Toward Automated Preprocessing of Untargeted LC-MS-Based Metabolomics Feature Lists from Human Biofluids

Maximizing the extraction of true, high-quality, nonredundant features from biofluids analyzed via LC-MS systems is challenging. Here, the R packages IPO and AutoTuner were used to optimize XCMS parameter settings for the retrieval of metabolite or lipid features in both ionization modes from either faecal or urine samples from two cohorts (