A Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification

DOI10.1111/j.1541-0420.2006.00611.xzbMath1114.62130OpenAlexW2168963004WikidataQ42605603 ScholiaQ42605603MaRDI QIDQ5295368

Ming Yuan, Christina Kendziorski

Publication date: 27 July 2007

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2006.00611.x

zbMATH Keywords

empirical Bayesmodel-based clusteringfinite mixture model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (8)

Gamma-based clustering via ordered means with application to gene-expression analysis ⋮ Multiple hypothesis testing and clustering with mixtures of non-central \(t\)-distributions applied in microarray data analysis ⋮ Detecting Strong Signals in Gene Perturbation Experiments: An Adaptive Approach With Power Guarantee and FDR Control ⋮ The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data ⋮ Gaga: a parsimonious and flexible model for differential expression analysis ⋮ Bayesian testing of many hypotheses \(\times \) many genes: a study of sleep apnea ⋮ Dynamic Tensor Clustering ⋮ Simultaneous inference for multiple testing and clustering via a Dirichlet process mixture model

Uses Software

• EBarrays

Cites Work

• The analysis of gene expression data. Methods and software
• Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection
• Robust Locally Weighted Regression and Smoothing Scatterplots
• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• A Direct Approach to False Discovery Rates
• Detecting differential gene expression with a semiparametric hierarchical mixture method
• Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data
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