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MCEE: a data preprocessing approach for metabolic confounding effect elimination

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 expe... Full description

Journal Title: Analytical and Bioanalytical Chemistry 2018, Vol.410(11), pp.2689-2699
Main Author: Li, Yitao
Other Authors: Li, Mengci , Jia, Wei , Ni, Yan , Chen, Tianlu
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1618-2642 ; E-ISSN: 1618-2650 ; DOI: 10.1007/s00216-018-0947-4
Link: http://dx.doi.org/10.1007/s00216-018-0947-4
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recordid: springer_jour10.1007/s00216-018-0947-4
title: MCEE: a data preprocessing approach for metabolic confounding effect elimination
format: Article
creator:
  • Li, Yitao
  • Li, Mengci
  • Jia, Wei
  • Ni, Yan
  • Chen, Tianlu
subjects:
  • Metabolomics
  • Confounding factor
  • Generalized linear model
  • Principal component analysis
  • Direct orthogonal signal correction
ispartof: Analytical and Bioanalytical Chemistry, 2018, Vol.410(11), pp.2689-2699
description: 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
language: eng
source:
identifier: ISSN: 1618-2642 ; E-ISSN: 1618-2650 ; DOI: 10.1007/s00216-018-0947-4
fulltext: fulltext
issn:
  • 1618-2650
  • 16182650
  • 1618-2642
  • 16182642
url: Link


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titleMCEE: a data preprocessing approach for metabolic confounding effect elimination
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subjectMetabolomics ; Confounding factor ; Generalized linear model ; Principal component analysis ; Direct orthogonal signal correction
descriptionIt 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
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abstractIt 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
copBerlin/Heidelberg
pubSpringer Berlin Heidelberg
doi10.1007/s00216-018-0947-4
pages2689-2699
date2018-04