FeatureMAP enables network-guided interpretation of untargeted metabolomics

Wang, Y.; Zhang, F.; Peng, G.; Qiu, L.; Zeng, L.; Liu, Y.; Kinchagawat, J.; Yang, X.; Liu, C.; Johnson, C.; Su, Q.; Wang, C.; Shen, X.

doi:10.64898/2026.11.04.764165

bioRxiv · 10.64898/2026.11.04.764165

FeatureMAP enables network-guided interpretation of untargeted metabolomics

Wang, Y.·Zhang, F.·Peng, G.·Qiu, L.·Zeng, L.·Liu, Y.·Kinchagawat, J.·Yang, X.·Liu, C.·Johnson, C.·Su, Q.·Wang, C.·Shen, X.

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Abstract

Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics generates thousands of features, but incomplete metabolite annotation and reliance on predefined pathways limit their biological interpretation. Here we present FeatureMAP, an LC-MS feature-centric computational framework that integrates multi-evidence metabolite annotation, network-guided refinement, metabolic module discovery, and structured large language model-based functional interpretation. Across ten public datasets, FeatureMAP improved MS1-based metabolite candidate prioritization while reducing annotation redundancy. In semi-synthetic benchmarks, FeatureMAP recovered coordinated metabolic perturbations, achieving 92% recovery of a simulated perturbation, and distinguished concurrent metabolic processes. Controlled benchmarks further evaluated functional interpretation under competing metabolite-level signals. Based on FeatureMAP, we additionally developed PhenoMAP, a phenotype-aware knowledge graph comprising 12,929 dysregulated metabolic modules from 501 human metabolomics studies spanning 326 phenotypes. Module-level comparisons revealed reproducible metabolic similarities across independent studies and biologically supported relationships among phenotypes. Application to Alzheimer's disease cerebrospinal fluid and cellular datasets identified convergent membrane-lipid alterations and implicated galactose-linked glycosphingolipid remodeling. Together, FeatureMAP and PhenoMAP enable network-grounded interpretation of incompletely annotated LC-MS metabolomics data and systematic investigation of metabolic dysregulation across biological conditions.

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Wang, Y., Zhang, F., Peng, G., Qiu, L., Zeng, L., Liu, Y., Kinchagawat, J., Yang, X., Liu, C., Johnson, C., Su, Q., Wang, C., Shen, X.. 2026-11-05. FeatureMAP enables network-guided interpretation of untargeted metabolomics. https://doi.org/10.64898/2026.11.04.764165

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