PT - JOURNAL ARTICLE AU - Nasim Bararpour AU - Federica Gilardi AU - Cristian Carmeli AU - Jonathan Sidibe AU - Julijana Ivanisevic AU - Tiziana Caputo AU - Marc Augsburger AU - Silke Grabherr AU - Béatrice Desvergne AU - Nicolas Guex AU - Murielle Bochud AU - Aurelien Thomas TI - Visualization and normalization of drift effect across batches in metabolome-wide association studies AID - 10.1101/2020.01.22.914051 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.01.22.914051 4099 - http://biorxiv.org/content/early/2020/02/04/2020.01.22.914051.short 4100 - http://biorxiv.org/content/early/2020/02/04/2020.01.22.914051.full AB - 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.