Functional Hierarchical Models for Identifying Genes with Different Time‐Course Expression Profiles

DOI10.1111/j.1541-0420.2005.00505.xzbMath1097.62127OpenAlexW2064533818WikidataQ51934956 ScholiaQ51934956MaRDI QIDQ5492093

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Publication date: 12 October 2006

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2005.00505.x

zbMATH Keywords

empirical BayesB-splinesGibbs samplingfalse discovery ratemicroarray

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Monte Carlo methods (65C05) Biochemistry, molecular biology (92C40)

Related Items (7)

A multivariate empirical Bayes statistic for replicated microarray time course data ⋮ Identifying temporally differentially expressed genes through functional principal components analysis ⋮ A statistical analysis of memory CD8 T cell differentiation: An application of a hierarchical state space model to a short time course microarray experiment ⋮ Bayesian models for two-sample time-course microarray experiments ⋮ A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data ⋮ On Gene Ranking Using Replicated Microarray Time Course Data ⋮ A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data

Cites Work

• Functional data analysis
• The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
• Empirical Bayes Analysis of a Microarray Experiment
• Improved statistical tests for differential gene expression by shrinking variance components estimates
• Detecting differential gene expression with a semiparametric hierarchical mixture method

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