A hierarchical approach to removal of unwanted variation for large-scale metabolomics data - Taiyun Kim (Nature Portfolio, 2021)
Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.