Analytical and Bioanalytical Chemistry

, Volume 410, Issue 11, pp 2689–2699 | Cite as

MCEE: a data preprocessing approach for metabolic confounding effect elimination

Paper in Forefront

Abstract

It is well recognized that physiological and environmental factors such as race, age, gender, and diurnal cycles often have a definite influence on metabolic results that statistically manifests as confounding variables. Currently, removal or controlling of confounding effects relies heavily on experimental design. There are no available data processing techniques focusing on the compensation of their effects. We therefore proposed a new method, Metabolic confounding effect elimination (MCEE), to remove the influence of specified confounding factors and make the data more accurate. The method consists of three steps: metabolites grouping, confounder-related metabolites selection, and metabolites modification. Its effectiveness and advantages were evaluated comprehensively by several simulated models and real datasets, and were compared with two typical methods, the principal component analysis (PCA)- and the direct orthogonal signal correction (DOSC)-based methods. MCEE is simple, effective, and safe, and is independent of sample number, association degree, and missing value. Hence, it may serve as a good complement to existing metabolomics data preprocessing methods and aid in better understanding the metabolic and biological status of interest.

Graphical Abstract

Algorithm flow and demo performance of MCEE

Keywords

Metabolomics Confounding factor Generalized linear model Principal component analysis Direct orthogonal signal correction 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (31501079, 31500954 and 81772530), the National Key R&D Program of China (2017YFC0906800), and the Seventh Framework Programme of the European Union (294923). The authors thank the support of Biobank of Shanghai 6th People’s Hospital.

Compliance with ethical standards

The protocol of HCC was approved by the Zhongshan Hospital Institutional Review Board and written consents were signed by all participants before the study. The protocol of arthritis was approved by the Review Board in Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, and all participants gave informed consent before they were involved in the study.

Conflict of Interest

The authors declare that they have no competing interests.

Supplementary material

216_2018_947_MOESM1_ESM.pdf (724 kb)
ESM 1 (PDF 723 kb)
216_2018_947_MOESM2_ESM.xlsx (69 kb)
ESM 2 (XLSX 69 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yitao Li
    • 1
  • Mengci Li
    • 1
  • Wei Jia
    • 1
    • 2
  • Yan Ni
    • 2
  • Tianlu Chen
    • 1
  1. 1.Center for Translational MedicineShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
  2. 2.University of Hawaii Cancer CenterHonoluluUSA

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