Evaluation of Normalization Methods to Pave the Way Towards Large-Scale LC-MS-Based Metabolomics Profiling Experiments
Evaluation of Normalization Methods to Pave the Way Towards Large-Scale LC-MS-Based Metabolomics Profiling Experiments
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DOI：
10.1089/omi.2013.0010
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发表时间：
2013-09-01
影响因子：
3.3
通讯作者：
Berg, Maya
中科院分区：
文献类型：
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作者：
Ejigu, Bedilu Alamirie;Valkenborg, Dirk;Berg, Maya
结合液相色谱-质谱（LC-MS）为基础的代谢组学实验是在很长一段时间内收集的，由于LC-MS测量之间的系统变异性，仍然存在问题。到目前为止，LC-MS数据的大多数规范化方法都是模型驱动的，基于内部标准或中间质量控制运行，其中外部模型被外推到感兴趣的数据集。在本文的第一部分中，我们评估了几种现有的LC-MS代谢组学实验数据驱动的归一化方法，这些方法不需要使用内部标准。根据可变性度量，每种归一化方法的性能都相对较好，这表明使用任何归一化方法都将极大地改善源自多次实验运行的数据分析。在第二部分中，我们将循环-黄土归一化应用于利什曼原虫样本。这种归一化方法可以消除两个测量块之间随时间的系统性变异性，并保持差异代谢物。综上所述，标准化允许汇集来自不同测量块的数据集，并增加分析的统计能力，从而为增加LC-MS代谢组学实验的规模铺平了道路。根据我们的调查，如果只使用少数内部标准，我们建议使用数据驱动的规范化方法，而不是模型驱动的规范化方法。此外，数据驱动的规范化方法是对非靶向LC-MS实验数据集进行规范化的最佳选择。
Combining liquid chromatography-mass spectrometry (LC-MS)-based metabolomics experiments that were collected over a long period of time remains problematic due to systematic variability between LC-MS measurements. Until now, most normalization methods for LC-MS data are model-driven, based on internal standards or intermediate quality control runs, where an external model is extrapolated to the dataset of interest. In the first part of this article, we evaluate several existing data-driven normalization approaches on LC-MS metabolomics experiments, which do not require the use of internal standards. According to variability measures, each normalization method performs relatively well, showing that the use of any normalization method will greatly improve data-analysis originating from multiple experimental runs. In the second part, we apply cyclic-Loess normalization to a Leishmania sample. This normalization method allows the removal of systematic variability between two measurement blocks over time and maintains the differential metabolites. In conclusion, normalization allows for pooling datasets from different measurement blocks over time and increases the statistical power of the analysis, hence paving the way to increase the scale of LC-MS metabolomics experiments. From our investigation, we recommend data-driven normalization methods over model-driven normalization methods, if only a few internal standards were used. Moreover, data-driven normalization methods are the best option to normalize datasets from untargeted LC-MS experiments.