scientific-metabolomics-network

To build a metabolite network from KEGG and Reactome data, provide your metabolomics dataset to construct network graphs using glasso or pearson correlation methods, revealing metabolic relationships through reproducible Python code.

Hub metabolites are highly connected nodes within a metabolite network. You identify them by constructing a thresholded graph or GGM from your dataset and running hub detection analysis to pinpoint metabolites with high connectivity.

Yes, you can perform integrated pathway enrichment reporting on your metabolite correlation networks. The tool combines network construction with MetaboAnalyst enrichment to report enriched pathways across diverse metabolomics datasets.

Yes, the tool uses Gaussian Graphical Models (GGM) for partial correlation network construction. It applies glasso or pearson methods to threshold graphs and construct metabolite interaction networks from your data.

The best way to integrate Reactome pathway graphs with metabolomics data is to construct pathway graphs directly from Reactome, then overlay your metabolite correlations to identify network-level insights and metabolic relationships.

Thresholded graphs for metabolite correlation analysis rely on accurate input data and chosen thresholds. Use glasso for partial correlations to mitigate indirect connections, but network accuracy depends on dataset quality and parameter tuning.