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Leveraging the Cell Ontology to classify unseen cell types
Sheng Wang1,2, Angela Oliveira Pisco3, Aaron McGeever4
1Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Nature Communications
|September 22, 2021
Summary
OnClass is a new algorithm that automatically classifies cells into types using the Cell Ontology. It can identify cell types not seen in training data and discover marker genes for all Cell Ontology terms.
Area of Science:
- Computational biology
- Genomics
- Bioinformatics
Background:
- Single-cell technologies generate vast datasets for biological system analysis.
- Inconsistent cell type terminology hinders joint analysis of independent single-cell datasets.
Purpose of the Study:
- To present OnClass, an algorithm and software for automated cell classification.
- To enable classification of cells into Cell Ontology terms, including those not in training data.
- To identify marker genes for all Cell Ontology categories.
Main Methods:
- Utilizes the Cell Ontology graph to infer cell type relationships.
- Employs a novel algorithm for automated cell classification.
- Software implementation for practical application in single-cell data analysis.
Main Results:
- OnClass accurately classifies cells into Cell Ontology categories.
- The algorithm successfully classifies cell types absent from training data.
- Marker genes are identified for all Cell Ontology terms, enhancing biological insight.
Conclusions:
- OnClass overcomes challenges in single-cell data integration by standardizing cell type annotation.
- The algorithm advances beyond simple annotation, offering marker gene discovery for the entire Cell Ontology.
- OnClass provides a data-centric approach for potential refinement of the Cell Ontology.
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