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Variable Selection in Untargeted Metabolomics and the Danger of Sparsity

The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminat...

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Autores principales: Tinnevelt, Gerjen H., Engelke, Udo F.H., Wevers, Ron A., Veenhuis, Stefanie, Willemsen, Michel A., Coene, Karlien L.M., Kulkarni, Purva, Jansen, Jeroen J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698561/
https://www.ncbi.nlm.nih.gov/pubmed/33213095
http://dx.doi.org/10.3390/metabo10110470
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author Tinnevelt, Gerjen H.
Engelke, Udo F.H.
Wevers, Ron A.
Veenhuis, Stefanie
Willemsen, Michel A.
Coene, Karlien L.M.
Kulkarni, Purva
Jansen, Jeroen J.
author_facet Tinnevelt, Gerjen H.
Engelke, Udo F.H.
Wevers, Ron A.
Veenhuis, Stefanie
Willemsen, Michel A.
Coene, Karlien L.M.
Kulkarni, Purva
Jansen, Jeroen J.
author_sort Tinnevelt, Gerjen H.
collection PubMed
description The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia–telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables.
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spelling pubmed-76985612020-11-29 Variable Selection in Untargeted Metabolomics and the Danger of Sparsity Tinnevelt, Gerjen H. Engelke, Udo F.H. Wevers, Ron A. Veenhuis, Stefanie Willemsen, Michel A. Coene, Karlien L.M. Kulkarni, Purva Jansen, Jeroen J. Metabolites Article The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia–telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables. MDPI 2020-11-17 /pmc/articles/PMC7698561/ /pubmed/33213095 http://dx.doi.org/10.3390/metabo10110470 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tinnevelt, Gerjen H.
Engelke, Udo F.H.
Wevers, Ron A.
Veenhuis, Stefanie
Willemsen, Michel A.
Coene, Karlien L.M.
Kulkarni, Purva
Jansen, Jeroen J.
Variable Selection in Untargeted Metabolomics and the Danger of Sparsity
title Variable Selection in Untargeted Metabolomics and the Danger of Sparsity
title_full Variable Selection in Untargeted Metabolomics and the Danger of Sparsity
title_fullStr Variable Selection in Untargeted Metabolomics and the Danger of Sparsity
title_full_unstemmed Variable Selection in Untargeted Metabolomics and the Danger of Sparsity
title_short Variable Selection in Untargeted Metabolomics and the Danger of Sparsity
title_sort variable selection in untargeted metabolomics and the danger of sparsity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698561/
https://www.ncbi.nlm.nih.gov/pubmed/33213095
http://dx.doi.org/10.3390/metabo10110470
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