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

ManiNetCluster: A Manifold Learning Approach to Reveal the Functional Linkages Across Multiple Gene Networks

Nam D Nguyen, Ian K Blaby, View ORCID ProfileDaifeng Wang
doi: https://doi.org/10.1101/470195
Nam D Nguyen
Stony Brook University;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ian K Blaby
Brookhaven National Laboratory
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Daifeng Wang
Stony Brook University;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Daifeng Wang
  • For correspondence: daifeng.wang@stonybrookmedicine.edu
  • Abstract
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

The coordination of genome encoded function is a critical and complex process in biological systems, especially across phenotypes or states (e.g., time, disease, organism). Understanding how the complexity of genome-encoded function relates to these states remains a challenge. To address this, we have developed a novel computational method based on manifold learning and comparative analysis, ManiNetCluster, which simultaneously aligns and clusters multiple molecular networks to systematically reveal function links across multiple datasets. Specifically, ManiNetCluster employs manifold learning to match local and non-linear structures among the networks of different states, to identify cross-network linkages. By applying ManiNetCluster to the developmental gene expression datasets across model organisms (e.g., worm, fruit fly), we found that our tool significantly better aligns the orthologous genes than existing state-of-the-art methods, indicating the non-linear interactions between evolutionary functions in development. Moreover, we applied ManiNetCluster to a series of transcriptomes measured in the green alga Chlamydomonas reinhardtii, to determine the function links between various metabolic processes between the light and dark periods of a diurnally cycling culture. For example, we identify a number of genes putatively regulating processes across each lighting regime, and how comparative analyses between ManiNetCluster and other clustering tools can provide additional insights. ManiNetCluster is available as an R package together with a tutorial at https://github.com/namtk/ManiNetCluster.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted November 15, 2018.
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.
ManiNetCluster: A Manifold Learning Approach to Reveal the Functional Linkages Across Multiple Gene Networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
ManiNetCluster: A Manifold Learning Approach to Reveal the Functional Linkages Across Multiple Gene Networks
Nam D Nguyen, Ian K Blaby, Daifeng Wang
bioRxiv 470195; doi: https://doi.org/10.1101/470195
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
ManiNetCluster: A Manifold Learning Approach to Reveal the Functional Linkages Across Multiple Gene Networks
Nam D Nguyen, Ian K Blaby, Daifeng Wang
bioRxiv 470195; doi: https://doi.org/10.1101/470195

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 (996)
  • Biochemistry (1485)
  • Bioengineering (941)
  • Bioinformatics (6806)
  • Biophysics (2414)
  • Cancer Biology (1782)
  • Cell Biology (2518)
  • Clinical Trials (106)
  • Developmental Biology (1685)
  • Ecology (2556)
  • Epidemiology (1489)
  • Evolutionary Biology (5006)
  • Genetics (3603)
  • Genomics (4618)
  • Immunology (1159)
  • Microbiology (4228)
  • Molecular Biology (1618)
  • Neuroscience (10753)
  • Paleontology (81)
  • Pathology (236)
  • Pharmacology and Toxicology (407)
  • Physiology (553)
  • Plant Biology (1448)
  • Scientific Communication and Education (410)
  • Synthetic Biology (542)
  • Systems Biology (1870)
  • Zoology (258)