212 lines
7.0 KiB
R
Executable File
212 lines
7.0 KiB
R
Executable File
#!/usr/bin/env Rscript
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#============================================================================
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# PPI Network Visualization
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# 蛋白互作网络可视化
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#============================================================================
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# Parse arguments using commandArgs (no external dependencies)
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args <- commandArgs(trailingOnly = TRUE)
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network_file <- NULL
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output_file <- NULL
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prefix <- "ppi"
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# Parse arguments
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i <- 1
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while (i <= length(args)) {
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if (args[i] == "--network" && i < length(args)) {
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network_file <- args[i + 1]
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i <- i + 2
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} else if (args[i] == "--output" && i < length(args)) {
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output_file <- args[i + 1]
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i <- i + 2
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} else if (args[i] == "--prefix" && i < length(args)) {
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prefix <- args[i + 1]
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i <- i + 2
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} else {
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i <- i + 1
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}
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}
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# Validate arguments
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if (is.null(network_file) || is.null(output_file)) {
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cat("Usage: Rscript draw_ppi_network.R --network <file> --output <file> [--prefix <name>]\n")
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quit(status = 1)
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}
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cat("=" ,rep("=", 60), "\n", sep="")
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cat("PPI Network Visualization\n")
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cat("=" ,rep("=", 60), "\n\n")
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has_igraph <- requireNamespace("igraph", quietly = TRUE)
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has_ggraph <- requireNamespace("ggraph", quietly = TRUE)
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has_ggplot2 <- requireNamespace("ggplot2", quietly = TRUE)
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if (has_igraph) {
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library(igraph)
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}
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if (has_ggraph && has_ggplot2) {
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library(ggraph)
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library(ggplot2)
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}
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#============================================================================
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# Step 1: Read network data
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#============================================================================
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cat("Step 1: Reading network data...\n")
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if (!file.exists(network_file)) {
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stop(paste("Network file not found:", network_file))
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}
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edges <- read.table(network_file, header = TRUE, sep = "\t", stringsAsFactors = FALSE)
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cat(" Edges:", nrow(edges), "\n")
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cat(" Columns:", paste(colnames(edges), collapse = ", "), "\n\n")
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score_col <- if ("combined_score" %in% colnames(edges)) "combined_score" else "score"
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if (!score_col %in% colnames(edges)) {
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stop("Network file must contain score or combined_score column")
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}
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edge_scores <- as.numeric(edges[[score_col]])
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edge_weights <- ifelse(edge_scores > 1, edge_scores / 1000, edge_scores)
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if (!has_igraph) {
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cat("igraph not installed; using base R fallback plot\n")
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nodes <- sort(unique(c(edges$protein1, edges$protein2)))
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n <- length(nodes)
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angle <- seq(0, 2 * pi, length.out = n + 1)[1:n]
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coords <- data.frame(name = nodes, x = cos(angle), y = sin(angle))
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rownames(coords) <- coords$name
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pdf(output_file, width = 12, height = 10)
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plot(coords$x, coords$y, type = "n", axes = FALSE, xlab = "", ylab = "",
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main = paste(prefix, "PPI Network\n", n, "proteins,", nrow(edges), "interactions"))
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for (idx in seq_len(nrow(edges))) {
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a <- coords[edges$protein1[idx], ]
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b <- coords[edges$protein2[idx], ]
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col <- ifelse(edge_weights[idx] >= 0.7, "red",
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ifelse(edge_weights[idx] >= 0.4, "orange", "gray"))
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segments(a$x, a$y, b$x, b$y, col = col, lwd = 1 + edge_weights[idx] * 3)
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}
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points(coords$x, coords$y, pch = 21, bg = "lightblue", cex = 2)
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text(coords$x, coords$y, labels = coords$name, pos = 3, cex = 0.7)
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legend("bottomright",
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legend = c("High (>=0.7)", "Medium (0.4-0.7)", "Low (<0.4)"),
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col = c("red", "orange", "gray"), lty = 1, lwd = 2,
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title = "Confidence", cex = 0.8)
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dev.off()
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cat("Base R network plot generated:", output_file, "\n")
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quit(status = 0)
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}
