{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71a34d71",
   "metadata": {},
   "source": [
    "---\n",
    "title: 单细胞空间邻域统计：细胞空间分布与邻近类型分析\n",
    "author: SeekGene\n",
    "date: 2026-01-29\n",
    "tags:\n",
    "  - 空间转录组\n",
    "  - Notebooks\n",
    "  - 生态位分析\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007e9c7d-2c77-4441-9f97-773dfe0fd03a",
   "metadata": {},
   "source": [
    "# 单细胞空间邻域统计：细胞空间分布与邻近类型分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78340c22-a62c-4c3e-ad80-ca790319e156",
   "metadata": {},
   "source": [
    "## 加载分析 R 包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61c7bbb8-7218-43bc-98db-8ebe365252c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:18:24.083494Z",
     "iopub.status.busy": "2025-08-22T08:18:24.053979Z",
     "iopub.status.idle": "2025-08-22T08:18:50.107491Z",
     "shell.execute_reply": "2025-08-22T08:18:50.105985Z"
    },
    "scrolled": true,
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Attaching SeuratObject\n",
      "\n",
      "qs 0.27.3. Announcement: https://github.com/qsbase/qs/issues/103\n",
      "\n",
      "Warning message:\n",
      "“package ‘dplyr’ was built under R version 4.3.3”\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "Warning message:\n",
      "“package ‘tibble’ was built under R version 4.3.3”\n",
      "Warning message:\n",
      "“package ‘stringr’ was built under R version 4.3.3”\n",
      "Warning message:\n",
      "“package ‘ggplot2’ was built under R version 4.3.3”\n"
     ]
    }
   ],
   "source": [
    "library(Seurat)\n",
    "library(qs)\n",
    "library(dplyr)\n",
    "library(tibble)\n",
    "library(stringr)\n",
    "library(ggplot2)\n",
    "library(phenoptr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5123546-e65f-4d2a-9f5d-a364c2665405",
   "metadata": {},
   "source": [
    "## 填写输入对象"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b50515b-b031-4679-8025-853ac9cb885f",
   "metadata": {},
   "source": [
    "* **col_SAM**: metadata 对应的样本列名\n",
    "* **SAMple_name**: 分析的样本名\n",
    "* **col_celltype**：metadata 对应的细胞类型列名\n",
    "* **analysis_celltype**：分析的细胞类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e577a86a-28ab-4b93-88c0-1f461db3b3f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:18:50.110977Z",
     "iopub.status.busy": "2025-08-22T08:18:50.109620Z",
     "iopub.status.idle": "2025-08-22T08:18:50.124266Z",
     "shell.execute_reply": "2025-08-22T08:18:50.123034Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "col_sam = \"Sample\"\n",
    "sample_name = \"T1\"\n",
    "col_celltype = \"large_celltype\"\n",
    "analysis_celltype = \"Neutrophils\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad317ea-3231-4ac3-9e58-a4b6376dd0c0",
   "metadata": {},
   "source": [
    "## 读取 rds 和 metadata，对数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d0aaedf-400e-472d-ba90-195b2f23ec6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:19:43.136739Z",
     "iopub.status.busy": "2025-08-22T08:19:43.135540Z",
     "iopub.status.idle": "2025-08-22T08:19:53.767720Z",
     "shell.execute_reply": "2025-08-22T08:19:53.766178Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "data = readRDS(\"../../data/AY1739779861133/input.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bcb844e8-575a-4976-a4bd-1c31933802d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:19:53.771191Z",
     "iopub.status.busy": "2025-08-22T08:19:53.770102Z",
     "iopub.status.idle": "2025-08-22T08:19:54.613845Z",
     "shell.execute_reply": "2025-08-22T08:19:54.612344Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "meta = read.delim(\"../../data/AY1739779861133/meta.tsv\")\n",
    "rownames(meta) = meta$barcode\n",
    "data@meta.data = meta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d838d25-ae66-406b-bb9c-6862ff269670",
   "metadata": {},
   "source": [
    "* **提取 rds 里想要分析的空间样本信息**  \n",
    "* **对 SeekSpace 数据提供的空间位置信息进行转变，像素点→实际点（转化倍数 0.2653）**\n",
    "* **提取想要分析的细胞类型**\n",
    "* **计算每个细胞点之间的距离**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09df66e7-b374-48cf-9545-c8799ba98e57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:19:59.081115Z",
     "iopub.status.busy": "2025-08-22T08:19:59.079917Z",
     "iopub.status.idle": "2025-08-22T08:20:03.828427Z",
     "shell.execute_reply": "2025-08-22T08:20:03.826923Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "sub = subset(data,cells = rownames(data@meta.data[data@meta.data[[col_sam]] %in% sample_name,]))\n",
    "sub@meta.data$barcode = rownames(sub@meta.data)\n",
    "df_labels = sub@meta.data[,c(\"barcode\",col_celltype)]\n",
    "sub_position = Embeddings(sub,\"spatial\")\n",
    "sub_position = sub_position*0.2653\n",
    "sub_position = as.data.frame(sub_position)\n",
    "\n",
    "Neu_barcode = rownames(sub@meta.data[sub@meta.data[[col_celltype]] %in% analysis_celltype,])\n",
    "dist_matrix <- as.matrix(dist(sub_position, method = \"euclidean\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8ad91b-7766-4f2f-8a9c-9ccbc986d717",
