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MultiVI: deep generative model for the integration of multi-modal data

View ORCID ProfileTal Ashuach, Mariano I. Gabitto, Michael I. Jordan, Nir Yosef
doi: https://doi.org/10.1101/2021.08.20.457057
Tal Ashuach
1Center for Computational Biology, University of California, Berkeley, CA, USA
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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  • ORCID record for Tal Ashuach
Mariano I. Gabitto
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
3Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
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Michael I. Jordan
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
3Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
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Nir Yosef
1Center for Computational Biology, University of California, Berkeley, CA, USA
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
4Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
5Chan Zuckerberg BioHub, San Francisco, CA, USA
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  • For correspondence: niryosef@berkeley.edu
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Abstract

The ability to jointly profile the transcriptional and chromatin land-scape of single-cells has emerged as a powerful technique to identify cellular populations and shed light on their regulation of gene expression. Current computational methods analyze jointly profiled (paired) or individual data modalities (unpaired), but do not offer a principled method to analyze both paired and unpaired samples jointly. Here we present MultiVI, a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA + scATAC) data. MultiVI creates an informative low-dimensional latent space that accurately reflects both chromatin and transcriptional properties of the cells even when one of the modalities is missing. MultiVI accounts for technical effects in both scRNA and scATAC-seq while correcting for batch effects in both data modalities. We use public datasets to demonstrate that MultiVI is stable, easy to use, and outperforms current approaches for the joint analysis of paired and unpaired data. MultiVI is available as an open source package, implemented in the scvi-tools frame-work: https://docs.scvi-tools.org/.

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-NC-ND 4.0 International license.
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Posted August 20, 2021.
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MultiVI: deep generative model for the integration of multi-modal data
Tal Ashuach, Mariano I. Gabitto, Michael I. Jordan, Nir Yosef
bioRxiv 2021.08.20.457057; doi: https://doi.org/10.1101/2021.08.20.457057
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MultiVI: deep generative model for the integration of multi-modal data
Tal Ashuach, Mariano I. Gabitto, Michael I. Jordan, Nir Yosef
bioRxiv 2021.08.20.457057; doi: https://doi.org/10.1101/2021.08.20.457057

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