单细胞 RNA 测序数据分析中执行和基准测试八种计算二聚体检测方法的协议。

Protocol for executing and benchmarking eight computational doublet-detection methods in single-cell RNA sequencing data analysis.

机构信息

Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL 60660, USA.

Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095-1554, USA.

出版信息

STAR Protoc. 2021 Jul 28;2(3):100699. doi: 10.1016/j.xpro.2021.100699. eCollection 2021 Sep 17.

DOI:10.1016/j.xpro.2021.100699

PMID:34382023

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8339294/

Abstract

The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational techniques have been developed for detecting doublets from scRNA-seq data. We developed an R package DoubletCollection to integrate the installation and execution of eight doublet detection methods. DoubletCollection provides a unified interface to perform and visualize downstream analysis after doublet detection. Here, we present a protocol of using DoubletCollection to benchmark doublet-detection methods. This protocol can accommodate new doublet-detection methods in the fast-growing scRNA-seq field. For details on the use and execution of this protocol, please refer to Xi and Li (2020).

摘要

二聚体的存在是单细胞 RNA 测序 (scRNA-seq) 数据分析中的一个关键混杂因素。已经开发了一些计算技术来从 scRNA-seq 数据中检测二聚体。我们开发了一个 R 包 DoubletCollection 来集成八个二聚体检测方法的安装和执行。DoubletCollection 提供了一个统一的接口，用于在进行二聚体检测后执行和可视化下游分析。在这里，我们介绍了使用 DoubletCollection 来对二聚体检测方法进行基准测试的方案。该方案可以适用于快速发展的 scRNA-seq 领域中的新的二聚体检测方法。有关使用和执行此方案的详细信息，请参阅 Xi 和 Li (2020)。