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MCEE 2.0: more options and enhanced performance.

Yitao Li1, Xiaojiao Zheng1, Dandan Liang1

  • 1Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China.

Analytical and Bioanalytical Chemistry
|July 7, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an upgraded metabolic confounding effect elimination (MCEE) tool to improve metabolomics research by addressing noise from physiological and environmental factors. The enhanced MCEE software offers new methods for more accurate analysis of complex biological data.

Keywords:
Canonical correlation analysisConfounding effectGeneralized linear modelMetabolomics

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Area of Science:

  • Computational metabolomics
  • Bioinformatics
  • Systems biology

Background:

  • Confounding factors like age, diet, and gender can introduce bias into metabolomics studies.
  • Existing methods for eliminating confounding effects in metabolomics may not fully capture complex biological correlations.
  • Accurate analysis of metabolic data is crucial for understanding diseases and biological processes.

Purpose of the Study:

  • To introduce an upgraded version of the metabolic confounding effect elimination (MCEE) software with enhanced performance and new analytical options.
  • To provide a robust tool for identifying and processing multiple confounding factors in metabolomics data.
  • To offer guidance on selecting appropriate methods and parameters for confounding effect elimination.

Main Methods:

  • Development of an upgraded GUI-based metabolic confounding effect elimination (MCEE) tool.
  • Integration of canonical correlation analysis (CCA) alongside the generalized linear model (GLM) for metabolite selection.
  • Validation using simulated datasets (basic, varying sample sizes, sparse) and real-world datasets (Type 2 Diabetes, Arthritis).

Main Results:

  • The upgraded MCEE tool effectively eliminates confounding effects in metabolomics data.
  • Canonical correlation analysis (CCA) demonstrates effectiveness in handling complex correlations and collinearity within the metabolome.
  • Performance evaluation using diverse datasets confirms the tool's utility and provides insights into method selection.

Conclusions:

  • The enhanced MCEE software offers improved capabilities for noise reduction in metabolomics studies.
  • The inclusion of CCA provides a powerful multivariate approach for analyzing complex metabolic data.
  • The updated MCEE tool and accompanying guidance facilitate more accurate and reliable metabolomics research.