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R Venn Diagram package Venerable unavailable - alternative package?

开发者 https://www.devze.com 2023-03-06 05:56 出处:网络
I need to plot area proportional Venn Diagrams with at least 5 variables. I tried to install Vennerable pack开发者_如何学Pythonage but its just not available anymore. Link to windows build doesn\'t w

I need to plot area proportional Venn Diagrams with at least 5 variables.

I tried to install Vennerable pack开发者_如何学Pythonage but its just not available anymore. Link to windows build doesn't work (page not found).

Is there an alternative package?


The source files, last updated in 2007, are downloadable from Sourceforge as a tar.gz file:

http://sourceforge.net/projects/vennerable/files/R%20Source%20package/1.1.1.1/Vennerable_1.1.1.1.tar.gz/download

Better yet, what appears to be more updated (Edit: and broken) source files, last updated in 2009, can be checked out from R-Forge using SVN:

svn checkout svn://svn.r-forge.r-project.org/svnroot/vennerable

Can you build it yourself with Rtools? The Linux x86_64 build log suggests a problem with the vignette causing the build to fail, and the source tar.gz and binaries to be unavailable. Perhaps building it yourself without the vignette will work. The package was last updated 19 months ago, so it does not appear to be currently maintained.

Edit

It seems the most recent revisions from R-Forge break the package. There is a problem with the compute.Venn() function, and a number of tests fail. I built the Venerable_1.1.1.1 revision from Sourceforge for you for x86_64 architecture. You can download it at:

http://commondatastorage.googleapis.com/jthetzel-public/Vennerable_1.1.1.1.zip

I haven't actually tested the package, but it successfully installs and loads into R 2.13.0 x86_64 on Windows 7. If you have further questions, you can email the author of the package, Jonathan Swinton, whose email you will find in the DESCRIPTION file in the zip archive.

Note the rare correct use of all caps to refer to the DESCRIPTION file, which was so named for presumably historic reasons (though I'd be interested if anyone has a definitive explanation). Most other uses of all caps are generally frowned upon, unless you specifically intend to raise the systolic pressure of the SO community.


Follow this:

source("http://bioconductor.org/biocLite.R")       
biocLite("graph")       
biocLite("RBGL")       
biocLite("RColorBrewer")       
biocLite("gtools")       
biocLite("reshape" 
install.packages("Vennerable", repos="http://R-Forge.R-project.org") 


A look at CRAN (the place to be for R packages) revealed at least two promising packages: VennDiagram and venneuler.


Vennerable 3.0 is available. It cand be downloaded eg from within R by
install.packages("Vennerable", repos="http://R-Forge.R-project.org")


Thorough inspection leads to this bioinformatics engine, of higher performance than available R packages for Venn diagrams out there so far. It is written in Perl by Belgium author Lieven Sterck (lieven.sterck@psb.vib-ugent.be) from Ghent University.

The webtool generates as output not only the requested diagram (provided the input consists of 5 sets or less, already surpassing capabilities of most R packages available for Venn diagrams) but also produces a text file listing the elements contained in each intersection group for detailed inspection. Lists, although not geometric diagrams as such, can be generated even for comparisons exceeding 5 sets. Furthermore, this webtool also outputs the total number of elements per dataset in a tabulated manner for confirmation of unique entries.

The only missing feature: a weighted option equivalent to the "Chow-Ruskey" in the Vennerable R package, which however seems to suffer from some issues (besides flaky installation as mentioned above) adding an extra element to the last group list provided in the data frame for overlap. Vennerable (and other packages of the same ilk) only generates a figure with group labels and numbers the elements contained per intersection, not listing them. Venn diagrams and intersection data in general, although simple in principle are of great utility to illustrate coverage of "multiomics" biological data just to provide with an example.

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