[BioC] large amount of slides/ out of memory
jmacdon at med.umich.edu
Wed Jun 9 04:06:09 CEST 2004
Note too that if you are simply doing RMA on the chips, you can use
justRMA which will give identical results, but using far less RAM.
James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
Ann Arbor MI 48109
>>> "Simon Lin" <simon.lin at duke.edu> 06/08/04 5:28 PM >>>
"Out of memory error" is a common challenge of running a very large
Affymetrix chips with RMA or gcRMA.
To solve this program, I am working on a dcExpresso function. It is in
stage now. I will move it into beta release in the next two weeks.
Simon M. Lin, M.D.
Manager, Duke Bioinformatics Shared Resource
Assistant Research Professor, Biostatistics and Bioinformatics
Box 3958, Duke University Medical Center
Rm 286 Hanes, Trent Dr, Durham, NC 27710
Ph: (919) 681-9646 FAX: (919) 681-8028
Lin00025 (at) mc.duke.edu
Date: Mon, 7 Jun 2004 08:36:24 +0200 (MEST)
From: "R.G.W. Verhaak" <r.verhaak at erasmusmc.nl>
Subject: Re: [BioC] large amount of slides
To: bioconductor at stat.math.ethz.ch
<437220.127.116.11.114.1086590184.squirrel at 18.104.22.168>
I have succesfully ran GCRMA on a dataset of 285 HGU133a chips, on a
machine with 8 Gb RAM installed; I noticed a peak memory use of 5,5 Gb
(although I have not been monitoring it continuously). I would say 200
chips use equally less memory, so around 4 Gb.
> Message: 9
> Date: Fri, 04 Jun 2004 10:06:14 -0500
> From: "Vada Wilcox" <v_wilcox at hotmail.com>
> Subject: [BioC] large amount of slides
> To: bioconductor at stat.math.ethz.ch
> Message-ID: <BAY19-F34SDGAIXWb9D0002ec89 at hotmail.com>
> Content-Type: text/plain; format=flowed
> Dear all,
> I have been using RMA succesfully for a while now, but in the past I
> only used it on a small amount of slides. I would like to do my study
> larger scale now, with data (series of experiments) from other
> as well. My questions is the following: if I want to study, let's say
> slides, do I have to read them all into R at once (so together I mean,
> read.affy() in package affy), or is it OK to read them series by
> all wild types and controls of one researcher at a time)?
> If it is really necessary to read all of them in at one time how much
> would I need (for let's say 200 CELfiles) and how can I raise the RAM?
> it's possible to raise it by using 'max vsize = ...' but I haven't
> to do it succesfully for 200 experiments though. Can somebody help me
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