metabolomics-pathway-enrichment

name	metabolomics-pathway-enrichment
description	Metabolomics pathway analysis using MetaboAnalystR (KEGG, Reactome), pathview visualization, MSEA, mummichog, and network-based topology analysis.
version	0.1.0
author	OmicsClaw
license	MIT
tags	["metabolomics","pathway","KEGG","enrichment","MetaboAnalyst","pathview"]
metadata	{"omicsclaw":{"domain":"metabolomics","emoji":"🗺️","trigger_keywords":["metabolomics pathway","KEGG","MetaboAnalyst","enrichment","mummichog"],"allowed_extra_flags":["--method"],"legacy_aliases":["met-pathway"],"saves_h5ad":false}}

🗺️ Metabolomics Pathway Analysis

Map metabolites to biological pathways and perform enrichment, topology, and network analysis.

Core Capabilities

1. KEGG pathway enrichment: Over-representation analysis (ORA) using MetaboAnalystR
2. Quantitative enrichment analysis (QEA): For continuous data (fold changes)
3. Topology-based analysis: Considers pathway structure (betweenness, degree)
4. pathview visualization: KEGG pathway maps with metabolite data overlay
5. Network-based analysis: Metabolite-pathway bipartite networks
6. MSEA: Metabolite Set Enrichment using SMPDB or HMDB sets

CLI Reference

python omicsclaw.py run met-pathway --demo
python omicsclaw.py run met-pathway --input <metabolites.csv> --output <dir>

Algorithm / Methodology

KEGG Pathway Enrichment (MetaboAnalystR)

library(MetaboAnalystR)

mSet <- InitDataObjects('conc', 'pathora', FALSE)
mSet <- SetOrganism(mSet, 'hsa')  # Human

# Load metabolite list (HMDB IDs or compound names)
metabolites <- c('HMDB0000001', 'HMDB0000005', 'HMDB0000010')

mSet <- Setup.MapData(mSet, metabolites)
mSet <- CrossReferencing(mSet, 'hmdb')  # Or 'name', 'kegg', 'pubchem'

# Pathway analysis
mSet <- SetKEGG.PathLib(mSet, 'hsa', 'current')
mSet <- SetMetabolomeFilter(mSet, FALSE)
mSet <- CalculateOraScore(mSet, 'rbc', 

pathway_results  mSetanalSetora.mat

Quantitative Enrichment Analysis (QEA)

mSet <- InitDataObjects('conc', 'pathqea', FALSE)
mSet <- SetOrganism(mSet, 'hsa')

metabolite_data <- data.frame(
    compound = c('Glucose', 'Lactate', 'Pyruvate'),
    fc = c(1.5, 2.3, 0.7)
)

mSet <- Setup.MapData(mSet, metabolite_data)
mSet <- CrossReferencing(mSet, 'name')
mSet <- SetKEGG.PathLib(mSet, 'hsa', 'current')
mSet <- CalculateQeaScore(mSet, 'rbc', 'gt'

qea_results  mSetanalSetqea.mat

Topology-Based Analysis

mSet <- InitDataObjects('conc', 'pathinteg', FALSE)
mSet <- SetOrganism(mSet, 'hsa')
mSet <- Setup.MapData(mSet, metabolites)
mSet <- CrossReferencing(mSet, 'hmdb')
mSet <- SetKEGG.PathLib(mSet, 'hsa', 'current')
mSet <- SetMetabolomeFilter(mSet, FALSE)
mSet <- CalculateHyperScore(mSet)  # Combined ORA + topology

topo_results <- mSet$analSet$topo.mat

Pathview Visualization

library(pathview)

metabolite_data <- c('C00031' = 1.5, 'C00186' = 2.3, 'C00022' = 0.7)

pathview(cpd.data = metabolite_data,
         pathway.id = '00010',  # Glycolysis
         species = 'hsa',
         cpd.idtype = 'kegg',
         out.suffix = 'glycolysis_mapped')
# Output: hsa00010.glycolysis_mapped.png

