注释
计算生物学
鉴定(生物学)
转录组
电池类型
计算机科学
基因注释
基因组
生物
细胞
RNA序列
生物信息学
基因
遗传学
基因表达
植物
作者
Xin Shao,Jie Liao,Xiaoyan Lu,Rui Xue,Ai Ni,Xiaohui Fan
出处
期刊:iScience
[Elsevier]
日期:2020-03-01
卷期号:23 (3): 100882-100882
被引量:189
标识
DOI:10.1016/j.isci.2020.100882
摘要
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.
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