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Scatter Plot

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

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Related Experiment Video

Updated: Aug 17, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Generating colorblind-friendly scatter plots for single-cell data.

Tejas Guha¹, Elana J Fertig^2,3,4,5,6, Atul Deshpande^2,3,4

¹Department of Electrical and Computer Engineering, A. James Clark School of Engineering, The University of Maryland, College Park, United States.

|December 16, 2022

Summary

This summary is machine-generated.

This study introduces scatterHatch, an R package for creating accessible scatter plots in single-cell bioinformatics. It uses both color and patterns to differentiate cell groups, improving clarity for all readers, including those with color vision deficiencies.

Keywords:

UMAP accessibility genetics genomics none single-cell software tool spatial data visualization

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Last Updated: Aug 17, 2025

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Area of Science:

Bioinformatics
Computational Biology
Data Visualization

Background:

Scatter plots are crucial for visualizing single-cell data, often relying solely on color to distinguish cell groups.
Increasing data complexity diminishes the effectiveness of color-only differentiation, particularly for individuals with color vision deficiencies (CVDs).

Purpose of the Study:

To introduce scatterHatch, an R package designed to enhance the interpretability of scatter plots in single-cell bioinformatics.
To provide a visualization method that redundantly encodes cell groups using both color and patterns for improved accessibility.

Main Methods:

Development of the scatterHatch R package for generating scatter plots.
Implementation of redundant coding by combining color and pattern fills for cell groups.
Simulation of various color vision deficiency (CVD) types to assess plot accessibility.

Main Results:

ScatterHatch plots demonstrate superior interpretability compared to traditional color-only scatter plots.
Redundant coding with patterns significantly improves the differentiation of cell groups under simulated CVD conditions.
The package offers a practical solution for creating more inclusive scientific visualizations.

Conclusions:

ScatterHatch enhances the accessibility of single-cell data visualizations by employing pattern fills alongside color.
This approach ensures that critical biological insights are discernible to a broader audience, irrespective of their color vision status.
The R package facilitates the creation of more robust and universally understandable bioinformatics graphics.