scGate: Marker-Based Cell Type Purification for Single-Cell Sequencing Data

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.

Version: 1.6.2
Depends: R (≥ 4.3.0)
Imports: Seurat (≥ 4.0.0), UCell (≥ 2.6.0), dplyr, stats, utils, methods, patchwork, ggridges, reshape2, ggplot2, BiocParallel
Suggests: ggparty, partykit, knitr, rmarkdown
Published: 2024-04-23
DOI: 10.32614/CRAN.package.scGate
Author: Massimo Andreatta ORCID iD [aut, cre], Ariel Berenstein ORCID iD [aut], Josep Garnica [aut], Santiago Carmona ORCID iD [aut]
Maintainer: Massimo Andreatta <massimo.andreatta at unil.ch>
BugReports: https://github.com/carmonalab/scGate/issues
License: GPL-3
URL: https://github.com/carmonalab/scGate
NeedsCompilation: no
Citation: scGate citation info
Materials: NEWS
CRAN checks: scGate results

Documentation:

Reference manual: scGate.pdf
Vignettes: Index of scGate vignettes

Downloads:

Package source: scGate_1.6.2.tar.gz
Windows binaries: r-devel: scGate_1.6.2.zip, r-release: scGate_1.6.2.zip, r-oldrel: scGate_1.6.2.zip
macOS binaries: r-release (arm64): scGate_1.6.2.tgz, r-oldrel (arm64): scGate_1.4.1.tgz, r-release (x86_64): scGate_1.6.2.tgz, r-oldrel (x86_64): scGate_1.4.1.tgz
Old sources: scGate archive

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