Bio Data Visualization Upset Plots

Version Compatibility

Reference examples tested with: ComplexUpset 1.3+ (R, Krassowski), UpSetR 1.4.0 (last 2019 release; effectively unmaintained), upsetplot 0.9+ (Python).

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

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

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

UpSet Plots

"Show set intersections for 4+ sets" -> Replace Venn diagrams (which become illegible past 4 sets) with UpSet (Lex 2014 IEEE TVCG 20:1983). The display: a matrix of dots indicating which sets participate in each intersection, with a vertical bar above each column showing intersection size and horizontal bars on the left showing per-set total size. Sort by intersection size (cardinality) for "biggest overlaps first" or by degree (number of sets) for grouped layout.

• R: ComplexUpset::upset (Krassowski; ggplot2-native, recommended), UpSetR::upset (Conway 2017; legacy, unmaintained)
• Python: upsetplot.UpSet

The Single Most Important Modern Insight -- UpSetR Is Effectively Unmaintained

UpSetR (Conway, Lex, Gehlenborg 2017 Bioinformatics 33:2938) is the original R implementation but has had no CRAN release since v1.4.0 (2019). ComplexUpset (Krassowski; CRAN active through 2025-07) is the actively maintained ggplot2-grammar replacement. For new work in 2026, prefer ComplexUpset. Caveat: ggplot2 4.0 (mid-2025) broke ComplexUpset's upset() function (issue #213); pin to compatible versions until patched.

The Lex 2014 paper and underlying UpSet visualization concept are not affected — the visualization is the same; the difference is which R package implements it best in the current ecosystem.

ComplexUpset (Modern Default)

Goal: Render a set-intersection plot with cardinality-sorted bars, optional metadata stacks (e.g., percent of intersection significant), and pre-specified queries highlighting biologically relevant intersections.

Approach: Convert set memberships to a long-format data frame with one row per element and binary columns per set; pass to upset() with intersections='all' or pre-specified subset; use ComplexUpset::upset_query to highlight intersections.

library(ComplexUpset)
library(ggplot2)

# Convert from list of sets to long format
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'))

# Long-format binary membership matrix
all_elements <- unique(unlist(sets))
df <- data.frame(element = all_elements)
for (s in names(sets)) df[[s]] <- df$element %in% sets[[s]]

# UpSet
upset(df,
      intersect = names(sets),                          # which columns are sets
      n_intersections = 20,                             # show top 20 intersections
      sort_intersections = 'descending',                # by cardinality
      sort_intersections_by = 'cardinality',            # 'cardinality' OR 'degree'
      base_annotations = list(
          'Intersection size' = intersection_size(
              counts = TRUE,
              text = list(size = 3))),
      themes = upset_modify_themes(
          list('Intersection size' = theme(panel.grid = element_blank()))))

Sorting -- Cardinality vs Degree

Cardinality sort (default): intersections ordered by size (largest first). Reveals "the biggest overlap is A∩B."

Degree sort: intersections grouped by number of sets they include (1-set intersections, then 2-set, then 3-set, etc.). Reveals "how distributed are the overlaps across set counts?"

Choose based on the scientific question. Cardinality is the default for "find the biggest overlap"; degree is appropriate when comparing across "exclusive to 1 set" vs "shared by all."

Pre-Specified Queries / Highlighting

upset(df,
      intersect = names(sets),
      queries = list(
          upset_query(intersect = c('SetA', 'SetB'),
                       color = '#D55E00', fill = '#D55E00',
                       only_components = c('intersections_matrix', 'Intersection size')),
          upset_query(intersect = c('SetA', 'SetC', 'SetD'),
                       color = '#0072B2', fill = '#0072B2',
                       only_components = c('intersections_matrix', 'Intersection size'))))

Attribute Panels (ComplexUpset Strength)

Unlike UpSetR's "boxplot.summary," ComplexUpset supports arbitrary ggplot annotations stacked above the intersection bars:

upset(df,
      intersect = names(sets),
      annotations = list(
          'log2 FC' = ggplot(mapping = aes(x = intersection, y = log2FC)) +
                       geom_boxplot() + theme_classic(),
          'Significant fraction' = ggplot(mapping = aes(x = intersection, fill = significant)) +
                                    geom_bar(position = 'fill') +
                                    scale_fill_manual(values = c('TRUE' = '#D55E00', 'FALSE' = 'grey80')) +
                                    theme_classic()))

upsetplot (Python)

from upsetplot import from_contents, UpSet
import matplotlib.pyplot as plt

sets = {'SetA': ['Gene1','Gene2','Gene3','Gene4'],
        'SetB': ['Gene2','Gene3','Gene5','Gene6'],
        'SetC': ['Gene1','Gene3','Gene6','Gene7']}
data = from_contents(sets)

upset = UpSet(data,
              subset_size='count',
              show_counts=True,
              sort_by='cardinality',                    # 'cardinality' OR 'degree'
              sort_categories_by='cardinality',
              facecolor='#0072B2',
              element_size=40)
upset.style_subsets(present=['SetA', 'SetB'], facecolor='#D55E00')   # highlight specific intersection
fig = plt.figure(figsize=(8, 5))
upset.plot(fig=fig)
plt.savefig('upset.pdf', bbox_inches='tight')

UpSetR (Legacy — Use Only for Reproducibility)

library(UpSetR)
upset(fromList(sets),
      nsets = 4, nintersects = 20,
      order.by = 'freq',
      decreasing = TRUE,
      mb.ratio = c(0.6, 0.4),
      point.size = 3,
      line.size = 1,
      text.scale = c(1.5, 1.3, 1.3, 1, 1.5, 1.3))

UpSetR works but lacks ggplot2 grammar and active maintenance. Reproducing a paper's UpSetR figure is the main reason to use it in 2026.

Per-Method Failure Modes

Using UpSetR for new work in 2026

Trigger: Following older tutorials that default to UpSetR.

Mechanism: UpSetR has not had a CRAN release since 2019; integration with current ggplot2 / R ecosystem stale.

Symptom: Limited customization; ggplot2 layer not available; eventual breakage.

Fix: Switch to ComplexUpset for new figures. UpSetR is fine for reproducing old figures.

ggplot2 4.0 broke ComplexUpset

Trigger: ggplot2 4.0 (mid-2025) introduced API changes; ComplexUpset's upset() errored.

Mechanism: Upstream ggplot2 changes affected ComplexUpset internals (issue #213).

Symptom: "Error in upset(): ..." after ggplot2 upgrade.

Fix: Pin compatible versions (renv::install('ggplot2@3.5.2')) until ComplexUpset patches. Check GitHub issues for fix status.

Too many sets makes UpSet unreadable

Trigger: UpSet with 10+ sets and n_intersections = Inf.

Mechanism: Number of possible intersections is 2^N − 1; with 10 sets that's 1023 columns.

Symptom: Vertical bars too thin to read; matrix dots unrecognizable.

Fix: Set n_intersections = 20 (or whatever fits); pre-filter to relevant intersections via intersections = list(c('SetA','SetB'), c('SetA','SetC','SetD')).