期刊
ANALYTICAL SCIENCES
卷 28, 期 8, 页码 801-805出版社
JAPAN SOC ANALYTICAL CHEMISTRY
DOI: 10.2116/analsci.28.801
关键词
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资金
- Korea Health 21 RD Project [A070001]
- National Project for Personalized Genomic Medicine [A111218-PG02]
- Ministry of Health & Welfare, Republic of Korea
- Basic Science Research Program through the National Research Foundation of Korea (NRF)
- Ministry of Education, Science and Technology, Republic of Korea [2010-0022996]
- National Research Foundation of Korea [2010-0022996] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In metabolomic research, it is important to reduce systematic error in experimental conditions. To ensure that metabolomic data from different studies are comparable, it is necessary to remove unwanted systematic factors by data normalization. Several normalization methods are used for metabolomic data, but the best method has not yet been identified. In this study, to reduce variation from non-biological systematic errors, we applied 1-norm, 2-norm, and quantile normalization methods to liquid chromatography-mass spectrometry (LC-MS)-based metabolomic data from human urine samples after oral administration of cyclosporine (high- and low-dose) in healthy volunteers and compared the effectiveness of the three methods. The principal component analysis (PCA) score plot showed more obvious groupings according to the cyclosporine dose after quantile normalization than after the other two methods and prior to normalization. Quantile normalization is a simple and effective method to reduce non-biological systematic variation from human LC-MS-based metabolomic data, revealing the biological variance.
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