aPCoA: Covariate Adjusted Principal Coordinates Analysis

TL;DR

aPCoA is a new tool that adjusts for confounding covariates in principal coordinates analysis, improving data visualization in fields like ecology and genomics where non-Euclidean distances are common.

Contribution

It introduces a covariate-adjusted PCoA method implemented as an R package and Shiny app, enhancing the interpretability of complex dissimilarity data.

Findings

Improves visualization of dissimilarity data

Helps reveal patterns obscured by confounders

Available as user-friendly software

Abstract

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.