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James, >From the presentation that I saw on refRMA (and the paper), the quantile normalization process would coerce the distribution of the low-level probe data to have the same shape as the "reference" data. In GeneLogic's case, they used sets of biological samples from their large data base to define this reference distribution. Assuming that the reference distribution used is acceptable for your samples/problem, this should remove many of the systematic effects that you may have in your data. After googling, it seems that the package is only available form the authors at this time, so I can't be exact. As I mentioned earlier today, I have similar code that I'd be willing to share. It works very similar to the affy RMA functions. Max -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of James Anderson Sent: Wednesday, March 07, 2007 3:10 PM To: bioconductor Subject: [BioC] RMA, RefRMA questions Hi, I roughly understand the issue of RMA and RefRMA. When using RefRMA, one RMA model is generated first based on the samples from one lab. If some additional arrays measured in a different lab needs to be normalized, does RefRMA automatically take care of the systematic difference between the two labs or except RefRMA, there are still some extra work needs to be done to correct the systematic difference between the two labs? Thanks, James --------------------------------- The fish are biting. [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor ---------------------------------------------------------------------- LEGAL NOTICE\ Unless expressly stated otherwise, this messag...{{dropped}}