{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8a787e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressMessages(library(rmarkdown))\n",
    "suppressMessages(library(dplyr))\n",
    "suppressMessages(library(Seurat))\n",
    "suppressMessages(library(patchwork))\n",
    "suppressMessages(library(grDevices))\n",
    "suppressMessages(library(ggplot2))\n",
    "suppressMessages(library(viridis))\n",
    "suppressMessages(library(DT))\n",
    "suppressMessages(library(ggrepel))\n",
    "suppressMessages(library(DESeq2))\n",
    "suppressMessages(library(gridExtra))\n",
    "suppressMessages(library(stringr))\n",
    "suppressMessages(library(ggblur))\n",
    "suppressMessages(library(patchwork))\n",
    "suppressMessages(library(enrichR))\n",
    "#source(\"/home/rlancione/scripts/utils.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "edd63667",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA <- \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/\"\n",
    "OUTPATH <- \"/projects/ps-epigen/users/kdang/ASAP/progressReport/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30df077c",
   "metadata": {},
   "source": [
    "# mirror barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "46251c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'Astrocytes'</li><li>'GABAergic'</li><li>'Glutamatergic'</li><li>'Macrophages'</li><li>'Meis2'</li><li>'Non-neuronal'</li><li>'Oligo'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Astrocytes'\n",
       "\\item 'GABAergic'\n",
       "\\item 'Glutamatergic'\n",
       "\\item 'Macrophages'\n",
       "\\item 'Meis2'\n",
       "\\item 'Non-neuronal'\n",
       "\\item 'Oligo'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Astrocytes'\n",
       "2. 'GABAergic'\n",
       "3. 'Glutamatergic'\n",
       "4. 'Macrophages'\n",
       "5. 'Meis2'\n",
       "6. 'Non-neuronal'\n",
       "7. 'Oligo'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Astrocytes\"    \"GABAergic\"     \"Glutamatergic\" \"Macrophages\"  \n",
       "[5] \"Meis2\"         \"Non-neuronal\"  \"Oligo\"        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "broad_cts <- list.files(paste0(DATA, \"broad_ct/\"), pattern = \".txt\")\n",
    "broad_cts <- broad_cts[-2]\n",
    "broad_cts <- gsub(\".txt\",\"\",broad_cts)\n",
    "broad_cts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "9d4b7587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>celltype</th><th scope=col>regulation</th><th scope=col>counts</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>GABAergic.6</td><td>UP      </td><td>310</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>GABAergic.6</td><td>DOWN    </td><td>268</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>GABAergic.6</td><td>COMBINED</td><td>578</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Meis2      </td><td>UP      </td><td>175</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Meis2      </td><td>DOWN    </td><td> 32</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Meis2      </td><td>COMBINED</td><td>207</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & celltype & regulation & counts\\\\\n",
