factor_intensities: Infer a matrix of non-negative intensities in NMF with...
In vrnmf: Volume-Regularized Structured Matrix Factorization

factor_intensities

R Documentation

Infer a matrix of non-negative intensities in NMF with offset/nmf-offset.

Description

factor_intensities estimates a non-negative matrix D that optimizes the objective function F = ||X - C*D - offset||^2, where offset is either column-specific offset or a "1-rank nmf term": product of row vector and column vector

Usage

factor_intensities(
  C,
  X,
  fit.nmf = TRUE,
  fit.factor = FALSE,
  qp.exact = FALSE,
  n.iter = 200,
  qp.iter = 10,
  rel.error.cutoff = 1e-05,
  extrapolate = TRUE,
  extrapolate.const = TRUE,
  extrapolate.convex = FALSE,
  q.factor = 1,
  verbose = TRUE,
  n.cores = 1
)

Arguments

`C`	Numeric matrices.
`X`	Numeric matrices.
`fit.nmf`	A boolean. Fit both intensities and spectrum of the offset residuals.
`fit.factor`	A boolean. Fit only spectrum of the offset residuals (keep intensities constant across samples).
`qp.exact`	A boolean. Estimate intensities using exact quadratic programming (qp.exact = TRUE) or inexact QP via gradient decent with extrapolation (qp.exact = FALSE).
`n.iter`	An integer. Number of iterations.
`qp.iter`	= 1e+1 An integer. Number of iterations of inexact QP.
`rel.error.cutoff`	A numeric. Relative error cutoff between iterations to stop iterations.
`extrapolate`	A boolean. Use Nesterov-like extrapolation at each iteration.
`extrapolate.const`	A boolean. Use extrapolation scheme that adds a constant extrapolation q.factor (described below) at each iteration.
`extrapolate.convex`	A boolean. Use Nesterov extrapolation scheme.
`q.factor`	A numeric. Specification of a a constant extrapolation factor used in case of extrapolate.const = T.
`verbose`	A boolean. Print per-iteration information (by default TRUE).
`n.cores`	An integer. Number of cores to use.