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This paper introduces a network propagation protocol to integrate multi-omics data by mapping significant gene hits from individual datasets onto a prior knowledge molecular interaction network and diffusing their signals. By mathematically integrating these propagated networks and performing subsequent clustering and pathway enrichment, the method identifies convergent molecular subnetworks that represent shared biological mechanisms across disparate molecular layers. The key result is the ability to pinpoint core biological drivers that manifest across orthogonal omics data, overcoming challenges of limited gene-level overlap.
Shared molecular subnetworks, not just individual genes, drive phenotypes across multi-omics data.
Phenotypic outcomes arise from coordinated changes across molecular layers. Although multi-omics data capture complementary molecular signals, integrating them into coherent biological mechanisms remains challenging due to limited overlap at the gene level. Network propagation maps significant gene hits from each dataset onto a prior knowledge molecular interaction network and diffuses their signals to neighboring genes. Mathematical integration of propagated networks, followed by clustering and pathway enrichment, reveals shared molecular networks, pinpointing the core biology captured across orthogonal molecular layers.