Joint sparse canonical correlation analysis for detecting differential imaging genetics modules.

ABSTRACT:

Motivation

Imaging genetics combines brain imaging and genetic information to identify the relationships between genetic variants and brain activities. When the data samples belong to different classes (e.g. disease status), the relationships may exhibit class-specific patterns that can be used to facilitate the understanding of a disease. Conventional approaches often perform separate analysis on each class and report the differences, but ignore important shared patterns.

Results

In this paper, we develop a multivariate method to analyze the differential dependency across multiple classes. We propose a joint sparse canonical correlation analysis method, which uses a generalized fused lasso penalty to jointly estimate multiple pairs of canonical vectors with both shared and

SUBMITTER: Fang J 

PROVIDER: S-EPMC5181564 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature