Hi,

Sorry to bother all of you who are not interested in this question. I am thinking of this for a long time, but does not seem to get the answer out of this:

For affymetrix data, there are several kinds of options to do transform before doing classification. One way is to mean centering and scaling each gene across all the samples. This will destroy the correlation structure between different samples. Things might get complicated when there is batch effect between two batches A and B, one way to delete batch effect is to mean centering and scaling each gene across all the samples within each batch. However, when I do this, I noticed that the classification performance gets worse. I know this will also destroy the correlation structure between samples, but not clear why it deteriorate classification performance. Is this always the case or just happen to be that way on my data? If it is always the case, why it can deteriorate classification performance? 

I am getting very frustrated on this, I would appreciate it very much if I can get some reply. 

Thanks,

James

       
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