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Multilevel regularized regression for simultaneous taxa selection and network construction with metagenomic count

Zhenqiu Liu1, Fengzhu Sun1, Jonathan Braun1

  • 1Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA, Molecular and Computational Biology Program, Department of Biological Sciences, USC, Los Angeles, CA 90089, USA, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA and F. Widjaja Foundation - Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

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This study introduces a novel multilevel regularized regression method to simultaneously identify disease-associated bacteria and construct their interaction networks. The approach efficiently handles large datasets, improving the understanding of microbial communities in different health conditions.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Disease-associated taxa identification and bacteria interaction network construction are typically studied independently.
  • Interactions between taxa and their differentiation can influence each other, impacting network structures across clinical conditions.
  • Current network comparison methods, like permutation tests, are computationally intensive.

Purpose of the Study:

  • To develop a unified framework for simultaneously identifying disease-associated taxa and constructing microbial interaction networks.
  • To enable the construction of both common and condition-specific networks.
  • To address the computational challenges of analyzing large-scale microbiome data.

Main Methods:

  • A multilevel regularized regression method is proposed, incorporating an Lp (p ∈ [0, 2]) penalty.
  • The method jointly models taxa abundance differentiation and correlation.
  • An efficient algorithm using dual formulation is developed for the n ≪ m problem (small samples, large number of taxa).

Main Results:

  • The proposed method successfully identifies true and biologically significant genera.
  • It accurately reconstructs underlying microbial network structures.
  • The framework allows for the simultaneous construction of common and differentiated networks.

Conclusions:

  • The developed method provides an efficient and integrated approach for microbiome network analysis.
  • It enhances the ability to identify disease-specific microbial patterns and interactions.
  • The software MLRR is available for MATLAB implementation.