Analytical and Bioanalytical Chemistry

, Volume 411, Issue 20, pp 5089–5098 | Cite as

MCEE 2.0: more options and enhanced performance

  • Yitao Li
  • Xiaojiao Zheng
  • Dandan Liang
  • Aihua Zhao
  • Wei JiaEmail author
  • Tianlu ChenEmail author
Research Paper


A confounding factor is an unstudied factor that affects one or more of the variables that are being studied in an investigation, so the presence of a confounder may lead to inaccurate or biased results. It is well recognized that physiological and environmental factors such as race, diet, age, gender, blood pressure, and diurnal cycle affect mammalian metabolism. To eliminate the noise introduced by confounders into metabolomics studies, a GUI-based method denoted metabolic confounding effect elimination (MCEE) was developed and has since been applied successfully in a wide range of metabolomics studies. To keep up with recent developments in computational metabolomics and a growing number of user requests, an upgraded version of MCEE with more options and enhanced performance was designed and developed. Besides the generalized linear model (GLM) method, a multivariate method for selecting affected metabolites—canonical correlation analysis (CCA)—was introduced, which accounts for complicated correlations and collinearity within the metabolome. Multiple confounders are acceptable and can be identified and processed separately or simultaneously. The effectiveness of this new version of MCEE as well as the pros and cons of the two methodological options were examined using three simulated data sets (a basic model, a model with different sample size ratios, and a sparse model) and two real-world data sets (a human type 2 diabetes mellitus data set and a human arthritis data set). As well as presenting the results of this examination of the new version of MCEE, some instructions on appropriate method selection and parameter setting are provided here. The freely available MATLAB code for MCEE with a GUI has also been updated accordingly at

Graphical abstract


Metabolomics Confounding effect Canonical correlation analysis Generalized linear model 



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). We thank the Biobank of Shanghai Sixth People’s Hospital.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethics statement

The Ethics Committee of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital approved the Shanghai Diabetes Study (SHDS) study, in accordance with the World Medical Association’s Declaration of Helsinki. Written informed consent was obtained from all participants before the start of the study. The human type 2 diabetes mellitus data set used in the present work was obtained from that study.

The human arthritis study was approved by the Review Board of the Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, and all participants gave informed consent before they were included in the study. The human arthritis data set used in the present work was obtained from that study.

Supplementary material

216_2019_1874_MOESM1_ESM.pdf (572 kb)
ESM 1 (PDF 572 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational MedicineShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
  2. 2.University of Hawaii Cancer CenterHonoluluUSA

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