Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Gromov-Wasserstein optimal transport to align single-cell multi-omics data

Pinar Demetci, Rebecca Santorella, Björn Sandstede, View ORCID ProfileWilliam Stafford Noble, View ORCID ProfileRitambhara Singh
doi: https://doi.org/10.1101/2020.04.28.066787
Pinar Demetci
1Department of Computer Science, Brown University
2Center for Computational Molecular Biology, Brown University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rebecca Santorella
3Division of Applied Mathematics, Brown University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Björn Sandstede
3Division of Applied Mathematics, Brown University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
William Stafford Noble
4Department of Genome Sciences, University of Washington
5Paul G. Allen School of Computer Science and Engineering, University of Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for William Stafford Noble
Ritambhara Singh
1Department of Computer Science, Brown University
2Center for Computational Molecular Biology, Brown University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ritambhara Singh
  • For correspondence: ritambhara@brown.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Data integration of single-cell measurements is critical for understanding cell development and disease, but the lack of correspondence between different types of measurements makes such efforts challenging. Several unsupervised algorithms can align heterogeneous single-cell measurements in a shared space, enabling the creation of mappings between single cells in different data domains. However, these algorithms require hyperparameter tuning for high-quality alignments, which is difficult in an unsupervised setting without correspondence information for validation. We present Single-Cell alignment using Optimal Transport (SCOT), an unsupervised learning algorithm that uses Gromov Wasserstein-based optimal transport to align single-cell multi-omics datasets. We compare the alignment performance of SCOT with state-of-the-art algorithms on four simulated and two real-world datasets. SCOT performs on par with state-of-the-art methods but is faster and requires tuning fewer hyperparameters. Furthermore, we provide an algorithm for SCOT to use Gromov Wasserstein distance to guide the parameter selection. Thus, unlike previous methods, SCOT aligns well without using any orthogonal correspondence information to pick the hyperparameters. Our source code and scripts for replicating the results are available at https://github.com/rsinghlab/SCOT.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* Equal Contribution

  • Method, Experimental Setup, and Result Sections updated with new results; Supplementary information updated

  • https://github.com/rsinghlab/SCOT

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.
Back to top
PreviousNext
Posted November 11, 2020.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Gromov-Wasserstein optimal transport to align single-cell multi-omics data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Gromov-Wasserstein optimal transport to align single-cell multi-omics data
Pinar Demetci, Rebecca Santorella, Björn Sandstede, William Stafford Noble, Ritambhara Singh
bioRxiv 2020.04.28.066787; doi: https://doi.org/10.1101/2020.04.28.066787
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Gromov-Wasserstein optimal transport to align single-cell multi-omics data
Pinar Demetci, Rebecca Santorella, Björn Sandstede, William Stafford Noble, Ritambhara Singh
bioRxiv 2020.04.28.066787; doi: https://doi.org/10.1101/2020.04.28.066787

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (2518)
  • Biochemistry (4968)
  • Bioengineering (3473)
  • Bioinformatics (15185)
  • Biophysics (6886)
  • Cancer Biology (5380)
  • Cell Biology (7717)
  • Clinical Trials (138)
  • Developmental Biology (4521)
  • Ecology (7135)
  • Epidemiology (2059)
  • Evolutionary Biology (10211)
  • Genetics (7503)
  • Genomics (9773)
  • Immunology (4825)
  • Microbiology (13185)
  • Molecular Biology (5130)
  • Neuroscience (29368)
  • Paleontology (203)
  • Pathology (836)
  • Pharmacology and Toxicology (1461)
  • Physiology (2131)
  • Plant Biology (4738)
  • Scientific Communication and Education (1008)
  • Synthetic Biology (1337)
  • Systems Biology (4003)
  • Zoology (768)