Scientific work that specifically focuses on the generation of an area-proportional Venn diagram for the quantification of the relationships between studied data sets is also available (e.g., – ).Įxamples of area-proportional 3-Venn diagrams drawn with circles (A–C) and polygons (D–F) in literature. Such diagrams have also been used in various other disciplines, such as: neuroscience biosciences microbiology botany ecology public health museum conservation criminology information search and filtering. Small multiples of such diagrams can also facilitate the analysis of a collection of data sets for different attributes (e.g., Venn diagrams depicting overlapping disease symptoms for different countries, ).Ĭonsequently, area-proportional 3-Venn diagrams have been used to, for instance: compare the cell-type of differentially regulated genes after an anti-cancer drug treatment ( Figure 1A) summarize prognostic indicators of severe malaria ( Figure 1B) analyse differences and similarities between chicken egg white proteome in three different studies ( Figure 1C) and between gene libraries ( Figure 1D) study transcriptome variation of different tissue types of the male field cricket ( Figure 1E) summarize genes affecting DNA damage in three different studies ( Figure 1F). Thus, a Venn diagram can easily depict the data set relations as well as their cardinality or other associated quantitative data. Size is processed preattentively and is easily noticeable due to its pop-out effect. A Venn diagram can also be area-proportional, such that the area of each region in the diagram is directly proportional to quantitative data corresponding to the depicted set relation. Ī Venn diagram with n curves is referred to as an n-Venn diagram and its regions depict all of the 2 n different combinations of the curve overlaps. Closed curves also aid set analysis due to the perceptual grouping principles of common regions and closure.
Both the curves and their spatial relationships are often easily visible, as closed curves are processed preattentively and pop out as complete distinct objects, particularly when the curves are smooth and have good continuation. Each data set is represented by a closed curve and each set relation is represented by one of the spatial relationships between the curves. To aid analysis, Venn diagrams are often used. For instance, relationships between groups of genes are studied to understand biological processes, improve health care, find cures to illnesses, and solve problems in agriculture.
We compare euler APE and various other methods that are currently available and we discuss differences between their generated diagrams in terms of accuracy and ease of understanding for real world data.ĭata is routinely generated and analysed. We describe the drawing method adopted by euler APE and we discuss our evaluation of the effectiveness of euler APE and ellipses for drawing random 3-set data.
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We present euler APE as the first method and software that uses ellipses for automatically drawing accurate area-proportional Venn diagrams for 3-set data. Ellipses are more flexible than circles and are similarly smooth, but none of the current automatic drawing methods use ellipses. However, polygons are non-smooth and non-symmetric, so the curves are not easily distinguishable and the diagrams are difficult to comprehend. Other methods use polygons to draw accurate diagrams. However, circles cannot draw accurate diagrams for most 3-set data and so the generated diagrams often have misleading region areas. Most methods depict the data sets as circles, as they perceptually pop out as complete distinct objects due to their smoothness and regularity. Drawing these diagrams manually is difficult and current automatic drawing methods do not always produce appropriate diagrams.
The area of the regions formed by the overlapping curves is often directly proportional to the cardinality of the depicted set relation or any other related quantitative data. Venn diagrams with three curves are used extensively in various medical and scientific disciplines to visualize relationships between data sets and facilitate data analysis.
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