VoronoiBiomedPlot-package: Projection Visualization Plots for Dimensionally Reduced Data

In VoronoiBiomedPlot: Projection Visualization Plots for Dimensionally Reduced Data

Projection Visualization Plots for Dimensionally Reduced Data

Description

Creates visualization plots for 2D projected data including ellipse plots, Voronoi diagram plots, and combined ellipse-Voronoi plots. Designed to visualize class separation in dimensionally reduced data from techniques like principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or others. For more details see Lotsch and Ultsch (2024) <doi:10.1016/j.imu.2024.101573>.

Details

The VoronoiBiomedPlot package provides functions for creating visualization plots of 2D projected data, particularly useful for biomedical data analysis and dimensionality reduction results.

The package includes two main functions:

• create_projection_plots: Creates three types of plots (ellipse, Voronoi, and combined)

• create_voronoi_plot: Creates standalone Voronoi tessellation plots

These functions are designed to visualize class separation in dimensionally reduced data from techniques like PCA, PLS-DA, t-SNE, or other projection methods commonly used in biomedical research.

Voronoi tessellation divides the plot space into regions based on proximity to data points, providing an intuitive visualization of class boundaries and decision regions. Confidence ellipses show the distribution spread and correlation structure within each class.

Author(s)

Jorn Lotsch <[e-mail]>

References

Lötsch, J. and A. Ultsch (2024). Comparative assessment of projection and clustering method combinations in the analysis of biomedical data. Informatics in Medicine Unlocked 50: 101573. https://www.sciencedirect.com/science/article/pii/S2352914824001291

See Also

• https://github.com/JornLotsch/voronoi_projection_plot

• Report bugs at https://github.com/JornLotsch/voronoi_projection_plot/issues

Examples

# Load the iris dataset
data <- iris[, c("Sepal.Length", "Petal.Length", "Species")]

# Create comprehensive projection plots
plots <- create_projection_plots(
  data = data,
  class_column = "Species",
  legend_position = "bottom",
  add_grid_lines = FALSE
)

# Access individual plots
# plots$ellipse_plot
# plots$voronoi_plot
# plots$voronoi_plot_plus_ellipse

# Create standalone Voronoi plot
voronoi_plot <- create_voronoi_plot(
  data = data,
  class_column = "Species",
  legend_position = "bottom",
  add_grid_lines = FALSE
)

VoronoiBiomedPlot documentation built on Aug. 11, 2025, 1:07 a.m.