Concordance-Based Batch Effect Correction for Large-Scale Metabolomics.

Area of Science:

• Metabolomics
• Bioinformatics
• Data Science

Background:

• Large-scale metabolomics studies face batch effects due to long experimental durations.
• Existing batch effect correction (BEC) methods often rely on quality control (QC) samples or limited internal standards.
• Deteriorating QC sample quality and incomplete metabolome coverage limit current BEC approaches.

Purpose of the Study:

• To develop a novel, data-driven batch effect correction (BEC) method for large-scale metabolomics.
• To address limitations of QC-based and isotope-labeled standard methods.
• To introduce CordBat for robust BEC without requiring QC samples.

Main Methods:

• CordBat utilizes a reference batch to establish metabolite correlation coordinates.
• A Gaussian graphical model is constructed on combined reference and other batches.
• BEC is achieved by optimizing correction coefficients to harmonize metabolite correlations across batches.

Main Results:

• CordBat demonstrated effective batch effect removal across three real-world metabolomics datasets.
• The method successfully achieved concordance of metabolite correlations post-correction.
• CordBat performed comparably to QC-based methods and excelled in preserving biological effects.

Conclusions:

• CordBat offers an effective alternative for batch effect correction in large-scale metabolomics.
• The method is particularly valuable for studies lacking sufficient or stable QC samples.
• CordBat ensures data integrity and enhances the reliability of biological findings in metabolomics research.