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

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 methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow.

Automated summary

LC-MS-based untargeted metabolomics heavily relies on automated peak detection and data preprocessing algorithms to handle complex and large raw data. However, current algorithms often generate numerous false positive peaks, complicating downstream data processing and analysis. To address this issue, we introduce the CPC algorithm, which allows automated characterization of detected peaks and filtering of low-quality peaks using quality criteria familiar to analytical chemists. In practical application, the algorithm significantly reduces the number of false positive peaks detected by XCMS.