Ludwig Institute for Cancer Research, Lausanne Branch, and Department of Oncology, CHUV and University of Lausanne, 1011 Lausanne, Switzerland.
Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
Bioinformatics. 2022 Apr 28;38(9):2642-2644. doi: 10.1093/bioinformatics/btac141.
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.
scGate is available as an R package at https://github.com/carmonalab/scGate (https://doi.org/10.5281/zenodo.6202614). Several reproducible workflows describing the main functions and usage of the package on different single-cell modalities, as well as the code to reproduce the benchmark, can be found at https://github.com/carmonalab/scGate.demo (https://doi.org/10.5281/zenodo.6202585) and https://github.com/carmonalab/scGate.benchmark. Test data are available at https://doi.org/10.6084/m9.figshare.16826071.
Supplementary data are available at Bioinformatics online.
单细胞数据分析中的一个常见生物信息学任务是从异质数据集纯化感兴趣的细胞类型或细胞群体。在这里，我们提出了 scGate，这是一种自动化算法，可以在不需要训练数据或参考基因表达谱的情况下，基于标记纯化特定的细胞群体。scGate 使用一组标记物来纯化感兴趣的细胞群体，这些标记物按照层次结构组织，类似于流式细胞术使用的门控策略。scGate 优于最新的单细胞分类器，并且可以应用于多种单细胞数据模式（例如 RNA-seq、ATAC-seq、CITE-seq）。scGate 作为一个 R 包实现，并与 Seurat 框架集成，为从异质单细胞数据集中分离感兴趣的细胞群体提供了一个直观的工具。
scGate 作为一个 R 包可在 https://github.com/carmonalab/scGate 上获得（https://doi.org/10.5281/zenodo.6202614）。几个可重现的工作流程描述了该包在不同单细胞模式上的主要功能和用法，以及重现基准的代码，可在 https://github.com/carmonalab/scGate.demo（https://doi.org/10.5281/zenodo.6202585）和 https://github.com/carmonalab/scGate.benchmark 上找到。测试数据可在 https://doi.org/10.6084/m9.figshare.16826071 获得。
补充数据可在 Bioinformatics 在线获得。
