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This study introduces a novel tree-based method to identify microbial taxa linked to diseases. The approach uses knockoff copies for accuracy, outperforming existing methods in simulations and a gut microbiome analysis.

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Area of Science:

  • Microbiome research
  • Computational biology
  • Statistical genetics

Background:

  • Microbiota significantly impacts human health and disease.
  • Identifying disease-associated microbial taxa is crucial for medical advancements.
  • Taxonomic and evolutionary information can reveal microbial abundance patterns.

Purpose of the Study:

  • To develop a flexible tree-based method for identifying disease-associated microbial taxa.
  • To improve the accuracy of microbial feature selection in microbiome studies.
  • To assess and refine the selection process using noise-injected features.

Main Methods:

  • Utilized a tree structure to analyze microbial community data.
  • Introduced auxiliary knockoff copies (noise) to assess false positives.
  • Performed extensive numerical simulations to evaluate performance.
  • Applied the method to a gut microbiome dataset linked to body mass index.

Main Results:

  • The proposed methodology demonstrated superior selection accuracy compared to existing methods.
  • Successfully identified microbial taxa associated with body mass index in a real-world dataset.
  • The knockoff approach effectively refined the selection process, reducing false positives.

Conclusions:

  • The tree-based method with knockoffs offers a more accurate approach to identifying disease-associated taxa in microbiome research.
  • This technique enhances the precision of microbial biomarker discovery.
  • The findings have implications for understanding host-microbe interactions and developing targeted interventions.