| Literature DB >> 20563291 |
Abstract
Having observed an m x n matrix X whose rows are possibly correlated, we wish to test the hypothesis that the columns are independent of each other. Our motivation comes from microarray studies, where the rows of X record expression levels for m different genes, often highly correlated, while the columns represent n individual microarrays, presumably obtained independently. The presumption of independence underlies all the familiar permutation, cross-validation, and bootstrap methods for microarray analysis, so it is important to know when independence fails. We develop nonparametric and normal-theory testing methods. The row and column correlations of X interact with each other in a way that complicates test procedures, essentially by reducing the accuracy of the relevant estimators.Entities:
Year: 2009 PMID: 20563291 PMCID: PMC2887702 DOI: 10.1214/09-AOAS236
Source DB: PubMed Journal: Ann Appl Stat ISSN: 1932-6157 Impact factor: 2.083