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aPCoA: covariate adjusted principal coordinates analysis.

Yushu Shi1, Liangliang Zhang1, Kim-Anh Do1

  • 1Department of Biostatistics.

Bioinformatics (Oxford, England)
|April 28, 2020
PubMed
Summary
This summary is machine-generated.

Adjusted principal coordinates analysis (aPCoA) offers a new tool for visualizing ecological and genomic data. This method helps reveal underlying patterns obscured by confounding factors, improving scientific data presentation.

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Area of Science:

  • Ecology
  • Microbiology
  • Genomics

Background:

  • Non-Euclidean distances are frequently used to quantify sample dissimilarity in ecological, microbiological, and genomic studies.
  • Principal coordinates analysis (PCoA) is a common technique for visualizing these distance-based data.
  • Confounding covariates can obscure important patterns in PCoA visualizations.

Purpose of the Study:

  • To introduce an accessible tool for improving data visualization in the presence of confounding covariates.
  • To enable clearer presentation of scientific effects of interest in complex datasets.

Main Methods:

  • Development of an adjusted principal coordinates analysis (aPCoA) method.
  • Implementation of aPCoA as an R package.
  • Creation of an interactive aPCoA Shiny application.

Main Results:

  • The aPCoA tool effectively visualizes data by accounting for confounding variables.
  • Enhanced presentation of scientific patterns is achieved, facilitating interpretation.
  • The R package and Shiny app provide user-friendly access to the methodology.

Conclusions:

  • Adjusted principal coordinates analysis is a valuable tool for researchers in ecology, microbiology, and genomics.
  • The aPCoA package and app simplify the visualization of complex, covariate-influenced biological data.
  • Improved data visualization leads to enhanced understanding and presentation of scientific findings.