OmicNetR: Network-Based Integration of Multi-Omics Data Using Sparse CCA

Provides an end-to-end workflow for integrative analysis of two omics layers using sparse canonical correlation analysis (sCCA), including sample alignment, feature selection, network edge construction, and visualization of gene-metabolite relationships. The underlying methods are based on penalized matrix decomposition and sparse CCA (Witten, Tibshirani and Hastie (2009) ), with design principles inspired by multivariate integrative frameworks such as mixOmics (Rohart et al. (2017) ).