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A novel SCCA approach via truncated ?1-norm and truncated group lasso for brain imaging genetics.

ABSTRACT: MOTIVATION:Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ?1-norm or its variants to induce sparsity. The ?0-norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem. RESULTS:In this paper, we propose the truncated ?1-norm penalized SCCA to improve the performance and effectiveness of the ?1-norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning ?. It can avoid the time intensive parameter tuning if given a reasonable small ?. Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations. AVAILABILITY AND IMPLEMENTATION:The Matlab code and sample data are freely available at http://www.iu.edu/?shenlab/tools/tlpscca/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Du L

PROVIDER: S-EPMC5860211 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics.

Du Lei L Liu Kefei K Zhang Tuo T Yao Xiaohui X Yan Jingwen J Risacher Shannon L SL Han Junwei J Guo Lei L Saykin Andrew J AJ Shen Li L

Bioinformatics (Oxford, England) 20180101 2

<h4>Motivation</h4>Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its po ...[more]

PMID: 28968815

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Project description:Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics. To investigate the intricate genetic basis of cerebrum imaging phenotypes, a great deal of other standard SCCA methods combining different interested structed have now appeared. For example, some models use group lasso penalty, and some use the fused lasso or the graph/network guided fused lasso for feature selection. However, prior knowledge may not be completely available and the group lasso methods have limited capabilities in practical applications. The graph/network guided approaches can use sample correlation to define constraints, thereby overcoming this problem. Unfortunately, this also has certain limitations. The graph/network conducted methods are susceptible to the sign of the sample correlation of the data, which will affect the stability of the model. To improve the efficiency and stability of SCCA, a sparse canonical correlation analysis model with GraphNet regularization (FGLGNSCCA) is proposed in this manuscript. Based on the FGLSCCA model, the GraphNet regularization penalty is imposed in our study and an optimization algorithm is presented to optimize the model. The structural Magnetic Resonance Imaging (sMRI) and gene expression data are used in this study to find the genotype and characteristics of brain regions associated with Alzheimer's disease (AD). Experiment results shown that the new FGLGNSCCA model proposed in this manuscript is superior or equivalent to traditional methods in both artificially synthesized neuroimaging genetics data or actual neuroimaging genetics data. It can select essential features more powerfully compared with other multivariate methods and identify significant canonical correlation coefficients as well as captures more significant typical weight patterns which demonstrated its excellent ability in finding biologically important imaging genetic relations.

Dataset Information

A novel SCCA approach via truncated ?1-norm and truncated group lasso for brain imaging genetics.

Publications

A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics.

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