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Robust covariance estimation for high-dimensional compositional data with application to microbial communities

Yong He1, Pengfei Liu2, Xinsheng Zhang3

  • 1Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong, China.

Statistics in Medicine
|April 11, 2021
PubMed
Summary
This summary is machine-generated.

Analyzing microbial community data is challenging due to its complex nature. This study introduces a robust covariance estimation method for high-dimensional, compositional data, improving co-occurrence analysis in microbiome research.

Keywords:
adaptive thresholdingcompositional datamedian of meansmicrobiomesparse covariance matrix

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Modeling

Background:

  • High-throughput sequencing generates high-dimensional, compositional microbial data.
  • Conventional correlation analysis is unsuitable for complex microbial datasets.
  • Challenges exist in accurately estimating covariance for microbial community data.

Purpose of the Study:

  • To develop a robust covariance estimation method for high-dimensional, compositional microbial data.
  • To address the limitations of existing methods in analyzing microbial taxon relationships.
  • To improve the understanding of microbial co-occurrence and co-exclusion patterns.

Main Methods:

  • Utilized a proxy matrix: the centered log-ratio (CLR) covariance matrix.
  • Constructed a Median-of-Means (MOM) estimator for the CLR covariance matrix.
  • Developed an adaptive thresholding procedure for robust covariance estimation.

Main Results:

  • Derived optimal convergence rates under spectral norm with weaker conditions than sub-Gaussianity.
  • Provided theoretical guarantees for support recovery in covariance estimation.
  • Demonstrated the robustness and advantages of the MOM estimator over state-of-the-art methods through simulations.

Conclusions:

  • The proposed adaptive MOM thresholding procedure offers a robust and efficient method for microbial covariance estimation.
  • This approach enhances the analysis of microbial community structures and interactions.
  • Successfully applied the method to a human gut microbiome dataset, showcasing its practical utility.