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Open Access Methodology article

Multivariate search for differentially expressed gene combinations

Yuanhui Xiao1, Robert Frisina2, Alexander Gordon1, Lev Klebanov13 and Andrei Yakovlev1*

Author Affiliations

1 Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA

2 Departments of Otolaryngology, Neurobiology and Anatomy, and Biomedical Engineering, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA

3 Department of Probability and Statistics, Charls University, Sokolovska 83, Praha-8, CZ-18675, Czech Republic

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BMC Bioinformatics 2004, 5:164  doi:10.1186/1471-2105-5-164

Published: 26 October 2004

Abstract

Background

To identify differentially expressed genes, it is standard practice to test a two-sample hypothesis for each gene with a proper adjustment for multiple testing. Such tests are essentially univariate and disregard the multidimensional structure of microarray data. A more general two-sample hypothesis is formulated in terms of the joint distribution of any sub-vector of expression signals.

Results

By building on an earlier proposed multivariate test statistic, we propose a new algorithm for identifying differentially expressed gene combinations. The algorithm includes an improved random search procedure designed to generate candidate gene combinations of a given size. Cross-validation is used to provide replication stability of the search procedure. A permutation two-sample test is used for significance testing. We design a multiple testing procedure to control the family-wise error rate (FWER) when selecting significant combinations of genes that result from a successive selection procedure. A target set of genes is composed of all significant combinations selected via random search.

Conclusions

A new algorithm has been developed to identify differentially expressed gene combinations. The performance of the proposed search-and-testing procedure has been evaluated by computer simulations and analysis of replicated Affymetrix gene array data on age-related changes in gene expression in the inner ear of CBA mice.