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Sparse least trimmed squares regression with compositional covariates for high-dimensional data.

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This study introduces a robust statistical method for analyzing microbiome data, effectively selecting relevant microbes associated with health outcomes while identifying unusual data points. The approach enhances prediction accuracy in microbiome research.

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Area of Science:

  • Microbiome research
  • Statistical modeling
  • Bioinformatics

Background:

  • High-throughput sequencing generates vast microbiome data, often sparse and compositional.
  • Identifying microbes linked to clinical outcomes and detecting outliers are critical challenges.

Purpose of the Study:

  • To develop a robust and sparse variable selection method for compositional microbiome data.
  • To improve prediction accuracy by addressing the challenges of high-dimensional microbiome data.

Main Methods:

  • A linear log-contrast model accounts for the compositional nature of covariates.
  • Elastic-net regularization induces sparsity in regression coefficients.
  • Robustness is achieved through trimming, with reweighting for efficiency and outlier diagnostics.

Main Results:

  • The proposed method effectively connects robustness and sparsity for variable selection.
  • Simulation studies demonstrate the numerical performance of the approach.
  • The method is applied to predict caffeine intake from gut microbiome composition.

Conclusions:

  • The developed method offers a robust solution for analyzing compositional microbiome data.
  • It enhances the ability to identify microbe-host associations and improve predictive models.
  • The R-package 'RobZS' is available for practical application.