bio-metabolomics-pathway-mapping

1

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot 43270938365c

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

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

# 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

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')