APCoA: Covariate Adjusted Principal Coordinates Analysis

• Activity Number: 353 - Visual Stories That Count!
• Type: Contributed
• Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistical Graphics
• Author(s): Yushu Shi* and Liangliang Zhang and Kim-Anh Do and Christine B. Peterson and Robert Jenq
• Companies: University of Texas MD Anderson Cancer Center and M.D. Anderson Cancer Center and The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center
• Keywords: Microbiome; Classical multidimensional scaling; Non-Euclidean distance

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.