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The main function “ggVennDiagram()” accepts a list input and outputs a ggplot object. By measuring the length of input list, it automatically applies internal functions to build a plot in two steps: data pre-processing and visualization. The second step relies on ggplot2’s functions; therefore, we mainly focus on explaining the first step as follows.

Data pre-processing then can be divided into two procedures: shape generation, which defines the edges of Venn sets and regions and region value calculation which calculates the region items and performs necessary statistics, such as counting and calculating percentages.

Since the returned data after data pre-processing are compatible with the sf object, these data are directly passed into “geom_sf()”/“geom_sf_label()”/“geom_sf_text()” functions intrinsically provided by ggplot2. Filling colors are mapped to the counts of region items, and a color bar legend is generated automatically to show the difference between different regions (Figure 1A).

In ggVennDiagram, we treated all the edges, labels, and polygons as simple features, which refer to a standard to describe how the objects in the real world can be presented in computers, with emphasis on the spatial geometry of these objects. A total of 15 types of simple features are implemented in R, three of which are used to describe all the components of a Venn diagram.

Firstly, the edges of sets are inherited from LINESTRING, which is a sequence of points connected by straight non-self-intersecting lines. Secondly, all the possible intersecting regions are inherited from POLYGON, which is formed by a sequence of closed points. Thirdly, the labels of sets are inherited from POINT, which is a single point used to anchor a short text. Simple features are to define the coordinates of Venn plot components. It is the first time for simple features to be employed in a Venn diagram. Such a design enhances the ability to describe Venn diagram components, making it possible to calculate intersection and overlapping regions between different sets.

To simplify the calculation of simple features, we introduce an S4 class Polygon object which expands the S4 class Venn object derived from RVenn. As those methods are implemented in RVenn, set operation methods are implemented for Polygon object, resulting in the unified set operation functions for the set object Venn and the shape object Polygon.

The shape used in the Venn diagram with less than four sets can be a simple structure, such as a circle or an ellipse, but when the Venn diagram has more than four sets, irregular polygons are required. It is hard to generate irregular polygons with simple geometric functions. Therefore, ggVennDiagram is designed to bear a built-in preprocessed shape data set imported from venn, VennDiagram, and some online materials, which undoubtedly increases the efficiency of shape generation on the user side.

Region value calculation depends on the RVenn package and new functions written on its defined Venn object. There are a total of 2ⁿ - 1 regions in a Venn diagram, in which n indicates the number of sets. The member and its number in each region are stored with region IDs in a tibble and joined with the region shape object through unique IDs. Likewise, the member and its number in a set are assigned to the SetEdge through unique IDs in parallel. By doing this, a complete VennPlotData object is generated for subsequent plotting (Figure1B).

After data pre-processing, ggVennDiagram calls native ggplot2 functions to draw Venn diagrams in four layers (Figures 2A,B). The first layer is to show the number of members in each region, with gradient color filling exhibiting the differences in member number among various regions. The second layer is to show set edges. When an irregular polygon rather than an ellipse and circle is used to draw a Venn diagram, set edges are essential for distinguishing the boundary between different sets. The third layer is to display set labels, and the fourth layer is to exhibit region labels. The data pre-processing function is accessible to users. Thus, it is easy for those familiar with the ggplot2 syntax to revise the details of the image including the region fill color, line color/thickness, text style, and so on (Figures 2C,D).

As has been noted above, a set of built-in shapes from ggVennDiagram is used to plot the Venn diagram. By default, only the most appropriate shape is used when the main function “ggVennDiagram()” is called. However, other applicable shapes can be specified in a stepwise plot, which has been described in the previous section (Figure 3A). In addition, ggVennDiagram provides a series of functions to help users with a novel shape when they know shape coordinates. For example, a six-set Venn diagram can be made up of only six triangles (Figure 3B). To this end, we just need to pass the vertex coordinates and set label coordinates to the “triangle()” function and “label_position()” function, respectively, and then construct a VennPlotData object with the constructor function “VennPlotData()” (Figure 1B). The generated VennPlotData object now can join with set and calculated region values through “plotData_add_venn()” function, and the resultant data can be used in stepwise customization of the Venn diagram (Figure 3B).

