Joint sparse canonical correlation analysis for detecting differential imaging genetics modules

Fang, Jian; Lin, Dongdong; Schulz, S. Charles; Zhao, Xu; Calhoun, Vince D.; Wang, Yu Ping

doi:10.1093/bioinformatics/btw485

Cited by 69 publications

(47 citation statements)

References 47 publications

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“…As a result, for increasing values of λ 2 , more and more components of x 1 , .., x K will be identical: it encourages different classes to share SCP components. Such penalty has been successfully applied before to joint Gaussian graphical model estimation[22] as well as cross-correlation analysis formulation for extracting imaging genomic modules, as in our recent work [23]. A schematic diagram illustrating our approach can be seen in Figure 1.…”

Section: Identifying Shared and Differential Networkmentioning

confidence: 99%

Fused Estimation of Sparse Connectivity Patterns From Rest fMRI—Application to Comparison of Children and Adult Brains

Zillé

Calhoun

Stephen

et al. 2018

IEEE Trans. Med. Imaging

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In this work, we consider the problem of estimating multiple sparse, co-activated brain regions from functional magnetic resonance imaging (fMRI) observations belonging to different classes. More precisely, we propose a method to analyze similarities and differences in functional connectivity between children and young adults. Often, analysis is conducted on each class separately, and differences across classes are identified with an additional postprocessing step using various statistical tools. Here, we propose to rely on a generalized fused Lasso penalty, which allows us to make use of the entire dataset in order to estimate connectivity patterns that are either shared across classes, or specific to a given group. By using the entire population during the estimation, we hope to increase the power of our analysis. The proposed model falls in the category of population-wise matrix decomposition, and a simple and efficient alternating direction method of multipliers (ADMM) algorithm is introduced to solve the associated optimization problem. After validating our approach on simulated data, experiments are performed on resting-state fMRI imaging from the Philadelphia Neurodevelopmental Cohort (PNC) dataset, comprised of normally developing children from ages 8 to 21. Developmental differences were observed in various brain regions, as a total of 3 class-specific resting-state components were identified. Statistical analysis of the estimated subject-specific features, as well as classification results (based on age groups, up to 81% accuracy, n = 583 samples) related to these components demonstrate that the proposed method is able to properly extract meaningful shared and class-specific sub-networks.

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Section: Identifying Shared and Differential Networkmentioning

confidence: 99%

Fused Estimation of Sparse Connectivity Patterns From Rest fMRI—Application to Comparison of Children and Adult Brains

Zillé

Calhoun

Stephen

et al. 2018

IEEE Trans. Med. Imaging

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“…Journal of Medical Imaging 026501-9 Apr-Jun 2019 • Vol. 6 (2) function and then solving an optimization problem. The ability to detect nonlinear group-group associations makes DCCA more suitable for analyzing complex multi-omics and imaging-genetic associations, in which both genetic factors and brain ROIs may work as groups when regulating a phenotype or performing a specific brain function.…”

Section: Discussionmentioning

confidence: 99%

“…10 Therefore, it is of interest Journal of Medical Imaging 026501-2 Apr-Jun 2019 • Vol. 6 (2) to identify two subsets/groups of variables X sub ∈ R n×r ð1 ≤ r ≤ pÞ and Y sub ∈ R n×s ð1 ≤ s ≤ qÞ which are significantly dependent. However, the scale of simultaneous inference in this case is very large, i.e., 2 pþq , making it more difficult to detect significantly dependent subsets.…”

Section: Distance Canonical Correlation Analysismentioning

confidence: 99%

“…Journal of Medical Imaging 026501-5 Apr-Jun 2019 • Vol. 6 (2) dysfunction in the brain and a number of mental disorders. 21 Pathway "oxidative damage," which is related to cell signaling, may lead to damage of cell and the death of neurons.…”

