pathway analysis
Definition
Pathway analysis in metabolomics is a computational approach that maps experimentally measured metabolite changes onto known biochemical pathways to identify biological processes affected by experimental conditions. This method integrates quantitative metabolomics data with curated pathway databases (KEGG, Reactome, MetaCyc) to determine which metabolic routes are significantly enriched or perturbed. By contextualizing individual metabolite alterations within their broader biochemical networks, pathway analysis reveals coordinated changes in metabolism, identifies rate-limiting steps, and uncovers regulatory mechanisms. It employs statistical methods including over-representation analysis (ORA), functional class scoring, and topology-based approaches that consider pathway structure and metabolite positions to prioritize biologically meaningful pathways over random fluctuations.
Visualize pathway analysis in Nodes Bio
Researchers can visualize metabolic pathway analysis results as interactive network graphs in Nodes Bio, where nodes represent metabolites and edges show enzymatic reactions or regulatory relationships. Significantly altered pathways can be highlighted with color-coding based on enrichment scores, while node size reflects fold-change magnitude. This enables intuitive exploration of metabolic flux changes, identification of pathway crosstalk, and discovery of key regulatory metabolites at pathway branch points.
Visualization Ideas:
- Metabolic pathway networks with differentially abundant metabolites highlighted by statistical significance
- Multi-pathway interaction maps showing crosstalk between carbohydrate, lipid, and amino acid metabolism
- Time-course metabolic networks displaying temporal progression of pathway perturbations
Example Use Case
A pharmaceutical team investigating drug-induced liver toxicity performs untargeted metabolomics on hepatocyte samples treated with a candidate compound. Pathway analysis reveals significant enrichment in glutathione metabolism, fatty acid β-oxidation, and the TCA cycle. Network visualization shows depleted glutathione conjugates alongside elevated lipid peroxidation products, indicating oxidative stress. The team identifies N-acetylcysteine as a potential co-therapy and modifies the drug structure to reduce reactive metabolite formation, ultimately improving the safety profile before clinical trials.