498 lines
15 KiB
R
498 lines
15 KiB
R
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#!/usr/bin/env Rscript
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## =========================================================
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## plot_motif.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
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## 右：MEME motif 矩形条
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## =========================================================
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args <- commandArgs(trailingOnly = TRUE)
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if (length(args) < 3) {
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stop(
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"用法:\n",
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" Rscript plot_motif.R <treefile> <subfamily_file> <meme_xml> [output_prefix]\n\n",
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"说明:\n",
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" treefile : IQ-TREE Newick 树文件\n",
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" subfamily_file : 亚家族文件路径（可为空字符串）\n",
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" meme_xml : MEME XML 结果文件\n",
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" output_prefix : 输出文件前缀（可选，默认与 treefile 同名）\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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# 第四个参数：输出前缀
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if (length(args) >= 4 && nzchar(args[4])) {
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output_prefix <- args[4]
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} else {
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output_prefix <- sub("\\.[^.]*$", "", treefile)
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}
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cat("树文件: ", treefile, "\n")
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cat("亚家族文件: ", ifelse(nzchar(subfamily_file), subfamily_file, "(未提供)"), "\n")
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cat("MEME XML 文件: ", meme_xml, "\n")
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cat("输出前缀: ", output_prefix, "\n\n")
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## ===================== 2. 加载 R 包 =====================
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script_args_all <- commandArgs(trailingOnly = FALSE)
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script_file_arg <- grep("^--file=", script_args_all, value = TRUE)
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script_dir <- if (length(script_file_arg) > 0) dirname(normalizePath(sub("^--file=", "", script_file_arg[1]))) else getwd()
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source(file.path(script_dir, "subfamily_palette.R"))
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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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make_distinct_cols <- function(n) {
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if (n <= 0) return(character(0))
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base <- c(
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"#0072B2", "#D55E00", "#009E73", "#CC79A7", "#E69F00",
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"#56B4E9", "#F0E442", "#000000", "#882255", "#44AA99",
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"#AA4499", "#117733", "#332288", "#DDCC77", "#88CCEE"
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)
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if (n <= length(base)) return(base[seq_len(n)])
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if (exists("hcl.colors", where = asNamespace("grDevices"), inherits = FALSE)) {
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return(grDevices::hcl.colors(n, palette = "Dark 3"))
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}
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grDevices::colorRampPalette(base)(n)
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}
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## ===================== 3. 读亚家族文件 + 深色配色 =====================
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add_confidence_labels <- function(subfam_df, subfamily_file) {
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add_subfamily_confidence_labels(subfam_df, subfamily_file)
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}
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# 检查亚家族文件是否存在
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has_subfamily <- nzchar(subfamily_file) && file.exists(subfamily_file)
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if (has_subfamily) {
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subfam <- read_subfamily_plot_table(subfamily_file)
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subfam <- add_confidence_labels(subfam, subfamily_file)
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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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warning("在亚家族文件中没有检测到任何 subfamily，将使用默认着色")
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has_subfamily <- FALSE
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} else {
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subfam_cols <- make_subfamily_cols(subfam_levels)
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cat("检测到", n_subfam, "个亚家族\n")
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}
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} else {
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cat("未提供亚家族文件，将使用默认着色\n")
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# 创建空的 subfam 数据框
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subfam <- data.frame(label = character(), subfamily = character(), low_confidence = logical(), display_label = character(), stringsAsFactors = FALSE)
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subfam_levels <- character()
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subfam_cols <- character()
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}
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## ===================== 4. 树绘图函数 =====================
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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$tip.label)
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if (nrow(subfam_df) > 0) {
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tree_df <- dplyr::left_join(tree_df, subfam_df, by = "label")
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} else {
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tree_df$subfamily <- NA_character_
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tree_df$display_label <- tree_df$label
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}
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tree_df$display_label <- ifelse(is.na(tree_df$display_label), tree_df$label, tree_df$display_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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geom_text2(
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aes(label = ifelse(!isTip & !is.na(support), round(support), "")),
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hjust = -0.2, vjust = -0.4, size = 2.2,
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na.rm = TRUE, show.legend = FALSE
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)
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# 只有提供亚家族时才着色
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if (nrow(subfam_df) > 0) {
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p <- p + 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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na.value = "grey70"
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)
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} else {
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p <- p + geom_tippoint(size = 2, color = "grey50")
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}
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p <- p + geom_tiplab(aes(label = display_label), 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.position = "right",
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legend.background = element_rect(color = "#969696", 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 =====================
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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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## 兼容可能出现的 Length / length 大小写问题
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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 + site 信息
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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$gene]
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## motif 终点坐标（同样用 XML 的真实坐标）
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sites$end <- sites$start + sites$width - 1
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sites
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}
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## ===================== 6. 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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## 给 gene ID 单独留一块"标签区域"，宽度与树宽成比例，且不少于 1.5
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label_panel_width <- max(1.5, tree_core_width * 0.5)
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## Panel A（树 + gene ID）的右边界
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x_A_right <- x_label_left + label_panel_width
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x_A_left <- x_tree_min
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## ===================== 7. 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$label)
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if (nrow(motif_sites) == 0) {
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stop("在 MEME 结果中没有找到与树 tip.label 匹配的基因。", call. = FALSE)
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}
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## 合并 tip 的 y / subfamily
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motif_sites <- motif_sites %>%
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dplyr::left_join(tip_dat, by = c("gene" = "label"))
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## 只保留 seqlen 不缺失的条目（完全依赖 XML 的 length）
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motif_sites <- motif_sites %>%
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dplyr::filter(!is.na(seqlen))
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## 检查长度范围
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cat("motif 对应序列长度范围: ",
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min(motif_sites$seqlen, na.rm = TRUE), " ~ ",
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max(motif_sites$seqlen, na.rm = TRUE), "\n")
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## ===================== 8. motif 高对比配色（不循环复用） =====================
