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. In in silico and/or experimental datasets, the robust methods, relative to several popular existing strategies, improved replicate concordance, and reduced drift and batch effects. The robust methods, with improved recovery of the underlying signal demonstrated in simulation, produced distinct differential abundance results, highlighting the impact of normalization on downstream statistical inference. Overall, tGAM-based normalization suggested the best performance across scenarios and is proposed as default choice. Metanorm is versatile, supporting normalization in metabolomics studies across scales and experimental setups.