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scCODA: A Bayesian model for compositional single-cell data analysis
View ORCID ProfileM. Büttner, View ORCID ProfileJ. Ostner, View ORCID ProfileCL. Müller, View ORCID ProfileFJ. Theis, View ORCID ProfileB. Schubert
doi: https://doi.org/10.1101/2020.12.14.422688
M. Büttner
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
J. Ostner
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
2Department of Statistics, Ludwig-Maximilians-Universität München, München, Germany
CL. Müller
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
2Department of Statistics, Ludwig-Maximilians-Universität München, München, Germany
3Center for Computational Mathematics, Flatiron Institute, New York, New York, USA
FJ. Theis
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
4Department of Mathematics, Technische Universität München, Garching bei München, Germany
5TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
B. Schubert
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
4Department of Mathematics, Technische Universität München, Garching bei München, Germany
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Posted December 15, 2020.
scCODA: A Bayesian model for compositional single-cell data analysis
M. Büttner, J. Ostner, CL. Müller, FJ. Theis, B. Schubert
bioRxiv 2020.12.14.422688; doi: https://doi.org/10.1101/2020.12.14.422688
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