UpSet Plot
Chart overview
UpSet plots encode set membership as a binary dot matrix and display intersection sizes as bar charts, providing a scalable alternative to Venn diagrams for four or more sets.
Key points
- Bioinformaticians and clinical researchers use UpSet plots to visualize overlapping genomic variant lists, patient cohort memberships across multiple databases, multi-omics data integration, and co-morbidity patterns.
- The layout allows precise reading of intersection sizes without the visual overlap ambiguity that makes Venn diagrams unreadable with many sets.
Python Tutorial
How to create a upset plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Python Scatter Plot TutorialExample Visualization
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View example prompt
"Create an UpSet plot from my data. Display intersection size bars vertically sorted by size at the top. Below, show a binary dot matrix where filled circles and connecting lines indicate which sets are in each intersection. Add horizontal set size bars on the left side. Label all sets clearly on the y-axis. Use journal formatting with Arial font and no top or right spines."
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Python Code Example
Console Output
Figure saved: plotivy-upset-plot.png
Common Use Cases
- 1Visualizing overlapping gene lists from multiple differential expression analyses in RNA-seq studies
- 2Comparing patient cohorts across clinical registries and identifying unique versus shared populations
- 3Displaying co-occurrence of comorbidities or symptom clusters across patient subgroups
- 4Summarizing multi-omics feature overlap in integrative cancer genomics studies
Pro Tips
Sort intersections by size by default, but allow sorting by degree for biological interpretation
Use the upsetplot Python library which provides a high-level interface for correct layout
Highlight the exclusive (single-set) intersections with a distinct color
Limit to the top 20-30 intersections when sets have many small intersections to maintain readability
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for upset-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for upset-plot analysis tasks.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.