扩增子分析|基于R包ggClusterNet包进行生态网络分析—十种可视化布局包括igraph，Gephi和ma

一、引言

         用于微生物组网络分析的工具包括MENA、WGCNA、igraph、ggraph、SpiecEasi、Cytoscape、Gephi、NetworkX和SparCC等。每个工具在网络构建和可视化上各具优势，例如MENA基于随机矩阵理论，具备良好的抗噪性能；WGCNA构建无尺度加权基因网络；SpiecEasi结合数据转换与图形模型推理框架。一些工具如Cytoscape、Gephi和R包（igraph、ggraph）提供可视化功能，但存在操作繁琐、重复性差和美观性不足的问题。

         为解决这些局限，南京农业大学袁军和中科院刘永鑫团队开发了R包ggClusterNet，该包于2022年发表在国内主办的微生物领域全球第一期刊iMeta上，截止推文发稿日期已经被引141次，广受微生物生态研究者关注和喜爱。特点包括分析快速、通过少量代码即可完成网络分析且具有可重复性，最大的特点是提供了多种微生物生态网络可视化工具，及网络特性的深度挖掘。本期带给大家ggClusterNet高达十种网络布局，带你摆脱绘制出的网络图不好看以及专业软件Cytoscape和Gephi入门难得问题。下期我们会接着探讨网络特性深度挖掘，包括网络属性，节点属性，zi-pi图等。         如下展示了ggClusterNet的分析流程：

图1 分析流程图

二、基本知识

图2 ggClusterNet中网络可视化的十种布局算法

       ggClusterNet特点之一就是提供了十种可供选择的可视化布局，这大大减少了网络绘制的时间和难度，提升科研效率，如下详细介绍了十种网络图特点。

randomClusterG: 随机布局，环状模块。模块（组）的所有节点都排列成一个环。多个模块绘制为相同半径的多个圆。接着，设计了一个函数，可以在绘图面板中随机排列这些圆环。

ArtifCluster: 环状模块，人工布局。模块（组）的所有节点都排列成一个环。多个模块绘制为相同半径的多个圆，然后通过设置坐标值手动排列这些圆。

randSNEClusterG: sna包中的布局按照模块布局。模块（组）的所有节点分别按 sna 包中的多种布局排列。多个模块随机排列在绘图面板中。

PolygonModsquareG: 环状布局，顺序行列排布。模块（组）的所有节点都排列成一个环。多个模块绘制为不同半径的多个圆（节点数量越多，圆的半径越大）。然后这些圆手动排列成一行或多行。

model_maptree: 按照maptree算法布局模块。首先对网络进行模块化分析，并按网络模块化对节点进行分组，然后用于计算坐标。节点的相对位置根据 Weixin Wang 等人开发的算法计算，该算法试图在添加圆时找到最密集的排列方式。

model_igraph: 模仿igraph布局。所有节点使用 Fruchterman 和 Reingold 的力导向布局算法放置在平面上。通过 R 包 igraph 计算的高度节点趋向于聚集，而低度节点分布在周围网络中。

model_Gephi.2: 模仿Gephi布局。所有节点绘制为一个圆，并计算坐标。然后使用每个节点的坐标值来构建簇，并重新分配给节点。

PolyRdmNotdCirG: 根据模块信息，节点随机分布在不同半径的多个圆中（节点数量越多，圆的半径越大）。然后这些模块按规则排列成一个多边形的顶点（边的数量等于模块的数量），以坐标轴的原点为中心。

PolygonRrClusterG: 环状模块，模块半径正比于节点数量，环状布局。模块（组）的所有节点都排列成一个环。多个模块绘制为不同半径的多个圆（节点数量越多，圆的半径越大）。然后这些圆按规则排列成多边形的顶点，以坐标轴的中心为中心。

PolygonClusterG: 环状模块，环状布局。模块（组）的所有节点都排列成一个环。多个模块绘制为相同半径的多个圆。然后这些圆按规则排列成多边形的顶点（边的数量等于模块的数量），以坐标轴的原点为中心。

