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An Empirical Bayes Method for Differential Expression Analysis of Single Cells with Deep Generative Models
View ORCID ProfilePierre Boyeau, View ORCID ProfileJeffrey Regier, View ORCID ProfileAdam Gayoso, Michael I. Jordan, View ORCID ProfileRomain Lopez, Nir Yosef
doi: https://doi.org/10.1101/2022.05.27.493625
Pierre Boyeau
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Jeffrey Regier
2Department of Statistics, University of Michigan, Ann Arbor
Adam Gayoso
3Center for Computational Biology, University of California, Berkeley
Michael I. Jordan
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
3Center for Computational Biology, University of California, Berkeley
4Department of Statistics, University of California, Berkeley
Romain Lopez
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Nir Yosef
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
3Center for Computational Biology, University of California, Berkeley
5Ragon Institute of MGH, MIT and Harvard, USA
6Chan Zuckerberg Biohub, San Francisco, USA

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Posted May 29, 2022.
An Empirical Bayes Method for Differential Expression Analysis of Single Cells with Deep Generative Models
Pierre Boyeau, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Romain Lopez, Nir Yosef
bioRxiv 2022.05.27.493625; doi: https://doi.org/10.1101/2022.05.27.493625
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