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.

@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"
}