   "metadata": {},
   "source": [
    "## 分别计算 4 个不同范围内的空间邻域组成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "619c7155-efd2-4da8-b8f9-f96d819d81da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:20:07.767907Z",
     "iopub.status.busy": "2025-08-22T08:20:07.766689Z",
     "iopub.status.idle": "2025-08-22T08:20:17.564965Z",
     "shell.execute_reply": "2025-08-22T08:20:17.563572Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mJoining with `by = join_by(barcode)`\n",
      "\u001b[1m\u001b[22mJoining with `by = join_by(barcode)`\n",
      "\u001b[1m\u001b[22mJoining with `by = join_by(barcode)`\n",
      "\u001b[1m\u001b[22mJoining with `by = join_by(barcode)`\n"
     ]
    }
   ],
   "source": [
    "neigh_freq1 = data.frame()\n",
    "for(threshold in c(20,50,100,200)){\n",
    "\n",
    "    # 找出每个点周围内的邻近点（排除自身）\n",
    "    neighbors = apply(dist_matrix, 1, function(row) {\n",
    "      nearby = which(row <= threshold & row > 0)  # row > 0 排除自身\n",
    "      if (length(nearby) > 0) {\n",
    "        paste(names(nearby), collapse = \";\")  # 用分号分隔邻近点\n",
    "      } else {\n",
    "        NA  # 如果没有邻近点，返回NA\n",
    "      }\n",
    "    })\n",
    "\n",
    "    # 将结果转换为数据框\n",
    "    result = data.frame(\n",
    "      Point = rownames(sub_position),\n",
    "      X = sub_position$spatial_1,\n",
    "      Y = sub_position$spatial_2,\n",
    "      Neighbors = neighbors,\n",
    "      stringsAsFactors = FALSE\n",
    "    )\n",
    "\n",
    "\n",
    "    result_Neu = result[result$Point %in% Neu_barcode,]\n",
    "    map_neighbors_to_labels = function(neighbor_ids, label_df) {\n",
    "      if (is.na(neighbor_ids)) return(NA)\n",
    "      ids = strsplit(neighbor_ids, \";\")[[1]]  # 拆分ID\n",
    "      labels = label_df$large_celltype[match(ids, label_df$barcode)]  # 匹配细胞类型\n",
    "      paste(labels, collapse = \"; \")  # 合并为字符串\n",
    "    }\n",
    "\n",
    "    # 应用到 Neighbors 列\n",
    "    result_Neu$Neighbor_Labels = sapply(\n",
    "      result_Neu$Neighbors, \n",
    "      map_neighbors_to_labels, \n",
    "      label_df = df_labels\n",
    "    )\n",
    "\n",
    "    merged_string = paste(result_Neu$Neighbors, collapse = \";\")\n",
    "    a = str_split(merged_string,\";\")[[1]]\n",
    "    neigh = data.frame(barcode = unique(a))\n",
    "    neigh = left_join(neigh,sub@meta.data[,c(\"barcode\",col_celltype)])\n",
    "    neigh_freq = as.data.frame(table(neigh[[col_celltype]]))\n",
    "    colnames(neigh_freq) = c(\"Celltype\",\"Cell_number\")\n",
    "    neigh_freq$Distance = paste0(threshold,\"um\")\n",
    "    neigh_freq1 = rbind(neigh_freq1,neigh_freq)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7bd25a7c-edf0-40d8-94f8-bbb98a6fe934",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:20:20.549578Z",
     "iopub.status.busy": "2025-08-22T08:20:20.548347Z",
     "iopub.status.idle": "2025-08-22T08:20:20.583110Z",
     "shell.execute_reply": "2025-08-22T08:20:20.581811Z"
    },
    "scrolled": true,
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 44 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Celltype</th><th scope=col>Cell_number</th><th scope=col>Distance</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>B_cells          </td><td>  49</td><td>20um </td></tr>\n",
       "\t<tr><td>Endothelial_cells</td><td>   9</td><td>20um </td></tr>\n",
       "\t<tr><td>Epithelial_cells </td><td>  68</td><td>20um </td></tr>\n",
       "\t<tr><td>Fibroblasts      </td><td> 105</td><td>20um </td></tr>\n",
       "\t<tr><td>Macrophages      </td><td>  35</td><td>20um </td></tr>\n",
       "\t<tr><td>Mast_cells       </td><td>   2</td><td>20um </td></tr>\n",
       "\t<tr><td>Nerve            </td><td>   3</td><td>20um </td></tr>\n",
       "\t<tr><td>Neutrophils      </td><td>  39</td><td>20um </td></tr>\n",
       "\t<tr><td>Plasma_cells     </td><td>  34</td><td>20um </td></tr>\n",
       "\t<tr><td>SMC              </td><td>   4</td><td>20um </td></tr>\n",
       "\t<tr><td>T_cells          </td><td>  94</td><td>20um </td></tr>\n",
       "\t<tr><td>B_cells          </td><td> 188</td><td>50um </td></tr>\n",
       "\t<tr><td>Endothelial_cells</td><td>  41</td><td>50um </td></tr>\n",
       "\t<tr><td>Epithelial_cells </td><td> 443</td><td>50um </td></tr>\n",
       "\t<tr><td>Fibroblasts      </td><td> 312</td><td>50um </td></tr>\n",
       "\t<tr><td>Macrophages      </td><td> 106</td><td>50um </td></tr>\n",
       "\t<tr><td>Mast_cells       </td><td>   4</td><td>50um </td></tr>\n",
       "\t<tr><td>Nerve            </td><td>   7</td><td>50um </td></tr>\n",
       "\t<tr><td>Neutrophils      </td><td> 162</td><td>50um </td></tr>\n",
       "\t<tr><td>Plasma_cells     </td><td> 119</td><td>50um </td></tr>\n",
       "\t<tr><td>SMC              </td><td>  35</td><td>50um </td></tr>\n",
       "\t<tr><td>T_cells          </td><td> 310</td><td>50um </td></tr>\n",
       "\t<tr><td>B_cells          </td><td> 386</td><td>100um</td></tr>\n",
