Abstract
Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis’ sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.
Highlights
Metabolomics data mining describes the application of strategic data analysis methods incorporating artificial intelligence, machine learning, statistics and database operations to extract meaningful and useful information from high-dimensional and high-volume metabolomics datasets.This is a complex, time-consuming process including many steps with many possible options at each step
In recognition of the complexity of data analysis in metabolomics, we propose that distinct reporting guidelines be drawn up for separate sections of the data analysis pipeline as their various steps are often carried out at different times, by different individuals or groups of various skill sets, or even often outsourced to different locations
The main document informing these guidelines is the original proposed guidelines for data analysis reporting in metabolomics by Goodacre et al [11], which covers the reporting of every part of the data analysis of a metabolomics experiment from design of experiment through pre-processing to data pre-treatment and final analysis
Summary
Metabolomics data mining describes the application of strategic data analysis methods incorporating artificial intelligence, machine learning, statistics and database operations to extract meaningful and useful information from high-dimensional and high-volume metabolomics datasets. This is a complex, time-consuming process including many steps with many possible options at each step. For detailed discussion and reviews of the data analysis step in metabolomics and its complexities, the reader is referred to the following publications [3,4,5,6,7]. Metabolomics data analysis sections have been found to be plagued by inconsistent reporting— with regards to structure, details reported, and performance metrics used [3]
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