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UpSetR generates static UpSet plots. The UpSet technique visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes.
For further details about the original technique see the UpSet website. You can also check out the UpSetR shiny app. Here is the source code for the shiny wrapper.
A Python package called py-upset to create UpSet plots has been created by GitHub user ImSoErgodic.
If you use UpSetR in a paper, please cite:
Jake R Conway, Alexander Lex, Nils Gehlenborg UpSetR: An R Package for the Visualization of Intersecting Sets and their Properties doi: https://doi.org/10.1093/bioinformatics/btx364
The original technique and the interactive visualization tool implementing the approach are described here:
Alexander Lex, Nils Gehlenborg, Hendrik Strobelt, Romain Vuillemot, Hanspeter Pfister,
UpSet: Visualization of Intersecting Sets,
IEEE Transactions on Visualization and Computer Graphics (InfoVis ’14), vol. 20, no. 12, pp. 1983–1992, 2014.
doi: https://doi.org/10.1109/TVCG.2014.2346248
Sample data sets for UpSetR are included in the package and can be loaded like this:
movies <- read.csv( system.file("extdata", "movies.csv", package = "UpSetR"), header=T, sep=";" )
mutations <- read.csv( system.file("extdata", "mutations.csv", package = "UpSetR"), header=T, sep = ",")The movie data set created by the GroupLens Lab and curated by Bilal Alsallakh and the mutations data set was originally created by the TCGA Consortium and represents mutations for the 100 most mutated genes in a glioblastoma multiforme cohort.
In addition to the examples shown here, we have included a range of UpSetR plots in the paper about the R package, which can be found in a separate GitHub repository.
There are currently four vignettes that explain how to use the features included in the UpSetR package: * Basic Usage * Queries * Attribute Plots * Set Metadata
A view of the UpSet plot with additional plots based on elements in the intersections.
upset(movies,attribute.plots=list(gridrows=60,plots=list(list(plot=scatter_plot, x="ReleaseDate", y="AvgRating"),
list(plot=scatter_plot, x="ReleaseDate", y="Watches"),list(plot=scatter_plot, x="Watches", y="AvgRating"),
list(plot=histogram, x="ReleaseDate")), ncols = 2))A view of UpSetR mimicking the plot published by Lex & Gehlenborg http://www.nature.com/nmeth/journal/v11/n8/abs/nmeth.3033.html
upset(mutations, sets = c("PTEN", "TP53", "EGFR", "PIK3R1", "RB1"), sets.bar.color = "#56B4E9",
order.by = "freq", empty.intersections = "on")An example using two set queries (war movies and noir movies) along with attribute plots comparing the average rating (top) and average rating vs the number of times the movies have been watched (bottom).
upset(movies, attribute.plots=list(gridrows = 100, ncols = 1,
plots = list(list(plot=histogram, x="AvgRating",queries=T),
list(plot = scatter_plot, y = "AvgRating", x = "Watches", queries = T))),
sets = c("Action", "Adventure", "Children", "War", "Noir"),
queries = list(list(query = intersects, params = list("War"), active = T),
list(query = intersects, params = list("Noir"))))Install the latest released version from CRAN
install.packages("UpSetR")Download the latest development code of UpSetR from GitHub using devtools with
devtools::install_github("hms-dbmi/UpSetR")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.