View source: R/network_visualization.R
plot_tf_network | R Documentation |
This function integrate R package igraph to visualize consequence of regulatory network analysis
plot_tf_network(
TFs_list,
layout = "grid",
group.cols = NULL,
title.name = NULL,
vertex.size = 13,
vertex.size.add = 3,
vertex.label.color = "black",
edge.label.color = "black",
legend = TRUE,
vertex.label.cex = 0.8,
vertex.label.family = "ArialMT",
frame.color = "white",
arrow.size = 0.2,
arrow.width = 0.5,
edge.width = 1.8,
edge.curved = 0,
edge.color = c("#FDD1B0", "#B3B3B3")
)
TFs_list |
TFs_list generated by |
layout |
the layout to display the network, options: 'grid','sphere', 'circle','random' |
group.cols |
colors for group in network |
title.name |
the name of the title |
vertex.size |
size of vertex whose edge numbers are last 25% |
vertex.size.add |
expansion size of vertex whose edge numbers are last 25%-75% and top25% |
vertex.label.color |
ertex label color |
edge.label.color |
edge label color |
legend |
logic, indicating whether to show the legend |
vertex.label.cex |
The label size of vertex |
vertex.label.family |
vertex label family |
frame.color |
frame.color |
arrow.size |
arrow size |
arrow.width |
arrow width |
edge.width |
edge width |
edge.curved |
edge curvature |
edge.color |
edge color. You need to input two colors, first one indicate 'Positive' regulation, second one indicate 'Negative' regulation. |
figure
#'
load(system.file("extdata", "test_clustering.rda", package = "IReNA"))
Kmeans_clustering <- add_ENSID(test_clustering, Spec1 = "Hs")
cor0.6 <- get_cor(Kmeans_clustering, Tranfac201803_Hs_MotifTFsF, 0.7, start_column=3)
TFs_list <- network_analysis(cor0.6,Kmeans_clustering)
plot_tf_network(TFs_list)
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