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

An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures

View ORCID ProfileSahir R Bhatnagar, Yi Yang, Mathieu Blanchette, Budhachandra Khundrakpam, Alan Evans, Luigi Bouchard, Celia MT Greenwood
doi: https://doi.org/10.1101/102475
Sahir R Bhatnagar
Department of Epidemiology, Biostatistics and Occupational Health, McGill UniversityLady Davis Institute, Jewish General Hospital Montréal, QC
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Sahir R Bhatnagar
Yi Yang
Department of Mathematics and Statistics, McGill University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mathieu Blanchette
Montreal Neurological Institute, McGill University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Budhachandra Khundrakpam
Department of Computer Science, McGill University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alan Evans
Department of Computer Science, McGill University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Luigi Bouchard
Department of Biochemistry, Université de Sherbrooke
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Celia MT Greenwood
Department of Epidemiology, Biostatistics and Occupational Health, McGill UniversityLady Davis Institute, Jewish General Hospital Montréal, QCDepartments of Oncology and Human Genetics, McGill University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Computational approaches to variable selection have become increasingly important with the advent of high-throughput technologies in genomics and brain imaging studies, where the data has become massive, yet where it is believed that the number of truly important variables is small relative to the total number of variables. Although many approaches have been developed for main effects, less attention has been paid to interaction models. Here, starting from the hypothesis that a binary exposure variable can alter correlation patterns between clusters of high-dimensional variables, i.e. alter network properties of the variables, we explore whether such exposure-dependent clustering relationships can improve predictive modelling of an outcome or phenotype variable. Hence, we propose a modelling framework called ECLUST to test this hypothesis, and evaluate performance through extensive simulations. We see improved model fit in many scenarios. We further illustrate the framework through the analysis of three data sets from very different fields, each with high dimensional data, a binary exposure, and a phenotype of interest. Our method is available in the eclust CRAN package.

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-ND 4.0 International license.
Back to top
PreviousNext
Posted January 24, 2017.
Download PDF

Supplementary Material

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.
An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures
(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
An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures
Sahir R Bhatnagar, Yi Yang, Mathieu Blanchette, Budhachandra Khundrakpam, Alan Evans, Luigi Bouchard, Celia MT Greenwood
bioRxiv 102475; doi: https://doi.org/10.1101/102475
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures
Sahir R Bhatnagar, Yi Yang, Mathieu Blanchette, Budhachandra Khundrakpam, Alan Evans, Luigi Bouchard, Celia MT Greenwood
bioRxiv 102475; doi: https://doi.org/10.1101/102475

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

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (1546)
  • Biochemistry (2502)
  • Bioengineering (1759)
  • Bioinformatics (9734)
  • Biophysics (3930)
  • Cancer Biology (2996)
  • Cell Biology (4237)
  • Clinical Trials (135)
  • Developmental Biology (2655)
  • Ecology (4133)
  • Epidemiology (2033)
  • Evolutionary Biology (6936)
  • Genetics (5247)
  • Genomics (6534)
  • Immunology (2209)
  • Microbiology (7019)
  • Molecular Biology (2786)
  • Neuroscience (17431)
  • Paleontology (127)
  • Pathology (433)
  • Pharmacology and Toxicology (712)
  • Physiology (1069)
  • Plant Biology (2516)
  • Scientific Communication and Education (647)
  • Synthetic Biology (836)
  • Systems Biology (2700)
  • Zoology (439)