Improving molecular cancer class discovery through sparse non-negative matrix factorization
Department of Genetics, Harvard Medical School Boston, MA 02115, USA
*To whom correspondence should be addressed.
Motivation: Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process.
Results: We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.
Contact: g1m1c1{at}receptor.med.harvard.edu, ygao{at}receptor.med.harvard.edu
Supplementary information: http://arep.med.harvard.edu/snmf/supplement.htm
Received on April 7, 2005; revised on July 27, 2005; accepted on August 30, 2005
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