bio-metabolomics-pathway-mapping

name	bio-metabolomics-pathway-mapping
description	Map metabolites to KEGG, Reactome, and SMPDB pathways and run enrichment/topology analysis with MetaboAnalyst. Use when: user asks which pathways are affected, wants pathway enrichment from a metabolite list, needs to visualize metabolites on KEGG maps, or interpret metabolomics results biologically. Triggers: pathway enrichment, pathway analysis, KEGG pathway, MetaboAnalyst, which pathways, pathway topology, over-representation analysis, metabolite set enrichment, MSEA, pathway impact, map metabolites to pathways.
tool_type	r
primary_tool	MetaboAnalystR
upstream	{"repo":"https://github.com/GPTomics/bioSkills","license":"MIT","original_author":"GPTomics"}

Version Compatibility

Reference examples tested with: ReactomePA 1.46+, clusterProfiler 4.10+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Metabolomics Pathway Mapping

"Map my metabolites to pathways" → Perform pathway enrichment and topology analysis using KEGG, Reactome, or MetaboAnalyst to interpret metabolomics results in biochemical context.

• R: MetaboAnalystR::SetMetabolomeFilter() → PerformDetailMatch() → pathway topology

KEGG Pathway Enrichment

library(MetaboAnalystR)

# Initialize MetaboAnalyst
mSet <- InitDataObjects('conc', 'pathora', FALSE)

# Set organism
mSet <- SetOrganism(mSet, 'hsa')  # Human

# Load metabolite list (HMDB IDs or compound names)
metabolites <- c('HMDB0000001', 'HMDB0000005', 'HMDB0000010')  # Example HMDB IDs
# Or use names: c('Glucose', 'Lactate', 'Pyruvate')

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', 'hyperg')  # Over-representation

# Get results
pathway_results <- mSet$analSet$ora.mat
print(pathway_results)

Quantitative Enrichment Analysis (QEA)

# For continuous data (fold changes or concentrations)
mSet <- InitDataObjects('conc', 'pathqea', FALSE)
mSet <- SetOrganism(mSet, 'hsa')

# Load data with values
metabolite_data <- data.frame(
    compound = c('Glucose', 'Lactate', 'Pyruvate'),
    fc = c(1.5, 2.3, 0.7)  # Fold changes
)

mSet <- Setup.MapData(mSet, metabolite_data)
mSet <- CrossReferencing(mSet, 'name')

# QEA analysis
mSet <- SetKEGG.PathLib(mSet, 'hsa', 'current')
mSet <- CalculateQeaScore(mSet, 'rbc', 'gt')

# Results
qea_results <- mSet$analSet$qea.mat

Topology-Based Analysis

# Considers pathway structure (betweenness, degree)
mSet <- InitDataObjects('conc', 'pathinteg', FALSE)
mSet <- SetOrganism(mSet, 'hsa')

mSet <- Setup.MapData(mSet, metabolites)
mSet <- CrossReferencing(mSet, 'hmdb')

# Topology analysis
mSet <- SetKEGG.PathLib(mSet, 'hsa', 'current')
mSet <- SetMetabolomeFilter(mSet, FALSE)
mSet <- CalculateHyperScore(mSet)  # Combined ORA + topology

topo_results <- mSet$analSet$topo.mat

Reactome Pathways

library(ReactomePA)
library(clusterProfiler)

# Convert to Reactome IDs (if available)
reactome_ids <- c('R-HSA-70171', 'R-HSA-1428517')  # Example

# Enrichment
enriched <- enrichPathway(gene = reactome_ids, organism = 'human', pvalueCutoff = 0.05)
print(enriched)

KEGG Mapper (Direct API)

library(KEGGREST)

# Get pathway information
pathway_info <- keggGet('hsa00010')  # Glycolysis

# Map compounds to pathways
kegg_ids <- c('C00031', 'C00186', 'C00022')  # Glucose, Lactate, Pyruvate

# Find pathways containing these compounds
find_pathways <- function(kegg_id) {
    pathways <- keggLink('pathway', kegg_id)
    return(pathways)
}

all_pathways <- lapply(kegg_ids, find_pathways)

Pathway Visualization

library(pathview)

# Visualize KEGG pathway with metabolite data
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

Network-Based Analysis

Goal: Visualize metabolite-pathway relationships as a bipartite network for identifying pathway crosstalk and hub metabolites.

Approach: Extract metabolite-pathway edges from enrichment results, build an igraph network, and annotate nodes by type for interactive visualization.

library(igraph)

# Build metabolite-pathway network
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 = FALSE)

    # Add attributes
    V(g)$type <- ifelse(V(g)$name %in% edges$from, 'metabolite', 'pathway')

    return(g)
}

network <- build_network(pathway_results)
plot(network, vertex.size = ifelse(V(network)$type == 'pathway', 15, 5))

Metabolite Set Enrichment

# MSEA using predefined metabolite sets
mSet <- InitDataObjects('conc', 'msetora', FALSE)

# Use SMPDB (Small Molecule Pathway Database)
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

Combine with Gene Expression

# Integrated pathway analysis (metabolites + genes)
library(IMPaLA)

# Prepare gene list
genes <- c('HK1', 'PFKM', 'ALDOA')  # Glycolysis enzymes

# Prepare metabolite list
metabolites <- c('HMDB0000122', 'HMDB0000190')  # Glucose, Lactate

# Joint pathway analysis
# (Use MetaboAnalyst joint pathway analysis or custom integration)

Export Results

# Format for publication
export_pathways <- function(results, output_file) {
    results_df <- as.data.frame(results)
    results_df$pathway <- rownames(results)

    # Select relevant columns
    results_df <- results_df[, c('pathway', 'Total', 'Expected', 'Hits',
                                   'Raw p', 'Holm adjust', 'FDR', 'Impact')]

    # Sort by FDR
    results_df <- results_df[order(results_df$FDR), ]

    write.csv(results_df, output_file, row.names = FALSE)
    return(results_df)
}

export_pathways(pathway_results, 'pathway_enrichment.csv')

Related Skills

• metabolite-annotation - Identify metabolites first
• statistical-analysis - Get significant metabolites
• pathway-analysis/kegg-pathways - Similar enrichment concepts for genes