Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.

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 p

SUBMITTER: Jiang MZ 

PROVIDER: S-EPMC10237647 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature