Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology
Open accessAbstract
In the last decade, advances in high-throughput technologies such as DNA microarrays have made it possible to simultaneously measure the expression levels of tens of thousands of genes and proteins. This has resulted in large amounts of biological data requiring analysis and interpretation. Nonnegative matrix factorization (NMF) was introduced as an unsupervised, parts-based learning paradigm involving the decomposition of a nonnegative matrix V into two nonnegative matrices, W and H, via a multiplicative updates algorithm. In the context of a pxn gene expression matrix V consisting of observations on p genes from n samples, each column of W defines a metagene, and each column of H represents the metagene expression pattern of the corresponding sample. NMF has been primarily applied in an unsupervised setting in image and natural language processing. More recently, it has been successfully utilized in a variety of applications in computational biology. Examples include molecular pattern discovery, class comparison and prediction, cross-platform and cross-species analysis, functional characterization of genes and biomedical informatics. In this paper, we review this method as a data analytical and interpretive tool in computational biology with an emphasis on these applications.
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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 [82]
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- Authors
- Devarajan, K.
- Affiliations
- Fox Chase Cancer Center (US)
- Corresponding author
- Devarajan, Karthik
- ORCID
- Devarajan, Karthik
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-07-25
- Volume
- 4
- Issue
- 7
- Pages
- e1000029
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2447881
- OpenAlex
- W2132692097
- Cited by
- 454
- References
- 75
- Citations by year
- 2026: 9 · 2025: 30 · 2024: 41 · 2023: 22 · 2022: 28 · 2021: 32 · 2020: 35 · 2019: 28 · 2018: 25 · 2017: 28 · 2016: 27 · 2015: 26 · 2014: 35 · 2013: 25 · 2012: 20
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Pennsylvania Department of Health
- National Institutes of Health (CA-06927)
- National Cancer Institute (P30CA006927)
- 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, DNA microarrays, unsupervised learning, cross-platform analysis, cross-species comparison, biomedical informatics
- MeSH terms
- Artificial Intelligence, Pattern Recognition, Automated, Systems Integration, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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