Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments

DOI10.1111/j.1541-0420.2011.01618.xzbMath1274.62839OpenAlexW1998128037WikidataQ47373827 ScholiaQ47373827MaRDI QIDQ2893379

Shigeyuki Matsui, Hisashi Noma

Publication date: 20 June 2012

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2011.01618.x

zbMATH Keywords

empirical Bayessample sizepowermicroarrayseffect sizehierarchical mixture modelsgene screening

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (6)

An empirical Bayes optimal discovery procedure based on semiparametric hierarchical mixture models ⋮ Efficient testing and effect size estimation for set‐based genetic association inference via semiparametric multilevel mixture modeling ⋮ Effect-size estimation using semiparametric hierarchical mixture models in disease-association studies with neuroimaging data ⋮ Sample size reassessment for a two-stage design controlling the false discovery rate ⋮ Multi-subgroup gene screening using semi-parametric hierarchical mixture models and the optimal discovery procedure: Application to a randomized clinical trial in multiple myeloma ⋮ General power and sample size calculations for high-dimensional genomic data

Cites Work

• Size, power and false discovery rates
• Empirical Bayes Confidence Intervals Based on Bootstrap Samples
• Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
• A Direct Approach to False Discovery Rates
• Empirical Bayes Estimates for Large-Scale Prediction Problems
• Sample size determination in microarray experiments for class comparison and prognostic classification
• Sample Size Calculations Based on Ranking and Selection in Microarray Experiments
• Statistical significance for genomewide studies
• Estimating the Proportion of True Null Hypotheses, with application to DNA Microarray Data
• Bayesian Robust Inference for Differential Gene Expression in Microarrays with Multiple Samples
• Large-Scale Simultaneous Hypothesis Testing
• Detecting differential gene expression with a semiparametric hierarchical mixture method
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