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To ensure the \u2018thorough\u2019 removal of unwanted variations, the collective consideration of multiple criteria (\u2018intragroup variation\u2019, \u2018marker stability\u2019 and \u2018classification capability\u2019) was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were \u2018first\u2019 discovered to be non-CWP. \u2018Then\u2019, 21 new strategies that combined the \u2018sample\u2019-based method with the \u2018metabolite\u2019-based one were found to be CWP. \u2018Finally\u2019, a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.<\/jats:p>","DOI":"10.1093\/bib\/bbz137","type":"journal-article","created":{"date-parts":[[2019,10,8]],"date-time":"2019-10-08T19:26:32Z","timestamp":1570562792000},"page":"2142-2152","source":"Crossref","is-referenced-by-count":49,"title":["A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies"],"prefix":"10.1093","volume":"21","author":[{"given":"Qingxia","family":"Yang","sequence":"first","affiliation":[{"name":"Ph.D. candidates of Zhejiang University, China, and jointly cultivated by the School of Pharmaceutical Sciences in Chongqing University, China. Their main research interests include OMICs-based bioinformatics and statistical metabolomics"}]},{"given":"Jiajun","family":"Hong","sequence":"additional","affiliation":[{"name":"Ph.D. candidates of Zhejiang University, China, and jointly cultivated by the School of Pharmaceutical Sciences in Chongqing University, China. Their main research interests include OMICs-based bioinformatics and statistical metabolomics"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Ph.D. candidates of Zhejiang University, China, and jointly cultivated by the School of Pharmaceutical Sciences in Chongqing University, China. Their main research interests include OMICs-based bioinformatics and statistical metabolomics"}]},{"given":"Weiwei","family":"Xue","sequence":"additional","affiliation":[{"name":"Ph.D. candidates of Zhejiang University, China, and jointly cultivated by the School of Pharmaceutical Sciences in Chongqing University, China. Their main research interests include OMICs-based bioinformatics and statistical metabolomics"}]},{"given":"Song","family":"Li","sequence":"additional","affiliation":[{"name":"Ph.D. candidates of Zhejiang University, China, and jointly cultivated by the School of Pharmaceutical Sciences in Chongqing University, China. Their main research interests include OMICs-based bioinformatics and statistical metabolomics"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[{"name":"Ph.D. candidates of Zhejiang University, China, and jointly cultivated by the School of Pharmaceutical Sciences in Chongqing University, China. Their main research interests include OMICs-based bioinformatics and statistical metabolomics"}]},{"given":"Feng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Ph.D. candidates of Zhejiang University, China, and jointly cultivated by the School of Pharmaceutical Sciences in Chongqing University, China. 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