615 lines
17 KiB
R
Executable File
615 lines
17 KiB
R
Executable File
#!/usr/bin/env Rscript
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## =========================================================
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## combo_motif_structure.R
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## 左：系统发育树（含基因 ID）
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## 中：亚家族竖线（不压 gene ID）
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## 右一：MEME motif（按 XML 实际长度比例）
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## 右二：GFF3 基因结构（按 GFF3 实际长度比例；只画 CDS + UTR）
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## 顶部：三个表头：Phylogenetic tree / Motif / Gene structure
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##
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## 用法:
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## Rscript combo_motif_structure.R \
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## <treefile> <subfamily_file> <meme_xml> <gff3_file> [output_pdf]
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## =========================================================
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args <- commandArgs(trailingOnly = TRUE)
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if (length(args) < 4) {
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stop(
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"用法:\n",
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" Rscript combo_motif_structure.R ",
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"<treefile> <subfamily_file> <meme_xml> <gff3_file> [output_pdf]\n\n",
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call. = FALSE
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)
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}
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treefile <- args[1]
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subfamily_file <- args[2]
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meme_xml <- args[3]
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gff3_file <- args[4]
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## 第五个参数：输出 pdf 名称
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if (length(args) >= 5 && nzchar(args[5])) {
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output_pdf <- args[5]
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} else {
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prefix <- sub("\\.[^.]*$", "", treefile)
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output_pdf <- paste0(prefix, ".tree_motif_structure.pdf")
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}
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cat("树文件: ", treefile, "\n")
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cat("亚家族文件: ", subfamily_file, "\n")
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cat("MEME XML 文件: ", meme_xml, "\n")
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cat("GFF3 文件: ", gff3_file, "\n")
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cat("输出文件: ", output_pdf, "\n\n")
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## ===================== 2. 加载 R 包 =====================
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suppressPackageStartupMessages({
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library(ape)
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library(ggtree)
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library(tidytree)
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library(dplyr)
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library(RColorBrewer)
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library(ggplot2)
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library(xml2)
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library(ggnewscale)
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})
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## ===================== 3. 读亚家族文件 + 深色配色 =====================
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subfam <- read.table(subfamily_file,
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header = FALSE,
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stringsAsFactors = FALSE)
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colnames(subfam) <- c("label", "subfamily")
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subfam_levels <- sort(unique(subfam$subfamily))
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n_subfam <- length(subfam_levels)
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if (n_subfam == 0) {
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stop("在亚家族文件中没有检测到任何 subfamily，请检查文件格式。", call. = FALSE)
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}
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## 深色调：Dark2，若类别多就循环使用
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base_cols_sf <- RColorBrewer::brewer.pal(8, "Dark2")
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subfam_cols_vec <- rep_len(base_cols_sf, n_subfam)
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subfam_cols <- setNames(subfam_cols_vec, subfam_levels)
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## ===================== 4. 树绘图函数（只画树 + tip/subfamily，不画竖线） =====================
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plot_iqtree_by_subfamily <- function(treefile, subfam_df, subfam_cols, label_offset = 0.4) {
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tree <- read.tree(treefile)
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tree_df <- as_tibble(tree)
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tree_df$isTip <- tree_df$node <= length(tree$tip.label)
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tree_df <- dplyr::left_join(tree_df, subfam_df, by = "label")
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tree_df$support <- ifelse(
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tree_df$isTip,
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NA_real_,
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suppressWarnings(as.numeric(tree_df$label))
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)
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p <- ggtree(tree, branch.length = "none") %<+% tree_df +
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## bootstrap
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geom_nodepoint(
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aes(fill = cut(support, c(0, 50, 75, 100))),
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shape = 21,
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size = 2,
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na.rm = TRUE
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) +
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scale_fill_manual(
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values = c("black", "grey40", "grey80"),
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guide = "legend",
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name = "Bootstrap Percentage (BP)",
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breaks = c("(75,100]", "(50,75]", "(0,50]"),
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labels = expression(BP > 75,
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50 < BP * "<=75",
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BP <= 50)
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) +
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## tip 颜色 = subfamily
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geom_tippoint(
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aes(color = subfamily),
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size = 2,
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na.rm = TRUE
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) +
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scale_color_manual(
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values = subfam_cols,