From version 1.0, ggVennDiagram supports Venn diagrams with up to seven sets (Figures 3C–E). This feature is dependent on the shapes imported from another R package venn (Dusa, 2020). However, we insist that Venn diagrams with more than four sets may not be a good choice to display their relationships.

To date, there are three major methods to display set relationships: Venn diagram, Euler diagram, and UpSet plot (Conway et al., 2017). The UpSet plot is a state-of-the-art visualization technique for the quantitative analysis of sets (Lex et al., 2014), and it supports an unlimited number of sets. When the number of sets is very large, it is more justified to choose the UpSet plot.

The first version of ggVennDiagram was released on October 9th, 2019 (version 0.3). Since then, it has been applied to many biomedical research fields. For example, Cook et al. (2020) used ggVennDiagram to show overlapping differentially expressed genes across three sample times (days 1, 3, and 5) in both the root and the shoot of canola. Besides, Harris et al. (2020) used ggVennDiagram to display that 22.5% of differentially expressed genes were shared by treated mice and human patients. Furthermore, Maguire et al. (2020) used ggVennDiagram to confirm that their novel method has low bias and is more sensitive than three other methods for small RNA library preparation. In addition, ggVennDiagram is also used for analyzing the differences between several spatially varied oral metabolomics samples (Ciurli et al., 2021) and for comparing single-nucleotide variants between tumor and non-tumor tissues (Horny et al., 2021). So far, ggVennDiagram has been cited in more than 20 peer-reviewed articles and open-access preprints, as retrieved by Google Scholar. It could be speculated that ggVennDiagram has a very wide range of application scenarios in biomedical studies.

Table 1 presents the features of currently available Venn plot tools (see also Figure 4 for the comparison of the generated plots by these tools). First of all, the support for grammar of graphics by nine R packages and two web tools was assessed. Grammar of graphics is a general scheme for data visualization, which breaks up graphs into semantic components, such as scales and layers. Except ggVennDiagram, none of these tools fully support this feature in plotting Venn diagrams.

Additionally, ggVennDiagram takes the lead in the following three aspects of data processing capacity. (1) We can get access to region members by querying the VennPlotData object. (2) It should be noted that we only implement the input of list (as input format). This design is simple enough to understand and prepare, and it is easy to store set members, which is essential for the calculation of region members. (3) Via the design of a layered object, ggVennDiagram can store plotting data into the VennPlotData object (Figure 1B), thus making it possible to query and reuse the target data.

Furthermore, ggVennDiagram is superior in four aspects of visualization. (1) Region filling allows the user to easily identify the differences between various parts of the Venn diagram, and this is one of the key features of ggVennDiagram. Although several other tools have this feature, only ggVennDiagram is fully automatic since it is driven by ggplot2’s aesthetic mapping. (2) The ggVennDiagram has built-in shapes consisting of circles, ellipses, and others. Besides, we also provide functions to help users to import self-defined shapes (Figures 3A,B). (3) The ggVennDiagram supports two- to seven-set Venn diagrams, which is adequate for daily use. (4) Element control in ggVennDiagram can be applied for set edge/label and region filling/label, so that it is convenient to set their color/line type/size, and so on (Figures 1A, 2B,D,3A–E).

Notably, several tools support both Venn and Euler diagrams. However, an Euler diagram has two shortages: firstly, it is area proportional, but the human eye is less sensitive to area than to color; secondly, it only shows relevant relationships, but sometimes, it is impossible to show all intersection regions merely by using simple geometric shapes, such as circles and ellipses. Therefore, we assume that it is more appropriate to use color filling for displaying the difference between different regions in ordinary biomedical studies.

Overall, ggVennDiagram integrates and optimizes a Venn diagram plotting method, exhibiting multiple advantages in performance over current existing tools. Compared with webtool, R scripts are easier to integrate into the existing bioinformatics analysis pipelines to realize automation and batch drawing of Venn diagrams. Therefore, it is necessary and useful to develop ggVennDiagram.