Computer Science ›› 2019, Vol. 46 ›› Issue (4): 66-72.doi: 10.11896/j.issn.1002-137X.2019.04.010

• Big Data & Data Science • Previous Articles     Next Articles

High Order Statistics Structured Sparse Algorithm for Image Genetic Association Analysis

RU Feng, XU Jin, CHANG Qi, KAN Dan-hui   

  1. School of Electronic Control,Chang’an University,Xi’an 710064,China
  • Received:2018-08-27 Online:2019-04-15 Published:2019-04-23

Abstract: The development of neuroimaging technology and molecular genetics has produced a large number of imaging genetic data,which has greatly promoted the study of complex mental diseases.However,because the dimensions of the data are too high and the correlation measure is based on the assumption that data obey Gaussian distribution,traditionalalgorithms often fail to explain the dependencies between two types of data.In order to solve the shortcomings of traditional algorithms,this paper proposed a method for correlation analysis of a large number of SNP and fMRI data.This method guides fused lasso to perform feature selection by constructing a network structure of features,and uses higher-order statistics to extract statistically significant variables.Thus,biomarkers associated with mental illness are identified.The experimental results show that the distribution of typical vector values obtained by the algorithm in simulation data are almost consistent with the real data,and the correlation coefficient obtained is the closest to the correlation coefficient in the real dataset.The average correlation coefficient of the proposed algorithm is up to 81%,which is about 20% higher than L1-SCCA and about 3% higher than FL-SCCA.Compared with the other two algorithms in real data,the proposed algorithm can find more genes and brain regions that have potential effects on schizophrenia.The experimental results show that the proposed algorithm can effectively identify risk genes and abnormal brain regions within a reasonable time.

Key words: Correlation analysis, Feature selection, Higher-order statistics, Image genetics, Sparse representation

CLC Number: 

