forked from admin/PlantAI-rag-new
422 lines
14 KiB
R
Executable File
422 lines
14 KiB
R
Executable File
#!/usr/bin/env Rscript
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# 启动子分析增强可视化脚本
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# 包含8种可视化方式
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# 适配testpy311环境
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rm(list = ls())
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####----加载R包----####
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cat("=== 加载R包 ===\n")
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# 检查并加载必需包
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required_packages <- c("tidyverse", "ggtree", "treeio", "ape",
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"patchwork", "aplot", "RColorBrewer", "ggnewscale")
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for(pkg in required_packages) {
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if(!require(pkg, character.only = TRUE, quietly = TRUE)) {
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cat("错误: 包", pkg, "未安装\n")
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cat("请在testpy311环境中安装: conda install r-", tolower(pkg), "\n", sep="")
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quit(save = "no", status = 1)
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}
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}
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# 加载可选包
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optional_packages <- c("UpSetR", "circlize", "ComplexHeatmap")
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for(pkg in optional_packages) {
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if(require(pkg, character.only = TRUE, quietly = TRUE)) {
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cat("✓ 已加载:", pkg, "\n")
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} else {
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cat("⚠ 可选包未安装:", pkg, "\n")
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}
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}
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cat("✓ 所有必需包加载完成\n\n")
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####----命令行参数----####
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args <- commandArgs(trailingOnly = TRUE)
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if (length(args) < 3) {
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cat("
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启动子分析增强可视化
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用法:
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Rscript promoter_visualization_enhanced.R [树文件] [分组文件] [启动子文件] [元件描述文件] [输出前缀]
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可视化类型:
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1. 系统发育树 + 亚家族标注
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2. 元件数量热图
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3. 组合图（树+热图）
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4. 元件分布条形图
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5. 类别堆叠条形图
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6. 启动子结构示意图（基因箭头+元件位置）
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7. UpSet图（元件共现）
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8. 环形图（元件类别分布）
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")
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quit(save = "no", status = 0)
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}
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tree_file <- args[1]
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group_file <- args[2]
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promoter_file <- args[3]
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element_desc_file <- ifelse(length(args) >= 4, args[4], NA)
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output_prefix <- ifelse(length(args) >= 5, args[5], "promoter_analysis")
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cat("=== 启动子增强可视化 ===\n")
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cat("树文件:", tree_file, "\n")
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cat("分组文件:", group_file, "\n")
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cat("启动子文件:", promoter_file, "\n")
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cat("输出前缀:", output_prefix, "\n\n")
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####----读取数据----####
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# 1. 读取树
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tree <- treeio::read.newick(tree_file)
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# 2. 读取分组
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group_df <- read.delim(group_file, header = FALSE, stringsAsFactors = FALSE)
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if(ncol(group_df) >= 2) {
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colnames(group_df) <- c("label", "Subgroup")
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}
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# 清理基因ID（保留基础ID用于匹配）
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group_df$label <- group_df$label %>%
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str_trim()
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# 创建基础ID列用于匹配
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group_df$label_base <- group_df$label %>%
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str_remove("\\.\\d+\\.T\\d+$") %>%
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str_remove("\\.T\\d+$") %>%
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str_remove("\\.\\d+$")
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# 3. 读取启动子数据
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promoter <- read.delim(promoter_file, header = FALSE, stringsAsFactors = FALSE)
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if(ncol(promoter) >= 8) {
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promoter <- promoter[, c(1, 2, 4, 5, 6, 8)]
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colnames(promoter) <- c("gene", "element", "position", "length", "strand", "description")
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}
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# 启动子数据基因ID（已经是基础ID）
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promoter$gene <- promoter$gene %>% str_trim()
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# 4. 读取元件描述（如果有）
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if(!is.na(element_desc_file) && file.exists(element_desc_file)) {
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element_desc <- read.csv(element_desc_file, stringsAsFactors = FALSE)
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# 添加分类
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element_desc$category <- sapply(element_desc$element, function(x) {
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if(grepl("TATA|CAAT|Inr|Y-patch", x, ignore.case = TRUE)) return("Core promoter")
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if(grepl("ABRE", x, ignore.case = TRUE)) return("ABA signaling")
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if(grepl("P-box|GARE", x, ignore.case = TRUE)) return("GA signaling")
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if(grepl("CGTCA|TGACG", x, ignore.case = TRUE)) return("JA signaling")
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if(grepl("TCA|as-1", x, ignore.case = TRUE)) return("SA signaling")
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if(grepl("TGA|AuxRE", x, ignore.case = TRUE)) return("Auxin response")
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if(grepl("ERE", x, ignore.case = TRUE)) return("Ethylene response")
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if(grepl("DRE|LTR|ARE|STRE|W.box|W-box", x, ignore.case = TRUE)) return("Stress response")
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if(grepl("^MYB$|MYB-like|MRE", x, ignore.case = TRUE)) return("MYB-related")
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if(grepl("^MYC$|E-box", x, ignore.case = TRUE)) return("MYC-related")
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if(grepl("WRKY", x, ignore.case = TRUE)) return("WRKY-related")
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if(grepl("G-box|GT1|I-box|Sp1|Box.4|GATA", x, ignore.case = TRUE)) return("Light response")
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if(grepl("RY|AAGAA|CAT|GCN4", x, ignore.case = TRUE)) return("Development")
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return("Other")
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})
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# 合并分类到promoter数据
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promoter <- promoter %>%
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left_join(element_desc %>% select(element, category), by = "element")
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} else {
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promoter$category <- "Other"
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}
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# 只保留树中存在的基因
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tree_labels <- tree$tip.label
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# 创建树标签的基础ID
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tree_labels_base <- tree_labels %>%
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str_remove("\\.\\d+\\.T\\d+$") %>%
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str_remove("\\.T\\d+$") %>%
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str_remove("\\.\\d+$")
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# 使用基础ID进行匹配
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group_df <- group_df %>% filter(label_base %in% tree_labels_base)
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promoter <- promoter %>% filter(gene %in% tree_labels_base)
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# 更新树标签为匹配格式
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names(tree_labels_base) <- tree_labels
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cat("数据读取完成:\n")
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cat(" 树中基因数:", length(tree_labels), "\n")
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cat(" 分组基因数:", nrow(group_df), "\n")
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cat(" 启动子记录数:", nrow(promoter), "\n")
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cat(" 唯一元件数:", length(unique(promoter$element)), "\n\n")
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####----可视化1: 系统发育树 + 亚家族----####
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cat("[1/8] 绘制系统发育树...\n")
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# 准备亚家族颜色
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subfam_colors <- brewer.pal(n = length(unique(group_df$Subgroup)), name = "Dark2")
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names(subfam_colors) <- unique(group_df$Subgroup)
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# 绘制树
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# 创建分组数据框，使用原始树标签
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group_df_for_tree <- group_df %>%
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mutate(label = label_base) %>%
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select(-label_base)
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p_tree <- ggtree(tree) %<+% group_df_for_tree +
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geom_tippoint(aes(color = Subgroup), size = 3) +
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geom_tiplab(aes(color = Subgroup), size = 2.5, offset = 0.005) +
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scale_color_manual(values = subfam_colors, name = "Subfamily") +
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theme_tree2() +
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theme(legend.position = "right")
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# 保存
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ggsave(paste0(output_prefix, "_01_tree.pdf"), p_tree, width = 10, height = 8, device = cairo_pdf)
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tryCatch({
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ggsave(paste0(output_prefix, "_01_tree.png"), p_tree, width = 10, height = 8, dpi = 150, type = "cairo")
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}, error = function(e) {
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cat(" 警告: PNG保存失败，仅PDF可用\n")
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})
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cat(" 已保存: _01_tree\n")
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####----可视化2: 元件数量热图----####
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cat("[2/8] 绘制元件热图...\n")
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# 准备热图数据（使用基础ID）
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heatmap_data <- promoter %>%
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group_by(gene, element) %>%
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summarise(count = n(), .groups = "drop") %>%
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complete(gene = tree_labels_base, element, fill = list(count = 0)) %>%
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mutate(gene = factor(gene, levels = rev(tree_labels_base)))
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# 绘制热图
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p_heatmap <- ggplot(heatmap_data, aes(x = element, y = gene, fill = count)) +
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geom_tile(color = "white", size = 0.1) +
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scale_fill_gradient(low = "white", high = "#08519c", name = "Count") +
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labs(x = "Cis-acting Element", y = "Gene") +
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theme_bw() +
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theme(
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axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 6),
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axis.text.y = element_text(size = 7),
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panel.grid = element_blank()
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)
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ggsave(paste0(output_prefix, "_02_heatmap.pdf"), p_heatmap, width = 16, height = 10, device = cairo_pdf)
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cat(" 已保存: _02_heatmap\n")
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####----可视化3: 组合图（树+热图）----####
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cat("[3/8] 绘制组合图...\n")
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# 简化热图（无y轴标签）
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p_heatmap_simple <- p_heatmap +
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theme(axis.text.y = element_blank(), axis.title.y = element_blank())
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# 组合
