ArticleBMC bioinformatics2010
A flexible R package for nonnegative matrix factorization.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 926 papers.
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926 citing papers in PubMed.
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- Residual lipid-related proteomic and metabolomic signatures associated with major adverse cardiovascular events after revascularization in peripheral artery disease.BMC cardiovascular disorders · 2026Article
- TOR kinase inhibition reverses azole resistance in Candida auris by targeting heme biosynthesis.Archives of microbiology · 2026Article
- Ensemble clustering: A practical tutorial.Behavior research methods · 2026Article
- α2,6-Sialoglycan-regulated antitumor immunity of tumor-infiltrating CD8Nature chemical biology · 2026Article
- From Anatomy to Outcome: Linking Glioma Location Patterns to Survival Using Non-Negative Matrix Factorization.Clinical neuroradiology · 2026Article
- Spatial analysis of head and neck cancer identifies two ecosystems with distinct modes of epithelial-to-mesenchymal transition.Nature genetics · 2026Article
- Article
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- Identification of colorectal cancer molecular subtypes and prognostic features based on basement membrane-related genes.Journal of applied genetics · 2026Article
866 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community.
resultsOur objective is to provide the bioinformatics community with an open-source, easy-to-use and unified interface to standard NMF algorithms, as well as with a simple framework to help implement and test new NMF methods. For that purpose, we have developed a package for the R/BioConductor platform. The package ports public code to R, and is structured to enable users to easily modify and/or add algorithms. It includes a number of published NMF algorithms and initialization methods and facilitates the combination of these to produce new NMF strategies. Commonly used benchmark data and visualization methods are provided to help in the comparison and interpretation of the results.
conclusionsThe NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications. Documentation, source code and sample data are available from CRAN.
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