RMet: An automated R based software for analyzing GC-MS and GCxGC-MS untargeted metabolomic data

Gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS) are powerful techniques for measurement of all metabolites in complex metabolic samples. However, analyzing GC-MS and especially GCxGC-MS metabolomic data is a major challenge to the researchers in the field of metabolomics mainly due to the complexity and large data size. In this regard, an automated R based software entitled RMet has been developed to overcome the challenges in the metabolomic analysis workflow of GC-MS and GCxGC-MS data sets. Additionally, it is able to facilitate the complex process of extracting reliable and useful biological information from these data sets. Moreover, RMet can greatly accelerate the time-consuming data analysis process of large GC-MS and GCxGC-MS datasets by the means of modem chemometric methods. In fact, RMet transforms raw GC-MS and GCxGC-MS data files into the elution profiles and mass spectra of important (significantly affected metabolites) which can be imported into NIST MS search software for the final identification of these metabolites. To show the performance of the developed software, large GCxGC-MS data sets of a previously reported environmental metabolomics study on lettuce samples exposed to contaminants of emerging concerns (CECs) were analyzed by RMet. The procedure for analyzing GC-MS metabolic data with RMet is as same as GCxGC-MS data sets but some steps can be skipped due to the lower size of GCMS data sets. The software, its manual, sample data sets and source code are freely available on https://github.com/SUTChemometricsGroup/RMet.