Multivariate search for differentially expressed gene combinations

Background: To identify differentially expressed genes, it is standard practice to test a twosample 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. Crossvalidation is used to provide replication stability of the search procedure. A permutation twosample 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 sele...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Yuanhui Xiao, Robert D. Frisina, Alexander Gordon, Lev Klebanov, Andrei Yakovlev
• Keywords: BMCBI 2004, Expressed Gene, Gene, Gene Combinations