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 other omics layers.
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 other omics layers.
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:
"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)
|
v
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)
|
v
Phase 2: Quality Control & Filtering
|-- CV in QC samples (<30%)
|-- Blank subtraction (sample/blank > 3)
|-- Remove features with >50% missing
|
v
Phase 3: Normalization
|-- Sample-wise: TIC, PQN, or internal standards
|-- Transformation: log2, Pareto, or auto-scaling
|-- Batch effect correction (if multi-batch)
|
v
Phase 4: Exploratory Analysis
|-- PCA for sample clustering
|-- PLS-DA for supervised separation
|-- Outlier detection
|
v
Phase 5: Differential Analysis
|-- t-test / ANOVA / Wilcoxon
|-- Fold change + FDR correction
|-- Volcano plots, heatmaps
|
v
Phase 6: Pathway Analysis
|-- Metabolite set enrichment (MSEA)
|-- KEGG/Reactome pathway mapping
|-- Pathway topology (hub/bottleneck metabolites)
|
v
Phase 7: Multi-Omics Integration
|-- Metabolite-enzyme Spearman correlation
|-- Pathway-level concordance scoring
|-- Metabolic flux inference
|
v
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.
