Integrated workflow for univariate and multivariate evaluation of batch correction reliability

Area of Science:

• Metabolomics
• Bioinformatics
• Data Science

Background:

• Assessing batch correction methods in metabolomics lacks a standardized evaluation strategy.
• Batch effects significantly impact downstream statistical analyses, compromising reproducibility and validity.
• A robust framework for batch effect assessment is crucial for reliable metabolomics data.

Purpose of the Study:

• To present a comprehensive workflow for evaluating batch correction performance in metabolomics.
• To introduce innovative numerical indicators and diagnostic plots for multi-dimensional assessment.
• To ensure the reliability and validity of metabolomics data harmonization.

Main Methods:

• Developed a novel Batch Conformity Index (BCI) for multivariate, covariance-aware variability quantification.
• Utilized visualization tools like factorization methods, hierarchical clustering, and convex hulls for global diagnostics.
• Integrated univariate metrics and chemistry-based validation (isotopic ratio consistency) for compound-level and biochemical integrity assessment.

Main Results:

• Demonstrated the workflow's utility by comparing LOESS and ComBat batch correction methods on a large serum metabolomics dataset.
• Effectively captured the complementary strengths and limitations of different correction methods.
• Provided an objective and interpretable basis for evaluating batch correction strategies.

Conclusions:

• The developed framework offers a unified strategy for evaluating batch correction reliability.
• Addresses multivariate, univariate, and chemical dimensions for comprehensive assessment.
• Represents a significant advancement towards standardized and reproducible metabolomics data harmonization.