Package: scGate
Type: Package
Title: Marker-Based Cell Type Purification for Single-Cell Sequencing
        Data
Version: 1.7.3
Authors@R: c(
  person('Massimo', 'Andreatta',
    email = 'massimo.andreatta@unige.ch',
    role = c('aut','cre'),
    comment = c(ORCID = '0000-0002-8036-2647')), 
  person('Ariel','Berenstein',
    email = 'arieljberenstein@gmail.com',
    role = c('aut'),
    comment = c(ORCID = '0000-0001-8540-5389')), 
  person('Josep','Garnica',
    email = 'josep.garnicacaparros@unige.ch',
    role = c('aut')),  
  person('Santiago', 'Carmona',
    email = 'santiago.carmona@unige.ch',
    role = c('aut'),
    comment = c(ORCID = '0000-0002-2495-0671')) 
  )
Description: 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>.
biocViews:
Depends: R (>= 4.3.0)
Imports: Seurat (>= 4.0.0), UCell (>= 2.6.0), dplyr, stats, utils,
        methods, patchwork, ggridges, colorspace, reshape2, ggplot2,
        BiocParallel
Suggests: ggparty, partykit, knitr, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/carmonalab/scGate
BugReports: https://github.com/carmonalab/scGate/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libpng-dev
        libuv1-dev libxml2-dev libssl-dev python3 zlib1g-dev
Repository: https://carmonalab.r-universe.dev
Date/Publication: 2026-03-13 11:17:21 UTC
RemoteUrl: https://github.com/carmonalab/scGate
RemoteRef: HEAD
RemoteSha: 14dcede8f60298fddd2c47aa139e3edffbb31386
NeedsCompilation: no
Packaged: 2026-06-21 09:13:25 UTC; root
Author: Massimo Andreatta [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-8036-2647>),
  Ariel Berenstein [aut] (ORCID: <https://orcid.org/0000-0001-8540-5389>),
  Josep Garnica [aut],
  Santiago Carmona [aut] (ORCID: <https://orcid.org/0000-0002-2495-0671>)
Maintainer: Massimo Andreatta <massimo.andreatta@unige.ch>