       "\t<tr><td>Endothelial_cells</td><td>  82</td><td>100um</td></tr>\n",
       "\t<tr><td>Epithelial_cells </td><td>1401</td><td>100um</td></tr>\n",
       "\t<tr><td>Fibroblasts      </td><td> 596</td><td>100um</td></tr>\n",
       "\t<tr><td>Macrophages      </td><td> 188</td><td>100um</td></tr>\n",
       "\t<tr><td>Mast_cells       </td><td>   5</td><td>100um</td></tr>\n",
       "\t<tr><td>Nerve            </td><td>  10</td><td>100um</td></tr>\n",
       "\t<tr><td>Neutrophils      </td><td> 289</td><td>100um</td></tr>\n",
       "\t<tr><td>Plasma_cells     </td><td> 235</td><td>100um</td></tr>\n",
       "\t<tr><td>SMC              </td><td>  79</td><td>100um</td></tr>\n",
       "\t<tr><td>T_cells          </td><td> 556</td><td>100um</td></tr>\n",
       "\t<tr><td>B_cells          </td><td> 603</td><td>200um</td></tr>\n",
       "\t<tr><td>Endothelial_cells</td><td> 111</td><td>200um</td></tr>\n",
       "\t<tr><td>Epithelial_cells </td><td>2902</td><td>200um</td></tr>\n",
       "\t<tr><td>Fibroblasts      </td><td> 800</td><td>200um</td></tr>\n",
       "\t<tr><td>Macrophages      </td><td> 262</td><td>200um</td></tr>\n",
       "\t<tr><td>Mast_cells       </td><td>   6</td><td>200um</td></tr>\n",
       "\t<tr><td>Nerve            </td><td>  14</td><td>200um</td></tr>\n",
       "\t<tr><td>Neutrophils      </td><td> 383</td><td>200um</td></tr>\n",
       "\t<tr><td>Plasma_cells     </td><td> 307</td><td>200um</td></tr>\n",
       "\t<tr><td>SMC              </td><td> 121</td><td>200um</td></tr>\n",
       "\t<tr><td>T_cells          </td><td> 750</td><td>200um</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 44 × 3\n",
       "\\begin{tabular}{lll}\n",
       " Celltype & Cell\\_number & Distance\\\\\n",
       " <fct> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t B\\_cells           &   49 & 20um \\\\\n",
       "\t Endothelial\\_cells &    9 & 20um \\\\\n",
       "\t Epithelial\\_cells  &   68 & 20um \\\\\n",
       "\t Fibroblasts       &  105 & 20um \\\\\n",
       "\t Macrophages       &   35 & 20um \\\\\n",
       "\t Mast\\_cells        &    2 & 20um \\\\\n",
       "\t Nerve             &    3 & 20um \\\\\n",
       "\t Neutrophils       &   39 & 20um \\\\\n",
       "\t Plasma\\_cells      &   34 & 20um \\\\\n",
       "\t SMC               &    4 & 20um \\\\\n",
       "\t T\\_cells           &   94 & 20um \\\\\n",
       "\t B\\_cells           &  188 & 50um \\\\\n",
       "\t Endothelial\\_cells &   41 & 50um \\\\\n",
       "\t Epithelial\\_cells  &  443 & 50um \\\\\n",
       "\t Fibroblasts       &  312 & 50um \\\\\n",
       "\t Macrophages       &  106 & 50um \\\\\n",
       "\t Mast\\_cells        &    4 & 50um \\\\\n",
       "\t Nerve             &    7 & 50um \\\\\n",
       "\t Neutrophils       &  162 & 50um \\\\\n",
       "\t Plasma\\_cells      &  119 & 50um \\\\\n",
       "\t SMC               &   35 & 50um \\\\\n",
       "\t T\\_cells           &  310 & 50um \\\\\n",
       "\t B\\_cells           &  386 & 100um\\\\\n",
       "\t Endothelial\\_cells &   82 & 100um\\\\\n",
       "\t Epithelial\\_cells  & 1401 & 100um\\\\\n",
       "\t Fibroblasts       &  596 & 100um\\\\\n",
       "\t Macrophages       &  188 & 100um\\\\\n",
       "\t Mast\\_cells        &    5 & 100um\\\\\n",
       "\t Nerve             &   10 & 100um\\\\\n",
       "\t Neutrophils       &  289 & 100um\\\\\n",
       "\t Plasma\\_cells      &  235 & 100um\\\\\n",
       "\t SMC               &   79 & 100um\\\\\n",
       "\t T\\_cells           &  556 & 100um\\\\\n",
       "\t B\\_cells           &  603 & 200um\\\\\n",
       "\t Endothelial\\_cells &  111 & 200um\\\\\n",
       "\t Epithelial\\_cells  & 2902 & 200um\\\\\n",
       "\t Fibroblasts       &  800 & 200um\\\\\n",
       "\t Macrophages       &  262 & 200um\\\\\n",
       "\t Mast\\_cells        &    6 & 200um\\\\\n",
       "\t Nerve             &   14 & 200um\\\\\n",
       "\t Neutrophils       &  383 & 200um\\\\\n",
       "\t Plasma\\_cells      &  307 & 200um\\\\\n",
       "\t SMC               &  121 & 200um\\\\\n",
       "\t T\\_cells           &  750 & 200um\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 44 × 3\n",
       "\n",
       "| Celltype &lt;fct&gt; | Cell_number &lt;int&gt; | Distance &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| B_cells           |   49 | 20um  |\n",
       "| Endothelial_cells |    9 | 20um  |\n",
       "| Epithelial_cells  |   68 | 20um  |\n",
       "| Fibroblasts       |  105 | 20um  |\n",
       "| Macrophages       |   35 | 20um  |\n",
       "| Mast_cells        |    2 | 20um  |\n",
       "| Nerve             |    3 | 20um  |\n",
       "| Neutrophils       |   39 | 20um  |\n",
       "| Plasma_cells      |   34 | 20um  |\n",
       "| SMC               |    4 | 20um  |\n",
       "| T_cells           |   94 | 20um  |\n",
       "| B_cells           |  188 | 50um  |\n",
       "| Endothelial_cells |   41 | 50um  |\n",
       "| Epithelial_cells  |  443 | 50um  |\n",
