Hi,
  I have a question for RMA normalization. Since RMA is an across sample normalization, suppose I have 50 training samples (cel files) and 50 test samples (cel files). There are two ways to perform normalization:
1. Combine all the 100 samples together and use RMA to do normalization. Then train the training set of 50 samples to classify the 50 test samples. 
2. Use the 50 training samples to do RMA, then each cel file is converted to gene expression vector. Suppose the mapping from cel file to expression vector is:
Expression = f(cel). The form of f is determined by the 50 training cel files. Then apply the same mapping to the test cel files. 

  I would think method 2 is more reasonable and trully blind. However, it is not clear how to determine the function f from the 50 training cel files. method 1 is easy to implement, but it is not trully blind, since the normalization of cel files from training samples actually utilized the information from test cel files. 
  Could anybody tell me how to determine the function f from the 50 training cel files? 

  Many thanks,
  James

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