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p_combined <- p_heatmap_simple %>% insert_left(p_tree, width = 0.4)
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ggsave(paste0(output_prefix, "_03_combined.pdf"), p_combined, width = 18, height = 10, device = cairo_pdf)
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cat(" 已保存: _03_combined\n")
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####----可视化4: 元件分布条形图----####
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cat("[4/8] 绘制元件分布条形图...\n")
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element_stats <- promoter %>%
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group_by(element) %>%
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summarise(count = n(), .groups = "drop") %>%
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arrange(desc(count)) %>%
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head(30)
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p_bar <- ggplot(element_stats, aes(x = reorder(element, count), y = count)) +
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geom_bar(stat = "identity", fill = "steelblue") +
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coord_flip() +
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labs(x = "Cis-acting Element", y = "Count",
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title = "Top 30 Most Abundant Cis-acting Elements") +
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theme_bw() +
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theme(
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plot.title = element_text(size = 14, face = "bold"),
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axis.text = element_text(size = 9)
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)
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ggsave(paste0(output_prefix, "_04_element_barplot.pdf"), p_bar, width = 10, height = 8, device = cairo_pdf)
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cat(" 已保存: _04_element_barplot\n")
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####----可视化5: 类别堆叠条形图----####
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cat("[5/8] 绘制类别堆叠条形图...\n")
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category_stats <- promoter %>%
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group_by(gene, category) %>%
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summarise(count = n(), .groups = "drop") %>%
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complete(gene = tree_labels, category, fill = list(count = 0))
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# 计算每个基因的类别比例
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category_props <- category_stats %>%
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group_by(gene) %>%
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mutate(prop = count / sum(count)) %>%
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ungroup() %>%
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mutate(gene = factor(gene, levels = rev(tree_labels)))
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# 定义类别颜色
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category_colors <- c(
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"Core promoter" = "#e41a1c",
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"Light response" = "#377eb8",
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"ABA signaling" = "#4daf4a",
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"GA signaling" = "#984ea3",
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"JA signaling" = "#ff7f00",
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"SA signaling" = "#ffff33",
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"Auxin response" = "#a65628",
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"Ethylene response" = "#f781bf",
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"Stress response" = "#999999",
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"MYB-related" = "#66c2a5",
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"MYC-related" = "#fc8d62",
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"WRKY-related" = "#8da0cb",
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"Development" = "#e78ac3",
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"Cell cycle" = "#a6d854",
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"Other" = "#ffd92f"
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)
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p_stacked <- ggplot(category_props, aes(x = gene, y = prop, fill = category)) +
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geom_bar(stat = "identity", position = "stack", width = 0.8) +
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scale_fill_manual(values = category_colors, name = "Category") +
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coord_flip() +
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labs(x = "Gene", y = "Proportion",
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title = "Cis-acting Element Category Distribution by Gene") +
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theme_bw() +
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theme(
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plot.title = element_text(size = 14, face = "bold"),
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axis.text.y = element_text(size = 7),
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legend.position = "right"
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)
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ggsave(paste0(output_prefix, "_05_category_stacked.pdf"), p_stacked, width = 12, height = 10, device = cairo_pdf)
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cat(" 已保存: _05_category_stacked\n")
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####----可视化6: 启动子结构示意图----####
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cat("[6/8] 绘制启动子结构示意图...\n")
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# 选择Top 5最复杂的基因进行展示
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top_genes <- promoter %>%
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group_by(gene) %>%
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summarise(n_elements = n(), .groups = "drop") %>%
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arrange(desc(n_elements)) %>%
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head(5) %>%
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pull(gene)
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# 准备结构数据
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structure_data <- promoter %>%
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filter(gene %in% top_genes) %>%
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mutate(
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# 将位置转换为相对于ATG的负坐标
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start = -2000 + position - length,
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end = -2000 + position
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) %>%
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select(gene, element, start, end, category) %>%
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mutate(
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gene = factor(gene, levels = top_genes),
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category = factor(category, levels = names(category_colors))
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)
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# 绘制结构图
