NOREVA: normalization and evaluation of MS-based metabolomics data

By Jing Tang, Qingxia Yang, Shuang Li, Xuejiao Cui, Yinghong Li, Yuzong Chen, Weiwei Xue, Xiaofeng Li, Feng Zhu. Nucleic Acids Res., 2017

332 citations recorded by the source corpus.

Abstract 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/.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/70bc86c9ec0bad094f2aae9eac91ec4a69ff8dc8

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In Molecular Biology

1. Jasmine Chong
2. Jianguo Xia
3. Shuzhao Li
4. Zhiqian Pang
5. David S. Wishart
6. Guangyan Zhou
7. Igor Sinelnikov

In Spectroscopy

1. Jasmine Chong
2. Shuzhao Li
3. Jianguo Xia
4. Zhiqian Pang
5. David S. Wishart

Citations in the corpus

• MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics (2020, background): “Over the course of spectral collection, chromatographic conditions can change and baselines can drift [17].”