Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China.
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Nucleic Acids Res. 2017 Jul 3;45(W1):W162-W170. doi: 10.1093/nar/gkx449.
Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.
基于质谱的代谢组学数据中存在多种形式的不需要的信号变化，这些变化会降低代谢物分析的准确性。已经开发了多种归一化方法来解决这个问题。然而，它们的性能差异很大，并且严重依赖于所研究数据的性质。此外，由于实际数据的复杂性，通过单一标准评估方法的性能是不可行的。因此，我们开发了 NOREVA，以便从多个角度评估各种归一化方法的性能。NOREVA 集成了五个成熟的标准（每个标准都有不同的基本理论），以确保比任何单一标准更全面的评估。它提供了最完整的可用归一化方法集，具有独特的功能，可以根据质量控制代谢物去除整体不需要的变化，并允许基于质量控制样本的顺序校正，然后再进行数据归一化。NOREVA 的创新性和算法的可靠性通过对五个基准数据集的案例研究得到了广泛验证。总之，NOREVA 因其能够考虑多个标准来识别性能良好的归一化方法而与众不同，并且可以成为其他可用工具的不可或缺的补充。NOREVA 可以在 http://server.idrb.cqu.edu.cn/noreva/ 上免费访问。
