microbiomeMarker

microbiomeMarker identifies microbiome markers by applying normalization, differential analysis, and supervised learning to detect microbial signatures relevant to translational and precision medicine.

Key Features:

• Normalization and Differential Analysis (DA): Implements recognized normalization techniques alongside differential analysis methods to process microbiome data and identify significant markers.
• Supervised Learning Models: Incorporates three supervised learning models specifically tailored to identify microbiome markers.
• Method Comparison and Confounder Analysis: Compares different DA methods and performs confounder analysis to evaluate robustness of marker identification.
• Standardized Formats: Utilizes standardized input and output formats to support scalability and interoperability with other microbiome packages and tools.
• Visualization and Interpretation Functions: Includes functions to visualize and interpret identified microbiome markers.

Scientific Applications:

• Translational and Precision Medicine: Identifies microbial markers associated with health conditions or treatment responses to inform personalized treatment strategies and diagnostic development.

Methodology:

Statistical normalization, differential analysis (DA), supervised machine learning (three models), method comparison, and confounder analysis applied to microbiome data.