       "  & <chr> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & GABAergic.6 & UP       & 310\\\\\n",
       "\t2 & GABAergic.6 & DOWN     & 268\\\\\n",
       "\t3 & GABAergic.6 & COMBINED & 578\\\\\n",
       "\t4 & Meis2       & UP       & 175\\\\\n",
       "\t5 & Meis2       & DOWN     &  32\\\\\n",
       "\t6 & Meis2       & COMBINED & 207\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | celltype &lt;chr&gt; | regulation &lt;chr&gt; | counts &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | GABAergic.6 | UP       | 310 |\n",
       "| 2 | GABAergic.6 | DOWN     | 268 |\n",
       "| 3 | GABAergic.6 | COMBINED | 578 |\n",
       "| 4 | Meis2       | UP       | 175 |\n",
       "| 5 | Meis2       | DOWN     |  32 |\n",
       "| 6 | Meis2       | COMBINED | 207 |\n",
       "\n"
      ],
      "text/plain": [
       "  celltype    regulation counts\n",
       "1 GABAergic.6 UP         310   \n",
       "2 GABAergic.6 DOWN       268   \n",
       "3 GABAergic.6 COMBINED   578   \n",
       "4 Meis2       UP         175   \n",
       "5 Meis2       DOWN        32   \n",
       "6 Meis2       COMBINED   207   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'Astrocytes'</li><li>'GABAergic.1'</li><li>'GABAergic.10'</li><li>'GABAergic.11'</li><li>'GABAergic.12'</li><li>'GABAergic.13'</li><li>'GABAergic.14'</li><li>'GABAergic.15'</li><li>'GABAergic.2'</li><li>'GABAergic.3'</li><li>'GABAergic.4'</li><li>'GABAergic.5'</li><li>'GABAergic.6'</li><li>'GABAergic.7'</li><li>'GABAergic.8'</li><li>'GABAergic.9'</li><li>'Glutamatergic.1'</li><li>'Glutamatergic.2'</li><li>'Glutamatergic.3'</li><li>'Glutamatergic.4'</li><li>'Glutamatergic.5'</li><li>'Macrophages'</li><li>'Meis2'</li><li>'Non-neuronal'</li><li>'Oligo'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Astrocytes'\n",
       "\\item 'GABAergic.1'\n",
       "\\item 'GABAergic.10'\n",
       "\\item 'GABAergic.11'\n",
       "\\item 'GABAergic.12'\n",
       "\\item 'GABAergic.13'\n",
       "\\item 'GABAergic.14'\n",
       "\\item 'GABAergic.15'\n",
       "\\item 'GABAergic.2'\n",
       "\\item 'GABAergic.3'\n",
       "\\item 'GABAergic.4'\n",
       "\\item 'GABAergic.5'\n",
       "\\item 'GABAergic.6'\n",
       "\\item 'GABAergic.7'\n",
       "\\item 'GABAergic.8'\n",
       "\\item 'GABAergic.9'\n",
       "\\item 'Glutamatergic.1'\n",
       "\\item 'Glutamatergic.2'\n",
       "\\item 'Glutamatergic.3'\n",
       "\\item 'Glutamatergic.4'\n",
       "\\item 'Glutamatergic.5'\n",
       "\\item 'Macrophages'\n",
       "\\item 'Meis2'\n",
       "\\item 'Non-neuronal'\n",
       "\\item 'Oligo'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Astrocytes'\n",
       "2. 'GABAergic.1'\n",
       "3. 'GABAergic.10'\n",
       "4. 'GABAergic.11'\n",
       "5. 'GABAergic.12'\n",
       "6. 'GABAergic.13'\n",
       "7. 'GABAergic.14'\n",
       "8. 'GABAergic.15'\n",
