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

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State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_C00E7D1EAE17
Type
Article: article from journal or magazin.
Publication sub-type
Case report (case report): feedback on an observation with a short commentary.
Collection
Publications
Institution
Title
DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies.
Journal
Scientific reports
Author(s)
Bararpour N., Gilardi F., Carmeli C., Sidibe J., Ivanisevic J., Caputo T., Augsburger M., Grabherr S., Desvergne B., Guex N., Bochud M., Thomas A.
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Publication state
Published
Issued date
11/03/2021
Peer-reviewed
Oui
Volume
11
Number
1
Pages
5657
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
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.
Keywords
Multidisciplinary
Pubmed
Open Access
Yes
Funding(s)
Swiss National Science Foundation / 310030_156771
Swiss National Science Foundation / 33CM30-124087
Create date
11/03/2021 14:49
Last modification date
27/03/2021 7:32
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