A comprehensive automatic data analysis strategy for gas chromatography-mass spectrometry based untargeted metabolomics - Zhang, Yu-Ying; Zhang, Qian; Zhang, Yue-Ming; Wang, Wei-Wei; Zhang, Li; Yu, Yong-Jie; Bai, Chang-Cai; Guo, Ji-Zhao; Fu, Hai-Yan; She, Yuanbin (Journal of Chromatography A, 2020)
•We propose a comprehensive data analysis workflow for GC-MS-based metabolomics.•An automatic TIC peak detection and resolution methodology is proposed.•A new time-shift correction and component registration algorithm is developed.•A MATLAB GUI is developed based on the developed strategy for users. Automatic data analysis for gas chromatography-mass spectrometry (GC-MS) is a challenging task in untargeted metabolomics. In this work, we provide a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (autoGCMSDataAnal) by developing a new automatic strategy for performing TIC peak detection and resolution and proposing a novel time-shift correction and component registration algorithm. autoGCMSDataAnal uses original acquired GC-MS datafiles as input to automatically perform TIC peak detection, component resolution, time-shift correction and component registration, statistical analysis, and compound identification. We utilize standards and complex plant samples to comprehensively investigate the performance of autoGCMSDataAnal. The results suggest that the developed strategy is comparable with several state-of-the-art methods that are widely used in GC-MS-based untargeted metabolomics. Based on the proposed strategy, we develop a user-friendly MATLAB GUI for users who are unfamiliar with programming languages to facilitate their routine analysis, which can be freely downloaded at: http://software.tobaccodb.org/software/autogcmsdataanal.