{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dcb8ef50",
   "metadata": {},
   "source": [
    "# Niche-differential expression analysis using `nicheDE`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "833e8ca8-b82a-46ca-9df1-3c7c0723dad8",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "set.seed(20240709)\n",
    "\n",
    "library(spacexr)\n",
    "library(nicheDE)\n",
    "library(MCube)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82892b33",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "DATA_PATH <- \"/import/home/share/zw/pql/data/breast_cancer\"\n",
    "RESULT_PATH <- \"/import/home/share/zw/pql/results/breast_cancer\"\n",
    "\n",
    "if (!dir.exists(RESULT_PATH)) {\n",
    "    dir.create(RESULT_PATH, recursive = TRUE)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800cd97b",
   "metadata": {},
   "source": [
    "## Applying `RCTD` and `nicheDE` to the ST dataset segmented by `UCS`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88519dcf",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "seg_method <- \"UCS_10X\"\n",
    "myRCTD <- readRDS(file.path(RESULT_PATH, seg_method, \"myRCTD.rds\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b2566fb",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Computing average expression profile matrix\"\n",
      "[1] \"Average expression matrix computed\"\n"
     ]
    }
   ],
   "source": [
    "# read in data\n",
    "# create library matrix\n",
    "librarymatrix <- CreateLibraryMatrix(\n",
    "    t(as.matrix(myRCTD@reference@counts)),\n",
    "    cbind(\n",
    "        colnames(myRCTD@reference@counts),\n",
    "        as.character(myRCTD@reference@cell_types)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d94aa4c8",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Creating Niche-DE object\"\n",
      "[1] \"Niche-DE object created with 17201 observations, 287 genes, 1 batch(es), and 17 cell types.\"\n"
     ]
    }
   ],
   "source": [
    "weights_RCTD <- as.matrix(myRCTD@results$weights)\n",
    "NDE_obj <- CreateNicheDEObject(\n",
    "    t(as.matrix(myRCTD@spatialRNA@counts)), myRCTD@spatialRNA@coords,\n",
    "    librarymatrix, weights_RCTD / rowSums(weights_RCTD),\n",
    "    sigma = c(50, 100, 200)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15521f6a",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Calculating effective niche for kernel bandwith 50(1 out of 3 values).\"\n",
      "[1] \"Calculating effective niche for kernel bandwith 100(2 out of 3 values).\"\n",
      "[1] \"Calculating effective niche for kernel bandwith 200(3 out of 3 values).\"\n",
      "[1] \"Effective niche calculated\"\n"
     ]
    }
   ],
   "source": [
    "NDE_obj <- CalculateEffectiveNiche(NDE_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5cca2b76",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Starting Niche-DE analysis with parameters C = 150, M = 10, gamma = 0.8.\"\n",
      "[1] \"Performing Niche-DE analysis with kernel bandwidth:50 (number 1 out of 3 values)\"\n",
      "[1] \"Running Niche-DE in parallel\"\n",
      "[1] \"Splitting Data into 1 chunks in order to avoid memory overload. Each chunk is less than 1 gigabytes.\"\n",
      "[1] \"Initializing cluster\"\n",
      "[1] \"Evaluating chunk 1 out of 1\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in e$fun(obj, substitute(ex), parent.frame(), e$data):\n",
      "“already exporting variable(s): constant_param, niche_DE_core, counts_chunk”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Closing cluster\"\n",
      "[1] \"Cleaning disk for next iteration\"\n",
      "[1] \"Performing Niche-DE analysis with kernel bandwidth:100 (number 2 out of 3 values)\"\n",
      "[1] \"Running Niche-DE in parallel\"\n",
      "[1] \"Splitting Data into 1 chunks in order to avoid memory overload. Each chunk is less than 1 gigabytes.\"\n",
      "[1] \"Initializing cluster\"\n",
      "[1] \"Evaluating chunk 1 out of 1\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in e$fun(obj, substitute(ex), parent.frame(), e$data):\n",
      "“already exporting variable(s): constant_param, niche_DE_core, counts_chunk”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Closing cluster\"\n",
      "[1] \"Cleaning disk for next iteration\"\n",
      "[1] \"Performing Niche-DE analysis with kernel bandwidth:200 (number 3 out of 3 values)\"\n",