三、示例数据和代码

这套代码基于ggClusterNet包自带的数据进行分析，同时我们也提供了读取自己数据的方法，代码均可留言，小编会及时发送原始代码和示例数据。

🌟数据基本操作之包安装，微生物网络模型构建

# 清理工作环境中的所有对象 rm(list = ls()) #检测并安装所需R包# if (!require("phyloseq")) { install.packages("phyloseq") } if (!require("igraph")) { install.packages("igraph") } if (!require("network")) { install.packages("network") } if (!require("sna")) { install.packages("sna") } if (!require("tidyverse")) { install.packages("tidyverse") } library(devtools) if (!require("ggClusterNet")) {remotes::install_github("taowenmicro/ggClusterNet") } if (!require("ggrepel")) { install.packages("ggrepel") } if (!require("tidyfst")) { install.packages("tidyfst") } #导入所需R包 library(phyloseq) library(igraph) library(network) library(sna) library(tidyverse) library(ggClusterNet) library(ggrepel) #加载包里的数据 data(ps) #计算微生物网络 result = corMicro (ps = ps, N = 250, r.threshold=0.8, p.threshold=0.05 ) #提取数据 cor = result[[1]] dim(cor) ps_net = result[[3]] #导出表格 otu_table = as.data.frame(t(vegan_otu(ps_net))) tax_table = as.data.frame(vegan_tax(ps_net)) #OTU模块化分组 netClu = modulGroup( cor = cor,cut = 3,method = "cluster_fast_greedy" ) head(netClu) #微生物模块化分组，得到微生物分组信息（可选） netClu = data.frame(ID = row.names(tax_table),group =rep(1:5,length(row.names(tax_table)))[1:length(row.names(tax_table))] ) netClu$group = as.factor(netClu$group) 3.1 randomClusterG布局 # 设置随机种子以确保结果的可重复性 set.seed(1112) # 创建节点分组信息，将tax_table的行名作为节点ID，每个节点随机分配到1-12的组别 netClu = data.frame(ID = row.names(tax_table), group = rep(1:12, length(row.names(tax_table)))[1:length(row.names(tax_table))]) # 将group列转换为因子类型 netClu$group = as.factor(netClu$group) # 使用随机聚类函数randomClusterG生成节点布局，cor为相关性矩阵，netClu为节点分组信息 result2 = randomClusterG(cor = cor, nodeGroup = netClu) # 从result2中提取节点信息 node = result2[[1]] # 将节点坐标、OTU表格、分类学表格等信息整合到一个数据框中 nodes = nodeadd(plotcord = node, otu_table = otu_table, tax_table = tax_table) # -----构建边（连接）信息----- # 基于相关性矩阵构建节点之间的边（连接） edge = edgeBuild(cor = cor, node = node) # 显示边的数据框的维度 dim(edge) # 修改edge数据框中第8列的列名为"cor"，代表相关性值 colnames(edge)[8] = "cor" # 绘制网络图 p1 <- ggplot() + # 使用geom_segment绘制边，颜色由相关性值确定，边的透明度和宽度设定 geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2, color = cor), data = edge, linewidth = 0.2, alpha = 0.6) + # 使用geom_point绘制节点，节点大小由平均值决定，节点填充颜色由分类信息(Phylum)决定 geom_point(aes(X1, X2, fill = Phylum, size = mean), pch = 21, data = nodes) + # 可选：添加节点标签 # geom_text_repel(aes(X1, X2, label = elements), pch = 21, data = nodes) + # geom_text(aes(X1, X2, label = elements), pch = 21, data = nodes) + # 设置边的颜色使用的调色板 scale_colour_manual(values = c("#377EB8", "#E41A1C")) + # 设置节点大小范围 scale_size(range = c(4, 14)) + # 移除坐标轴上的刻度 scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + # 设置主题，移除背景和网格线，使图形背景为白色 theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) # 显示绘制的网络图 p1 # 保存绘制的网络图为PDF文件，设置宽度和高度，并允许保存超出限制的图像 ggsave("1.pdf", p1, width = 20, height = 19, limitsize = FALSE)

3.2 PolygonClusterG布局 ##################PolygonClusterG布局################### netClu = data.frame(ID = row.names(tax_table),group =rep(1:12,length(row.names(tax_table)))[1:length(row.names(tax_table))] ) netClu$group = as.factor(netClu$group) set.seed(12) result2 = PolygonClusterG(cor = cor,nodeGroup =netClu,zoom = 0.8,zoom2 = 0.8 ) node = result2[[1]] head(node) #node节点注释 nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #计算边 edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 ggsave("2.pdf",p1,width = 20,height = 19)

3.3 PolygonRrClusterG布局 ######################PolygonRrClusterG布局#################### netClu = modulGroup( cor = cor,cut = 3,method = "cluster_fast_greedy" ) head(netClu) netClu$group = as.factor(netClu$group) set.seed(12) result2 = PolygonRrClusterG(cor = cor,nodeGroup =netClu,zoom2 = 2,zoom = 0.01) node = result2[[1]] head(node) #node节点注释 nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #计算边 edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 ggsave("3.pdf",p1,width = 17,height = 16)

