tooluniverse-metabolomics-analysis
SKILL.md
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:
- "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"
- "Characterize the metabolic phenotype of this cell line"
- "Identify metabolites associated with drug response"
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 metabolite databases (HMDB, KEGG)
|-- Annotate with chemical IDs, formulas, pathways
|-- Confidence scoring for IDs
|
v
Phase 2: Quality Control & Filtering
|-- Assess peak quality (CV, blank ratios)
|-- Remove background peaks
|-- Filter low-quality metabolites
|-- Internal standard check
|
v
Phase 3: Normalization
|-- Sample-wise normalization (TIC, PQN, internal standards)
|-- Batch effect correction
|-- Log-transform or scaling
|
v
Phase 4: Exploratory Analysis
|-- PCA for sample clustering
|-- Quality assessment plots
|-- Outlier detection
|-- Sample correlation
|
v
Phase 5: Differential Analysis
|-- Statistical testing (t-test, ANOVA, Wilcoxon)
|-- Fold change calculation
|-- Multiple testing correction
|-- Volcano plots, heatmaps
|
v
Phase 6: Pathway Analysis
|-- Metabolite set enrichment (MSEA)
|-- Pathway topology analysis
|-- KEGG/Reactome pathway mapping
|-- Identify dysregulated pathways
|
v
Phase 7: Multi-Omics Integration
|-- Correlate with enzyme expression (RNA/protein)
|-- Metabolite-gene associations
|-- Pathway-level integration
|-- Metabolic flux inference
|
v
Phase 8: Generate Report
|-- Summary statistics
|-- Differential metabolites
|-- Pathway diagrams
|-- Multi-omics integration plots
|-- Biomarker panel
Phase Details
Phase 1: Data Import & Metabolite Identification
Objective: Load data and identify metabolites from features.
Supported data types:
- Peak tables: Pre-processed metabolite abundance
- Raw spectra: LC-MS (.mzML, .mzXML), GC-MS
- NMR spectra: 1D/2D NMR data
Peak table format (typical):
Sample_ID | Glucose | Lactate | Glutamine | ... | Cholesterol
----------|---------|---------|-----------|-----|------------
Control_1 | 125000 | 45000 | 78000 | ... | 23000
Control_2 | 130000 | 43000 | 82000 | ... | 25000
Disease_1 | 85000 | 92000 | 45000 | ... | 45000
Data loading:
def load_metabolomics_data(file_path, file_type='peak_table'):
"""
Load metabolomics data.
file_type options:
- 'peak_table': CSV/TSV with metabolites as columns
- 'mzml': Raw LC-MS data (requires processing)
- 'nmr': NMR spectra
"""
import pandas as pd
if file_type == 'peak_table':
# Load peak table
data = pd.read_csv(file_path, index_col=0)
# Rows = samples, Columns = metabolites
return data
elif file_type == 'mzml':
# Process raw MS data (requires pymzml or similar)
# Peak detection, alignment, quantification
pass
Metabolite identification:
def identify_metabolites(feature_data, mass_list, rt_list=None):
"""
Match features to metabolite databases.
Uses accurate mass and retention time (if available).
Queries: HMDB, KEGG Compound, PubChem
"""
from tooluniverse import ToolUniverse
tu = ToolUniverse()
identified_metabolites = []
for i, mass in enumerate(mass_list):
# Query HMDB by mass (±5 ppm tolerance)
hmdb_result = tu.run_one_function({
"name": "hmdb_search_by_mass",
"arguments": {
"mass": mass,
"mass_tolerance": 0.005 # 5 ppm
}
})
if hmdb_result and 'data' in hmdb_result:
matches = hmdb_result['data']
# Rank by confidence
# (exact mass match, pathway context, etc.)
identified_metabolites.append({
'feature_id': i,
'metabolite_name': matches[0]['name'],
'hmdb_id': matches[0]['accession'],
'formula': matches[0]['chemical_formula'],
'confidence': calculate_confidence(matches[0])
})
return identified_metabolites
Confidence scoring:
Level 1: Confirmed with authentic standard (MS + RT match)
Level 2: Probable structure (accurate mass + MS/MS)
Level 3: Tentative match (accurate mass only)
Level 4: Unknown metabolite
Phase 2: Quality Control & Filtering
Objective: Remove low-quality features and background noise.
Quality control metrics:
def metabolomics_qc(data, sample_metadata):
"""
Quality control for metabolomics data.
