[Article] Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.
Summary: They proposed Independence and Structural sparsity canonical correlation analysis (ISCCA) for imaging genetics study. They combined ICA and CCA to reduce the collinear effects, which also incorporate graph structure of the data into the model to improve the accuracy of feature selection. This method helps identify risk genes and abnormal brain regions in schizophrenia.
Zhang, Yipu, et al. “Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.” IEEE journal of biomedical and health informatics 24.9 (2020): 2621-2629.