aPCoA

aPCoA adjusts principal coordinates analysis (PCoA) by incorporating covariate adjustment into distance matrices to remove confounding effects from non-Euclidean dissimilarities for clearer visualization in ecology, microbiology, and genomics.

Key Features:

• Covariate Adjustment: Modifies the PCoA distance matrix to adjust for specified confounding covariates, isolating biological signal from extraneous variation.
• Visualization Enhancement: Mitigates the influence of extraneous variables to improve clarity and interpretability of PCoA-derived visualizations of non-Euclidean dissimilarities.

Scientific Applications:

• Ecology: Visualizes species distributions and community compositions while accounting for environmental covariates.
• Microbiology: Distinguishes microbial community structure differences related to health conditions or treatments from confounding factors such as age or diet.
• Genomics: Visualizes genetic variation across populations while adjusting for population stratification and other demographic variables.

Methodology:

Integrates covariate adjustment directly into the PCoA framework by modifying the distance matrix used in traditional PCoA to account for specified covariates.