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

M. Büttner, J. Ostner, C. L. Müller (), F. J. Theis and B. Schubert ()
Additional contact information
M. Büttner: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health
J. Ostner: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health
C. L. Müller: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health
F. J. Theis: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health
B. Schubert: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract 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.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
https://www.nature.com/articles/s41467-021-27150-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27150-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-27150-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().