Quantile Normalization Approach for Liquid Chromatography-Mass Spectrometry-based Metabolomic Data from Healthy Human Volunteers
Quantile Normalization Approach for Liquid Chromatography-Mass Spectrometry-based Metabolomic Data from Healthy Human Volunteers
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DOI：
10.2116/analsci.28.801
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发表时间：
2012-08-01
影响因子：
1.6
通讯作者：
Yoon, Young-Ran
中科院分区：
文献类型：
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作者：
Lee, Joomi;Park, Jeonghyeon;Yoon, Young-Ran
在代谢组学研究中，减少实验条件的系统误差是很重要的。为了确保来自不同研究的代谢组学数据具有可比性，有必要通过数据归一化来去除不需要的系统因素。几种归一化方法用于代谢组学数据，但最好的方法尚未确定。在这项研究中，为了减少非生物系统误差的变化，我们应用1-范数，2-范数和分位数归一化方法，以液相色谱-质谱（LC-MS）为基础的代谢组学数据，从健康志愿者口服环孢素（高剂量和低剂量）后的人尿液样本，并比较了三种方法的有效性。主成分分析（PCA）评分图显示更明显的分组后，分位数归一化后，比其他两种方法和归一化前的环孢素剂量。分位数归一化是一种简单有效的方法，可以减少基于LC-MS的人体代谢组学数据的非生物系统变异，揭示生物学变异。
In metabolomic research, it is important to reduce systematic error in experimental conditions. To ensure that metabolomic data from different studies are comparable, it is necessary to remove unwanted systematic factors by data normalization. Several normalization methods are used for metabolomic data, but the best method has not yet been identified. In this study, to reduce variation from non-biological systematic errors, we applied 1-norm, 2-norm, and quantile normalization methods to liquid chromatography-mass spectrometry (LC-MS)-based metabolomic data from human urine samples after oral administration of cyclosporine (high- and low-dose) in healthy volunteers and compared the effectiveness of the three methods. The principal component analysis (PCA) score plot showed more obvious groupings according to the cyclosporine dose after quantile normalization than after the other two methods and prior to normalization. Quantile normalization is a simple and effective method to reduce non-biological systematic variation from human LC-MS-based metabolomic data, revealing the biological variance.