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We adapted sparse multiple canonical correlation analysis (SMCCA) for multi-omics data integration. Our methods revealed transferable associations between proteomics and blood cell counts across cohorts.

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Area of Science:

  • Systems Biology
  • Genomics
  • Proteomics
  • Biostatistics

Background:

  • Integrative multi-omics analysis provides holistic biological system views.
  • Canonical Correlation Analysis (CCA) extracts shared latent features between assays.
  • Large-scale cohort data availability enables new multi-omics applications.

Purpose of the Study:

  • To adapt and apply sparse multiple CCA (SMCCA) for multi-omics data integration in large cohorts.
  • To develop enhanced SMCCA methods (SMCCA-GS and SSMCCA) addressing specific data challenges.
  • To identify cohort-agnostic biological relationships between multi-omics data and phenotypic traits.

Main Methods:

  • Adapted sparse multiple CCA (SMCCA) for proteomics and methylomics data.
  • Incorporated Gram-Schmidt (GS) algorithm for improved canonical variable (CV) orthogonality.
  • Developed Sparse Supervised Multiple CCA (SSMCCA) for supervised multi-assay integration.
  • Applied methods to Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS) data.

Main Results:

  • Identified strong associations between blood cell counts and protein abundance.
  • Demonstrated transferability of learned CVs across independent cohorts (MESA and JHS).
  • Proteomic CVs explained significant phenotypic variance in blood cell counts (38.9%–50.0%) across cohorts.
  • Other omics-CV-trait pairs also showed similar transferability.

Conclusions:

  • Adapted SMCCA methods (SMCCA-GS, SSMCCA) are effective for large-scale multi-omics integration.
  • CVs capture biologically meaningful, cohort-agnostic variation.
  • Findings suggest considering blood cell composition in protein-based association studies.
  • The developed methods facilitate discovery of robust multi-omics relationships across diverse populations.