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Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells

View ORCID ProfileAdam Gayoso, View ORCID ProfilePhilipp Weiler, View ORCID ProfileMohammad Lotfollahi, View ORCID ProfileDominik Klein, View ORCID ProfileJustin Hong, View ORCID ProfileAaron Streets, View ORCID ProfileFabian J. Theis, View ORCID ProfileNir Yosef
doi: https://doi.org/10.1101/2022.08.12.503709
Adam Gayoso
1Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
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Philipp Weiler
2Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
3Department of Mathematics, Technical University of Munich, Munich, Germany
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Mohammad Lotfollahi
2Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
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Dominik Klein
2Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
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Justin Hong
1Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
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Aaron Streets
1Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
4Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
5Chan Zuckerberg Biohub, San Francisco, CA, USA
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Fabian J. Theis
2Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
3Department of Mathematics, Technical University of Munich, Munich, Germany
6TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
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  • For correspondence: fabian.theis@helmholtz-muenchen.de niryosef@berkeley.edu
Nir Yosef
1Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
7Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
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  • For correspondence: fabian.theis@helmholtz-muenchen.de niryosef@berkeley.edu
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Abstract

RNA velocity has been rapidly adopted to guide the interpretation of transcriptional dynamics in snapshot single-cell transcriptomics data. Current approaches for estimating and analyzing RNA velocity can empirically reveal complex dynamics but lack effective strategies for quantifying the uncertainty of the estimate and its overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show in a series of examples that veloVI compares favorably to previous approaches for inferring RNA velocity with improvements in fit to the data, consistency across transcriptionally similar cells, and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that properties unique to veloVI, such as posterior velocity uncertainty, can be used to assess the appropriateness of analysis with velocity to the data at hand. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.

Competing Interest Statement

F.J.T consults for Immunai Inc., Singularity Bio B.V., CytoReason Ltd, and Omniscope Ltd, and has ownership interest in Dermagnostix GmbH and Cellarity. N.Y. is an advisor and/or has equity in Cellarity, Celsius Therapeutics, and Rheos Medicine.

Footnotes

  • https://github.com/YosefLab/velovi

  • https://github.com/YosefLab/velovi_reproducibility

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 15, 2022.
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Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
Adam Gayoso, Philipp Weiler, Mohammad Lotfollahi, Dominik Klein, Justin Hong, Aaron Streets, Fabian J. Theis, Nir Yosef
bioRxiv 2022.08.12.503709; doi: https://doi.org/10.1101/2022.08.12.503709
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Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
Adam Gayoso, Philipp Weiler, Mohammad Lotfollahi, Dominik Klein, Justin Hong, Aaron Streets, Fabian J. Theis, Nir Yosef
bioRxiv 2022.08.12.503709; doi: https://doi.org/10.1101/2022.08.12.503709

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