Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCL), Voie du Roman Pays 20, bte L1.04.01, 1348, Louvain-la-Neuve, Belgium.
Machine Learning Group, Université Catholique de Louvain (UCL), 1348, Louvain-la-Neuve, Belgium.
Metabolomics. 2020 Mar 18;16(4):42. doi: 10.1007/s11306-020-01662-6.
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.
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.
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 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.
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.
使用 2D NMR 数据源（本文中的 COSY）可以获得至少与使用 1D NMR 数据获得的结果一样好的综合代谢组学结果，并且预处理的水平较低且不复杂。但是，仍然存在一个主要问题，并且在很大程度上阻碍了 2D 数据源在代谢组学中的广泛应用：实验时间。
本文的目的是通过考虑更快的 2D COSY 采集技术来克服我们最近发表的 MIC 策略中的实验时间问题：具有更少的瞬变数的常规 COSY 和使用非均匀采样（NUS）方法。这些更快的替代方案都经过了新的 2D 预处理工作流程和代谢组学信息内容分析的处理。最终，将结果与常规 COSY 光谱的结果进行比较。
为了预处理 2D 数据源，使用全局峰列表（GPL）工作流程和矢量化工作流程。为了比较这些数据源并检测更具信息量的数据源，使用基于聚类和质量惯性度量的代谢组学信息含量（MIC）指数。
根据多因素实验设计（该设计是无监督的，并且基于人类尿液样本）讨论结果。显示了描述性 PCA 结果和 MIC 指数，从而可以直接和客观地比较不同的数据组。
总之，已经证明，仅使用每个增量一个瞬变记录的常规 COSY 光谱和使用 50％非均匀采样记录的 COSY 光谱与初始记录的 COSY 光谱具有非常相似的 MIC 结果，但是在更短的时间内完成。因此，使用减少瞬变次数或 NUS 等技术确实可以为在代谢组学中潜在的高通量使用 2D COSY 光谱开辟大门。
