HolobiomicsLab Metabolomics Statistics and Biomarker Discovery
Summary
End-to-end metabolomics statistics: from a raw feature table to normalized data, multivariate structure, differential features, pathway context, and ranked biomarkers.
When to use
Use when you have a metabolomics feature/quant table and want a statistically rigorous comparison and candidate biomarkers — cleaning and normalization, multivariate analysis, differential features, enrichment/pathway analysis, and biomarker selection with ROC.
When NOT to use
- The data is not LC-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — clean_normalize
Goal: clean + normalize the feature table
EDAM operation: operation_3435
Inputs: feature-table · Outputs: feature-table
Candidate leaf skills: metabolite-feature-normalization-across-batches (primary), metabolite-feature-intensity-normalization, metabolomics-feature-transformation, metabolomics-data-normalization, metabolite-feature-matrix-manipulation
Tools (primary): R, Jupyter Notebook, Google Colab, FBMN-STATS web app
Other candidate tools: R ≥4.1.2, OUKS (Omics Untargeted Key Script), MAI package, MetCorR, GetFeatistics, ggplot2, XCMS, MS-Dial, dbnorm, sva, ber, pcaMethods, limma, impute, BiocParallel, Biobase, mixOmics, statTarget, MInfer, MetaboAnalyst, NormalizeMets, RStudio, NormQcmets, LogTransform, MissingValues, RlaPlots, PcaPlots
Grounding: 7 KB(s); DOIs: 10.1007/s11306-018-1347-7, 10.1007/s12561-013-9081-1, 10.1016/j.cmpb.2025.108672, 10.1021/acs.jproteome.1c00392 …
Stage 2 — multivariate
Goal: multivariate analysis (PCA / PLS-DA / OPLS-DA, VIP)
EDAM operation: operation_3659
Inputs: feature-table · Outputs: tsv
Candidate leaf skills: multivariate-statistical-analysis-metabolomics (primary), multivariate-ordination-analysis, metabolomics-feature-selection-significance-filtering, multivariate-ordination-analysis-nmds-pca, principal-component-analysis-for-metabolomics
Tools (primary): R, Jupyter Notebook, Google Colab, MZmine3
Other candidate tools: vegan, MetaboDirect, R prcomp, ggplot2 (R), Python, mbpls, pandas, numpy, scikit-learn, matplotlib, MAMSI, MamsiStructSearch, Python 3.8, R 4.0.2, seaborn, SYNCSA, ggpubr, factoextra, ggplot2, metaboprep
Grounding: 5 KB(s); DOIs: 10.1021/acs.analchem.5c01327, 10.1038/s41596-024-01046-3, 10.1093/bioinformatics/btac059/6522114, 10.1186/s40168-023-01476-3 …
Stage 3 — differential
Goal: differential feature analysis (univariate, volcano, FDR)
EDAM operation: operation_3659
Inputs: feature-table · Outputs: tsv
Candidate leaf skills: metabolite-feature-anova-analysis (primary), fold-change-calculation, multiple-testing-correction-and-p-value-adjustment, fold-change-calculation-metabolomics, fold-change-calculation-across-groups
Tools (primary): margheRita, R, MS-DIAL
Other candidate tools: Python (pandas, NumPy, SciPy), R (base stats, tidyverse, or similar), pandas, NumPy, SciPy, edgeR.R, DESeq2, RankProd, ggplot2, ComplexHeatmap, edgeR, RankProduct, LargeMetabo, pytest, fermo_core
Grounding: 6 KB(s); DOIs: 10.1021/acs.analchem.4c05039, 10.1038/s41467-024-50111-8, 10.1093/bib/bbac455, 10.1093/bioadv/vbae175 …
Stage 4 — enrichment_pathway
Goal: enrichment + pathway / functional analysis
EDAM operation: operation_3928
Inputs: tsv · Outputs: tsv
Candidate leaf skills: metabolite-set-enrichment-analysis (primary), metabolite-kegg-pathway-enrichment, untargeted-metabolomics-feature-analysis, metabolic-network-mapping, pathway-metabolite-mapping-integration
Tools (primary): R, fgsea, readr, readxl, KEGG, enrichmet, KEGGREST, igraph
Other candidate tools: ggplot2, KEGG_Enrich_PlotPanel, Enrichment, KEGG_Enrich_Plot, Mummichog 3, metDataModel, JMS, mass2chem
Grounding: 3 KB(s); DOIs: 10.1093/bib/bbac455, 10.1101/2025.08.28.672951v2, 10.1371/journal.pcbi.1003123
Stage 5 — biomarker
Goal: biomarker selection + ROC / importance
EDAM operation: operation_3659
Inputs: tsv, tsv · Outputs: tsv
Candidate leaf skills: feature-importance-ranking (primary), model-performance-evaluation-roc-curves, variable-importance-ranking-and-interpretation, random-forest-classification-for-metabolite-prediction, metabolomic-biomarker-pathway-association
Tools (primary): MeTEor, R
Other candidate tools: randomForest, ggplot2, Omu, read.metabo, igraph, KEGG_Enrich_PlotPanel, Enrichment, KEGG_Enrich_Plot
Grounding: 4 KB(s); DOIs: 10.1007/978-3-319-47656-8_6, 10.1093/bib/bbac455, 10.1093/bioadv/vbae178, 10.1128/mra.00129-19
Grounding
Each stage carries the kb_slugs/dois of the leaves it draws on. Ground any stage against its source paper with the collection's /ground command or bin/perspicacite_kb_bind.py (Perspicacité KB; serverless local-clone fallback).
Verification contract
workflow.yaml is gradable by asb solve-workflow (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.
Provenance
Generated by compose_workflows.py (semantic binding + EDAM-aware primary selection). derived_from_workflows lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via release_gate.py.