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Front Mol Biosci. 2015 Feb 2;2:4. doi: 10.3389/fmolb.2015.00004. eCollection 2015.

A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis.

Author information

1
Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian, China ; Department of Entomology and Nematology, University of California, Davis Davis, CA, USA.
2
Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian, China.
3
School of Computer Science and Technology, Dalian University of Technology Dalian, China.

Abstract

HighlightsDeveloped a data preprocessing strategy to cope with missing values and mask effects in data analysis from high variation of abundant metabolites.A new method- 'x-VAST' was developed to amend the measurement deviation enlargement.Applying the above strategy, several low abundant masked differential metabolites were rescued. Metabolomics is a booming research field. Its success highly relies on the discovery of differential metabolites by comparing different data sets (for example, patients vs. controls). One of the challenges is that differences of the low abundant metabolites between groups are often masked by the high variation of abundant metabolites. In order to solve this challenge, a novel data preprocessing strategy consisting of three steps was proposed in this study. In step 1, a 'modified 80%' rule was used to reduce effect of missing values; in step 2, unit-variance and Pareto scaling methods were used to reduce the mask effect from the abundant metabolites. In step 3, in order to fix the adverse effect of scaling, stability information of the variables deduced from intensity information and the class information, was used to assign suitable weights to the variables. When applying to an LC/MS based metabolomics dataset from chronic hepatitis B patients study and two simulated datasets, the mask effect was found to be partially eliminated and several new low abundant differential metabolites were rescued.

KEYWORDS:

biomarkers; data preprocessing; differential metabolites; metabolomics; pattern recognition

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