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aPCoA: covariate adjusted principal coordinates analysis.
Bioinformatics ( IF 5.8 ) Pub Date : 2020-04-27 , DOI: 10.1093/bioinformatics/btaa276
Yushu Shi 1 , Liangliang Zhang 1 , Kim-Anh Do 1 , Christine B Peterson 1 , Robert R Jenq 2
Affiliation  

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 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 adjusted principal coordinates analysis as an easy-to-use tool, available as both an R package and a Shiny app, to improve data visualization in this context, enabling enhanced presentation of the effects of interest.

中文翻译:

aPCoA:协变量调整后的主坐标分析。

在生态学,微生物学和基因组学等领域,非欧几里德距离被广泛用于描述样本之间的成对相异性。给定这些成对的距离,通常使用主坐标分析来构造数据的可视化。但是,混杂的协变量会使与所关注的科学问题相关的模式难以观察。我们提供经过调整的主坐标分析,这是一种易于使用的工具,既可以作为R程序包也可以作为Shiny应用程序使用,以改善这种情况下的数据可视化,从而增强对感兴趣效果的表示。
更新日期:2020-07-03
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