文章基本信息

标题：CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology
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作者：Matthew N. Bernstein ; Zhongjie Ma ; Michael Gleicher 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：1
页码：1-42
DOI：10.1016/j.isci.2020.101913
语种：English
出版社：Elsevier
摘要：SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification of cell clusters by considering the rich hierarchical structure of known cell types. Furthermore, CellO comes pre-trained on a comprehensive data set of human, healthy, untreated primary samples in the Sequence Read Archive. CellO's comprehensive training set enables it to run out of the box on diverse cell types and achieves competitive or even superior performance when compared to existing state-of-the-art methods. Lastly, CellO's linear models are easily interpreted, thereby enabling exploration of cell-type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO's models across the ontology.Graphical abstractDisplay OmittedHighlights•CellO hierarchically annotates single-cell RNA-seq data using the Cell Ontology•CellO is pre-trained on a comprehensive data set comprising diverse cell types•CellO achieves superior or comparable performance with existing methods•The CellO Viewer is a novel web application for exploring CellO's trained modelsGenomics; Classification of Bioinformatical Subject; Genomic Analysis