FeatureMSEA: Metabolic Feature-based Metabolite Set Enrichment Analysis
Abstract
Liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics detects thousands of metabolic features, but converting these chemical signals into metabolite set-level biological knowledge remains challenging. This is because most features lack unambiguous metabolite identities. Conventional metabolite set enrichment analysis (MSEA) generally requires identified metabolites and metabolite-level ranked inputs, leaving much of the untargeted feature space unused. Here, we present FeatureMSEA, a feature rank-based framework for metabolite set enrichment directly from metabolic features with ambiguous annotations. FeatureMSEA integrates multi-evidence feature-to-metabolite annotation, feature rank-based enrichment scoring, permutation-based inference, and iterative leading-edge-guided annotation refinement, with an optional LLM-assisted module for post-enrichment interpretation. In null comparisons of randomly split healthy samples, FeatureMSEA detected no significant metabolite sets, whereas metabolite-set spike-in simulations showed recovery of implanted signals. In a cerebrospinal fluid metabolomics study of Huntingtons disease, FeatureMSEA identified dysregulated metabolite sets related to amino acid metabolism, mitochondrial energy metabolism, and neuroactive signaling. MS/MS-based annotation analysis further showed that FeatureMSEA refinement reduced annotation ambiguity and prioritized chemically consistent candidate metabolites. In summary, FeatureMSEA provides a general framework for extracting metabolite set-level biological insights from LC-MS untargeted metabolomics in which confident metabolite identification remains incomplete.
Full-text reasoning
From the deep-tier full-text analysis of this preprint.
Reasoning review — 8 key claims, 8 well-supported
- supported FeatureMSEA is a rank-based framework for MSEA directly from metabolic features with ambiguous annotations.
- supported FeatureMSEA avoids arbitrary feature-level significance cutoffs by using the full phenotype-ranked feature list.
- supported FeatureMSEA preserves multiple candidate annotations per feature, performing MSEA without definitive metabolite identification.
- supported FeatureMSEA identifies leading-edge feature-metabolite relationships, using them to iteratively refine and prioritize annotations.
- supported FeatureMSEA was evaluated using simulated datasets with spike-in metabolite sets.
- supported FeatureMSEA was applied to a Huntington's disease case study, demonstrating disease-relevant metabolite set-level interpretation.
- supported Annotation refinement in FeatureMSEA was assessed using confident MS/MS-supported annotations as partial reference evidence.
- supported FeatureMSEA is an accessible computational tool, implemented with code and a Shiny application, integrating with existing workflows.
Claims and gaps are read from the full text by a language model, shown for transparency; they do not affect ranking or selection.
Lifecycle
- biorxiv v1 2026-06-19 source ↗
Discussion
Moderated digest of third-party discussion on Bluesky — substantive endorsement and critique.
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Kermit Murray @kkmurray.bsky.social · 811 followers critical
(BioRxiv All) FeatureMSEA: Metabolic Feature-based Metabolite Set Enrichment Analysis: Liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics detects thousands of metabolic features, but converting these chemical signals into metabolite set-level biological… #BioRxiv #MassSpecRSS
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#NeuroDegeneration preprints @prepub-neurodegen.bsky.social · 230 followers neutral
FeatureMSEA: Metabolic Feature-based Metabolite Set Enrichment Analysis #NeuroDegeneration 🧪🧠 https://www.biorxiv.org/content/10.64898/2026.06.15.732313v1
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bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · 4920 followers neutral
FeatureMSEA: Metabolic Feature-based Metabolite Set Enrichment Analysis https://www.biorxiv.org/content/10.64898/2026.06.15.732313v1
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bioRxivpreprint @biorxivpreprint.bsky.social · 8895 followers neutral
FeatureMSEA: Metabolic Feature-based Metabolite Set Enrichment Analysis https://www.biorxiv.org/content/10.64898/2026.06.15.732313v1