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Optimization of metabolomic data processing using NOREVA
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DOI：10.1038/s41596-021-00636-9.png)
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Abstract
En 中文
A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data. Peak table processing is essential for metabolomics, but finding the best workflow is challenging. This protocol describes NOREVA, an out-of-the-box software tool that can process and evaluate thousands of workflows in a single experiment.
Keywords:
MASS-SPECTROMETRY
NORMALIZATION
DISCOVERY
MICROBIOME
SOFTWARE
PATHWAY
QUALITY
TOOLS
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