Multivariate search for differentially expressed gene combinations

• Published in: BMC bioinformatics Vol. 5; no. 1; p. 164
• Main Authors: Xiao, Yuanhui, Frisina, Robert, Gordon, Alexander, Klebanov, Lev, Yakovlev, Andrei
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 26.10.2004; BioMed Central; BMC
• Subjects: Aging - genetics; Algorithms; Animals; Computer Simulation; Gene Expression Profiling - methods; Gene Expression Regulation - genetics; Methodology; Mice; Mice, Inbred CBA; Models, Genetic; Multivariate Analysis; Organ of Corti - chemistry; Organ of Corti - metabolism; Stria Vascularis - chemistry; Stria Vascularis - metabolism
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-5-164

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. 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. 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.