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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 |
Please use the canonical form https://CRAN.R-project.org/package=aPCoA to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.