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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
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Jeffrey Regier
2Department of Statistics, University of Michigan, Ann Arbor
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Adam Gayoso
3Center for Computational Biology, University of California, Berkeley
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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
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Romain Lopez
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
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  • For correspondence: romain_lopez@berkeley.edu niryosef@berkeley.edu
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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  • For correspondence: romain_lopez@berkeley.edu niryosef@berkeley.edu
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Abstract

Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. Deep generative models have been extensively applied to scRNA-seq data, with a special focus on embedding cells into a low-dimensional latent space and correcting for batch effects. However, little attention has been given to the problem of utilizing the uncertainty from the deep generative model for differential expression. Furthermore, the existing approaches do not allow controlling for the effect size or the false discovery rate. Here, we present lvm-DE, a generic Bayesian approach for performing differential expression from using a fitted deep generative model, while controlling the false discovery rate. We apply the lvm-DE framework to scVI and scSphere, two deep generative models. The resulting approaches outperform the state-of-the-art methods at estimating the log fold change in gene expression levels, as well as detecting differentially expressed genes between subpopulations of cells.

Competing Interest Statement

The authors have declared no competing interest.

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 4.0 International license.
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Posted May 29, 2022.
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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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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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