3.4 ranSNEClusterG布局 #########################ranSNEClusterG布局################## netClu = data.frame(ID = row.names(tax_table),group =rep(1:5,length(row.names(tax_table)))[1:length(row.names(tax_table))] ) netClu$group = as.factor(netClu$group) #?ranSNEClusterG result2 = ranSNEClusterG (cor= cor,layout ="circrand") node = result2[[1]] head(node) #node节点注释 nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #计算边# edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + geom_point(aes(X1, X2,fill = Phylum),pch = 21, data = nodes,size = 5) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 # ggsave("4.pdf",p1,width = 12,height = 10) result2 = ranSNEClusterG (cor= cor,layout ="random") node = result2[[1]] head(node) #node节点注释 nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #计算边 edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + geom_point(aes(X1, X2,fill = Phylum),pch = 21, data = nodes,size = 4) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 ggsave("5.pdf",p1,width = 12,height = 10)

3.5 PolygonModsquareG布局 ########################PolygonModsquareG布局##################### netClu = data.frame(ID = row.names(tax_table),group =rep(1:50,length(row.names(tax_table)))[1:length(row.names(tax_table))] ) netClu$group = as.factor(netClu$group) result2 <- PolygonModsquareG(cor = cor,nodeGroup =netClu,r1 = 10,N = 10,cut = 8) node = result2[[1]] #node节点注释 nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #计算边 edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + geom_point(aes(X1, X2,fill = Phylum),pch = 21, data = nodes,size =3) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 ggsave("6.pdf",p1,width = 50,height =50,limitsize = FALSE)

3.6 PolyRdmNotdCirG布局 netClu = data.frame(ID = row.names(tax_table),group =rep(1:3,length(row.names(tax_table)))[1:length(row.names(tax_table))] ) netClu$group = as.factor(netClu$group) result2 = PolyRdmNotdCirG (cor = cor,nodeGroup =netClu ) node = result2[[1]] head(node) # ---node节点注释#----------- nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #-----计算边#-------- edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + geom_point(aes(X1, X2,fill = Phylum),pch = 21, data = nodes,size = 3) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 ggsave("8.pdf",p1,width = 10,height =9)

3.7 rArtifCluster布局（人工布局） #################rArtifCluster布局（人工布局）##################### if(!require("gganatogram")){devtools::install_github("jespermaag/gganatogram")} if(!require("viridis")){install.packages("viridis")} library(gganatogram) library(viridis) ##-----人工置顶半径大小和圆心位置 #--这里我设置r都是相同的，也可以设置不同，然后包装成一个向量就可以了#------- art = netClu art$group = 1:5 xs = as.data.frame(table(art$group)) r = rep(15,length(xs$Freq)) r #----准备圆心坐标，往往与你的设计有关#--------- # 有多少个模块就提供多少个坐标 #--指定坐标吮顺序按照一下指定 #-------人工准备坐标 ax1 = c(120,0) ax2 = c(130,-30) ax3 = c(140,-70) ax4 = c(130,-110) ax5 = c(120,-140) da = rbind(ax1,ax2,ax3,ax4,ax5) #--------计算布局#--------- #-------计算网络布局-得到节点坐标=node#--------- result2 = ArtifCluster(cor = cor,nodeGroup =art,r = r,da =da) node = result2[[1]] head(node) # ---node节点注释#----------- nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #-----计算边#-------- edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) library(gganatogram) library(viridis) organism = "solanum_lycopersicum.whole_plant" data =other_key[[organism]] library(scales) show_col(data$colour) data$colour =c("#4DAF4A") # data$colour[1] = "" # , sex='female', fill="colour" p = gganatogram(data=data, organism=organism,ggplot2_only = TRUE,fillOutline='#a6bddb',fill = "colour") + # theme_void() + ggtitle(organism) + theme(plot.title = element_text(hjust=0.5, size=9)) p p1 <- p + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 #--------按照每个部分添加一个箭头 da2 = data.frame(x = da[,1] -50,y = da[,2]) da = as.data.frame(da) colnames(da) = c("x0","y0") da3 = cbind(da2,da) da3 p2 = p1 + geom_segment(aes(x = x, y = y, xend = x0, yend = y0),data = da3,arrow = arrow(length = unit(0.2,"cm")),size = 0.7) p2 ggsave("9.pdf",p2,width = 12,height = 11) 3.8 model_Gephi.2布局 result2 <- model_Gephi.2(cor = cor, method = "cluster_fast_greedy", seed = 12 ) node = result2[[1]] head(node) #node节点注释 nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #计算边 edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 ggsave("10.pdf",p1,width = 12,height = 11)

3.9 model_maptree布局 result2 <- model_maptree(cor = cor, method = "cluster_fast_greedy", seed = 12 ) node = result2[[1]] head(node) #---node节点注释#----------- nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #-----计算边#-------- edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), data = edge, size = 0.5,alpha = 0.6) + # geom_curve(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor), # data = edge, size = 0.5,alpha = 0.3,curvature = -0.2) + geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) + # geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodes) + # geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) + scale_colour_manual(values = c("#377EB8","#E41A1C")) + scale_size(range = c(4, 14)) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) p1 ggsave("./11.pdf",p1,width = 12,height = 12)