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p_structure <- ggplot(structure_data) +
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# 绘制启动子区域背景
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geom_rect(aes(xmin = -2000, xmax = 0, ymin = as.numeric(gene) - 0.3,
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ymax = as.numeric(gene) + 0.3),
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fill = "grey95", color = "grey70") +
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# 绘制元件
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geom_rect(aes(xmin = start, xmax = end, ymin = as.numeric(gene) - 0.25,
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ymax = as.numeric(gene) + 0.25, fill = category),
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color = "black", size = 0.2) +
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scale_fill_manual(values = category_colors, name = "Category") +
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scale_y_continuous(breaks = 1:length(top_genes), labels = top_genes) +
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labs(x = "Position relative to ATG (bp)", y = "Gene",
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title = "Promoter Structure (Top 5 Most Complex)") +
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theme_bw() +
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theme(
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plot.title = element_text(size = 14, face = "bold"),
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panel.grid.minor = element_blank()
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)
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ggsave(paste0(output_prefix, "_06_promoter_structure.pdf"), p_structure, width = 14, height = 6, device = cairo_pdf)
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cat(" 已保存: _06_promoter_structure\n")
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####----可视化7: UpSet图（元件共现）----####
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cat("[7/8] 绘制UpSet图...\n")
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# 准备UpSet数据
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upset_data <- promoter %>%
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group_by(gene, element) %>%
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summarise(present = 1, .groups = "drop") %>%
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pivot_wider(names_from = element, values_from = present, values_fill = 0)
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# 选择Top 20元件进行UpSet分析
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top_elements <- promoter %>%
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group_by(element) %>%
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summarise(count = n(), .groups = "drop") %>%
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arrange(desc(count)) %>%
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head(20) %>%
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pull(element)
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upset_subset <- upset_data %>%
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select(gene, all_of(top_elements)) %>%
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as.data.frame()
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# 保存UpSet数据
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write.csv(upset_subset, paste0(output_prefix, "_07_upset_data.csv"), row.names = FALSE)
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# 如果UpSetR可用，绘制UpSet图
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if("UpSetR" %in% installed.packages()[,"Package"]) {
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tryCatch({
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p_upset <- upset(upset_subset,
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sets = top_elements,
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order.by = "freq",
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nsets = 20,
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nintersects = 40,
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main.bar.color = "steelblue",
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sets.bar.color = "darkred",
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matrix.color = "gray20")
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pdf(paste0(output_prefix, "_07_upset.pdf"), width = 14, height = 10)
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print(p_upset)
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dev.off()
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png(paste0(output_prefix, "_07_upset.png"), width = 1400, height = 1000, res = 100)
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print(p_upset)
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dev.off()
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cat(" 已保存: _07_upset\n")
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}, error = function(e) {
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cat(" UpSet图绘制失败:", e$message, "\n")
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})
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} else {
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cat(" UpSetR包未安装，跳过UpSet图\n")
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}
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####----可视化8: 环形图（类别分布）----####
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cat("[8/8] 绘制环形图...\n")
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# 计算类别总数
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category_totals <- promoter %>%
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group_by(category) %>%
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summarise(count = n(), .groups = "drop") %>%
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arrange(desc(count))
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# 绘制环形图
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p_donut <- ggplot(category_totals, aes(x = 2, y = count, fill = category)) +
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geom_bar(stat = "identity", width = 1, color = "white") +
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coord_polar("y", start = 0) +
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xlim(0.5, 2.5) +
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scale_fill_manual(values = category_colors, name = "Category") +
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labs(title = "Cis-acting Element Category Distribution") +
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theme_void() +
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theme(
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plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
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legend.position = "right"
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)
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ggsave(paste0(output_prefix, "_08_category_donut.pdf"), p_donut, width = 10, height = 8, device = cairo_pdf)
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cat(" 已保存: _08_category_donut\n")
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####----完成----####
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cat("\n===== 所有可视化完成 =====\n")
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cat("生成的文件:\n")
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for(i in 1:8) {
|
||
pdf_file <- paste0(output_prefix, "_0", i, "_*.pdf")
|
||
cat(sprintf("%d. %s\n", i, pdf_file))
|
||
} |