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    "---\n",
    "title: 甲基化 + RNA 双组学：MethSCAn 差异甲基化分析\n",
    "author: SeekGene\n",
    "date: 2026-03-16\n",
    "tags:\n",
    "  - 甲基化 + RNA 双组学\n",
    "  - Notebooks\n",
    "---"
   ]
  },
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    "# 甲基化 + RNA 双组学：MethSCAn 差异甲基化分析\n",
    "\n",
    "## 模块简介\n",
    "\n",
    "本模块基于 **[MethSCAn](https://anders-biostat.github.io/MethSCAn/)** 开发，用于开展单细胞甲基化数据的系统性分析。\n",
    "\n",
    "**MethSCAn** 是一套命令行工具集，支持单细胞甲基化数据的预处理、质量控制、变异区域识别及下游分析。该工具通过检测基因组范围内的甲基化变异，实现以下核心分析目标：\n",
    "\n",
    "*   **变异区域发现 (VMR Detection)**：自动识别细胞间甲基化变异区域（Variably Methylated Regions, VMRs），这些区域通常与细胞类型、发育阶段或疾病状态相关。\n",
    "*   **细胞聚类分群 (Cell Clustering)**：基于甲基化特征矩阵，对细胞进行聚类分群，有效区分不同的细胞类型或细胞状态，方法思路与单细胞 RNA 测序中的细胞聚类分析相似。\n",
    "*   **差异甲基化分析 (DMR Analysis)**：识别不同细胞群体之间的差异甲基化区域（Differentially Methylated Regions, DMRs），用于刻画表观遗传层面的细胞异质性。\n",
    "\n",
    "\n",
    "## 输入文件准备\n",
    "\n",
    "本模块支持从 `compact_data` 目录作为分析起点。`compact_data` 目录通常由以下数据预处理流程生成：\n",
    "\n",
    "### 数据预处理流程\n",
    "\n",
    "1. **甲基化感知比对和提取**：使用 allcools 分析流程进行比对和甲基化位点提取，生成包含甲基化和非甲基化位点信息的 `allc` 文件。\n",
    "2. **格式转换**：使用 `allc_to_bismarkCov.py` 将 `allc` 文件转换为 bismark 的 coverage 格式（`.cov` 文件）。\n",
    "3. **数据准备**：使用 `methscan prepare` 将 `.cov` 文件转换为高效的 `compact_data` 格式，供后续分析使用。\n",
    "\n",
    "**注意**：\n",
    "*   虽然 MethSCAn 支持 `allc` 格式的输入，但其处理效率较低，通常不推荐在正式分析中直接使用，建议先转换为 bismark coverage 格式后再进行分析。\n",
    "*   **一般情况下，寻因云平台已默认完成 `allc_to_bismarkCov.py` 和 `methscan prepare` 的步骤**，可直接使用现有的 `compact_data` 目录开展分析。\n",
    "\n",
    "### 文件结构示例\n",
    "\n",
    "`compact_data` 目录结构如下：\n",
    "\n",
    "```text\n",
    "compact_data/\n",
    "├── chr1.npz\n",
    "├── chr2.npz\n",
    "├── chr3.npz\n",
    "├── ...\n",
    "├── chrX.npz\n",
    "├── column_header.txt\n",
    "└── cell_stats.csv\n",
    "```\n",
    "\n",
    "## MethSCAn 分析流程\n",
    "\n",
    "脚本说明：\n",
    "以下代码为 MethSCAn 分析流程的初始化示例，包含环境配置、路径设置及辅助函数定义，供后续各步骤调用。"
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    "options(warn = -1)\n",
    "# 加载所需的 R 包\n",
    "suppressPackageStartupMessages({\n",
    "  library(ggplot2)      # 用于数据可视化\n",
    "  library(dplyr)        # 用于数据处理\n",
    "  library(readr)        # 用于读取 CSV 文件\n",
    "  library(tidyverse)    # 数据整理和可视化工具集\n",
    "  library(data.table)   # 高效的数据读取和处理\n",
    "  library(irlba)        # 用于迭代 PCA（填补缺失值）\n",
    "  library(Seurat)       # 单细胞分析工具（版本 v5）\n",
    "  library(Matrix)       # 稀疏矩阵处理\n",
    "})\n",
    "\n",
    "# --- 输入参数配置 ---\n",
    "\n",
    "## methscan_path：MethSCAn 命令路径\n",
    "# 如果 methscan 在系统 PATH 中，设置为 NULL\n",
    "# 如果 methscan 在不同环境中，指定完整路径\n",
    "methscan_path <- \"/jp_envs/envs/methscan/bin/methscan\"\n",
    "\n",
    "## outdir：结果输出目录\n",
    "outdir <- \"./DMRs\"\n",
    "# 创建输出目录（如不存在）\n",
    "dir.create(outdir, recursive = TRUE, showWarnings = FALSE)\n",
    "\n",
    "## compact_data_dir：compact_data 目录（由 methscan prepare 生成）\n",
    "compact_data_dir <- file.path(\"../../data/AY1768874914782/methylation/demoWTJW969-task-1/WTJW969/methscan/compact_data\")\n",
    "\n",
    "## n_threads：并行计算线程数\n",
    "# 注意：MethSCAn 会自动检测系统的最大线程数，当 n_threads 设置大于系统的最大线程数时会出错\n",
    "# 建议设置为小于或等于系统可用 CPU 核心数\n",
    "n_threads <- 8\n",
    "\n",
    "# 定义函数：在 R 中调用 MethSCAn 命令\n",
    "run_methscan <- function(command, args = character(), methscan_path = NULL) {\n",
    "  # 确定命令路径：优先使用参数，其次使用全局变量，最后使用系统 PATH\n",
    "  if (is.null(methscan_path) && exists(\"methscan_path\", envir = .GlobalEnv)) {\n",
    "    methscan_path <- get(\"methscan_path\", envir = .GlobalEnv)\n",
    "  }\n",
    "  cmd <- if (is.null(methscan_path)) \"methscan\" else methscan_path\n",
    "  \n",
    "  cat(\"执行：\", cmd, command, paste(args, collapse = \" \"), \"\\n\")\n",
    "  \n",
    "  # 创建临时文件用于捕获 stdout 和 stderr\n",
    "  stdout_file <- tempfile()\n",
    "  stderr_file <- tempfile()\n",
    "  \n",
    "  # 执行命令，捕获输出\n",
    "  result <- system2(\n",
    "    cmd, \n",
    "    args = c(command, args), \n",
    "    wait = TRUE,\n",
    "    stdout = stdout_file,\n",
    "    stderr = stderr_file\n",
    "  )\n",
    "  \n",
    "  return(invisible(result))\n",
    "}"
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    "### 关键参数设置\n",
    "\n",
