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Robust multivariate regression controlling false discoveries for microbiome data.

Gianna Serafina Monti1, Meritxell Pujolassos Tanyà2, Malu Calle Rosingana2,3

  • 1Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan 20126, Italy.

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Summary
This summary is machine-generated.

This study introduces a robust regression model to identify microbial species linked to health indicators. The new method effectively handles complex microbiome data, improving disease signature discovery.

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Area of Science:

  • Microbiome research
  • Statistical modeling
  • Bioinformatics

Background:

  • Microbiome signatures are crucial for understanding diseases like obesity and liver disease.
  • Analyzing microbiome data presents challenges due to compositionality, high dimensionality, sparsity, and outliers.

Purpose of the Study:

  • To develop a robust multivariate compositional regression model for identifying microbiome-health indicator associations.
  • To address the limitations of existing methods in analyzing complex microbiome data.

Main Methods:

  • Developed a robust multivariate compositional regression model.
  • Incorporated outlier robustness and a derandomization step.
  • Ensured control of the false discovery rate (FDR) for reliable results.

Main Results:

  • The proposed method outperforms the Multi-Response Knockoff Filter (MRKF) in simulation studies regarding FDR control, power, and robustness.
  • Successfully identified microbial species associated with specific clinical parameters in real-world data applications.
  • Enhanced stability and reproducibility of microbiome data analysis.

Conclusions:

  • The developed robust regression model offers a superior approach for analyzing microbiome data and discovering disease-associated microbial signatures.
  • Provides valuable biological insights by reliably linking microbial species to clinical health indicators.
  • The method is available as R code with comprehensive documentation.