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 |
| 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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=aPCoA
to link to this page.