# 基于CRAN安装R包，检测没有则安装p_list = c("psych", "magrittr", "reshape2", "dplyr", "readxl", "igraph", "tidyverse", "RColorBrewer", "ggraph")for(p in p_list){if (!requireNamespace(p)){install.packages(p)}    library(p, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)}
# 加载R包 Load the packagesuppressWarnings(suppressMessages(library(psych)))suppressWarnings(suppressMessages(library(magrittr)))suppressWarnings(suppressMessages(library(reshape2)))suppressWarnings(suppressMessages(library(dplyr)))suppressWarnings(suppressMessages(library(readxl)))suppressWarnings(suppressMessages(library(igraph)))suppressWarnings(suppressMessages(library(tidyverse)))suppressWarnings(suppressMessages(library(RColorBrewer)))suppressWarnings(suppressMessages(library(ggraph)))

#1.加载数据#1.Load datadata5 <- read.csv("data/data5_used.csv", row.names = 1, header = TRUE, check.names = FALSE)#2.计算Spearman相关系数并调整p值#2. Calculate the Spearman correlation coefficient and adjust the p-valueCor_selected <- corr.test(data5, method="spearman", adjust="BH")Cor <- as.data.frame(Cor_selected$r)# 准备相关性数据# Prepare correlation datar.cor <- data.frame(Cor_selected$r)[1:36, 37:78]#3.创建数据框并设置对角块为零#3. Create a data frame and set the diagonal blocks to zerodata <- Cor %>% as.data.frame()data[1:36, 1:36] <- 0data[37:78, 37:78] <- 0# 添加id列用于melt# Add id column for meltdata$id <- colnames(data)#4.准备用于绘图的相关性Connect数据#4. Prepare the correlation data for plotting# 定义相关性计算函数，这里只计算组件对象的相关性，不计算组内对象的相关性# Define the correlation calculation function. Here, only the correlation of component objects is calculated, not the correlation of objects within the group.calculate_correlations <- function(data, species_index, start_col, end_col) {  rows <- end_col - start_col + 1  Correlations <- data.frame(    variable = character(length = rows),    correlation = numeric(length = rows),    p_adj = numeric(length = rows),    stringsAsFactors = FALSE  )  for (i in 1:rows) {    temp1 <- colnames(data5)[i + start_col - 1]    temp2 <- corr.test(data5[, species_index], data5[, i + start_col - 1], method="spearman", adjust="BH")    temp3 <- temp2$p.adj    Correlations[i, 1] <- temp1    Correlations[i, 2] <- temp2$r    Correlations[i, 3] <- temp3  }  Correlations$species <- colnames(data5)[species_index]  return(Correlations)}# 计算相关性# Calculate correlationstart_col <- 37end_col <- ncol(data5)species_count <- 36all_correlations <- list()for (species_index in 1:species_count) {  correlations <- calculate_correlations(data5, species_index, start_col, end_col)  all_correlations[[species_index]] <- correlations}# 合并所有相关性数据# Merge all related datafinal_correlations <- do.call(rbind, all_correlations)#筛选p_adj <= 0.05和r的绝对值>=0.4的数据#Filter data with p_adj <= 0.05 and absolute value of r >= 0.4final_correlations <- final_correlations[abs(final_correlations$correlation) >= 0.4 ,]final_correlations <- final_correlations[final_correlations$p_adj <= 0.05 ,]#data2 <- read.csv("species_gene_correlation_36S_42genes_selected.csv", row.names = 1, header = TRUE, check.names = FALSE)data2 <- data.frame(from = final_correlations$species, to = final_correlations$variable, r = final_correlations$correlation, p_adj = final_correlations$p_adj)cor.data2 <- data2 %>%  data.frame() %>%  mutate(    linecolor = ifelse(r > 0, "positive", "negative"),    linesize = abs(r)  )colnames(cor.data2) <- c("to", "from", "r", "FDR", "linecolor", "linesize")connect_n <- cor.data2[, c(2, 1, 3, 5, 6)]#5. 移除id列并创建分层边数据#5. Remove the id column and create hierarchical edge datadata <- data %>% select(-id)edge <- data.frame(from = 'Origin', to = colnames(data))vertices <- data.frame(name = c('Origin', as.character(edge$to)))colnames(vertices) <- "name"# 对顶点进行分组# Group the verticesvertices$group <- "group"vertices$group[1:37] <- "Species"vertices$group[38:79] <- "Gene"#6.计算角度并调整标签位置#6. Calculate the angle and adjust the label positionall_leaves <- which(is.na(match(vertices$name, edge$from)))nleaves <- length(all_leaves)vertices$id[all_leaves] <- seq(1, nleaves)vertices$angle <- 90 - 360 * vertices$id / nleavesvertices$hjust <- ifelse(vertices$angle < -90, 1, 0)vertices$angle <- ifelse(vertices$angle < -90, vertices$angle + 180, vertices$angle)#7.将连接与顶点匹配#7. Match connections to verticesfrom <- match(connect_n$from, vertices$name)to <- match(connect_n$to, vertices$name)connect1 <- connect_n[connect_n$r >= 0, ]from1 <- match(connect1$from, vertices$name)to1 <- match(connect1$to, vertices$name)connect2 <- connect_n[connect_n$r < 0, ]from2 <- match(connect2$from, vertices$name)to2 <- match(connect2$to, vertices$name)#8.创建图对象并生成图#8. Create a graph object and generate a graphmygraph <- graph_from_data_frame(edge, vertices = vertices)# 生成图# Generate graphpdf("results/EdgeBundingPlot01.pdf",family = "serif",width = 6,height = 7)ggraph(mygraph, layout = 'dendrogram', circular = TRUE) +  geom_conn_bundle(data = get_con(from = from, to = to),                    aes(edge_width = rep(connect_n$linesize, 3), edge_colour = rep(connect_n$linecolor, 3), edge_alpha = 0.8),                   tension = 0.8) +  geom_node_point(aes(filter = leaf, x = 1.05 * x, y = 1.05 * y, size = 5, color = group, alpha = 0.2)) +  geom_node_text(aes(filter = leaf, x = 1.1 * x, y = 1.1 * y, label = name, angle = angle, hjust = hjust, color = group), size = 2.5) +  scale_edge_color_manual(values = c("#BA55D3", "#CCEEFF")) +  scale_edge_width_continuous(range = c(0.2,1.6)) +  scale_size_continuous(range = c(0.1, 1.5)) +  scale_color_manual(values = rep(c('#6A5ACD', '#189078', '#54278f'), 30)) +  theme_void() +  theme(plot.margin = unit(c(0, 0, 0, 0), 'cm')) +  coord_fixed() +  expand_limits(x = c(-1.2, 1.2), y = c(-1.2, 1.2))dev.off()#> png #>   2