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

• Main Authors: Andreatta, M. (Author), Berenstein, A.J (Author), Carmona, S.J (Author)
• Format: Article
• Language: English
• Published: Oxford University Press 2022
• Online Access: View Fulltext in Publisher

 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. © 2022 The Author(s). Published by Oxford University Press.