A dynamic probabilistic principal components model for the analysis of longitudinal metabolomic data

Abstract

In a longitudinal metabolomics study, multiple metabolites are measured from several observations at many time points. Interest lies in reducing the dimensionality of such data and in highlighting influential metabolites which change over time. A dynamic probabilistic principal components analysis (DPPCA) model is proposed to achieve dimension reduction while appropriately modelling the correlation due to repeated measurements. This is achieved by assuming an autoregressive model for some of the model parameters. Linear mixed models are subsequently used to identify influential metabolites which change over time. The proposed model is used to analyse data from a longitudinal metabolomics animal study.