Pasquini Giovanni, Rojo Arias Jesus Eduardo, Schäfer Patrick, Busskamp Volker
Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden 01307, Germany.
Universitäts-Augenklinik Bonn, University of Bonn, Department of Ophthalmology, Bonn 53127, Germany.
Comput Struct Biotechnol J. 2021 Jan 19;19:961-969. doi: 10.1016/j.csbj.2021.01.015. eCollection 2021.
The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.
单细胞测序的出现开启了转录组学和基因组学研究的新纪元，推动了我们对细胞异质性和动态变化的认识。细胞类型注释是分析单细胞RNA测序数据的关键步骤，但手动注释既耗时又具有一定主观性。作为一种替代方法，已经开发出了用于自动识别细胞类型的工具。出现了不同的策略，最终通过使用精心策划的标记基因数据库、关联参考表达数据或通过监督分类转移标签，将单细胞的基因表达谱与细胞类型联系起来。在这篇综述中，我们概述了可用的工具以及对scRNA-seq数据进行自动细胞类型注释的潜在方法。
