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.
Highlights
Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining
To principal components analysis (PCA) or independent component analysis (ICA), the objective of non-negative matrix factorization (NMF) is to explain the observed data using a limited number of basis components, which when combined together approximate the original data as accurately as possible
The NMF package currently implements the classical random initialization, the non-negative double singular value decomposition (NNSVD) approach from [27], as well as an initialization based on Independent Component Analysis (ICA), where the entries for W and H are set to the positive part of the result from an ICA
Summary
Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. 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. Non-negative Matrix Factorization The factorization of matrices representing complex multidimensional datasets is the basis of several commonly applied techniques for pattern recognition and unsupervised clustering. Applications and motivations Since its formal definition in [1,2], the NMF approach has been applied successfully in several fields including image and pattern recognition, signal processing and text mining [3]. For a comprehensive review of applications of NMF in computational molecular biology see [3].
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