A TESTING BASED APPROACH TO THE DISCOVERY OF DIFFERENTIALLY CORRELATED VARIABLE SETS

被引：4

作者：

Bodwin, Kelly ^{[1
]}

Zhang, Kai ^{[1
]}

Nobel, Andrew ^{[1
]}

机构：

[1] Univ North Carolina Chapel Hill, Dept Stat & Operat Res, Chapel Hill, NC 27599 USA

来源：

ANNALS OF APPLIED STATISTICS | 2018年 / 12卷 / 02期

基金：

美国国家科学基金会;

关键词：

Differential correlation mining; association mining; biostatistics; genomics; high-dimensional data; COVARIANCE-MATRIX; R PACKAGE; EXPRESSION; NETWORKS; CONNECTIVITY; ACTIVATION; HYPOTHESIS; DEPENDENCE; ELEMENTS; MODELS;

D O I：

10.1214/17-AOAS1083

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：1180 / 1203

页数：24

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