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A Python library for probabilistic analysis of single-cell omics data

Nature Biotechnology volume 40, pages 163–166 (2022)Cite this article

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To the Editor — Methods for analyzing single-cell data1,2,3,4 perform a core set of computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state annotation, removal of unwanted variation, analysis of differential expression, identification of spatial patterns of gene expression, and joint analysis of multi-modal omics data. Many of these methods rely on likelihood-based models to represent variation in the data; we refer to these as ‘probabilistic models’. Probabilistic models provide principled ways to capture uncertainty in biological systems and are convenient for decomposing the many sources of variation that give rise to omics data5.

Despite the appeal of probabilistic models, several obstacles impede their community-wide adoption. The first obstacle, coming from the perspective of the end user, relates to the difficulty of implementing and running such models. Because probabilistic models are often implemented using Python machine-learning libraries, users are often required to interact with interfaces and objects that are lower level in nature than those used in popular environments for single-cell data analysis like Bioconductor6, Seurat7 or Scanpy8.

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Fig. 1: User perspective of scvi-tools.
Fig. 2: The scvi-tools API for developers and reimplementation of Stereoscope.

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Acknowledgements

We acknowledge members of the Streets and Yosef laboratories for general feedback. We thank all the GitHub users who contributed code to scvi-tools over the years. We thank Nicholas Everetts for help with the analysis of the Drosophila data. We thank David Kelley and Nick Bernstein for help implementing Solo. We thank Marco Wagenstetter and Sergei Rybakov for help with the transition of the scGen package to use scvi-tools, as well as feedback on the scArches implementation. We thank Hector Roux de Bézieux for insightful discussions about the R ecosystem. We thank Kieran Campbell and Allen Zhang for clarifying aspects of the original CellAssign implementation. We thank the Pyro team, including Eli Bingham, Martin Jankowiak and Fritz Obermeyer, for help integrating Pyro in scvi-tools. Research reported in this manuscript was supported by the NIGMS of the National Institutes of Health under award number R35GM124916 and by the Chan-Zuckerberg Foundation Network under grant number 2019-02452. O.C. is supported by the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning (EP/S023151/1, studentship 2420649). A.G. is supported by NIH Training Grant 5T32HG000047-19. A.S. and N.Y. are Chan Zuckerberg Biohub investigators.

Author information

Author notes

  1. Maxime Langevin

    Present address: Pasteur, Department of Chemistry, École Normale Supérieure, PSL University, Paris, France

  2. Maxime Langevin

    Present address: Molecular Design Sciences – Integrated Drug Discovery, Sanofi R&D, Vitry-sur-Seine, France

  3. Yining Liu & Achille Nazaret

    Present address: Department of Computer Science, Columbia University, New York, NY, USA

  4. Gabriel Misrachi

    Present address: Gleamer, Paris, France

  5. Oscar Clivio

    Present address: Department of Statistics, University of Oxford, Oxford, UK

  6. These authors contributed equally: Adam Gayoso, Romain Lopez and Galen Xing

Authors and Affiliations

  1. Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA

    Adam Gayoso, Galen Xing, Valeh Valiollah Pour Amiri, Justin Hong, Chenling Xu, Tal Ashuach, Mariano Gabitto, Aaron Streets, Michael I. Jordan & Nir Yosef

  2. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA

    Romain Lopez, Pierre Boyeau, Valeh Valiollah Pour Amiri, Justin Hong, Katherine Wu, Michael Jayasuriya, Yining Liu, Mariano Gabitto, Michael I. Jordan & Nir Yosef

  3. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Galen Xing, Aaron Streets & Nir Yosef

  4. École Normale Supérieure Paris-Saclay, Gif-sur-Yvette, France

    Pierre Boyeau, Edouard Mehlman & Oscar Clivio

  5. Centre de Mathématiques Appliquées, École polytechnique, Palaiseau, France

    Edouard Mehlman, Maxime Langevin, Gabriel Misrachi & Achille Nazaret

  6. Mines Paristech, PSL University, Paris, France

    Jules Samaran

  7. Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany

    Mohammad Lotfollahi & Fabian J. Theis

  8. School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany

    Mohammad Lotfollahi & Fabian J. Theis

  9. Serqet Therapeutics, Cambridge, MA, USA

    Valentine Svensson

  10. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Eduardo da Veiga Beltrame & Lior Pachter

  11. Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK

    Vitalii Kleshchevnikov & Carlos Talavera-López

  12. EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK

    Carlos Talavera-López

  13. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Lior Pachter

  14. Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA

    Aaron Streets

  15. Department of Statistics, University of California, Berkeley, Berkeley, CA, USA

    Michael I. Jordan

  16. Department of Statistics, University of Michigan, Ann Arbor, MI, USA

    Jeffrey Regier

  17. Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA, USA

    Nir Yosef

Authors
  1. Adam Gayoso
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  2. Romain Lopez
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  7. Katherine Wu
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  9. Edouard Mehlman
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  10. Maxime Langevin
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  11. Yining Liu
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  14. Achille Nazaret
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  17. Tal Ashuach
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  18. Mariano Gabitto
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  19. Mohammad Lotfollahi
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  20. Valentine Svensson
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  21. Eduardo da Veiga Beltrame
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  25. Fabian J. Theis
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  28. Jeffrey Regier
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  29. Nir Yosef
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Contributions

A.G., R.L and G.X. contributed equally. A.G. designed the scvi-tools application programming interface with input from G.X. and R.L. G.X. and A.G. led development of scvi-tools with input from R.L. G.X. reimplemented scVI, totalVI, AutoZI and scANVI with input from A.G. R.L. implemented Stereoscope with input from A.G. Data analysis in this manuscript was led by A.G., R.L. and G.X, with input from N.Y. A.G., R.L., P.B., E.M., M. Langevin., Y.L., J.S., G.M. and A.N., O.C. worked on the initial version of the codebase (scvi package), with input from M.I.J, J.R. and N.Y. R.L., E.M. and C.X. contributed the scANVI model, with input from J.R. and N.Y. A.G. implemented totalVI with input from A.S. and N.Y. T.A. implemented peakVI with input from A.G. A.G implemented scArches with input from M. Lotfollahi., F.J.T and N.Y. V.S. made several contributions to the codebase, including the LDVAE model. P.B. contributed the differential expression programming interface. E.d.V.B. and C.T.-L. provided tutorials on differential expression and deconvolution of spatial transcriptomics, with input from L.P. K.W. implemented CellAssign in the codebase with input from A.G. V.V.P.A., J.H. and M.J. made general code contributions and helped maintain scvi-tools. J.H. implemented LDA. T.A. and M.G. implemented MultiVI. V.K. improved Pyro support in scvi-tools and ported Cell2Location to use scvi-tools. N.Y. supervised all research. A.G., R.L., G.X., J.R. and N.Y. wrote the manuscript.

Corresponding author

Correspondence to Nir Yosef.

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Competing interests

V.S. is a full-time employee of Serqet Therapeutics and has ownership interest in Serqet Therapeutics. F.J.T. reports consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc. N.Y. is an advisor to and/or has equity in Cellarity, Celsius Therapeutics and Rheos Medicines. The remaining authors declare no competing interests.

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Nature Biotechnology thanks Martin Hemberg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Supplementary information

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Supplementary Figs. 1–8, Notes 1–5, Tables 1–3 and References

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Gayoso, A., Lopez, R., Xing, G. et al. A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol 40, 163–166 (2022). https://doi.org/10.1038/s41587-021-01206-w

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