Bio Metabolomics Pathway Mapping

Version Compatibility

Reference examples tested with: MetaboAnalystR 4.0+, FELLA 1.22+

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

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

The single most important input fact: whether the metabolites are confidently identified (KEGG/HMDB IDs) determines which method is even possible. An untargeted LC-MS feature table with no IDs cannot run ORA; it requires mummichog/PSEA. Verify the input type before choosing a tool.

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" -> Test whether a metabolite set or an m/z feature table is statistically enriched for biochemical pathways, given an explicit background.

• Identified compound list -> ORA / MSEA: CalculateOraScore() (MetaboAnalystR)
• Raw m/z peak table (no IDs) -> mummichog / PSEA: PerformPSEA() (MetaboAnalystR)
• Mechanism (which enzymes/reactions link the hits) -> network diffusion: runDiffusion() (FELLA)

The Single Most Important Modern Insight -- Pathway Enrichment Launders Annotation Uncertainty Into Confident Biology

Enrichment is the one workflow step where uncertainty is structurally destroyed: compounds enter as names with no error bars, and the hypergeometric/permutation machinery cannot represent "this is a 40%-confident guess." A pile of MSI-level-3 tentative annotations emerges as a p-value with three decimals. Wieder 2021 simulated this directly: even a 4% misidentification rate manufactured both false-positive and false-negative pathways across five real datasets, and real untargeted annotation is far worse than 4%. The errors do not average out, because a single wrong hub-adjacent compound (alanine, glutamate, a TCA intermediate) can flip a pathway by itself. No untargeted pathway claim can be stronger than its annotation layer. The honest ceiling is "features consistent with perturbation of pathway X co-varied with phenotype, conditional on the chosen annotations, background, database boundary, and ionization settings" -- never "pathway X is upregulated."

Two Starting Points -> Method -> Tool

The field's most common category error is conflating identified-compound enrichment with raw-feature activity prediction. They are disjoint entry points.

Mummichog exists because identification is the rate-limiter: only ~2-10% of untargeted features are ever confidently identified. It predicts network activity directly from the feature table, then the network context retro-prioritizes which annotation was probably right (Li 2013). Its existence is an admission of the annotation bottleneck, not a triumph -- use it knowing systems-level inference is bought with per-metabolite certainty.

ORA vs MSEA vs Topology vs Mummichog

ORA on an Identified Compound List

Goal: Test whether a list of confidently identified metabolites is over-represented in KEGG/SMPDB pathways, with a defensible background.

Approach: Map names/IDs to the internal library, set the pathway library and metabolome filter (the background), then run the hypergeometric score; report mapping coverage alongside p-values.

library(MetaboAnalystR)

# 'pathora' = pathway ORA; 'conc' = concentration-style input
mSet <- InitDataObjects('conc', 'pathora', FALSE)
mSet <- SetOrganism(mSet, 'hsa')

# Confidently identified compounds (MSI level 1-2); names, HMDB, or KEGG IDs
compounds <- c('Pyruvate', 'L-Lactate', 'Citrate', 'Succinate', 'Fumarate', 'L-Alanine')
mSet <- Setup.MapData(mSet, compounds)
mSet <- CrossReferencing(mSet, 'name')          # 'name' | 'hmdb' | 'kegg' | 'pubchem'
mSet <- CreateMappingResultTable(mSet)          # inspect mapping coverage before trusting any p-value

mSet <- SetKEGG.PathLib(mSet, 'hsa', 'current')

# SetMetabolomeFilter(mSet, TRUE) restricts the background to a user-supplied
# reference metabolome (the assay-coverage set). FALSE uses the whole library
# (all of KEGG) -- the inflated default that manufactures false positives.
mSet <- SetMetabolomeFilter(mSet, FALSE)
mSet <- CalculateOraScore(mSet, 'rbc', 'hyperg') # node-importance 'rbc'|'dgr'; test 'hyperg'|'fisher'

ora <- as.data.frame(mSet$analSet$ora.mat)       # columns include Raw p, FDR, Impact, Hits, Total

Mummichog / PSEA on a Raw m/z Peak Table

Goal: Predict perturbed pathway activity from an untargeted LC-MS feature table when no compound identities exist.

