
Hi Matt, Thanks a lot for the suggestion. I read the papers and think frma is perfect for my task. But I still have a few questions: 1) For the multiple arrays, the only summarize method is random_effect, right? 2) In your frmaTools paper (BMC Bioinformatics) you mentioned that the latest version of the frma package has the option to use the version 13 Entrez Gene probe annotation (section 3.2 Alternative CDF). But I could not find any method to apply this option in manual frma.pdf (Feb 14, 2012) downloaded from Bioconductor frma page. Is this option still available? 3) Is the data file installed automatically when I install firma package or I need to install it by myself like biocLite("hgu133plus2frmavecs")?  
4) When you built the reference distribution for U133Plus2, did you pay attention to the experiment protocol used for each sample, such as the starting RNA type (total RNA or mRNA), the amount of total RNA used (~5ug or 10-100ng)? Does it make sense to run SVA after frma to correct for the possible batch effects due to different protocols used? Thanks, Ying > Date: Tue, 14 Feb 2012 17:17:48 -0500
> Subject: Re: [BioC] Best way to normalize GEO gene expression datasets from different labs/sources?
> From: mccallm@gmail.com
> To: ying_chen@live.com
> CC: bioconductor@r-project.org
> 
> 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]]
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
> 
> 
> 
> -- 
> Matthew N McCall, PhD
> 112 Arvine Heights
> Rochester, NY 14611
> Cell: 202-222-5880
 		 	   		  
	[[alternative HTML version deleted]]

