Integrated workflow for univariate and multivariate evaluation of batch correction reliability

Assessing batch correction methods remains a major challenge in metabolomics, as no consensus currently exists for a generic and reliable evaluation strategy. Given the strong influence of batch effects on downstream statistical analyses, establishing a robust framework for their assessment is crucial to ensure result reproducibility and validity.

This study presents a comprehensive workflow that combines innovative numerical indicators and diagnostic plots to assess multiple dimensions of batch correction performance. It relies on a newly developed indicator, the Batch Conformity Index (BCI), a multivariate, covariance-aware metric quantifying within- and between-batch variability. Complementary visualization tools, including single and multiblock factorization methods, hierarchical clustering and convex hull representations, provide interpretable global diagnostics. These are complemented by compound-level analyses employing classical univariate metrics such as the coefficient of variation, and intra/inter-batch dispersion indices. The workflow also integrates chemistry-based validation via isotopic ratio consistency to ensure that corrections preserve true biochemical information, enabling the detection of potential overfitting or overcorrection.

The benefits offered by the proposed strategy were illustrated by comparing two widely used correction methods, i.e. LOESS and ComBat, applied to a large-scale serum metabolomics dataset. The results highlighted the complementary strengths and limitations of each method, successfully captured by the proposed workflow, thus providing an objective and interpretable basis for method evaluation.

The developed framework offers a unified strategy for evaluating batch correction reliability across multivariate, univariate, and chemical dimensions, representing a significant step toward standardized and reproducible metabolomics data harmonization.