CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology
iScience — 2021
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, a source of prior
knowledge that is not utilized by any existing methods. Furthermore, CellO comes pre-trained
on a novel, comprehensive dataset of human, healthy, untreated primary samples in the
Sequence Read Archive, which to the best of our knowledge, is the most diverse curated
collection of primary cell data to date. 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.
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BibTex references
@Article{BMGD21, author = "Bernstein, Matthew and Ma, Zhongie and Gleicher, Michael and Dewey, Colin", title = "CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology", journal = "iScience", year = "2021", doi = "https://doi.org/10.1016/j.isci.2020.101913", projecturl = "https://uwgraphics.github.io/CellOViewer/)", url = "http://graphics.cs.wisc.edu/Papers/2021/BMGD21" }