联系我们
Isaac Scientific Publishing
Modern Clinical Medicine Research
MCMR > Volume 4, Number 1, April 2021

Hyper-graph based Adaptive Sparse Multi-view Canonical Correlation Analysis with Application to Neuroimaging Genetics Study of Alzheimer’s Disease

Download PDF  (455.6 KB)PP. 1-9,  Pub. Date:April 13, 2021
DOI: 10.22606/mcmr.2021.41001

Author(s)
Lei Wang, Wei Kong, Shuaiqun Wang
Affiliation(s)
College of Information Engineering, Shanghai Maritime University, Shanghai, China
College of Information Engineering, Shanghai Maritime University, Shanghai, China
College of Information Engineering, Shanghai Maritime University, Shanghai, China
Abstract
Neuroimaging genetics has gained more and more attention on account of detecting the linkage between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants (i.e., Single Nucleotide Polymorphism (SNP) in Alzheimer’s disease (AD)). To overcome the problem of sparse multi-view canonical correlation (SMCCA) ‘unfair combination of pairwise convariance’, introducing adaptive weights when combining pairwise covariances, a novel formulation of SMCCA, named adaptive SMCCA. In this paper, we integrate multi-modal genomic data from postmortem AD brain and proposed a hyper-graph based sparse multi-view canonical correlation analysis (HGSMCCA) method to extract the most correlated multi-modal biomarkers. Specifically, we utilized the adaptive sparse multi-view canonical correlation analysis (AdsSMCCA) framework, consider the benefit of hyper-graph-based regularization term into consideration that will contribute to the selection of more discriminative biomarkers. We propose a hyper-graph optimization strategy based on the adaptive SMCCA model, and we apply it to neuroimaging genetics data. All these results demonstrate the capability of HGSMCCA in identifying diagnostically genotype-phenotype patterns.
Keywords
neuroimaging genetics, hyper-graph, Alzheimer’s disease, sparse multi-view canonical correlation analysis.
References
  • [1]  Witten DM, Tibshirani R, Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics. 2009: kxp008.
  • [2]  Du L, Huang H, Yan J, Kim S, Risacher SL, Inlow M, et al. Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method. Bioinformatics. 2016: btw033.
  • [3]  Lin D, Calhoun VD, Wang YP. Correspondence between fMRI and SNP data by group sparse canonical correlation analysis. Medical image analysis. 2014; 18:891–902. [PubMed: 24247004]
  • [4]  J. Saugstad, J. Wiedrick, J. Lapidus, U. Sandau, T. Lusardi, C. Harrington, et al., “Validation of human cerebrospinal fluid microRNAs as biomarkers for Alzheimer’s disease,” Journal of Extracellular Vesicles, vol. 7, pp. 199-199, 2018.
  • [5]  Du L, Huang H, Yan J, Kim S, Risacher SL, Inlow M, Moore JH, Saykin AJ, Shen L. Structured sparse cca for brain imaging genetics via graph oscar. BMC Systems Biology. 2016:335–345.
  • [6]  Friedman JH, Hastie T, Hofling H, Tibshirani R. Pathwise coordinate optimization. The Annals of Applied Statistics. 2007; 1(2):302–332.
  • [7]  Shen L, Kim S, Risacher SL, Nho K, Swaminathan S, West JD, Foroud T, Pankratz N, Moore JH, Sloan CD, et al. Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage. 2010; 53(3):1051–63. [PubMed: 20100581]
  • [8]  Shen,L. and Thompson,P.M. (2020) Brain imaging genomics: integrated analysis and machine learning. Proc. IEEE, 108, 125–162.
  • [9]  Mukherjee,S. et al. (2018) Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups. Mol. Psychiatr. doi: 10.1038/s41380-018- 0298-8 .
  • [10]  Hao, X., et al., Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease. Neuroinformatics, 14(4): p. 439-452, 2016.