    "本节对 MethSCAn 分析流程中涉及的关键参数进行集中说明。这些参数将在后续的`methscan filter`、`methscan scan`、`methscan matrix` 和 `methscan diff`步骤中被调用，用于控制细胞质量过滤、变异区域检测及差异分析等关键行为。\n",
    "\n",
    "#### `methscan filter` 参数\n",
    "\n",
    "低质量细胞过滤相关的关键参数（如 `min_sites`、`min_meth`、`max_meth` 等）的具体含义及推荐取值，详见**第 3.2.2 节**中过滤执行代码前的参数说明表。\n",
    "\n",
    "#### `methscan diff` 参数\n",
    "\n",
    "用于比较不同 cluster 间差异的甲基化区域（DMR）分析所涉及的关键参数如下：\n",
    "\n",
    "| 参数名称 | 中文释义 | 默认值 | 详细说明 |\n",
    "| :--- | :--- | :--- | :--- |\n",
    "| `min_cells` | **最小细胞数要求** | 6 | **质量控制 (QC) 参数**。<br>要求每个基因组区域在每个比较组中至少有该数量的细胞具有测序覆盖度，方可纳入差异分析。例如，设置为 6 表示仅对在每个组中至少有 6 个细胞具备覆盖度的区域进行差异检验。<br>**注意**：该参数用于过滤覆盖度不足的区域，有助于提高 DMR 检测的稳定性。如果某个组中细胞数量较少，可适当降低此值，但可能会增加假阳性风险。\n",
    "\n",
    "#### `threads` 参数（通用参数）\n",
    "\n",
    "用于控制计算密集型步骤（如 `scan`、`matrix`、`diff`）的并行计算线程数。\n",
    "\n",
    "| 参数名称 | 中文释义 | 默认值/建议值 | 详细说明 |\n",
    "| :--- | :--- | :--- | :--- |\n",
    "| `threads` | **并行计算线程数** | 8 | **性能优化参数**。<br>用于加速计算密集型步骤（如 `scan`、`matrix`、`diff`），建议根据实际可用 CPU 核心数进行设置。<br>**重要提示**：MethSCAn 会自动检测系统的最大线程数。**当 `threads` 设置大于系统的最大线程数时，程序会报错**。因此，建议将 `threads` 设置为小于或等于系统可用 CPU 核心数的值。\n",
    "\n",
    "#### 最佳实践建议\n",
    "\n",
    "1.  关于 `methscan filter` 参数的选择：\n",
    "    \n",
    "    **在参数设定前，强烈建议先运行质量评估步骤（见 3.2.1 节）**，通过可视化细胞的甲基化位点数和全局甲基化百分比分布，识别潜在离群细胞，并据此确定 `min_sites`、`min_meth` 和 `max_meth` 的取值范围。\n",
    "    \n",
    "    `min_sites` 参数策略：当过滤后保留的细胞数量较少时，可适当降低该阈值（例如降至 20000），但可能引入更多噪声；在数据质量较高的情况下，可适当提高阈值以增强结果可靠性。\n",
    "    \n",
    "    全局甲基化百分比阈值（`min_meth` 和 `max_meth`）：不同物种的全局甲基化水平通常存在差异，例如：\n",
    "    *   小鼠：通常为 70-80%。\n",
    "    *   人类：通常为 60-70%。\n",
    "\n",
    "2.  关于计算资源：\n",
    "    \n",
    "    `methscan scan`、`matrix`、`diff`：默认使用 8c64g 资源。**在常见数据规模下，整个运行过程通常需要 1-2 小时**。若在寻因云平台环境中运行，建议适当预留更长的任务时间窗口。\n",
    "\n",
    "3.  关于 DMR 分析的 `min_cells` 参数：\n",
    "    \n",
    "    默认值：每组 6 个细胞\n",
    "    \n",
    "    参数作用：要求每个比较组中有足够数量的细胞在某一区域具有测序覆盖度，从而提高差异甲基化分析的统计稳定性。\n",
    "    \n",
    "    经验参考：在实践中，当参与比较的两个 cluster 的细胞数量均大于约 200 个时，通常更容易获得满足显著性阈值的 DMR 结果；在细胞数量较少的情况下，可能难以检出显著差异，可尝试适当降低 `min_cells` 参数进行探索性分析，并结合生物学背景对结果进行综合判断。"
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     "text": [
      "✓ 使用已有的 compact_data 目录： ../../data/AY1768874914782/methylation/demoWTJW969-task-1/WTJW969/methscan/compact_data \n"
     ]
    }
   ],
   "source": [
    "# --- 平台说明 ---\n",
    "# 在寻因云平台环境中，methscan prepare 步骤通常已在上游流程中完成，\n",
    "# 因此本流程默认直接使用已有的 compact_data 目录。\n",
    "# 以下被注释的代码仅作为在本地环境或需要重新执行 prepare 步骤时的参考实现；\n",
    "# 如需重新运行 methscan prepare，请取消相应注释。\n",
    "\n",
    "\n",
    "# # 检查输入目录是否存在\n",
    "# if (!dir.exists(input_cov_dir)) {\n",
    "#   stop(\"输入目录不存在：\", input_cov_dir, \n",
    "#        \"\\n请先运行 allc_to_bismarkCov.py 转换数据\")\n",
    "# }\n",
    "# \n",
    "# # 获取所有 coverage 文件\n",
    "# cov_files <- list.files(input_cov_dir, full.names = TRUE, pattern = \"\\\\.cov$\")\n",
    "# if (length(cov_files) == 0) {\n",
    "#   stop(\"在 \", input_cov_dir, \" 中未找到 .cov 文件\")\n",
    "# }\n",
    "# \n",
    "# cat(\"找到 \", length(cov_files), \" 个 coverage 文件\\n\")\n",
    "# \n",
    "# # 准备输出目录\n",
    "# compact_data_dir <- file.path(outdir, \"compact_data\")\n",
    "# if (!dir.exists(compact_data_dir)) {\n",
    "#   dir.create(compact_data_dir, recursive = TRUE)\n",
    "# }\n",
    "# \n",
    "# # 执行 methscan prepare\n",
    "# run_methscan(\n",
    "#   command = \"prepare\",\n",
    "#   args = c(cov_files, compact_data_dir)\n",
    "# )\n",
    "\n",
    "# 直接使用已有的 compact_data 目录\n",
    "if (!dir.exists(compact_data_dir)) {\n",
    "  stop(\"compact_data 目录不存在：\", compact_data_dir, \n",
    "       \"\\n请确保已在云平台完成 methscan prepare 步骤\")\n",
    "}\n",
    "cat(\"✓ 使用已有的 compact_data 目录：\", compact_data_dir, \"\\n\")"
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   "source": [
    "### 质量评估和低质量细胞过滤\n",
    "\n",
    "在执行细胞过滤之前，建议先对细胞质量进行系统评估，并结合可视化结果确定合适的过滤参数。\n",
    "\n",
    "#### 细胞统计信息读取与质量指标可视化"
   ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m2196\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): cell_name\n",
      "\u001b[32mdbl\u001b[39m (3): n_obs, n_meth, global_meth_frac\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