      "[1] \"Running Niche-DE in parallel\"\n",
      "[1] \"Splitting Data into 1 chunks in order to avoid memory overload. Each chunk is less than 1 gigabytes.\"\n",
      "[1] \"Initializing cluster\"\n",
      "[1] \"Evaluating chunk 1 out of 1\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in e$fun(obj, substitute(ex), parent.frame(), e$data):\n",
      "“already exporting variable(s): constant_param, niche_DE_core, counts_chunk”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Closing cluster\"\n",
      "[1] \"Cleaning disk for next iteration\"\n",
      "[1] \"Computing Positive Niche-DE Pvalues\"\n",
      "[1] \"Computing Gene Level Pvalues\"\n",
      "[1] \"Combining Gene Level Pvalues Across Kernel Bandwidths\"\n",
      "[1] \"Computing Cell Type Level Pvalues\"\n",
      "[1] \"Combining Cell Type  Level Pvalues Across Kernel Bandwidths\"\n",
      "[1] \"Computing and Combining interaction Level Pvalues Across Kernel bandwidths\"\n",
      "[1] \"Computing Negative Niche-DE Pvalues\"\n",
      "[1] \"Computing Gene Level Pvalues\"\n",
      "[1] \"Combining Gene Level Pvalues Across Kernel Bandwidths\"\n",
      "[1] \"Computing Cell Type Level Pvalues\"\n",
      "[1] \"Combining Cell Type  Level Pvalues Across Kernel Bandwidths\"\n",
      "[1] \"Computing and Combining interaction Level Pvalues Across Kernel bandwidths\"\n",
      "[1] \"Niche-DE analysis complete. Number of Genes with niche-DE T-stat equal to 227\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in niche_DE(NDE_obj, num_cores = 36, outfile = \"\"):\n",
      "“Less than 1000 genes pass. This could be due to insufficient read depth of data or size of C parameter. Consider changing choice of C parameter”\n"
     ]
    }
   ],
   "source": [
    "NDE_obj <- niche_DE(NDE_obj, num_cores = 36, outfile = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "26800f0e",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "saveRDS(\n",
    "    object = NDE_obj,\n",
    "    file = file.path(\n",
    "        RESULT_PATH, seg_method,\n",
    "        paste0(\n",
    "            \"NDE_obj\",\n",
    "            \".rds\"\n",
    "        )\n",
    "    )\n",
    ")\n",
    "# NDE_obj <- readRDS(\n",
    "#     file = file.path(\n",
    "#         RESULT_PATH, seg_method,\n",
    "#         paste0(\n",
    "#             \"NDE_obj\",\n",
    "#             \".rds\"\n",
    "#         )\n",
    "#     )\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e349bf2b",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Returning Niche-DE Genes\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 17 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Genes</th><th scope=col>Pvalues.Interaction</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>6</th><td>KLF5    </td><td>0.000000e+00</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>CDC42EP1</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>SERHL2  </td><td>0.000000e+00</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ACTA2   </td><td>1.421085e-14</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>KRT7    </td><td>1.746159e-12</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>FSTL3   </td><td>7.120960e-08</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>TACSTD2 </td><td>3.502333e-07</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>BACE2   </td><td>1.428245e-03</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>C6orf132</td><td>4.869276e-03</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>MYO5B   </td><td>5.190509e-03</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>MZB1    </td><td>9.895541e-03</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>USP53   </td><td>1.268764e-02</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>TPD52   </td><td>1.391316e-02</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>SEC11C  </td><td>3.428432e-02</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SVIL    </td><td>3.810915e-02</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>OCIAD2  </td><td>4.262528e-02</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>KRT8    </td><td>4.418016e-02</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 17 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Genes & Pvalues.Interaction\\\\\n",
       "  & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t6 & KLF5     & 0.000000e+00\\\\\n",
