infer_intensities

A character. Keep elements of matrix <code>D</code> non-negative ("pos") or not ("all). (default="pos")

esign

An integer. Number of cores to use. (default=1)

n.cores

<code>infer_intensities</code> estimates a non-negative matrix <code>D</code> that optimizes the objective function \(F = ||X - C*D||^2\)
using per-row quadratic programming.

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.

Evan Biederstedt

infer_intensities: Infer a matrix of non-negative intensities in NMF

Description

Usage

Arguments

Value