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An adaptive direction-assisted test for microbiome compositional data.

Wei Zhang1, Aiyi Liu2, Zhiwei Zhang3

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

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|May 31, 2022
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A new statistical test enhances microbiome analysis for complex diseases by addressing zero counts and compositional data. This method improves the detection of differences in microbial communities, aiding disease understanding and treatment strategies.

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Area of Science:

  • Microbiome research
  • Computational biology
  • Statistical genetics

Background:

  • Microbial communities are linked to complex diseases like cancer and cardiovascular disease.
  • Identifying differential abundance of microbial taxa is crucial for understanding disease pathology and developing therapies.
  • Analyzing microbiome data presents challenges due to compositional constraints, excessive zeros, and high dimensionality.

Purpose of the Study:

  • To develop a novel microbiome-based statistical test for detecting differences in microbial relative abundances between health conditions.
  • To address the unique characteristics of microbiome data, including compositional constraints and excessive zeros.
  • To provide a powerful and adaptive procedure for high-dimensional microbiome data analysis.

Main Methods:

  • A direction-assisted test that clusters taxa based on mean difference directions to account for compositional data.
  • Incorporation of a burden-type test to collapse multiple taxa, effectively managing excessive zeros.
  • An adaptive procedure designed for high-dimensional settings, enhancing power across various alternative hypotheses.

Main Results:

  • Extensive simulation studies demonstrated substantial power gains compared to existing microbiome analysis tests.
  • The proposed method showed superior performance across a wide range of simulated scenarios.
  • Validation with real-world microbiome datasets confirmed the approach's effectiveness.

Conclusions:

  • The novel direction-assisted test offers a powerful and robust method for microbiome differential abundance analysis.
  • This approach effectively handles the complexities of microbiome data, including compositional nature and excessive zeros.
  • The developed R package, MiDAT, facilitates the application of this advanced statistical method in microbiome research.