QC metrics:
- Coefficient of variation (CV) in QC samples
- Blank ratios (signal in samples vs blanks)
- Missing values per metabolite
- Total ion current per sample
"""
# 1. CV in QC samples (should be < 30%)
qc_samples = sample_metadata['sample_type'] == 'QC'
qc_data = data[qc_samples]
cv_per_metabolite = qc_data.std() / qc_data.mean()
high_cv = cv_per_metabolite > 0.3
print(f"Metabolites with CV > 30%: {high_cv.sum()}")
# 2. Blank subtraction
blank_samples = sample_metadata['sample_type'] == 'Blank'
blank_data = data[blank_samples]
# Calculate blank ratios
blank_means = blank_data.mean()
sample_means = data[~blank_samples & ~qc_samples].mean()
blank_ratio = sample_means / blank_means
# Filter: keep metabolites with sample/blank > 3
keep_metabolites = blank_ratio > 3
# 3. Missing values (remove if >50% missing)
missing_per_metabolite = (data == 0).sum() / data.shape[0]
keep_metabolites &= (missing_per_metabolite < 0.5)
# Filter data
filtered_data = data.loc[:, keep_metabolites]
return filtered_data
Phase 3: Normalization
Objective: Account for technical variation and enable fair comparison.
Normalization methods:
1. Total Ion Current (TIC):
def normalize_tic(data):
"""
Normalize by total ion current.
Assumes total metabolite abundance is similar across samples.
"""
tic = data.sum(axis=1)
median_tic = tic.median()
norm_factors = median_tic / tic
normalized = data.multiply(norm_factors, axis=0)
return normalized
2. Probabilistic Quotient Normalization (PQN):
def normalize_pqn(data, reference_sample=None):
"""
Probabilistic quotient normalization.
More robust than TIC to large metabolite changes.
"""
import numpy as np
# Use median sample as reference
if reference_sample is None:
reference = data.median(axis=0)
else:
reference = data.loc[reference_sample]
# Calculate quotients
quotients = data.div(reference, axis=1)
# Median quotient per sample
norm_factors = quotients.median(axis=1)
# Normalize
normalized = data.div(norm_factors, axis=0)
return normalized
3. Internal Standard Normalization:
def normalize_internal_standard(data, is_metabolite):
"""
Normalize by spiked-in internal standard.
Most accurate if added before sample processing.
"""
is_abundance = data[is_metabolite]
norm_factors = is_abundance.median() / is_abundance
normalized = data.multiply(norm_factors, axis=0)
# Remove internal standard from data
normalized = normalized.drop(columns=[is_metabolite])
return normalized
Transformation:
def transform_data(data, method='log'):
"""
Transform metabolite abundances.
Methods:
- 'log': log2 transform (stabilize variance)
- 'pareto': Pareto scaling (mean-center, divide by sqrt(std))
- 'auto': Auto-scaling (mean-center, divide by std)
"""
import numpy as np
if method == 'log':
# Add small constant to avoid log(0)
transformed = np.log2(data + 1)
elif method == 'pareto':
# Pareto scaling (common in metabolomics)
mean = data.mean(axis=0)
std = data.std(axis=0)
transformed = (data - mean) / np.sqrt(std)
elif method == 'auto':
# Auto-scaling (z-score)
mean = data.mean(axis=0)
std = data.std(axis=0)
transformed = (data - mean) / std
return transformed
Phase 4: Exploratory Analysis
Objective: Visualize data structure and detect outliers.
PCA:
def perform_pca_metabolomics(data, sample_groups):
"""
Principal component analysis for sample clustering.
"""
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
# PCA
pca = PCA(n_components=2)
pca_result = pca.fit_transform(data)
# Plot
plt.figure(figsize=(8, 6))
for group in sample_groups.unique():
mask = sample_groups == group
plt.scatter(pca_result[mask, 0], pca_result[mask, 1], label=group)
plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.1%})')
plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.1%})')
plt.legend()
plt.title('PCA - Metabolomics Data')
PLS-DA (Partial Least Squares Discriminant Analysis):
def plsda_analysis(X, y, n_components=2):
"""
PLS-DA for supervised dimensionality reduction.
Better separation than PCA for classification tasks.
"""
from sklearn.cross_decomposition import PLSRegression
from sklearn.preprocessing import LabelEncoder
# Encode labels
le = LabelEncoder()
y_encoded = le.fit_transform(y)
# PLS
pls = PLSRegression(n_components=n_components)
X_pls = pls.fit_transform(X, y_encoded)[0]
# Plot
plt.scatter(X_pls[:, 0], X_pls[:, 1], c=y_encoded)
plt.xlabel('PLS Component 1')
plt.ylabel('PLS Component 2')
plt.title('PLS-DA')
return X_pls
Phase 5: Differential Metabolite Analysis
Objective: Identify metabolites with significant abundance changes.
Statistical testing:
def differential_metabolites(data, group1_samples, group2_samples):
"""
Identify differential metabolites between two groups.
