Multivariate search for differentially expressed gene combinations.
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.
Xiao
Yuanhui
Y
Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA. yxiao@bst.rochester.edu <yxiao@bst.rochester.edu>
Frisina
Robert
R
Gordon
Alexander
A
Klebanov
Lev
L
Yakovlev
Andrei
A
D000375:Aging / Q000235:genetics; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics*; D051379:Mice; D008808:Mice, Inbred CBA; D008957:Models, Genetic; D015999:Multivariate Analysis; D009925:Organ of Corti / Q000737:chemistry / Q000378:metabolism; D013316:Stria Vascularis / Q000737:chemistry / Q000378:metabolism