Integrative analysis of time course metabolic data and biomarker discovery

TL;DR

This paper introduces a Bayesian mixed-effects model with a CAR component for analyzing time-course metabolic data, enabling better understanding of biological systems and discovery of biomarkers by addressing overfitting and complex data relationships.

Contribution

It presents a novel integrative statistical framework combining Bayesian, mixed-effects, and CAR models for improved analysis of longitudinal omic data.

Findings

Reduces overfitting in time-series analysis

Captures experimental design effects effectively

Identifies multiple associations between omic variables

Abstract

Metabonomics time-course experiments provide the opportunity to understand the changes to an organism by observing the evolution of metabolic profiles in response to internal or external stimuli. Along with other omic longitudinal profiling technologies, these techniques have great potential to complement the analysis of complex relations between variations across diverse omic variables and provide unique insights into the underlying biology of the system. However, many statistical methods currently used to analyse short time-series omic data are i) prone to overfitting or ii) do not take into account the experimental design or iii) do not make full use of the multivariate information intrinsic to the data or iv) unable to uncover multiple associations between different omic data. The model we propose is an attempt to i) overcome overfitting by using a weakly informative Bayesian model, ii) capture experimental design conditions through a mixed-effects model, iii) model interdependencies between variables by augmenting the mixed-effects model with a conditional auto-regressive (CAR) component and iv) identify potential associations between heterogeneous omic variables .