Robust metabolomics data normalization across scales and experimental designs

Metabolomics studies employing liquid chromatography-mass spectrometry are affected by signal drift and batch effects, introducing technical variance that impedes biological knowledge discovery. Quality control (QC) sample-based normalization strategies are widely implemented but remain vulnerable to outliers, thereby reducing normalization performance. We introduce rLOESS, rGAM and tGAM, three robust normalization methods that improve resistance to outliers by downweighting or accommodating them. Leveraging additive models, the rGAM and tGAM methods allow flexible non-linear modeling, differential sample weighting, and data-driven QC representativeness evaluation. Implementations of these methods are gathered in the Metanorm R package, integrating robust normalization with visualization for performance verification, while supporting efficient parallel processing. Spanning in silico and experimental datasets, the robust methods, relative to existing strategies, consistently demonstrated a reduction in false positive and false negative differentially abundant metabolites, improved replicate concordance, and reduced batch effects. Metanorm is versatile, supporting normalization in metabolomics studies across scales and experimental setups.