[BioC] rMAT tutorial. Data file too large?

[Patrick Schorderet]

Hello everybody,

I wanted to use rMAT for some of my ChIP-chip analysis and tried to  
follow the tutorial (http://wiki.rglab.org/index.php? 
title=Public:RMAT). I have updated everything (R version 2.11.0 and  
rMAT version 2.4.0) and have downloaded the tutorial data file (EM). I  
have managed to do everything up to the rbind part. I have the same  
summary as in the tutorial (summary(ER)), but as soon as I try to  
normalize the data, my R crashes. I am wondering if any of you have  
experienced similar outcomes? Could the amount of processing be too  
large? Has any of you been able to successfully follow the tutorial?

Thanks for any useful help,

Patrick











R version 2.11.0 (2010-04-22)
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[R.app GUI 1.32 (5573) i386-apple-darwin9.8.0]

 > library(rMAT)
Le chargement a nécessité le package : IRanges

Attachement du package : 'IRanges'

The following object(s) are masked from 'package:base':

     cbind, Map, mapply, order, paste, pmax, pmax.int, pmin, pmin.int,  
rbind, rep.int,
     table

Le chargement a nécessité le package : Biobase

Welcome to Bioconductor

   Vignettes contain introductory material. To view, type
   'openVignette()'. To cite Bioconductor, see
   'citation("Biobase")' and for packages 'citation(pkgname)'.


Attachement du package : 'Biobase'

The following object(s) are masked from 'package:IRanges':

     updateObject

Le chargement a nécessité le package : affxparser
 > bpmapA<-"P1_CHIP_A.Anti-Sense.hs.NCBIv35.NR.bpmap"
 > celA<-c("MCF_ER_A1.CEL","MCF_ER_A3.CEL", "MCF_ER_A4.CEL",  
"MCF_INP_A1.CEL", "MCF_INP_A3.CEL","MCF_INP_A4.CEL")
 > ERA<-BPMAPCelParser(bpmapA, celA, seqName="chr2")
 > bpmapB<-"P1_CHIP_B.Anti-Sense.hs.NCBIv35.NR.bpmap"
 > celB<-c("MCF_ER_B1.CEL","MCF_ER_B3.CEL", "MCF_ER_B4.CEL",  
"MCF_INP_B1.CEL", "MCF_INP_B3.CEL","MCF_INP_B4.CEL")
 > ERB<-BPMAPCelParser(bpmapB, celB, seqName="chr2")
 > bpmapC<-"P1_CHIP_C.Anti-Sense.hs.NCBIv35.NR.bpmap"
 > celC<-c("MCF_ER_C1.CEL","MCF_ER_C3.CEL", "MCF_ER_C4.CEL",  
"MCF_INP_C1.CEL", "MCF_INP_C3.CEL","MCF_INP_C4.CEL")
 > ERC<-BPMAPCelParser(bpmapC, celC, seqName="chr2")
 >
 >
 > ER<-rbind(ERA,ERB,ERC)
 >
 > summary(ER)
    Genome interrogated:  P1_CHIP_A.Anti-Sense.hs.NCBIv35.NR  
P1_CHIP_B.Anti-Sense.hs.NCBIv35.NR P1_CHIP_C.Anti-Sense.hs.NCBIv35.NR
    Chromosome(s) interrogated: chr21, chr22
    Sample name(s):  MCF_ER_A1 MCF_ER_A3 MCF_ER_A4 MCF_INP_A1  
MCF_INP_A3 MCF_INP_A4
    The total number of probes is:  1015922
    Preprocessing Information
      - Transformation: log
      - Normalization: none