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scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data
- Source :
- iScience, Vol 23, Iss 3, Pp-(2020), iScience
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- 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.<br />Graphical Abstract<br />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<br />Quantitative Genetics; Cell Biology; Bioinformatics; Automation in Bioinformatics
- Subjects :
- 0301 basic medicine
Cell type
Bioinformatics
Computer science
Cell
Sequencing data
02 engineering and technology
Computational biology
Article
Quantitative Genetics
03 medical and health sciences
Annotation
medicine
Transcriptome profiling
lcsh:Science
Gene
Automation in Bioinformatics
Multidisciplinary
RNA
Cell Biology
021001 nanoscience & nanotechnology
030104 developmental biology
medicine.anatomical_structure
lcsh:Q
Identification (biology)
0210 nano-technology
Subjects
Details
- ISSN :
- 25890042
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- iScience
- Accession number :
- edsair.doi.dedup.....8f9f2a7960ada192725680843c888f57