How to make a plot that groups different type of nodes by cluster?

Usage
1

I have a dataframe df with nodes information like below:

dput(df)

structure(list(Names = c("A4GALT", "AASS", "ABCA10", "ABCA7", 
"ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2"), type = c("typeA", 
"typeA", "typeC", "typeA", "typeC", "typeC", "typeB", "typeB", 
"typeB"), type_num = c(1L, 1L, 3L, 1L, 3L, 3L, 2L, 2L, 2L), Clusters = c("Cluster1", 
"Cluster1", "Cluster2", "Cluster3", "Cluster3", "Cluster1", "Cluster2", 
"Cluster3", "Cluster2")), row.names = c(NA, 9L), class = "data.frame")

So, in the df dataframe, there are 4 columns. Names is the gene name, type is different types A, B and C, type_num is the number given to each type and column Clusters show 3 clusters in which each gene belong to.

Similarly, I have other dataframe df2 with with edges information. And below I’m showing that:

dput(df2)

structure(list(fromNode = c("A4GALT", "A4GALT", "A4GALT", "A4GALT", 
"A4GALT", "A4GALT", "A4GALT", "A4GALT", "AASS", "AASS", "AASS", 
"AASS", "AASS", "AASS", "AASS", "ABCA10", "ABCA10", "ABCA10", 
"ABCA10", "ABCA10", "ABCA10", "ABCA7", "ABCA7", "ABCA7", "ABCA7", 
"ABCA7", "ABCD4", "ABCD4", "ABCD4", "ABCD4", "ABHD4", "ABHD4", 
"ABHD4", "ABTB1", "ABTB1", "AC006978.2"), toNode = c("AASS", 
"ABCA10", "ABCA7", "ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2", 
"ABCA10", "ABCA7", "ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2", 
"ABCA7", "ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2", 
"ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2", "ABHD4", 
"ABTB1", "AC006978.2", "AC009119.2", "ABTB1", "AC006978.2", "AC009119.2", 
"AC006978.2", "AC009119.2", "AC009119.2"), weight = c(0.005842835, 
0.002253695, 0.014513253, 0.004851739, 0.066702792, 0.009418991, 
0.001136938, 0.000474221, 0.004405601, 0.000666001, 0.005625977, 
0.0333554, 0.004666223, 0.000103131, 0.00026302, 0.004514819, 
0.029632695, 0.001825839, 0.028379806, 0.001403298, 0.008339397, 
0.02393394, 0.004782329, 0.024767355, 0.002986813, 0.00559471, 
0.005961539, 0.064831874, 0.013023138, 0.027935729, 0.006618816, 
0.001134219, 0.012798368, 0.007961242, 0.01640476, 0.007997743
), direction = c("undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected")), row.names = c(NA, -36L), class = "data.frame")

I wanted to create a network like below which represents the clusters with types having different colors in each cluster. It should look something like this:

Screen Shot 2020-08-28 at 23.53.37
Screen Shot 2020-08-28 at 23.53.37778×718 89.2 KB

In the above image the numbers represent different clusters and colors represent different types which belong to each cluster and connected.

Can anyone please help me how to create a network like above to collapse each cluster into a single network node and visualize a network with clusters as nodes. Any help is appreciated. thank you in advance.

2

To generate the network you can use graph_from_data_frame.

You can cluster the graph with several different algorithms, e.g. using cluster_infomap.

You can collapse each cluster in a single network node by using contract. You can simply provide the membership vector of the resulting clustering. You probably also want to simplify.

H <- contract(G, membership(communities))
H <- simplify(H, remove.loops=FALSE)

You can then simply plot the resulting graph H.

3

Thank you for your reply. What is the object G here. Which data did you take?

And I tried doing the network but it didn’t look good and I wanted to increase the distance between the clusters here [How to increase the distance between the clusters using igraph?]

4

This is the graph object, please see the documentation. I have not used any data, this is just an example. You should construct the graph G yourself by using graph_from_data_frame, as said.