aPCoA: Covariate Adjusted Principal Coordinates Analysis
Quantitative Methods
2020-03-24 v1
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.
Keywords
Cite
@article{arxiv.2003.09544,
title = {aPCoA: Covariate Adjusted Principal Coordinates Analysis},
author = {Yushu Shi and Liangliang Zhang and Kim-Anh Do and Christine Peterson and Robert Jenq},
journal= {arXiv preprint arXiv:2003.09544},
year = {2020}
}