如何使用其他信息创建代表不同集群的网络？

Question

我有一个数据框 nodes，其信息如下所示：

dput(nodes)

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") 

因此，在 nodes 数据框中，有 4 列。 Names 是基因名称，type 是不同类型，type_num 是每个基因类型的编号，Clusters 列显示每个基因所属的 3 个簇。

同样，我还有其他数据框 edges，其信息如下：

输入（边）

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")

尝试使用igraph，但看起来不像我想要的那样。

library(igraph)
net <- graph_from_data_frame(d=edges, vertices=nodes, directed=F)

as_edgelist(net, names=T)
as_adjacency_matrix(net, attr="weight")

# Removing loops from the graph:
net <- simplify(net, remove.multiple = F, remove.loops = T) 

# Let's and reduce the arrow size and remove the labels:
plot(net, edge.arrow.size=.4,vertex.label=NA)

它看起来像这样：

谁能帮我用上面给出的数据创建一个像上面这样的网络。任何帮助表示赞赏。提前谢谢你。

Answer 1

这主要是对Grouped layout based on attribute的答案的重复。

我认为您想通过Clusters 属性对顶点进行分组，并使用type 属性为它们着色。我会在这个答案中这样做。 您创建网络的代码很好，但简单的绘图不会按集群对顶点进行分组（我添加了按类型为顶点着色）。

plot(net, edge.arrow.size=.4,vertex.label=NA, 
    vertex.color=as.numeric(factor(nodes$type)))

您需要的是一个强调集群的布局。上面引用的先前答案显示了如何通过生成具有相同顶点但在同一簇中的顶点之间具有重边权重的不同图来做到这一点。在你的情况下，它会是

Grouped.net = net
E(Grouped.net)$weight = 1

## Add edges with high weight between all nodes in the same group
for(Clus in unique(nodes$Clusters)) {
    GroupV = which(nodes$Clusters == Clus)
    Grouped.net = add_edges(Grouped.net, combn(GroupV, 2), attr=list(weight=80))
} 

## Now create a layout based on G_Grouped
set.seed(567)
LO = layout_with_fr(Grouped.net)

## Use the layout to plot the original graph
plot(net, layout=LO, edge.arrow.size=.4,vertex.label=NA, 
    vertex.color=as.numeric(factor(nodes$type)))

如果您有大量顶点，您可能还希望通过 vertex.size=4 减小它们的大小

Answer 2

我不确定下面的代码是否有效

plot(net,
     edge.width = E(net)$weight,
     vertex.color = factor(V(net)$name),
     mark.groups = split(V(net)$name,V(net)$Clusters))

这给了