Bio data visualization upset plots

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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

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