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Color Palettes - Usage Guide

Overview

Proper color selection ensures figures are accessible, publication-ready, and effectively communicate data patterns.

Prerequisites

# R
install.packages(c('viridis', 'RColorBrewer', 'ggsci', 'colorspace'))
# Python
pip install matplotlib seaborn

Quick Start

Tell your AI agent what you want to do:

  • "Apply a colorblind-friendly palette to my plot"
  • "Use a diverging color scheme for my heatmap"
  • "Get the Nature journal color palette"

Example Prompts

Colorblind-Friendly

"Apply viridis colors to my heatmap"

"Use a colorblind-safe palette for my categorical data"

Journal Styles

"Use the Nature journal color palette for my figure"

"Apply Cell-style colors to my plot"

Custom Palettes

"Create a custom palette matching my lab colors (#1A5276, #F39C12, #27AE60)"

"Generate a gradient from blue to red for my heatmap"

Data-Appropriate

"Use a diverging palette centered at zero for my log2FC data"

"Apply a sequential palette for my p-value heatmap"

What the Agent Will Do

  1. Identify the data type (continuous, diverging, categorical)
  2. Select appropriate palette type
  3. Apply palette to the visualization
  4. Ensure accessibility (colorblind-friendly)
  5. Test visual contrast

Palette Selection Guide

Data Type Palette Type Examples
Continuous (0 to max) Sequential viridis, Blues
Centered (-x to +x) Diverging RdBu, coolwarm
Categories Qualitative Set1, tab10, npg

Tips

  • Avoid red-green combinations (colorblind unfriendly)
  • Use viridis or cividis for safe continuous palettes
  • Test with colorblind simulators
  • Use consistent colors throughout all figures in a paper
  • Check journal requirements (some require CMYK for print)

Related Skills

  • data-visualization/ggplot2-fundamentals - Apply palettes
  • data-visualization/heatmaps-clustering - Heatmap colors