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Citation: An Yanpeng, Liu Si, Hao Fuhua, Wang Yulan, Tang Huiru. Development and validation of an improved probabilistic quotient normalization method for LC/MS- and NMR-based metabonomic analysis[J]. Chinese Chemical Letters, ;2020, 31(7): 1827-1830. doi: 10.1016/j.cclet.2019.12.011 shu

Development and validation of an improved probabilistic quotient normalization method for LC/MS- and NMR-based metabonomic analysis

An Yanpeng ^a,1 ,
Liu Si ^a,b,1 ,
Hao Fuhua ^b ,
Wang Yulan ^c ,
Tang Huiru ^a,*,

a.
State Key Laboratory of Genetic Engineering, Zhongshan Hospital and School of Life Sciences, Human Phenome Institute, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Fudan University, Shanghai 200438, China
b.
CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, University of Chinese Academy of Sciences, Wuhan 430071, China
c.
Singapore Phenome Center, Lee Kong Chian School of Medicine, School of Biological Sciences, Nanyang Technological University, Singapore 636921, Singapore

^*

huiru_tang@fudan.edu.cn

¹

Received Date: 11 November 2019
Revised Date: 3 December 2019
Accepted Date: 4 December 2019
Available Online: 5 December 2019

Figures(3)

Robust normalization is a prerequisite for reliable metabonomic analysis especially when intervention treatments cause drastic metabolomic changes or when spot urinary samples are employed without knowing the drinking water quantity. With the simulated and real datasets, here, we report a probabilistic quotient normalization method based on the mode-of-quotients (mPQN) which is suitable for metabonomic analysis of both NMR and LC-MS data with little and/or drastic metabolite changes. When applied to metabonomic analysis of both animal plasma samples and human urinary samples, this newly proposed method has clearly shown better robustness than all classical normalization methods especially when drastic changes of some metabolites occur.
- Metabonomics,
- Normalization,
- Probabilistic quotient normalization,
- NMR,
- LC-MS
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