Covariate Adjusted PCoA Plot

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 to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) .


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install.packages("aPCoA")

1.0 by Yushu Shi, 15 days ago


Browse source code at https://github.com/cran/aPCoA


Authors: Yushu Shi


Documentation:   PDF Manual  


GPL (>= 2) license


Imports vegan, randomcoloR, mvabund, ape, car, cluster


See at CRAN