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  "Title": "Covariate Adjusted PCoA Plot",
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  "Date": "2021-12-12",
  "Author": "Yushu Shi",
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  "Description": "In fields such as ecology, microbiology, and genomics,\nnon-Euclidean distances are widely applied to describe pairwise\ndissimilarity between samples. Given these pairwise distances,\nprincipal coordinates analysis (PCoA) is commonly used to\nconstruct a visualization of the data. However, confounding\ncovariates can make patterns related to the scientific question\nof interest difficult to observe. We provide 'aPCoA' as an\neasy-to-use tool to improve data visualization in this context,\nenabling enhanced presentation of the effects of interest.\nDetails are described in Yushu Shi, Liangliang Zhang, Kim-Anh\nDo, Christine Peterson and Robert Jenq (2020) Bioinformatics,\nVolume 36, Issue 13, 4099-4101.",
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