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CellMentor

Supervised Non-negative Matrix Factorization for Dimensional Reduction in Single-Cell Analysis

Bioconductor version: 3.24 · Package version: 1.1.2

Implements supervised cell type-aware non-negative matrix factorization (NMF) for dimensional reduction in single-cell RNA sequencing analysis. The package provides methods for incorporating cell type information into the dimensionality reduction process, enabling improved visualization and downstream analysis of single-cell data while preserving biological structure. CellMentor employs a unique loss function that simultaneously minimizes variation within known cell populations while maximizing distinctions between different cell types, enabling effective transfer of learned patterns from labeled reference datasets to new unlabeled data.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("CellMentor")

Details

MaintainerEkaterina Petrenko <petrenko.kate@icloud.com>
AuthorEkaterina Petrenko [aut, cre] (ORCID: <https://orcid.org/0000-0003-3549-834X>)
LicenseApache License (>= 2)
URLhttps://github.com/petrenkokate/CellMentor
Bug Reportshttps://github.com/petrenkokate/CellMentor/issues
Downloads rank200
Source branchdevel
biocViewsDimensionReduction, SingleCell, Software, Transcriptomics

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageCellMentor_1.1.2.tar.gz
Windows binary (x86_64)CellMentor_1.1.2.zip
macOS binary (arm64)CellMentor_1.1.2.tgz
macOS binary (x86_64)CellMentor_1.1.2.tgz
Dependencies

Depends: R (>= 4.5.0)

Imports: methods, Matrix, BiocParallel, SingleR, Seurat (>= 4.0.0), utils, stats, parallel, progress, ggplot2, data.table, magrittr, graphics, RMTstat, sparsesvd, cluster, skmeans, MLmetrics, tibble, lsa, nnls, SingleCellExperiment, entropy, irlba, aricode

Suggests: testthat (>= 3.0.0), covr, withr, rmarkdown, knitr, BiocStyle, scater, scRNAseq