MTA

MTA analyzes longitudinal, phylogenetically based high-dimensional microbiome data to identify community-level dynamic trends, compare temporal dynamics between groups, and classify individual subjects by their longitudinal microbial profiles.

Key Features:

• High-Dimensional Data Handling: Processes phylogenetically based, high-dimensional microbiome datasets typical of longitudinal studies.
• Dynamic Trend Capture: Detects common microbial dynamic trends at the community level and identifies dominant taxa over time.
• Group Comparison: Quantifies differences in overall microbial dynamic trends between distinct groups.
• Subject Classification: Classifies individual subjects using their longitudinal microbial profiles.

Scientific Applications:

• Longitudinal microbiome analysis: Characterizing temporal changes in the human microbiome.
• Hypothesis testing: Identifying significant taxonomic shifts in longitudinal studies.
• Phenotypic association: Classifying subjects by microbial trajectories for health-related and disease-related phenotypic research.
• Validation and benchmarking: Applied and validated via simulations and real data analyses, including a longitudinal study in mice.

Methodology:

Computational methodology comprises three explicit tasks: trend analysis (identifying common within-group community-level trends), comparative analysis (assessing between-group differences in microbial dynamics), and classification (assigning subjects based on longitudinal profiles).