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Metabolomics Analysis
Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.
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 :
- "Analyze this LC-MS metabolomics data for differential metabolites"
- "Which metabolic pathways are dysregulated between conditions?"
- "Identify metabolite biomarkers for disease classification"
- "Correlate metabolite levels with enzyme expression"
- "Perform pathway enrichment for differential metabolites"
- "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
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
| 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 :
Databases :
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)
Weekly Installs
127
Repository
mims-harvard/to…universe
GitHub Stars
1.2K
First Seen
Feb 19, 2026
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Installed on
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