A Dynamic Probabilistic Principal Components Model for the Analysis of Longitudinal Metabolomics Data. Issue 5 (10th March 2014)

• Record Type: Journal Article
• Authors: Nyamundanda, Gift; Gormley, Isobel Claire; Brennan, Lorraine
• Is Part Of: Journal of the Royal Statistical Society. Volume 63:Issue 5(2014:Nov.)
• Page Start: 763
• Page End: 782
• Publication Date: 2014-03-10
• Subjects: Auto-regressive model -- Linear mixed model -- Longitudinal metabolomic data -- Metabolomics -- Principal components analysis -- Probabilistic principal components analysis; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12060
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: 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 model is proposed to achieve dimension reduction while appropriately modelling the correlation due to repeated measurements. This is achieved by assuming an auto-regressive model for some of the model parameters. Linear mixed models are subsequently used to identify influential metabolites which change over time. The model proposed is used to analyse data from a longitudinal metabolomics animal study.