venn-diagram-lab 2.0.1

Headless Venn diagram analysis and rendering — companion to the Venn Diagram Lab web tool.

venn-diagram-lab

Headless Python companion to the Venn Diagram Lab web tool. Build, render, and statistically analyse Venn / UpSet diagrams from CSV / TSV / GMT / GMX inputs — same 44 SVG models, same intersection/Jaccard/hypergeometric statistics, same PDF report layout — but in a notebook, a Snakemake rule, or a CI job, with no browser involved.

Install

pip install venn-diagram-lab

That's it — all bundled SVG templates, sample datasets, and the vdl CLI ship with the wheel.

System deps (cairosvg): the PDF/PNG render path uses cairosvg, which needs the cairo native library. On Linux the wheel works out of the box once you have libcairo2. On macOS run brew install cairo pango. On Windows install the GTK3 runtime (cairo bundled). CI is currently Linux-only for this reason — see CHANGELOG.md "Known limitations".

For development (clone + editable):

git clone https://github.com/ZoliQua/Venn-Diagram-Lab.git
cd Venn-Diagram-Lab
python -m venv .venv && source .venv/bin/activate
python python/scripts/sync_data.py        # populates _data/ from the React side
pip install -e "python/[dev]"

Quickstart (30 seconds)

from venn_diagram_lab import load_sample, analyze

result = analyze(load_sample("dataset_real_cancer_drivers_4"))
print(result.set_sizes)
# {'Vogelstein': 138, 'COSMIC_CGC': 581, 'OncoKB': 1231, 'IntOGen': 633}

# Render the Venn diagram
result.render_venn().save("cancer_drivers.svg")

# Or a full multi-page PDF report
result.to_pdf_report("cancer_drivers_report.pdf")

Loading your own data

from venn_diagram_lab import load_csv, load_tsv, load_gmt, load_gmx, Dataset, analyze

# Binary 0/1 columns
ds = load_csv("genes.csv", binary=True)

# Aggregated (each column = a set, cells = item names)
ds = load_csv("pathways.csv", binary=False)

# GMT (one set per line)
ds = load_gmt("hallmark.gmt")

# In-memory dict
ds = Dataset.from_dict({
    "Set A": ["x", "y", "z"],
    "Set B": ["y", "z", "w"],
})

result = analyze(ds)

Visualisations

Method	Output	Best for
result.render_venn()	SVG (vector)	Publication; up to 9 sets via 44 bundled templates
result.render_venn(model='proportional')	SVG	Area-proportional; 2-3 sets only
result.render_upset()	matplotlib Figure	5+ sets where Venn is hard to read
result.render_network()	matplotlib Figure	Pairwise relationships at a glance
result.to_pdf_report(path)	Multi-page PDF	One-shot publication-ready report

All visualisation methods accept the same kwargs as the underlying render.* functions — see their docstrings for full reference.

Statistics

stats = result.statistics       # lazy compute
print(stats.jaccard)            # square pandas DataFrame
print(stats.hypergeometric)     # long-form: pair, intersection, expected, p_value, p_adjusted, ...

compute_pairwise produces 5 metric tables: Jaccard, Sørensen-Dice, Overlap Coefficient, Fold Enrichment, and the hypergeometric long-form (with Benjamini-Hochberg FDR correction).

Export to TSV (matches the web tool byte-for-byte)

result.to_region_summary_tsv("regions.tsv")     # depth-sorted region table
result.to_matrix_tsv("matrix.tsv")              # one row per item with set membership
result.to_statistics_tsv("statistics.tsv")      # pairwise stats with FDR

These match the React web tool's three Export buttons exactly — including float formatting and spreadsheet-formula escaping. The Phase 7 parity tests (pytest python/tests/test_parity_with_webapp.py) prove this for all 5 bundled samples.

Command-line interface

The wheel installs a vdl console script:

Command	Purpose
vdl version	Print the package version
vdl list-models	Table of the 44 bundled SVG models
vdl list-samples	Table of bundled sample datasets
vdl analyze <input> [--model M] [--mode binary|aggregated] [--format csv|tsv|gmt|gmx] [--output-dir D] [--venn FILE] [--upset FILE] [--network FILE] [--pdf FILE] [--statistics-tsv FILE]	Main entry point: load, analyse, optionally write outputs
vdl render-sample <name> [...same output flags...]	Bundled-sample shortcut

Without any output flags, both commands print a Rich-styled summary table. With --output-dir, all five outputs (svg, png upset, png network, pdf, tsv) are written.

Notebook gallery

Eight executable notebooks live under python/examples/:

#	Notebook	Topic
01	01_quickstart.ipynb	First analysis in 10 cells
02	02_real_cancer_drivers.ipynb	Biological walkthrough (cancer driver catalogs)
03	03_proportional_diagrams.ipynb	Area-proportional 2/3-set demos
04	04_upset_vs_venn_vs_network.ipynb	Choosing the right visualisation
05	05_statistics_deep_dive.ipynb	Jaccard / Dice / Hypergeometric / BH-FDR
06	06_pipeline_integration.ipynb	Snakemake + Nextflow templates
07	07_pdf_reports.ipynb	Multi-page PDF reports
08	08_custom_styling_and_export.ipynb	lxml SVG post-processing + multi-format export

Each notebook is built from a python/scripts/notebooks/_build_NN_*.py script and executed nightly on CI to prevent bit-rot.

Bundled sample datasets

Name	Sets	Items	Source
dataset_real_cancer_drivers_4	4	1394	Vogelstein / COSMIC CGC / OncoKB / IntOGen catalogs
dataset_real_msigdb_cancer_pathways	5	777	MSigDB Hallmark cancer pathways
dataset_real_msigdb_immune_pathways	4	521	MSigDB Hallmark immune pathways
dataset_mock_gene_sets	6	3288	Synthetic for demos
dataset_mock_streaming_platforms	8	800	TV/movie titles across 8 streaming services

from venn_diagram_lab import list_samples, load_sample
list_samples()
ds = load_sample("dataset_real_cancer_drivers_4")

Contributing

The repo monorepos the React web tool and this Python package. After cloning:

cd Venn-Diagram-Lab
python -m venv .venv && source .venv/bin/activate
python python/scripts/sync_data.py
pip install -e "python/[dev]"
pytest python/tests/ -q

Run the slow notebook suite (~3 min):

pytest python/tests/test_notebooks.py -m slow

Regenerate the parity-test fixtures (requires Node 20+):

npm install
npm run fixtures:parity

Conventional commit prefixes used: feat(python):, fix(python):, chore(python):, docs(python):, test(python):.

Versioning

Strict SemVer. Pre-1.0 minor bumps may include behavior changes; see CHANGELOG.md.

License

MIT — see LICENSE.

Citation

If you use this package in research, please cite:

Dul Z., Ölbei M., Thomas N. S. B., Si Ammour A., Csikász-Nagy A. (2026).
Venn Diagram Lab — interactive Venn / UpSet diagrams (v2.0.0).
https://www.venndiagramlab.org/
DOI: 10.5281/zenodo.20000599

See CITATION.cff for machine-readable citation metadata.