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name = "Subfamily"
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) +
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## 基因 ID 标签，靠树一点
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geom_tiplab(offset = label_offset, size = 3, hjust = 0) +
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ggtitle(NULL) +
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theme_tree() +
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theme(
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legend.background = element_rect(color = "#969696",
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fill = "#d9d9d9")
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)
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attr(p, "label_offset") <- label_offset
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return(p)
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}
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## ===================== 5. 解析 MEME XML，得到 motif 位点 + 序列长度 =====================
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parse_meme_xml <- function(meme_file) {
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doc <- xml2::read_xml(meme_file)
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## 所有序列的 id/name/length
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seq_nodes <- xml2::xml_find_all(doc, ".//sequence")
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seq_ids <- xml2::xml_attr(seq_nodes, "id")
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seq_names <- xml2::xml_attr(seq_nodes, "name")
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## 兼容 length / Length 大小写
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seq_lengths <- xml2::xml_attr(seq_nodes, "length")
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if (any(is.na(seq_lengths))) {
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seq_lengths2 <- xml2::xml_attr(seq_nodes, "Length")
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idx <- which(is.na(seq_lengths) & !is.na(seq_lengths2))
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seq_lengths[idx] <- seq_lengths2[idx]
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}
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seq_lengths <- suppressWarnings(as.numeric(seq_lengths))
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name_map <- setNames(seq_names, seq_ids) # id -> gene
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length_map <- setNames(seq_lengths, seq_names) # gene -> seqlen
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motif_nodes <- xml2::xml_find_all(doc, ".//motif")
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site_list <- list()
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motif_index <- 1L
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for (m in motif_nodes) {
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motif_id <- xml2::xml_attr(m, "id")
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if (is.na(motif_id) || motif_id == "") {
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motif_id <- xml2::xml_attr(m, "name")
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}
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if (is.na(motif_id) || motif_id == "") {
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motif_id <- paste0("Motif_", motif_index)
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}
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motif_width <- xml2::xml_attr(m, "width")
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motif_width <- suppressWarnings(as.numeric(motif_width))
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if (is.na(motif_width)) motif_width <- 10
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sites <- xml2::xml_find_all(m, ".//contributing_site")
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if (length(sites) == 0) next
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seq_id <- xml2::xml_attr(sites, "sequence_id")
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start <- xml2::xml_attr(sites, "position")
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start <- suppressWarnings(as.numeric(start))
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width <- rep(motif_width, length(sites))
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df <- data.frame(
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seq_id = seq_id,
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start = start,
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width = width,
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motif = motif_id,
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stringsAsFactors = FALSE
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)
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site_list[[length(site_list) + 1L]] <- df
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motif_index <- motif_index + 1L
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}
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if (length(site_list) == 0) {
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stop("在 MEME XML 中没有找到任何 motif site，请检查文件或 XML 结构。", call. = FALSE)
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}
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sites <- dplyr::bind_rows(site_list)
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## 序列名 & 序列长度来自 XML 中的 sequence 区
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sites$gene <- unname(name_map[sites$seq_id])
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sites$seqlen <- length_map[sites$gene]
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## motif 终点坐标
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sites$end <- sites$start + sites$width - 1
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sites
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}
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## ===================== 6. 解析 GFF3，提取基因结构 =====================
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parse_gff3_gene_structure <- function(gff3_file, tree_genes) {
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gff <- read.table(gff3_file,
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sep = "\t",
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comment.char = "#",
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quote = "",
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stringsAsFactors = FALSE,
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fill = TRUE)
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if (ncol(gff) < 9) {
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stop("GFF3 文件列数不足 9 列，请检查格式。", call. = FALSE)
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}
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colnames(gff)[1:9] <- c("seqid", "source", "type", "start",
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"end", "score", "strand", "phase",
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"attributes")
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gff$start <- as.numeric(gff$start)
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gff$end <- as.numeric(gff$end)
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## 只保留关心的 feature 类型
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feat_types <- c("exon", "CDS", "UTR",
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"five_prime_UTR", "three_prime_UTR",
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"5UTR", "3UTR")
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feat_df <- gff[gff$type %in% feat_types, ]
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if (nrow(feat_df) == 0) {
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stop("在 GFF3 中没有找到 exon/CDS/UTR 等特征。", call. = FALSE)
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}
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## 解析 attributes 里的 Parent=...
