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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.3 |
rolling linux/jammy R-4.5 | aPCoA_1.3.tar.gz |
27.1 KiB |
1.3 |
rolling linux/noble R-4.5 | aPCoA_1.3.tar.gz |
26.9 KiB |
1.3 |
rolling source/ R- | aPCoA_1.3.tar.gz |
6.3 KiB |
1.3 |
latest linux/jammy R-4.5 | aPCoA_1.3.tar.gz |
27.1 KiB |
1.3 |
latest linux/noble R-4.5 | aPCoA_1.3.tar.gz |
26.9 KiB |
1.3 |
latest source/ R- | aPCoA_1.3.tar.gz |
6.3 KiB |
1.3 |
2026-04-26 source/ R- | aPCoA_1.3.tar.gz |
6.3 KiB |
1.3 |
2026-04-23 source/ R- | aPCoA_1.3.tar.gz |
6.3 KiB |
1.3 |
2026-04-09 windows/windows R-4.5 | aPCoA_1.3.zip |
29.8 KiB |
1.3 |
2025-04-20 source/ R- | aPCoA_1.3.tar.gz |
6.3 KiB |