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首页> 美国卫生研究院文献>Frontiers in Molecular Biosciences >A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis
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A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis

机译：代谢组学的数据预处理策略可减少数据分析中的掩盖效应

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>Highlights class="unordered" style="list-style-type:disc">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.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.
机译：>突出显示 class =“ unordered” style =“ list-style-type：disc”> <！-list-behavior = unordered prefix-word = mark-type = disc max-label-size = 0-> 开发了一种数据预处理策略，以应对丰富代谢物的高变化数据分析中的缺失值和掩盖效应。 开发了一种新方法“ x-VAST” 采用上述策略，挽救了几种低丰度掩盖的微分代谢物。 Metabolomics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）来发现差异代谢物。挑战之一是，群体之间低丰度代谢物的差异通常被丰度代谢物的高变化所掩盖。为了解决这一挑战，本研究提出了一种由三个步骤组成的新型数据预处理策略。在第1步中，使用“修改后的80％”规则来减少缺失值的影响；在第2步中，使用单位方差和Pareto缩放方法来减少大量代谢物的掩盖效应。在第3步中，为了解决缩放的不利影响，从强度信息和类信息得出的变量的稳定性信息用于为变量分配合适的权重。当应用于来自慢性乙型肝炎患者研究的基于LC / MS的代谢组学数据集和两个模拟数据集时，发现掩盖效应被部分消除，并且拯救了几种新的低丰度差异代谢物。

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