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Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method
- Source :
- Bioinformatics (Oxford, England). 32(10)
- Publication Year :
- 2015
-
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.
- Subjects :
- 0301 basic medicine
Statistics and Probability
Computer science
Feature selection
Neuroimaging
Machine learning
computer.software_genre
Biochemistry
030218 nuclear medicine & medical imaging
Correlation
03 medical and health sciences
0302 clinical medicine
Lasso (statistics)
Humans
Molecular Biology
business.industry
Brain
Pattern recognition
Original Papers
Computer Science Applications
Constraint (information theory)
Computational Mathematics
030104 developmental biology
Computational Theory and Mathematics
Feature (computer vision)
Artificial intelligence
Canonical correlation
business
computer
Algorithms
Sign (mathematics)
Subjects
Details
- ISSN :
- 13674811
- Volume :
- 32
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- Bioinformatics (Oxford, England)
- Accession number :
- edsair.doi.dedup.....a361d048a7dcce4fb4f35ea6dfbe7c23