Volume-Regularized Structured Matrix Factorization
Non-negative tri-factorization of co-occurence matrix using minimum vo...
Infer a matrix of non-negative intensities in NMF with offset/nmf-offs...
Infer a matrix of non-negative intensities in NMF
Project vector onto a probabilistic simplex.
Simulate matrices to explores vrnmf
Preprocess the data for downstream volume analysis.
Update volume-regularized matrix R using det volume approximation
Alternating optimization of volume-regularized NMF
Update volume-regularized matrix R using logdet volume approximation...
Volume-regularized NMF
Procrustes algorithm estimates orthonormal transformation between two ...
Update of a matrix in NMF with equality contstraints on columns.
Update of a matrix in NMF with equality contstraints on rows.
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. <doi:10.1126/science.aba7408>. 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.