Analyze metabolomics data end-to-end — metabolite identification, quantification (TIC normalization, batch correction), differential analysis, and pathway interpretation. Use for processing mass-spec metabolomics output, normalization choice, untargeted metabolomics workflows, and integrating with o

Metabolomics Analysis Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers. Domain Reasoning Metabolomics quantification depends critically on normalization. Total ion current (TIC) normalization corrects for sample loading variation and works well for global abundance changes; internal standard normalization is more accurate for targeted analysis where specific metabolite concentrations matter. Missing values in a peak table may reflect signal below the detection limit — not true absence — and should be imputed or handled explicitly rather than treated as zero. Failing to account for batch effects across instrument runs is a frequent source of spurious differential metabolites. LOOK UP DON'T GUESS Metabolite identities: use Metabolite search and Metabolite get info to confirm names, CIDs, and HMDB IDs; never assume identity from m/z alone. Pathway memberships: query KEGG, MetaCyc, or Reactome tools; do not list pathways from memory. Disease associations: retrieve from CTD via Metabolite get diseases ; do not infer clinical relevance without database evidence. CV thresholds and QC criteria: apply the values defined in this workflow (CV < 30%, blank ratio 3x); do not override with guesses. When to Use This Skill Triggers : User has metabolomics data (LC MS, GC MS, NMR) Questions about metabolite abundance or concentrations Differential metabolite analysis requests Metabolic pathway analysis Multi omics integration with metabolomics Metabolic biomarker discovery Flux balance analysis or metabolic modeling Metabolite enzyme correlation Example Questions : 1. "Analyze this LC MS metabolomics data for differential metabolites" 2. "Which metabolic pathways are dysregulated between conditions?" 3. "Identify metabolite biomarkers for disease classification" 4. "Correlate metabolite levels with enzyme expression" 5. "Perform pathway enrichment for differential metabolites" 6. "Integrate metabolomics with transcriptomics data" Core Capabilities Capability Description Data Import LC MS, GC MS, NMR, targeted/untargeted platforms Metabolite Identification Match to HMDB, KEGG, PubChem, spectral libraries Quality Control Peak quality, blank subtraction, internal standard normalization Normalization Probabilistic quotient, total ion current, internal standards Statistical Analysis Univariate and multivariate (PCA, PLS DA, OPLS DA) Differential Analysis Identify significant metabolite changes Pathway Enrichment KEGG, Reactome, BioCyc metabolic pathway analysis Metabolite Enzyme Integration Correlate with expression data Flux Analysis Metabolic flux balance analysis (FBA) Biomarker Discovery Multi metabolite signatures Workflow Overview Phase Summaries Phase 1: Data Import & Identification Load peak tables (CSV/TSV) or process raw spectra (mzML). Match features to HMDB by accurate mass (+/ 5 ppm). Assign confidence levels: L1 (standard match), L2 (MS/MS), L3 (mass only), L4 (unknown). Phase 2: Quality Control Assess CV in QC samples (reject 30%), compute blank ratios (keep 3x blank), filter features with 50% missing values. Check internal standard recovery (95 105% acceptable). Phase 3: Normalization Three methods available: TIC (simple, assumes similar total abundance), PQN (robust to large changes, recommended), Internal Standard (most accurate with spiked standards). Follow with log2 transform or Pareto scaling. Phase 4: Exploratory Analysis PCA reveals sample grouping and batch effects. PLS DA provides supervised separation (report R2 and Q2 for model quality). Flag and investigate outliers. Phase 5: Differential Analysis Welch's t test (two groups) or ANOVA (multiple groups) with Benjamini Hochberg FDR correction. Significance thresholds: adj. p < 0.05 and log2FC 1.0. Phase 6: Pathway Analysis Map differential metabolites to KEGG compound IDs. Perform MSEA for pathway enrichment. Consider topology: metabolites at pathway hubs (high degree/betweenness centrality) have greater impact. Phase 7: Multi Omics Integration Correlate metabolite levels with enzyme expression (Spearman). Expected: substrate enzyme negative correlation (consumption), product enzyme positive correlation (production). Score pathway dysregulation using combined metabolite + gene evidence. Phase 8: Report See [report template.md](report template.md) for full example output. Integration with ToolUniverse Skill Used For Phase tooluniverse gene enrichment Pathway enrichment Phase 6 tooluniverse rnaseq deseq2 Enzyme expression for integration Phase 7 tooluniverse proteomics analysis Protein levels for integration Phase 7 tooluniverse multi omics integration Comprehensive integration Phase 7 Quantified Minimums Component Requirement Metabolites At least 50 identified metabolites Replicates At least 3 per condition QC CV < 30% in QC samples, blank subtraction Statistical test t test or Wilcoxon with FDR correction Pathway analysis MSEA with KEGG or Reactome Report QC, differential metabolites, pathways, visualizations Limitations Identification : Many features remain unidentified (Level 4) Coverage : Cannot detect all metabolites (depends on method) Quantification : Relative abundance (not absolute without standards) Isomers : Difficult to distinguish structural isomers Ion suppression : Matrix effects can affect quantification Dynamic range : Limited compared to targeted methods References Methods : MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 XCMS: https://doi.org/10.1021/ac051437y MSEA: https://doi.org/10.1186/1471 2105 11 395 Databases : HMDB: https://hmdb.ca KEGG Compound: https://www.genome.jp/kegg/compound/ Reactome: https://reactome.org Reference Files [code examples.md](code examples.md) Python code for all phases (data loading, QC, normalization, statistics, pathway analysis) [report template.md](report template.md) Full example report (LC MS disease vs control)

tooluniverse-metabolomics-analysis