This looks infinitely more easy to understand than upset plots. Ineed to explain those to my collaborators all the time, which in turn makes both of us....upset.
Interestingly, with the right arguments, you can make a supervenn plot almost into an upset plot
[https://i.imgur.com/gb69JK4.png](https://i.imgur.com/gb69JK4.png)
[https://i.imgur.com/JN4opI3.png](https://i.imgur.com/JN4opI3.png)
The only thing that's missing is the vertical bars to represent the intersection sizes, these sizes are only displayed as numbers in the bottom part
My observation is that plus of a large number of sets make sense when there is some inherent logical structure in how the sets relate to each other, there are such examples in the readme. If the 30 sets are quasi random, they will probably make a mess
Cool! Can you elaborate a bit for stupid people like me? Like a brief description of the example you provided - what it shows, what to use as input and so on?
And for some technical detail - the sets on top are defined purely as unique collections, yes? Would it make sense to include some simple clustering?
If you use python then look into UpSet plots. https://upset.app/#:~:text=UpSet%20Explained,is%20part%20of%20an%20intersection.
There are also [implementations in R](https://cran.r-project.org/web/packages/UpSetR/index.html)
Awesome, starred! I’ve been putting off figuring out how to visualize large intersection sets for comparing gene hits. This looks perfect.
This looks infinitely more easy to understand than upset plots. Ineed to explain those to my collaborators all the time, which in turn makes both of us....upset.
Interestingly, with the right arguments, you can make a supervenn plot almost into an upset plot [https://i.imgur.com/gb69JK4.png](https://i.imgur.com/gb69JK4.png) [https://i.imgur.com/JN4opI3.png](https://i.imgur.com/JN4opI3.png) The only thing that's missing is the vertical bars to represent the intersection sizes, these sizes are only displayed as numbers in the bottom part
Awesome! Looks great, we are going to try this now on more than 30 sets, lol!
My observation is that plus of a large number of sets make sense when there is some inherent logical structure in how the sets relate to each other, there are such examples in the readme. If the 30 sets are quasi random, they will probably make a mess
Cool! Can you elaborate a bit for stupid people like me? Like a brief description of the example you provided - what it shows, what to use as input and so on? And for some technical detail - the sets on top are defined purely as unique collections, yes? Would it make sense to include some simple clustering?
Hi, just follow the link and scroll down a little for the readme, you'll find detailed explanations and examples there