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Deep Generative Models for Detecting Differential Expression in Single Cells

View ORCID ProfilePierre Boyeau, View ORCID ProfileRomain Lopez, Jeffrey Regier, View ORCID ProfileAdam Gayoso, Michael I. Jordan, Nir Yosef
doi: https://doi.org/10.1101/794289
Pierre Boyeau
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
2Department of Applied Mathematics and Computer Science, Ecole des Ponts ParisTech
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  • ORCID record for Pierre Boyeau
Romain Lopez
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
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Jeffrey Regier
3Department of Statistics, University of Michigan, Ann Arbor
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Adam Gayoso
4Center 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
4Center for Computational Biology, University of California, Berkeley
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Nir Yosef
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
4Center for Computational Biology, University of California, Berkeley
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  • For correspondence: niryosef@berkeley.edu
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Abstract

Detecting differentially expressed genes is important for characterizing subpopulations of cells. However, in scRNA-seq data, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. First, we show that deep generative models, which combined Bayesian statistics and deep neural networks, better estimate the log-fold-change in gene expression levels between subpopulations of cells. Second, we use Bayesian decision theory to detect differentially expressed genes while controlling the false discovery rate. Our experiments on simulated and real datasets show that our approach out-performs state-of-the-art DE frameworks. Finally, we introduce a technique for improving the posterior approximation, and show that it also improves differential expression performance.

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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 October 04, 2019.
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Deep Generative Models for Detecting Differential Expression in Single Cells
Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef
bioRxiv 794289; doi: https://doi.org/10.1101/794289
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Deep Generative Models for Detecting Differential Expression in Single Cells
Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef
bioRxiv 794289; doi: https://doi.org/10.1101/794289

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