"""
from scipy import stats
import numpy as np
results = []
for metabolite in data.columns:
# Extract abundances
group1 = data.loc[group1_samples, metabolite]
group2 = data.loc[group2_samples, metabolite]
# Statistics
mean1 = group1.mean()
mean2 = group2.mean()
fold_change = mean2 / mean1
log2fc = np.log2(fold_change)
# t-test
t_stat, p_value = stats.ttest_ind(group1, group2, equal_var=False)
results.append({
'metabolite': metabolite,
'fold_change': fold_change,
'log2FC': log2fc,
'mean_group1': mean1,
'mean_group2': mean2,
'p_value': p_value,
't_statistic': t_stat
})
results_df = pd.DataFrame(results)
# Multiple testing correction
from statsmodels.stats.multitest import multipletests
results_df['adj_p_value'] = multipletests(results_df['p_value'], method='fdr_bh')[1]
# Significance
results_df['significant'] = (
(results_df['adj_p_value'] < 0.05) &
(np.abs(results_df['log2FC']) > 1.0)
)
return results_df
Volcano plot:
def plot_metabolite_volcano(de_results):
"""Visualize differential metabolite results."""
plt.figure(figsize=(8, 6))
# Non-significant
non_sig = de_results[~de_results['significant']]
plt.scatter(non_sig['log2FC'], -np.log10(non_sig['p_value']),
c='gray', alpha=0.5, s=20)
# Significant
sig = de_results[de_results['significant']]
plt.scatter(sig['log2FC'], -np.log10(sig['p_value']),
c='red', alpha=0.7, s=30)
plt.axhline(-np.log10(0.05), color='blue', linestyle='--')
plt.axvline(-1, color='blue', linestyle='--')
plt.axvline(1, color='blue', linestyle='--')
plt.xlabel('log2 Fold Change')
plt.ylabel('-log10(p-value)')
plt.title('Differential Metabolites')
Phase 6: Metabolic Pathway Analysis
Objective: Interpret metabolite changes at pathway level.
Metabolite Set Enrichment Analysis (MSEA):
def pathway_enrichment_metabolites(metabolite_list, organism='human'):
"""
Perform pathway enrichment for differential metabolites.
Uses KEGG metabolic pathways.
"""
from tooluniverse import ToolUniverse
tu = ToolUniverse()
# Get KEGG compound IDs for metabolites
kegg_ids = []
for metabolite in metabolite_list:
# Convert HMDB ID to KEGG Compound ID
result = tu.run_one_function({
"name": "kegg_find_compound",
"arguments": {"query": metabolite}
})
if result and 'data' in result:
kegg_ids.append(result['data'][0]['entry_id'])
# Pathway enrichment
enrichment = tu.run_one_function({
"name": "kegg_enrich_pathway",
"arguments": {
"compound_list": ",".join(kegg_ids),
"organism": organism
}
})
return enrichment
Pathway topology analysis:
def pathway_topology_analysis(metabolites, pathway_id):
"""
Analyze pathway dysregulation considering topology.
Metabolites at key pathway positions (hubs, bottlenecks)
have more impact than peripheral metabolites.
"""
# Load pathway structure from KEGG
# Calculate impact score based on:
# - Betweenness centrality (bottleneck metabolites)
# - Degree centrality (hub metabolites)
# - Pathway position (early vs late)
pass
Phase 7: Multi-Omics Integration
Objective: Integrate metabolomics with transcriptomics/proteomics.
Metabolite-enzyme correlation:
def correlate_metabolite_enzyme(metabolite_data, enzyme_expression):
"""
Correlate metabolite levels with enzyme expression.
Expected correlations:
- Substrate + enzyme → negative correlation (consumption)
- Product + enzyme → positive correlation (production)
"""
from scipy.stats import spearmanr
# For each metabolite-enzyme pair
correlations = {}
for metabolite in metabolite_data.columns:
# Find enzymes that produce/consume this metabolite
enzymes = find_metabolite_enzymes(metabolite)
for enzyme in enzymes:
if enzyme in enzyme_expression.index:
met_levels = metabolite_data[metabolite]
enz_expr = enzyme_expression.loc[enzyme]
r, p = spearmanr(met_levels, enz_expr)
correlations[f'{metabolite}_{enzyme}'] = {
'r': r,
'p': p,
'relationship': 'product' if r > 0 else 'substrate'
}
return correlations
Pathway-level integration:
def integrate_omics_pathway(metabolite_fc, gene_fc, pathway_id):
"""
Integrate metabolite and gene fold changes at pathway level.
