2009/09/01 by Bradley Efron
Biochemistry, Genetics and Molecular Biology · Mathematics · #Bioinformatics and Genomic Networks #Gene expression and cancer classification #Genetic Mapping and Diversity in Plants and Animals #stat.AP
paper · pdf · doi:10.1214/09-aoas236
published as Annals of Applied Statistics 2009, Vol. 3, No. 3, 922-942 · Published in at http://dx.doi.org/10.1214/09-AOAS236 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2009/09/01 · arxiv created 2009/10/08 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.