  • TP301.6
[1]RIPKE S,NEALE B M,CORVIN A,et al.Biological insights from 108 schizophrenia-associated genetic loci[J].Nature,2014,511(7510):421.
[2]LIU J,CALHOUN V D.A review of multivariate analyses in imaging genetics.Frontiers in Neuroinformatics,2014,8(29):1-11.
[3]EDITION F.Diagnostic and statistical manual of mental disorders[M].Arlington:American Psychiatric Publishing,2013.
[4]LIU J,PEARLSON G,WINDEMUTH A,et al.Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA[J].Human Brain Mapping,2009,30(1):241-255.
[5]LE FLOCH É,GUILLEMOT V,FROUIN V,et al.Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares[J].Neuroimage,2012,63(1):11-24.
[6]CHI E C,ALLEN G I,ZHOU H,et al.Imaging genetics via sparse canonical correlation analysis[C]∥2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI).IEEE,2013:740-743.
[7]DU L,HUANG H,YAN J,et al.Structured sparse canonical correlation analysis for brain imaging genetics:an improved GraphNet method[J].Bioinformatics,2016,32(10):1544-1551.
[8]BOGDAN R,SALMERON B J,CAREY C E,et al.Imaging Genetics and Genomics in Psychiatry:A Critical Review of Progress and Potential[J].Biological Psychiatry,2017,82(3):165-175.
[9]HU W,LIN D,CAO S,et al.Adaptive sparse multiple canonical correlation analysis with application to imaging (epi)genomics study of schizophrenia[J].IEEE Transactions on Biomedical Engineering,2018,65(2):390-399.
[10]DU L,HUANG H,YAN J,et al.Structured sparse CCA for brain imaging genetics via graph OSCAR[J].BMC Systems Bio-logy,2016,10(3):68-77.
[11]HOTELLING H.Relations Between Two Sets of Variates[J].Biometrika,1936,28(3/4):321-377.
[12]WITTEN D M,TIBSHIRANI R J.Extensions of Sparse Cano- nical Correlation Analysis with Applications to Genomic Data[J].Statistical Applications in Genetics & Molecular Biology,2009,8(1):1-27.
[13]TIBSHIRANI R,SAUNDERS M,ROSSET S,et al.Sparsity and smoothness via the fused lasso[J].Journal of the Royal Statistical Society,2010,67(1):91-108.
[14]HYVÄRINEN A.Fast and Robust Fixed-Point Algorithms for Independent Component Analysis[J].IEEE Transactions on Neural Networks,1999,10(3):626-634.
[15]HYVÄRINEN A.New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit[J].Advances in Neural Information Processing Systems,1997,10:273-279.
[16]CHEN X,LIU H.An Efficient Optimization Algorithm for Structured Sparse CCA,with Applications to eQTL Mapping[J].Statistics in Biosciences,2012,4(1):3-26.
[17]HASTIE T.A penalized matrix decomposition,with applications to sparse principal components and canonical correlation analysis[J].Biostatistics,2009,10(3):515-534.
[18]FANG J,LIN D,SCHULZ C,et al.Joint sparse canonical correlation analysis for detecting differential imaging genetics mo-dules[J].Bioinformatics,2016,32(22):3480-3488.
[19]HU W,LIN D,CALHOUN V D,et al.Integration of SNPs-FMRI-methylation data with sparse multi-CCA for schizophrenia study∥Engineering in Medicine & Biology Society.IEEE,2016.
[20]CAO H,LIN D,DUAN J,et al.Biomarker Identification for Dia- gnosis of Schizophrenia with Integrated Analysis of fMRI and SNPs[C]∥IEEE International Conference on Bioinformatics and Biomedicine.2012:223-228.
[21]LAW M H,COTTON R G,BERGER G E.The role of phospholipases A2 in schizophrenia[J].Molecular Psychiatry,2006,11(6):547-556.
[22]SANDERS A R,DUAN J,DRIGALENKO E I,et al.Transcriptome study of differential expression in schizophrenia[J].Human Molecular Genetics,2013,22(24):5001-5014.
[23]CAO H,DUAN J,LIN D,et al.Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method[J].Bmc Medical Genomics,2013,6 (3):1-8.
[24]OZDEMIR H,ERTUGRUL A,BASAR K,et al.Differential effects of antipsychotics on hippocampal presynaptic protein expressions and recognition memory in a schizophrenia model in mice[J].Progress in neuro-psychopharmacology & biological psychiatry,2012,39(1):62-68.
[25]KIRCHER T T,THIENEL R.Functional brain imaging of symptoms and cognition in schizophrenia[J].Progress in Brain Research,2005,150(2):299-308.
[1] LI Bin, WAN Yuan. Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment [J]. Computer Science, 2022, 49(8): 86-96.
[2] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[3] YANG Xiao, WANG Xiang-kun, HU Hao, ZHU Min. Survey on Visualization Technology for Equipment Condition Monitoring [J]. Computer Science, 2022, 49(7): 89-99.
[4] KANG Yan, WANG Hai-ning, TAO Liu, YANG Hai-xiao, YANG Xue-kun, WANG Fei, LI Hao. Hybrid Improved Flower Pollination Algorithm and Gray Wolf Algorithm for Feature Selection [J]. Computer Science, 2022, 49(6A): 125-132.
[5] CHU An-qi, DING Zhi-jun. Application of Gray Wolf Optimization Algorithm on Synchronous Processing of Sample Equalization and Feature Selection in Credit Evaluation [J]. Computer Science, 2022, 49(4): 134-139.
[6] SUN Lin, HUANG Miao-miao, XU Jiu-cheng. Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [J]. Computer Science, 2022, 49(4): 152-160.
[7] LI Zong-ran, CHEN XIU-Hong, LU Yun, SHAO Zheng-yi. Robust Joint Sparse Uncorrelated Regression [J]. Computer Science, 2022, 49(2): 191-197.
[8] ZHANG Ye, LI Zhi-hua, WANG Chang-jie. Kernel Density Estimation-based Lightweight IoT Anomaly Traffic Detection Method [J]. Computer Science, 2021, 48(9): 337-344.
[9] SUN Lin, PING Guo-lou, YE Xiao-jun. Correlation Analysis for Key-Value Data with Local Differential Privacy [J]. Computer Science, 2021, 48(8): 278-283.
[10] YANG Lei, JIANG Ai-lian, QIANG Yan. Structure Preserving Unsupervised Feature Selection Based on Autoencoder and Manifold Regularization [J]. Computer Science, 2021, 48(8): 53-59.
[11] HOU Chun-ping, ZHAO Chun-yue, WANG Zhi-peng. Video Abnormal Event Detection Algorithm Based on Self-feedback Optimal Subclass Mining [J]. Computer Science, 2021, 48(7): 199-205.
[12] HU Yan-mei, YANG Bo, DUO Bin. Logistic Regression with Regularization Based on Network Structure [J]. Computer Science, 2021, 48(7): 281-291.
[13] ZHOU Gang, GUO Fu-liang. Research on Ensemble Learning Method Based on Feature Selection for High-dimensional Data [J]. Computer Science, 2021, 48(6A): 250-254.
[14] DING Si-fan, WANG Feng, WEI Wei. Relief Feature Selection Algorithm Based on Label Correlation [J]. Computer Science, 2021, 48(4): 91-96.
[15] LI Pei-guan, YU Zhi-yong, HUANG Fang-wan. Power Load Data Completion Based on Sparse Representation [J]. Computer Science, 2021, 48(2): 128-133.
Full text



No Suggested Reading articles found!