2020/03/21 by Yushu Shi, Liangliang Zhang, Shi, Yushu +7
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Chemistry · #FOS: Biological sciences #Metabolomics and Mass Spectrometry Studies #Quantitative Methods (q-bio.QM) #Sensory Analysis and Statistical Methods #Spectroscopy and Chemometric Analyses
paper · pdf · doi:10.48550/arxiv.2003.09544
openalex publication_date 2020/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.