       "9. 'GABAergic.2'\n",
       "10. 'GABAergic.3'\n",
       "11. 'GABAergic.4'\n",
       "12. 'GABAergic.5'\n",
       "13. 'GABAergic.6'\n",
       "14. 'GABAergic.7'\n",
       "15. 'GABAergic.8'\n",
       "16. 'GABAergic.9'\n",
       "17. 'Glutamatergic.1'\n",
       "18. 'Glutamatergic.2'\n",
       "19. 'Glutamatergic.3'\n",
       "20. 'Glutamatergic.4'\n",
       "21. 'Glutamatergic.5'\n",
       "22. 'Macrophages'\n",
       "23. 'Meis2'\n",
       "24. 'Non-neuronal'\n",
       "25. 'Oligo'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Astrocytes\"      \"GABAergic.1\"     \"GABAergic.10\"    \"GABAergic.11\"   \n",
       " [5] \"GABAergic.12\"    \"GABAergic.13\"    \"GABAergic.14\"    \"GABAergic.15\"   \n",
       " [9] \"GABAergic.2\"     \"GABAergic.3\"     \"GABAergic.4\"     \"GABAergic.5\"    \n",
       "[13] \"GABAergic.6\"     \"GABAergic.7\"     \"GABAergic.8\"     \"GABAergic.9\"    \n",
       "[17] \"Glutamatergic.1\" \"Glutamatergic.2\" \"Glutamatergic.3\" \"Glutamatergic.4\"\n",
       "[21] \"Glutamatergic.5\" \"Macrophages\"     \"Meis2\"           \"Non-neuronal\"   \n",
       "[25] \"Oligo\"          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DEG_file <- read.table(paste0(DATA,\"sub_ct/DEG_countSummary.txt\"))\n",
    "colnames(DEG_file) <- c(\"celltype\",\"regulation\",\"counts\")\n",
    "head(DEG_file)\n",
    "sort(unique(DEG_file$celltype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c1a28378",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Astrocytes\"\n",
      "     celltype regulation counts\n",
      "56 Astrocytes       DOWN     48\n",
      "[1] -48\n",
      "     celltype regulation counts\n",
      "55 Astrocytes         UP    481\n",
      "[1] 481\n",
      "   celltypes up_deg down_deg\n",
      "1 Astrocytes    481      -48\n",
      "   celltypes up_deg down_deg\n",
      "1 Astrocytes    481      -48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_text(data = up_down_df, aes(x = celltypes, y = down_deg, :\n",
      "“\u001b[1m\u001b[22mIgnoring unknown aesthetics: \u001b[32mfill\u001b[39m”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"GABAergic\"\n",
      "       celltype regulation counts\n",
      "2   GABAergic.6       DOWN    268\n",
      "11  GABAergic.2       DOWN    144\n",
      "23  GABAergic.4       DOWN    297\n",
      "29  GABAergic.1       DOWN    181\n",
      "32  GABAergic.3       DOWN    185\n",
      "35  GABAergic.8       DOWN     43\n",
      "41  GABAergic.7       DOWN     54\n",
      "44  GABAergic.5       DOWN     94\n",
      "50 GABAergic.10       DOWN     78\n",
      "53  GABAergic.9       DOWN     59\n",
      "59 GABAergic.11       DOWN    587\n",
      "62 GABAergic.14       DOWN    831\n",
      "68 GABAergic.12       DOWN    145\n",
      "71 GABAergic.15       DOWN    359\n",
      "74 GABAergic.13       DOWN    188\n",
      " [1] -268 -144 -297 -181 -185  -43  -54  -94  -78  -59 -587 -831 -145 -359 -188\n",
      "       celltype regulation counts\n",
