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Unsupervised manifold alignment for single-cell multi-omics data

View ORCID ProfileRitambhara Singh, Pinar Demetci, Giancarlo Bonora, Vijay Ramani, Choli Lee, He Fang, Zhijun Duan, Xinxian Deng, Jay Shendure, Christine Disteche, View ORCID ProfileWilliam Stafford Noble
doi: https://doi.org/10.1101/2020.06.13.149195
Ritambhara Singh
1Department of Computer Science, Brown University
2Center for Computational Molecular Biology, Brown University
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Pinar Demetci
2Center for Computational Molecular Biology, Brown University
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Giancarlo Bonora
3Department of Genome Sciences, University of Washington
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Vijay Ramani
4UC San Francisco
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Choli Lee
3Department of Genome Sciences, University of Washington
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He Fang
5Department of Pathology, University of Washington
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Zhijun Duan
6Division of Hematology, University of Washington
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Xinxian Deng
5Department of Pathology, University of Washington
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Jay Shendure
3Department of Genome Sciences, University of Washington
7Howard Hughes Medical Institute
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Christine Disteche
5Department of Pathology, University of Washington
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William Stafford Noble
3Department of Genome Sciences, University of Washington
8Paul G. Allen School of Computer Science and Engineering, University of Washington
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  • ORCID record for William Stafford Noble
  • For correspondence: wnoble@uw.edu
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Abstract

Integrating single-cell measurements that capture different properties of the genome is vital to extending our understanding of genome biology. This task is challenging due to the lack of a shared axis across datasets obtained from different types of single-cell experiments. For most such datasets, we lack corresponding information among the cells (samples) and the measurements (features). In this scenario, unsupervised algorithms that are capable of aligning single-cell experiments are critical to learning an in silico co-assay that can help draw correspondences among the cells. Maximum mean discrepancy-based manifold alignment (MMD-MA) is such an unsupervised algorithm. Without requiring correspondence information, it can align single-cell datasets from different modalities in a common shared latent space, showing promising results on simulations and a small-scale single-cell experiment with 61 cells. However, it is essential to explore the applicability of this method to larger single-cell experiments with thousands of cells so that it can be of practical interest to the community. In this paper, we apply MMD-MA to two recent datasets that measure transcriptome and chromatin accessibility in ~2000 single cells. To scale the runtime of MMD-MA to a more substantial number of cells, we extend the original implementation to run on GPUs. We also introduce a method to automatically select one of the user-defined parameters, thus reducing the hyperparameter search space. We demonstrate that the proposed extensions allow MMD-MA to accurately align state-of-the-art single-cell experiments.

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 4.0 International license.
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Posted June 15, 2020.
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Unsupervised manifold alignment for single-cell multi-omics data
Ritambhara Singh, Pinar Demetci, Giancarlo Bonora, Vijay Ramani, Choli Lee, He Fang, Zhijun Duan, Xinxian Deng, Jay Shendure, Christine Disteche, William Stafford Noble
bioRxiv 2020.06.13.149195; doi: https://doi.org/10.1101/2020.06.13.149195
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Unsupervised manifold alignment for single-cell multi-omics data
Ritambhara Singh, Pinar Demetci, Giancarlo Bonora, Vijay Ramani, Choli Lee, He Fang, Zhijun Duan, Xinxian Deng, Jay Shendure, Christine Disteche, William Stafford Noble
bioRxiv 2020.06.13.149195; doi: https://doi.org/10.1101/2020.06.13.149195

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