aPCoA app introduces an innovative visualization technique for adjusted Principal Coordinates Analysis (PCoA), enabling the incorporation of covariate adjustments into the PCoA projection. Principal Coordinates Analysis, often referred to as classic or metric multidimensional scaling, provides a means to visualize sample variation and potentially identify clusters by reducing data dimensions. Prior to the development of aPCoA, the challenge with PCoA was that confounding covariates could overshadow the primary covariate's effect. For instance, in a study exploring the microbiome's response to diet, site-related clustering might dominate the visual representation if patients were recruited from multiple locations. While several methods have been proposed to account for covariates in Principal Component Analysis, there were no existing solutions for adjusting covariates in PCoA. Original article: Shi et al. Bioinformatics, Volume 36, Issue 13, July 2020, Pages 4099–4101

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