Comprehensive Peak Characterization (CPC) in Untargeted LC-MS Analysis.

ABSTRACT: LC-MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the metho

SUBMITTER: Pirttila K 

PROVIDER: S-EPMC8878835 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature