Fully automatic resolution of untargeted GC-MS data with deep learning assistance.

Journal: Talanta
Published Date: Mar 26, 2022

Abstract

DeepResolution (Deep learning-assisted multivariate curve Resolution) has been proposed to solve the co-eluting problem for GC-MS data. However, DeepResolution models must be retrained when encountering unknown components, which is undoubtedly time-consuming and burdensome. In this study, a new pipeline named DeepResoution2 was proposed to overcome these limitations. DeepResolution2 utilizes deep neural networks to divide the profile into segments, estimate the number of components in each segment, and predict the elution region of each component. Subsequently, the information obtained by these deep learning models is used to assist the multivariate curve resolution procedure. Only seven models (1 + 1 + 5) are required to automate the whole analysis procedure of untargeted GC-MS data, which is an important improvement over DeepResolution. These seven models are stable and universal. Once established, they can be used to resolve most GC-MS data. Compared with MS-DIAL, ADAP-GC, and AMDIS, DeepResolution2 can obtain more reasonable mass spectra, chromatograms and peak areas to identify and quantify compounds. DeepResoution2 (0.955) outperformed AMDIS (0.939), MS-DIAL (0.948) and ADAP-GC (0.860) in terms of the linear correlation between concentrations and peak areas on overlapped peaks in fatty acid dataset. In real biological samples of human male infertility plasma, the peak areas and mass spectra of 136 untargeted GC-MS files were automatically extracted by DeepResolution2 without any prior information and manual intervention. DeepResolution2 includes all the functions for analyzing untargeted GC-MS datasets from the feature extraction of raw data files to the establishment of discriminant models.

Authors

  • Xiaqiong Fan
    College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, P. R. China.
  • Zhenbo Xu
    College of Chemistry and Chemical Engineering, Central South University, Changsha, China.
  • Hailiang Zhang
    Department of Urology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
  • Dabiao Liu
    College of Chemistry and Chemical Engineering, Central South University, Changsha, China.
  • Qiong Yang
    Institute of Modern Physics, Chinese Academy of Science, Lanzhou 730000, China.
  • Qiaotao Tao
    College of Chemistry and Chemical Engineering, Central South University, Changsha, China.
  • Ming Wen
    College of Chemistry and Chemical Engineering, Central South University, Changsha, People's Republic of China.
  • Xiao Kang
    College of Chemistry and Chemical Engineering, Central South University, Changsha, China.
  • Zhimin Zhang
    School of Control Science and Engineering, Shandong University, Jinan, People's Republic of China. School of Information Technology and Electrical Engineering, University of Queensland, Queensland, Australia.
  • Hongmei Lu
    College of Chemistry and Chemical Engineering, Central South University, Changsha, People's Republic of China.

Keywords

External Resources

Popular Topics
Recent Journals