  In fields such as ecology, microbiology, and genomics, non-Euclidean
distances are widely applied to describe pairwise dissimilarity between
samples. Given these pairwise distances, principal coordinates analysis (PCoA)
is commonly used to construct a visualization of the data. However, confounding
covariates can make patterns related to the scientific question of interest
difficult to observe. We provide aPCoA as an easy-to-use tool, available as
both an R package and a Shiny app, to improve data visualization in this
context, enabling enhanced presentation of the effects of interest.