       "\t7 & CDC42EP1 & 0.000000e+00\\\\\n",
       "\t10 & SERHL2   & 0.000000e+00\\\\\n",
       "\t2 & ACTA2    & 1.421085e-14\\\\\n",
       "\t8 & KRT7     & 1.746159e-12\\\\\n",
       "\t11 & FSTL3    & 7.120960e-08\\\\\n",
       "\t16 & TACSTD2  & 3.502333e-07\\\\\n",
       "\t17 & BACE2    & 1.428245e-03\\\\\n",
       "\t12 & C6orf132 & 4.869276e-03\\\\\n",
       "\t9 & MYO5B    & 5.190509e-03\\\\\n",
       "\t3 & MZB1     & 9.895541e-03\\\\\n",
       "\t1 & USP53    & 1.268764e-02\\\\\n",
       "\t14 & TPD52    & 1.391316e-02\\\\\n",
       "\t4 & SEC11C   & 3.428432e-02\\\\\n",
       "\t15 & SVIL     & 3.810915e-02\\\\\n",
       "\t5 & OCIAD2   & 4.262528e-02\\\\\n",
       "\t13 & KRT8     & 4.418016e-02\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 17 × 2\n",
       "\n",
       "| <!--/--> | Genes &lt;chr&gt; | Pvalues.Interaction &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 6 | KLF5     | 0.000000e+00 |\n",
       "| 7 | CDC42EP1 | 0.000000e+00 |\n",
       "| 10 | SERHL2   | 0.000000e+00 |\n",
       "| 2 | ACTA2    | 1.421085e-14 |\n",
       "| 8 | KRT7     | 1.746159e-12 |\n",
       "| 11 | FSTL3    | 7.120960e-08 |\n",
       "| 16 | TACSTD2  | 3.502333e-07 |\n",
       "| 17 | BACE2    | 1.428245e-03 |\n",
       "| 12 | C6orf132 | 4.869276e-03 |\n",
       "| 9 | MYO5B    | 5.190509e-03 |\n",
       "| 3 | MZB1     | 9.895541e-03 |\n",
       "| 1 | USP53    | 1.268764e-02 |\n",
       "| 14 | TPD52    | 1.391316e-02 |\n",
       "| 4 | SEC11C   | 3.428432e-02 |\n",
       "| 15 | SVIL     | 3.810915e-02 |\n",
       "| 5 | OCIAD2   | 4.262528e-02 |\n",
       "| 13 | KRT8     | 4.418016e-02 |\n",
       "\n"
      ],
      "text/plain": [
       "   Genes    Pvalues.Interaction\n",
       "6  KLF5     0.000000e+00       \n",
       "7  CDC42EP1 0.000000e+00       \n",
       "10 SERHL2   0.000000e+00       \n",
       "2  ACTA2    1.421085e-14       \n",
       "8  KRT7     1.746159e-12       \n",
       "11 FSTL3    7.120960e-08       \n",
       "16 TACSTD2  3.502333e-07       \n",
       "17 BACE2    1.428245e-03       \n",
       "12 C6orf132 4.869276e-03       \n",
       "9  MYO5B    5.190509e-03       \n",
       "3  MZB1     9.895541e-03       \n",
       "1  USP53    1.268764e-02       \n",
       "14 TPD52    1.391316e-02       \n",
       "4  SEC11C   3.428432e-02       \n",
       "15 SVIL     3.810915e-02       \n",
       "5  OCIAD2   4.262528e-02       \n",
       "13 KRT8     4.418016e-02       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Returning Niche-DE Genes\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Genes</th><th scope=col>Pvalues.Interaction</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>4</th><td>SERPINA3</td><td>0.000000000</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>RHOH    </td><td>0.001215106</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>FLNB    </td><td>0.015552298</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>MLPH    </td><td>0.016036460</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ANKRD30A</td><td>0.024360175</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Genes & Pvalues.Interaction\\\\\n",
       "  & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t4 & SERPINA3 & 0.000000000\\\\\n",
       "\t2 & RHOH     & 0.001215106\\\\\n",
       "\t3 & FLNB     & 0.015552298\\\\\n",
       "\t1 & MLPH     & 0.016036460\\\\\n",
       "\t5 & ANKRD30A & 0.024360175\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 2\n",
       "\n",
       "| <!--/--> | Genes &lt;chr&gt; | Pvalues.Interaction &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 4 | SERPINA3 | 0.000000000 |\n",
       "| 2 | RHOH     | 0.001215106 |\n",
       "| 3 | FLNB     | 0.015552298 |\n",
       "| 1 | MLPH     | 0.016036460 |\n",
       "| 5 | ANKRD30A | 0.024360175 |\n",
       "\n"
      ],
      "text/plain": [
       "  Genes    Pvalues.Interaction\n",
       "4 SERPINA3 0.000000000        \n",
       "2 RHOH     0.001215106        \n",
       "3 FLNB     0.015552298        \n",
       "1 MLPH     0.016036460        \n",
       "5 ANKRD30A 0.024360175        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target cell type: DCIS 1\n",
    "# Niche cell type: Invasive Tumor\n",
    "get_niche_DE_genes(\n",
    "    NDE_obj, test.level = \"I\", \n",
