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

Files in This Item:
File Description SizeFormat 
DPPCA_Paper.pdf180.13 kBAdobe PDFDownload
Title: A dynamic probabilistic principal components model for the analysis of longitudinal metabolomics data
Authors: Nyamundanda, Gift
Gormley, Isobel Claire
Brennan, Lorraine
Permanent link: http://hdl.handle.net/10197/7107
Date: Nov-2014
Online since: 2015-11-01T04:00:27Z
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 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.
Funding Details: Health Research Board
Irish Research Council for Science, Engineering and Technology
University College Dublin
Type of material: Journal Article
Publisher: Wiley
Journal: Journal of the Royal Statistical Society: Series C (Applied Statistics)
Volume: 63
Issue: 5
Start page: 763
End page: 782
Copyright (published version): 2014 Royal Statistical Society
Keywords: Auto-regressive modelLinear mixed modelLongitudinal metabolomic dataMetabolomicsPrincipal components analysisProbabilistic principal components analysis
DOI: 10.1111/rssc.12060
Language: en
Status of Item: Peer reviewed
Appears in Collections:Conway Institute Research Collection
Mathematics and Statistics Research Collection
Agriculture and Food Science Research Collection

Show full item record

Citations 50

Last Week
Last month
checked on Feb 19, 2019

Download(s) 50

checked on May 25, 2018

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.