  Hi,
    I think the rough idea of RefRMA is to use the training set cel images to do RMA normalization, then we have a model how to map every cel image to an intensity vector for training sample. For the cel image file for test samples, apply the same mapping to get the intensity for each test sample, based on the model obtained by RMA normalization with training samples ONLY. My questions are:
  
   Could      anybody explain to me EXACTLY what RMA does? I know roughly what RMA does,      but not clearly about the process of converting cel image file to an      intensity vector for each sample
   Suppose      my case is I have two sites (say, they are both affy files, each site has      50 samples), I want to train the samples measured at site 1 to classify      the samples in site 2. Suppose there are some obvious systematic      difference between the two sites, for example, site 1 is measured with      much brighter light than sight 2, so that the image of site 2 files are      always dimmer than site 1. Does RefRMA (or RefPlus) account for this (systematic bias) when      normalizing each sample in test site 2 to test site 1? 
   
  Thank you very much!
   
  James
  
 
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