代码

remove(list = ls()) ##清空当前环境
setwd("D:/生信代码复现/R语言绘制网络组合图") ##💚💚💚设置路径



######加载R包


rm(list=ls())  
####----loadRPackage----####  
library(tidyverse)  
library(readxl)  
library(tidygraph)  
library(ggraph)  
library(igraph)  
library(cowplot)  
library(ggnewscale)  
source("layout_function.R")  
source("combine_plot.R")

####----loadData(加载数据）----####  
#nodefile  
nodes<-readxl::read_xlsx(path="nodes.xlsx")  

#edgefile  
edges<-readxl::read_xlsx(path="edges.xlsx")  

#ppifile  
ppi<-readxl::read_xlsx(path="ppi.xlsx")  

####----Plot----####  
graph<-as_tbl_graph(ppi)%>%  
  tidygraph::mutate(Popularity=centrality_degree(mode='out'))%>%  
  tidygraph::left_join(nodes,by=c("name"="name"))

####----Plot----####
graph<-as_tbl_graph(ppi)%>%
  tidygraph::mutate(Popularity=centrality_degree(mode='out'))%>%
  tidygraph::left_join(nodes,by=c("name"="name"))

ly4<-layout_function(graph)

p_ppi<-ggraph(ly4)+
  geom_edge_diagonal(aes(color=type))+
  geom_node_point(aes(size=Popularity,color=type),alpha=1)+
  geom_node_text(data=ly4%>%
                   tidygraph::filter(name%in%c("Cis","Ctrl")),
                 aes(label=name),
                 size=5,
                 color="#ffffff")+
  scale_size(range=c(3,15))+
  coord_fixed()+
  theme_void()+
  theme(legend.position="none")

#### 加载节点数据 ####
nodes <- readxl::read_xlsx("nodes.xlsx")

#### 基于节点数据生成 data_ex ####
data_ex <- nodes %>%
  mutate(
    id = row_number(),                          # 为每个节点添加顺序编号
    data1 = sample(letters[1:5], n(), replace = TRUE), # 分类数据
    data2 = runif(n(), min = 0, max = 20),      # 连续数据1
    data3 = runif(n(), min = 0, max = 16),      # 连续数据2
    data4 = runif(n(), min = 0, max = 30)       # 柱形图数据
  )


####然后是依次画圈####
p2<-ggplot(data=data_ex)+
  geom_tile(aes(x=id,y=1,fill=data1),height=0.2)+
  scale_fill_manual(values=c('#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3'),na.value="transparent")+
  new_scale_fill()+
  geom_tile(aes(x=id,y=1.25,fill=data2),height=0.2)+
  scale_fill_gradient(low="#fcc5c0",high="#dd3497",na.value="transparent")+
  new_scale_fill()+
  geom_tile(aes(x=id,y=1.5,fill=data3),height=0.2)+
  scale_fill_gradient(low="#dadaeb",high="#807dba",na.value="transparent")+
  coord_polar(
    start=1.17*pi
  )+
  scale_y_continuous(
    limits=c(-1,2.5)
  )+
  theme_void()

p2

# 保存图片到本地
ggsave(filename = "Output/p2_plot.png",  # 保存路径和文件名
       plot = p2,                        # 要保存的图表对象
       width = 10,                       # 宽度（单位：英寸）
       height = 10,                      # 高度（单位：英寸）
       dpi = 300)                        # 分辨率


#####然后是最外面的柱形图#####
data_ex_add_angle<-data_ex%>%  
  dplyr::mutate(angle=90-360*(id-0.5)/nrow(.)+(1.17*pi))%>%  
  dplyr::mutate(hjust=ifelse(angle<-90,0,1))%>%  
  dplyr::mutate(angle=ifelse(angle<-90,angle+150,angle-25))  


p3<-ggplot(data=data_ex_add_angle)+  
  geom_bar(aes(x=id,y=data4),stat="identity",fill="#d9d9d9")+  
  geom_text(data=data_ex_add_angle%>%dplyr::filter(!name%in%c("Cis","Ctrl")),  
            aes(x=id,y=data4+1,label=name,hjust=hjust),  
            angle=data_ex_add_angle%>%dplyr::filter(!name%in%c("Cis","Ctrl"))%>%pull(angle))+  
  coord_polar(  
    start=1.17*pi  
  )+  
  scale_y_continuous(  
    limits=c(-50,30)  
  )+  
  theme_void()  


p3


#####最后是拼图#####
####----combine----####  
p_combine<-combine_plot(p2,p_ppi,p3)  

p_combine  


ggsave(filename="Output/p_combine.pdf",  
       height=12,  
       width=12)