Normalizing and Correcting Variable and Complex LC-MS Metabolomic Data with the R Package pseudoDrift.

ABSTRACT: In biological research domains, liquid chromatography-mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC-MS metabolomic data. Using data simulation, we demonstrate here that our appr

SUBMITTER: Rodriguez J 

PROVIDER: S-EPMC9144304 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature