aPCoA: 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) Bioinformatics, Volume 36, Issue 13, 4099-4101.
||R (≥ 3.5.0)
||vegan, randomcoloR, ape, car, cluster
||Yushu Shi <shiyushu2006 at gmail.com>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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