Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST.

Area of Science:

• Metabolomics
• Computational Biology
• Bioinformatics

Background:

• Untargeted metabolomics generates large datasets requiring efficient analysis.
• Organizing and prioritizing metabolite features is a significant challenge.
• Current methods often rely on mass fragmentation-based spectral similarity grouping.

Purpose of the Study:

• To present msFeaST, a feature-set testing and visualization workflow for LC-MS/MS untargeted metabolomics data.
• To streamline the organization and prioritization of metabolite features.
• To integrate experimental data with mass-spectral structural information for enhanced analysis.

Main Methods:

• Utilized k-medoids clustering for spectral similarity-based feature grouping.
• Applied feature-set testing using the globaltest package for group-wise statistical analysis.
• Developed an interactive workflow for integrating experimental and spectral data.

Main Results:

• msFeaST enables statistically assessing differential abundance patterns for metabolite feature groups.
• The workflow leverages spectral clustering to group potentially related metabolites.
• Provides enhanced prioritization of features and feature sets in exploratory data analysis.

Conclusions:

• msFeaST revolutionizes untargeted metabolomics by automating feature organization and prioritization.
• The workflow enhances the interpretation of metabolomics data through integrated analysis.
• Facilitates the discovery of meaningful biological insights from complex datasets.