Leveraging the Cell Ontology to classify unseen cell types

Sheng Wang, Angela Oliveira Pisco (), Aaron McGeever, Maria Brbic, Marinka Zitnik, Spyros Darmanis, Jure Leskovec, Jim Karkanias and Russ B. Altman ()
Additional contact information
Sheng Wang: Stanford University
Angela Oliveira Pisco: Chan Zuckerberg Biohub
Aaron McGeever: Chan Zuckerberg Biohub
Maria Brbic: Stanford University
Marinka Zitnik: Stanford University
Spyros Darmanis: Chan Zuckerberg Biohub
Jure Leskovec: Chan Zuckerberg Biohub
Jim Karkanias: Chan Zuckerberg Biohub
Russ B. Altman: Stanford University

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

Date: 2021
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DOI: 10.1038/s41467-021-25725-x

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