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

A Fast Lasso-Based Method for Inferring Higher-Order Interactions

View ORCID ProfileKieran Elmes, Astra Heywood, View ORCID ProfileZhiyi Huang, View ORCID ProfileAlex Gavryushkin
doi: https://doi.org/10.1101/2021.12.13.471844
Kieran Elmes
1Department of Computer Science, University of Otago, New Zealand
3School of Mathematics and Statistics, University of Canterbury, New Zeleand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kieran Elmes
  • For correspondence: kieran.elmes@postgrad.otago.ac.nz
Astra Heywood
2Department of Biochemistry, University of Otago, New Zealand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zhiyi Huang
1Department of Computer Science, University of Otago, New Zealand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zhiyi Huang
Alex Gavryushkin
3School of Mathematics and Statistics, University of Canterbury, New Zeleand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Alex Gavryushkin
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Large-scale genotype-phenotype screens provide a wealth of data for identifying molecular alterations associated with a phenotype. Epistatic effects play an important role in such association studies. For example, siRNA perturbation screens can be used to identify combinatorial gene-silencing effects. In bacteria, epistasis has practical consequences in determining antimicrobial resistance as the genetic background of a strain plays an important role in determining resistance. Recently developed tools scale to human exome-wide screens for pairwise interactions, but none to date have included the possibility of three-way interactions. Expanding upon recent state-of-the art methods, we make a number of improvements to the performance on large-scale data, making consideration of three-way interactions possible. We demonstrate our proposed method, Pint, on both simulated and real data sets, including antibiotic resistance testing and siRNA perturbation screens. Pint outperforms known methods in simulated data, and identifies a number of biologically plausible gene effects in both the antibiotic and siRNA models. For example, we have identified a combination of known tumor suppressor genes that is predicted (using Pint) to cause a significant increase in cell proliferation.

Author Summary In recent years, large-scale genetic datasets have become available for analysis. These large datasets often stretch the limits of classic computational methods, requiring too much memory or simply taking a prohibitively long time to run. Due to the enormous number of potential interactions, each gene or variation in the data is often modeled on its own, without considering interactions between them. Recently, methods have been developed to solve regression problems that include these interacting effects. Even the fastest of these cannot include threeway interactions, however. We improve upon one such method, developing an approach that is significantly faster than the current state of the art. Moreover, our method scales to three-way interactions among thousands of genes, while avoiding a number of the limitations of previous approaches. We analyse large-scale simulated data, antibiotic resistance, and gene-silencing datasets to demonstrate the accuracy and performance of our approach.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵✉ E-mail addresses: alex{at}biods.org.

  • We acknowledge support from the Royal Society Te Apārangi through a Rutherford Discovery Fellowship (RDF-UOC1702). This work was partially supported by Ministry of Business, Innovation, and Employment of New Zealand through an Endeavour Smart Ideas grant (UOOX1912) and a Data Science Programmes grant (UOAX1932).

  • A section has been added on effect strength vs. accuracy. Several new options in the package are now described: using non-binary input, reducing running times with an approximate hierarchy, and excluding identical effects. Simulations have been re-run without a strong hierarchy assumption.

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 August 31, 2022.
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.
A Fast Lasso-Based Method for Inferring Higher-Order Interactions
(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
A Fast Lasso-Based Method for Inferring Higher-Order Interactions
Kieran Elmes, Astra Heywood, Zhiyi Huang, Alex Gavryushkin
bioRxiv 2021.12.13.471844; doi: https://doi.org/10.1101/2021.12.13.471844
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A Fast Lasso-Based Method for Inferring Higher-Order Interactions
Kieran Elmes, Astra Heywood, Zhiyi Huang, Alex Gavryushkin
bioRxiv 2021.12.13.471844; doi: https://doi.org/10.1101/2021.12.13.471844

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 (4087)
  • Biochemistry (8768)
  • Bioengineering (6481)
  • Bioinformatics (23348)
  • Biophysics (11752)
  • Cancer Biology (9150)
  • Cell Biology (13256)
  • Clinical Trials (138)
  • Developmental Biology (7417)
  • Ecology (11371)
  • Epidemiology (2066)
  • Evolutionary Biology (15091)
  • Genetics (10402)
  • Genomics (14012)
  • Immunology (9122)
  • Microbiology (22050)
  • Molecular Biology (8780)
  • Neuroscience (47381)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2482)
  • Physiology (3705)
  • Plant Biology (8054)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2209)
  • Systems Biology (6016)
  • Zoology (1250)