[BioC] large amount of slides/ out of memory

[James MacDonald]

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.

HTH,

Jim



James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
>>> "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
batch of
Affymetrix chips with RMA or gcRMA.
To solve this program, I am working on a dcExpresso function. It is in
alpha
stage now. I will move it into beta release in the next two weeks.
Best regards,

Simon


=================================================
  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
  http://dbsr.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
Message-ID:
<43766.130.115.244.114.1086590184.squirrel at 130.115.244.114>
Content-Type: text/plain;charset=iso-8859-1

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.

Roel Verhaak

>
> 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
have
> only used it on a small amount of slides. I would like to do my study
on a
> larger scale now, with data (series of experiments) from other
researchers
> as well. My questions is the following: if I want to study, let's say
200
> slides, do I have to read them all into R at once (so together I mean,
> with
> read.affy() in package affy), or is it OK to read them series by
series
> (so
> 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
RAM
> would I need (for let's say 200 CELfiles) and how can I raise the RAM?
I
> now
> it's possible to raise it by using 'max vsize = ...' but I haven't
been
> able
> to do it succesfully for 200 experiments though. Can somebody help me
on
> this?
>

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