aPCoA v1.0

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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) <arXiv:2003.09544>.

Functions in aPCoA

Name Description
aPCoA Covariate Adjusted PCoA Plot
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Details

Type Package
Date 2020-03-23
License GPL (>= 2)
NeedsCompilation no
Repository CRAN
Packaged 2020-03-24 02:46:28 UTC; yshi7
Date/Publication 2020-03-25 16:50:02 UTC

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