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motif_levels <- sort(unique(motif_sites$motif))
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n_motif <- length(motif_levels)
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motif_cols_vec <- make_distinct_cols(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. 三个 Panel 的 X 布局 =====================
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## 间隔和比例（可调）：
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gap1 <- max(0.3, tree_core_width * 0.1) # Panel A 和 B 之间空隙
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gap2 <- max(0.3, tree_core_width * 0.1) # Panel B 和 C 之间空隙
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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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## Panel B（亚家族竖线区域）
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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 区域）
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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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## 用"XML 中最长的序列长度"作为统一尺子
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max_len <- max(motif_sites$seqlen, na.rm = TRUE)
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scale_factor <- width_C / max_len # 每 1 个 aa/nt 对应多少 x 轴单位
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cat("最长序列长度: ", max_len,
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"；motif 面板宽度: ", width_C,
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"；scale_factor: ", scale_factor, "\n")
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## ===================== 10. 基因主干线 + motif 矩形坐标 =====================
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## 基因主干线：从 0 到 seqlen（在 Panel C 中）
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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_factor
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)
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## motif 矩形：start / end 完全用 XML 的坐标，乘以全局比例尺
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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_factor,
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xmax = x_C_left + end * scale_factor,
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ymin = y - 0.25,
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ymax = y + 0.25
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)
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## ===================== 11. 亚家族竖线的 Y 范围（完全跟树对齐） =====================
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if (has_subfamily) {
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subfam_ranges <- make_subfamily_ranges(tip_dat)
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} else {
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# 没有亚家族时创建空数据框
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subfam_ranges <- data.frame(
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subfamily = character(),
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ymin = numeric(),
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ymax = numeric(),
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ymid = numeric(),
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stringsAsFactors = FALSE
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)
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}
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## ===================== 12. 顶部两个表头的位置 =====================
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tree_header_x <- (x_A_left + x_A_right) / 2
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motif_header_x <- (x_C_left + x_C_right) / 2
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header_y <- max_y + 1.2
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## ===================== 13. 拼合：树 + 基因主干线 + motif + 竖线 + 表头 =====================
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p_combo <- p_tree +
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## 先画基因主干线（浅灰）
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geom_segment(
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data = gene_tracks,
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inherit.aes = FALSE,
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aes(
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x = x_gene_start,
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xend = x_gene_end,
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y = y,
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yend = y
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),
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colour = "grey70",
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linewidth = 0.4
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) +
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## motif 用新的 fill scale（不影响 bootstrap 的 fill）
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ggnewscale::new_scale_fill() +
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geom_rect(
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data = motif_rect,
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inherit.aes = FALSE,
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aes(
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xmin = xmin,
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xmax = xmax,
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ymin = ymin,
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ymax = ymax,
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fill = motif
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),
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color = NA
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) +
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scale_fill_manual(values = motif_cols, name = "Motif")
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# 只有有亚家族时才添加亚家族竖线
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if (nrow(subfam_ranges) > 0) {
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p_combo <- p_combo +
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geom_segment(
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data = subfam_ranges,
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inherit.aes = FALSE,
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aes(
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x = x_subfam,
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xend = x_subfam,
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y = ymin,
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yend = ymax,
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colour = subfamily
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),
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linewidth = 1
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) +
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geom_text(
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data = subfam_ranges,
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inherit.aes = FALSE,
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aes(
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x = x_subfam + width_B * 0.05,
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y = ymid,
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label = subfamily,
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colour = subfamily
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),
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hjust = 0,
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size = 3
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) +
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scale_color_manual(values = subfam_cols, name = "Subfamily")
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}
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p_combo <- p_combo +
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## 顶部两个表头（各在自己 panel 正上方）
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annotate(
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"text",
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x = tree_header_x,
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y = header_y,
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label = "Phylogenetic tree",
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fontface = "bold",
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size = 4
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) +
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annotate(
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"text",
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x = motif_header_x,
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y = header_y,
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label = "Motif",
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fontface = "bold",
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size = 4
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) +
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## 画板 x 轴范围 = 从树最左到 motif panel 右边，再留一点边距
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coord_cartesian(
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xlim = c(x_A_left, x_C_right + tree_core_width * 0.1)
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) +
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## 给表头留出一点空间
|
|||
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expand_limits(y = header_y + 0.5) +
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theme(
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legend.position = "right",
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legend.background = element_rect(color = "#969696", fill = "#d9d9d9")
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)
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## 输出 PDF 和 PNG
|
|||
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output_pdf <- paste0(output_prefix, ".tree_motif.pdf")
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|||
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output_png <- paste0(output_prefix, ".tree_motif.png")
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|||
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# 保存PDF
|
|||
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ggsave(output_pdf, p_combo, width = 14, height = 10)
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|||
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# 保存PNG（使用png()函数避免conda R的API冲突）
|
|||
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png(filename = output_png, width = 4200, height = 3000, res = 300)
|
|||
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print(p_combo)
|
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dev.off()
|
|||
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|||
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cat("[OK] PDF: ", output_pdf, "\n", sep = "")
|
|||
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cat("[OK] PNG: ", output_png, "\n", sep = "")
|
|||
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|
cat("已输出树 + motif 组合图\n")
|