       "| Fibroblasts       |  312 | 50um  |\n",
       "| Macrophages       |  106 | 50um  |\n",
       "| Mast_cells        |    4 | 50um  |\n",
       "| Nerve             |    7 | 50um  |\n",
       "| Neutrophils       |  162 | 50um  |\n",
       "| Plasma_cells      |  119 | 50um  |\n",
       "| SMC               |   35 | 50um  |\n",
       "| T_cells           |  310 | 50um  |\n",
       "| B_cells           |  386 | 100um |\n",
       "| Endothelial_cells |   82 | 100um |\n",
       "| Epithelial_cells  | 1401 | 100um |\n",
       "| Fibroblasts       |  596 | 100um |\n",
       "| Macrophages       |  188 | 100um |\n",
       "| Mast_cells        |    5 | 100um |\n",
       "| Nerve             |   10 | 100um |\n",
       "| Neutrophils       |  289 | 100um |\n",
       "| Plasma_cells      |  235 | 100um |\n",
       "| SMC               |   79 | 100um |\n",
       "| T_cells           |  556 | 100um |\n",
       "| B_cells           |  603 | 200um |\n",
       "| Endothelial_cells |  111 | 200um |\n",
       "| Epithelial_cells  | 2902 | 200um |\n",
       "| Fibroblasts       |  800 | 200um |\n",
       "| Macrophages       |  262 | 200um |\n",
       "| Mast_cells        |    6 | 200um |\n",
       "| Nerve             |   14 | 200um |\n",
       "| Neutrophils       |  383 | 200um |\n",
       "| Plasma_cells      |  307 | 200um |\n",
       "| SMC               |  121 | 200um |\n",
       "| T_cells           |  750 | 200um |\n",
       "\n"
      ],
      "text/plain": [
       "   Celltype          Cell_number Distance\n",
       "1  B_cells             49        20um    \n",
       "2  Endothelial_cells    9        20um    \n",
       "3  Epithelial_cells    68        20um    \n",
       "4  Fibroblasts        105        20um    \n",
       "5  Macrophages         35        20um    \n",
       "6  Mast_cells           2        20um    \n",
       "7  Nerve                3        20um    \n",
       "8  Neutrophils         39        20um    \n",
       "9  Plasma_cells        34        20um    \n",
       "10 SMC                  4        20um    \n",
       "11 T_cells             94        20um    \n",
       "12 B_cells            188        50um    \n",
       "13 Endothelial_cells   41        50um    \n",
       "14 Epithelial_cells   443        50um    \n",
       "15 Fibroblasts        312        50um    \n",
       "16 Macrophages        106        50um    \n",
       "17 Mast_cells           4        50um    \n",
       "18 Nerve                7        50um    \n",
       "19 Neutrophils        162        50um    \n",
       "20 Plasma_cells       119        50um    \n",
       "21 SMC                 35        50um    \n",
       "22 T_cells            310        50um    \n",
       "23 B_cells            386        100um   \n",
       "24 Endothelial_cells   82        100um   \n",
       "25 Epithelial_cells  1401        100um   \n",
       "26 Fibroblasts        596        100um   \n",
       "27 Macrophages        188        100um   \n",
       "28 Mast_cells           5        100um   \n",
       "29 Nerve               10        100um   \n",
       "30 Neutrophils        289        100um   \n",
       "31 Plasma_cells       235        100um   \n",
       "32 SMC                 79        100um   \n",
       "33 T_cells            556        100um   \n",
       "34 B_cells            603        200um   \n",
       "35 Endothelial_cells  111        200um   \n",
       "36 Epithelial_cells  2902        200um   \n",
       "37 Fibroblasts        800        200um   \n",
       "38 Macrophages        262        200um   \n",
       "39 Mast_cells           6        200um   \n",
       "40 Nerve               14        200um   \n",
       "41 Neutrophils        383        200um   \n",
       "42 Plasma_cells       307        200um   \n",
       "43 SMC                121        200um   \n",
       "44 T_cells            750        200um   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neigh_freq1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5290f284-3ec9-437c-b2f5-aa5afd9bb362",
   "metadata": {},
   "source": [
    "* **计算每个距离范围内分析目标细胞类型周围邻域细胞类型占比**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "00a71369-5ed7-4bf9-aab7-8cfd5e9a64a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:20:25.435863Z",
     "iopub.status.busy": "2025-08-22T08:20:25.434667Z",
     "iopub.status.idle": "2025-08-22T08:20:25.466751Z",
     "shell.execute_reply": "2025-08-22T08:20:25.465467Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "neigh_freq1 = neigh_freq1 %>%\n",
    "  group_by(Distance) %>% # 第一步：按Region分组\n",
    "  mutate(Region_Pct = Cell_number / sum(Cell_number) * 100) %>% # 第二步：计算组内占比\n",
    "  ungroup()\n",
    "neigh_freq1$Distance = factor(neigh_freq1$Distance,c(\"20um\",\"50um\",\"100um\",\"200um\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb7902b1-f64b-4317-acf5-1c61c0dbf5d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:20:27.436018Z",
     "iopub.status.busy": "2025-08-22T08:20:27.434825Z",
