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) doi:10.1093/biostatistics/kxp008), with design principles inspired by multivariate integrative frameworks such as mixOmics (Rohart et al. (2017) doi:10.1371/journal.pcbi.1005752).