Dear Matt,

I just went to link you provided and took a look at fRMA. It is a very
useful package. May I ask two questions regarding fRMA?

1. Does fRMA have the precomputed vectors for Affy's Human Exon arrays to
estimate both gene-level and exon-level data?
2. Can the Random Eff ct Model in fRMA handle two random batch effects
which are known in my exon array data?

Thanks,
Shirley

On Tue, Feb 14, 2012 at 5:17 PM, Matthew McCall <mccallm@gmail.com> wrote:

> Ying,
>
> You might consider fRMA:
> McCall MN, Bolstad BM, and Irizarry RA* (2010). Frozen Robust
> Multi-Array Analysis (fRMA), Biostatistics, 11(2):242-253.
> http://bioconductor.org/packages/release/bioc/html/frma.html
>
> This preprocessing algorithm was designed to handle such multi-batch
> analyses.
>
> Best,
> Matt
>
> On Tue, Feb 14, 2012 at 4:49 PM, ying chen <ying_chen@live.com> wrote:
> >
> >
> > Hi, I collected dozens of breast cancer GEO datasets (same platform,
> Affy U133Plus2) and wonder if there is a way to normalize these datasets so
> I can compare the gene expression levels across all the datasets even
> though they are from different labs? I think about doing a RMA to all the
> datasets together first then followed by SVA to correct for batch effect,
> or doing RMAs dataset by dataset then follwed by mean-scaling. Does any of
> these make sense? Or what is the best approach? Any suggestion? Thanks a
> lot for the help! Ying
> >        [[alternative HTML version deleted]]
> >
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>
> --
> Matthew N McCall, PhD
> 112 Arvine Heights
> Rochester, NY 14611
> Cell: 202-222-5880
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