scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

• Published in: iScience Vol. 23; no. 3; p. 100882
• Main Authors: Shao, Xin, Liao, Jie, Lu, Xiaoyan, Xue, Rui, Ai, Ni, Fan, Xiaohui
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 27.03.2020; Elsevier
• Subjects: Automation in Bioinformatics; Bioinformatics; Cell Biology; Quantitative Genetics; Automation in Bioinformatics; Quantitative Genetics; Bioinformatics; Cell Biology
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2020.100882

Recent advancements in single-cell RNA sequencing (scRNA-seq) have facilitated the classification of thousands of cells through transcriptome profiling, wherein accurate cell type identification is critical for mechanistic studies. In most current analysis protocols, cell type-based cluster annotation is manually performed and heavily relies on prior knowledge, resulting in poor replicability of cell type annotation. This study aimed to introduce a single-cell Cluster-based Automatic Annotation Toolkit for Cellular Heterogeneity (scCATCH, https://github.com/ZJUFanLab/scCATCH). Using three benchmark datasets, the feasibility of evidence-based scoring and tissue-specific cellular annotation strategies were demonstrated by high concordance among cell types, and scCATCH outperformed Seurat, a popular method for marker genes identification, and cell-based annotation methods. Furthermore, scCATCH accurately annotated 67%–100% (average, 83%) clusters in six published scRNA-seq datasets originating from various tissues. The present results show that scCATCH accurately revealed cell identities with high reproducibility, thus potentially providing insights into mechanisms underlying disease pathogenesis and progression. [Display omitted] •Construction of a comprehensive tissue-specific reference database of cell markers•Paired comparisons to identify potential marker genes for clusters to ensure accuracy•Evidence-based scoring and annotation for clustered cells from scRNA-seq data•Accurate and replicable annotation on cell types of clusters without prior knowledge Quantitative Genetics; Cell Biology; Bioinformatics; Automation in Bioinformatics