Metagenes and molecular pattern discovery using matrix factorization
Open accessAbstract
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.
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We previously identified three robust and reproducible transcriptomic sub-groups of KRAS-mutant LUAD by applying non-negative matrix factorization consensus clustering to RNASeq data from 68 tumors from TCGA.
“We previously identified three robust and reproducible transcriptomic sub-groups of KRAS-mutant LUAD by applying non-negative matrix factorization consensus clustering81–83 to RNASeq data from 68 tumors from The Cancer Genome Atlas (TCGA) dataset67.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [81]
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- Authors
- Brunet, J.-P., Tamayo, P., Golub, T. R., & Mesirov, J. P.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Mesirov, Jill P.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2004-03-11
- Volume
- 101
- Issue
- 12
- Pages
- 4164-4169
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC384712
- OpenAlex
- W2136787567
- Cited by
- 2,183
- References
- 16
- Citations by year
- 2026: 113 · 2025: 113 · 2024: 168 · 2023: 164 · 2022: 205 · 2021: 161 · 2020: 143 · 2019: 137 · 2018: 102 · 2017: 118 · 2016: 87 · 2015: 92 · 2014: 92 · 2013: 88 · 2012: 68
- FWCI
- 10.18
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- AstraZeneca
- Massachusetts Institute of Technology
- Harvard University
- Broad Institute
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- non-negative matrix factorization, metagenes, gene expression data, dimensionality reduction, cancer microarray data, model selection, stochastic clustering, hierarchical clustering comparison
- MeSH terms
- Algorithms, Data Interpretation, Statistical, Leukemia, Medulloblastoma, Models, Genetic, Neoplasms, Central Nervous System Neoplasms, Computational Biology
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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