Empirical Bayes analysis of unreplicated microarray data
type: Item
instance of: scholarly article
title: Empirical Bayes analysis of unreplicated microarray data
zbMATH Open document ID: 1190.62011
author: B. E. Eshmatov
published in: Computational Statistics
publication date: 2010-04-22T00:00:00Z
review text: A microarray analysis technique is developed for the analysis of unreplicated gene expression data. It is based on a two-way ANOVA model with heterogeneous errors. A Bayesisan approach is used for the parameter estimation with Markov chain Monte-Carlo techniques for the evaluation of the estimates. An empirical Bayes approach is proposed for the data variance priors specifications. The performance of the proposed methods is assessed by real data and via simulations.
Mathematics Subject Classification ID: 62C12
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62J10
Mathematics Subject Classification ID: 65C40
Mathematics Subject Classification ID: 92C40
zbMATH DE Number: 5697316
zbMATH Keywords: two-way ANOVA
zbMATH Keywords: heterogeneous error
zbMATH Keywords: Markov chain Monte-Carlo
zbMATH Keywords: MCMC
describes a project that uses: EBarrays
MaRDI profile type: Publication
full work available at URL: s00180-008-0133-9
OpenAlex ID: W2023892763
cites work: Q4839349
cites work: Robbins, empirical Bayes and microarrays
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cites work: Practical Markov Chain Monte Carlo
cites work: Bayesian Models for Gene Expression With DNA Microarray Data
cites work: Detecting Differentially Expressed Genes in Microarrays Using Bayesian Model Selection
cites work: Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations
cites work: Significance analysis of microarrays applied to the ionizing radiation response
DOI: 10.1007/S00180-008-0133-9
description: scientific article; zbMATH DE number 5697316