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) <arXiv:2003.09544>.
| Version: | 1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | vegan, randomcoloR, ape, car, cluster |
| Published: | 2020-06-11 |
| 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_1.1.tar.gz |
| Windows binaries: | r-devel: aPCoA_1.1.zip, r-release: aPCoA_1.1.zip, r-oldrel: aPCoA_1.1.zip |
| macOS binaries: | r-release: aPCoA_1.1.tgz, r-oldrel: aPCoA_1.1.tgz |
| Old sources: | aPCoA archive |
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