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jrSiCKLSNMF: Multimodal Single-Cell Omics Dimensionality Reduction

Methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization ('jrSiCKLSNMF', pronounced "junior sickles NMF") on quality controlled single-cell multimodal omics count data. 'jrSiCKLSNMF' specifically deals with dual-assay scRNA-seq and scATAC-seq data. This package contains functions to extract meaningful latent factors that are shared across omics modalities. These factors enable accurate cell-type clustering and facilitate visualizations. Methods for pre-processing, clustering, and mini-batch updates and other adaptations for larger datasets are also included. For further details on the methods used in this package please see Ellis, Roy, and Datta (2023) <doi:10.3389/fgene.2023.1179439>.

Version:	1.2.3
Depends:	R (≥ 3.5.0)
Imports:	Rcpp (≥ 1.0.9),igraph,umap,kknn,ggplot2, methods, stats,rlang,Matrix,data.table, parallel,pbapply,cluster,MASS,clValid,factoextra,foreach,irlba,scran,Rdpack,ggrepel
LinkingTo:	Rcpp,RcppArmadillo,RcppProgress
Suggests:	knitr,rmarkdown
Published:	2025-07-11
DOI:	10.32614/CRAN.package.jrSiCKLSNMF
Author:	Dorothy Ellis [aut, cre], Susmita Datta [ths], Kenneth Perkins [ctb] (Util.h function author, http://programmingnotes.org/), Renaud Gaujoux [ctb] (Author of .nndsvd R adaptation)
Maintainer:	Dorothy Ellis <ddemoreellis at gmail.com>
License:	GPL-3
NeedsCompilation:	yes
Citation:	jrSiCKLSNMF citation info
Materials:	README,NEWS
CRAN checks:	jrSiCKLSNMF results

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