scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.

Details

Serval ID
serval:BIB_241F222A7909
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.
Journal
Bioinformatics
Author(s)
Andreatta M., Berenstein A.J., Carmona S.J.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Publication state
In Press
Peer-reviewed
Oui
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
Abstract
A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. scGate outperforms state-of-the-art single-cell classifiers and it can be applied to multiple modalities of single-cell data (e.g. RNA-seq, ATAC-seq, CITE-seq). scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from heterogeneous single-cell datasets.
R package source code and reproducible tutorials are available at https://github.com/carmonalab/scGate.
Supplementary data are available at Bioinformatics online.
Pubmed
Web of science
Create date
14/03/2022 9:24
Last modification date
22/07/2022 6:38
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