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get_attr <- function(attr_vec, key) {
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sapply(strsplit(attr_vec, ";"), function(fields) {
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kv <- strsplit(fields, "=")
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res <- NA_character_
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for (p in kv) {
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if (length(p) == 2 && p[1] == key) {
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res <- p[2]
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break
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}
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}
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res
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})
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}
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feat_df$Parent <- get_attr(feat_df$attributes, "Parent")
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## 只保留 Parent 出现在树 tip.label 的转录本
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feat_df <- feat_df[!is.na(feat_df$Parent) &
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feat_df$Parent %in% tree_genes, ]
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if (nrow(feat_df) == 0) {
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stop("GFF3 中的 Parent 与树 tip.label 无交集，请检查 ID 是否一致。", call. = FALSE)
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}
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feat_df$gene <- feat_df$Parent
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## 每个 gene 的基因区域：真实 genomic 坐标范围
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gene_ranges <- feat_df %>%
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dplyr::group_by(gene) %>%
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dplyr::summarise(
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gene_start = min(start, end, na.rm = TRUE),
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gene_end = max(start, end, na.rm = TRUE),
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gene_len = gene_end - gene_start + 1,
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.groups = "drop"
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)
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## 特征相对该基因起点的坐标
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feat_df <- feat_df %>%
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dplyr::left_join(gene_ranges, by = "gene") %>%
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dplyr::mutate(
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rel_start = pmin(start, end) - gene_start,
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rel_end = pmax(start, end) - gene_start
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) %>%
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dplyr::mutate(
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Type = dplyr::case_when(
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type %in% c("five_prime_UTR", "three_prime_UTR",
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"5UTR", "3UTR", "UTR") ~ "UTR",
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TRUE ~ type
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)
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)
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list(features = feat_df, ranges = gene_ranges)
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}
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## ===================== 7. 画树 & 基本坐标信息 =====================
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label_offset <- 0.4
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p_tree <- plot_iqtree_by_subfamily(treefile, subfam, subfam_cols, label_offset = label_offset)
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tree_dat <- p_tree$data
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tip_dat <- tree_dat %>%
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dplyr::filter(isTip) %>%
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dplyr::select(label, x, y, subfamily)
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if (nrow(tip_dat) == 0) {
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stop("树中没有 tip 节点，请检查 treefile。", call. = FALSE)
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}
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x_tree_min <- min(tree_dat$x, na.rm = TRUE)
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x_tip_max <- max(tip_dat$x, na.rm = TRUE)
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max_y <- max(tree_dat$y, na.rm = TRUE)
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## 树的“主干宽度”（不算标签）
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tree_core_width <- x_tip_max - x_tree_min
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if (!is.finite(tree_core_width) || tree_core_width <= 0) {
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stop("树的宽度计算异常，请检查 treefile。", call. = FALSE)
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}
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## gene ID 左边界（文本起点）
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x_label_left <- x_tip_max + label_offset
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## Panel A（树 + gene ID）
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label_panel_width <- max(1.5, tree_core_width * 0.5)
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x_A_left <- x_tree_min
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x_A_right <- x_label_left + label_panel_width
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## ===================== 8. 解析 MEME XML，并与树对齐（Y 对齐） =====================
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motif_sites <- parse_meme_xml(meme_xml)
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motif_sites <- motif_sites %>%
