Lee Joomi, Park Jeonghyeon, Lim Mi-sun, Seong Sook Jin, Seo Jeong Ju, Park Sung Min, Lee Hae Won, Yoon Young-Ran
Department of Biomedical Science and Clinical Trial Center, Kyungpook National University Graduate School and Hospital, Daegu, Korea.
Anal Sci. 2012;28(8):801-5. doi: 10.2116/analsci.28.801.
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.
在代谢组学研究中，减少实验条件下的系统误差很重要。为确保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去除不必要的系统因素。几种归一化方法用于代谢组学数据，但尚未确定最佳方法。在本研究中，为减少非生物系统误差引起的变异，我们将1-范数、2-范数和分位数归一化方法应用于健康志愿者口服环孢素（高剂量和低剂量）后人类尿液样本的基于液相色谱-质谱（LC-MS）的代谢组学数据，并比较了这三种方法的有效性。主成分分析（PCA）得分图显示，与其他两种方法及归一化之前相比，分位数归一化后根据环孢素剂量的分组更明显。分位数归一化是一种简单有效的方法，可减少基于人类LC-MS的代谢组学数据中的非生物系统变异，揭示生物变异。
