[BioC] Reading 80 CEL files.

[cstrato]

Since so many people replied already, please let me mention package xps 
which allows you to do RMA for 80 CEL files on laptops with 1-2 GB RAM 
only. You can even run Exon arrays or HuGene arrays. xps is availble 
from the Bioconductor development branch, but works with R-2.5.0 and 
R-2.6.0, too. However, currently xps is supported only for Linux and 
MacOS X.

Best regards
Christian
_._._._._._._._._._._._._._._._
C.h.i.s.t.i.a.n S.t.r.a.t.o.w.a
V.i.e.n.n.a       A.u.s.t.r.i.a
e.m.a.i.l:    cstrato at aon.at
_._._._._._._._._._._._._._._._


Weiwei Shi wrote:
> Oscar,
>
> I am curious how long it runs and the configuration of the linux cluster.
>
>
> weiwei
>
> On 11/17/07, olsen <olsen2002 at yahoo.com> wrote:
>   
>>> Hello~~~
>>>
>>>
>>>
>>> I am trying to read 80 HG-U133A arrays so I did as
>>> follows:
>>>
>>>
>>>
>>>       
>>>> memory.limit(size=4095)
>>>>         
>>>> options(object.size=10000000000, digits=8,
>>>>         
>>> scipen=100, memory=3200483647,
>>> contrasts=c("contr.treatment", "contr.poly"))
>>>
>>>       
>>>> library(affy)
>>>>         
>>>> cel.file <- ReadAffy(celfile.path = ¡¨D://CEL)
>>>>         
>>>
>>> However, it showed
>>>
>>>
>>>
>>> Error: cannot allocate vector of size 309.4 Mb
>>>
>>>
>>>
>>> I have tried adding the --max-mem-size=2Gb tag onto
>>> my shortcut.
>>>
>>> The machine has 2G RAM and a 3.30GHz processor.
>>>
>>> Is there any idea to let R use more memory anywhere?
>>>
>>> Thank you~
>>>
>>>
>>>
>>> Best Regards,
>>>
>>> Pure
>>>
>>>       
>> Pure,
>>
>> My advice: run them in Linux. We have run up to 1000
>> arrays in a linux cluster.
>>
>> Oscar Puig
>>
>>
>>
>>
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>>     
>
>
>