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dplyr::filter(!is.na(gene)) %>%
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dplyr::filter(gene %in% tip_dat$label)
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if (nrow(motif_sites) == 0) {
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stop("在 MEME 结果中没有找到与树 tip.label 匹配的基因。", call. = FALSE)
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}
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motif_sites <- motif_sites %>%
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dplyr::left_join(tip_dat, by = c("gene" = "label")) %>%
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dplyr::filter(!is.na(seqlen))
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cat("motif 序列长度范围 (XML): ",
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min(motif_sites$seqlen, na.rm = TRUE), " ~ ",
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max(motif_sites$seqlen, na.rm = TRUE), "\n")
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## motif 配色
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motif_levels <- sort(unique(motif_sites$motif))
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n_motif <- length(motif_levels)
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base_cols_m <- RColorBrewer::brewer.pal(8, "Dark2")
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motif_cols_vec <- rep_len(base_cols_m, n_motif)
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motif_cols <- setNames(motif_cols_vec, motif_levels)
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motif_sites$motif <- factor(motif_sites$motif, levels = motif_levels)
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## ===================== 9. 解析 GFF3（gene structure），并与树对齐 =====================
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gs <- parse_gff3_gene_structure(gff3_file, tip_dat$label)
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gene_feat <- gs$features
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gene_ranges <- gs$ranges
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gene_ranges <- gene_ranges %>%
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dplyr::left_join(tip_dat, by = c("gene" = "label"))
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gene_feat <- gene_feat %>%
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dplyr::left_join(tip_dat, by = c("gene" = "label"))
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gene_ranges <- gene_ranges %>% dplyr::filter(!is.na(y))
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gene_feat <- gene_feat %>% dplyr::filter(!is.na(y))
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if (nrow(gene_feat) == 0) {
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stop("GFF3 中匹配到树 tip.label 的基因结构为空。", call. = FALSE)
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}
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cat("基因长度范围 (GFF3): ",
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min(gene_ranges$gene_len, na.rm = TRUE), " ~ ",
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max(gene_ranges$gene_len, na.rm = TRUE), "\n")
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## ===================== 10. 四个 Panel 的 X 布局 =====================
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## 间隔和比例（可以微调）：
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gap1 <- max(0.3, tree_core_width * 0.10) # A-B 间隔
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gap2 <- max(0.3, tree_core_width * 0.10) # B-C 间隔
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gap3 <- max(0.3, tree_core_width * 0.10) # C-D 间隔
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r_B <- 0.4 # Panel B 宽度 = 树宽 * 0.4
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r_C <- 1.6 # Panel C 宽度 = 树宽 * 1.6
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r_D <- 2.2 # Panel D 宽度 = 树宽 * 2.2（结构面板更大）
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## Panel B（亚家族竖线）
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x_B_left <- x_A_right + gap1
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width_B <- tree_core_width * r_B
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x_B_right <- x_B_left + width_B
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x_subfam <- (x_B_left + x_B_right) / 2
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## Panel C（motif）
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x_C_left <- x_B_right + gap2
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width_C <- tree_core_width * r_C
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x_C_right <- x_C_left + width_C
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## Panel D（gene structure）
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x_D_left <- x_C_right + gap3
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width_D <- tree_core_width * r_D
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x_D_right <- x_D_left + width_D
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## ===================== 11. motif 坐标（按 XML 实际长度） =====================
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max_len_motif <- max(motif_sites$seqlen, na.rm = TRUE)
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scale_motif <- width_C / max_len_motif
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cat("最长 motif 序列长度: ", max_len_motif,
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"；motif 面板宽度: ", width_C,
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"；scale_motif: ", scale_motif, "\n")
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## motif 基因主干线
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motif_gene_tracks <- motif_sites %>%
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dplyr::distinct(gene, y, seqlen) %>%
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dplyr::mutate(
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x_gene_start = x_C_left,