      "1   GABAergic.6         UP    310\n",
      "10  GABAergic.2         UP    273\n",
      "22  GABAergic.4         UP    348\n",
      "28  GABAergic.1         UP    310\n",
      "31  GABAergic.3         UP    262\n",
      "34  GABAergic.8         UP    236\n",
      "40  GABAergic.7         UP    259\n",
      "43  GABAergic.5         UP    247\n",
      "49 GABAergic.10         UP    326\n",
      "52  GABAergic.9         UP    228\n",
      "58 GABAergic.11         UP    544\n",
      "61 GABAergic.14         UP    799\n",
      "67 GABAergic.12         UP    540\n",
      "70 GABAergic.15         UP    767\n",
      "73 GABAergic.13         UP    469\n",
      " [1] 310 273 348 310 262 236 259 247 326 228 544 799 540 767 469\n",
      "      celltypes up_deg down_deg\n",
      "1   GABAergic.6    310     -268\n",
      "2   GABAergic.2    273     -144\n",
      "3   GABAergic.4    348     -297\n",
      "4   GABAergic.1    310     -181\n",
      "5   GABAergic.3    262     -185\n",
      "6   GABAergic.8    236      -43\n",
      "7   GABAergic.7    259      -54\n",
      "8   GABAergic.5    247      -94\n",
      "9  GABAergic.10    326      -78\n",
      "10  GABAergic.9    228      -59\n",
      "11 GABAergic.11    544     -587\n",
      "12 GABAergic.14    799     -831\n",
      "13 GABAergic.12    540     -145\n",
      "14 GABAergic.15    767     -359\n",
      "15 GABAergic.13    469     -188\n",
      "      celltypes up_deg down_deg\n",
      "1   GABAergic.6    310     -268\n",
      "2   GABAergic.2    273     -144\n",
      "3   GABAergic.4    348     -297\n",
      "4   GABAergic.1    310     -181\n",
      "5   GABAergic.3    262     -185\n",
      "6   GABAergic.8    236      -43\n",
      "7   GABAergic.7    259      -54\n",
      "8   GABAergic.5    247      -94\n",
      "9  GABAergic.10    326      -78\n",
      "10  GABAergic.9    228      -59\n",
      "11 GABAergic.11    544     -587\n",
      "12 GABAergic.14    799     -831\n",
      "13 GABAergic.12    540     -145\n",
      "14 GABAergic.15    767     -359\n",
      "15 GABAergic.13    469     -188\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_text(data = up_down_df, aes(x = celltypes, y = down_deg, :\n",
      "“\u001b[1m\u001b[22mIgnoring unknown aesthetics: \u001b[32mfill\u001b[39m”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Glutamatergic\"\n",
      "          celltype regulation counts\n",
      "8  Glutamatergic.1       DOWN    370\n",
      "26 Glutamatergic.2       DOWN    392\n",
      "38 Glutamatergic.3       DOWN    386\n",
      "47 Glutamatergic.4       DOWN    743\n",
      "65 Glutamatergic.5       DOWN    448\n",
      "[1] -370 -392 -386 -743 -448\n",
      "          celltype regulation counts\n",
      "7  Glutamatergic.1         UP    354\n",
      "25 Glutamatergic.2         UP    351\n",
      "37 Glutamatergic.3         UP    264\n",
      "46 Glutamatergic.4         UP    682\n",
      "64 Glutamatergic.5         UP    550\n",
