Identification of Differentially Expressed Genes in High-Density Oligonucleotide Arrays Accounting for the Quantification Limits of the Technology

DOI10.1111/1541-0420.00064zbMath1210.62209OpenAlexW2084878514WikidataQ47401890 ScholiaQ47401890MaRDI QIDQ3079148

Joseph G. Ibrahim, George L. Mutter, Mahlet G. Tadesse

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1541-0420.00064

zbMATH Keywords

Bayesian inferencecensored dataDNA microarraygene expressionANCOVAhierarchical priorsHPD intervalsendometrial cancer

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Bayesian inference (62F15) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (5)

A new class of mixture models for differential gene expression in DNA microarray data ⋮ A two-stage empirical Bayes method for identifying differentially expressed genes ⋮ A simple method for screening variables before clustering microarray data ⋮ Integrative model-based clustering of microarray methylation and expression data ⋮ Bayesian Robust Inference for Differential Gene Expression in Microarrays with Multiple Samples

Cites Work

• Unnamed Item
• Importance-Weighted Marginal Bayesian Posterior Density Estimation
• Bayesian Models for Gene Expression With DNA Microarray Data
• Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models
• Adaptive Rejection Sampling for Gibbs Sampling
• Experimental design for gene expression microarrays
• Contemporary Bayesian Econometrics and Statistics

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