Section: Imaging-genetic Associationsmentioning

confidence: 99%

“…Association analyses, e.g., canonical correlation analysis (CCA), 1 have been conducted to study brain connectivity and how genetic factors and endophenotypes interact. 2 However, these methods typically use Pearson correlation which only captures linear relationships while nonlinear correlations may exist among brain regions. 3 To address the limitation of Pearson correlation-based methods, Székely et al 4 proposed a correlation measurement, distance correlation, which evaluates the dependence between two single variables or two sets of variables.…”

Section: Introductionmentioning

confidence: 99%

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Distance canonical correlation analysis with application to an imaging-genetic study

Zhang

Cai

et al. 2019

J. Med. Imag.

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Distance canonical correlation analysis with application to an imaging-genetic study,"Abstract. Distance correlation is a measure that can detect both linear and nonlinear associations. However, applying distance correlation to imaging genetic studies often needs multiple testing correction due to the large number of multiple inferences. As a result, the sensitivity of its detection may be low. We propose a new model, distance canonical correlation analysis (DCCA), which overcomes this problem by searching a combination of features with the highest distance correlation. This is achieved by constructing a distance kernel function followed by solving a subsequent optimization problem. The ability to detect both linear and nonlinear associations makes DCCA suitable for analyzing complex multimodal and imaging-genetic associations. When applied to a brain imaging-genetic study from the Philadelphia Neurodevelopmental Cohort (PNC), DCCA detected several mental disorder-related gene pathways and brain networks. Experiments on brain connectivity found that the default mode network had strong nonlinear connections with other brain networks. When applied to the study of age effects, DCCA revealed that the connections of brain networks were relatively weak in younger groups but became stronger at older age stages. It indicates that adolescence is a vital stage for brain development. DCCA thus reveals a number of interesting findings and demonstrates a powerful new approach for analyzing multimodal brain imaging data. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis—An application in renal clear cell carcinoma

Dutta,

Sen,

Satagopan

2024

Genetic Epidemiology

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Somatic changes like copy number aberrations (CNAs) and epigenetic alterations like methylation have pivotal effects on disease outcomes and prognosis in cancer, by regulating gene expressions, that drive critical biological processes. To identify potential biomarkers and molecular targets and understand how they impact disease outcomes, it is important to identify key groups of CNAs, the associated methylation, and the gene expressions they impact, through a joint integrative analysis. Here, we propose a novel analysis pipeline, the joint sparse canonical correlation analysis (jsCCA), an extension of sCCA, to effectively identify an ensemble of CNAs, methylation sites and gene (expression) components in the context of disease endpoints, especially tumor characteristics. Our approach detects potentially orthogonal gene components that are highly correlated with sets of methylation sites which in turn are correlated with sets of CNA sites. It then identifies the genes within these components that are associated with the outcome. Further, we aggregate the effect of each gene expression set on tumor stage by constructing “gene component scores” and test its interaction with traditional risk factors. Analyzing clinical and genomic data on 515 renal clear cell carcinoma (ccRCC) patients from the TCGA‐KIRC, we found eight gene components to be associated with methylation sites, regulated by groups of proximally located CNA sites. Association analysis with tumor stage at diagnosis identified a novel association of expression of ASAH1 gene trans‐regulated by methylation of several genes including SIX5 and by CNAs in the 10q25 region including TCF7L2. Further analysis to quantify the overall effect of gene sets on tumor stage, revealed that two of the eight gene components have significant interaction with smoking in relation to tumor stage. These gene components represent distinct biological functions including immune function, inflammatory responses, and hypoxia‐regulated pathways. Our findings suggest that jsCCA analysis can identify interpretable and important genes, regulatory structures, and clinically consequential pathways. Such methods are warranted for comprehensive analysis of multimodal data especially in cancer genomics.

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Joint sparse canonical correlation analysis for detecting differential imaging genetics modules

Cited by 69 publications

References 47 publications

Fused Estimation of Sparse Connectivity Patterns From Rest fMRI—Application to Comparison of Children and Adult Brains

Fused Estimation of Sparse Connectivity Patterns From Rest fMRI—Application to Comparison of Children and Adult Brains

Distance canonical correlation analysis with application to an imaging-genetic study

Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis—An application in renal clear cell carcinoma

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