  • [11]  Zhou T, Liu M, Thung KH, Shen D. Latent Representation Learning for Alzheimer’s Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data. IEEE Trans Med Imaging. 38(10): 2411–2422, 2019.
  • [12]  Wang, Q., et al., Machine Learning in Medical Imaging: 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, Proceedings. Vol. 10541. 2017, Cham: Springer International Publishing AG, 2017.
  • [13]  Witten DM, Tibshirani R, Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics. 10(3): 515–34, 2009.
  • [14]  Chen X, Liu H. An efficient optimization algorithm for structed sparse cca, with applications to eqtl mapping. Statics in Biosciences. 4(1): 3-26, 2012.
  • [15]  Hao X, Li C, Du L, et al. Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease. Sci Rep. 7: 44272, 2017.
  • [16]  Du L, Yan J, Kim S, Risacher SL, Huang H, et al. A novel structure-aware sparse learning algorithm for brain imaging genetics. MICCAI. 329–336, 2014.
  • [17]  Du L, Zhang T, Liu K, et al. Sparse Canonical Correlation Analysis via Truncated ?1-norm with Application to Brain Imaging Genetics. Proceedings (IEEE Int Conf Bioinformatics Biomed). 707–711, 2016.
  • [18]  Yan, J., et al., Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm. Bioinformatics, 30(17): p. i564-i571, 2014.
  • [19]  Viivi Uurtio, Sahely Bhadra, and Juho Rousu. Large-scale sparse kernel canonical correlation analysis. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 6383–6391, Long Beach, California, USA, 09–15. PMLR, 2019.
  • [20]  Shao, Wei, et al. "Hyper-graph based Sparse Canonical Correlation Analysis for the Diagnosis of Alzheimer’s Disease from Multi-dimensional Genomic Data." Methods (2020).
  • [21]  Zheng W, Zhou X, Zou C, Zhao L. Facial expression recognition using kernel canonical correlation analysis (KCCA). IEEE Trans Neural Netw. 17(1):233–238, 2006.
  • [22]  Liu Y, Tan L, Wang HF, et al. Multiple Effect of APOE Genotype on Clinical and Neuroimaging Biomarkers across Alzheimer’s Disease Spectrum. Mol Neurobiol. 53(7): 4539–4547, 2016.
  • [23]  Du L, Zhang T, Liu K, et al. Sparse Canonical Correlation Analysis via Truncated ?1-norm with Application to Brain Imaging Genetics. Proceedings (IEEE Int Conf Bioinformatics Biomed). 707–711, 2016.
  • [24]  Du L, Liu K, Zhu L, et al. Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort. Bioinformatics. 35(14): i474–i483, 2019.
  • [25]  Larson NB, Jenkins GD, Larson MC, et al. Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer. Eur J Hum Genet. 22(1):126–131, 2014.
  • [26]  Iusem, A. On the convergence properties of the projected gradient method for convex optimization. Computational & Applied Mathematics. 22(1): 37–52, 2003.
  • [27]  Voxel-Based Morphometry. In: Schmidt R., Willis W. (eds) Encyclopedia of Pain. Springer, Berlin, Heidelberg, 2007.
  • [28]  Hu, Wenxing, et al. "Adaptive Sparse Multiple Canonical Correlation Analysis With Application to Imaging (Epi) Genomics Study of Schizophrenia." IEEE Transactions on Biomedical Engineering 65.2(2018):390-399.
  • [29]  Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mnimri single-subject brain. Neuro Image, 15: 273–289, 2002.
  • [30]  S. Purcell, B. Neale, K. Todd-Brown, L. Thomas, M. A. Ferreira, D. Bender, J. Maller, P. Sklar, P. I. De Bakker, and M. J. Daly, "PLINK: a tool set for whole-genome association and population-based linkage analyses," The American Journal of Human Genetics, vol. 81, pp. 559-575, 2007.
Copyright © 2020 Isaac Scientific Publishing Co. All rights reserved.