「微生物组」基于LorMe包进行微生物组数据分析—网络分析！！！

###安装LorMe包install.packages("LorMe")##加载MicrobiomeStat包library(LorMe)# Lightening One-Code Resolving Microbial Ecology Solutionlibrary(ggplot2)# Create Elegant Data Visualisations Using the Grammar of Graphics

data("Two_group")

#将数据按照处理组和对照组分开Treat_obj<- sub_tax_summary(taxobj =Two_group, Group=="Treatment")Control_obj<- sub_tax_summary(taxobj =Two_group, Group=="Control")
###网络分析##处理组Treat_network_results<- network_analysis(  taxobj = Treat_obj,  taxlevel = "Base",  n = 4,  threshold = 0.6)# Total degree: 172# Total edges/links: 86# Total vertices: 141# Connectance: 0.00871327254305978# Average degree: 1.21985815602837# Diameter: 4# Average path length: 1.44094488188976# Global clustering coefficient: 0# Number of clusters: 57# Betweenness centralization: 0.000631698722654529# Degree centralization: 0.0127152988855117

##提取节点信息nodes_table <-Treat_network_results$Nodes_info##提取邻接关系Adjacency_table <-Treat_network_results$Adjacency_column_table##提取邻接矩阵Adjacency_matrix <-Treat_network_results$Adjacency_matrix

###对照组Control_network_results<- network_analysis(  taxobj = Control_obj,  taxlevel = "Base",  n = 4,  threshold = 0.6)# Total degree: 154# Total edges/links: 77# Total vertices: 132# Connectance: 0.0089058524173028# Average degree: 1.16666666666667# Diameter: 3# Average path length: 1.31428571428571# Global clustering coefficient: 0# Number of clusters: 55# Betweenness centralization: 0.000562097421254813# Degree centralization: 0.0139949109414758##提取节点信息nodes_table2 <-Control_network_results$Nodes_info##提取邻接关系Adjacency_table2 <-Control_network_results$Adjacency_column_table##提取邻接矩阵Adjacency_matrix2 <-Control_network_results$Adjacency_matrix

###基于网络分析数据进行网络可视化Treat_visual<- network_visual(  network_obj=Treat_network_results,#数据  mode = "major_module", #可视化类型, "major_module"或"major_tax".  major_num = 6, #需要显示的模块数  taxlevel = NULL, #仅针对“major_tax”模式显示分类级别  select_tax = NULL, #要显示的特定分类，仅适用于“major_tax”模式  palette = "Set1", #颜色  vertex.size = 6 #节点大小)

Control_visual<- network_visual(  network_obj=Control_network_results,#数据  mode = "major_module", #可视化类型, "major_module"或"major_tax".  major_num = 6, #需要显示的模块数  taxlevel = NULL, #仅针对“major_tax”模式显示分类级别  select_tax = NULL, #要显示的特定分类，仅适用于“major_tax”模式  palette = "Set1", #颜色  vertex.size = 6 #节点大小)

##模块物种组成module_results <- Module_composition(  network_obj = Treat_network_results,  No.module = c(2,3,6), #Module number to analysis  taxlevel = "Phylum", #summarise level  palette = "Plan10",    rmprefix = "p__")module_results$Module2$Pie_plot_Module2module_results$Module3$Pie_plot_Module3module_results$Module6$Pie_plot_Module6

##各样本数据在各模块中的相应丰度module_rel<- Module_abundance(Treat_network_results,No.module = c(2,3,6))module_rel$rowframe

#### Analysisnetwork_results <- network_analysis(  taxobj = Two_group,  taxlevel = "Genus",  n = 10,  threshold = 0.8)# Total degree: 278# Total edges/links: 139# Total vertices: 115# Connectance: 0.0212051868802441# Average degree: 2.41739130434783# Diameter: 10# Average path length: 3.7350192413414# Global clustering coefficient: 0.298828125# Number of clusters: 23# Betweenness centralization: 0.0870461222446653# Degree centralization: 0.110373760488177

indicator_results <- indicator_analysis(  taxobj = Two_group,  taxlevel = "Genus")##结合差异性分析进行可视化network_diff_obj <- network_withdiff(  network_obj = network_results,  diff_frame = indicator_results)

##对特定模块进行可视化，如显示control中差异性标签最多的两个模块network_visual_re(  network_visual_obj = network_diff_obj,  module_paint = TRUE,  module_num = c(1, 4))##更多见?network_withdiff

####计算每个样本的模块丰度moduleframe<- Module_abundance(network_obj =network_results,No.module = c(1,4) )#统计moduleframe$plotlist$Plotobj_Module1$Statistics#可视化moduleframe$plotlist$Plotobj_Module1$Barplotmoduleframe$plotlist$Plotobj_Module1$Boxplotmoduleframe$plotlist$Plotobj_Module1$Violinplot