     "iopub.status.idle": "2025-08-22T08:20:27.470921Z",
     "shell.execute_reply": "2025-08-22T08:20:27.469676Z"
    },
    "scrolled": true,
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 44 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Celltype</th><th scope=col>Cell_number</th><th scope=col>Distance</th><th scope=col>Region_Pct</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>B_cells          </td><td>  49</td><td>20um </td><td>11.08597285</td></tr>\n",
       "\t<tr><td>Endothelial_cells</td><td>   9</td><td>20um </td><td> 2.03619910</td></tr>\n",
       "\t<tr><td>Epithelial_cells </td><td>  68</td><td>20um </td><td>15.38461538</td></tr>\n",
       "\t<tr><td>Fibroblasts      </td><td> 105</td><td>20um </td><td>23.75565611</td></tr>\n",
       "\t<tr><td>Macrophages      </td><td>  35</td><td>20um </td><td> 7.91855204</td></tr>\n",
       "\t<tr><td>Mast_cells       </td><td>   2</td><td>20um </td><td> 0.45248869</td></tr>\n",
       "\t<tr><td>Nerve            </td><td>   3</td><td>20um </td><td> 0.67873303</td></tr>\n",
       "\t<tr><td>Neutrophils      </td><td>  39</td><td>20um </td><td> 8.82352941</td></tr>\n",
       "\t<tr><td>Plasma_cells     </td><td>  34</td><td>20um </td><td> 7.69230769</td></tr>\n",
       "\t<tr><td>SMC              </td><td>   4</td><td>20um </td><td> 0.90497738</td></tr>\n",
       "\t<tr><td>T_cells          </td><td>  94</td><td>20um </td><td>21.26696833</td></tr>\n",
       "\t<tr><td>B_cells          </td><td> 188</td><td>50um </td><td>10.88592936</td></tr>\n",
       "\t<tr><td>Endothelial_cells</td><td>  41</td><td>50um </td><td> 2.37405906</td></tr>\n",
       "\t<tr><td>Epithelial_cells </td><td> 443</td><td>50um </td><td>25.65141865</td></tr>\n",
       "\t<tr><td>Fibroblasts      </td><td> 312</td><td>50um </td><td>18.06601042</td></tr>\n",
       "\t<tr><td>Macrophages      </td><td> 106</td><td>50um </td><td> 6.13781123</td></tr>\n",
       "\t<tr><td>Mast_cells       </td><td>   4</td><td>50um </td><td> 0.23161552</td></tr>\n",
       "\t<tr><td>Nerve            </td><td>   7</td><td>50um </td><td> 0.40532716</td></tr>\n",
       "\t<tr><td>Neutrophils      </td><td> 162</td><td>50um </td><td> 9.38042849</td></tr>\n",
       "\t<tr><td>Plasma_cells     </td><td> 119</td><td>50um </td><td> 6.89056167</td></tr>\n",
       "\t<tr><td>SMC              </td><td>  35</td><td>50um </td><td> 2.02663578</td></tr>\n",
       "\t<tr><td>T_cells          </td><td> 310</td><td>50um </td><td>17.95020266</td></tr>\n",
       "\t<tr><td>B_cells          </td><td> 386</td><td>100um</td><td>10.08622942</td></tr>\n",
       "\t<tr><td>Endothelial_cells</td><td>  82</td><td>100um</td><td> 2.14267050</td></tr>\n",
       "\t<tr><td>Epithelial_cells </td><td>1401</td><td>100um</td><td>36.60830938</td></tr>\n",
       "\t<tr><td>Fibroblasts      </td><td> 596</td><td>100um</td><td>15.57355631</td></tr>\n",
       "\t<tr><td>Macrophages      </td><td> 188</td><td>100um</td><td> 4.91246407</td></tr>\n",
       "\t<tr><td>Mast_cells       </td><td>   5</td><td>100um</td><td> 0.13065064</td></tr>\n",
       "\t<tr><td>Nerve            </td><td>  10</td><td>100um</td><td> 0.26130128</td></tr>\n",
       "\t<tr><td>Neutrophils      </td><td> 289</td><td>100um</td><td> 7.55160700</td></tr>\n",
       "\t<tr><td>Plasma_cells     </td><td> 235</td><td>100um</td><td> 6.14058009</td></tr>\n",
       "\t<tr><td>SMC              </td><td>  79</td><td>100um</td><td> 2.06428011</td></tr>\n",
       "\t<tr><td>T_cells          </td><td> 556</td><td>100um</td><td>14.52835119</td></tr>\n",
       "\t<tr><td>B_cells          </td><td> 603</td><td>200um</td><td> 9.63412686</td></tr>\n",
       "\t<tr><td>Endothelial_cells</td><td> 111</td><td>200um</td><td> 1.77344624</td></tr>\n",
       "\t<tr><td>Epithelial_cells </td><td>2902</td><td>200um</td><td>46.36523406</td></tr>\n",
       "\t<tr><td>Fibroblasts      </td><td> 800</td><td>200um</td><td>12.78159450</td></tr>\n",
       "\t<tr><td>Macrophages      </td><td> 262</td><td>200um</td><td> 4.18597220</td></tr>\n",
       "\t<tr><td>Mast_cells       </td><td>   6</td><td>200um</td><td> 0.09586196</td></tr>\n",
       "\t<tr><td>Nerve            </td><td>  14</td><td>200um</td><td> 0.22367790</td></tr>\n",
       "\t<tr><td>Neutrophils      </td><td> 383</td><td>200um</td><td> 6.11918837</td></tr>\n",
       "\t<tr><td>Plasma_cells     </td><td> 307</td><td>200um</td><td> 4.90493689</td></tr>\n",
       "\t<tr><td>SMC              </td><td> 121</td><td>200um</td><td> 1.93321617</td></tr>\n",
       "\t<tr><td>T_cells          </td><td> 750</td><td>200um</td><td>11.98274485</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 44 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Celltype & Cell\\_number & Distance & Region\\_Pct\\\\\n",
       " <fct> & <int> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t B\\_cells           &   49 & 20um  & 11.08597285\\\\\n",
       "\t Endothelial\\_cells &    9 & 20um  &  2.03619910\\\\\n",
