Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study
- 1 June 2020
- journal article
- research article
- Published by Taylor & Francis Ltd in Journal of Applied Statistics
- Vol. 48 (10), 1-25
- https://doi.org/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.Keywords
This publication has 35 references indexed in Scilit:
- Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effectsBMC Bioinformatics, 2012
- Analysis of factorial time-course microarrays with application to a clinical study of burn injuryProceedings of the National Academy of Sciences of the United States of America, 2010
- Brd2 disruption in mice causes severe obesity without Type 2 diabetesBiochemical Journal, 2009
- Nature, Nurture, or Chance: Stochastic Gene Expression and Its ConsequencesCell, 2008
- Hidden Markov Models for Microarray Time Course Data in Multiple Biological ConditionsJournal of the American Statistical Association, 2006
- Bayesian Measures of Model Complexity and FitJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- Model-Based Clustering, Discriminant Analysis, and Density EstimationJournal of the American Statistical Association, 2002
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determinationBiometrika, 1995
- Inference from Iterative Simulation Using Multiple SequencesStatistical Science, 1992
- Comparing partitionsJournal of Classification, 1985