Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments

DOI10.1016/j.csda.2006.08.009zbMath1445.62261OpenAlexW1971907611WikidataQ59397301 ScholiaQ59397301MaRDI QIDQ1020203

Alessio Farcomeni, Luca Tardella, Marco Alfo'

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.08.009

zbMATH Keywords

mixture modelsfalse discovery ratemicroarray datacounting distributionup-regulated genes

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Related Items

Editorial: Advances in mixture models

Uses Software

• daMA

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Efficient design and analysis of two colour factorial microarray experiments
• Identifying differentially expressed genes in unreplicated multiple-treatment microarray timecourse experiments
• Identification of interaction patterns and classification with applications to microarray data
• Multiple hypothesis testing in microarray experiments.
• The analysis of gene expression data. Methods and software
• Thresholding rules for recovering a sparse signal from microarray experiments
• Significance analysis of microarrays applied to the ionizing radiation response
• A new Bayesian method for nonparametric capture-recapture models in presence of heterogeneity
• Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
• Operating Characteristics and Extensions of the False Discovery Rate Procedure
• Adaptive Rejection Sampling for Gibbs Sampling
• A Full Likelihood Procedure for Analysing Exchangable Binary Data
• Nearest neighbor pattern classification
• Experimental design for gene expression microarrays