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scCODA is a Bayesian model for compositional single-cell data analysis.

Nat. Commun. 12:6876 (2021)
Publ. Version/Full Text DOI
Open Access Gold
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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
Quellenangaben Volume: 12, Issue: 1, Pages: , Article Number: 6876 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
Grants Projekt DEAL