Feature Extraction for LC–MS via Hierarchical Density Clustering

Abstract

Liquid chromatography coupled with mass spectrometry (LC–MS) is a popular analytical platform for metabolomic studies. Accurate and sensitive feature detection is a key step before further analysis. It is still challenging due to the large quantity and high complexity of LC–MS data sets. Pure ion chromatogram (PIC) consists of ions produced from metabolite without interferences. Therefore, hierarchical density-based spatial clustering of applications with noise (HDBSCAN) was applied to extract PICs from LC to MS data sets in this study. Since metabolites generate high-density and continuous ions in both m/z and elution time axes, HDBSCAN can cluster ions of the same metabolite into the same group and avoid the definition of m/z tolerance. Compared to centWave and PITracer, the proposed method achieved higher recall and comparable levels of precision for feature detection on simulated, MM48 and Arabidopsis thaliana (L.) Heynh data sets. It was implemented in Python and opensourced at http://www.github.com/zmzhang/HPIC .