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

Controlling false discoveries in Bayesian gene networks with lasso regression p-values

View ORCID ProfileLingfei Wang, View ORCID ProfileTom Michoel
doi: https://doi.org/10.1101/288217
Lingfei Wang
Department of Genetics & Genomics, The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Lingfei Wang
  • For correspondence: lingfei.wang@roslin.ed.ac.uk
Tom Michoel
Department of Genetics & Genomics, The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tom Michoel
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Motivation Bayesian networks can represent directed gene regulations and therefore are favored over co-expression networks. However, hardly any Bayesian network study concerns the false discovery control (FDC) of network edges, leading to low accuracies due to systematic biases from inconsistent false discovery levels in the same study.

Results We design four empirical tests to examine the FDC of Bayesian networks from three p-value based lasso regression variable selections — two existing and one we originate. Our method, lassopv, computes p-values for the critical regularization strength at which a predictor starts to contribute to lasso regression. Using null and Geuvadis datasets, we find that lassopv obtains optimal FDC in Bayesian gene networks, whilst existing methods have defective p-values. The FDC concept and tests extend to most network inference scenarios and will guide the design and improvement of new and existing methods. Our novel variable selection method with lasso regression also allows FDC on other datasets and questions, even beyond network inference and computational biology.

Availability Lassopv is implemented in R and freely available at https://github.com/lingfeiwang/lassopv and https://cran.r-project.org/package=lassopv.

Contact Lingfei.Wang{at}roslin.ed.ac.uk

Supplementary information Supplementary data are available at Bioinformatics online.

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 March 27, 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.
Controlling false discoveries in Bayesian gene networks with lasso regression p-values
(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
Controlling false discoveries in Bayesian gene networks with lasso regression p-values
Lingfei Wang, Tom Michoel
bioRxiv 288217; doi: https://doi.org/10.1101/288217
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Controlling false discoveries in Bayesian gene networks with lasso regression p-values
Lingfei Wang, Tom Michoel
bioRxiv 288217; doi: https://doi.org/10.1101/288217

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

  • Systems Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4086)
  • Biochemistry (8759)
  • Bioengineering (6479)
  • Bioinformatics (23339)
  • Biophysics (11748)
  • Cancer Biology (9148)
  • Cell Biology (13245)
  • Clinical Trials (138)
  • Developmental Biology (7416)
  • Ecology (11369)
  • Epidemiology (2066)
  • Evolutionary Biology (15086)
  • Genetics (10397)
  • Genomics (14009)
  • Immunology (9119)
  • Microbiology (22039)
  • Molecular Biology (8779)
  • Neuroscience (47357)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2482)
  • Physiology (3704)
  • Plant Biology (8049)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2208)
  • Systems Biology (6015)
  • Zoology (1249)