KEGG Mapper (Direct API)

library(KEGGREST)

pathway_info <- keggGet('hsa00010')  # Glycolysis

kegg_ids <- c('C00031', 'C00186', 'C00022')

find_pathways <- function(kegg_id) {
    pathways <- keggLink('pathway', kegg_id)
    return(pathways)
}

all_pathways <- lapply(kegg_ids, find_pathways)

Network-Based Analysis

library(igraph)

build_network <- function(pathway_results) {
    edges <- data.frame()
    for (i in 1:nrow(pathway_results)) {
        pathway <- rownames(pathway_results)[i]
        metabolites <- strsplit(pathway_results$Metabolites[i], '; ')[[1]]
        for (met in metabolites) {
            edges <- rbind(edges, data.frame(from = met, to = pathway))
        }
    }
    g <- graph_from_data_frame(edges, directed  
    Vgtype  ifelseVgname  edgesfrom  
    g


network  build_networkpathway_results
plotnetwork vertex.size  ifelseVnetworktype    

Metabolite Set Enrichment (MSEA)

mSet <- InitDataObjects('conc', 'msetora', FALSE)
mSet <- SetMetaboliteFilter(mSet, FALSE)
mSet <- SetCurrentMsetLib(mSet, 'smpdb_pathway', 2)
mSet <- Setup.MapData(mSet, metabolites)
mSet <- CrossReferencing(mSet, 'hmdb')
mSet <- CalculateHyperScore(mSet)
msea_results <- mSet$analSet$ora.mat

Export Results

export_pathways <- function(results, output_file) {
    results_df <- as.data.frame(results)
    results_df$pathway <- rownames(results)
    results_df <- results_df[, c('pathway', 'Total', 'Expected', 'Hits',
                                   'Raw p', 'Holm adjust', 'FDR', 'Impact')]
    results_df <- results_df[order(results_df$FDR), ]
    write.csv(results_df, output_file, row.names = FALSE)
    return(results_df)
}

Parameters

Parameter	Default	Description
--method	ora	ora, qea, topology, msea, mummichog
--species	hsa	KEGG organism code
--id-type	hmdb	hmdb, kegg, name, pubchem
--fdr-cutoff	0.05	FDR threshold

Why This Exists

• Without it: Finding 50 significant metabolites offers loose biological intuition but lacks systemic proof
• With it: Validates functional perturbations by mapping features onto definitive KEGG/Reactome architectures
• Why OmicsClaw: Runs fast local enrichment caches utilizing rigorous topology metrics (Degree/Betweenness)

Workflow

1. Calculate: Map chemical IDs (HMDB/PubChem) to target Database indices.
2. Execute: Hypergeometric tests and pathway impact score calculations.
3. Assess: Perform FDR multiple testing adjustments.
4. Generate: Output structural network graphs.
5. Report: Tabulate key functionally enriched terms.

Example Queries

• "Perform KEGG pathway analysis on these significant metabolites"
• "Run mummichog enrichment directly on m/z features"

Output Structure

output_directory/
├── report.md
├── result.json
├── pathways.csv
├── figures/
│   ├── pathway_overview.png
│   ├── pathway_map.png
│   └── metabolite_network.png
├── tables/
│   ├── pathway_enrichment.csv
│   └── topology_scores.csv
└── reproducibility/
    ├── commands.sh
    ├── requirements.txt
    └── checksums.sha256

Safety

• Local-first: Local database matching where possible; transparent interactions for external APIs (like KEGG).
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• met-diff — Upstream source of significant feature hits

Version Compatibility

Reference examples tested with: MetaboAnalystR 4.0+, ReactomePA 1.46+

Dependencies

Required: numpy, pandas Optional: MetaboAnalystR (R), pathview (R), KEGGREST (R), igraph (R), ReactomePA (R)

Citations

• MetaboAnalyst — Pang et al., Nucleic Acids Research 2021
• pathview — Luo & Brouwer, Bioinformatics 2013
• mummichog — Li et al., PLoS Computational Biology 2013
• FELLA — Picart-Armada et al., PLoS Computational Biology 2018

Related Skills

• met-annotate — Identify metabolites first
• met-diff — Get significant metabolites for enrichment
• xcms-preprocess — Feature extraction upstream