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.

Version: 1.3
Depends: R (≥ 3.5.0)
Imports: vegan, randomcoloR, ape, car, cluster
Published: 2021-12-13
DOI: 10.32614/CRAN.package.aPCoA
Author: Yushu Shi
Maintainer: Yushu Shi <shiyushu2006 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: aPCoA results


Reference manual: aPCoA.pdf


Package source: aPCoA_1.3.tar.gz
Windows binaries: r-devel: aPCoA_1.3.zip, r-release: aPCoA_1.3.zip, r-oldrel: aPCoA_1.3.zip
macOS binaries: r-release (arm64): aPCoA_1.3.tgz, r-oldrel (arm64): aPCoA_1.3.tgz, r-release (x86_64): aPCoA_1.3.tgz, r-oldrel (x86_64): aPCoA_1.3.tgz
Old sources: aPCoA archive


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