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scCODA is a Bayesian model for compositional single-cell data analysis.
Nat. Commun. 12:6876 (2021)
Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA ( https://github.com/theislab/scCODA ), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses.
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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
2041-1723
e-ISSN
2041-1723
Journal
Nature Communications
Quellenangaben
Volume: 12,
Issue: 1,
Article Number: 6876
Publisher
Nature Publishing Group
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Projekt DEAL