Metabolomics is becoming a mature part of analytical chemistry as evidenced by the growing number of publications and attendees of international conferences dedicated to this topic. Yet, a systematic treatment of the fundamental structure and properties of metabolomics data is lagging behind. We want to fill this gap by introducing two fundamental theories concerning metabolomics data: data theory and measurement theory. Our approach is to ask simple questions, the answers of which require applying these theories to metabolomics. We show that we can distinguish at least four different levels of metabolomics data with different properties and warn against confusing data with numbers. This treatment provides a theoretical underpinning for preprocessing and postprocessing methods in metabolomics and also argues for a proper match between type of metabolomics data and the biological question to be answered. The approach can be extended to other omics measurements such as proteomics and is thus of relevance for a large analytical chemistry community.

中文翻译：

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代谢系统的数值表示

代谢组学正在成为分析化学的成熟部分，这一主题的国际会议的出版物和参加者的数量不断增加证明了这一点。但是，对代谢组学数据的基本结构和性质的系统处理仍然落后。我们想通过介绍有关代谢组学数据的两种基本理论来填补这一空白：数据理论和测量理论。我们的方法是提出简单的问题，要想回答这些问题，就需要将这些理论应用于代谢组学。我们表明，我们可以区分具有不同属性的至少四个不同水平的代谢组学数据，并警告不要将数据与数字混淆。这种治疗为代谢组学中的预处理和后处理方法提供了理论基础，并主张在代谢组学数据类型和要回答的生物学问题之间进行适当匹配。该方法可以扩展到其他蛋白质组学测量方法，例如蛋白质组学，因此与大型分析化学界相关。