Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from – either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

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代谢组学中的数据标准化策略：当前的挑战、方法和工具

数据标准化是定量代谢组学方法中的一大挑战，无论是靶向还是非靶向。如果没有适当的标准化，质谱和光谱数据可能会提供错误的、次优的数据，这会导致误导和混淆生物学结果，从而导致无法应用于人类医疗保健、临床和其他研究途径。为了解决这个问题，文献中提出了许多统计方法和软件工具，并多年来实施，从而提供了多种方法可供选择——基于样本或基于数据的归一化策略。近年来，用于代谢组学数据标准化的新专用软件工具也出现了。在这篇帐户文章中，