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

GLUER: integrative analysis of single-cell omics and imaging data by deep neural network

Tao Peng, Gregory M. Chen, View ORCID ProfileKai Tan
doi: https://doi.org/10.1101/2021.01.25.427845
Tao Peng
1Center for Childhood Cancer Research, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
2Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gregory M. Chen
3Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kai Tan
1Center for Childhood Cancer Research, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
2Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
4Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kai Tan
  • For correspondence: tank1@email.chop.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

Single-cell omics assays have become essential tools for identifying and characterizing cell types and states of complex tissues. While each single-modality assay reveals distinctive features about the sequenced cells, true multi-omics assays are still in early stage of development. This notion signifies the importance of computationally integrating single-cell omics data that are conducted on various samples across various modalities. In addition, the advent of multiplexed molecular imaging assays has given rise to a need for computational methods for integrative analysis of single-cell imaging and omics data. Here, we present GLUER (inteGrative anaLysis of mUlti-omics at single-cEll Resolution), a flexible tool for integration of single-cell multi-omics data and imaging data. Using multiple true multi-omics data sets as the ground truth, we demonstrate that GLUER achieved significant improvement over existing methods in terms of the accuracy of matching cells across different data modalities resulting in ameliorating downstream analyses such as clustering and trajectory inference. We further demonstrate the broad utility of GLUER for integrating single-cell transcriptomics data with imaging-based spatial proteomics and transcriptomics data. Finally, we extend GLUER to leverage true cell-pair labels when available in true multi-omics data, and show that this approach improves co-embedding and clustering results. With the rapid accumulation of single-cell multi-omics and imaging data, integrated data holds the promise of furthering our understanding of the role of heterogeneity in development and disease.

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-NC 4.0 International license.
Back to top
PreviousNext
Posted January 26, 2021.
Download PDF
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.
GLUER: integrative analysis of single-cell omics and imaging data by deep neural network
(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
GLUER: integrative analysis of single-cell omics and imaging data by deep neural network
Tao Peng, Gregory M. Chen, Kai Tan
bioRxiv 2021.01.25.427845; doi: https://doi.org/10.1101/2021.01.25.427845
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
GLUER: integrative analysis of single-cell omics and imaging data by deep neural network
Tao Peng, Gregory M. Chen, Kai Tan
bioRxiv 2021.01.25.427845; doi: https://doi.org/10.1101/2021.01.25.427845

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 (3514)
  • Biochemistry (7367)
  • Bioengineering (5346)
  • Bioinformatics (20324)
  • Biophysics (10045)
  • Cancer Biology (7776)
  • Cell Biology (11352)
  • Clinical Trials (138)
  • Developmental Biology (6453)
  • Ecology (9980)
  • Epidemiology (2065)
  • Evolutionary Biology (13356)
  • Genetics (9373)
  • Genomics (12611)
  • Immunology (7725)
  • Microbiology (19102)
  • Molecular Biology (7465)
  • Neuroscience (41153)
  • Paleontology (301)
  • Pathology (1235)
  • Pharmacology and Toxicology (2142)
  • Physiology (3178)
  • Plant Biology (6879)
  • Scientific Communication and Education (1276)
  • Synthetic Biology (1900)
  • Systems Biology (5328)
  • Zoology (1091)