Network propagation in bipartite metabolite–reaction graphs for metabolomic data exploration

Introduction: Interpretation of metabolomic data is frequently limited by incomplete metabolite coverage and the predefined representation of biochemical organization provided by pathway-based approaches.

Objectives: This study describes and evaluates a graph-based network propagation method designed to enhance metabolomic data exploration by diffusing node-level statistical relevance scores across a metabolic network.

Methods: An undirected bipartite metabolite–reaction network was reconstructed using the KEGG database, comprising 8236 nodes (1601 metabolites and 6635 reactions). Simulated metabolomic datasets with 75% sparsity mimicking two metabolic perturbations and two no-effect control groups, and real data from a previous study were used to test the strategy. Node-level statistical signal scores were distributed across the network topology using a random walk-based diffusion operator combined with a supervised clamping procedure to preserve initially observed measurements. A topological distance mask was subsequently applied to restrict propagation to nodes located within a predefined network distance from experimentally measured metabolites.

Results: Network propagation redistributed statistical relevance across connected subgraphs, expanding the number of metabolic features carrying topology-informed statistical scores beyond the experimentally observed metabolome in the analysis of simulated and real data. In both oxidative stress and mitochondrial dysfunction simulations, significant nodes displayed non-random topological organization consistent with the simulated perturbations. Propagation also identified additional unmeasured metabolites that were topologically connected to experimentally observed signals. In multivariate analyses, network propagation substantially expanded the feature space and showed that it could improve predictive classification performance.