Metabolite-Investigator: an integrated user-friendly workflow for metabolomics multi-study analysis

Area of Science:

• Metabolomics
• Bioinformatics
• Systems Biology

Background:

• Metabolic dysregulation underlies many diseases, driving interest in high-throughput metabolomics.
• Integrating multi-cohort metabolomics data is crucial for robust findings but challenging due to data variability.

Purpose of the Study:

• To develop a scalable workflow, Metabolite-Investigator, for comprehensive quantitative metabolomics data analysis across multiple studies.
• To provide a user-friendly interface for data integration, pre-processing, and advanced analytical methods.

Main Methods:

• Developed Metabolite-Investigator, a web-tool and Shiny-app, for quantitative metabolomics data.
• Implemented features for data integration, cleaning, transformation, and batch correction.
• Included uni- and multivariable association analyses, network analysis, and factor prioritization.

Main Results:

• Metabolite-Investigator supports end-to-end analysis of multi-cohort metabolomics data.
• The tool facilitates identification of cohort-specific interactions and common covariate models.
• A graphical user interface enables intuitive data exploration and analysis.

Conclusions:

• Metabolite-Investigator offers a scalable and integrated solution for multi-study metabolomics research.
• The open-source tool enhances reproducibility and facilitates discovery in metabolic disease research.
• Accessible via web and as a stand-alone app, promoting widespread adoption.