INTRODUCTION The use of 2D NMR data sources (COSY in this paper) allows to reach general metabolomics results which are at least as good as the results obtained with 1D NMR data, and this with a less advanced and less complex level of pre-processing. But a major issue still exists and can largely slow down a generalized use of 2D data sources in metabolomics: the experiment duration. OBJECTIVE The goal of this paper is to overcome the experiment duration issue in our recently published MIC strategy by considering faster 2D COSY acquisition techniques: a conventional COSY with a reduced number of transients and the use of the Non-Uniform Sampling (NUS) method. These faster alternatives are all submitted to novel 2D pre-processing workflows and to Metabolomic Informative Content analyses. Eventually, results are compared to those obtained with conventional COSY spectra. METHODS To pre-process the 2D data sources, the Global Peak List (GPL) workflow and the Vectorization workflow are used. To compare this data sources and to detect the more informative one(s), MIC (Metabolomic Informative Content) indexes are used, based on clustering and inertia measures of quality. RESULTS Results are discussed according to a multi-factor experimental design (which is unsupervised and based on human urine samples). Descriptive PCA results and MIC indexes are shown, leading to the direct and objective comparison of the different data sets. CONCLUSION In conclusion, it is demonstrated that conventional COSY spectra recorded with only one transient per increment and COSY spectra recorded with 50% of non-uniform sampling provide very similar MIC results as the initial COSY recorded with four transients, but in a much shorter time. Consequently, using techniques like the reduction of the number of transients or NUS can really open the door to a potential high-throughput use of 2D COSY spectra in metabolomics.

中文翻译：

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将快速的2D NMR实验与新颖的预处理工作流程和代谢组学的MIC质量测量相结合。

简介使用2D NMR数据源（本文中的COSY）可以达到一般的代谢组学结果，其结果至少与使用1D NMR数据获得的结果一样好，而且预处理的水平较低且复杂程度较低。但是仍然存在一个主要问题，并且可能大大降低代谢组学中二维数据源的普遍使用：实验持续时间。目的本文的目的是通过考虑更快的二维COZY采集技术来克服我们最近发布的MIC策略中的实验持续时间问题：减少瞬态次数的常规COZY以及使用非均匀采样（NUS）方法。这些更快的选择都提交给了新颖的2D预处理工作流程和代谢组学信息含量分析。最终，将结果与使用常规COYY光谱获得的结果进行比较。方法为了预处理2D数据源，使用了全局峰列表（GPL）工作流和矢量化工作流。为了比较此数据源并检测更多信息，基于聚类和质量惯性度量，使用了MIC（代谢信息含量）指标。结果根据多因素实验设计（无监督且基于人尿液样品）讨论了结果。显示了描述性PCA结果和MIC索引，从而可以直接和客观比较不同的数据集。结论总而言之，结果表明，传统的COZY光谱每增量仅记录一个瞬态信号，而COZY光谱以50％的非均匀采样记录，其MIC结果与初始COZY四个瞬态记录的结果非常相似，但时间要短得多。因此，使用减少瞬态次数或NUS之类的技术确实可以为代谢组学中潜在的2D COZY光谱高通量使用打开大门。