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Methodology for microbiome data analysis: An overview.

Irene Creus-Martí1, Andrés Moya2, Francisco J Santonja3

  • 1Department of Applied Mathematics, Universitat Politècnica de València, Valencia, Spain.

Computers in Biology and Medicine
|April 25, 2025
PubMed
Summary
This summary is machine-generated.

The human microbiome significantly impacts health and offers clinical potential. This review details statistical methods for analyzing complex microbiome data, especially longitudinal datasets, aiding research in bacterial behavior and health.

Keywords:
Compositional dataLongitudinal dataMicrobiome modeling

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Biostatistics

Background:

  • The human microbiome plays a crucial role in health and disease.
  • Emerging research highlights the microbiome's potential clinical applications.
  • Analyzing microbiome data presents unique statistical challenges due to its complex nature.

Purpose of the Study:

  • To review the characteristics of microbiome data.
  • To summarize prevalent statistical methods for microbiome data analysis, distinguishing between longitudinal and non-longitudinal approaches.
  • To provide a reference for researchers in biology and statistics.

Main Methods:

  • Classification of statistical methods based on analytical objectives and mathematical properties.
  • Categorization of methods considering their applicability to longitudinal versus non-longitudinal data.
  • Structured overview of established and emerging analytical strategies.

Main Results:

  • Microbiome data exhibits distinct characteristics complicating statistical analysis.
  • A range of statistical methods exist for analyzing microbiota, with varying suitability for different data types (longitudinal/non-longitudinal).
  • Methods are presented based on biological aims and mathematical foundations.

Conclusions:

  • This review serves as a foundational reference for microbiome data analysis.
  • It emphasizes the importance of specialized methodologies for studying longitudinal microbiome datasets.
  • The work aims to guide future research directions in microbiome analytics.