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BMCBI
2004

Multivariate search for differentially expressed gene combinations

8 years 12 months ago
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...
Yuanhui Xiao, Robert D. Frisina, Alexander Gordon,
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2004
Where BMCBI
Authors Yuanhui Xiao, Robert D. Frisina, Alexander Gordon, Lev Klebanov, Andrei Yakovlev
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