Abstract Gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) are powerful techniques for measurement of all metabolites in complex metabolic samples. However, analyzing GC-MS and especially GC×GC-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 GC×GC-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 GC×GC-MS datasets by the means of modern chemometric methods. In fact, RMet transforms raw GC-MS and GC×GC-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 GC×GC-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 GC×GC-MS data sets but some steps can be skipped due to the lower size of GC-MS data sets. The software, its manual, sample data sets and source code are freely available on https://github.com/SUTChemometricsGroup/RMet .

中文翻译：

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RMet：一种基于 R 的自动化软件，用于分析 GC-MS 和 GC×GC-MS 非靶向代谢组学数据

摘要 气相色谱-质谱 (GC-MS) 和综合二维气相色谱-质谱 (GC×GC-MS) 是测量复杂代谢样品中所有代谢物的强大技术。然而，分析 GC-MS 尤其是 GC×GC-MS 代谢组学数据是代谢组学领域研究人员面临的主要挑战，主要是由于数据的复杂性和大数据量。在这方面，开发了一种名为 RMet 的基于 R 的自动化软件，以克服 GC-MS 和 GC×GC-MS 数据集代谢组学分析工作流程中的挑战。此外，它能够促进从这些数据集中提取可靠和有用的生物信息的复杂过程。而且，RMet 可以通过现代化学计量学方法大大加快大型 GC-MS 和 GC×GC-MS 数据集的耗时数据分析过程。事实上，RMet 将原始 GC-MS 和 GC×GC-MS 数据文件转换为重要（显着影响的代谢物）的洗脱图谱和质谱图，可以将其导入 NIST MS 搜索软件以最终鉴定这些代谢物。为了显示开发软件的性能，RMet 分析了先前报告的环境代谢组学研究的大型 GC×GC-MS 数据集，该研究针对暴露于新兴问题 (CEC) 污染物的生菜样品。使用 RMet 分析 GC-MS 代谢数据的过程与 GC×GC-MS 数据集相同，但由于 GC-MS 数据集较小，可以跳过某些步骤。软件，它的手册，