We propose an integrative self-organizing map (iSOM) for exploring differential expression patterns across multiple microarray experiments. The algorithm is based on the assumption that observed differential expressions are random samples of a mean pattern model which is unknowna priori . The learning mechanism of iSOM is similar to the conventional SOM. The mean pattern model which underlies the proposed iSOM models mean differential expressions using a one-dimension of mean differential expressions for the mean differential expressions. The feature map of an iSOM model can be used to reveal correlation between multiple medically/biologically related disease types or multiple platform experiments for one disease. We illustrate applications of iSOM using simulated data and real data.
KeywordsIntegrative Study of MicroarraysPattern DiscoveryDifferential ExpressionsSelf-Organizing Map
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