       "\t Epithelial\\_cells  &   68 & 20um  & 15.38461538\\\\\n",
       "\t Fibroblasts       &  105 & 20um  & 23.75565611\\\\\n",
       "\t Macrophages       &   35 & 20um  &  7.91855204\\\\\n",
       "\t Mast\\_cells        &    2 & 20um  &  0.45248869\\\\\n",
       "\t Nerve             &    3 & 20um  &  0.67873303\\\\\n",
       "\t Neutrophils       &   39 & 20um  &  8.82352941\\\\\n",
       "\t Plasma\\_cells      &   34 & 20um  &  7.69230769\\\\\n",
       "\t SMC               &    4 & 20um  &  0.90497738\\\\\n",
       "\t T\\_cells           &   94 & 20um  & 21.26696833\\\\\n",
       "\t B\\_cells           &  188 & 50um  & 10.88592936\\\\\n",
       "\t Endothelial\\_cells &   41 & 50um  &  2.37405906\\\\\n",
       "\t Epithelial\\_cells  &  443 & 50um  & 25.65141865\\\\\n",
       "\t Fibroblasts       &  312 & 50um  & 18.06601042\\\\\n",
       "\t Macrophages       &  106 & 50um  &  6.13781123\\\\\n",
       "\t Mast\\_cells        &    4 & 50um  &  0.23161552\\\\\n",
       "\t Nerve             &    7 & 50um  &  0.40532716\\\\\n",
       "\t Neutrophils       &  162 & 50um  &  9.38042849\\\\\n",
       "\t Plasma\\_cells      &  119 & 50um  &  6.89056167\\\\\n",
       "\t SMC               &   35 & 50um  &  2.02663578\\\\\n",
       "\t T\\_cells           &  310 & 50um  & 17.95020266\\\\\n",
       "\t B\\_cells           &  386 & 100um & 10.08622942\\\\\n",
       "\t Endothelial\\_cells &   82 & 100um &  2.14267050\\\\\n",
       "\t Epithelial\\_cells  & 1401 & 100um & 36.60830938\\\\\n",
       "\t Fibroblasts       &  596 & 100um & 15.57355631\\\\\n",
       "\t Macrophages       &  188 & 100um &  4.91246407\\\\\n",
       "\t Mast\\_cells        &    5 & 100um &  0.13065064\\\\\n",
       "\t Nerve             &   10 & 100um &  0.26130128\\\\\n",
       "\t Neutrophils       &  289 & 100um &  7.55160700\\\\\n",
       "\t Plasma\\_cells      &  235 & 100um &  6.14058009\\\\\n",
       "\t SMC               &   79 & 100um &  2.06428011\\\\\n",
       "\t T\\_cells           &  556 & 100um & 14.52835119\\\\\n",
       "\t B\\_cells           &  603 & 200um &  9.63412686\\\\\n",
       "\t Endothelial\\_cells &  111 & 200um &  1.77344624\\\\\n",
       "\t Epithelial\\_cells  & 2902 & 200um & 46.36523406\\\\\n",
       "\t Fibroblasts       &  800 & 200um & 12.78159450\\\\\n",
       "\t Macrophages       &  262 & 200um &  4.18597220\\\\\n",
       "\t Mast\\_cells        &    6 & 200um &  0.09586196\\\\\n",
       "\t Nerve             &   14 & 200um &  0.22367790\\\\\n",
       "\t Neutrophils       &  383 & 200um &  6.11918837\\\\\n",
       "\t Plasma\\_cells      &  307 & 200um &  4.90493689\\\\\n",
       "\t SMC               &  121 & 200um &  1.93321617\\\\\n",
       "\t T\\_cells           &  750 & 200um & 11.98274485\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 44 × 4\n",
       "\n",
       "| Celltype &lt;fct&gt; | Cell_number &lt;int&gt; | Distance &lt;fct&gt; | Region_Pct &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| B_cells           |   49 | 20um  | 11.08597285 |\n",
       "| Endothelial_cells |    9 | 20um  |  2.03619910 |\n",
       "| Epithelial_cells  |   68 | 20um  | 15.38461538 |\n",
       "| Fibroblasts       |  105 | 20um  | 23.75565611 |\n",
       "| Macrophages       |   35 | 20um  |  7.91855204 |\n",
       "| Mast_cells        |    2 | 20um  |  0.45248869 |\n",
       "| Nerve             |    3 | 20um  |  0.67873303 |\n",
       "| Neutrophils       |   39 | 20um  |  8.82352941 |\n",
       "| Plasma_cells      |   34 | 20um  |  7.69230769 |\n",
       "| SMC               |    4 | 20um  |  0.90497738 |\n",
       "| T_cells           |   94 | 20um  | 21.26696833 |\n",
       "| B_cells           |  188 | 50um  | 10.88592936 |\n",
       "| Endothelial_cells |   41 | 50um  |  2.37405906 |\n",
       "| Epithelial_cells  |  443 | 50um  | 25.65141865 |\n",
       "| Fibroblasts       |  312 | 50um  | 18.06601042 |\n",
       "| Macrophages       |  106 | 50um  |  6.13781123 |\n",
       "| Mast_cells        |    4 | 50um  |  0.23161552 |\n",
       "| Nerve             |    7 | 50um  |  0.40532716 |\n",
       "| Neutrophils       |  162 | 50um  |  9.38042849 |\n",
       "| Plasma_cells      |  119 | 50um  |  6.89056167 |\n",
       "| SMC               |   35 | 50um  |  2.02663578 |\n",
       "| T_cells           |  310 | 50um  | 17.95020266 |\n",
       "| B_cells           |  386 | 100um | 10.08622942 |\n",
       "| Endothelial_cells |   82 | 100um |  2.14267050 |\n",
       "| Epithelial_cells  | 1401 | 100um | 36.60830938 |\n",
       "| Fibroblasts       |  596 | 100um | 15.57355631 |\n",
       "| Macrophages       |  188 | 100um |  4.91246407 |\n",
       "| Mast_cells        |    5 | 100um |  0.13065064 |\n",
       "| Nerve             |   10 | 100um |  0.26130128 |\n",
       "| Neutrophils       |  289 | 100um |  7.55160700 |\n",
       "| Plasma_cells      |  235 | 100um |  6.14058009 |\n",
       "| SMC               |   79 | 100um |  2.06428011 |\n",
