inmf: Perform Integrative Non-negative Matrix Factorization

In RcppPlanc: Parallel Low-Rank Approximation with Nonnegativity Constraints

Perform Integrative Non-negative Matrix Factorization

Description

Performs integrative non-negative matrix factorization (iNMF) (J.D. Welch, 2019) to return factorized H, W, and V matrices. The objective function is stated as

\arg\min_{H\ge0,W\ge0,V\ge0}\sum_{i}^{d}||E_i-(W+V_i)Hi||^2_F+ \lambda\sum_{i}^{d}||V_iH_i||_F^2

where E_i is the input non-negative matrix of the i'th dataset, d is the total number of datasets. E_i is of size m \times n_i for m features and n_i sample points, H_i is of size k \times n_i, V_i is of size m \times k, and W is of size m \times k.

inmf optimizes the objective with ANLS strategy, while onlineINMF optimizes the same objective with an online learning strategy.

Usage

inmf(
  objectList,
  k = 20,
  lambda = 5,
  niter = 30,
  nCores = 2,
  Hinit = NULL,
  Vinit = NULL,
  Winit = NULL,
  verbose = FALSE
)

Arguments

objectList	list of input datasets. List elements should all be of the same class. Viable classes include: matrix, dgCMatrix, H5Mat, H5SpMat.
k	Integer. Inner dimensionality to factorize the datasets into. Default 20.
lambda	Regularization parameter. Larger values penalize dataset-specific effects more strongly (i.e. alignment should increase as lambda increases). Default 5.
niter	Integer. Total number of block coordinate descent iterations to perform. Default 30.
nCores	The number of parallel tasks that will be spawned. Default 2
Hinit	Initial values to use for H matrices. A list object where each element is the initial H matrix of each dataset. Each should be dense matrix of size n_i \times k. Default NULL.
Vinit	Similar to Hinit, but each should be of size m \times k.
Winit	Initial values to use for W matrix. A matrix object of size m \times k. Default NULL.
verbose	Logical scalar. Whether to show information and progress. Default FALSE.

Value

A list of the following elements:

• H - a list of result H_i matrices of size n_i \times k

• V - a list of result V_i matrices

• W - the result W matrix

• objErr - the final objective error value.

Author(s)

Yichen Wang

References

Joshua D. Welch and et al., Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity, Cell, 2019

Examples

library(Matrix)
set.seed(1)
result <- inmf(list(ctrl.sparse, stim.sparse), k = 10, niter = 10, verbose = FALSE)

RcppPlanc documentation built on April 15, 2025, 1:11 a.m.