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

Author

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

Abstract

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.

Suggested Citation

  • Shoaib Bin Masud & Conor Jenkins & Erika Hussey & Seth Elkin-Frankston & Phillip Mach & Elizabeth Dhummakupt & Shuchin Aeron, 2021. "Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn’s disease with a publicly available untargeted metabolomics dataset," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0255240
    DOI: 10.1371/journal.pone.0255240
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