      "[1] 354 351 264 682 550\n",
      "        celltypes up_deg down_deg\n",
      "1 Glutamatergic.1    354     -370\n",
      "2 Glutamatergic.2    351     -392\n",
      "3 Glutamatergic.3    264     -386\n",
      "4 Glutamatergic.4    682     -743\n",
      "5 Glutamatergic.5    550     -448\n",
      "        celltypes up_deg down_deg\n",
      "1 Glutamatergic.1    354     -370\n",
      "2 Glutamatergic.2    351     -392\n",
      "3 Glutamatergic.3    264     -386\n",
      "4 Glutamatergic.4    682     -743\n",
      "5 Glutamatergic.5    550     -448\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_text(data = up_down_df, aes(x = celltypes, y = down_deg, :\n",
      "“\u001b[1m\u001b[22mIgnoring unknown aesthetics: \u001b[32mfill\u001b[39m”\n"
     ]
    },
    {
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JrZs3HDrtPW7TUZUfiYqpWPWPXzzyu3JplVBwEAANJEIAQAACCHbProhvJXvJ3wNtBj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\n3BU6t8dHA5qUTDoVu2JMhwui7n3/9637vUbMnJ4tO4xL+Gh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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Macrophages\"\n",
      "      celltype regulation counts\n",
      "20 Macrophages       DOWN     17\n",
      "[1] -17\n",
      "      celltype regulation counts\n",
      "19 Macrophages         UP    249\n",
      "[1] 249\n",
      "    celltypes up_deg down_deg\n",
      "1 Macrophages    249      -17\n",
      "  celltypes up_deg down_deg\n",
      "1 Microglia    249      -17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_text(data = up_down_df, aes(x = celltypes, y = down_deg, :\n",
      "“\u001b[1m\u001b[22mIgnoring unknown aesthetics: \u001b[32mfill\u001b[39m”\n"
     ]
    },
    {
     "data": {
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45k0zqqiqpfV7zRahPiP0wf3HL6MTUtNzy6a3LgEAACAASURBVGtbkBFCYmJi4tJK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\na72VlJSwAUIcjpIUpQQC5sMMR1lvAAAAAIBhgu5ZiHKCn6b1r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5z1sP+SXYwYF11R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\n5CalSxf2b28S19BQIoi0mnZVd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ZjxMi5N8Od+rTalxp1PZln2zMjcuyeW/1mP3zcft3TiPx8+YKpB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Jl+c28OZ/cW5lO8zYdidOJvXQIBsyIg4B0YeM6ENG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\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MFsZJ4ZWYCz/8rbeHlEZvzFGcEGHAmz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\nrU8tOQkz9dyiPk/6G3Y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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Meis2\"\n",
      "  celltype regulation counts\n",
      "5    Meis2       DOWN     32\n",
      "[1] -32\n",
      "  celltype regulation counts\n",
      "4    Meis2         UP    175\n",
      "[1] 175\n",
      "  celltypes up_deg down_deg\n",
      "1     Meis2    175      -32\n",
      "  celltypes up_deg down_deg\n",
      "1     Meis2    175      -32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_text(data = up_down_df, aes(x = celltypes, y = down_deg, :\n",
