Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis. Use when interpreting metabolomics results in the context of biochemical pathways.
Reference examples tested with: ReactomePA 1.46+, clusterProfiler 4.10+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Map my metabolites to pathways" → Perform pathway enrichment and topology analysis using KEGG, Reactome, or MetaboAnalyst to interpret metabolomics results in biochemical context.
MetaboAnalystR::SetMetabolomeFilter() → PerformDetailMatch() → pathway topologylibrary(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)
# 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
# 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
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)
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)
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
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)$type == 'pathway', 15, 5))
# MSEA using predefined metabolite sets
mSet <- InitDataObjects('conc', 'msetora', FALSE)
# Use SMPDB (Small Molecule Pathway Database)
mSet <- SetMetaboliteFilter(mSet, FALSE)
mSet <- SetCurrentMsetLib(mSet, 'smpdb_pathway', 2)
mSet <- Setup.MapData(mSet, metabolites)
mSet <- CrossReferencing(mSet, 'hmdb')
mSet <- CalculateHyperScore(mSet)
msea_results <- mSet$analSet$ora.mat
# Integrated pathway analysis (metabolites + genes)
library(IMPaLA)
# Prepare gene list
genes <- c('HK1', 'PFKM', 'ALDOA') # Glycolysis enzymes
# Prepare metabolite list
metabolites <- c('HMDB0000122', 'HMDB0000190') # Glucose, Lactate
# Joint pathway analysis
# (Use MetaboAnalyst joint pathway analysis or custom integration)
# Format for publication
export_pathways <- function(results, output_file) {
results_df <- as.data.frame(results)
results_df$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')