An Empirical Bayesian Approach for Identifying Differential Coexpression in High-Throughput Experiments

DOI10.1111/j.1541-0420.2011.01688.xzbMath1251.62042OpenAlexW2047820517WikidataQ36352750 ScholiaQ36352750MaRDI QIDQ2912340

Christina Kendziorski, John A. Dawson

Publication date: 14 September 2012

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3482945

zbMATH Keywords

empirical Bayesgene expressionsprostate cancermeta-analysismicroarrayscoexpression

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40) Genetics and epigenetics (92D10) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (2)

Modeling dynamic correlation in zero‐inflated bivariate count data with applications to single‐cell RNA sequencing data ⋮ Meta-analytic framework for modeling genetic coexpression dynamics

Uses Software

• mclust
• R
• Bioconductor
• BOBYQA
• gcrma
• EBarrays
• CoXpress

Cites Work

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• Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis
• A statistical framework for testing functional categories in microarray data
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• A Direct Approach to False Discovery Rates
• Exponential Numbers
• An Inverse Matrix Adjustment Arising in Discriminant Analysis

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