vennDiagramLab

vennDiagramLab is the R companion to the Venn Diagram Lab web tool and the Python venn-diagram-lab package. It provides headless Venn / UpSet / Network diagram analysis and rendering for bioinformaticians and biostatisticians who work natively in R, with byte-equivalent outputs that match the web tool and the Python package down to the byte.

1. Features

44 SVG diagram models (2- to 9-set) from a dozen construction methods (Venn 1880, Edwards 1996, Anderson 1988, Grünbaum 1984/1992, Bannier-Bodin 2017, Carroll 2000, Mamakani 2012, SUMO-Venn) — the same library shipped by the web tool.
Area-proportional 2- and 3-set layouts via analytical (solve_2set) and approximate (solve_3set) solvers.
UpSet plots via ComplexUpset with sort-by-size / sort-by-degree, depth / heatmap / custom color modes, and threshold cutoffs.
Force-directed network views via ggraph + tidygraph, with configurable edge metric (intersection / Jaccard / fold enrichment / overlap coefficient) and significance coloring.
Five pairwise statistical metrics (Jaccard, Dice, overlap coefficient, fold enrichment, hypergeometric over-representation) with BH-FDR adjustment.
Multi-page PDF reports combining overview, Venn + UpSet, statistics tables, network, and methodology pages in a single US-Letter-landscape document.
ggplot2 layer (geom_venn()) and broom-compatible S3 methods (tidy() / glance() / augment()) for tidyverse + targets / drake pipeline integration.
Byte-equivalent TSV exports tested against the React webapp’s golden fixtures — the same region-summary, item-matrix, and statistics files the web tool’s “Export” buttons emit.
Cross-implementation parity verified by 12 byte-equivalence tests against the Python package’s golden fixtures (4 sample datasets × 3 export types).

2. Install

2.1. From CRAN (recommended)

vennDiagramLab is on CRAN (current version: 2.4.2):

install.packages("vennDiagramLab")

CRAN binaries are built for the three current major Windows / macOS / Linux R versions. Source-only? install.packages("vennDiagramLab", type = "source").

2.2. From Bioconductor (alternate channel once published)

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("vennDiagramLab")

2.3. Development version (GitHub)

The package source lives in the r/ subdirectory of the monorepo:

install.packages("remotes")
remotes::install_github("ZoliQua/Venn-Diagram-Lab", subdir = "r")

A standalone mirror (no subdir = needed) is kept in sync on every push to main for Bioconductor’s Single Package Builder, which requires DESCRIPTION at repo root:

remotes::install_github("ZoliQua/vennDiagramLab")

Both installs produce the same package; the mirror exists purely to satisfy Bioc’s submission tooling.

3. Quickstart

library(vennDiagramLab)

# Load a bundled dataset (4 cancer driver source catalogs)
ds     <- load_sample("dataset_real_cancer_drivers_4")
result <- analyze(ds)

# Render the Venn diagram as SVG
svg <- render_venn_svg(result, title = "Cancer driver overlap")
writeLines(svg, "cancer_drivers.svg")

# Or get a tidy summary
broom::tidy(result)

# Or generate a multi-page PDF report
to_pdf_report(result, "cancer_drivers.pdf")

See vignette("v01_quickstart") for the full intro and browseVignettes("vennDiagramLab") for the complete eight-vignette gallery.

3.1 Complete User Guide

You can reach the complete USER GUIDE here in markdown format or downlad it as a PDF File.

4. Statistics surfaces

Two additional statistics surfaces complement the pairwise tables, matching the web tool’s Statistics panel and the Python package:

Function	Purpose
`item_share_distribution(matrix)`	Histogram of how many sets each item belongs to (1, 2, …, n)
`cluster_set_order(D, linkage = "average")`	UPGMA / complete / single hierarchical linkage on a distance matrix; returns leaf order plus dendrogram merges
`render_share_distribution(dataset)`	SVG bar chart of the item-share distribution
`render_cluster_heatmap(result, linkage = "average")`	Pairwise-Jaccard heatmap with UPGMA-reordered axes and side dendrograms

library(vennDiagramLab)

ds  <- load_sample("dataset_real_cancer_drivers_4")
res <- analyze(ds)

img <- render_share_distribution(ds)
substr(slot(img, "content"), 1L, 200L)

heatmap <- render_cluster_heatmap(res, linkage = "average")
substr(slot(heatmap, "content"), 1L, 200L)

Both renderers return an SvgImage S4 object (slots: content, width, height), the same shape as render_venn_svg().

5. PDF report — About + Credits

to_pdf_report() closes with the unified About This Report appendix shared across the webtool, the Python package, and this companion: 12 structured sections (intro, Plots, Statistics) followed by a Credits and Cite footer listing authors, package URLs, and the Zenodo DOI. Section titles render in bold, bodies in plain weight, and the content auto-paginates across as many landscape pages as needed.

