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❝最近交流群内有读者询问网络图的绘制方法，正好最近看到有论文中正好有此内容。本节就来分享iMeta论文中此类网络图的绘制方法，论文提供了的数据的处理过程代码，绘图则是使用Gephi软件进行，小编在作者提供的数据结果基础上。根据个人对数据的理解来使用ggraph包进行绘图，与原文有所不同，仅供参考，具体的内容请参考论文。
论文
The crop mined phosphorus nutrition via modifying root traits and rhizosphere micro-food web to meet the increased growth demand under elevated CO2
论文数据代码
https://github.com/xhhhhhhhhhhx/2024imate_zhou/tree/main/imate_2024
论文原图
❝绘制此图的难点应该在于如何针对原始数据进行筛选过滤得到绘图所需的边文件及点文件，关于数据的处理过程论文代码中均有介绍，请仔细阅读。
加载R包
library(igraph)
library(Hmisc)
library(tidyverse)
library(MetBrewer)
library(ggnewscale)
library(tidygraph)
library(ggraph)
数据过滤
bacteria <- read.table("otu.txt", header = T, check.names = F)
bacteria <- bacteria[which(rowSums(bacteria) >= 0.001), ]
# dim(bacteria)
bacteria1 <- bacteria
bacteria1[bacteria1>0] <- 1
bacteria<- bacteria[which(rowSums(bacteria1) >= 1), ]
write.csv(bacteria,"selectbacteria.csv")
bacteria <- t(bacteria)
bac_corr <- rcorr(bacteria, type = 'spearman')
r <- bac_corr$r
r[abs(r) < 0.6] <- 0
p <- bac_corr$P
p <- p.adjust(p, method = 'BH')
p[p>=0.05] <- -1
p[p<0.05 & p>=0] <- 1
p[p==-1] <- 0
z <- r * p
diag(z) <- 0
write.table(data.frame(z, check.names = FALSE), 'bac_corr.matrix.txt',
col.names = NA, sep = '\t', quote = FALSE)
igraph <- graph.adjacency(z, weighted = TRUE, mode = 'undirected')
igraph
vcount(igraph)
igraph <- delete_vertices(igraph, names(degree(igraph)[degree(igraph) == 0]))
vcount(igraph)
E(igraph)$correlation <- E(igraph)$weight
E(igraph)$weight <- abs(E(igraph)$weight)
plot(igraph)
write.graph(igraph, 'network.gml', format = 'gml')
adj_matrix <- as.matrix(get.adjacency(igraph, attr = 'correlation'))
write.table(data.frame(adj_matrix, check.names = FALSE), 'bac_network.adj_matrix.txt', col.names = NA, sep = '\t', quote = FALSE)
叠加注释信息
tax <- read.delim('taxonomy.txt',
check.names = FALSE, stringsAsFactors = FALSE)
row.names(tax)<-make.names(tax[,1],TRUE)
tax<-tax[,-1]
dim(tax)
tax <- tax[as.character(V(igraph)$name), ]
write.csv(tax,"tax.csv")
V(igraph)$phylum <- tax$phylum
V(igraph)$class <- tax$class
V(igraph)$order <- tax$order
V(igraph)$family <- tax$family
V(igraph)$genus <- tax$genus
V(igraph)$specie<- tax$specie
V(igraph)$abundance <- tax$Abundance
V(igraph)$type <- tax$Type
igraph
plot(igraph)
构建边文件
edge <- data.frame(as_edgelist(igraph))
edge_list <- data.frame(
source = edge[[1]],
target = edge[[2]],
weight = E(igraph)$weight,
correlation = E(igraph)$correlation
)
head(edge_list)
write.table(edge_list, 'network.edge_list.txt', sep = '\t', row.names = FALSE, quote = FALSE)
构建点文件
node_list <- data.frame(
label = names(V(igraph)),
phylum = V(igraph)$phylum,
class = V(igraph)$class,
order = V(igraph)$order,
family = V(igraph)$family,
genus = V(igraph)$genus,
specie = V(igraph)$specie,
abundance = V(igraph)$abundance,
Type = V(igraph)$type)
head(node_list)
write.table(node_list, 'network.node_list.txt', sep = '\t', row.names = FALSE, quote = FALSE)
#graphml format, which can be opened and visually edited using gephi software
write.graph(igraph, 'gephi_network.graphml', format = 'graphml')
ggraph绘图
graph <- graph_from_data_frame(edge_list,node_list, directed = FALSE)
tidy_graph <- tidygraph::as_tbl_graph(graph)
set.seed(123)
ggraph::ggraph(graph = tidy_graph, layout = "fr",niter = 666) +
geom_edge_link(aes(color=correlation)) +
scale_edge_color_gradientn(colors=met.brewer("Cassatt1")) +
new_scale_color() +
geom_node_point(aes(color=Type),show.legend = FALSE) +
scale_color_manual(values = c("#5785C1")) +
scale_edge_width(range = c(0.5, 1)) +
ggtitle(paste0('Nodes=', gorder(graph), ', Edges=', gsize(graph))) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5, size = 14,color="black"))
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