[BioC] memory usage ReadAffy() and probe level information

[Tan, MinHan]

The limit for justRMA that I have encountered is approximately 110-120
HGU133 Plus 2.0 CEL files, with 2 GB RAM, R 1.9.0 beta running on
Windows. I do not encounter a memory error directly - the R GUI
application just gracefully folds up and reminds me to send an error
report to Bill Gates. Anyway, I'm not sure if it directly translates in
terms of number of transcripts, but that limit should allow >200 HGU133A
chips?

I am told that the maximum addressable memory for a 32 bit processor is
4GB - would it make a difference if the actual physical memory is
increased from 2 GB to 4 GB (even though my memory.limit has already
been set to 4095)?

Min-Han Tan


-----Original Message-----
From: James MacDonald [mailto:jmacdon at med.umich.edu] 
Sent: Tuesday, April 06, 2004 8:52 AM
To: r.verhaak at erasmusmc.nl; bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] memory usage ReadAffy() and probe level information


Simply put, you are not going to be able to read in >300 cel files with
only(!) 2 Gb RAM. You should be able to do justRMA with this much RAM on
100 or so chips, but if you really want to be able to do huge numbers of
chips you are going to have to upgrade to a 64 bit architecture, which
at this point in time also means you have to switch to Linux.

To get the pm probes I think you want to do something like this:

my.pms <- probes(abatch, "pm", "1007_s_at") # for e.g., the 1007_s_at
probes

Best,

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

>>> Roel Verhaak <r.verhaak at erasmusmc.nl> 04/06/04 08:32AM >>>
Hi,

I have two questions regarding the use of the affy package:
- I have a large series of cel-files and am trying to read
them at once. Unfortunately, the ReadAffy-function seems to
use a lot of memory. My workstation has 2 Gig of RAM
installed, but trying to read >100 cel-files (HGU133a) is a no-go. This
is R1.8.1 with Bioconductor 1.3 on a Windows machine. Reading in 50
cel-files already means a (peak-)memory usage of 800 meg. Is there any
solution to this, because I would like to read 300 cel-files at the same
time if possible. I have played around with the memory.limit()-function,
but with no success.
- Second, after data import I would like to retrieve all information on
a probe level for several probe sets. I do this using the pm()-function,
for instance
>pm(CelFiles)[1:16, 1:50]. The problem is that I have to find out first
where which exact "location" the probe set of interest has. This is not
a big problem, but I thought there might be a more elegant solution to
this. 

Thanks in advance,
Roel Verhaak

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