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Statistical evaluation of diet-microbe associations.

Xiang Zhang1, Max Nieuwdorp2, Albert K Groen3

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When analyzing microbial abundance and diet, different statistical methods yield varying results. Researchers should use multiple methods to identify robust associations and control for false discoveries.

Keywords:
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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical analysis

Background:

  • Statistical methods for analyzing microbial abundance and dietary variables lack consensus.
  • Method selection is often based on tradition, software availability, or desired outcomes.

Purpose of the Study:

  • To compare the performance of four popular statistical methods for evaluating microbial-diet associations.
  • To identify optimal methods for analyzing the relationship between diet and microbial abundance.

Main Methods:

  • Applied edgeR, limma, metagenomeSeq, and shotgunFunctionalizeR to analyze microbial-diet data.
  • Conducted simulation studies to assess method performance and identify biases.

Main Results:

  • Significant differences were observed in the results obtained from the four tested methods.
  • No single method demonstrated optimal performance across all simulation scenarios.
  • Simulation studies highlighted potential limitations and variability in each method's output.

Conclusions:

  • Researchers should employ multiple statistical methods for analyzing microbial-diet associations.
  • Focusing on findings consistent across several methods can improve the control of false discovery rates.
  • Substantial false discovery rates may still occur, necessitating careful interpretation of results.