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

LonGP: an additive Gaussian process regression model for longitudinal study designs

Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzen, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki
doi: https://doi.org/10.1101/259564
Lu Cheng
1Department of Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Siddharth Ramchandran
1Department of Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tommi Vatanen
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Niina Lietzen
3Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku 20520, Finland.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Riitta Lahesmaa
3Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku 20520, Finland.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aki Vehtari
1Department of Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Harri Lähdesmäki
1Department of Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.
  • 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

Motivation Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models have become the standard workhorse for statistical analysis of data from longitudinal study designs. However, analysis of longitudinal data can be complicated for both practical and theoretical reasons, including difficulties in modelling, correlated outcome values, functional (time-varying) covariates, nonlinear effects, and model inference.

Results We present LonGP, an additive Gaussian process regression model for analysis of experimental data from longitudinal study designs. LonGP implements a flexible, non-parametric modelling framework that solves commonly faced challenges in longitudinal data analysis. In addition to inheriting all standard features of Gaussian processes, LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We develop an accurate Bayesian inference and model selection method, and implement an efficient model search algorithm for our additive Gaussian process model. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets. Our work is accompanied by a versatile software implementation.

Availability LonGP software tool is available at http://research.cs.aalto.fi/csb/software/longp/.

Contact lu.cheng.ac{at}gmail.com, harri.lahdesmaki{at}aalto.fi

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 February 06, 2018.
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.
LonGP: an additive Gaussian process regression model for longitudinal study designs
(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
LonGP: an additive Gaussian process regression model for longitudinal study designs
Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzen, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki
bioRxiv 259564; doi: https://doi.org/10.1101/259564
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
LonGP: an additive Gaussian process regression model for longitudinal study designs
Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzen, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki
bioRxiv 259564; doi: https://doi.org/10.1101/259564

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 (3482)
  • Biochemistry (7329)
  • Bioengineering (5301)
  • Bioinformatics (20212)
  • Biophysics (9985)
  • Cancer Biology (7706)
  • Cell Biology (11273)
  • Clinical Trials (138)
  • Developmental Biology (6425)
  • Ecology (9923)
  • Epidemiology (2065)
  • Evolutionary Biology (13292)
  • Genetics (9353)
  • Genomics (12559)
  • Immunology (7681)
  • Microbiology (18964)
  • Molecular Biology (7421)
  • Neuroscience (40915)
  • Paleontology (298)
  • Pathology (1226)
  • Pharmacology and Toxicology (2130)
  • Physiology (3145)
  • Plant Biology (6842)
  • Scientific Communication and Education (1271)
  • Synthetic Biology (1893)
  • Systems Biology (5299)
  • Zoology (1086)