      "“\u001b[1m\u001b[22mIgnoring unknown aesthetics: \u001b[32mfill\u001b[39m”\n"
     ]
    },
    {
     "data": {
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1gXobD2f52E/THtr+eiAi3HkcObqDQ+AAAAABApzgGhIAhv/L7/at6VVIWfJ948dubp\ne4UfAwAAQCFVj+zKg7R/NmwVbcen59H3cxX7yHy6c2D7EUejxWVWXVef3NS3SjH/ggtAkZPT\nu3+//Y7q6/kEQRCkr0+N9xh1THyTbuPvJ7dT/KCo1rO3g/jTzJsL+005r3yH5cSg6V/OuiJe\nP6hVt3dP+5xuMf7sx0kNRYsIk05N9fzjnuLft4KQHPyz50z/FFGJSc/vR3EAIQAAAAD16RT2\nAPLg1cnx7Xr8cTM5pzoxp8b1/LFR4MKmLCIEAABQi8pHduXeuz1rdrz+eGnRd8wXqm0NGn1s\nVLsBOyLEi3rKuP12YtfwWrqaHSGAIiT1yYmfFxzduPSrMWOHefZqXaVUztWl8Xd3/zR2wq9n\nIsWpncRu/PIJtXKaSGA/fv5Xy7ps/jj/IO3mL51bx/65Y+mAWgq6TA7Z8U3fEauvy/w4tfpq\nwYQ6OQ9RUv/7lWM3uSx/+N/T7N3pCe16ZuzdNLGZeZYljpnRAb8M6DklQJxVGrn8tKC3Sc6d\nAACAYqtOm8IeAYBPWnGdYC2NPj62bXcl6aAgCIKQdnPJoB8DUpTWAwAAwEfqHNmVSwknp0w7\n+HF/Ua0G477tqMqkrlj/7z16rQtJFxUZNZ9z7MCkhgaaHSCAIuh9dNCm2YNcqlnbuvQZOe3n\nv/advXbv4dOomMS09xkp8W+jnoXdOL13w69TB7lUs677xWLZdFAwd/tlz7wWejl3YdR5we89\ny8kUJQSvG1ivimO/aSt2+t14/PJtQlpGWmLMy/Cbp3eumv5Vkyp1+66+Lru00ar77z91V74v\ns0HLRdumNxANSPri6DfOtVsMnr3h+PVHke9S0pJjIh9ePbZ+5oBmdVpPOSmzbNri89V/f22n\nLd8mAAAAAKhAIpVKlddSXdzFNUuOPJUrNHf5elKHHI5eUJf05dFRbj3XZr/1iq6ubnp6umyZ\nlt13ATeXNOeV0adm7ty5s2bNqlq16uPHjwt7LAAAfFKkLzZ2qe0p3pTPYvjxqLUdNDi57N2p\n0Y4dVj/+8FXUxutE6F/tSyu7Len6Ao+20/1jRUUGjb49dvpnVzOF9wD4NBweYNBta04bcOZM\nYu668MThKY5KnzOCIAgp1xe1bzPNX/nOotkydl509uQUByVLHP+T+XS3p0vfzeHqHY5h7jr/\n4P7pzqa5GB7wSZt94UJhDwElxWxn58IeAgAAeaLhLUafbZw69qdzsr9rdJ1/HTJfc11IXxwa\n7tb7rwdp2X1o2GD8vr29TnZr9+s9cUaYGbp04h8jA7+roblhAAAAFAcnp3c/WX/hj33rqrEF\nnfpHdgmCIAj3Vg1bU+a7n/rVUuHl+5vTU9p/JkoHBesBqxYrTwfTQ1b37iibDuraj9xzgnQQ\nQM4M7frMXb9ikouVqsvtDBym/nOxwneDxv957Z16XZk5jli+6deBdVVMBwVB0KrU2yfgsE3P\n/kuCYlS7Q7/GV2uPbhhUU8lSSAAAAABQTLNbjN5eveqc/KzHqqOWjNVYMJf5/ICXq6J0sLTT\n90dOL/Oo4bJ476JWcttTpQct+GGXij+2AAAAPhmpT0783K+hXfPBczaff5KktLo0/u6uqW4N\nOq24Ld6gXfmRXYIgCO+j/Zf1b1jP47sNFyKz/bL2r6TQfbO6Nem85HrihyJtuzGbVnxmoaT9\nzGc7BrUfe0y8v552tQF/n/qzs6WSOwF8Gjr9Eezr89OY7k4VS2U5oE8RfZsmfaeuOX3v5s5v\nVU8H/1W67sBVgbdOLx/Xxd5MlR/OWqa1Oo1b5nsrcM3AukZq9SQIWuU7Lva/e+43zyZWOU/i\nlZg1+mrx8Qe3t5AOAgAAAMgbTW4xmnF6bAW3VTJnIghGPTY/2j9AMy9tMp/u9Wzbd/PD9Ow+\nNGkx48ixua3+v79K5sPf29T/xl/mhEKtxotCL0+prpGhoGhgi1EAAJQR78gnMa7RysO9ZdOm\nTZs62Fe0LFu2bBkT/cykuNjYt5GhN69du3TmwPY9WWNEc7dfTx/+pr7S0wFvz7avP+eBIAiC\noF+hWY9ePTp3atesdpXy1mX0UmKio6OfPQg8deTw/n3HrkfJ5IeWnVZfODjSVunGFuG/NK72\n3VVV//CctPkj8szXGtz/HkBBS495eDXgnH/g9dv3Q0JDH0ZExcTFxyemCgbGpqampqZmlpXt\nGzo1buzUpKVrK/uyeT+kLyniwvHj5wIvXbp8/X7Eq3exse9iEzJ0SpuYmZmZWVSyb9SkabPm\nrTt0bFlV9WWDiqS+CDq4a/8xv4vX7zx4EhUTnyKUKmNpZVWuvJ2jq0fnzl3aNa1kpHI8CpRE\nbDGKAsMWowCA4k6DW4xKz+/eGy1XZjPwm76aSQffP9k1qG3/vx9nZPehmcuC40emNf04S1Or\nxrilE1c3WRgiSj8zr6xcceHb35w5wh0AAJRM0viH53c/PL97req3SMxdF544qEI6KCv1edDO\n5UE7l/+grKJ25Z6rT/w9THk6CAAiumVqNO9ao3nXIQXUX6nKzp8Pd/58eAF0pV++WZ8JzfpM\nKICuAAAAAJRoGtxi9NLBwy/liuqNHOOiidc9GeHbv3LtpyAdNG/3m98xcTooCIIg6Dh9P6O7\nXNnTrZtOZ2pgOAAAACWAoV2fn8/cPTXFUYUzBXNDv+pnS85e2z2sln7+