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aPCoA
View on CRAN: Click
here
Download and install aPCoA package within the R console
Install from CRAN:
install.packages("aPCoA")
Install from Github:
library("remotes")
install_github("cran/aPCoA")
Install by package version:
library("remotes")
install_version("aPCoA", "1.3")
Attach the package and use:
library("aPCoA")
Maintained by
Yushu Shi
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-03-25
Latest Update: 2021-12-13
Description:
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.
How to cite:
Yushu Shi (2020). aPCoA: Covariate Adjusted PCoA Plot. R package version 1.3, https://cran.r-project.org/web/packages/aPCoA. Accessed 06 Nov. 2024.
Previous versions and publish date:
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Complete documentation for aPCoA
Functions, R codes and Examples using
the aPCoA R package
Some associated functions: Tasmania . aPCoA .
Some associated R codes: aPCoA.R . Full aPCoA package functions and examples
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