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

Author

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  • 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
    Ludwig-Maximilians-Universität München)

  • C. L. Müller

    (Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health
    Ludwig-Maximilians-Universität München
    Center for Computational Mathematics, Flatiron Institute)

  • F. J. Theis

    (Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health
    Technische Universität München
    TUM School of Life Sciences Weihenstephan, Technical University of Munich)

  • B. Schubert

    (Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health
    Technische Universität München)

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.

Suggested Citation

  • M. Büttner & J. Ostner & C. L. Müller & F. J. Theis & B. Schubert, 2021. "scCODA is a Bayesian model for compositional single-cell data analysis," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27150-6
    DOI: 10.1038/s41467-021-27150-6
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