Set-based differential covariance testing for genomics.

ABSTRACT: The problem of detecting the changes in covariance for a single pair of genomic features has been studied in some detail but may be limited in importance or general applicability. For testing equality of covariance matrices of a set of features, many methods have been limited to the two-sample problem and involve varying assumptions on the number of features p versus the sample size n. More general covariance regression approaches are appealing but have been insufficiently structured to provide interpretable testing. To address these deficiencies, we propose a simple uniform framework to test association of covariance matrices with an experimental variable, whether discrete or continuous. We describe four different summary statistics, to ensure power and flexibility under var

SUBMITTER: Zhou YH 

PROVIDER: S-EPMC6853199 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature