Hi

I usually do a quality control of the data before I turn the data set into an ExpressionSet. If I use justRMA, how can I then quality control the data to see if any array should be removed? Will it not be difficult if all the arrays are corrected as they are read into the program?

Regards,
Ingrid



-----Original Message-----
From: Sean Davis [mailto:sdavis2@mail.nih.gov]
Sent: Tue 12/12/2006 12:39
To: Ingrid H. G. Østensen
Cc: bioconductor@stat.math.ethz.ch
Subject: Re: [BioC] Compare results from different analysis (Affymetrix)
 
On Tuesday 12 December 2006 05:14, Ingrid H. G. Østensen wrote:
> Hi
>
> More information:
> 1,50 GB RAM
>
> > sessionInfo()
>
> R version 2.4.0 (2006-10-03)
> i386-pc-mingw32
>
> locale:
> LC_COLLATE=Norwegian (Bokmål)_Norway.1252;LC_CTYPE=Norwegian
> (Bokmål)_Norway.1252;LC_MONETARY=Norwegian
> (Bokmål)_Norway.1252;LC_NUMERIC=C;LC_TIME=Norwegian (Bokmål)_Norway.1252
>
> attached base packages:
> [1] "methods"   "stats"     "graphics"  "grDevices" "utils"     "datasets"
> [7] "base"
>
> From the runing of the script:
> > library(limma)
> > library(affyQCReport)
>
> Loading required package: Biobase
> Loading required package: tools
>
> Welcome to Bioconductor
>
>     Vignettes contain introductory material. To view, type
>     'openVignette()' or start with 'help(Biobase)'. For details
>     on reading vignettes, see the openVignette help page.
>
> Loading required package: affy
> Loading required package: affyio
> Loading required package: simpleaffy
> Loading required package: genefilter
> Loading required package: survival
> Loading required package: splines
> Welcome to 'affyQCReport' V 1.6-1
> Further information available at: www.bifix.org
>       mailto: craig.parman@bifix.org
>
> > library(RColorBrewer)
> > library(xtable)
> > library(simpleaffy)
> > memory.limit(4000)
>
> NULL
>
> > dataSet <- read.affy()

Thanks, Ingrid, for the details.  As Jim already suggested, using justRMA 
should probably allow you to load all of your arrays.  You will get back an 
ExpressionSet which you can then use for further analyses.  Normalizing and 
analyzing all your data simultaneously has significant advantages over the 
piecemeal approach.

Sean


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