best to your work!
>
>
You could try using the xps or aroma.affymetrix packages.  I think both are
designed to deal with large datasets.

Sean

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