Venn plots are intuitive and widely adopted graphical tools for comparing multiple datasets, particularly useful for highlighting both shared and distinct elements. Commonly employed in fields such as genomics, transcriptomics, and systems biology, these plots help distill complex datasets into interpretable summaries. This article outlines the principles and advantages of Venn diagrams, provides guidance on their interpretation, and demonstrates their generation in R using the VennDiagram, ggvenn, and eulerr packages. Through practical examples, this guide equips researchers and data scientists with the skills to extract meaningful biological or analytical insights from overlapping data sets.
What Are Venn Plots?
Venn plots-more formally referred to as Venn diagrams-illustrate the logical relationships between sets of data. First introduced by the British logician John Venn in the late 19th century, these diagrams use overlapping geometrical shapes, most commonly circles, to depict intersections (shared elements) and mutually exclusive regions (distinct elements). In the context of biological sciences, Venn diagrams have become a standard visualization approach for exploring commonalities among gene expression profiles, protein targets, or metabolomic features across different experimental conditions or sample groups. Their utility lies in their simplicity and their capacity to summarize complex relationships in a format that is both accessible and informative.
The Advantages of Venn Plots
1. Intuitive Interpretation
Venn plots offer a visually accessible method for identifying the degree of overlap between datasets. By observing intersecting and non-overlapping regions, users can quickly pinpoint elements that are common or exclusive, without requiring extensive computational analysis.
2. Facilitation of Comparative Analysis
These plots are invaluable in comparative biological studies. For example, in transcriptomic analyses, Venn diagrams help reveal genes commonly regulated across conditions or uniquely expressed under specific stimuli, enabling the identification of condition-specific markers or signaling pathways.
3. Effective Data Reduction
When working with large and complex datasets, Venn plots enable efficient summarization by focusing on key intersecting subsets. This can streamline downstream analyses and assist in selecting candidates for experimental validation.
4. Versatility Across Disciplines
Beyond the life sciences, Venn diagrams are employed in marketing, business analytics, and machine learning-for tasks such as customer segmentation, feature selection, and comparison of model outputs across categories.
How to Interpret a Venn Plot
Reading a Venn diagram involves understanding the basic principles of set theory. The key components include:
Shapes (typically circles): Represent different datasets or experimental groups.
Overlapping areas: Indicate elements common to two or more datasets.
Non-overlapping regions: Contain elements unique to one dataset.
Labels or counts: Denote either the number or identity of items in each region.
In transcriptomic studies, for instance, a five-group Venn diagram comparing gene expression across subtypes such as Her2, Basal, Normallike, LumA, and LumB can visualize both shared and uniquely regulated genes across these biological conditions.
The venn plot of differentially expressed mRNAs in different samples.( C Erdogan.,et.al, 2024)
How to Draw a Venn Plot in R
So, how do we draw venn plot in the R language? Here, the editor brings you an example. We will start with simple random data.
First, we need to install and load the VennDiagram package: The VennDiagram package is one of the most widely used tools for generating Venn plots in R. It provides flexibility in terms of customization and output formats.
BiocManager::install("VennDiagram")
library(VennDiagram)
Next, we can use the draw.quad.venn () function to draw a venn plot.
To generate a Venn plot in R, the data must be structured as lists representing different datasets. In this example, we generated four random data sets containing 400, 600, 350 and 550 genes, respectively.
set.seed(123)
A <- paste0("Gene", sample(1:1000, 400, replace = FALSE))
B <- paste0("Gene", sample(1:1000, 600, replace = FALSE))
C <- paste0("Gene", sample(1:1000, 350, replace = FALSE))
D <- paste0("Gene", sample(1:1000, 550, replace = FALSE))
venn_data <- list(A = A, B = B, C = C, D = D)
Using the draw.quad.venn () function to visualize them.
venn.plot <- draw.quad.venn(
area1 = length(A),
area2 = length(B),
area3 = length(C),
area4 = length(D),
n12 = length(intersect(A, B)),
n13 = length(intersect(A, C)),
n14 = length(intersect(A, D)),
n23 = length(intersect(B, C)),
n24 = length(intersect(B, D)),
n34 = length(intersect(C, D)),
n123 = length(Reduce(intersect, list(A, B, C))),
n124 = length(Reduce(intersect, list(A, B, D))),
n134 = length(Reduce(intersect, list(A, C, D))),
n234 = length(Reduce(intersect, list(B, C, D))),
n1234 = length(Reduce(intersect, list(A, B, C, D))),
category = c("A", "B", "C", "D"),
fill = c("blue", "red", "green", "yellow"),
alpha = 0.5,
cat.col = c("blue", "red", "green", "yellow"),
cat.cex = 1.5,
margin = 0.05
)
The random dataset venn plot result.
For a more modern and ggplot2-compatible visualization, the ggvenn package provides a clean and customizable alternative:
install.packages("ggvenn")
library(ggvenn)
ggvenn(venn_data, show_percentage = TRUE, fill_color = c("#1E90FF", "#FF8C00", "#4DAF4A", "#FFD700"))
The random dataset venn plot result.
For those needing proportional Venn plots where the area of each section accurately reflects the data distribution, the eulerr package is a powerful tool:
install.packages("eulerr")
library(eulerr)
venn_list <- list(A = A, B = B, C = C, D = D)
plot(euler(venn_list), quantities = TRUE)
The random dataset venn plot result.
Venn diagrams are essential visualization tools for analyzing categorical overlap across datasets. Their intuitive format and broad applicability make them valuable across diverse disciplines, from genomics to marketing analytics. With R packages like VennDiagram, ggvenn, and eulerr, researchers can generate publication-quality plots that facilitate data interpretation, highlight key relationships, and support evidence-based decision-making. Whether applied to gene expression studies, customer profiling, or machine learning model evaluation, Venn plots remain a versatile and powerful instrument for comparative analysis.
Reference
- Erdogan C, Suer I, Kaya M. et al. Bioinformatics analysis of the potentially functional circRNA-miRNA-mRNA network in breast cancer. PLoS One (2024). https://doi.org/10.1371/journal.pone.0301995
