Volume-Regularized Structured Matrix Factorization

Implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. . This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at < https://github.com/kharchenkolab/vrnmf>. The size of the 'simulatedNMF' package is approximately 8 MB.


Reference manual

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1.0.1 by Evan Biederstedt, 8 days ago


Report a bug at https://github.com/kharchenkolab/vrnmf/issues

Browse source code at https://github.com/cran/vrnmf

Authors: Ruslan Soldatov [aut] , Peter Kharchenko [aut] , Viktor Petukhov [aut] , Evan Biederstedt [cre, aut]

Documentation:   PDF Manual  

GPL-3 license

Imports graphics, ica, lpSolveAPI, Matrix, nnls, parallel, quadprog, stats

Suggests knitr, rmarkdown, testthat

See at CRAN