From Genomics Alterations to Expression Dynamics: A Hierarchical Multi-Omics Integration Framework with gINTomics

Despite the abundance of multi-omic integration methods, few adopt a hierarchical approach, which is crucial for understanding how different genomic alterations influence downstream molecular processes. We present gINTomics, a hierarchical computational framework that captures directional biological information flow across genomic, transcriptomic, and epigenomic layers. Unlike existing meta-dimensional approaches, gINTomics follows a multi-staged paradigm reflecting biological hierarchy, modeling molecular modifications by linking genomic alterations to downstream transcriptional consequences. The framework offers flexible integration strategies for different omic combinations and tailored statistical modeling with optional Random Forest for variable selection. A key feature is the comprehensive interactive Shiny application that provides intuitive visualization through interactive plots and circos diagrams, enabling users to explore complex regulatory relationships across multiple analytical perspectives. Validation on TCGA ovarian cancer data confirmed the framework's effectiveness in identifying biologically relevant regulatory mechanisms and prognostic biomarkers. Available as a Bioconductor package, gINTomics provides researchers with a powerful and accessible tool for mechanistic multi-omic integration and biomarker discovery.