tooluniverse-metabolomics-analysis

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 This Skill Solves:

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"
7. "Characterize the metabolic phenotype of this cell line"
8. "Identify metabolites associated with drug response"

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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

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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 metabolite databases (HMDB, KEGG)
    |-- Annotate with chemical IDs, formulas, pathways
    |-- Confidence scoring for IDs
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Phase 2: Quality Control & Filtering
    |-- Assess peak quality (CV, blank ratios)
    |-- Remove background peaks
    |-- Filter low-quality metabolites
    |-- Internal standard check
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Phase 3: Normalization
    |-- Sample-wise normalization (TIC, PQN, internal standards)
    |-- Batch effect correction
    |-- Log-transform or scaling
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Phase 4: Exploratory Analysis
    |-- PCA for sample clustering
    |-- Quality assessment plots
    |-- Outlier detection
    |-- Sample correlation
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Phase 5: Differential Analysis
    |-- Statistical testing (t-test, ANOVA, Wilcoxon)
    |-- Fold change calculation
    |-- Multiple testing correction
    |-- Volcano plots, heatmaps
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Phase 6: Pathway Analysis
    |-- Metabolite set enrichment (MSEA)
    |-- Pathway topology analysis
    |-- KEGG/Reactome pathway mapping
    |-- Identify dysregulated pathways
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Phase 7: Multi-Omics Integration
    |-- Correlate with enzyme expression (RNA/protein)
    |-- Metabolite-gene associations
    |-- Pathway-level integration
    |-- Metabolic flux inference
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Phase 8: Generate Report
    |-- Summary statistics
    |-- Differential metabolites
    |-- Pathway diagrams
    |-- Multi-omics integration plots
    |-- Biomarker panel