microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization

Area of Science:

• Microbiome research
• Bioinformatics
• Computational biology

Background:

• Microbiome profiles are crucial for translational and precision medicine.
• Identifying reliable microbiome biomarkers is essential for clinical applications.

Purpose of the Study:

• To introduce microbiomeMarker, an R/Bioconductor package for microbiome marker identification.
• To provide a scalable and extensible tool for analyzing microbiome data.

Main Methods:

• Implementation of normalization and differential analysis (DA) methods.
• Integration of three supervised learning models for marker identification.
• Inclusion of functions for comparing DA methods and confounder analysis.

Main Results:

• microbiomeMarker facilitates the identification and interpretation of microbiome markers.
• The package supports standardized input/output for seamless integration with other tools.
• It enables comparison of different analytical approaches for microbiome data.

Conclusions:

• microbiomeMarker offers a comprehensive solution for microbiome biomarker discovery.
• The package enhances the potential of microbiome profiling in translational and precision medicine.
• Its design promotes scalability, extensibility, and user-friendly interpretation of results.