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TV4bkn/dr0CeXn6I1TcLULWNBAKkRP+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\n/s6EqKhC0cpciKzsLCHUMYF/xlSwdX5eyW56TJphrVtDeGRLcFG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FxcTEcLno89d37qXC7jszGQxGfHy81Wptb7KiXXsAACAASURBVG93N+e02+0qlYrB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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 525,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 读取 cell_stats.csv 文件\n",
    "cell_stats_path <- file.path(compact_data_dir,  \"cell_stats.csv\")\n",
    "\n",
    "cell_stats <- read_csv(cell_stats_path)\n",
    "\n",
    "#if (file.exists(cell_stats_path)) {\n",
    "#  cell_stats <- read_csv(cell_stats_path)\n",
    "#  cat(\"成功读取细胞统计信息，共\", nrow(cell_stats), \"个细胞\\n\")\n",
    "#  head(cell_stats)\n",
    "#} else {\n",
    "#  stop(\"找不到 cell_stats.csv 文件，请检查路径是否正确\")\n",
    "#}\n",
    "\n",
    "# 可视化甲基化位点数和全局甲基化百分比\n",
    "# 根据可视化结果，制定过滤参数\n",
    "\n",
    "options(repr.plot.width = 8, repr.plot.height = 7, repr.plot.res = 150)\n",
    "p <- cell_stats %>% \n",
    "  ggplot(aes(x = global_meth_frac * 100, y = n_obs)) +\n",
    "  geom_point(alpha = 0.6) +\n",
    "  # 在横坐标65和86处添加红色垂直线（示例阈值）\n",
    "  geom_vline(xintercept = c(65, 86), color = \"red\", linetype = \"solid\", linewidth = 1) +\n",
    "  # 在纵坐标60000处添加红色水平线（示例阈值）\n",
    "  geom_hline(yintercept = 60000, color = \"red\", linetype = \"solid\", linewidth = 1) +\n",
    "  labs(\n",
    "    x = \"Global DNA methylation %\", \n",
    "    y = \"# of observed CpG sites\",\n",
    "    title = \"Cell Quality Assessment\"\n",
    "  ) +\n",
    "  theme_minimal() +\n",
    "  theme(\n",
    "    plot.title = element_text(hjust = 0.5, size = 14, face = \"bold\")\n",
    "  )\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d8ea6a3-9b41-44ea-a250-bbe66d82445c",
   "metadata": {},
   "source": [
    "#### 根据可视化结果执行过滤\n",
    "\n",
    "基于上述可视化结果，确定过滤参数并执行细胞过滤:\n",
    "\n",
    "`methscan filter` **关键参数**：\n",
    "\n",
    "| 参数名称 | 中文释义 | 默认值/建议值 | 详细说明 |\n",
    "| :--- | :--- | :--- | :--- |\n",
    "| `min_sites` | **最小观察到的甲基化位点数** | 25000-60000 | **质量控制 (QC) 参数**。<br>要求每个细胞观察到的甲基化位点数必须大于此值。用于剔除测序深度不足、无法准确推断甲基化状态的低质量细胞。<br>**注意**：此阈值取决于测序深度，深度越高，阈值可设置越高。 |\n",
    "| `min_meth` | **最小全局甲基化百分比** | 60-70 | **质量控制 (QC) 参数**。<br>要求每个细胞的全局甲基化百分比必须大于此值。用于剔除可能由于不完全亚硫酸氢盐转化或技术问题导致的异常细胞。<br>**注意**：不同物种和细胞类型的正常甲基化水平差异很大（如小鼠通常为 70-80%，人类为 60-70%）。 |\n",
    "| `max_meth` | **最大全局甲基化百分比** | 70-85 | **质量控制 (QC) 参数**。<br>要求每个细胞的全局甲基化百分比必须小于此值。用于剔除可能由于技术问题导致的异常高甲基化细胞。<br>**注意**：不同物种和细胞类型的正常甲基化水平差异很大，请根据实际数据情况设置合理的阈值范围。 |\n",
    "| `threads` | **并行计算线程数** | 8 | 用于加速计算密集型步骤（如 `scan`、`matrix`）。建议根据系统资源设置，通常设置为可用 CPU 核心数。 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2bf79539-a7e3-4c8b-8191-bfc3e00aee19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T08:47:49.648428Z",
     "iopub.status.busy": "2026-02-06T08:47:49.647543Z",
     "iopub.status.idle": "2026-02-06T09:07:57.132649Z",
     "shell.execute_reply": "2026-02-06T09:07:57.131491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "执行： /jp_envs/envs/methscan/bin/methscan filter --min-sites 60000 --min-meth 65 --max-meth 86 ../../data/AY1768874914782/methylation/demoWTJW969-task-1/WTJW969/methscan/compact_data ./DMRs/filtered_data \n"
     ]
    }
   ],
   "source": [
    "# 过滤参数（请根据质量评估结果调整）\n",
    "min_sites <- 60000    # 最小观察到的甲基化位点数\n",
    "min_meth <- 65       # 最小全局甲基化百分比\n",
    "max_meth <- 86        # 最大全局甲基化百分比\n",
    "\n",
    "# 准备输出目录\n",
    "filtered_data_dir <- file.path(outdir, \"filtered_data\")\n",
    "if (!dir.exists(filtered_data_dir)) {\n",
    "  dir.create(filtered_data_dir, recursive = TRUE)\n",
    "}\n",
    "\n",
    "# 执行 methscan filter\n",
    "run_methscan(\n",
    "  command = \"filter\",\n",
    "  args = c(\n",
    "    \"--min-sites\", min_sites,\n",
    "    \"--min-meth\", min_meth,\n",
    "    \"--max-meth\", max_meth,\n",
    "    compact_data_dir,\n",
    "    filtered_data_dir\n",
    "  )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f69ac31-dda7-471b-85a0-f785e0a43fa0",
   "metadata": {},
   "source": [
    "### 数据平滑：methscan smooth\n",
    "\n",
    "在进行 VMR/DMR 检测之前，需要对甲基化数据进行平滑处理。`methscan smooth` 将所有单细胞视为伪 bulk 样本，计算全基因组范围内的平滑平均甲基化水平。该结果是后续 VMR 检测及甲基化矩阵构建步骤的必要输入。\n",
    "\n",
    "**输出结果**：平滑处理后的结果文件输出至 `${outdir}/filtered_data/smoothed` 目录。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef680617-23f1-484e-9735-d0cee1ace819",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T09:07:57.135113Z",
