Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.
Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.
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.
Metabolite_search and Metabolite_get_info to confirm names, CIDs, and HMDB IDs; never assume identity from m/z alone.Metabolite_get_diseases; do not infer clinical relevance without database evidence.Triggers:
Example Questions:
| 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 |
Input: Metabolomics Data (Peak Table or Spectra)
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Phase 1: Data Import & Metabolite Identification
|-- Load peak table or process raw spectra
|-- Match features to HMDB, KEGG (accurate mass +/- 5 ppm)
|-- Confidence scoring (Level 1-4)
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Phase 2: Quality Control & Filtering
|-- CV in QC samples (<30%)
|-- Blank subtraction (sample/blank > 3)
|-- Remove features with >50% missing
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Phase 3: Normalization
|-- Sample-wise: TIC, PQN, or internal standards
|-- Transformation: log2, Pareto, or auto-scaling
|-- Batch effect correction (if multi-batch)
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Phase 4: Exploratory Analysis
|-- PCA for sample clustering
|-- PLS-DA for supervised separation
|-- Outlier detection
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Phase 5: Differential Analysis
|-- t-test / ANOVA / Wilcoxon
|-- Fold change + FDR correction
|-- Volcano plots, heatmaps
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Phase 6: Pathway Analysis
|-- Metabolite set enrichment (MSEA)
|-- KEGG/Reactome pathway mapping
|-- Pathway topology (hub/bottleneck metabolites)
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Phase 7: Multi-Omics Integration
|-- Metabolite-enzyme Spearman correlation
|-- Pathway-level concordance scoring
|-- Metabolic flux inference
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Phase 8: Generate Report
|-- Summary statistics, differential metabolites
|-- Pathway diagrams, biomarker panel
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).
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).
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.
PCA reveals sample grouping and batch effects. PLS-DA provides supervised separation (report R2 and Q2 for model quality). Flag and investigate outliers.
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.
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.
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.
See report_template.md for full example output.
| 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 |
| 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 |
Methods:
Databases: