By Caimei Li, Senior Scientist at Creative Proteomics (LinkedIn). RUO. Last updated: January 2026
Exploratory ordinations are helpful, but they rarely tell the full biology story. If your metabolomics bioinformatics analysis still ends at PCA or a pretty PLS-DA plot, you're likely missing what translational teams need most: pathway analysis metabolomics that's transparent, metabolomics network analysis that's interpretable, and batch correction in metabolomics that preserves biology. This guide lays out an evidence-led roadmap—pathways, networks, and explainable multi-omics—paired with diagnostics, reporting, and reproducibility so results stand up across cohorts.
Key takeaways
- PCA/PLS-DA are for exploration; advanced means evidence-based interpretation plus reproducible workflows.
- Start with a clear question (discovery, mechanism, prediction) to choose methods and define credible outputs.
- Show diagnostics for batch correction, feature selection, and enrichment; avoid "cleaner plots" without evidence.
- Report mapping tables, database versions, and unmapped proportions to keep pathway claims honest.
- Package parameters, versions, seeds, and QC reports into a simple reproducibility pack.
Why PCA and PLS-DA stop being enough (and what "advanced" really means)
PCA and PLS-DA are great for spotting trends, but they can be dominated by batch/run-order drift, missingness patterns, and confounders. "Advanced" should mean interpretable and reproducible biology, not just heavier math. That includes transparent QC design, correction strategies matched to study design (known vs unknown variation), defensible feature selection with leakage prevention, pathway mapping with database/version transparency, network modules with stability checks, and explainable multi-omics integration.
According to the community QA/QC initiative mQACC and NIST, the strongest LC–MS untargeted workflows prioritize pooled QC design, system suitability, and reporting diagnostics rather than cosmetic plots; see the mQACC/NIST workshop overview in the 2023–2024 period for guidance in the Metabolomics QA/QC workshop report. Implementation examples (non-prescriptive): R (stats::prcomp; mixOmics::plsda) and Python (scikit-learn PCA/PLSRegression).
A roadmap for metabolomics bioinformatics analysis beyond PCA—focused on interpretable, reproducible outputs.
Start with the question: discovery, mechanism, or prediction?
Before touching models, decide what success looks like.
- Discovery: You're building a defensible story and a shortlist. Expect transparent mapping tables, enrichment outputs, and module summaries. Validation happens later, often with targeted assays.
- Mechanism: You want pathway/network narratives grounded in identified metabolites, assumptions, and confidence levels. Expect joint pathway evidence and module-level patterns.
- Prediction: You need leakage-proof evaluation with nested cross‑validation or holdouts, stability-aware feature selection, and interpretable model explanations.
Set these expectations upfront and stick to them. It's the easiest way to avoid overfitting and to keep outputs audit-ready.
Data readiness checklist: metadata, missingness, QC drift, and confounders
Metabolomics projects succeed or fail on data readiness. Minimums include:
- Complete metadata: sample IDs, timepoints, site, storage, run order, operator, batch/plate/instrument.
- QC design and evidence: pooled study QCs across runs, internal standards, blanks, and any reference materials.
- Missingness: describe patterns and imputation choices; avoid hiding MNAR mechanisms.
- Drift evidence: run-order trends in QC signals; feature-wise drift plots; PCA scores vs. injection order.
- Documentation: exclusions, reruns, SOPs, and criteria for acceptance.
Community efforts emphasize reporting diagnostics over universal thresholds. For LC–MS untargeted QA/QC context, see the mQACC and NIST guidance summarized in the 2023–2024 Metabolomics QA/QC workshop report.
Batch correction that preserves biology (without hiding problems)
When to correct and how to show it:
- Known batches with a balanced design: consider model-based adjustments such as ComBat (R: sva::ComBat) or limma::removeBatchEffect; report design balance and covariates.
- Unknown variation or confounding: consider SVA or RUV variants; protect biological variables during estimation and evaluate sensitivity.
- QC-based drift modeling: use pooled QC injections to correct run‑order drift per feature with LOESS/splines (QC‑RLSC/RSC) or ensemble methods like SERRF; keep per‑feature diagnostics.
Diagnostics to include (before/after): QC RSD distributions; run-order trend attenuation; PCA of QCs and study samples showing reduced drift axes with preserved group separation; sensitivity checks across ≥2 methods.
Example tool paths (non-prescriptive): R (sva::ComBat; limma::removeBatchEffect; ruv; stats::loess; SERRF web/CLI) and Python (neuroCombat/pyComBat; statsmodels LOESS; scikit‑learn-inspired forest approaches).
Good batch correction in metabolomics shows diagnostics and sensitivity checks, not just a cleaner plot.
Feature selection you can defend (and reproduce)
Feature selection strategies need to be defensible and replicable.
- Prevent leakage: perform selection inside cross‑validation folds; never pre‑filter using the full dataset when evaluating predictive performance.
- Control multiplicity: report FDR‑adjusted p‑values (e.g., Benjamini–Hochberg) for univariate screens.
- Stability awareness: bootstrap/subsample feature selection and report selection frequency; triangulate (e.g., univariate + LASSO + random forest) for confidence.
- Documentation: record parameters, seeds, and versions to enable re‑runs.
