UpSet Plots
UpSetR (R) - Basic Plot
library(UpSetR)
# From binary matrix (rows = elements, columns = sets)
upset(fromExpression(data), order.by = 'freq', nsets = 6)
# From list of sets
gene_sets <- list(
SetA = c('Gene1', 'Gene2', 'Gene3', 'Gene4'),
SetB = c('Gene2', 'Gene3', 'Gene5', 'Gene6'),
SetC = c('Gene1', 'Gene3', 'Gene6', 'Gene7'),
SetD = c('Gene3', 'Gene4', 'Gene7', 'Gene8')
)
upset(fromList(gene_sets), order.by = 'freq', nsets = 4)
UpSetR Customization
# Customized appearance
upset(fromList(gene_sets),
nsets = 6,
nintersects = 40,
order.by = 'freq',
decreasing = TRUE,
mb.ratio = c(0.6, 0.4), # Matrix to bar ratio
point.size = 3,
line.size = 1.5,
mainbar.y.label = 'Intersection Size',
sets.x.label = 'Set Size',
text.scale = c(1.5, 1.3, 1.3, 1, 1.5, 1.3),
set_size.show = TRUE,
set_size.scale_max = 500)
# Custom set colors
upset(fromList(gene_sets),
sets.bar.color = c('#E64B35', '#4DBBD5', '#00A087', '#3C5488'),
main.bar.color = '#7E6148',
matrix.color = '#7E6148')
UpSetR with Queries
# Highlight specific intersections
upset(fromList(gene_sets),
order.by = 'freq',
queries = list(
list(query = intersects,
params = list('SetA', 'SetB'),
color = '#E64B35',
active = TRUE),
list(query = intersects,
params = list('SetA', 'SetC', 'SetD'),
color = '#4DBBD5',
active = TRUE)
))
# Highlight elements matching criteria
# Requires attribute data frame with element names as row names
upset(fromList(gene_sets),
queries = list(
list(query = elements,
params = list('logFC', 1, 2), # column, min, max
color = 'red',
active = TRUE)
))
UpSetR with Metadata Boxplots
# Add attribute plots below intersection matrix
# Requires data frame with set membership columns + attribute columns
upset(data,
order.by = 'freq',
boxplot.summary = c('logFC', 'pvalue'))
# Custom attribute plots
upset(data,
order.by = 'freq',
attribute.plots = list(
gridrows = 50,
plots = list(
list(plot = histogram, x = 'logFC', queries = FALSE),
list(plot = scatter_plot, x = 'logFC', y = 'pvalue', queries = TRUE)
),
ncols = 2
))
upsetplot (Python) - Basic
from upsetplot import from_memberships, plot, UpSet
import matplotlib.pyplot as plt
# From membership lists
memberships = [
['SetA', 'SetB'],
['SetA'],
['SetB', 'SetC'],
['SetA', 'SetB', 'SetC'],
['SetC'],
['SetA', 'SetC']
]
data = from_memberships(memberships)
# Basic plot
plot(data, show_counts=True)
plt.savefig('upset.png', dpi=150, bbox_inches='tight')
upsetplot from DataFrame
import pandas as pd
from upsetplot import from_contents, UpSet
# From dict of sets
gene_sets = {
'SetA': ['Gene1', 'Gene2', 'Gene3', 'Gene4'],
'SetB': ['Gene2', 'Gene3', 'Gene5', 'Gene6'],
'SetC': ['Gene1', 'Gene3', 'Gene6', 'Gene7']
}
data = from_contents(gene_sets)
upset = UpSet(data, subset_size='count', show_counts=True, sort_by='cardinality')
upset.plot()
plt.savefig('upset.png', dpi=150, bbox_inches='tight')
upsetplot Customization
from upsetplot import UpSet
upset = UpSet(data,
subset_size='count',
show_counts=True,
show_percentages=True,
sort_by='cardinality', # or 'degree'
sort_categories_by='cardinality',
facecolor='#4DBBD5',
element_size=40,
intersection_plot_elements=10)
fig = plt.figure(figsize=(12, 8))
upset.plot(fig=fig)
upsetplot with Metadata
# Add data attributes for additional plots
df = pd.DataFrame({
'SetA': [True, True, False, True, False],
'SetB': [True, False, True, True, False],
'SetC': [False, True, True, False, True],
'logFC': [1.2, -0.8, 2.1, 0.5, -1.5],
'pvalue': [0.01, 0.05, 0.001, 0.2, 0.03]
})
df = df.set_index(['SetA', 'SetB', 'SetC'])
upset = UpSet(df, subset_size='count')
upset.add_stacked_bars(by='significant', colors=['gray', 'red'])
# Or: upset.add_catplot(value='logFC', kind='box')
upset.plot()
Save UpSet Plots
# R - to PDF
pdf('upset_plot.pdf', width = 10, height = 6)
upset(fromList(gene_sets), order.by = 'freq')
dev.off()
# R - to PNG
png('upset_plot.png', width = 10, height = 6, units = 'in', res = 300)
upset(fromList(gene_sets), order.by = 'freq')
dev.off()
# Python
fig = plt.figure(figsize=(10, 6))
upset.plot(fig=fig)
plt.savefig('upset.pdf', bbox_inches='tight')
plt.savefig('upset.png', dpi=300, bbox_inches='tight')
Related Skills
- data-visualization/heatmaps-clustering - Alternative for smaller sets
- pathway-analysis/enrichment-visualization - Gene set overlaps
- differential-expression/de-results - DE gene set comparisons
FridrichMethod