MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

Area of Science:

• Biochemistry
• Systems Biology
• Computational Biology

Background:

• Modern analytical techniques like LC-MS, GC-MS, and NMR enable simultaneous measurement of numerous metabolites.
• Non-targeted metabolomic studies generate vast datasets, posing challenges in identifying key metabolites and their dynamics.
• Existing software often lacks centralized clustering and user-driven focus for time-course metabolomic data analysis.

Purpose of the Study:

• To present an interactive software tool, MetaboClust, for time-course metabolomic data analysis.
• To introduce a dynamic, user-centric workflow for clustering metabolites with visual feedback.
• To demonstrate the application of MetaboClust in analyzing plant metabolomic time-course data.

Main Methods:

• Development of the MetaboClust software package for interactive time-course analysis.
• Implementation of a user-centric workflow including data correction, time-profile generation, and statistical analysis.
• Application of unbiased metabolite clustering to identify trends and facilitate pathway analysis.

Main Results:

• Demonstrated the utility of MetaboClust through two LC-MS time-course case studies on plants.
• Showcased a dynamic, user-driven clustering approach with intrinsic visual feedback.
• Enabled unbiased grouping of metabolites, leading to scoring of metabolic pathways based on observed trends.

Conclusions:

• MetaboClust provides an effective interactive platform for time-course metabolomic data analysis.
• The software facilitates unbiased metabolite clustering and pathway analysis, aiding biological discovery.
• The user-centric workflow enhances the exploration and interpretation of complex metabolomic datasets.