No abstract

Reduced-dimension or spatial in situ scatter plots are widely employed in bioinformatics papers analyzing single-cell data to present phenomena or cell-conditions of interest in cell groups. When displaying these cell groups, color is frequently the only graphical cue used to differentiate them. However, as the complexity of the information presented in these visualizations increases, the usefulness of color as the only visual cue declines, especially for the sizable readership with color-vision deficiencies (CVDs). In this paper, we present scatterHatch, an R package that creates easily interpretable scatter plots by redundant coding of cell groups using colors as well as patterns. We give examples to demonstrate how the scatterHatch plots are more accessible than simple scatter plots when simulated for various types of CVDs.

Summary: Color is often used as a primary differentiating factor in visualization of single-cell and multi-omics analyses. However, color-based visualizations are extremely limiting and require additional considerations to account for the wide range of color perceptions in the population. The scatterHatch package provides software for accessible single-cell visualizations that use patterns in conjunction with colors to amplify the distinction between different cell types, states, and groups. Availability: scatterHatch is available on Github at https://github.com/FertigLab/scatterHatch. Supplementary information: Supplementary figures are provided in the attached document.