A collection of research articles explores developments in interfacial transport and mixing, with wide-ranging practical applications.
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Communicated by Eric S. Lander, Massachusetts Institute of Technology, Cambridge, MA, December 20, 2003 (received for review November 1, 2003)
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.
Footnotes
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↵‡ To whom correspondence should be addressed. E-mail: mesirov{at}broad.mit.edu.
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Abbreviations: NMF, nonnegative matrix factorization; HC, hierarchical clustering; SOM, self-organizing maps; AML, acute myelogenous leukemia; ALL, acute lymphoblastic leukemia.
- Received November 1, 2003.
- Copyright © 2004, The National Academy of Sciences
- *The Eli and Edythe L. Broad Institute, Massachusetts Institute of Technology and Harvard University, 320 Charles Street, Cambridge, MA 02141; and †Dana–Farber Cancer Institute and Harvard Medical School, 44 Binney Street, Boston, MA 02115
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Communicated by Eric S. Lander, Massachusetts Institute of Technology, Cambridge, MA, December 20, 2003 (received for review November 1, 2003)
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.
Footnotes
-
↵‡ To whom correspondence should be addressed. E-mail: mesirov{at}broad.mit.edu.
-
Abbreviations: NMF, nonnegative matrix factorization; HC, hierarchical clustering; SOM, self-organizing maps; AML, acute myelogenous leukemia; ALL, acute lymphoblastic leukemia.
- Received November 1, 2003.
- Copyright © 2004, The National Academy of Sciences
Metagenes and molecular pattern discovery using matrix factorization
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