[BioC] segfault ReadAffy cause 'memory not mapped'

[Matthew McCall]

Brian,

I'm curious what you ended up doing with fRMA. Did you just use the
default implementation?

If you want something closer to RMA, you can obtain RMA-like
expression estimates by creating your own custom frma vectors using a
(in your case potentially fairly large) subset of the data. You can
even do this several times to get a sense of how much the estimates
depend on the subset used to create the frma vectors. This is
implemented in the frmaTools package. There is also a paper that
describes this:
McCall MN and Irizarry RA (2011). Thawing Frozen Robust Multi-array
Analysis (fRMA), BMC Bioinformatics, 12:369.

Best,
Matt


On Thu, Aug 22, 2013 at 9:49 PM, Brian D. Peyser PhD
<brian.peyser at nih.gov> wrote:
> On Thu, 2013-08-22 at 14:53 +0200, cstrato wrote:
>
>> Dear Brian,
>>
>> As I have already mentioned in the former case, package xps is able to
>> handle this amount of arrays.
>> (Quite some time ago a user did use xps to process about 23,000
>> hgu133plus2 arrays on his Mac, and memory consumption was only 4 GB RAM.)
>>
>> Best regards,
>> Christian
>>
>
> Thanks Christian,
>
> I did take a look at xps; it seems to be something that will certainly
> improve large scale analyses in R. In fact I may recommend investigating
> the ROOT interface to one of my personal friends who works in health
> informatics with large-scale (terabyte-sized, not petabyte) data. I
> think that was the one place SAS could still beat R, but that may be
> ending!
>
> In my case, I see a number of ways to accomplish the analysis, and did
> so with fRMA, but read.affybatch() seems to be failing when it could
> succeed.
>
> Thanks again,
> Brian
> --
> Brian D. Peyser PhD
> Special Assistant to the Associate Director
> Office of the Associate Director
> Developmental Therapeutics Program
> Division of Cancer Treatment and Diagnosis
> National Cancer Institute
> National Institutes of Health
> 301-524-5587 (mobile)
>
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-- 
Matthew N McCall, PhD
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