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Updated: Jun 2, 2025

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ISCAZIM: Integrated statistical correlation analysis for zero-inflated microbiome data.

Zhe Fan1,2, Jiali Lv1,2, Shuai Zhang1,2

  • 1Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.

Heliyon
|January 15, 2025
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Summary
This summary is machine-generated.

Analyzing gut microbiome and metabolome data is key for discovering disease biomarkers. ISCAZIM improves association analysis for zero-inflated microbiome data, enhancing multi-omics integration and biomarker discovery.

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Area of Science:

  • Microbiome research
  • Metabolomics
  • Computational biology
  • Statistical genetics

Background:

  • Microbiome-metabolome association analysis is crucial for identifying microbial biomarkers in chronic diseases.
  • Microbiome data's zero-inflation and over-dispersion characteristics challenge accurate association analysis.
  • Existing statistical methods may not adequately address these unique data properties.

Purpose of the Study:

  • To evaluate existing statistical methods for microbiome-metabolome association analysis.
  • To develop a robust computational framework addressing microbiome data complexities.
  • To enhance the accuracy and reliability of multi-omics integration studies.

Main Methods:

  • Developed Integrated Statistical Correlation Analysis for Zero-Inflated Microbiome data (ISCAZIM).
  • ISCAZIM accounts for zero-inflation rates (ZIRs), dispersion, and correlation patterns.
  • Benchmarked Pearson, Spearman, ZINB model, mutual information, and Maximal Information Coefficient, adapting methods based on ZIRs and correlation type (linear/non-linear).

Main Results:

  • ISCAZIM demonstrated superior accuracy compared to single methods in real-world microbiome-metabolomics data.
  • The framework successfully identified more truly significant association pairs.
  • ISCAZIM effectively handles complex microbiome data characteristics.

Conclusions:

  • ISCAZIM provides a significant advancement for association analysis in zero-inflated microbiome data.
  • The framework facilitates more reliable multi-omics integration for biomarker discovery.
  • ISCAZIM enhances the confidence in identifying gut microbiota-metabolite associations.