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.


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install.packages("vrnmf")

1.0.0 by Evan Biederstedt, a month ago


https://github.com/kharchenkolab/vrnmf


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] , Evan Biederstedt [cre, aut]


Documentation:   PDF Manual  


GPL-3 license


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

Suggests knitr, rmarkdown, testthat


See at CRAN