ggtangle: Draw Network with Data

Simple Network Visualization

library(yulab.utils)
library(igraph)
library(ggplot2)
library(ggtangle)
library(aplot)
library(ggfun)

net <- sample_gnp(10, .5)

V(net)$name = letters[1:10]
E(net)$weight = abs(rnorm(length(E(net))))
E(net)$type = sample(LETTERS[1:3], length(E(net)), replace=TRUE)

p <- ggplot(net) + geom_edge()
p

Node color and size

p1 <- p + geom_point(size=6, color='steelblue')
p2 <- p + geom_point(aes(color = label %in% letters[1:5]), size=6)
p3 <- p + geom_point(aes(size = igraph::degree(net)), color='steelblue') 
p4 <- p + geom_point(aes(shape = label %in% letters[1:5]), color='steelblue', size=8) 

plot_list(p1, p2, p3, p4, ncol=2)

Node label

p5 <- p + geom_label(aes(label=label, color=label %in% letters[1:5]), size=5)

p6 <- p + geom_label(aes(label=label), size=5, data=ggtree::td_filter(label %in% letters[1:5]))

plot_list(p5, p6)

Edge

p7 <- ggplot(net) + geom_edge(aes(linewidth=weight)) 
p8 <- ggplot(net) + geom_edge(aes(color=type)) 
p9 <- ggplot(net) + geom_edge(aes(linetype=type)) 

plot_list(p7, p8, p9, ncol=3)

Network with external data

set.seed(123)
expr <- abs(rnorm(10))
d <- data.frame(label=V(net)$name, expression=expr)
p %<+% d + geom_point(aes(color = expression), size=6) +
    scale_color_viridis_c()

Network with pies

flow_info <- data.frame(from = LETTERS[c(1,2,3,3,4,5,6)],
                        to = LETTERS[c(5,5,5,6,7,6,7)])

dd <- data.frame(
    label = LETTERS[1:7],
    v1 = abs(rnorm(7)),
    v2 = abs(rnorm(7)),
    v3 = abs(rnorm(7))
)

g <- igraph::graph_from_data_frame(flow_info)

p <- ggplot(g, layout='kk')  + geom_edge()

library(scatterpie)

## scatterpie v0.2.4 Learn more at https://yulab-smu.top/

p %<+% dd + 
    geom_scatterpie(cols = c("v1", "v2", "v3")) +
    geom_text(aes(label=label), nudge_y = .2) + 
    coord_fixed()

Category-Item association plot

set.seed(123)
x <- list(A = letters[1:10], B=letters[5:12], C=letters[sample(1:26, 15)])

p1 <- cnetplot(x, node_label = "none", 
          color_category='firebrick', color_item='steelblue')
p2 <- cnetplot(x, node_label = "all", size_category=2)
p3 <- cnetplot(x, node_label = "category")
p4 <- cnetplot(x, node_label = "item")
p5 <- cnetplot(x, node_label = "share")
p6 <- cnetplot(x, node_label = "exclusive")

plot_list(p1, p2, p3, p4, p5, p6, ncol=3, tag_levels = 'A')

node_label can be selected items/genes to specify which genes to be labelled and can also used to subset the graph.

g1 <- cnetplot(x, node_label = letters[1:5]) 

options(cnetplot_subset = TRUE)
g2 <- cnetplot(x, node_label = letters[1:5])

plot_list(g1, g2)

set.seed(123)
d <- setNames(rnorm(26), letters)

# color items/genes with associated data
p7 <- cnetplot(x, foldChange=d) + 
    scale_color_gradient2(name='associated data', low='darkgreen', high='firebrick')

# filter items/genes by log2FC values to label
p8 <- cnetplot(x, foldChange=d, node_labe = ">1") + 
    scale_color_gradient2(name='associated data', low='darkgreen', high='firebrick')

# Highlight categories
p9 <- cnetplot(x, foldChange=d, hilight=c("B", "C")) + 
    scale_color_gradient2(name='associated data', low='darkgreen', high='firebrick')

# label in separate layer allows more detail adjustment
p10 <- cnetplot(x, node_label="none")
p10 <- p10 + geom_cnet_label(node_label = 'all', fontface='bold')
plot_list(p7, p8, p9, p10, ncol=2, tag_levels = 'A')