Abstract As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and identification of metabolites having regulatory effect in various biological processes. While MS-based metabolomics assays are endowed with high-throughput and sensitivity, large-scale MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of true biologically relevant changes. We developed “ dbnorm ”, a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. “ dbnorm ” integrates advanced statistical tools to inspect dataset structure, at both macroscopic (sample batch) and microscopic (metabolic features) scales. To compare model performance on data correction, “ dbnorm ” assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how “ dbnorm ” efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.

Visualization and normalization of drift effect across batches in metabolome-wide association studies