Sparse multiway canonical correlation analysis for multimodal stroke recovery data

被引:0
作者
Das, Subham [1 ]
West, Franklin D. [2 ]
Park, Cheolwoo [3 ,4 ]
机构
[1] Univ Georgia, Dept Stat, Athens, GA USA
[2] Univ Georgia, Dept Anim & Dairy Sci, Athens, GA USA
[3] Korea Adv Inst Sci & Technol, Dept Math Sci, Daejeon, South Korea
[4] Korea Adv Inst Sci & Technol, Dept Math Sci, Daejeon 34141, South Korea
基金
美国国家卫生研究院; 新加坡国家研究基金会;
关键词
dimension reduction; multimodal data; multiway canonical correlation analysis; sparsity; stroke recovery; DECOMPOSITION;
D O I
10.1002/bimj.202300037
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Conventional canonical correlation analysis (CCA) measures the association between two datasets and identifies relevant contributors. However, it encounters issues with execution and interpretation when the sample size is smaller than the number of variables or there are more than two datasets. Our motivating example is a stroke-related clinical study on pigs. The data are multimodal and consist of measurements taken at multiple time points and have many more variables than observations. This study aims to uncover important biomarkers and stroke recovery patterns based on physiological changes. To address the issues in the data, we develop two sparse CCA methods for multiple datasets. Various simulated examples are used to illustrate and contrast the performance of the proposed methods with that of the existing methods. In analyzing the pig stroke data, we apply the proposed sparse CCA methods along with dimension reduction techniques, interpret the recovery patterns, and identify influential variables in recovery.
引用
收藏
页数:15
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