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@article{Jendoubi:2020:10.1186/s12859-019-3333-0,
author = {Jendoubi, Bedhiafi T and Ebbels, T},
doi = {10.1186/s12859-019-3333-0},
journal = {BMC Bioinformatics},
title = {Integrative analysis of time course metabolic data and biomarker discovery},
url = {http://dx.doi.org/10.1186/s12859-019-3333-0},
volume = {21},
year = {2020}
}

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TY  - JOUR
AB  - BackgroundMetabolomics 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 uncover 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, ii) do not fully take into account the experimental design or iii) do not make full use of the multivariate information intrinsic to the data or iv) are 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 by using a horseshoe prior.ResultsWe assess the performance of our model on synthetic and real datasets and show that it can outperform comparable models for metabolomic longitudinal data analysis. In addition, our proposed method provides the analyst with new insights on the data as it is able to identify metabolic biomarkers related to treatment, infer perturbed pathways as a result of treatment and find significant associations with additional omic variables. We also show through simulation that our model is fairly robust against inaccuracies in metabolite assignments. On real data, we demonstrate that the number of profiled metabolites slightly affects the predictive ability of the model.ConclusionsOur single model approach to longitudinal analysis of metabolomics data provides an approach simultane
AU  - Jendoubi,Bedhiafi T
AU  - Ebbels,T
DO  - 10.1186/s12859-019-3333-0
PY  - 2020///
SN  - 1471-2105
TI  - Integrative analysis of time course metabolic data and biomarker discovery
T2  - BMC Bioinformatics
UR  - http://dx.doi.org/10.1186/s12859-019-3333-0
UR  - http://hdl.handle.net/10044/1/75740
VL  - 21
ER  -

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