Open Access
June 2018 A testing based approach to the discovery of differentially correlated variable sets
Kelly Bodwin, Kai Zhang, Andrew Nobel
Ann. Appl. Stat. 12(2): 1180-1203 (June 2018). DOI: 10.1214/17-AOAS1083


Given data obtained under two sampling conditions, it is often of interest to identify variables that behave differently in one condition than in the other. We introduce a method for differential analysis of second-order behavior called Differential Correlation Mining (DCM). The DCM method identifies differentially correlated sets of variables, with the property that the average pairwise correlation between variables in a set is higher under one sample condition than the other. DCM is based on an iterative search procedure that adaptively updates the size and elements of a candidate variable set. Updates are performed via hypothesis testing of individual variables, based on the asymptotic distribution of their average differential correlation. We investigate the performance of DCM by applying it to simulated data as well as to recent experimental datasets in genomics and brain imaging.


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Kelly Bodwin. Kai Zhang. Andrew Nobel. "A testing based approach to the discovery of differentially correlated variable sets." Ann. Appl. Stat. 12 (2) 1180 - 1203, June 2018.


Received: 1 February 2016; Revised: 1 March 2017; Published: June 2018
First available in Project Euclid: 28 July 2018

zbMATH: 06980489
MathSciNet: MR3834299
Digital Object Identifier: 10.1214/17-AOAS1083

Keywords: association mining , biostatistics , Differential correlation mining , genomics , High-dimensional data

Rights: Copyright © 2018 Institute of Mathematical Statistics

Vol.12 • No. 2 • June 2018
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