For each reaction:
- Check if metabolites are changed
- Check if enzymes are changed
- Score pathway dysregulation (combined evidence)
"""
# Load pathway reactions
# For each reaction:
# - Metabolite substrate/product changes
# - Enzyme expression changes
# - Concordance score
pathway_score = calculate_pathway_dysregulation(
metabolite_fc, gene_fc, pathway_id
)
return pathway_score
Phase 8: Report Generation
Generate comprehensive metabolomics report:
# Metabolomics Analysis Report
## Dataset Summary
- **Platform**: LC-MS/MS (Orbitrap)
- **Method**: Untargeted metabolomics
- **Samples**: 40 (20 disease, 20 control)
- **Metabolites Identified**: 324 (Level 1/2 confidence)
- **Metabolites Quantified**: 298 (after QC)
## Quality Control
- **CV in QC samples**: 18% median (acceptable: <30%)
- **Blank ratios**: All metabolites > 3x blank signal
- **Missing values**: 8% average per metabolite
- **Internal standard**: Recovery 95-105% across samples
## Normalization
- **Method**: Probabilistic Quotient Normalization (PQN)
- **Transformation**: log2
- **Batch correction**: Not required (single batch)
## Exploratory Analysis
- **PCA**: Clear separation between groups (PC1: 28%, PC2: 18%)
- **PLS-DA**: Excellent discrimination (R2=0.89, Q2=0.75)
- **Outliers**: 1 sample removed (technical failure)
## Differential Metabolites
- **Significant metabolites**: 87 (adj. p < 0.05, |log2FC| > 1)
- Increased: 52 metabolites
- Decreased: 35 metabolites
### Top Increased Metabolites
1. **Lactate** (log2FC=3.2, p=1e-12) - Glycolysis
2. **Glutamine** (log2FC=2.8, p=1e-10) - Amino acid metabolism
3. **Palmitate** (log2FC=2.5, p=1e-9) - Fatty acid synthesis
### Top Decreased Metabolites
1. **Citrate** (log2FC=-2.9, p=1e-11) - TCA cycle
2. **ATP** (log2FC=-2.3, p=1e-9) - Energy metabolism
3. **NAD+** (log2FC=-2.1, p=1e-8) - Redox balance
## Pathway Enrichment
### Top Dysregulated Pathways
1. **Glycolysis/Gluconeogenesis** (p=1e-15)
- 12 metabolites: glucose, pyruvate, lactate, etc.
- Direction: Increased flux to lactate (Warburg effect)
2. **TCA Cycle** (p=1e-12)
- 8 metabolites: citrate, succinate, malate, etc.
- Direction: Decreased activity
3. **Glutaminolysis** (p=1e-10)
- 6 metabolites: glutamine, glutamate, α-KG, etc.
- Direction: Increased glutamine consumption
## Multi-Omics Integration
### Metabolite-Enzyme Correlations
- **LDHA (lactate dehydrogenase)**
- Expression: 3.5-fold increased (RNA + protein)
- Lactate: 3.2-fold increased
- Correlation: r=0.85 (p<0.001) - Concordant upregulation
- **IDH1 (isocitrate dehydrogenase)**
- Expression: 2.1-fold decreased
- Citrate: 2.9-fold decreased
- Correlation: r=0.78 (p<0.001) - TCA cycle suppression
### Metabolic Phenotype
Integration with RNA-seq and proteomics reveals:
- **Warburg effect**: Shift from oxidative to glycolytic metabolism
- **Glutamine addiction**: Increased glutaminolysis for anaplerosis
- **Redox imbalance**: Decreased NAD+/NADH ratio, oxidative stress
## Biomarker Discovery
### Top 10 Metabolites for Classification
Random Forest model (10-fold CV):
- **AUC**: 0.96 ± 0.03
- **Accuracy**: 92%
**Biomarker Panel**:
1. Lactate
2. Glutamine
3. Citrate
4. ATP
5. Palmitate
6. Pyruvate
7. Succinate
8. NAD+
9. α-ketoglutarate
10. Glucose-6-phosphate
## Biological Interpretation
Metabolomics reveals fundamental metabolic reprogramming in disease state:
1. **Glycolytic switch**: Increased glycolysis with lactate accumulation despite oxygen availability (Warburg effect), driven by LDHA upregulation.
2. **TCA cycle suppression**: Decreased citrate and TCA intermediates, consistent with IDH1 downregulation. Shunts carbon to biosynthesis.
3. **Glutamine dependence**: Elevated glutamine consumption and glutaminolysis provides alternative carbon source for anaplerosis and NADPH for biosynthesis.
4. **Biosynthetic activation**: Increased palmitate indicates active fatty acid synthesis, supporting membrane production for proliferation.
5. **Energy stress**: Despite active glycolysis, ATP levels are decreased, suggesting high energy demand outpacing production.
This metabolic signature is characteristic of proliferative, biosynthetically active cells typical of cancer or activated immune cells.
## Clinical Relevance
- **Therapeutic targets**: LDHA inhibitors, glutaminase inhibitors to disrupt metabolic dependencies
- **Biomarkers**: Lactate/citrate ratio as metabolic activity marker
- **Drug response**: Metabolic phenotype may predict sensitivity to metabolic inhibitors
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 concentration 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
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