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Jointly embedding multiple single-cell omics measurements

Jie Liu, Yuanhao Huang, Ritambhara Singh, Jean-Philippe Vert, View ORCID ProfileWilliam Stafford Noble
doi: https://doi.org/10.1101/644310
Jie Liu
1Department of Computational Medicine and Bioinformatics, University of Michigan,
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  • For correspondence: drjieliu@umich.edu
Yuanhao Huang
2Department of Computational Medicine and Bioinformatics, University of Michigan,
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  • For correspondence: hyhao@umich.edu
Ritambhara Singh
3Department of Genome Sciences, University of Washington,
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  • For correspondence: rsingh7@uw.edu
Jean-Philippe Vert
4Google Brain, Paris, Centre for Computational Biology, MINES ParisTech, PSL University,
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  • For correspondence: jpvert@google.com
William Stafford Noble
5Department of Genome Sciences, University of Washington, Paul G. Allen School of Computer Science and Engineering, University of Washington,
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  • ORCID record for William Stafford Noble
  • For correspondence: noble@gs.washington.edu william-noble@uw.edu
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Abstract

Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of cells. Effectively, MMD-MA performs an in silico co-assay by embedding cells measured in different ways into a learned latent space. In the MMD-MA algorithm, single-cell data points from multiple domains are aligned by optimizing an objective function with three components: (1) a maximum mean discrepancy (MMD) term to encourage the differently measured points to have similar distributions in the latent space, (2) a distortion term to preserve the structure of the data between the input space and the latent space, and (3) a penalty term to avoid collapse to a trivial solution. Notably, MMD-MA does not require any correspondence information across data modalities, either between the cells or between the features. Furthermore, MMD-MA’s weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data. We demonstrate the utility of MMD-MA in simulation experiments and using a real data set involving single-cell gene expression and methylation data.

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  • An incorrect author name was entered into biorxiv. The names were correct on the submitted PDF.

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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 4.0 International license.
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Posted May 21, 2019.
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Jointly embedding multiple single-cell omics measurements
Jie Liu, Yuanhao Huang, Ritambhara Singh, Jean-Philippe Vert, William Stafford Noble
bioRxiv 644310; doi: https://doi.org/10.1101/644310
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Jointly embedding multiple single-cell omics measurements
Jie Liu, Yuanhao Huang, Ritambhara Singh, Jean-Philippe Vert, William Stafford Noble
bioRxiv 644310; doi: https://doi.org/10.1101/644310

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