CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology.
Classification of Bioinformatical Subject
Genomic Analysis
Genomics
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
22 Jan 2021
22 Jan 2021
Historique:
received:
30
07
2020
revised:
28
10
2020
accepted:
02
12
2020
entrez:
28
12
2020
pubmed:
29
12
2020
medline:
29
12
2020
Statut:
epublish
Résumé
Cell 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.
Identifiants
pubmed: 33364592
doi: 10.1016/j.isci.2020.101913
pii: S2589-0042(20)31110-X
pmc: PMC7753962
doi:
Types de publication
Journal Article
Langues
eng
Pagination
101913Subventions
Organisme : NLM NIH HHS
ID : T15 LM007359
Pays : United States
Informations de copyright
© 2020 The Authors.
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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