Example tools: R (tidymodels with nested CV; glmnet; ranger; caret) and Python (scikit‑learn pipelines with nested CV; mlxtend stability selection; SHAP for model explanations).
Pathway analysis in metabolomics: what it can (and cannot) prove
Pathway conclusions are only as strong as mapping quality and assumptions. Over‑representation analysis (ORA) is sensitive to background choice, database mismatch, and misidentification. Recommendations from the PLOS Computational Biology community synthesis emphasize defining assay‑matched backgrounds, using organism‑specific sets, and applying multiple testing correction at both metabolite and pathway levels; see Wieder et al. 2021 ORA recommendations. Always report mapping tables (compound IDs, MSI confidence), database/source versions, and the proportion of unmapped features to keep claims honest and reproducible.
When reporting, include MSI levels or analogous confidence fields and mzTab‑M‑style identifiers where possible; HMDB provides organism‑linked compound data with versioned releases.
Pathway analysis metabolomics results are strongest when mapping tables, assumptions, and database versions are reported.
Enrichment approaches: over-representation vs ranked methods (how to choose)
Choose the enrichment approach that matches your data and ID confidence.
- ORA (MetPA/MSEA style) fits targeted datasets with high-confidence IDs and reasonable coverage.
- Ranked or untargeted approaches (including mummichog-like methods) fit untargeted datasets with partial or putative IDs. Customize background sets to your detection universe and consider permutation strategies.
- Topology-aware or differential-correlation methods can add nuance when justified by design; document parameters and pathway libraries.
Practical path: MetaboAnalyst implements mummichog with m/z+RT and joint pathway analysis features documented in the 2024 NAR update; see the MetaboAnalyst 6.0 overview for capabilities.
Network analysis: from correlation hairballs to interpretable modules
Naive Pearson correlation networks often produce dense "hairballs" that are hard to interpret. Prefer sparse, more direct association structures using partial correlations or graphical lasso to reduce indirect edges. Then detect modules (communities), summarize them (module scores), and annotate with pathway enrichment.
Report parameters (regularization strength, filtering thresholds) and perform robustness checks (e.g., subsampling to compute consensus edges/modules). For method context in metabolomics networks, see the 2022 survey framing module detection and parameter transparency in metabolite graphs in Frontiers' metabolomics network review. Tooling examples: R (glasso, huge, qgraph; igraph for communities) and Python (sklearn.covariance.GraphicalLasso, networkx, leidenalg via igraph). Visualization: Cytoscape for module layouts.
Metabolomics network analysis is most interpretable when it identifies stable modules rather than dense hairballs.
Multi-omics integration that stays explainable (metabolomics + RNA/protein)
Explainable integration patterns keep stakeholders aligned.
- Pathway-level joining: Run separate differential analyses, then perform joint pathway analysis to identify convergent pathways impacted at metabolite and gene/protein levels.
- Module linking: Summarize modules and test for cross-omics concordance.
- Supervised multi-omics signatures: DIABLO (mixOmics) selects cross-omics correlated features to discriminate groups with interpretable plots.
Prerequisites: matched sample IDs/timepoints; harmonized metadata; consistent normalization/scaling across omes; neutral reporting of parameters and versions. For platform context on joint pathway analysis, see the MetaboAnalyst 6.0 overview and mixOmics/DIABLO literature.
A practical decision tree: which method to use, when, and what you get
Use this decision frame to pre-commit outputs and evidence.
- Goal: mechanism vs prediction.
- Sample size: small vs large.
- QC readiness: strong vs weak.
- ID coverage: low vs high.
Expected outputs by branch include robust univariate tables with FDR, pathway mapping with versions and unmapped proportions, network module summaries with stability notes, and joint pathway/multi-omics signatures.
A practical decision tree for choosing pathway, network, and multi-omics methods in metabolomics bioinformatics analysis.
Reproducibility pack: what to save so results can be rerun next year
Treat your analysis like it needs to be audited. Save:
- Software and package versions; environment lockfiles or containers (renv, conda, Docker).
- Parameters for preprocessing, correction, modeling, enrichment; random seeds for stochastic steps.
- Databases and versions used for annotation and pathways (e.g., HMDB 5.0; organism-specific KEGG/Reactome versions); mapping tables and unmapped proportions.
- QC reports (RSDs, run-order plots, PCA before/after), SOPs, and any reference material identifiers.
- Literate scripts (Rmarkdown/Jupyter) and processed data snapshots; consider modular frameworks that auto‑log steps (e.g., maplet).
This "pack" makes your metabolomics bioinformatics analysis rerunnable across cohorts and batches.
Request analysis output examples (CTA)
If you'd like to compare audit‑ready outputs—batch diagnostics, pathway mapping tables, network modules, and multi‑omics summaries—please Request analysis output examples.
Selected references (limited for readability)
- QA/QC program overview for LC–MS untargeted workflows: mQACC/NIST Metabolomics QA/QC workshop report (2023–2024).
- Pathway enrichment guidance: Wieder et al., PLOS Computational Biology 2021 — ORA recommendations.
- Joint pathway analysis capabilities: MetaboAnalyst 6.0 overview (Nucleic Acids Research 2024).
