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Statistical Methods for Microbiome Compositional Data Network Inference: A Survey.

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  • 1School of Mathematical Sciences, Peking University, Beijing, China.

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Understanding microbial communities requires studying their interactions. This review explores network inference methods for analyzing complex microbiome data, highlighting challenges and future directions for ecological research.

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Area of Science:

  • Microbial ecology
  • Bioinformatics
  • Network science

Background:

  • Microbial communities are ubiquitous and interact with their environments.
  • Studying these interactions is crucial for understanding ecosystem dynamics.
  • Microbiome interaction network inference is a key approach, but faces data challenges.

Purpose of the Study:

  • To provide a comprehensive review of emerging microbiome interaction network inference methods.
  • To categorize and discuss different network types, their assumptions, and limitations.
  • To identify current challenges and future prospects in microbial network analysis.

Main Methods:

  • Review of existing literature on microbiome network inference.
  • Categorization of networks into correlation, conditional correlation, mixture, and differential networks.
  • Analysis of assumptions, advantages, and limitations of each network type.

Main Results:

  • Microbiome network inference is challenged by data properties (compositional, high-dimensional, sparse, heterogeneous) and external factors.
  • Four main categories of networks (correlation, conditional correlation, mixture, differential) are presented with their characteristics.
  • No single method currently captures all aspects of complex and dynamic microbial interactions.

Conclusions:

  • Current methods have limitations in fully representing microbial interactions.
  • Future research requires integrated statistical and experimental approaches.
  • Advancements in microbial network inference are essential for ecological and biological understanding.