A Novel Clustering Analysis Based on PCA and SOMs for Gene Expression Patterns

This paper proposes a novel clustering analysis algorithm based on principal component analysis (PCA) and self-organizing maps (SOMs) for clustering the gene expression patterns. This algorithm uses the PCA technique to direct the determination of the clusters such that the SOMs clustering analysis is not blind any longer. The integration of the PCA and the SOMs makes it possible to powerfully mine the underlying gene expression patterns with the practical meanings. In particular, our proposed algorithm can provide the informative clustering results like a hierarchical tree. Finally, the application on the leukemia data indicates that our proposed algorithm is efficient and effective, and it can expose the gene groups associated with the class distinction between the acute lymphoblastic leukemia (ALL) samples and the acute myeloid leukemia (AML) samples.