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Jointly Embedding Multiple Single-Cell Omics Measurements

Authors Jie Liu, Yuanhao Huang, Ritambhara Singh, Jean-Philippe Vert , William Stafford Noble

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Author Details

Jie Liu
  • Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
Yuanhao Huang
  • Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
Ritambhara Singh
  • Department of Genome Sciences, University of Washington, Seattle, WA, USA
Jean-Philippe Vert
  • Google Brain, Paris, France
  • Centre for Computational Biology, MINES ParisTech, PSL University, Paris, France
William Stafford Noble
  • Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

Funding

This work was supported by National Institutes of Health award U54 DK107979.

Cite As Get BibTex

Jie Liu, Yuanhao Huang, Ritambhara Singh, Jean-Philippe Vert, and William Stafford Noble. Jointly Embedding Multiple Single-Cell Omics Measurements. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 10:1-10:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.WABI.2019.10

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.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
  • Computing methodologies → Dimensionality reduction and manifold learning
  • Computing methodologies → Unsupervised learning
  • Computing methodologies → Machine learning algorithms
Keywords
  • Manifold alignment
  • single-cell sequencing

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