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

ABSTRACT:

Summary

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

SUBMITTER: Andreatta M 

PROVIDER: S-EPMC9048671 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature