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

Summary 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.
Graphical Abstract
Highlights • 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