bio-data-visualization-color-palettes

Version Compatibility

Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+, seaborn 0.13+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Color Palettes

"Choose colors for a biological visualization" → Select appropriate color palettes for sequential data (expression levels), diverging data (fold changes), or categorical data (cell types/clusters).

• Python: seaborn.color_palette(), matplotlib.colormaps
• R: RColorBrewer::brewer.pal(), viridis::viridis()

Palette Types

Type	Use Case	Example
Sequential	Continuous data (expression, coverage)	viridis, Blues
Diverging	Centered data (fold change, z-score)	RdBu, coolwarm
Qualitative	Categories (clusters, conditions)	Set1, tab10

viridis (Colorblind-Safe)

library(viridis)

# Continuous scale
ggplot(df, aes(x, y, color = value)) +
    geom_point() +
    scale_color_viridis_c()

# Discrete scale
ggplot(df, aes(x, y, color = group)) +
    geom_point() +
    scale_color_viridis_d()

# Options: viridis, magma, plasma, inferno, cividis, turbo
scale_color_viridis_c(option = 'magma')

import matplotlib.pyplot as plt

plt.scatter(x, y, c=values, cmap='viridis')
# Options: viridis, magma, plasma, inferno, cividis

RColorBrewer (R)

library(RColorBrewer)

# View all palettes
display.brewer.all()

# Sequential
scale_fill_brewer(palette = 'Blues')
scale_color_distiller(palette = 'YlOrRd', direction = 1)

# Diverging
scale_fill_brewer(palette = 'RdBu')
scale_color_gradient2(low = '#4DBBD5', mid = 'white', high = '#E64B35', midpoint = 0)

# Qualitative
scale_color_brewer(palette = 'Set1')
scale_fill_brewer(palette = 'Dark2')

# Get colors directly
brewer.pal(n = 5, name = 'Set1')