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: |
1.4.1 |
| Depends: |
R (≥ 4.2.0) |
| Imports: |
Seurat (≥ 4.0.0), UCell (≥ 2.1.3), dplyr, stats, utils, methods, patchwork, ggridges, reshape2, ggplot2, BiocParallel |
| Suggests: |
ggparty, partykit, knitr, rmarkdown |
| Published: |
2022-12-20 |
| Author: |
Massimo Andreatta 
[aut, cre],
Ariel Berenstein
[aut],
Changsheng Li [aut],
Santiago Carmona
[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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=scGate
to link to this page.