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#============================================================================
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# Step 2: Create graph
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#============================================================================
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cat("Step 2: Creating graph...\n")
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# 创建边列表
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edge_list <- as.matrix(edges[, c("protein1", "protein2")])
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# 创建图
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g <- graph_from_edgelist(edge_list, directed = FALSE)
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# 添加边权重（score）
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E(g)$weight <- edge_weights
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E(g)$confidence <- cut(edge_weights,
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breaks = c(0, 0.4, 0.7, 1.0),
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labels = c("Low", "Medium", "High"))
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cat(" Nodes:", vcount(g), "\n")
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cat(" Edges:", ecount(g), "\n\n")
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#============================================================================
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# Step 3: Calculate network metrics
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#============================================================================
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cat("Step 3: Calculating network metrics...\n")
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# 度中心性
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V(g)$degree <- degree(g)
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# 介数中心性
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V(g)$betweenness <- betweenness(g)
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# 聚类系数
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V(g)$clustering <- transitivity(g, type = "local")
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V(g)$clustering[is.na(V(g)$clustering)] <- 0 # 处理NA值
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cat(" Average degree:", mean(V(g)$degree), "\n")
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cat(" Network density:", edge_density(g), "\n")
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cat(" Clustering coefficient:", transitivity(g), "\n\n")
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#============================================================================
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# Step 4: Create visualization
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#============================================================================
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cat("Step 4: Creating visualization...\n")
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# 设置节点大小（基于度中心性）
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V(g)$size <- sqrt(V(g)$degree) * 3 + 2
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# 设置节点颜色（基于聚类系数）
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node_colors <- colorRampPalette(c("lightblue", "darkblue"))(100)
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color_indices <- cut(V(g)$clustering, breaks = 100, labels = FALSE)
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color_indices[is.na(color_indices)] <- 1 # 处理NA值
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V(g)$color <- node_colors[color_indices]
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# 创建PDF
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pdf(output_file, width = 12, height = 10)
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# 布局算法（使用Fruchterman-Reingold）
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set.seed(42)
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layout <- layout_with_fr(g, niter = 1000)
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# 绘制网络
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plot(g,
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layout = layout,
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vertex.size = V(g)$size,
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vertex.color = V(g)$color,
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vertex.label = V(g)$name,
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vertex.label.cex = 0.6,
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vertex.label.color = "black",
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edge.width = E(g)$weight * 2,
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edge.color = ifelse(E(g)$weight >= 0.7, "red",
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ifelse(E(g)$weight >= 0.4, "orange", "gray")),
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main = paste(prefix, "PPI Network\n",
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vcount(g), "proteins,", ecount(g), "interactions"),
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sub = "Edge color: Red (high confidence ≥0.7), Orange (medium 0.4-0.7), Gray (low <0.4)"
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)
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# 添加图例
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legend("bottomright",
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legend = c("High (≥0.7)", "Medium (0.4-0.7)", "Low (<0.4)"),
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col = c("red", "orange", "gray"),
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lty = 1,
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lwd = 2,
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title = "Confidence",
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cex = 0.8
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)
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# 使用ggraph绘制更美观的版本（如果包可用）
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if (has_ggraph && has_ggplot2) tryCatch({
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p <- ggraph(g, layout = "fr") +
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geom_edge_link(aes(edge_width = weight, edge_color = confidence), alpha = 0.6) +
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geom_node_point(aes(size = degree, color = clustering)) +
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geom_node_text(aes(label = name), repel = TRUE, size = 2) +
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scale_edge_width(range = c(0.5, 3)) +
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scale_color_gradient(low = "lightblue", high = "darkblue") +
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theme_graph() +
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labs(title = paste(prefix, "PPI Network"),
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subtitle = paste(vcount(g), "proteins,", ecount(g), "interactions"))
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print(p)
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}, error = function(e) {
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cat(" ggraph plot skipped:", e$message, "\n")
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})
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dev.off()
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cat("\n" ,rep("=", 60), "\n", sep="")
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cat("Completed!\n")
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cat("Output file:", output_file, "\n")
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cat("=" ,rep("=", 60), "\n", sep="")
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quit(status = 0)
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