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

Hypergraph factorisation for multi-tissue gene expression imputation

View ORCID ProfileRamon Viñas, View ORCID ProfileChaitanya K. Joshi, Dobrik Georgiev, View ORCID ProfileBianca Dumitrascu, View ORCID ProfileEric R. Gamazon, View ORCID ProfilePietro Liò
doi: https://doi.org/10.1101/2022.07.31.502211
Ramon Viñas
1Department of Computer Science and Technology, University of Cambridge
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ramon Viñas
Chaitanya K. Joshi
1Department of Computer Science and Technology, University of Cambridge
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Chaitanya K. Joshi
Dobrik Georgiev
1Department of Computer Science and Technology, University of Cambridge
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bianca Dumitrascu
1Department of Computer Science and Technology, University of Cambridge
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Bianca Dumitrascu
  • For correspondence: bmd39@cam.ac.uk eric.gamazon@vumc.org pl219@cam.ac.uk
Eric R. Gamazon
5Vanderbilt Genetics Institute and Data Science Institute, MRC Epidemiology Unit, University of Cambridge
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Eric R. Gamazon
  • For correspondence: bmd39@cam.ac.uk eric.gamazon@vumc.org pl219@cam.ac.uk
Pietro Liò
1Department of Computer Science and Technology, University of Cambridge
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pietro Liò
  • For correspondence: bmd39@cam.ac.uk eric.gamazon@vumc.org pl219@cam.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Integrating gene expression across scales and tissues is crucial for understanding the biological mechanisms that drive disease and characterise homeostasis. However, traditional multi-tissue integration methods cannot handle uncollected tissues or rely on genotype information, which is subject to privacy concerns and often unavailable. To address these challenges, we present HYFA (Hypergraph Factorisation), a novel method for joint imputation of multi-tissue and cell-type gene expression. HYFA imputes tissue-specific gene expression via a specialised graph neural network operating on a hypergraph of individuals, metagenes, and tissues. HYFA is genotype- agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues. In performance comparison on data from the Genotype Tissue Expression project, HYFA achieves superior performance over existing transcriptome imputation methods, especially when multiple reference tissues are available. Through transfer learning on a paired single-nucleus RNA-seq (snRNA-seq) dataset, we further show that HYFA can accurately resolve cell-type signatures from bulk gene expression, highlighting the method’s ability to leverage gene expression programs underlying cell-type identity, even in tissues that were never observed in the training set. Using Gene Set Enrichment Analysis, we find that the metagenes learned by HYFA capture information about known biological pathways. Notably, the HYFA-imputed dataset can be used to identify regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. Our framework can accelerate effective and scalable integration of tissue and cell-type gene expression biorepositories.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • rv340{at}cam.ac.uk; ckj24{at}cam.ac.uk; dgg30{at}cam.ac.uk; bmd39{at}cam.ac.uk; eric.gamazon{at}vumc.org; pl219{at}cam.ac.uk

  • Updated eQTL figures, extended data figures and panels

  • https://www.gtexportal.org/

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 August 08, 2022.
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.
Hypergraph factorisation for multi-tissue gene expression imputation
(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
Hypergraph factorisation for multi-tissue gene expression imputation
Ramon Viñas, Chaitanya K. Joshi, Dobrik Georgiev, Bianca Dumitrascu, Eric R. Gamazon, Pietro Liò
bioRxiv 2022.07.31.502211; doi: https://doi.org/10.1101/2022.07.31.502211
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Hypergraph factorisation for multi-tissue gene expression imputation
Ramon Viñas, Chaitanya K. Joshi, Dobrik Georgiev, Bianca Dumitrascu, Eric R. Gamazon, Pietro Liò
bioRxiv 2022.07.31.502211; doi: https://doi.org/10.1101/2022.07.31.502211

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 (4387)
  • Biochemistry (9611)
  • Bioengineering (7106)
  • Bioinformatics (24906)
  • Biophysics (12637)
  • Cancer Biology (9974)
  • Cell Biology (14375)
  • Clinical Trials (138)
  • Developmental Biology (7966)
  • Ecology (12127)
  • Epidemiology (2067)
  • Evolutionary Biology (16004)
  • Genetics (10936)
  • Genomics (14758)
  • Immunology (9883)
  • Microbiology (23700)
  • Molecular Biology (9490)
  • Neuroscience (50947)
  • Paleontology (370)
  • Pathology (1541)
  • Pharmacology and Toxicology (2688)
  • Physiology (4030)
  • Plant Biology (8675)
  • Scientific Communication and Education (1512)
  • Synthetic Biology (2402)
  • Systems Biology (6446)
  • Zoology (1346)