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

• Authors: Yipu Zhang, Chang’an University; Peng Peng, Chang’an University; Yongfeng Ju, Chang’an University; Gang Li, Chang’an University; Vince Calhoun, Emory University; Yu-Ping Wang, Tulane University
• Journal Title: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
• Volume: Volume 24, Number 9
• Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | 2020-09-01, Pages 2621-2629
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1109/JBHI.2020.2972581
• Subjects: Engineering, Biomedical
• Keywords: Science & Technology; Technology; Life Sciences & Biomedicine; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics; Computer Science; Correlation; Genetics; Functional magnetic resonance imaging; Brain modeling; Feature extraction; Data models; Canonical correlation analysis; Imaging Genetics; Statistical independence; Structural sparsity; COMPONENT ANALYSIS; BIPOLAR-DISORDER; SNP DATA; BRAIN; SCHIZOPHRENIA; FMRI; EXPRESSION; NETWORKS; SUPPORTS; GRIK4

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 incorporate graph structure of the data into the model to improve the accuracy of feature selection. The results from simulation studies demonstrate its higher accuracy in discovering correlations compared with other competing methods. Moreover, applying ISCCA to a real imaging genetics dataset collected by Mind Clinical Imaging Consortium (MCIC), a set of distinct gene-ROI interactions are identified, which are verified to be both statistically and biologically significant.