Sparse Canonical Correlation Analysis for Multiple Measurements With Latent Trajectories.

ABSTRACT: Canonical correlation analysis (CCA) is a widely used multivariate method in omics research for integrating high-dimensional datasets. CCA identifies hidden links by deriving linear projections of observed features that maximally correlate datasets. An important requirement of standard CCA is that observations are independent of each other. As a result, it cannot properly deal with repeated measurements. Current CCA extensions dealing with these challenges either perform CCA on summarized data or estimate correlations for each measurement. While these techniques factor in the correlation between measurements, they are suboptimal for high-dimensional analysis and exploiting this data's longitudinal qualities. We propose a novel extension of sparse CCA that incorporates time dynamics at the

SUBMITTER: Senar N 

PROVIDER: S-EPMC12573309 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature