A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis.
A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis.
复制标题
代谢组学的数据预处理策略，以减少数据分析中的掩模效应。
DOI：
10.3389/fmolb.2015.00004
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发表时间：
2015
影响因子：
5
通讯作者：
Xu G
中科院分区：
文献类型：
--
作者：
Yang J;Zhao X;Lu X;Lin X;Xu G
开发了一种数据预处理策略，以科普数据分析中大量代谢物高度变异的缺失值和掩蔽效应。提出了一种新的测量方法--“x-VAST”法。应用上述策略，拯救了几种低丰度掩蔽的差异代谢物。代谢组学是一个蓬勃发展的研究领域。它的成功高度依赖于通过比较不同的数据集（例如，患者与对照组）发现差异代谢物。挑战之一是组间低丰度代谢物的差异常常被丰度代谢物的高变异所掩盖。为了解决这一挑战，本研究提出了一种新的数据预处理策略，包括三个步骤。在步骤1中，使用“修改的80%”规则来减少缺失值的影响;在步骤2中，使用单位方差和帕累托缩放方法来减少来自丰富代谢物的掩蔽效应。在步骤3中，为了修复缩放的不利影响，使用从强度信息和类别信息推导出的变量的稳定性信息来为变量分配合适的权重。当应用于来自慢性肝炎B患者研究的基于LC/MS的代谢组学数据集和两个模拟数据集时，发现掩蔽效应被部分消除，并且拯救了几个新的低丰度差异代谢物。
Developed a data preprocessing strategy to cope with missing values and mask effects in data analysis from high variation of abundant metabolites. A new method- ‘x-VAST’ was developed to amend the measurement deviation enlargement. Applying the above strategy, several low abundant masked differential metabolites were rescued. Highlights Metabolomics is a booming research field. Its success highly relies on the discovery of differential metabolites by comparing different data sets (for example, patients vs. controls). One of the challenges is that differences of the low abundant metabolites between groups are often masked by the high variation of abundant metabolites. In order to solve this challenge, a novel data preprocessing strategy consisting of three steps was proposed in this study. In step 1, a ‘modified 80%’ rule was used to reduce effect of missing values; in step 2, unit-variance and Pareto scaling methods were used to reduce the mask effect from the abundant metabolites. In step 3, in order to fix the adverse effect of scaling, stability information of the variables deduced from intensity information and the class information, was used to assign suitable weights to the variables. When applying to an LC/MS based metabolomics dataset from chronic hepatitis B patients study and two simulated datasets, the mask effect was found to be partially eliminated and several new low abundant differential metabolites were rescued.