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A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

View ORCID ProfileAdam Gayoso, View ORCID ProfileRomain Lopez, View ORCID ProfileZoë Steier, View ORCID ProfileJeffrey Regier, View ORCID ProfileAaron Streets, Nir Yosef
doi: https://doi.org/10.1101/791947
Adam Gayoso
1Center for Computational Biology, University of California, Berkeley
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Romain Lopez
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
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Zoë Steier
3Department of Bioengineering, University of California, Berkeley
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Jeffrey Regier
4Department of Statistics, University of Michigan, Ann Arbor
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Aaron Streets
1Center for Computational Biology, University of California, Berkeley
3Department of Bioengineering, University of California, Berkeley
5Chan Zuckerberg Biohub, San Francisco, California
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  • For correspondence: astreets@berkeley.edu niryosef@berkeley.edu
Nir Yosef
1Center for Computational Biology, University of California, Berkeley
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
5Chan Zuckerberg Biohub, San Francisco, California
6Ragon Institute of MGH, MIT and Harvard
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  • For correspondence: astreets@berkeley.edu niryosef@berkeley.edu
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Abstract

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) combines unbiased single-cell transcriptome measurements with surface protein quantification comparable to flow cytometry, the gold standard for cell type identification. However, current analysis pipelines cannot address the two primary challenges of CITE-seq data: combining both modalities in a shared latent space that harnesses the power of the paired measurements, and handling the technical artifacts of the protein measurement, which is obscured by non-negligible background noise. Here we present Total Variational Inference (totalVI), a fully probabilistic end-to-end framework for normalizing and analyzing CITE-seq data, based on a hierarchical Bayesian model. In totalVI, the mRNA and protein measurements for each cell are generated from a low-dimensional latent random variable unique to that cell, representing its cellular state. totalVI uses deep neural networks to specify conditional distributions. By leveraging advances in stochastic variational inference, it scales easily to millions of cells. Explicit modeling of nuisance factors enables totalVI to produce denoised data in both domains, as well as a batch-corrected latent representation of cells for downstream analysis tasks.

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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 07, 2019.
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A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells
Adam Gayoso, Romain Lopez, Zoë Steier, Jeffrey Regier, Aaron Streets, Nir Yosef
bioRxiv 791947; doi: https://doi.org/10.1101/791947
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A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells
Adam Gayoso, Romain Lopez, Zoë Steier, Jeffrey Regier, Aaron Streets, Nir Yosef
bioRxiv 791947; doi: https://doi.org/10.1101/791947

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