     "iopub.status.busy": "2026-02-06T09:07:57.134250Z",
     "iopub.status.idle": "2026-02-06T09:21:39.919156Z",
     "shell.execute_reply": "2026-02-06T09:21:39.918009Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "执行： /jp_envs/envs/methscan/bin/methscan smooth ./DMRs/filtered_data \n"
     ]
    }
   ],
   "source": [
    "# 执行 methscan smooth\n",
    "run_methscan(\n",
    "  command = \"smooth\",\n",
    "  args = filtered_data_dir\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c668b33-46ed-4059-b591-a6cba09820db",
   "metadata": {},
   "source": [
    "### VMR 检测：methscan scan\n",
    "\n",
    "本步骤用于检测变异甲基化区域（Variable Methylation Region，VMR）。`methscan scan` 将在全基因组范围内自动识别细胞间甲基化变异显著的区域。\n",
    "\n",
    "**输出结果**：生成 BED 格式的结果文件，包含 VMR 的基因组坐标和甲基化方差。\n",
    "\n",
    "**替代方案**：若分析目标限定于特定的基因组区域（如启动子或基因体区域），可直接提供相应的 BED 文件，跳过 `scan` 步骤，直接使用 `methscan matrix` 分析这些区域。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07a1ded5-5a70-414c-86f7-8001e3b6752d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T09:21:39.921721Z",
     "iopub.status.busy": "2026-02-06T09:21:39.920866Z",
     "iopub.status.idle": "2026-02-06T09:31:32.504820Z",
     "shell.execute_reply": "2026-02-06T09:31:32.503562Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "执行： /jp_envs/envs/methscan/bin/methscan scan --threads 8 ./DMRs/filtered_data ./DMRs/VMRs.bed \n"
     ]
    }
   ],
   "source": [
    "# VMR 输出文件\n",
    "vmr_bed_file <- file.path(outdir, \"VMRs.bed\")\n",
    "\n",
    "# 执行 methscan scan\n",
    "result <- run_methscan(\n",
    "  command = \"scan\",\n",
    "  args = c(\n",
    "    \"--threads\", n_threads,\n",
    "    filtered_data_dir,\n",
    "    vmr_bed_file\n",
    "  )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7808c701-36d7-44ea-a11c-acc062bd9a9a",
   "metadata": {},
   "source": [
    "### VMR 矩阵生成：methscan matrix\n",
    "\n",
    "本步骤基于已识别的 VMR 区域构建甲基化矩阵，用于后续的细胞聚类及相关下游分析。\n",
    "\n",
    "**输出文件说明**：\n",
    "\n",
    "输出目录 `VMR_matrix`中包含以下文件：\n",
    "\n",
    "| 文件名 | 说明 |\n",
    "| :--- | :--- |\n",
    "| `mean_shrunken_residuals.csv.gz` | **收缩残差矩阵**（推荐用于下游分析）。受随机覆盖度变化影响较小，更适合用于降维和聚类分析。 |\n",
    "| `methylation_fractions.csv.gz` | **甲基化分数矩阵**。每个区域的甲基化分数（0-1 之间），表示该区域的平均甲基化水平。 |\n",
    "| `methylated_sites.csv.gz` | **甲基化位点数矩阵**（分子）。每个区域中甲基化的 CpG 位点数量。 |\n",
    "| `total_sites.csv.gz` | **总位点数矩阵**（分母）。每个区域中具有测序覆盖度的 CpG 位点总数。 |\n",
    "\n",
    "**矩阵特征**：\n",
    "\n",
    "*   缺失值较多：受单细胞测序覆盖度较低的影响，部分基因组区域在部分细胞中可能不存在有效测序 reads，从而产生较多缺失值。\n",
    "*   甲基化值呈小分母分数形式：甲基化水平通常以小分母分数表示（如 1/1、2/2、1/3 等），这是单细胞甲基化数据的典型特征。\n",
    "*   附加位点统计信息：输出目录中同时包含每个区域的甲基化 CpG 位点数及具有测序覆盖的 CpG 位点数，分别对应甲基化比例计算中的分子和分母信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3e9125d-fb10-497c-91a7-04fa79b356da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T09:31:32.507916Z",
     "iopub.status.busy": "2026-02-06T09:31:32.506943Z",
     "iopub.status.idle": "2026-02-06T10:15:54.287675Z",
     "shell.execute_reply": "2026-02-06T10:15:54.286503Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "执行： /jp_envs/envs/methscan/bin/methscan matrix --threads 8 ./DMRs/VMRs.bed ./DMRs/filtered_data ./DMRs/VMR_matrix \n",
      "✓ VMR 矩阵文件已生成：\n",
      "  - mean_shrunken_residuals.csv.gz (291.34 MB)\n",
      "  - methylation_fractions.csv.gz (60.59 MB)\n",
      "  - methylated_sites.csv.gz (43.48 MB)\n",
      "  - total_sites.csv.gz (56.35 MB)\n"
     ]
    }
   ],
   "source": [
    "# VMR 矩阵输出目录\n",
    "vmr_matrix_dir <- file.path(outdir, \"VMR_matrix\")\n",
    "\n",
    "# 执行 methscan matrix\n",
    "result <- run_methscan(\n",
    "  command = \"matrix\",\n",
    "  args = c(\n",
    "    \"--threads\", n_threads,\n",
    "    vmr_bed_file,\n",
    "    filtered_data_dir,\n",
    "    vmr_matrix_dir\n",
    "  )\n",
    ")\n",
    "\n",
    "# 检查输出文件是否生成\n",
    "if (result == 0 && dir.exists(vmr_matrix_dir)) {\n",
    "  expected_files <- c(\n",
    "    \"mean_shrunken_residuals.csv.gz\",\n",
    "    \"methylation_fractions.csv.gz\",\n",
    "    \"methylated_sites.csv.gz\",\n",
    "    \"total_sites.csv.gz\"\n",
    "  )\n",
    "  existing_files <- list.files(vmr_matrix_dir)\n",
    "  found_files <- expected_files[expected_files %in% existing_files]\n",
    "  \n",
    "  if (length(found_files) > 0) {\n",
    "    cat(\"✓ VMR 矩阵文件已生成：\\n\")\n",
