serval:BIB_C00E7D1EAE17
DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies.
10.1038/s41598-021-84824-3
000629619800035
33707505
Bararpour
N.
author
Gilardi
F.
author
Carmeli
C.
author
Sidibe
J.
author
Ivanisevic
J.
author
Caputo
T.
author
Augsburger
M.
author
Grabherr
S.
author
Desvergne
B.
author
Guex
N.
author
Bochud
M.
author
Thomas
A.
author
article
2021-03-11
Scientific reports
2045-2322
2045-2322
journal
11
1
5657
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.
Multidisciplinary
eng
60_published
SNF//310030_156771
SNF//33CM30-124087
true
peer-reviewed
Publication types: Journal Article
Publication Status: epublish
University of Lausanne
mailto:serval_help@unil.ch
http://www.unil.ch/serval
http://serval.unil.ch/disclaimer
https://serval.unil.ch/notice/serval:BIB_C00E7D1EAE17