NOREVA

NOREVA evaluates normalization methods for MS-based metabolomics data by integrating multiple evaluation criteria and enabling sequential QC-based corrections to reduce technical variation.

Key Features:

• Multi-Criteria Evaluation: Integrates five well-established evaluation criteria grounded in distinct theoretical frameworks to provide a holistic assessment of normalization methods for metabolomics data.
• Extensive Normalization Methods: Provides an extensive suite of available normalization methods and supports sequential quality control-based correction using QC metabolites prior to normalization to remove overall unwanted variations.
• Quality Control Integration: Enables corrections based on quality control (QC) samples and QC metabolites before normalization to ensure data reflect biological variation rather than technical artifacts in MS-based datasets.
• Validation and Reliability: Algorithms have been validated through case studies involving five benchmark datasets, demonstrating reliable identification of well-performing normalization methods across diverse datasets.

Scientific Applications:

• Metabolic profiling: Improves normalization in metabolic profiling studies to enhance detection of true biological signals in MS-based metabolomics.
• Biomarker discovery: Supports selection of normalization methods that increase robustness and reproducibility in biomarker discovery workflows.
• Comparative studies: Facilitates comparability of metabolomic measurements across cohorts and studies by reducing technical variability.
• Data analysis workflow refinement: Informs workflow decisions by evaluating and ranking normalization methods based on multiple criteria.

Methodology:

Operates on MS-based metabolomics data by integrating five evaluation criteria grounded in distinct theoretical frameworks and enabling sequential QC-based corrections using QC metabolites prior to normalization.