scientific-metabolomics

To identify differential metabolites from LC-MS data, you can run an end-to-end metabolomics analysis pipeline that applies Pareto scaling, PLS-DA with VIP scoring, and Fisher-exact pathway enrichment to pinpoint biomarker candidates.

The best way to validate PLS-DA results for metabolomics datasets is using permutation-based validation, which tests the statistical significance of your multivariate model and ensures the separation between sample groups is not random.

To visualize metabolite correlation networks and differential metabolites, you can generate volcano plots and boxplot visualizations directly from the statistical results of the metabolomics analysis pipeline to interpret metabolic pathway changes.

Yes, this metabolomics analysis pipeline supports GC-MS and NMR studies alongside LC-MS datasets, providing standardized quality control, feature filtering, and pathway enrichment across multiple analytical platforms.

You can integrate HMDB and Metabolomics Workbench to perform pathway enrichment by running the automated metabolomics pipeline, which queries these databases to map identified metabolites and interpret associated metabolic pathways.