       "| T_cells           |  556 | 100um | 14.52835119 |\n",
       "| B_cells           |  603 | 200um |  9.63412686 |\n",
       "| Endothelial_cells |  111 | 200um |  1.77344624 |\n",
       "| Epithelial_cells  | 2902 | 200um | 46.36523406 |\n",
       "| Fibroblasts       |  800 | 200um | 12.78159450 |\n",
       "| Macrophages       |  262 | 200um |  4.18597220 |\n",
       "| Mast_cells        |    6 | 200um |  0.09586196 |\n",
       "| Nerve             |   14 | 200um |  0.22367790 |\n",
       "| Neutrophils       |  383 | 200um |  6.11918837 |\n",
       "| Plasma_cells      |  307 | 200um |  4.90493689 |\n",
       "| SMC               |  121 | 200um |  1.93321617 |\n",
       "| T_cells           |  750 | 200um | 11.98274485 |\n",
       "\n"
      ],
      "text/plain": [
       "   Celltype          Cell_number Distance Region_Pct \n",
       "1  B_cells             49        20um     11.08597285\n",
       "2  Endothelial_cells    9        20um      2.03619910\n",
       "3  Epithelial_cells    68        20um     15.38461538\n",
       "4  Fibroblasts        105        20um     23.75565611\n",
       "5  Macrophages         35        20um      7.91855204\n",
       "6  Mast_cells           2        20um      0.45248869\n",
       "7  Nerve                3        20um      0.67873303\n",
       "8  Neutrophils         39        20um      8.82352941\n",
       "9  Plasma_cells        34        20um      7.69230769\n",
       "10 SMC                  4        20um      0.90497738\n",
       "11 T_cells             94        20um     21.26696833\n",
       "12 B_cells            188        50um     10.88592936\n",
       "13 Endothelial_cells   41        50um      2.37405906\n",
       "14 Epithelial_cells   443        50um     25.65141865\n",
       "15 Fibroblasts        312        50um     18.06601042\n",
       "16 Macrophages        106        50um      6.13781123\n",
       "17 Mast_cells           4        50um      0.23161552\n",
       "18 Nerve                7        50um      0.40532716\n",
       "19 Neutrophils        162        50um      9.38042849\n",
       "20 Plasma_cells       119        50um      6.89056167\n",
       "21 SMC                 35        50um      2.02663578\n",
       "22 T_cells            310        50um     17.95020266\n",
       "23 B_cells            386        100um    10.08622942\n",
       "24 Endothelial_cells   82        100um     2.14267050\n",
       "25 Epithelial_cells  1401        100um    36.60830938\n",
       "26 Fibroblasts        596        100um    15.57355631\n",
       "27 Macrophages        188        100um     4.91246407\n",
       "28 Mast_cells           5        100um     0.13065064\n",
       "29 Nerve               10        100um     0.26130128\n",
       "30 Neutrophils        289        100um     7.55160700\n",
       "31 Plasma_cells       235        100um     6.14058009\n",
       "32 SMC                 79        100um     2.06428011\n",
       "33 T_cells            556        100um    14.52835119\n",
       "34 B_cells            603        200um     9.63412686\n",
       "35 Endothelial_cells  111        200um     1.77344624\n",
       "36 Epithelial_cells  2902        200um    46.36523406\n",
       "37 Fibroblasts        800        200um    12.78159450\n",
       "38 Macrophages        262        200um     4.18597220\n",
       "39 Mast_cells           6        200um     0.09586196\n",
       "40 Nerve               14        200um     0.22367790\n",
       "41 Neutrophils        383        200um     6.11918837\n",
       "42 Plasma_cells       307        200um     4.90493689\n",
       "43 SMC                121        200um     1.93321617\n",
       "44 T_cells            750        200um    11.98274485"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neigh_freq1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514be2de-3839-48b6-8e50-410c98e77401",
   "metadata": {},
   "source": [
    "## 结果可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84272054-d815-4b77-a4d8-beaca3eb92cf",
   "metadata": {},
   "source": [
    "可视化目标细胞类型周围细胞类型的占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ed329c37-7549-41ee-a8e3-1ab882bac84f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:20:30.822717Z",
     "iopub.status.busy": "2025-08-22T08:20:30.821514Z",
     "iopub.status.idle": "2025-08-22T08:20:33.418085Z",
     "shell.execute_reply": "2025-08-22T08:20:33.416733Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(neigh_freq1, aes(fill = Celltype, y = Region_Pct, x = Distance)) +\n",
    "  theme_bw() +\n",
    "  geom_bar(position = \"fill\", stat = \"identity\") +\n",
    "  scale_fill_manual(\n",
    "    values = c(\"#93cc82\", \"#4d97cd\", \"#f6f5ee\", \"#ea9c9d\", \"#c74546\", \n",
    "               \"#db6968\", \"#4d97cd\", \"#99cbeb\", \"#459943\", \n",
    "               \"#fdc58f\", \"#e8c559\", \"#a3d393\", \"#f8984e\"), #可修改自己设定颜色\n",
    "    name = \"Celltype\"  # 自定义图例名称\n",
    "  ) +\n",
    "  theme(\n",
