Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis.
Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis.
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DOI：
10.1038/srep38881
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发表时间：
2016-12-13
影响因子：
4.6
通讯作者：
Zhu F
中科院分区：
文献类型：
--
作者：
Li B;Tang J;Yang Q;Cui X;Li S;Chen S;Cao Q;Xue W;Chen N;Zhu F
在非目标代谢组学分析中，多种因素（例如，不需要的实验和生物学变异以及技术错误）可能会阻碍差异代谢特征的识别，这需要在特征选择之前采用数据驱动的标准化方法。迄今为止，≥16种归一化方法已广泛应用于处理基于LC/MS的代谢组学数据。然而，这些方法的性能和样本量依赖性尚未得到详尽的比较，并且没有提供用于比较和全面评估所有 16 种归一化方法的性能的在线工具。本研究对这些方法进行了综合比较。结果，16 种方法根据不同样本量的标准化表现被分为三组。 VSN、对数变换和 PQN 被认为是标准化性能最佳的方法，而 Contrast 在不同基准数据的所有子数据集上始终表现不佳。此外，还构建了一个交互式网络工具，全面评估 16 种专门用于标准化基于 LC/MS 的代谢组学数据的方法的性能，并将其托管在 http://server.idrb.cqu.edu.cn/MetaPre/ 上。总之，本研究可以为分析基于 LC/MS 的代谢组学数据时选择合适的归一化方法提供有用的指导。
In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.
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