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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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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
ISSN (print) / ISBN
2041-1723
e-ISSN
2041-1723
Zeitschrift
Nature Communications
Quellenangaben
Band: 12,
Heft: 1,
Artikelnummer: 6876
Verlag
Nature Publishing Group
Verlagsort
London
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)
Förderungen
Projekt DEAL