Assessing Differential Gene Expression with Small Sample Sizes in Oligonucleotide Arrays Using a Mean‐Variance Model

DOI10.1111/j.1541-0420.2006.00675.xzbMath1206.62169OpenAlexW2138672317WikidataQ48404638 ScholiaQ48404638MaRDI QIDQ5427397

Jianhua Hu, Fred A. Wright

Publication date: 20 November 2007

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2006.00675.x

Mathematics Subject Classification ID

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

Related Items (3)

Estimating equation-based causality analysis with application to microarray time series data ⋮ Sample size calculation for a regularized \(t\)-statistic in microarray experiments ⋮ Statistical properties of gene-gene correlations in omics experiments

Uses Software

• limma

Cites Work

• The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
• Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection
• Significance analysis of microarrays applied to the ionizing radiation response
• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
• Empirical Bayes Analysis of a Microarray Experiment
• Bayesian Models for Gene Expression With DNA Microarray Data
• Statistical significance for genomewide studies
• Exploration, normalization, and summaries of high density oligonucleotide array probe level data
• Detecting differential gene expression with a semiparametric hierarchical mixture method
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