NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Area of Science:

• Metabolomics
• Bioinformatics
• Systems Biology

Background:

• Biological processes are dynamic, requiring metabolic variation monitoring over time.
• Metabolomics studies increasingly involve multi-class (N>2) rather than case-control (N=2) designs.
• Effective data normalization is critical for accurate analysis of time-course and multi-class metabolomics data.

Purpose of the Study:

• To develop and evaluate a tool for assessing normalization method performance in time-course and multi-class metabolomics.
• To address the lack of available tools for evaluating normalization in complex metabolomics study designs.

Main Methods:

• Updated NOREVA software to version 2.0.
• Integrated 144 normalization methods using a combination strategy.
• Assessed a total of 168 normalization methods, significantly expanding previous capabilities.
• Validated the tool's performance using benchmark datasets and case studies.

Main Results:

• NOREVA 2.0 enables normalization and evaluation for both time-course and multi-class metabolomic data.
• Identified well-performing normalization methods through comprehensive assessment.
• Demonstrated the tool's significance and effectiveness in case studies.

Conclusions:

• NOREVA 2.0 is a valuable tool for selecting appropriate normalization methods in advanced metabolomics research.
• It serves as an essential complement to existing bioinformatics tools for metabolomics data analysis.
• The tool enhances the reliability and interpretability of time-course and multi-class metabolomic studies.