ArticleBioinformatics (Oxford, England)2016
Joint sparse canonical correlation analysis for detecting differential imaging genetics modules.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- A kernel density estimation-based approach for quantifying O-GlcNAcylation dysregulation in cancer from gene expression data.Bioinformatics advances · 2026Article
- An Improved Deep Semi-supervised JNMF Method for Biomarker Extraction of Alzheimer's Disease.Journal of molecular neuroscience : MN · 2025Article
- Exploration of biomarkers of Alzheimer's disease based on orthogonal multi-task canonical correlation analysis.BMC medical imaging · 2025Article
- Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis-An application in renal clear cell carcinoma.Genetic epidemiology · 2024Article
- Clinical application of sparse canonical correlation analysis to detect genetic associations with cortical thickness in Alzheimer's disease.Frontiers in neuroscience · 2024Article
- Integrating Multimodal Neuroimaging and Genetics: A Structurally-Linked Sparse Canonical Correlation Analysis Approach.IEEE journal of translational engineering in health and medicine · 2024Article
- Fair Canonical Correlation Analysis.Advances in neural information processing systems · 2023Article
- Longitudinal Canonical Correlation Analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- A Novel Longitudinal Phenotype-Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer's Disease.Biomolecules · 2023Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
- Identification of multimodal brain imaging association via a parameter decomposition based sparse multi-view canonical correlation analysis method.BMC bioinformatics · 2022Article
- Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach.Journal of molecular neuroscience : MN · 2022Article
- Associating brain imaging phenotypes and genetic in Alzheimer's disease via JSCCA approach with autocorrelation constraints.Medical & biological engineering & computing · 2022Article
- Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study.IEEE journal of biomedical and health informatics · 2021Article
- Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method.IEEE transactions on medical imaging · 2020Article
- Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study.Bioinformatics (Oxford, England) · 2020Article
- Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.IEEE journal of biomedical and health informatics · 2020Article
- Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification.Bioinformatics (Oxford, England) · 2020Article
- Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data.IEEE transactions on medical imaging · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationImaging genetics combines brain imaging and genetic information to identify the relationships between genetic variants and brain activities. When the data samples belong to different classes (e.g. disease status), the relationships may exhibit class-specific patterns that can be used to facilitate the understanding of a disease. Conventional approaches often perform separate analysis on each class and report the differences, but ignore important shared patterns.
resultsIn this paper, we develop a multivariate method to analyze the differential dependency across multiple classes. We propose a joint sparse canonical correlation analysis method, which uses a generalized fused lasso penalty to jointly estimate multiple pairs of canonical vectors with both shared and class-specific patterns. Using a data fusion approach, the method is able to detect differentially correlated modules effectively and efficiently. The results from simulation studies demonstrate its higher accuracy in discovering both common and differential canonical correlations compared to conventional sparse CCA. Using a schizophrenia dataset with 92 cases and 116 controls including a single nucleotide polymorphism (SNP) array and functional magnetic resonance imaging data, the proposed method reveals a set of distinct SNP-voxel interaction modules for the schizophrenia patients, which are verified to be both statistically and biologically significant. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://sites.google.com/site/jianfang86/JSCCA CONTACT: wyp@tulane.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.