Approach: Declare instrument ppm and ionization mode, load the FULL feature table (m/z + p-value + t-score, optionally RT), set the query-defining p-cutoff, and run PSEA whose permutation null is sampled from R_all.

library(MetaboAnalystR)

mSet <- InitDataObjects('mass_all', 'mummichog', FALSE)
mSet <- SetPeakFormat(mSet, 'mpt')               # 'mpt' = m/z, p-value, t-score; 'mprt' adds RT (use with 'v2')

# ppm and ionization mode are chemistry-specific and mandatory; pos and neg use
# entirely different adduct tables. Mixed data needs a per-feature mode column.
mSet <- UpdateInstrumentParameters(mSet, 5.0, 'negative')

# CRITICAL: peaks.txt must be the ENTIRE feature table, not just significant peaks.
# The permutation null draws random feature lists from this file (R_all); supplying
# only significant features pre-enriches the pool and makes everything significant.
mSet <- Read.PeakListData(mSet, 'peaks.txt')
mSet <- SanityCheckMummichogData(mSet)

mSet <- SetPeakEnrichMethod(mSet, 'mum', 'v2')   # 'mum'|'gsea'|'integ'; 'v2' uses RT/empirical compounds
mSet <- SetMummichogPval(mSet, 0.2)              # query-defining cutoff; default is NOT 0.05 -- document it
mSet <- PerformPSEA(mSet, 'hsa_mfn', 'current', permNum = 1000) # library string encodes organism+network

psea <- mSet$mummi.resmat                         # predicted-active pathways; NOT a metabolite ID list

Network-Diffusion Enrichment (FELLA)

Goal: Return the intermediate enzymes, reactions, and modules that mechanistically link the affected metabolites, not just a ranked pathway list.

Approach: Build the KEGG knowledge graph once, then per-analysis map KEGG IDs and run heat diffusion; inspect excluded (unmapped) compounds explicitly.

library(FELLA)

# Build once, reuse. buildGraphFromKEGGREST hits the live KEGG API (slow); cache the DB.
graph <- buildGraphFromKEGGREST(organism = 'hsa')
buildDataFromGraph(keggdata.graph = graph, databaseDir = 'fella_hsa', internalDir = FALSE)
fella.data <- loadKEGGdata(databaseDir = 'fella_hsa', internalDir = FALSE)

cpd_ids <- c('C00022', 'C00186', 'C00158', 'C00042', 'C00122', 'C00041') # KEGG compound IDs only
analysis <- defineCompounds(compounds = cpd_ids, data = fella.data)
getExcluded(analysis)                              # compounds that did not map -- report this

# 'diffusion' is the recommended default; runHypergeom = plain ORA over the graph,
# runPagerank (lowercase r) = directed random walks. The method string is lowercase.
analysis <- runDiffusion(object = analysis, data = fella.data, approx = 'normality')
results <- generateResultsTable(object = analysis, data = fella.data, method = 'diffusion', threshold = 0.05)

Per-Method Failure Modes

Wrong background set (the silent controller)

• Trigger: ORA run with the full library ("all of KEGG"); mummichog run with only significant features as input.
• Mechanism: The background IS the null hypothesis made concrete. The KEGG-human library held ~3,373 compounds vs 286-1,110 actually measurable in real datasets; padding the denominator with undetectable compounds inflates every p-value. For mummichog, the permutation null samples from the input table, so a significant-only input pre-enriches the pool.
• Symptom: Many "significant" pathways; few survive once the background is the assay-specific metabolome (Wieder 2021: two of five datasets dropped to ZERO after FDR with the correct background).
• Fix: ORA -> SetMetabolomeFilter(mSet, TRUE) with the measured-metabolome reference. Mummichog -> supply the entire feature table as peaks.txt. State the background in one sentence or the p-values are uninterpretable.

Annotation laundering

• Trigger: ORA/MSEA run on MSI level-3 ("grey zone") tentative annotations as if they were level-1 confirmed.
• Mechanism: Enrichment cannot represent annotation confidence; a 4% misidentification rate already manufactures false pathways (Wieder 2021), and the error is not zero-mean because a wrong hub-adjacent compound flips a pathway alone.
• Symptom: Confident pathway claims downstream of unconfirmed IDs; results that do not replicate.
• Fix: Report the MSI level of the compounds driving the winning pathway; downgrade L3-driven claims to "consistent with." Consider metapone, which down-weights multiply-annotated features (weight inversely proportional to candidate count) instead of discarding the uncertainty.

