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Metagenes and molecular pattern discovery using matrix factorization

Publication TypeJournal Article
AuthorsBrunet, Jean-Philippe, Tamayo Pablo, Golub Todd R., and Mesirov Jill P.
AbstractWe describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of genes or initial conditions and able to detect alternative or context-dependent patterns of gene expression in complex biological systems. This ability, similar to semantic polysemy in text, provides a general method for robust molecular pattern discovery.
Year of Publication2004
JournalProceedings of the National Academy of Sciences of the United States of America
Volume101
Issue12
Pages4164 - 9
Date Published (YYYY/MM/DD)2004/03/23/
ISBN Number0027-8424
KeywordsAlgorithms, Cancer, Central Nervous System Neoplasms, Computational Biology, Data Interpretation, Genetic, Leukemia, Medulloblastoma, Models, Neoplasms, Statistical