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

Adam Gayoso; Romain Lopez; Galen Xing; Pierre Boyeau; Valeh Valiollah Pour Amiri; Justin Hong; Katherine Wu; Michael Jayasuriya; Edouard Mehlman; Maxime Langevin; Yining Liu; Jules Samaran; Gabriel Misrachi; Achille Nazaret; Oscar Clivio; Chenling Xu; Tal Ashuach; Mariano Gabitto; Mohammad Lotfollahi; Valentine Svensson; Eduardo da Veiga Beltrame; Vitalii Kleshchevnikov; Carlos Talavera-López; Lior Pachter; Fabian J. Theis; Aaron Streets; Michael I. Jordan; Jeffrey Regier; Nir Yosef

doi:10.1038/s41587-021-01206-w

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

Adam Gayoso
, Romain Lopez
, Galen Xing
, Pierre Boyeau
, Valeh Valiollah Pour Amiri
, Justin Hong
, Katherine Wu
, Michael Jayasuriya
, Edouard Mehlman
, Maxime Langevin
, Yining Liu
, Jules Samaran
, Gabriel Misrachi
, Achille Nazaret
, Oscar Clivio
, Chenling Xu
, Tal Ashuach
, Mariano Gabitto
, Mohammad Lotfollahi
, Valentine Svensson

Eduardo da Veiga Beltrame, Vitalii Kleshchevnikov, Carlos Talavera-López, Lior Pachter, Fabian J. Theis, Aaron Streets, Michael I. Jordan, Jeffrey Regier, Nir Yosef

Chair of Mathematical Modelling of Biological Systems

University of California at Berkeley
Department of Electrical Engineering and Computer Sciences
Chan Zuckerberg Biohub
Ecole Normale Superieure de Cachan
CNRS
Ecole Normale Supérieure
Hoechst AG.
Columbia University
Ecole Nationale Supérieure des Mines de Paris
Gleamer
University of Oxford
Helmholtz Zentrum München German Research Center for Environmental Health
Technical University of Munich
Serqet Therapeutics
Division of Biology and Biological Engineering
Wellcome Sanger Institute
European Bioinformatics Institute
California Institute of Technology Division of Engineering and Applied Science
Department of Bioengineering
University of Michigan, Ann Arbor
Massachusetts Institute of Technology

Research output: Contribution to journal › Letter › peer-review

434 Scopus citations

Original language	English
Pages (from-to)	163-166
Number of pages	4
Journal	Nature Biotechnology
Volume	40
Issue number	2
DOIs	https://doi.org/10.1038/s41587-021-01206-w
State	Published - Feb 2022

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10.1038/s41587-021-01206-w

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Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., Liu, Y., Samaran, J., Misrachi, G., Nazaret, A., Clivio, O., Xu, C., Ashuach, T., Gabitto, M., Lotfollahi, M., ... Yosef, N. (2022). A Python library for probabilistic analysis of single-cell omics data. Nature Biotechnology, 40(2), 163-166. https://doi.org/10.1038/s41587-021-01206-w

@article{8732b9796a0043f4888004aee01fc59e,

title = "A Python library for probabilistic analysis of single-cell omics data",

author = "Adam Gayoso and Romain Lopez and Galen Xing and Pierre Boyeau and \{Valiollah Pour Amiri\}, Valeh and Justin Hong and Katherine Wu and Michael Jayasuriya and Edouard Mehlman and Maxime Langevin and Yining Liu and Jules Samaran and Gabriel Misrachi and Achille Nazaret and Oscar Clivio and Chenling Xu and Tal Ashuach and Mariano Gabitto and Mohammad Lotfollahi and Valentine Svensson and \{da Veiga Beltrame\}, Eduardo and Vitalii Kleshchevnikov and Carlos Talavera-L{\'o}pez and Lior Pachter and Theis, \{Fabian J.\} and Aaron Streets and Jordan, \{Michael I.\} and Jeffrey Regier and Nir Yosef",

note = "Funding Information: 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 B{\'e}zieux 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. Funding Information: 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 B{\'e}zieux 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.",

year = "2022",

month = feb,

doi = "10.1038/s41587-021-01206-w",

language = "English",

volume = "40",

pages = "163--166",

journal = "Nature Biotechnology",

issn = "1087-0156",

publisher = "Nature Research",

number = "2",

}

Gayoso, A, Lopez, R, Xing, G, Boyeau, P, Valiollah Pour Amiri, V, Hong, J, Wu, K, Jayasuriya, M, Mehlman, E, Langevin, M, Liu, Y, Samaran, J, Misrachi, G, Nazaret, A, Clivio, O, Xu, C, Ashuach, T, Gabitto, M, Lotfollahi, M, Svensson, V, da Veiga Beltrame, E, Kleshchevnikov, V, Talavera-López, C, Pachter, L, Theis, FJ, Streets, A, Jordan, MI, Regier, J & Yosef, N 2022, 'A Python library for probabilistic analysis of single-cell omics data', Nature Biotechnology, vol. 40, no. 2, pp. 163-166. https://doi.org/10.1038/s41587-021-01206-w

TY - JOUR

T1 - A Python library for probabilistic analysis of single-cell omics data

AU - Gayoso, Adam

AU - Lopez, Romain

AU - Xing, Galen

AU - Boyeau, Pierre

AU - Valiollah Pour Amiri, Valeh

AU - Hong, Justin

AU - Wu, Katherine

AU - Jayasuriya, Michael

AU - Mehlman, Edouard

AU - Langevin, Maxime

AU - Liu, Yining

AU - Samaran, Jules

AU - Misrachi, Gabriel

AU - Nazaret, Achille

AU - Clivio, Oscar

AU - Xu, Chenling

AU - Ashuach, Tal

AU - Gabitto, Mariano

AU - Lotfollahi, Mohammad

AU - Svensson, Valentine

AU - da Veiga Beltrame, Eduardo

AU - Kleshchevnikov, Vitalii

AU - Talavera-López, Carlos

AU - Pachter, Lior

AU - Theis, Fabian J.

AU - Streets, Aaron

AU - Jordan, Michael I.

AU - Regier, Jeffrey

AU - Yosef, Nir

N1 - Funding Information: 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. Funding Information: 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.

PY - 2022/2

Y1 - 2022/2

UR - https://www.scopus.com/pages/publications/85124370073

U2 - 10.1038/s41587-021-01206-w

DO - 10.1038/s41587-021-01206-w

M3 - Letter

C2 - 35132262

AN - SCOPUS:85124370073

SN - 1087-0156

VL - 40

SP - 163

EP - 166

JO - Nature Biotechnology

JF - Nature Biotechnology

IS - 2

ER -

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

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