Hub-inflated topology / "impact"

• Trigger: Reporting MetaboAnalyst Pathway Impact as if it were an effect size.
• Mechanism: Impact = sum of relative-betweenness centrality of matched metabolites / sum over all pathway metabolites, computed inside an arbitrary isolated KEGG boundary without removing currency metabolites. A handful of cofactor-like hubs dominate betweenness (Tsouka & Masoodi 2023: L-alanine alone = ~95% of a pathway's total centrality). It is also sign-blind.
• Symptom: A pathway "lights up" with high impact because one promiscuous compound was hit by chance; the same hits give different impact in KEGG vs SMPDB.
• Fix: Treat impact as a visualization tiebreaker only. Read ORA and topology against each other; a pathway impact-driven by a single hub is a red flag, not a confirmation.

Pool size is not flux

• Trigger: Reporting "pathway X is activated / upregulated" from concentration-based enrichment.
• Mechanism: A metabolomics measurement is a steady-state pool size (production minus consumption), not a rate. Pool and flux can move in opposite directions: sildenafil RAISES the cGMP pool while LOWERING flux through it (it inhibits the degrading phosphodiesterase). A falling substrate pool can mean the pathway is MORE active.
• Symptom: Causal/activity language ("upregulated pathway") drawn from a concentration snapshot.
• Fix: Downgrade to "members of pathway X co-varied with phenotype." Activity claims require stable-isotope-resolved metabolomics (SIRM / 13C metabolic flux analysis), which traces label incorporation over time; concentration-based enrichment generates a flux hypothesis, never tests one.

Quantitative Thresholds

Common Errors

References

• Li S, Park Y, Duraisingham S, Strobel FH, Khan N, Soltow QA, Jones DP, Pulendran B. 2013. Predicting network activity from high throughput metabolomics. PLoS Comput Biol 9(7):e1003123.
• Pang Z, Lu Y, Zhou G, Hui F, Xu L, Viau C, Spigelman AF, MacDonald PE, Wishart DS, Li S, Xia J. 2024. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res 52(W1):W398-W406.
• Xia J, Wishart DS. 2010. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res 38(W):W71-W77.
• Picart-Armada S, Fernandez-Albert F, Vinaixa M, Yanes O, Perera-Lluna A. 2018. FELLA: an R package to enrich metabolomics data. BMC Bioinformatics 19(1):538.
• Wieder C, Frainay C, Poupin N, Rodriguez-Mier P, Vinson F, Cooke J, Lai RPJ, Bundy JG, Jourdan F, Ebbels T. 2021. Pathway analysis in metabolomics: recommendations for the use of over-representation analysis. PLOS Comput Biol 17(9):e1009105.
• Wieder C, Bundy JG, Frainay C, Poupin N, Rodriguez-Mier P, Vinson F, Cooke J, Lai RPJ, Jourdan F, Ebbels TMD. 2022. Avoiding the misuse of pathway analysis tools in environmental metabolomics. Environ Sci Technol 56(20):14219-14222.
• Karp PD, Midford PE, Caspi R, Khodursky A. 2021. Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics. BMC Genomics 22:191.
• Tsouka S, Masoodi M. 2023. Metabolic pathway analysis: advantages and pitfalls for the functional interpretation of metabolomics and lipidomics data. Biomolecules 13(2):244.
• Tian L, Yu T. 2022. Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data. Bioinformatics 38(14):3662-3669.
• Barupal DK, Fiehn O. 2017. Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets. Sci Rep 7:14567.
• Schymanski EL, Jeon J, Gulde R, Fenner K, Ruff M, Singer HP, Hollender J. 2014. Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ Sci Technol 48(4):2097-2098.

Related Skills

• metabolomics/metabolite-annotation - Annotation confidence levels (MSI) that feed ORA/MSEA and set the interpretive ceiling
• metabolomics/statistical-analysis - Upstream differential testing that produces the significant compound or feature list
• metabolomics/isotope-tracing - Flux versus pool: enrichment infers activity from steady-state abundance, which isotope labeling can contradict
• pathway-analysis/go-enrichment - Gene-set over-representation concepts (the ORA analogue for genes)
• pathway-analysis/gsea - Ranked-list enrichment concepts (the MSEA analogue for genes)
• multi-omics-integration/mofa-integration - Joint gene+metabolite integration and its coverage-asymmetry traps