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x_gene_end = x_C_left + seqlen * scale_motif
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)
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## motif 矩形
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motif_rect <- motif_sites %>%
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dplyr::mutate(
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xmin = x_C_left + (start - 1) * scale_motif,
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xmax = x_C_left + end * scale_motif,
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ymin = y - 0.25,
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ymax = y + 0.25
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)
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## ===================== 12. gene structure 坐标（按 GFF3 实际长度；只画 CDS + UTR） =====================
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max_gene_len <- max(gene_ranges$gene_len, na.rm = TRUE)
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scale_gene <- width_D / max_gene_len
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cat("最长基因长度: ", max_gene_len,
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"；gene 面板宽度: ", width_D,
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"；scale_gene: ", scale_gene, "\n")
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## 基因主干线（Panel D）
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gene_tracks <- gene_ranges %>%
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dplyr::mutate(
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x_gene_start = x_D_left,
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x_gene_end = x_D_left + gene_len * scale_gene
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)
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## 各 feature 矩形（先算坐标）
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gene_feat_rect <- gene_feat %>%
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dplyr::mutate(
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xmin = x_D_left + rel_start * scale_gene,
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xmax = x_D_left + rel_end * scale_gene,
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||
ymin = y - 0.25,
|
||
ymax = y + 0.25
|
||
)
|
||
|
||
## 只保留 CDS 和 UTR，不画 exon 等其它类型
|
||
gene_feat_rect <- gene_feat_rect %>%
|
||
dplyr::filter(Type %in% c("CDS", "UTR"))
|
||
|
||
## gene structure 深色配色（只针对 CDS 和 UTR）
|
||
gene_palette <- c(
|
||
CDS = "#1b9e77", # 深蓝绿
|
||
UTR = "#d95f02" # 深橙
|
||
)
|
||
|
||
gene_types <- sort(unique(gene_feat_rect$Type))
|
||
gene_cols_vec <- gene_palette[gene_types]
|
||
names(gene_cols_vec) <- gene_types
|
||
|
||
## ===================== 13. 亚家族竖线的 Y 范围 =====================
|
||
|
||
subfam_ranges <- tip_dat %>%
|
||
dplyr::filter(!is.na(subfamily)) %>%
|
||
dplyr::group_by(subfamily) %>%
|
||
dplyr::summarise(
|
||
ymin = min(y),
|
||
ymax = max(y),
|
||
ymid = (ymin + ymax) / 2,
|
||
.groups = "drop"
|
||
)
|
||
|
||
## ===================== 14. 顶部三个表头的位置 =====================
|
||
|
||
tree_header_x <- (x_A_left + x_A_right) / 2
|
||
motif_header_x <- (x_C_left + x_C_right) / 2
|
||
gene_header_x <- (x_D_left + x_D_right) / 2
|
||
header_y <- max_y + 1.2
|
||
|
||
## ===================== 15. 拼合：树 + motif + gene + 竖线 + 表头 =====================
|
||
|
||
p_combo <- p_tree +
|
||
## Panel C：motif 基因主干线（浅灰）
|
||
geom_segment(
|
||
data = motif_gene_tracks,
|
||
inherit.aes = FALSE,
|
||
aes(
|
||
x = x_gene_start,
|
||
xend = x_gene_end,
|
||
y = y,
|
||
yend = y
|
||
),
|
||
colour = "grey70",
|
||
linewidth = 0.4
|
||
) +
|
||
## motif 用新的 fill scale
|
||
ggnewscale::new_scale_fill() +
|
||
geom_rect(
|
||
data = motif_rect,
|
||
inherit.aes = FALSE,
|
||
aes(
|
||
xmin = xmin,
|
||
xmax = xmax,
|
||
ymin = ymin,
|
||
ymax = ymax,
|
||
fill = motif
|
||
),
|
||
color = NA
|
||
) +
|
||
scale_fill_manual(values = motif_cols, name = "Motif") +
|
||
## Panel D：gene 基因主干线
|
||
geom_segment(
|
||
data = gene_tracks,
|
||
inherit.aes = FALSE,
|
||
aes(
|
||
x = x_gene_start,
|
||
xend = x_gene_end,
|
||
y = y,
|
||
yend = y
|
||
),
|
||
colour = "grey70",
|
||
linewidth = 0.4
|
||
) +
|
||
## gene structure 用新的 fill scale（CDS / UTR 深色）
|
||
ggnewscale::new_scale_fill() +
|
||
geom_rect(
|
||
data = gene_feat_rect,
|
||
inherit.aes = FALSE,
|
||
aes(
|
||
xmin = xmin,
|
||
xmax = xmax,
|
||
ymin = ymin,
|
||
ymax = ymax,
|
||
fill = Type
|
||
),
|
||
color = NA
|
||
) +
|
||
scale_fill_manual(
|
||
values = gene_cols_vec,
|
||
name = "Gene Structure"
|
||
) +
|
||
## Panel B：亚家族竖线 + 标签
|
||
geom_segment(
|
||
data = subfam_ranges,
|
||
inherit.aes = FALSE,
|
||
aes(
|
||
x = x_subfam,
|
||
xend = x_subfam,
|
||
y = ymin,
|
||
yend = ymax,
|
||
colour = subfamily
|
||
),
|
||
linewidth = 1
|
||
) +
|
||
geom_text(
|
||
data = subfam_ranges,
|
||
inherit.aes = FALSE,
|
||
aes(
|
||
x = x_subfam + width_B * 0.05,
|
||
y = ymid,
|
||
label = subfamily,
|
||
colour = subfamily
|
||
),
|
||
hjust = 0,
|
||
size = 3
|
||
) +
|
||
## 顶部三个表头
|
||
annotate(
|
||
"text",
|
||
x = tree_header_x,
|
||
y = header_y,
|
||
label = "Phylogenetic tree",
|
||
fontface = "bold",
|
||
size = 4
|
||
) +
|
||
annotate(
|
||
"text",
|
||
x = motif_header_x,
|
||
y = header_y,
|
||
label = "Motif",
|
||
fontface = "bold",
|
||
size = 4
|
||
) +
|
||
annotate(
|
||
"text",
|
||
x = gene_header_x,
|
||
y = header_y,
|
||
label = "Gene structure",
|
||
fontface = "bold",
|
||
size = 4
|
||
) +
|
||
## X 轴范围从树最左到 gene panel 最右
|
||
coord_cartesian(
|
||
xlim = c(x_A_left, x_D_right + tree_core_width * 0.1)
|
||
) +
|
||
expand_limits(y = header_y + 0.5) +
|
||
theme(
|
||
legend.position = "right",
|
||
legend.background = element_rect(color = "#969696", fill = "#d9d9d9")
|
||
)
|
||
|
||
## 整体宽一点，容得下四个 panel
|
||
ggsave(output_pdf, p_combo, width = 18, height = 10)
|
||
|
||
cat("已输出 树 + motif + 基因结构 组合图到: ", output_pdf, "\n")
|