文章基本信息

标题：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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作者：Nasim Bararpour ; Federica Gilardi ; Cristian Carmeli 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2021
卷号：11
期号：1
页码：5657
DOI：10.1038/s41598-021-84824-3
出版社：Springer Nature
摘要：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.
其他摘要：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.