ArticlePLoS genetics2023
Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.
Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Exploration of multidimensional associations between craniomaxillofacial and dental arch structures.BMC oral health · 2026Article
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Omics Approaches in Hantavirus Research: Current Advances, Challenges, and Future Perspectives.Biotech (Basel (Switzerland)) · 2026Review
- OmicsTransformer: self-supervised masked consistency and uncertainty-aware fusion for robust multi-omics prediction.Bioinformatics (Oxford, England) · 2026Article
- Article
- AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices.Current issues in molecular biology · 2026Review
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Bottlenecks in advancing and applying multiomic data integration-common data resources as rate-limiting drivers-the high-impact use case of atherosclerotic cardiovascular disease.Briefings in bioinformatics · 2025Review
- IgG N-glycosylation contributes to different severities of insulin resistance: implications for 3P medical approaches.The EPMA journal · 2025Article
- The role of statistics in advancing nitric oxide research in plant biology: from data analysis to mechanistic insights.Frontiers in plant science · 2025Review
- Omics in mini-livestock: a genomic perspective on the future of sustainable food systems.Frontiers in genetics · 2025Review
- Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization.BMC bioinformatics · 2024Article
- Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine.Annual review of biomedical data science · 2024Review
- dCCA: detecting differential covariation patterns between two types of high-throughput omics data.Briefings in bioinformatics · 2024Article
- CAT Bridge: an efficient toolkit for gene-metabolite association mining from multiomics data.GigaScience · 2024Article
- Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease.Methods (San Diego, Calif.) · 2023Article
- Functional characterization of Alzheimer's disease genetic variants in microglia.Nature genetics · 2023Article
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28 authors.
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Abstract
Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.
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