Color Palettes
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')
matplotlib/seaborn (Python)
import matplotlib.pyplot as plt
import seaborn as sns
# Sequential
plt.scatter(x, y, c=values, cmap='Blues')
# Diverging
plt.scatter(x, y, c=values, cmap='RdBu_r', vmin=-2, vmax=2)
# Qualitative
palette = sns.color_palette('Set1', n_colors=5)
sns.scatterplot(x=x, y=y, hue=group, palette='Set1')
# Custom palette
custom_palette = {'Control': '#4DBBD5', 'Treatment': '#E64B35'}
sns.scatterplot(x=x, y=y, hue=group, palette=custom_palette)
Scientific Journal Palettes
library(ggsci)
# Nature Publishing Group
scale_color_npg()
scale_fill_npg()
# AAAS Science
scale_color_aaas()
# Lancet
scale_color_lancet()
# JAMA
scale_color_jama()
# JCO
scale_color_jco()
Custom Palettes
# Define custom colors
my_colors <- c(
'Control' = '#4DBBD5',
'Treatment' = '#E64B35',
'Vehicle' = '#00A087'
)
scale_color_manual(values = my_colors)
scale_fill_manual(values = my_colors)
# Create gradient
colorRampPalette(c('blue', 'white', 'red'))(100)
from matplotlib.colors import LinearSegmentedColormap
colors = ['#4DBBD5', 'white', '#E64B35']
cmap = LinearSegmentedColormap.from_list('custom_diverging', colors)
plt.imshow(data, cmap=cmap)
Heatmap Colors
library(circlize)
# For ComplexHeatmap
col_fun <- colorRamp2(c(-2, 0, 2), c('#4DBBD5', 'white', '#E64B35'))
# For pheatmap
pheatmap(mat, color = colorRampPalette(rev(brewer.pal(9, 'RdBu')))(100))
import seaborn as sns
sns.heatmap(data, cmap='RdBu_r', center=0, vmin=-2, vmax=2)
Colorblind Simulation
library(colorspace)
# Check if palette is colorblind safe
demoplot(rainbow(5), type = 'map')
demoplot(viridis(5), type = 'map')
# Simulate colorblindness
cvd_colors <- deutan(c('#E64B35', '#4DBBD5', '#00A087')) # deuteranopia
cvd_colors <- protan(c('#E64B35', '#4DBBD5', '#00A087')) # protanopia
Recommended Palettes
| Data Type | Recommended | Avoid |
|---|
| Expression heatmap | RdBu (diverging) | Rainbow |
| Categories (<8) | Set1, Dark2, npg | Too many colors |
| Categories (>8) | tab20, Paired | Qualitative sets |
| Continuous | viridis, plasma | Jet, rainbow |
| p-values | viridis (reversed) | Red-green |
Transparency
# Add alpha
scale_color_manual(values = alpha(c('#E64B35', '#4DBBD5'), 0.7))
# In geom
geom_point(alpha = 0.6)
# Add alpha to hex
def add_alpha(hex_color, alpha):
return hex_color + format(int(alpha * 255), '02x')
color_with_alpha = add_alpha('#E64B35', 0.7)
# In scatter
plt.scatter(x, y, c='#E64B35', alpha=0.7)
Extract Colors from Palette
# Get discrete colors
pal <- brewer.pal(8, 'Set1')
pal[1:3] # First 3 colors
# Interpolate more colors
colorRampPalette(brewer.pal(8, 'Set1'))(20)
import seaborn as sns
palette = sns.color_palette('Set1', n_colors=8)
palette[:3] # First 3 colors
# As hex
palette.as_hex()
Related Skills
- data-visualization/ggplot2-fundamentals - Apply colors
- data-visualization/heatmaps-clustering - Heatmap colors
- data-visualization/specialized-omics-plots - Plot styling
