Bayesian modeling of factorial time- course data with applications to a bone aging gene expression study

Wu, J., Gupta, M. , Hussein, A. I. and Gerstenfeld, L. (2021) Bayesian modeling of factorial time- course data with applications to a bone aging gene expression study. Journal of Applied Statistics, 48(10), pp. 1730-1754. (doi: 10.1080/02664763.2020.1772733)

Abstract

Many scientific studies, especially in the biomedical sciences, generate data measured simultaneously over a multitude of units, over a period of time, and under different conditions or combinations of factors. Often, an important question of interest asked relates to which units behave similarly under different conditions, but measuring the variation over time complicates the analysis significantly. In this article we address such a problem arising from a gene expression study relating to bone aging, and develop a Bayesian statistical method that can simultaneously detect and uncover signals on three levels within such data: factorial, longitudinal, and transcriptional. Our model framework considers both cluster and time-point-specific parameters and these parameters uniquely determine the shapes of the temporal gene expression profiles, allowing the discovery and characterization of latent gene clusters based on similar underlying biological mechanisms. Our methodology was successfully applied to discover transcriptional networks in a microarray data set comparing the transcriptomic changes that occurred during bone aging in male and female mice expressing one or both copies of the bromodomain (Brd2) gene, a transcriptional regulator which exhibits an age-dependent sex-linked bone loss phenotype.

• Item Type: Articles
• Additional Information: This research was supported by the National Institute of General Medical Sciences of the National Institute of Health under award number T32GM074905. It was conducted using the Linux Clusters for Genetic Analysis (LinGA) computing resources at the Boston University Medical Campus. The work on bone aging was supported by an Ellison Foundation (Ellison Medical Foundation) grant and NIH NIAMS 1RO1AR05974 to LCG, and CTSA award (National Center for Advancing Translational Sciences) UL1-TR000157 which funds the Microarray facility at BUSM.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gupta, Professor Mayetri
• Authors: Wu, J., Gupta, M., Hussein, A. I., and Gerstenfeld, L.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Journal of Applied Statistics
• Publisher: Taylor & Francis
• ISSN: 0266-4763
• ISSN (Online): 1360-0532
• Published Online: 01 June 2020
• Copyright Holders: Copyright © 2020 Informa UK Limited, trading as Taylor and Francis Group
• First Published: First published in Journal of Applied Statistics 48(10): 1730-1754
• Publisher Policy: Reproduced in accordance with the publisher copyright policy