MECAF

MECAF performs differential abundance testing on high-dimensional microbiome compositional data to detect condition-associated changes without assuming equal covariance across groups.

Key Features:

• Centered Log-Ratio Transformation: Employs centered log-ratio (CLR) transformation to analyze microbiome compositions and compare two high-dimensional abundance data matrices under different conditions.
• Closed-Form P-value Computation: Computes test p-values directly using a closed-form solution derived from the asymptotic null distribution.
• Maximum-Type Equal-Covariance-Assumption-Free Test: Implements a maximum-type test that operates without assuming equal covariance across groups.
• Asymptotic Power Analysis Against Sparse Alternatives: Investigates asymptotic statistical power specifically against sparse alternatives common in microbiome studies.
• Controlled Type I Error via Simulations: Demonstrates well-controlled type I error rates and improved statistical power relative to competing methods through simulation studies.
• Compatibility with High-Throughput Metagenomic Sequencing: Applicable to abundance data derived from high-throughput metagenomic sequencing technologies.

Scientific Applications:

• Differential Abundance Discovery: Detects shifts in microbiome abundances between disease and treatment conditions.
• Clinical Pathogen and Infectious Disease Studies: Supports analyses for clinical pathogen diagnosis and studies of infectious disease using metagenomic abundance data.
• Real-World Microbiome Data Analysis: Applied to real-world microbiome datasets to assess condition-associated microbial dynamics.

Methodology:

Computational steps explicitly include centered log-ratio transformation of compositional abundance matrices, a maximum-type equal-covariance-assumption-free test statistic, closed-form p-value derivation from the asymptotic null distribution, asymptotic power analysis against sparse alternatives, and evaluation via simulation studies.