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Nucleic Acids Res. 2017 Jul 3;45(W1):W162-W170. doi: 10.1093/nar/gkx449.

NOREVA: normalization and evaluation of MS-based metabolomics data.

Li B1, Tang J1, Yang Q1,2, Li S1, Cui X1, Li Y1, Chen Y3, Xue W1, Li X1, Zhu F1,2.

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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.
Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore.


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

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