    "    for (f in found_files) {\n",
    "      file_path <- file.path(vmr_matrix_dir, f)\n",
    "      file_size <- file.info(file_path)$size / 1024 / 1024  # MB\n",
    "      cat(\"  -\", f, sprintf(\"(%.2f MB)\\n\", file_size))\n",
    "    }\n",
    "  } else {\n",
    "    warning(\"警告：未找到预期的输出文件，请检查输出目录：\", vmr_matrix_dir)\n",
    "  }\n",
    "} else if (result != 0) {\n",
    "  stop(\"methscan matrix 执行失败，请检查上面的错误信息\")\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e89d5f5-5823-442f-bbd6-dcc3182da898",
   "metadata": {},
   "source": [
    "## 基于 VMR 的聚类分析\n",
    "\n",
    "本节基于 VMR 甲基化矩阵，使用 Seurat 进行降维与聚类分析，以识别具有相似甲基化特征的细胞群体。\n",
    "\n",
    "### 读取 VMR 矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb2220be-38a1-4608-8647-cb7821e218b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T10:15:54.290216Z",
     "iopub.status.busy": "2026-02-06T10:15:54.289352Z",
     "iopub.status.idle": "2026-02-06T10:16:15.671386Z",
     "shell.execute_reply": "2026-02-06T10:16:15.670146Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在读取 VMR 矩阵...\n",
      "矩阵维度： 2170 行（细胞）x 106967 列（VMR 区域）\n",
      "矩阵转换完成！\n",
      "缺失值比例： 75.31 %\n"
     ]
    }
   ],
   "source": [
    "# 使用 fread 读取压缩文件\n",
    "# 使用配置的 outdir 变量\n",
    "vmr_matrix_path <- file.path(outdir, \"VMR_matrix\", \"mean_shrunken_residuals.csv.gz\")\n",
    "\n",
    "if (file.exists(vmr_matrix_path)) {\n",
    "  cat(\"正在读取 VMR 矩阵...\\n\")\n",
    "  meth_mtx <- fread(vmr_matrix_path)\n",
    "  cat(\"矩阵维度：\", nrow(meth_mtx), \"行（细胞）x\", ncol(meth_mtx) - 1, \"列（VMR 区域）\\n\")\n",
    "  \n",
    "  # 将第一列设置为行名并转换为矩阵\n",
    "  row_names <- meth_mtx[[1]]\n",
    "  meth_mtx <- as.matrix(meth_mtx[, -1])\n",
    "  rownames(meth_mtx) <- row_names\n",
    "  \n",
    "  cat(\"矩阵转换完成！\\n\")\n",
    "  cat(\"缺失值比例：\", round(mean(is.na(meth_mtx)) * 100, 2), \"%\\n\")\n",
    "} else {\n",
    "  stop(\"找不到 VMR 矩阵文件，请检查路径是否正确\")\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6db1d1e-24bc-4a47-b6d5-b63b3e06cb44",
   "metadata": {},
   "source": [
    "### 迭代 PCA 填补缺失值\n",
    "\n",
    "VMR 甲基化矩阵在结构上与单细胞 RNA-seq 的 count 矩阵类似，但其显著特点是包含大量缺失值。这主要源于单细胞甲基化测序覆盖度较低，导致许多基因组区域在部分细胞中未获得有效测序 reads。\n",
    "\n",
    "为处理上述缺失问题，本分析采用迭代 PCA 方法对缺失值进行填补。具体过程为：首先将缺失值以 0 进行初始化填充；随后在填补后的矩阵上执行 PCA，并使用 PCA 重构得到的预测值替换原先填入的 0；该过程反复迭代，直至填补值在相邻迭代间变化趋于稳定。\n",
    "\n",
    "该方法通过迭代优化低维结构，对缺失甲基化值进行更合理的估计，从而为后续的降维和聚类分析提供更完整、稳定的数据基础。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4cb31e65-8a47-4459-bf9b-99c96130f92f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T10:16:15.673983Z",
     "iopub.status.busy": "2026-02-06T10:16:15.673109Z",
     "iopub.status.idle": "2026-02-06T10:24:20.498299Z",
     "shell.execute_reply": "2026-02-06T10:24:20.497173Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始执行迭代 PCA 填补缺失值...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Terminated after 32 iterations.\n",
      "\n",
      "\n",
      "\n",
      "Terminated after 32 iterations. Gain: 0.000974\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缺失值填补完成！\n"
     ]
    }
   ],
   "source": [
    "# 定义迭代 PCA 填补缺失值的函数\n",
    "prcomp_iterative <- function(x, n = 15, n_iter = 50, min_gain = 0.001, ...) {\n",
    "  mse <- rep(NA, n_iter)\n",
    "  na_loc <- is.na(x)\n",
    "  x[na_loc] <- 0  # zero is our first guess\n",
    "\n",
    "  for (i in 1:n_iter) {\n",
    "    prev_imp <- x[na_loc]  # what we imputed in the previous round\n",
    "    # PCA on the imputed matrix\n",
    "    pr <- prcomp_irlba(x, center = F, scale. = F, n = n, ...)\n",
    "    # impute missing values with PCA\n",
    "    new_imp <- (pr$x %*% t(pr$rotation))[na_loc]\n",
    "    x[na_loc] <- new_imp\n",
    "    # compare our new imputed values to the ones from the previous round\n",
    "    mse[i] <- mean((prev_imp - new_imp) ^ 2)\n",
    "    # if the values didn't change a lot, terminate the iteration\n",
    "    gain <- mse[i] / max(mse, na.rm = T)\n",
    "    if (gain < min_gain) {\n",
    "      message(paste(c(\"\\n\\nTerminated after \", i, \" iterations.\")))\n",
    "      break\n",
    "    }\n",
    "  }\n",
    "  message(paste(c(\"\\n\\nTerminated after \", i, \" iterations. Gain: \", round(gain, 6))))\n",
    "  pr$mse_iter <- mse[1:i]\n",
    "  list(pr = pr, imputed_matrix = x)\n",
    "}\n",
    "\n",
    "cat(\"开始执行迭代 PCA 填补缺失值...\\n\")\n",