    "    index = \"DCIS 1\", niche = \"Invasive Tumor\", \n",
    "    pos = TRUE, alpha = 0.05\n",
    ")\n",
    "get_niche_DE_genes(\n",
    "    NDE_obj, test.level = \"I\",\n",
    "    index = \"DCIS 1\", niche = \"Invasive Tumor\",\n",
    "    pos = FALSE, alpha = 0.05\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90dfd92a",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Returning Niche-DE Genes\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 16 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Genes</th><th scope=col>Pvalues.Interaction</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>8</th><td>SERHL2  </td><td>0.000000e+00</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>ERN1    </td><td>0.000000e+00</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>AGR3    </td><td>1.184238e-15</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>SCD     </td><td>2.482522e-08</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>LUM     </td><td>5.016727e-07</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>S100A14 </td><td>1.677298e-06</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>TENT5C  </td><td>3.242025e-05</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>SERPINA3</td><td>6.913058e-05</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>TOMM7   </td><td>7.185086e-04</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ESR1    </td><td>7.284165e-04</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>WARS    </td><td>5.464156e-03</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>CEACAM6 </td><td>5.550267e-03</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>FBLN1   </td><td>7.937503e-03</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>CAVIN2  </td><td>1.007103e-02</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>KRT8    </td><td>2.483159e-02</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>FASN    </td><td>3.017706e-02</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 16 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Genes & Pvalues.Interaction\\\\\n",
       "  & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t8 & SERHL2   & 0.000000e+00\\\\\n",
       "\t10 & ERN1     & 0.000000e+00\\\\\n",
       "\t4 & AGR3     & 1.184238e-15\\\\\n",
       "\t2 & SCD      & 2.482522e-08\\\\\n",
       "\t12 & LUM      & 5.016727e-07\\\\\n",
       "\t3 & S100A14  & 1.677298e-06\\\\\n",
       "\t13 & TENT5C   & 3.242025e-05\\\\\n",
       "\t9 & SERPINA3 & 6.913058e-05\\\\\n",
       "\t15 & TOMM7    & 7.185086e-04\\\\\n",
       "\t5 & ESR1     & 7.284165e-04\\\\\n",
       "\t6 & WARS     & 5.464156e-03\\\\\n",
       "\t11 & CEACAM6  & 5.550267e-03\\\\\n",
       "\t16 & FBLN1    & 7.937503e-03\\\\\n",
       "\t1 & CAVIN2   & 1.007103e-02\\\\\n",
       "\t14 & KRT8     & 2.483159e-02\\\\\n",
       "\t7 & FASN     & 3.017706e-02\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 16 × 2\n",
       "\n",
       "| <!--/--> | Genes &lt;chr&gt; | Pvalues.Interaction &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 8 | SERHL2   | 0.000000e+00 |\n",
       "| 10 | ERN1     | 0.000000e+00 |\n",
       "| 4 | AGR3     | 1.184238e-15 |\n",
       "| 2 | SCD      | 2.482522e-08 |\n",
       "| 12 | LUM      | 5.016727e-07 |\n",
       "| 3 | S100A14  | 1.677298e-06 |\n",
       "| 13 | TENT5C   | 3.242025e-05 |\n",
       "| 9 | SERPINA3 | 6.913058e-05 |\n",
       "| 15 | TOMM7    | 7.185086e-04 |\n",
       "| 5 | ESR1     | 7.284165e-04 |\n",
       "| 6 | WARS     | 5.464156e-03 |\n",
       "| 11 | CEACAM6  | 5.550267e-03 |\n",
       "| 16 | FBLN1    | 7.937503e-03 |\n",
       "| 1 | CAVIN2   | 1.007103e-02 |\n",
       "| 14 | KRT8     | 2.483159e-02 |\n",
       "| 7 | FASN     | 3.017706e-02 |\n",
       "\n"
      ],
      "text/plain": [
       "   Genes    Pvalues.Interaction\n",
       "8  SERHL2   0.000000e+00       \n",
       "10 ERN1     0.000000e+00       \n",
       "4  AGR3     1.184238e-15       \n",
       "2  SCD      2.482522e-08       \n",
       "12 LUM      5.016727e-07       \n",
       "3  S100A14  1.677298e-06       \n",
       "13 TENT5C   3.242025e-05       \n",
       "9  SERPINA3 6.913058e-05       \n",
       "15 TOMM7    7.185086e-04       \n",
       "5  ESR1     7.284165e-04       \n",
       "6  WARS     5.464156e-03       \n",
       "11 CEACAM6  5.550267e-03       \n",