3.10 model_igraph布局 ####################model_igraph布局###################### library(igraph) library(dplyr) library(Hmisc) p.threshold = 0.05 r.threshold = 0.7 x = ps %>% # scale_micro(method = "TMM") %>% vegan_otu() %>% t() %>% as.data.frame() occor<-WGCNA::corAndPvalue(t(x)/colSums(x),method = 'pearson') mtadj<-multtest::mt.rawp2adjp(unlist(occor$p),proc='BH') adpcor<-mtadj$adjp[order(mtadj$index),2] occor.p<-matrix(adpcor,dim(t(x)/colSums(x))[2]) ## R value occor.r<-occor$cor diag(occor.r) <- 0 # occor.r[occor.p > 0.05|abs(occor.r)<0.4] = 0 occor.r[occor.p > p.threshold | abs(occor.r)< r.threshold] = 0 occor.r[is.na(occor.r)]=0 cor = occor.r result= cor_Big_micro(ps = ps,N = 0,p.threshold = 0.05,r.threshold = 0.6,scale = FALSE) cor = result[[1]] p.threshold = 0.05 r.threshold = 0.6 x = ps %>% # scale_micro(method = "TMM") %>% vegan_otu() %>% t() %>% as.data.frame() occor<-WGCNA::corAndPvalue(t(x)/colSums(x),method = 'pearson') mtadj<-multtest::mt.rawp2adjp(unlist(occor$p),proc='BH') adpcor<-mtadj$adjp[order(mtadj$index),2] occor.p<-matrix(adpcor,dim(t(x)/colSums(x))[2]) ## R value occor.r<-occor$cor diag(occor.r) <- 0 # occor.r[occor.p > 0.05|abs(occor.r)<0.4] = 0 occor.r[occor.p > p.threshold | abs(occor.r)< r.threshold] = 0 occor.r[is.na(occor.r)]=0 cor = occor.r result2 <- model_igraph(cor = cor, method = "cluster_fast_greedy", seed = 12 ) node = result2[[1]] head(node) dat = result2[[2]] head(dat) tem = data.frame(mod = dat$model,col = dat$color) %>% dplyr::distinct( mod, .keep_all = TRUE) col = tem$col names(col) = tem$mod #node节点注释 otu_table = as.data.frame(t(vegan_otu(ps))) tax_table = as.data.frame(vegan_tax(ps)) nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table) head(nodes) #计算边 edge = edgeBuild(cor = cor,node = node) colnames(edge)[8] = "cor" head(edge) tem2 = dat %>% dplyr::select(OTU,model,color) %>% dplyr::right_join(edge,by =c("OTU" = "OTU_1" ) ) %>% dplyr::rename(OTU_1 = OTU,model1 = model,color1 = color) head(tem2) tem3 = dat %>% dplyr::select(OTU,model,color) %>% dplyr::right_join(edge,by =c("OTU" = "OTU_2" ) ) %>% dplyr::rename(OTU_2 = OTU,model2 = model,color2 = color) head(tem3) tem4 = tem2 %>%inner_join(tem3) head(tem4) edge2 = tem4 %>% mutate(color = ifelse(model1 == model2,as.character(model1),"across"), manual = ifelse(model1 == model2,as.character(color1),"#C1C1C1") ) head(edge2) col_edge = edge2 %>% dplyr::distinct(color, .keep_all = TRUE) %>% select(color,manual) col0 = col_edge$manual names(col0) = col_edge$color library(ggnewscale) p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = color), data = edge2, size = 1) + scale_colour_manual(values = col0) ggsave("./cs1.pdf",p1,width = 16,height = 14) p2 = p1 + new_scale_color() + geom_point(aes(X1, X2,color =model), data = dat,size = 4) + scale_colour_manual(values = col) + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + theme(panel.background = element_blank()) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white", colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) ggsave("./cs2.pdf",p2,width = 16,height = 14) # plot(igraph,layout=sub_net_layout,vertex.size=2) # p = ggplot(dat) + # geom_point(aes(X1, X2,fill = model),pch = 21) # p

四、参考文献

[1] Wen,Tao, Penghao Xie, Shengdie Yang, Guoqing Niu, Xiaoyu Liu, Zhexu Ding, Chao Xue, Yong-Xin Liu, Qirong Shen, and Jun Yuan. 2022. “ ggClusterNet: An R package for microbiome network analysis and modularity-based multiple network layouts.” iMeta 1, e32.

[2] Multiple_layout，github /taowenmicro/ggClusterNet/wiki/Multiple_layout

五、相关信息

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