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Quantile Normalization Approach for Liquid Chromatography— Mass Spectrometry-based Metabolomic Data from Healthy Human Volunteers

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Abstract

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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Authors and Affiliations

  1. Department of Biomedical Science and Clinical Trial Center, Kyungpook National University Graduate School and Hospital, Daegu, Korea

    Joomi Lee, Jeonghyeon Park, Mi-Sun Lim, Sook Jin Seong, Jeong Ju Seo, Sung Min Park, Hae Won Lee & Young-Ran Yoon

  2. BK21 Program, Kyungpook National University School of Medicine, Daegu, Korea

    Joomi Lee, Jeonghyeon Park, Sook Jin Seong, Jeong Ju Seo & Young-Ran Yoon

Authors
  1. Joomi Lee
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  2. Jeonghyeon Park
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  3. Mi-Sun Lim
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  4. Sook Jin Seong
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  5. Jeong Ju Seo
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  6. Sung Min Park
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  7. Hae Won Lee
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  8. Young-Ran Yoon
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Correspondence to Hae Won Lee or Young-Ran Yoon.

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Lee, J., Park, J., Lim, MS. et al. Quantile Normalization Approach for Liquid Chromatography— Mass Spectrometry-based Metabolomic Data from Healthy Human Volunteers. ANAL. SCI. 28, 801–805 (2012). https://doi.org/10.2116/analsci.28.801

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  • DOI: https://doi.org/10.2116/analsci.28.801

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