An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Lu Cheng (), Siddharth Ramchandran, Tommi Vatanen, Niina Lietzén, Riitta Lahesmaa, Aki Vehtari and Harri Lähdesmäki ()
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Lu Cheng: Aalto University School of Science
Siddharth Ramchandran: Aalto University School of Science
Tommi Vatanen: Broad Institute of MIT and Harvard
Niina Lietzén: University of Turku and Åbo Akademi University
Riitta Lahesmaa: University of Turku and Åbo Akademi University
Aki Vehtari: Aalto University School of Science
Harri Lähdesmäki: Aalto University School of Science

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract 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 are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. 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 demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets.

Date: 2019
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DOI: 10.1038/s41467-019-09785-8

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