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scGate  

Marker-Based Cell Type Purification for Single-Cell Sequencing Data
View on CRAN: Click here


Download and install scGate package within the R console
Install from CRAN:
install.packages("scGate")

Install from Github:
library("remotes")
install_github("cran/scGate")

Install by package version:
library("remotes")
install_version("scGate", "1.6.2")



Attach the package and use:
library("scGate")
Maintained by
Massimo Andreatta
[Scholar Profile | Author Map]
First Published: 2022-12-20
Latest Update: 2023-11-06
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
How to cite:
Massimo Andreatta (2022). scGate: Marker-Based Cell Type Purification for Single-Cell Sequencing Data. R package version 1.6.2, https://cran.r-project.org/web/packages/scGate. Accessed 29 Mar. 2025.
Previous versions and publish date:
1.4.1 (2022-12-20 12:00), 1.6.0 (2023-11-06 13:30)
Other packages that cited scGate R package
View scGate citation profile
Other R packages that scGate depends, imports, suggests or enhances
Complete documentation for scGate
Functions, R codes and Examples using the scGate R package
Some associated functions: combine_scGate_multiclass . gating_model . genes.blacklist.default . get_scGateDB . get_testing_data . load_scGate_model . performance.metrics . plot_UCell_scores . plot_levels . plot_tree . query.seurat . scGate . test_my_model . 
Some associated R codes: main.R . utils.R .  Full scGate package functions and examples
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