期刊
SCIENTIFIC REPORTS
卷 6, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/srep38881
关键词
-
资金
- National Natural Science Foundation of China [81202459, 21505009, 21302102]
- Innovation Project on Industrial Generic Key Technologies of Chongqing [cstc2015zdcy-ztzx120003]
- Chongqing Graduate Student Research Innovation Project [CYB14027]
- Fundamental Research Funds for the Central Universities [CDJZR14468801, CDJKXB14011, 2015CDJXY]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据