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Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

View ORCID ProfileOscar Clivio, View ORCID ProfileRomain Lopez, Jeffrey Regier, View ORCID ProfileAdam Gayoso, Michael I. Jordan, Nir Yosef
doi: https://doi.org/10.1101/794875
Oscar Clivio
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 Oscar Clivio
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
5Chan Zuckerberg Biohub, San Francisco, California
6Ragon Institute of MGH, MIT and Harvard
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  • For correspondence: niryosef@berkeley.edu
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Abstract

In single-cell RNA sequencing data, biological processes or technical factors may induce an overabundance of zero measurements. Existing probabilistic approaches to interpreting these data either model all genes as zero-inflated, or none. But the overabundance of zeros might be gene-specific. Hence, we propose the AutoZI model, which, for each gene, places a spike-and-slab prior on a mixture assignment between a negative binomial (NB) component and a zero-inflated negative binomial (ZINB) component. We approximate the posterior distribution under this model using variational inference, and employ Bayesian decision theory to decide whether each gene is zero-inflated. On simulated data, AutoZI outperforms the alternatives. On negative control data, AutoZI retrieves predictions consistent to a previous study on ERCC spike-ins and recovers similar results on control RNAs. Applied to several datasets and instances of the 10x Chromium protocol, AutoZI allows both biological and technical interpretations of zero-inflation. Finally, AutoZI’s decisions on mouse embyronic stem-cells suggest that zero-inflation might be due to transcriptional bursting.

Footnotes

  • Corrected wrong thresholds for AIC and BIC with respect to the difference of log-likelihoods. Note : this does not affect results as both criteria were directly computed from their definition.

  • https://github.com/oscarclivio/AutoZI_reproducibility

  • https://github.com/YosefLab/scVI

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-NC-ND 4.0 International license.
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Posted November 29, 2019.
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Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data
Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef
bioRxiv 794875; doi: https://doi.org/10.1101/794875
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Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data
Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef
bioRxiv 794875; doi: https://doi.org/10.1101/794875

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