Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.

ABSTRACT: Current developments of neuroimaging and genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9675402 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature