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

Workflow Overview

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

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 for full example output.

Integration with ToolUniverse

Quantified Minimums

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 - Python code for all phases (data loading, QC, normalization, statistics, pathway analysis)
• report_template.md - Full example report (LC-MS disease vs control)