title: Metagenes and molecular pattern discovery using matrix factorization.
abstract: We 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.
authors.affiliation: The Eli and Edythe L. Broad Institute, Massachusetts Institute of Technology and Harvard University, 320 Charles Street, Cambridge, MA 02141, USA.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Brunet
authors.forename: Pablo
authors.initials: P
authors.lastname: Tamayo
authors.forename: Todd R
authors.initials: TR
authors.lastname: Golub
authors.forename: Jill P
authors.initials: JP
authors.lastname: Mesirov
mesh_terms: D000465:Algorithms; D016543:Central Nervous System Neoplasms / Q000145:classification / Q000235:genetics; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D007938:Leukemia / Q000145:classification / Q000235:genetics; D008527:Medulloblastoma / Q000235:genetics; D008957:Models, Genetic; D009369:Neoplasms / Q000145:classification* / Q000235:genetics