    "    plot.title = element_text(hjust = 0.5),\n",
    "    panel.background = element_blank()\n",
    "  )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4915b79-bd75-4683-9bcd-0efb15b78b90",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "可视化目标细胞类型周围指定细胞类型的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be71eceb-4442-4050-a086-a3d576edca98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:20:36.818162Z",
     "iopub.status.busy": "2025-08-22T08:20:36.816889Z",
     "iopub.status.idle": "2025-08-22T08:20:36.829121Z",
     "shell.execute_reply": "2025-08-22T08:20:36.827780Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "plot = neigh_freq1[neigh_freq1$Celltype %in% \"T_cells\",]\n",
    "plot$Distance = factor(plot$Distance,levels = c(\"20um\",\"50um\",\"100um\",\"200um\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5989d49a-4476-4c8b-beb5-0fa5eff28b8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:20:38.861409Z",
     "iopub.status.busy": "2025-08-22T08:20:38.860175Z",
     "iopub.status.idle": "2025-08-22T08:20:38.881114Z",
     "shell.execute_reply": "2025-08-22T08:20:38.879752Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 4 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Celltype</th><th scope=col>Cell_number</th><th scope=col>Distance</th><th scope=col>Region_Pct</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>T_cells</td><td> 94</td><td>20um </td><td>21.26697</td></tr>\n",
       "\t<tr><td>T_cells</td><td>310</td><td>50um </td><td>17.95020</td></tr>\n",
       "\t<tr><td>T_cells</td><td>556</td><td>100um</td><td>14.52835</td></tr>\n",
       "\t<tr><td>T_cells</td><td>750</td><td>200um</td><td>11.98274</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 4 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Celltype & Cell\\_number & Distance & Region\\_Pct\\\\\n",
       " <fct> & <int> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t T\\_cells &  94 & 20um  & 21.26697\\\\\n",
       "\t T\\_cells & 310 & 50um  & 17.95020\\\\\n",
       "\t T\\_cells & 556 & 100um & 14.52835\\\\\n",
       "\t T\\_cells & 750 & 200um & 11.98274\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 4 × 4\n",
       "\n",
       "| Celltype &lt;fct&gt; | Cell_number &lt;int&gt; | Distance &lt;fct&gt; | Region_Pct &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| T_cells |  94 | 20um  | 21.26697 |\n",
       "| T_cells | 310 | 50um  | 17.95020 |\n",
       "| T_cells | 556 | 100um | 14.52835 |\n",
       "| T_cells | 750 | 200um | 11.98274 |\n",
       "\n"
      ],
      "text/plain": [
       "  Celltype Cell_number Distance Region_Pct\n",
       "1 T_cells   94         20um     21.26697  \n",
       "2 T_cells  310         50um     17.95020  \n",
       "3 T_cells  556         100um    14.52835  \n",
       "4 T_cells  750         200um    11.98274  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a05f62e-49a8-49dd-90b4-c45ac96db256",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-22T08:20:41.987568Z",
     "iopub.status.busy": "2025-08-22T08:20:41.986251Z",
     "iopub.status.idle": "2025-08-22T08:20:42.394281Z",
     "shell.execute_reply": "2025-08-22T08:20:42.392958Z"
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(plot, aes(x = Distance, y = Cell_number, fill = Distance)) +\n",
    "  geom_col() +  # 使用 geom_col() 代替 geom_bar(stat=\"identity\")\n",
    "  scale_fill_manual(\n",
    "    values = c(\"#8c510a\", \"#d8b365\", \"#f6e8c3\", \"#c7eae5\"),\n",
    "    name = \"Range\"  # 自定义图例名称\n",
    "  ) +\n",
    "  labs(\n",
    "    x = \"Neutrophils Neighborhood\",  # x轴标签\n",
    "    y = \"Epithelial Cells Number\",  # y轴标签\n",
    "  ) +\n",
    "  theme_minimal()+ # 使用简洁的主题\n",
    "geom_hline(yintercept = 750,color=\"red\")+\n",
    "  annotate(\n",
    "    \"text\", \n",
    "    x = Inf,  # 最右侧显示\n",
    "    y = 750,\n",
    "    label = \"T1 total Epithelial cells:750\", \n",
    "    color = \"red\",\n",
    "    hjust = 1.1,  # 靠右对齐\n",
    "    vjust = -1\n",
    "  )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113c8ce0-139b-4a2e-91bb-08583f762393",
   "metadata": {},
   "source": [
    "可视化目标细胞类型周围细胞类型密度分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a020ec84-2579-4d4e-8325-d0c6155c05d7",
   "metadata": {
    "scrolled": true,
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "sub_position = rownames_to_column(sub_position,\"Cell ID\")\n",
    "sub_position = cbind(sub_position,sub@meta.data[,col_celltype])\n",
    "colnames(sub_position)[2:4] = c(\"Cell X Position\",\"Cell Y Position\",\"Phenotype\")\n",
    "sub_position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "70e8e51b-826d-49ad-9bcc-4b94a0fcb32b",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distances=find_nearest_distance(sub_position)\n",
    "csd_with_distance=bind_cols(sub_position, distances)\n",
    "ggplot(csd_with_distance,aes(`Distance to Neutrophils`, color=Phenotype))+geom_density(size=1)+theme_bw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
