tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and...

Area of Science:

• Microbiology
• Bioinformatics
• Data Science

Background:

• Analyzing large-scale microbiome data presents challenges due to complex variations.
• Current analytical methods struggle to capture nonlinear patterns effectively.

Purpose of the Study:

• Introduce tmap, an integrative framework for population-scale microbiome stratification and association studies.
• Demonstrate tmap's superiority over existing methods in detecting complex patterns.

Main Methods:

• Utilize topological data analysis to build the tmap framework.
• Validate tmap's performance through various simulation scenarios.
• Apply tmap to diverse population-scale microbiome datasets.

Main Results:

• tmap effectively detects nonlinear patterns in microbiome data.
• The framework demonstrates superior performance compared to commonly used methods.
• tmap successfully reveals microbiome-associated host and environmental features.

Conclusions:

• tmap provides a powerful approach for microbiome stratification and association studies.
• The framework enhances understanding of systematic interrelations in microbiome associations.
• tmap offers a robust tool for exploring complex microbiome-host-environment interactions.