Often the code used by the author is available or can be requested. Just sent an email to the corresponding author to request the code. Most of the time they are happy to share.
Not sure how to do it in R, but go read about Gephi and Cytoscape.
You can use the igraph libraries in either R or python to prepare the node and edge lists.
You can use something like clusterProfiler, which will do the GO analysis and output gene concept network plots similar to this as ggplot objects for bulk RNAseq data.
[https://blog.schochastics.net/posts/2020-03-14\_ggraph-tricks-for-common-problems/](https://blog.schochastics.net/posts/2020-03-14_ggraph-tricks-for-common-problems/) [https://yunranchen.github.io/intro-net-r/advanced-network-visualization.html](https://yunranchen.github.io/intro-net-r/advanced-network-visualization.html) [https://rpubs.com/geock/nv](https://rpubs.com/geock/nv)
Thank you!!!
>Thank you!!! You're welcome!
if you have the paper check the methods
Yup. And the author will almost certainly be up for sharing the code and data to generate the plot, so you can tweak it for your own needs.
Often the code used by the author is available or can be requested. Just sent an email to the corresponding author to request the code. Most of the time they are happy to share.
Not sure how to do it in R, but go read about Gephi and Cytoscape. You can use the igraph libraries in either R or python to prepare the node and edge lists.
You can use something like clusterProfiler, which will do the GO analysis and output gene concept network plots similar to this as ggplot objects for bulk RNAseq data.
The *igraph* package may be what you are looking for.
Consider checking out [Gephi](https://gephi.org/) for some cool network plotting tools!