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

Buettner M, Ostner J, Mueller CL, Theis FJ, Schubert B (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Nature Communications Nature Publishing Group: Nature Communications

Book Volume: 12

Article Number: 6876

Journal Issue: 1

DOI: 10.1038/s41467-021-27150-6

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE)

How to cite

APA:

Buettner, M., Ostner, J., Mueller, C.L., Theis, F.J., & Schubert, B. (2021). scCODA is a Bayesian model for compositional single-cell data analysis. Nature Communications, 12(1). https://dx.doi.org/10.1038/s41467-021-27150-6

MLA:

Buettner, M., et al. "scCODA is a Bayesian model for compositional single-cell data analysis." Nature Communications 12.1 (2021).

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