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scatterbar: an R package for visualizing proportional data across spatially resolved coordinates.

Dee Velazquez1,2, Jean Fan1,2

  • 1Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States.

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Scatterbar, a new R package, improves visualization of proportional data in spatial transcriptomics. It uses scatter stacked bar plots to enhance interpretation compared to traditional scatter pie plots.

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Area of Science:

  • Bioinformatics
  • Data Visualization
  • Computational Biology

Background:

  • Visualizing proportional data across spatial coordinates is crucial for fields like spatial transcriptomics.
  • Existing methods, such as scatter pie plots, present perceptual challenges that hinder accurate interpretation.
  • Enhanced visual saliency is needed to improve the identification of proportional trends and differences.

Purpose of the Study:

  • To introduce scatterbar, an R package designed for visualizing proportional data in spatially resolved contexts.
  • To offer an alternative to scatter pie plots that addresses their perceptual limitations.
  • To improve the distinguishability of proportional distributions in complex datasets.

Main Methods:

  • Development of scatterbar, an open-source R package extending ggplot2.
  • Implementation of scatter stacked bar plots for proportional data visualization.
  • Application of scatterbar to deconvolved cell-type proportions from mouse brain spatial transcriptomics data.

Main Results:

  • Scatterbar effectively visualizes proportional data across numerous spatially resolved coordinates.
  • Scatter stacked bar plots generated by scatterbar enhance the distinguishability of proportional distributions.
  • The package demonstrates improved clarity compared to traditional scatter pie plots for spatial transcriptomics data.

Conclusions:

  • Scatterbar provides a valuable tool for researchers working with spatially resolved proportional data.
  • The scatter stacked bar plot approach offers superior perceptual clarity for complex biological datasets.
  • This visualization method facilitates more accurate interpretation of cell-type distributions in spatial transcriptomics.