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Article: MCEE: a data preprocessing approach for metabolic confounding effect elimination

TitleMCEE: a data preprocessing approach for metabolic confounding effect elimination
Authors
KeywordsConfounding factor
Direct orthogonal signal correction
Generalized linear model
Metabolomics
Principal component analysis
Issue Date2018
Citation
Analytical and Bioanalytical Chemistry, 2018, v. 410, n. 11, p. 2689-2699 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/342703
ISSN
2023 Impact Factor: 3.8
2023 SCImago Journal Rankings: 0.686
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Yitao-
dc.contributor.authorLi, Mengci-
dc.contributor.authorJia, Wei-
dc.contributor.authorNi, Yan-
dc.contributor.authorChen, Tianlu-
dc.date.accessioned2024-04-17T07:05:39Z-
dc.date.available2024-04-17T07:05:39Z-
dc.date.issued2018-
dc.identifier.citationAnalytical and Bioanalytical Chemistry, 2018, v. 410, n. 11, p. 2689-2699-
dc.identifier.issn1618-2642-
dc.identifier.urihttp://hdl.handle.net/10722/342703-
dc.description.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.-
dc.languageeng-
dc.relation.ispartofAnalytical and Bioanalytical Chemistry-
dc.subjectConfounding factor-
dc.subjectDirect orthogonal signal correction-
dc.subjectGeneralized linear model-
dc.subjectMetabolomics-
dc.subjectPrincipal component analysis-
dc.titleMCEE: a data preprocessing approach for metabolic confounding effect elimination-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s00216-018-0947-4-
dc.identifier.pmid29476235-
dc.identifier.scopuseid_2-s2.0-85044936082-
dc.identifier.volume410-
dc.identifier.issue11-
dc.identifier.spage2689-
dc.identifier.epage2699-
dc.identifier.eissn1618-2650-
dc.identifier.isiWOS:000429327900004-

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