       "16 FBLN1    7.937503e-03       \n",
       "1  CAVIN2   1.007103e-02       \n",
       "14 KRT8     2.483159e-02       \n",
       "7  FASN     3.017706e-02       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Returning Niche-DE Genes\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Genes</th><th scope=col>Pvalues.Interaction</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>TCIM</td><td>1.897227e-09</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>KRT7</td><td>1.374679e-02</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Genes & Pvalues.Interaction\\\\\n",
       "  & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & TCIM & 1.897227e-09\\\\\n",
       "\t2 & KRT7 & 1.374679e-02\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 2\n",
       "\n",
       "| <!--/--> | Genes &lt;chr&gt; | Pvalues.Interaction &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 1 | TCIM | 1.897227e-09 |\n",
       "| 2 | KRT7 | 1.374679e-02 |\n",
       "\n"
      ],
      "text/plain": [
       "  Genes Pvalues.Interaction\n",
       "1 TCIM  1.897227e-09       \n",
       "2 KRT7  1.374679e-02       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target cell type: DCIS 2\n",
    "# Niche cell type: Invasive Tumor\n",
    "get_niche_DE_genes(\n",
    "    NDE_obj, test.level = \"I\", \n",
    "    index = \"DCIS 2\", niche = \"Invasive Tumor\", \n",
    "    pos = TRUE, alpha = 0.05\n",
    ")\n",
    "get_niche_DE_genes(\n",
    "    NDE_obj, test.level = \"I\",\n",
    "    index = \"DCIS 2\", niche = \"Invasive Tumor\",\n",
    "    pos = FALSE, alpha = 0.05\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a84bcac0",
   "metadata": {},
   "source": [
    "## Comparing niche-associated genes with cell-type-specific SVGs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "644b903b",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mcubeGetSigGenes: Set adjust_method as BH and alpha as 0.05.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mcube_object <- readRDS(\n",
    "    file = file.path(\n",
    "        RESULT_PATH, seg_method,\n",
    "        paste0(\n",
    "            \"mcube_object\",\n",
    "            \".rds\"\n",
    "        )\n",
    "    )\n",
    ")\n",
    "sig_genes_list <- mcubeGetSigGenes(mcube_object@pvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ef8c893b",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Returning Niche-DE Genes\"\n",
      "[1] \"Returning Niche-DE Genes\"\n"
     ]
    }
   ],
   "source": [
    "# According to nicheDE tutorial, the niche cell type is unknown when the resoluation is \"CT\" (cell type).\n",
    "niche_genes_T <- get_niche_DE_genes(\n",
    "    NDE_obj, test.level = \"CT\",\n",
    "    index = \"DCIS 2\", niche = \"Invasive Tumor\", \n",
    "    pos = TRUE, alpha = 0.05\n",
    ")$Genes\n",
    "niche_genes_F <- get_niche_DE_genes(\n",
    "    NDE_obj, test.level = \"CT\",\n",
    "    index = \"DCIS 2\", niche = \"Invasive Tumor\", \n",
    "    pos = FALSE, alpha = 0.05\n",
    ")$Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8eb78ba",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘ggVennDiagram’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:spacexr’:\n",
      "\n",
      "    process_data\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggVennDiagram)\n",
    "celltype <- \"DCIS 2\"\n",
    "svg_list <- list(niche_genes_T, niche_genes_F, rownames(sig_genes_list[[celltype]]))\n",
    "name_list <- c(\n",
    "    \"Niche-DE positive genes\", \"Niche-DE-negative genes\",\n",
    "    paste0(\"SVGs specific to \", celltype)\n",
    ")\n",
    "p <- ggVennDiagram(svg_list, name_list, set_size = 8, label_size = 7, label_alpha = 0) +\n",
    "    scale_fill_gradient(low = \"white\", high = \"dodgerblue3\") +\n",
    "    # scale_fill_distiller(palette = \"RdBu\") +\n",
    "    theme(\n",
    "        text = element_text(size = 32),\n",
    "        title = element_text(size = 24),\n",
    "        legend.position = \"none\"\n",
    "    )\n",
    "ggsave(\n",
    "    filename = file.path(RESULT_PATH, \"niche_DE_genes_venn.pdf\"),\n",
    "    plot = p, width = 10, height = 6\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1da5f832",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "4.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}