scatterHatch

scatterHatch encodes group identity in scatter plots using both color and texture/pattern coding to improve interpretability of single-cell and spatial in situ datasets and to increase accessibility for readers with color-vision deficiencies.

Key Features:

• R package: Implemented as an R package that generates scatter plots integrating color and pattern coding.
• Redundant coding: Utilizes both colors and patterns to differentiate cell groups, providing redundant visual cues.
• Accessibility for CVD readers: Incorporates texture alongside color to improve clarity for individuals with various types of color-vision deficiencies (CVDs).
• Single-cell data application: Tailored for use with reduced-dimension and spatial in situ scatter plots to represent complex phenomena across cell groups.
• Demonstrated examples: Includes example visualizations demonstrating effectiveness across various types of CVDs.

Scientific Applications:

• Single-cell genomics visualization: Visualizes cellular heterogeneity and group-specific phenomena within single-cell datasets.
• Reduced-dimension scatter plots: Presents reduced-dimension embeddings to display cluster structure and group differences.
• Spatial in situ scatter plots: Represents spatially resolved single-cell data with combined color and pattern encoding.
• Accessible figure design for publications: Supports creation of figures that remain interpretable for audiences with CVDs to improve inclusive scientific communication.

Methodology:

Generates scatter plots that integrate both color and pattern (texture) coding to represent different cell groups and provides example visuals demonstrating effectiveness for various types of color-vision deficiencies.