    "result_residual <- meth_mtx %>% \n",
    "  scale(center = T, scale = F) %>% \n",
    "  prcomp_iterative(n = 15)\n",
    "\n",
    "# 提取填补后的残差矩阵\n",
    "residual_mtx_imputed <- result_residual$imputed_matrix\n",
    "cat(\"缺失值填补完成！\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e17d5a03-a15f-4861-a2fb-aaaee131fae6",
   "metadata": {},
   "source": [
    "### 创建 Seurat 对象\n",
    "\n",
    "使用经缺失值填补后的残差矩阵（`residual_mtx_imputed`）作为 Seurat 对象的输入。在构建 Seurat 对象时，`counts`、`data` 和 `scale.data` 三个矩阵均使用相同的填补后残差矩阵。\n",
    "\n",
    "**说明**：后续的 PCA、降维和聚类分析实际基于 `scale.data` 矩阵进行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0e071ab5-951e-4dd4-b7d8-01a69e6d530c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T10:24:20.500749Z",
     "iopub.status.busy": "2026-02-06T10:24:20.499901Z",
     "iopub.status.idle": "2026-02-06T10:24:36.086853Z",
     "shell.execute_reply": "2026-02-06T10:24:36.085564Z"
    }
   },
   "outputs": [],
   "source": [
    "# 使用填补缺失值后的残差矩阵创建 Seurat 对象\n",
    "# counts、data 和 scale.data 三个矩阵使用相同的填补后的残差矩阵\n",
    "seurat_obj <- CreateSeuratObject(\n",
    "    counts = as(t(residual_mtx_imputed), \"sparseMatrix\"),\n",
    "    project = \"MethSCAn\",\n",
    "    assay = \"VMR\"\n",
    ")\n",
    "# 将 data 和 scale.data 设置为与 counts 相同的矩阵\n",
    "# 注意：后续的 PCA、降维和聚类分析实际使用的是 scale.data 矩阵\n",
    "seurat_obj@assays$VMR@data <- seurat_obj@assays$VMR@counts\n",
    "seurat_obj@assays$VMR@scale.data <- as.matrix(seurat_obj@assays$VMR@counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c7b238-c75a-4bea-a2eb-5a1cfcb00e65",
   "metadata": {},
   "source": [
    "### PCA、UMAP 降维和聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "966ae35f-f45d-4b7e-98ca-906c7e579beb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T10:24:36.089451Z",
     "iopub.status.busy": "2026-02-06T10:24:36.088570Z",
     "iopub.status.idle": "2026-02-06T10:25:11.528588Z",
     "shell.execute_reply": "2026-02-06T10:25:11.527521Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UMAP 降维完成！\n",
      "KNN 图构建完成！\n",
      "聚类完成！\n"
     ]
    },
    {
     "data": {
      "image/png": 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AQAAEiUIAQAAEiUIAQAAEiUIAQAAEiU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\nbxe+89zEoT3aeTnrdDq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S6jJbDLKFST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\nY2NjLa8SFRVl4YSKOHbsmF6vNzPh8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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 525,
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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k/m3kqD4tF96tukQQBVgEAIAA7Nvecb0+8usUb9+e/f/yXZfHMA1YLu4OfT19xa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fkcazpfpqu2OiwOss1nk6QQC6eybOChkb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\nw0AOQT4X2evduAEAMFwWvKvPJE9n9ZUElg2d9Xz+8Y/Kz7sedXsDz8LQro2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fotNs02fSYGnQv30mZlTlOUWDAPBrbREGhAghHwrnd2SB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\nWURmz9x7z6nb8nzE1c4pwRC7Zk39hzJ6OJtYC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Z9z7Oiz6xtc3rH66l/cOjAOi6Fua8AYZ0DIakq/7C5636av5SxUaDc2D/fLIYQQOi/F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\nAQAAAECjCIQAAAAAoFH/D4p1ObKIz0iaAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 525,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. PCA 降维\n",
    "# 使用 Seurat 的 RunPCA 函数\n",
    "# 这会自动从 scale.data 层读取数据并计算 PCA\n",
    "n_pcs = 30\n",
    "seurat_obj <- RunPCA(\n",
    "    seurat_obj,\n",
    "    features = rownames(seurat_obj),  # 使用所有 VMRs\n",
    "    npcs = n_pcs,  # 计算前 30 个主成分\n",
    "    assay = \"VMR\",\n",
    "    verbose = FALSE\n",
    ")\n",
    "\n",
    "options(repr.plot.width = 8, repr.plot.height = 7, repr.plot.res = 150)\n",
    "\n",
    "# 可视化 Elbow Plot 以确定使用的主成分数量\n",
    "ElbowPlot(seurat_obj, ndims = 30)\n",
    "\n",
    "# 2. 根据 Elbow Plot 确定使用的主成分数量\n",
    "# 您可以根据 Elbow Plot 的结果调整这个参数\n",
    "n_pcs_use <- 10\n",
    "\n",
    "# 3. UMAP 降维\n",
    "seurat_obj <- RunUMAP(seurat_obj, dims = 1:n_pcs_use, reduction = \"pca\", verbose = FALSE)\n",
    "cat(\"UMAP 降维完成！\\n\")\n",
    "\n",
    "# 4. 构建 KNN 图\n",
    "seurat_obj <- FindNeighbors(seurat_obj, dims = 1:n_pcs_use, reduction = \"pca\", verbose = FALSE)\n",
    "cat(\"KNN 图构建完成！\\n\")\n",
    "\n",
