Lei Du1, Heng Huang2, Jingwen Yan1, Sungeun Kim1, Shannon L Risacher1, Mark Inlow3, Jason H Moore4, Andrew J Saykin1, Li Shen1. 1. Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA. 2. Department of Computer Science & Engineering, The University of Texas at Arlington, Arlington, TX, USA. 3. Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA and. 4. Institute for Biomedical Informatics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Abstract
MOTIVATION: Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated. RESULTS: We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations. AVAILABILITY AND IMPLEMENTATION: The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/ CONTACT: shenli@iu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated. RESULTS: We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations. AVAILABILITY AND IMPLEMENTATION: The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/ CONTACT: shenli@iu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Logan Grosenick; Brad Klingenberg; Kiefer Katovich; Brian Knutson; Jonathan E Taylor Journal: Neuroimage Date: 2013-01-05 Impact factor: 6.556
Authors: Lei Du; Yan Jingwen; Sungeun Kim; Shannon L Risacher; Heng Huang; Mark Inlow; Jason H Moore; Andrew J Saykin; Li Shen Journal: Med Image Comput Comput Assist Interv Date: 2014
Authors: V K Ramanan; S L Risacher; K Nho; S Kim; S Swaminathan; L Shen; T M Foroud; H Hakonarson; M J Huentelman; P S Aisen; R C Petersen; R C Green; C R Jack; R A Koeppe; W J Jagust; M W Weiner; A J Saykin Journal: Mol Psychiatry Date: 2013-02-19 Impact factor: 15.992
Authors: Jingwen Yan; Lei Du; Sungeun Kim; Shannon L Risacher; Heng Huang; Jason H Moore; Andrew J Saykin; Li Shen Journal: Bioinformatics Date: 2014-09-01 Impact factor: 6.937
Authors: Lei Du; Fang Liu; Kefei Liu; Xiaohui Yao; Shannon L Risacher; Junwei Han; Andrew J Saykin; Li Shen Journal: IEEE Trans Med Imaging Date: 2020-10-28 Impact factor: 10.048
Authors: Lei Du; Kefei Liu; Xiaohui Yao; Shannon L Risacher; Lei Guo; Andrew J Saykin; Li Shen Journal: Proc IEEE Int Symp Biomed Imaging Date: 2019-07-11
Authors: Lei Du; Kefei Liu; Tuo Zhang; Xiaohui Yao; Jingwen Yan; Shannon L Risacher; Junwei Han; Lei Guo; Andrew J Saykin; Li Shen Journal: Bioinformatics Date: 2018-01-15 Impact factor: 6.937
Authors: Chun Chieh Fan; Olav B Smeland; Andrew J Schork; Chi-Hua Chen; Dominic Holland; Min-Tzu Lo; V S Sundar; Oleksandr Frei; Terry L Jernigan; Ole A Andreassen; Anders M Dale Journal: Hum Mol Genet Date: 2018-05-01 Impact factor: 6.150
Authors: Lei Du; Tuo Zhang; Kefei Liu; Jingwen Yan; Xiaohui Yao; Shannon L Risacher; Andrew J Saykin; Junwei Han; Lei Guo; Li Shen Journal: Inf Process Med Imaging Date: 2017-05-23
Authors: Lei Du; Kefei Liu; Xiaohui Yao; Shannon L Risacher; Junwei Han; Andrew J Saykin; Lei Guo; Li Shen Journal: Med Image Anal Date: 2020-01-23 Impact factor: 8.545
Authors: Lei Du; Tuo Zhang; Kefei Liu; Xiaohui Yao; Jingwen Yan; Shannon L Risacher; Lei Guo; Andrew J Saykin; Li Shen Journal: Proceedings (IEEE Int Conf Bioinformatics Biomed) Date: 2017-01-19