metabolomics-pathway-enrichment

You can perform metabolomics pathway enrichment by running Over-Representation Analysis (ORA) on your metabolite list, which maps the differentially expressed metabolites to known metabolic pathways using hypergeometric tests and Benjamini-Hochberg FDR correction to identify significantly enriched biological functions.

In metabolomics pathway enrichment, Over-Representation Analysis (ORA) evaluates lists of significant metabolites, while Quantitative Enrichment Analysis (QEA) uses metabolite concentration data to directly map metabolic changes to known biological pathways across KEGG and Reactome databases.

Yes, metabolomics network analysis supports both KEGG and Reactome databases to perform pathway enrichment and generate network graphs that visualize metabolite-pathway interactions, integrating with MetaboAnalystR and pathview for comprehensive pathway mapping and visualization.

The best way to visualize metabolite-pathway interactions is to use network visualization tools that generate network graphs, integrating with MetaboAnalystR and pathview to map differentially expressed metabolites onto KEGG and Reactome pathways for comprehensive biological visualization.

Yes, metabolomics pathway enrichment supports topology-based analyses alongside ORA and QEA methods, integrating directly with MetaboAnalystR and pathview to perform comprehensive pathway mapping and visualization using KEGG and Reactome databases.

You should use hypergeometric tests with Benjamini-Hochberg FDR correction for pathway enrichment when you need to identify significantly enriched metabolic pathways from a list of differentially expressed metabolites while controlling the false discovery rate across multiple statistical tests.