Hi Zhijin,

A client pointed out a July 2007 article by Lim et al. testing 
different normalization/pre-processing methods for their effects on 
pairwise correlations between probesets (Bioinformatics 2007 
23(13):i282-i288; doi:10.1093/bioinformatics/btm201; full link 
below). They reported that GCRMA introduced severe artificial 
correlations between probesets; they looked for a cause and think 
it's due truncation of low-intensity values after Non-Specific 
Binding adjustment and then the Gene-Specific Binding adjustment on 
these truncated values. They also tested a specific correction to the 
GCRMA algorithm that appears to prevent the artificial correlation 
and suggest that it become an option or even a default in the R 
implementation of GCRMA.

What do you think of this article? Are there any plans to implement 
their suggestion?

Thanks,
Jenny

Comparative analysis of microarray normalization procedures: effects 
on reverse engineering gene networks

http://bioinformatics.oxfordjournals.org/cgi/content/full/23/13/i282?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=1&andorexacttitle=and&andorexacttitleabs=and&andorexactfulltext=and&searchid=1&FIRSTINDEX=0&sortspec=relevance&volume=23&firstpage=i282&resourcetype=HWCIT&eaf




Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA

ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich@uiuc.edu 
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