OOPS: Accidental commit, will restore version from 9/24/2022 by 9/26/2022 6pm EST.
RcppML is an R package for fast non-negative matrix factorization and divisive clustering using large sparse matrices.
See pkgdown site here: https://zdebruine.github.io/RcppML/
RcppML NMF is: * The fastest NMF implementation in any language for sparse and dense matrices * More interpretable than other implementations due to diagonal scaling * Easy to regularize with an L1 penalty
Installation
Install from CRAN or the development version from GitHub:
install.packages('RcppML') # install CRAN version
devtools::install_github("zdebruine/RcppML") # compile dev versionNOTE: RcppML is being actively developed. Please check that your packageVersion("RcppML") is current before raising issues.
Check out the CRAN manual.
Once installed and loaded, RcppML C++ headers defining classes can be used in C++ files for any R package using #include <RcppML.hpp>.
Matrix Factorization
Sparse matrix factorization by alternating least squares: * Non-negativity constraints * L1 regularization * Diagonal scaling * Rank-1 and Rank-2 specializations (~2x faster than irlba SVD equivalents)
Read (and cite) our bioRXiv manuscript on NMF for single-cell experiments.
R functions
The nmf function runs matrix factorization by alternating least squares in the form A = WDH. The project function updates w or h given the other, while the mse function calculates mean squared error of the factor model.
library(RcppML)
A <- Matrix::rsparsematrix(1000, 100, 0.1) # sparse Matrix::dgCMatrix
model <- RcppML::nmf(A, k = 10)
h0 <- predict(model, A)
evaluate(model, A) # calculate mean squared errorDivisive Clustering
Divisive clustering by rank-2 spectral bipartitioning. * 2nd SVD vector is linearly related to the difference between factors in rank-2 matrix factorization. * Rank-2 matrix factorization (optional non-negativity constraints) for spectral bipartitioning ~2x faster than irlba SVD * Sensitive distance-based stopping criteria similar to Newman-Girvan modularity, but orders of magnitude faster * Stopping criteria based on minimum number of samples
R functions
The dclust function runs divisive clustering by recursive spectral bipartitioning, while the bipartition function exposes the rank-2 NMF specialization and returns statistics of the bipartition.
library(RcppML)
A <- Matrix::rsparsematrix(1000, 1000, 0.1) # sparse Matrix::dgcMatrix
clusters <- dclust(A, min_dist = 0.001, min_samples = 5)
cluster0 <- bipartition(A)