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

DOI10.1080/02664763.2020.1772733OpenAlexW3033066603MaRDI QIDQ5861482

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Publication date: 1 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: http://eprints.gla.ac.uk/216256/7/216256.pdf

zbMATH Keywords

Markov chain Monte Carlomodel-based clusteringmixture modelsmicroarraysfactorial designstemporal gene expression

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• R
• CODA
• BayesDA

Cites Work

• Unnamed Item
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Analyzing gene expression time-courses based on multi-resolution shape mixture model
• Multimodal probabilistic generative models for time-course gene expression data and Gene Ontology (GO) tags
• A novel HMM-based clustering algorithm for the analysis of gene expression time-course data
• Inference from iterative simulation using multiple sequences
• Analyzing factorial designed microarray experiments
• A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data
• A temporal hidden Markov regression model for the analysis of gene regulatory networks
• Simulation Run Length Control in the Presence of an Initial Transient
• Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Cluster analysis of gene expression dynamics
• Bayesian Measures of Model Complexity and Fit
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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