MetabolomicsEngine: Pure Python Plasma Metabolomics with PLS-DA and KEGG Pathway Enrichment
Introduction
Metabolomics has emerged as a powerful approach for biomarker discovery, disease mechanism elucidation, and drug target identification [1]. While tools such as MetaboAnalyst provide comprehensive analysis pipelines, they require R installation and web access. MetabolomicsEngine provides a self-contained Python implementation of core metabolomics algorithms.
Methods
Differential Metabolite Analysis
For each metabolite, we apply the Mann-Whitney U test (non-parametric, robust to non-normality) between case and control groups. P-values are corrected using the Benjamini-Hochberg procedure. Significance threshold: FDR<0.05 and |log2FC|>0.5.
Principal Component Analysis
PCA is performed on autoscaled data (mean-centered, unit-variance scaled) using NumPy SVD. The first two principal components are visualized as a score plot.
PLS-DA via NIPALS
The NIPALS (Nonlinear Iterative Partial Least Squares) algorithm iteratively extracts latent variables (LVs) that maximize covariance between X (metabolite matrix) and y (class labels):
- Initialize weight vector w = X^T y / ||X^T y||
- Compute scores t = Xw
- Deflate: X = X - t p^T, y = y - t (t^T y / t^T t)
- Repeat for each LV
Classification uses nearest centroid in LV space. 5-fold stratified cross-validation estimates generalization accuracy.
KEGG Pathway Enrichment
For each of 12 metabolic pathways (TCA cycle, glycolysis, amino acid metabolism, fatty acid metabolism, purine/pyrimidine metabolism, ketone body metabolism, tryptophan metabolism, bile acid metabolism, phospholipid metabolism, gut microbiome metabolites, redox metabolism), we apply the hypergeometric test:
P(X ≥ k) = 1 - HyperGeom.cdf(k-1, N, K, n)where N=total metabolites, K=significant metabolites, n=pathway size, k=significant in pathway.
Results
Applied to a synthetic plasma metabolomics dataset (120 samples: 60 case, 60 control; 200 metabolites with realistic log-normal distributions and known differential effects):
Differential Metabolites: 33 significant metabolites (FDR<0.05, |log2FC|>0.5). Top hits include Cysteine (log2FC=+2.29), reflecting oxidative stress in cases, and multiple TCA cycle intermediates.
PCA: PC1 explains 5.7% and PC2 explains 2.4% of variance, with partial separation between groups visible in the score plot.
PLS-DA: 5-fold cross-validation achieves 100% accuracy, demonstrating strong discriminatory power of the metabolite panel. The NIPALS algorithm converges in 2 latent variables.
Pathway Enrichment: Tryptophan metabolism shows the strongest enrichment signal (p=0.26 after FDR correction), with 2/6 pathway members significant. The modest enrichment reflects the synthetic data design.
Conclusion
MetabolomicsEngine provides a complete, dependency-light metabolomics analysis pipeline. The pure NumPy NIPALS implementation is particularly valuable as a reference for understanding PLS-DA mechanics.
References
[1] Wishart et al. (2022) HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Research 50:D622-D631. [2] Wold et al. (2001) PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58:109-130.
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: MetabolomicsEngine version: 1.0.0 description: Plasma metabolomics analysis with PLS-DA and KEGG pathway enrichment allowed-tools: Bash(pip install *), Bash(python3 *), Bash(git clone *) --- # MetabolomicsEngine Skill ## Setup ```bash pip install numpy scipy pandas matplotlib scikit-learn git clone https://github.com/junior1p/MetabolomicsEngine cd MetabolomicsEngine ``` ## Run ```bash python3 metabolomics_engine.py ``` ## Expected Output ``` [MetabolomicsEngine] Generating synthetic plasma metabolomics dataset... Dataset: 120 samples (60 case, 60 control), 200 metabolites [MetabolomicsEngine] Differential metabolite analysis... Significant metabolites (FDR<0.05, |log2FC|>0.5): 33 Top metabolite: Cysteine (log2FC=2.29, FDR=0.0000) [MetabolomicsEngine] PCA... PC1: 5.7%, PC2: 2.4% [MetabolomicsEngine] PLS-DA (5-fold CV)... PLS-DA 5-fold CV accuracy: 100.0% [MetabolomicsEngine] KEGG pathway enrichment... Top pathway: Tryptophan metabolism [MetabolomicsEngine] Done in ~2s ``` ## Output Files - `metabo_output/differential_metabolites.csv` — all metabolite statistics - `metabo_output/pathway_enrichment.csv` — KEGG enrichment results - `metabo_output/metabo_dashboard.png` — 6-panel visualization - `metabo_output/summary.json` — key metrics
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