6. Latest additions (v2.2.3)

6.1. Render + PDF + bundle parity

render_enrichment_bar(result, metric) — Pairwise enrichment bar chart SVG.
render_enrichment_lollipop(result, metric) — Pairwise enrichment lollipop chart SVG.
to_excel_workbook(result, path) — 3-sheet xlsx (Jaccard / Sørensen-Dice / Enrichment).
to_zip_report(result, path) — Full Report ZIP bundle (PDF + SVGs + TSVs + xlsx + README).

to_pdf_report() also gains include_share = TRUE (Item Share Distribution page, on by default) and include_cluster = FALSE (Cluster Heatmap page, opt-in) flags.

6.2. Item display, Highlight, Region accessors, Boolean DSL

render_venn_svg(result, show_items = TRUE, item_options = list(...)) — Item names inside regions, with truncation and column layout.
render_venn_svg(result, highlight = c("AB", "ABC")) — Spotlight mode; desaturates sets that do not contribute to any highlighted region.
intersection_items(result, sets) — Items in every named set.
exclusive_items(result, sets) — Items in exactly this combination.
union_items(result, sets) — Items in any of the named sets.
parse_region_expression(expr, n_sets) — Boolean DSL parser returning a sorted integer vector of region bitmasks; composes with highlight = ....

The four new helpers chain naturally:

masks <- parse_region_expression("A & B + B & C", n_sets = 4L)
img   <- render_venn_svg(result, highlight = masks, show_items = TRUE)
items <- exclusive_items(result, c("A", "B"))

7. Documentation

Full reference site + vignettes: https://zoliqua.github.io/Venn-Diagram-Lab/r/
Eight RMarkdown vignettes (also accessible via vignette(package = "vennDiagramLab")):
1. v01_quickstart — five-step intro.
2. v02_real_cancer_drivers — long-form biological walkthrough (Vogelstein, COSMIC CGC, OncoKB, IntOGen).
3. v03_proportional_diagrams — area-proportional 2/3-set layouts and the low-level geometry helpers.
4. v04_upset_vs_venn_vs_network — choosing the right visualization per set count.
5. v05_statistics_deep_dive — Jaccard / Dice / hypergeometric / BH-FDR worked examples.
6. v06_pipeline_integration — broom + dplyr + targets pipeline sketch.
7. v07_pdf_reports — composite multi-page PDF generation.
8. v08_custom_styling_and_export — custom names / colors, geom_venn(), multi-format export.

vennDiagramLab is one of three coordinated implementations sharing the same SVG model library, statistics, and byte-equivalent TSV outputs:

Web tool — interactive viewer, editor, and visual analysis: https://www.venndiagramlab.org/
Python package (venn-diagram-lab on PyPI): https://pypi.org/project/venn-diagram-lab/
R package (this package — vennDiagramLab on CRAN + Bioconductor)

9. Source repositories

Monorepo (web tool + Python + R, primary development): https://github.com/ZoliQua/Venn-Diagram-Lab
R-only mirror (for Bioconductor Single Package Builder, auto-synced from monorepo main): https://github.com/ZoliQua/vennDiagramLab

The mirror is read-only — file changes should always be made in the monorepo. A GitHub Action splits the r/ subtree and force-pushes it (with a Bioc 0.99.z Version override) to the mirror on every push to main.

9. Citation

If you use vennDiagramLab in published work, please cite both the software and the version you used.

After install:

citation("vennDiagramLab")

returns a bibentry with the correct version + DOI for the installed copy.

10.1. DOIs

Two stable identifiers are available — pick whichever fits your reference style:

DOI	Resolves to	When to use
`10.32614/CRAN.package.vennDiagramLab` (link)	The CRAN package page	Citing the package as a CRAN release
`10.5281/zenodo.19510813` (link)	The Zenodo concept record — always resolves to the latest archived version	Citing the software as an archived artifact (“Cite all versions” — no need to update per release)

10.2. Plain-text citation

Dul Z., Ölbei M., Thomas N.S.B., Si Ammour A., Csikász-Nagy A. (2026).
vennDiagramLab: Headless Venn diagram analysis and rendering.
R package version 2.2.2.
https://CRAN.R-project.org/package=vennDiagramLab
DOI: 10.32614/CRAN.package.vennDiagramLab

10. Contributing + bug reports

Issues: https://github.com/ZoliQua/Venn-Diagram-Lab/issues
The repository accepts pull requests against main in the monorepo. Do not open PRs against the R-only mirror — they will be lost on the next subtree sync.

11. License

MIT — see LICENSE.