Nyamundanda, GiftGiftNyamundandaGormley, Isobel ClaireIsobel ClaireGormleyBrennan, LorraineLorraineBrennan2015-09-252015-11-012014 Royal2014-11Journal of the Royal Statistical Society: Series C (Applied Statistics)http://hdl.handle.net/10197/7107In 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.enThis is the author's version of the following article: Gift Nyamundanda, Isobel Claire Gormley and Lorraine Brennan (2014) "A dynamic probabilistic principal components model for the analysis of longitudinal metabolomics data" Journal of the Royal Statistical Society: Series C (Applied Statistics), 63(5): 763-782 which has been published in final form at http://dx.doi.org/10.1111/rssc.12060.Auto-regressive modelLinear mixed modelLongitudinal metabolomic dataMetabolomicsPrincipal components analysisProbabilistic principal components analysisJournal Article63576378210.1111/rssc.120602015-09-22https://creativecommons.org/licenses/by-nc-nd/3.0/ie/