Application of mixture models to detect differentially expressed genes

Authors:
Liat Ben-Tovim Jones;Richard Bean;Geoff McLachlan;Justin Zhu
Affiliations:
ARC Centre in Bioinformatics, Institute for Molecular Bioscience, UQ;ARC Centre in Bioinformatics, Institute for Molecular Bioscience, UQ;ARC Centre in Bioinformatics, Institute for Molecular Bioscience, UQ;ARC Centre in Bioinformatics, Institute for Molecular Bioscience, UQ
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

Citing 2
Cited 1

A mixture model approach for the analysis of microarray gene expression data

Computational Statistics & Data Analysis
A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments

Bioinformatics

Hierarchical mixture models: a probabilistic analysis

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

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Abstract

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.