    "# 5. 聚类（resolution 参数可以调整，值越大，聚类数越多）\n",
    "seurat_obj <- FindClusters(seurat_obj, resolution = 0.5, verbose = FALSE)\n",
    "cat(\"聚类完成！\\n\")\n",
    "\n",
    "# 6. 可视化聚类结果\n",
    "DimPlot(seurat_obj, group.by = 'VMR_snn_res.0.5', label = TRUE) + \n",
    "  ggtitle(\"UMAP (resolution = 0.5)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b79864-806d-4362-8268-131f5cc1896c",
   "metadata": {},
   "source": [
    "### 保存结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "448d366a-4143-4030-a0d8-5416de1a022e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T10:25:11.531008Z",
     "iopub.status.busy": "2026-02-06T10:25:11.530176Z",
     "iopub.status.idle": "2026-02-06T10:32:35.263678Z",
     "shell.execute_reply": "2026-02-06T10:32:35.262344Z"
    }
   },
   "outputs": [],
   "source": [
    "# 保存 Seurat 对象\n",
    "output_file <- \"MethSCAn.rds\"\n",
    "saveRDS(seurat_obj, file = output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1b5f1895-490e-4e0c-adc8-83a9b9947fa6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T10:32:35.266278Z",
     "iopub.status.busy": "2026-02-06T10:32:35.265396Z",
     "iopub.status.idle": "2026-02-06T10:32:35.413273Z",
     "shell.execute_reply": "2026-02-06T10:32:35.411973Z"
    }
   },
   "outputs": [],
   "source": [
    "clust_tbl <- seurat_obj@meta.data\n",
    "clust_tbl$cell <- row.names(clust_tbl)\n",
    "clust_tbl %>%\n",
    "  mutate(cell_group = case_when(\n",
    "    seurat_clusters == \"0\" ~ \"-\",\n",
    "    seurat_clusters == \"1\" ~ \"1\",\n",
    "    seurat_clusters == \"2\" ~ \"2\",\n",
    "    seurat_clusters == \"3\" ~ \"-\",\n",
    "    seurat_clusters == \"4\" ~ \"-\",\n",
    "    seurat_clusters == \"5\" ~ \"-\",\n",
    "    seurat_clusters == \"6\" ~ \"-\",\n",
    "    seurat_clusters == \"7\" ~ \"-\",\n",
    "    seurat_clusters == \"8\" ~ \"-\"    \n",
    "  )) %>% \n",
    "  dplyr::select(cell, cell_group) %>%\n",
    "  write_csv(\"cell_groups.csv\", col_names=F)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c82631-5579-4596-861a-2d7b77addb19",
   "metadata": {},
   "source": [
    "## 差异甲基化区域分析：methscan diff\n",
    "\n",
    "差异甲基化区域 (DMR) 分析用于比较不同条件下细胞之间的甲基化差异，例如不同 cluster、不同细胞类型或其他自定义分组。**通常建议在完成聚类分析后，基于聚类结果或预定义分组开展 DMR 分析。**\n",
    "\n",
    "### 输入文件准备\n",
    "\n",
    "**输入要求**：\n",
    "\n",
    "1. **数据目录**：`filtered_data` 目录（需已完成 `methscan smooth`步骤）。\n",
    "2. **细胞分组文件 (cell_groups_file)**：CSV 格式文件，其中第一列为细胞名称（cell barcode），第二列为分组标签。标签规则为：`group_A` 和 `group_B` 表示参与比较的两个细胞组；`-` 表示不参与本次比较的其他细胞。\n",
    "\n",
    "**文件格式示例**：\n",
    "\n",
    "```csv\n",
    "cell_01,-\n",
    "cell_02,-\n",
    "cell_03,-\n",
    "cell_04,group_A\n",
    "cell_05,group_A\n",
    "cell_06,group_A\n",
    "cell_07,group_B\n",
    "cell_08,group_B\n",
    "cell_09,group_B\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8c363a3-26f8-483e-bc34-d53b69f2dc35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T10:32:35.415861Z",
     "iopub.status.busy": "2026-02-06T10:32:35.414966Z",
     "iopub.status.idle": "2026-02-06T10:37:15.933097Z",
     "shell.execute_reply": "2026-02-06T10:37:15.931865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "执行： /jp_envs/envs/methscan/bin/methscan diff --threads 8 ./DMRs/filtered_data cell_groups.csv ./DMRs/DMRs.bed \n"
     ]
    }
   ],
   "source": [
    "# 细胞分组文件（用于 DMR 分析）\n",
    "cell_groups_file <- \"cell_groups.csv\"  # 请根据实际情况修改\n",
    "\n",
    "# DMR 输出文件\n",
    "dmr_bed_file <- file.path(outdir, \"DMRs.bed\")\n",
    "\n",
    "# 检查细胞分组文件是否存在\n",
    "if (file.exists(cell_groups_file)) {\n",
    "  # 执行 methscan diff\n",
    "  run_methscan(\n",
    "    command = \"diff\",\n",
    "    args = c(\n",
    "      \"--threads\", n_threads,\n",
    "      filtered_data_dir,\n",
    "      cell_groups_file,\n",
    "      dmr_bed_file\n",
    "    )\n",
    "  )\n",
    "} else {\n",
    "  warning(\"细胞分组文件不存在：\", cell_groups_file, \n",
    "          \"\\n跳过 DMR 分析。如需进行 DMR 分析，请先准备细胞分组文件。\")\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3df8825-e398-44c9-86e1-aea74b00acbd",
   "metadata": {},
   "source": [
    "### 输出文件格式说明\n",
    "\n",
    "**输出文件**：`DMRs.bed` \n",
    "\n",
    "结果以 BED 格式输出，包含以下字段：\n",
    "\n",
    "| 列号 | 列名 | 说明 | 示例值 |\n",
    "| :--- | :--- | :--- | :--- |\n",
    "| 1 | `chromosome` | 染色体名称 | 17 |\n",
