DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

Abstract As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabo...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nasim Bararpour, Federica Gilardi, Cristian Carmeli, Jonathan Sidibe, Julijana Ivanisevic, Tiziana Caputo, Marc Augsburger, Silke Grabherr, Béatrice Desvergne, Nicolas Guex, Murielle Bochud, Aurelien Thomas
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/90d0655bcafa4df9b625fb9bc7e81888
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:90d0655bcafa4df9b625fb9bc7e81888
record_format	dspace
spelling	oai:doaj.org-article:90d0655bcafa4df9b625fb9bc7e818882021-12-02T15:53:43ZDBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies10.1038/s41598-021-84824-32045-2322https://doaj.org/article/90d0655bcafa4df9b625fb9bc7e818882021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84824-3https://doaj.org/toc/2045-2322Abstract As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.Nasim BararpourFederica GilardiCristian CarmeliJonathan SidibeJulijana IvanisevicTiziana CaputoMarc AugsburgerSilke GrabherrBéatrice DesvergneNicolas GuexMurielle BochudAurelien ThomasNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nasim Bararpour Federica Gilardi Cristian Carmeli Jonathan Sidibe Julijana Ivanisevic Tiziana Caputo Marc Augsburger Silke Grabherr Béatrice Desvergne Nicolas Guex Murielle Bochud Aurelien Thomas DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies
description	Abstract As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.
format	article
author	Nasim Bararpour Federica Gilardi Cristian Carmeli Jonathan Sidibe Julijana Ivanisevic Tiziana Caputo Marc Augsburger Silke Grabherr Béatrice Desvergne Nicolas Guex Murielle Bochud Aurelien Thomas
author_facet	Nasim Bararpour Federica Gilardi Cristian Carmeli Jonathan Sidibe Julijana Ivanisevic Tiziana Caputo Marc Augsburger Silke Grabherr Béatrice Desvergne Nicolas Guex Murielle Bochud Aurelien Thomas
author_sort	Nasim Bararpour
title	DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies
title_short	DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies
title_full	DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies
title_fullStr	DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies
title_full_unstemmed	DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies
title_sort	dbnorm as an r package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/90d0655bcafa4df9b625fb9bc7e81888
work_keys_str_mv	AT nasimbararpour dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT federicagilardi dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT cristiancarmeli dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT jonathansidibe dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT julijanaivanisevic dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT tizianacaputo dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT marcaugsburger dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT silkegrabherr dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT beatricedesvergne dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT nicolasguex dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT muriellebochud dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies AT aurelienthomas dbnormasanrpackageforthecomparisonandselectionofappropriatestatisticalmethodsforbatcheffectcorrectioninmetabolomicstudies
_version_	1718385521723441152

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

Ejemplares similares