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

We developed “”, a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. “” 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, “” assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how “” efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.