    "| 2 | `DMR_start` | DMR 起始位置（基因组坐标） | 58270529 |\n",
    "| 3 | `DMR_end` | DMR 结束位置（基因组坐标） | 58285529 |\n",
    "| 4 | `t_statistic` | Welch's t 检验统计量 | -12.85232790631932 |\n",
    "| 5 | `n_sites` | 该区域内的甲基化位点总数（CpG 数量） | 561 |\n",
    "| 6 | `n_cells_group1` | 组 1 中有覆盖度的细胞数量 | 150 |\n",
    "| 7 | `n_cells_group2` | 组 2 中有覆盖度的细胞数量 | 66 |\n",
    "| 8 | `meth_frac_group1` | 组 1 的平均甲基化水平（0–1） | 0.3396857458943857 |\n",
    "| 9 | `meth_frac_group2` | 组 2 的平均甲基化水平（0–1） | 0.8429240842342447 |\n",
    "| 10 | `low_group_label` | 甲基化水平较低的组标签 | group_1 |\n",
    "| 11 | `p` | 原始 p 值（未调整） | 0.0 |\n",
    "| 12 | `adjusted_p` | 调整后的 p 值（FDR，Benjamini-Hochberg 方法） | 0.0 |\n",
    "\n",
    "**结果解读**：`low_group_label` 列表示甲基化水平较低的组标签；`adjusted_p` 列为多重检验校正后的显著性水平，通常建议以`adjusted_p < 0.05`作为筛选阈值；`t_statistic` 的符号可判断差异方向（负值表示 group_1 甲基化水平较低，正值表示 group_1 甲基化水平较高）。\n",
    "\n",
    "### 生成显著差异甲基化区域文件\n",
    "\n",
    "基于 `DMRs.bed` 文件，对显著差异甲基化区域进行筛选（`adjusted_p < 0.05`），并根据 `low_group_label` 对结果进行分组，生成两个 `BED` 文件。\n",
    "\n",
    "**输入**：  `DMRs.bed`：DMR 检测结果文件\n",
    "\n",
    "**输出**：根据 `low_group_label` 的值，生成以下两个显著 DMR `BED` 文件：\n",
    "\n",
    "*   `DMRs_significant_group_A.bed`：在 group_A 中甲基化水平显著较低的 DMR 区域。\n",
    "*   `DMRs_significant_group_B.bed`：在 group_B 中甲基化水平显著较低的 DMR 区域。\n",
    "\n",
    "上述输出文件均为 BED 格式，包含三列：chromosome、start、end。其中，若染色体名称为数字或性染色体（X/Y），将自动添加 \"chr\" 前缀；若原本已包含 \"chr\" 前缀，则保持不变。\n",
    "\n",
    "**说明**：仅保留 `adjusted_p < 0.05` 的显著差异甲基化区域，自动为数字染色体和性染色体添加 \"chr\" 前缀（若已有则保持不变），并跳过 `low_group_label` 为空或为 \"-\" 的行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "421a6d8f-35d1-4aeb-b4f1-8dfccdabc5b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-06T10:37:15.935792Z",
     "iopub.status.busy": "2026-02-06T10:37:15.934878Z",
     "iopub.status.idle": "2026-02-06T10:37:16.153635Z",
     "shell.execute_reply": "2026-02-06T10:37:16.152517Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始生成显著的 DMR 文件...\n",
      "处理完成！已生成显著的 DMR 文件。 \n",
      "\n",
      "生成的显著 DMR 文件：\n",
      "  -DMRs_significant_1.bed（23951 个区域）\n",
      "  -DMRs_significant_2.bed（30189 个区域）\n"
     ]
    }
   ],
   "source": [
    "# 生成显著的 DMR 文件（按 low_group_label 分组，adjusted_p < 0.05）\n",
    "# 在常染色体（数字）和性染色体（XY）前加上 \"chr\" 前缀\n",
    "# 检查 DMRs.bed 文件是否存在\n",
    "if (file.exists(dmr_bed_file)) {\n",
    "  cat(\"开始生成显著的 DMR 文件...\\n\")\n",
    "  \n",
    "  # 构建 awk 命令\n",
    "  awk_script <- sprintf('\n",
    "BEGIN {\n",
    "    outdir = \"%s\"\n",
    "}\n",
    "# 跳过表头或空行\n",
    "NR == 1 && $1 ~ /^chromosome|^#/ { next }\n",
    "NF < 12 { next }\n",
    "# 筛选 adjusted_p < 0.05 的行\n",
    "$12 < 0.05 {\n",
    "    # 获取 low_group_label（第10列）作为文件名\n",
    "    group_label = $10\n",
    "    if (group_label == \"\" || group_label == \"-\") next\n",
    "    \n",
    "    # 处理染色体名称：如果是数字或 X/Y，则加上 \"chr\" 前缀\n",
    "    chrom = $1\n",
    "    if (chrom ~ /^[0-9]+$/ || chrom ~ /^[XY]$/) {\n",
    "        chrom = \"chr\" chrom\n",
    "    }\n",
    "    \n",
    "    # 构建输出文件名\n",
    "    output_file = outdir \"/DMRs_significant_\" group_label \".bed\"\n",
    "    \n",
    "    # 输出前3列（chromosome, start, end）到对应的文件\n",
    "    print chrom \"\\\\t\" $2 \"\\\\t\" $3 > output_file\n",
    "}\n",
    "END {\n",
    "    print \"处理完成！已生成显著的 DMR 文件。\"\n",
    "}\n",
    "', outdir)\n",
    "  \n",
    "  # 在 R 中执行 awk 命令\n",
    "  awk_cmd <- paste(\"awk -F'\\\\t'\", shQuote(awk_script), shQuote(dmr_bed_file))\n",
    "  result <- system(awk_cmd, intern = TRUE)\n",
    "  \n",
    "  # 显示输出\n",
    "  cat(paste(result, collapse = \"\\n\"), \"\\n\")\n",
    "  \n",
    "  # 检查生成的文件\n",
    "  significant_files <- list.files(\n",
    "    path = outdir,\n",
    "    pattern = \"^DMRs_significant_.*\\\\.bed$\",\n",
    "    full.names = TRUE\n",
    "  )\n",
    "  \n",
    "  if (length(significant_files) > 0) {\n",
    "    cat(\"\\n生成的显著 DMR 文件：\\n\")\n",
    "    for (f in significant_files) {\n",
    "      n_lines <- length(readLines(f, warn = FALSE))\n",
    "      cat(\"  -\", basename(f), \"（\", n_lines, \" 个区域）\\n\", sep = \"\")\n",
    "    }\n",
    "  } else {\n",
    "    warning(\"未找到生成的显著 DMR 文件\")\n",
    "  }\n",
    "} else {\n",
    "  warning(\"DMRs.bed 文件不存在：\", dmr_bed_file, \n",
    "          \"\\n请先执行 methscan diff 命令\")\n",
    "}"
   ]
  }
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