PLoS ONE (Jan 2021)

Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.

  • Shoaib Bin Masud,
  • Conor Jenkins,
  • Erika Hussey,
  • Seth Elkin-Frankston,
  • Phillip Mach,
  • Elizabeth Dhummakupt,
  • Shuchin Aeron

DOI
https://doi.org/10.1371/journal.pone.0255240
Journal volume & issue
Vol. 16, no. 7
p. e0255240

Abstract

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Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers' attention for further analysis.