The matrix is also very useful because it can be sorted in various ways. This makes the size of intersections easy to compare. This layout is great because we can plot the size of the intersections (the “cardinality”) as bar charts right next ot the matrix, as you can see in the following example:

Finally, the last (violet) row represents the elements shared between alll sets. The orange (fifth) row represents elements that are shared by sets A and B, but not with C. The green (third) row corresponds to the elements that are only in set B, (not in A or C). The first row in the figure is completely empty – it corresponds to all the elements that are in none of the sets. Here you can see examples of how these intersections correspond to the segments in a Venn diagram. Also notice the lines connecting the filled-in cells: they show in which direction you should read the plot: Each row corresponds to a possible intersection: the filled-in cells show which set is part of an intersection. Each column corresponds to a set, and bar charts on top show the size of the set. UpSet plots the intersections of a set as a matrix, as shown in the following figure. If you want to look at pairwise intersections between sets, some sort of co-occurence matrix might be a better choice.Īlso take a look at the Nature Methods Points of View article discussing these trade-offs. UpSet shines when you want to look at all combinations of how sets intersect. If you want to see individual items in your set, you should probably go with a Euler Diagram. Items are abstracted away as “counts”, though attributes of the items can be visualized in integrated or adjacenct plots. UpSet is well suited for analyzing distributions and properties of many items. For fewer than 4 sets, an area-proportional Venn diagram might be the better choice, as they are immediately familiar to everyone. UpSet works best for set data with more than three and less than about 30 sets.

UpSetR: An R Package For The Visualization Of Intersecting Sets And Their Propertiesīioinformatics, 33(18): 2938-2940, doi:10.1093/bioinformatics/btx364, 2017.

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