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A robust knockoff filter for sparse regression analysis of microbiome compositional data

Author

Listed:
  • Gianna Serafina Monti

    (University of Milano Bicocca)

  • Peter Filzmoser

    (TU Wien)

Abstract

Microbiome data analysis often relies on the identification of a subset of potential biomarkers associated with a clinical outcome of interest. Robust ZeroSum regression, an elastic-net penalized compositional regression built on the least trimmed squares estimator, is a variable selection procedure capable to cope with the high dimensionality of these data, their compositional nature, and, at the same time, it guarantees robustness against the presence of outliers. The necessity of discovering “true” effects and to improve clinical research quality and reproducibility has motivated us to propose a two-step robust compositional knockoff filter procedure, which allows selecting the set of relevant biomarkers, among the many measured features having a nonzero effect on the response, controlling the expected fraction of false positives. We demonstrate the effectiveness of our proposal in an extensive simulation study, and illustrate its usefulness in an application to intestinal microbiome analysis.

Suggested Citation

  • Gianna Serafina Monti & Peter Filzmoser, 2024. "A robust knockoff filter for sparse regression analysis of microbiome compositional data," Computational Statistics, Springer, vol. 39(1), pages 271-288, February.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-022-01268-7
    DOI: 10.1007/s00180-022-01268-7
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    References listed on IDEAS

    as
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