tA8AAAAAQF6lR18/\n/NeSqcN6ujrWrlHJuqyRvq6+UdlylarbO7h8NvS7xesOXH2ZwzEbQNGluYAw+PDhCNkS7daj\nR9TLe8MZj7b2azNgR7j84YaCIAiScl1Wnjk8ySG7jVzMvpwwSG7rqOh9u7OckQgAAPAJK8Aj\nu+pOPvTPqm96NLTQVdpB+RaDFx27f2/fdy3N2SkPAAAAAEqE1zv6WEnkWU/0L7K9pIQemD+g\nRdVKjt2GTVn8176z1+8/ehYVk5iWkZYYE/3s8YMb5w9s+GXqiM8aV67crP/sfQ/kD+wAijiN\nbecUc+lSmGyJVqsvetnktdn0sE1fth2y71l2C/+0yn++zm+7Vy1FZ7PruvbvXX7Viheioujj\nx28Irk55HRUAAEAxoV2mltvgH9wG/7Ay34/skhjbeYz+1WP0/9i7z/goi7UPwBsgJLTQi3SQ\nJqB0RIoKKIIoKgr2QrGh2Dt2rNh9UbGCCCoqdkURUWki0kUEEVE4UqQnQCCUvB/OQTchQBI2\n2U32ur7NvfPM3Ht+4vG3f2aeIZuXThn3yZff/TB77vxFf6xevzExeU9s0RKlK9Wo06Bxi3ad\nupxy6gmNymb9v0Jr3jQz9aastwUAAED4bfjoumveW5tndkmc+8JV5980amGmQr+da2a8dV/P\nD986d8ioF65uVTIE20NuCFlAOG/evHSVpiedVPHQ1kz5dXivjv0/XplROliw+jmvT3zj/MMP\n9AVi2p7avezQl9cHlf6YOnVloEXlQ+sLACDvybVXdhUsefhx51x33Dk5vQ8AAAB5xOZx1w8Y\n/Xde2WXb7MHdOt49LTFrTyX/+tbAjsvWfT3h3qNz6E0dEFqhumL07/nz16StlDn66LqHsuKO\nRa/0PL5fxulg7OEXvzVp1IHTwUAgEIhp0+6YdN9w9uzZh9IVAAAAAACQaUlf3nTFyJUHnxcZ\nu6x584Ku6dPB2IptLr7v1Y+/X/zXusQdKTuS1v21ePonrw2+5JhKaV+ysXX6fSef98Zfh94E\n5IJQBYQrV6b/g1evfv3sL7dj4bAzjr/ss1WpGXxWuP5l700a3qtGZi6+SjjiiCppK1uXLl2T\n8VwAAAAAACCUtn57++WvLv/foFBcXKhCiZzZZcv4O2/6IM0lpYUP7/XyrKXfj7i776lt6lUu\nW6JwbOHiZSvXO/qUPncOn7Z09qvn14kLnr7h45tv/zwpm7tDbgrVn8XNmzenq5QrVy6bayUv\neK5HxwHj1mSUDhZpfM1H3w3rUTkmk2vVrFkzXeXPP//MZl8AAAAAAECmJU+949Ln//zfj/0F\nGtxwc4/YAz8Q3l1WvfbAa6uCC6W7PD/+7f5H7u/S0KKN+74x4aXT0oQha0Y9+IoUgjwg5wLC\nmJjMhnhpJM9/9tSOV4//O6N0sFizmz/99pmuFbOwcEJCQrpKYmIWbw4GAAAAAACyasf3d/cf\n+tveX/trXD7snqMLR/Iu6z79aGrwW88Kthn0fL/aB45RYmpc9Py9xwefIkyd/vGnOf7CRThk\noQoIY2PTB/IbN27M8irb5jzVvdO1X6/L6LMSre/8YuKQTmWztmKBAum/4bZt27LcV1RIXj5l\n1GM3nN/1mEbVy5csWrhQbJESZavUb3VC76sGv/bVr0kZJbYRtT4AAAAAAJEjZdbg/k8t2hu4\nVbrkuYeOKxrRu6ROnTQlOB8s0LF/n8Mz8VzlSy7rHpyQ7JnyXZp1ICKFKiAsU6ZMusrixYuz\ntsTW2Y9163zDN+sz+qxU+wcnfDW4faks97VuXYZpY+RbcG/jmGwqd/W3Wdtr99/fP9unVbXa\nHS685ak3v5y+cMW6xOSdu3dt37Jh5a8zv373+bv7dWlQtdFZD41fvjNb3yWn1wcAAAAAILLs\nmv9gvyELd/9vVLbnk491z/ov/Lm7y/pVq1KCxw3atk0ffGSsWLt2TdN0tXp1Hk0miCahCghL\nly6drrJ28uQsJIRJMx7u2vmWSRkeOizb8YmJX9zROv1doZmydu3adJWiRXPg7yjkYZt+eKxb\nk/bXjpi5fvcBZqUm/jJ20ElNjrt1fIavhgzj+gAAAAAARJjdCx7t9/C8vUdCSpw45Nlzyx3w\ngUjYZcOGDWnGlSpVyuST6Wfm2aNLRJNQBYTV69RJf6nv3Ne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uskZV+wTQHQRg6Y4MzFf/xxdp95JpACxN5Iur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\nxoEmvS+ZBsB4CDQAcsKnTgCyQZEGAHJEgxBmTPNPa5dmdG0zdv/znH5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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Non-neuronal\"\n",
      "       celltype regulation counts\n",
      "14 Non-neuronal       DOWN     53\n",
      "[1] -53\n",
      "       celltype regulation counts\n",
      "13 Non-neuronal         UP    396\n",
      "[1] 396\n",
      "     celltypes up_deg down_deg\n",
      "1 Non-neuronal    396      -53\n",
      "  celltypes up_deg down_deg\n",
      "1      OPCs    396      -53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_text(data = up_down_df, aes(x = celltypes, y = down_deg, :\n",
      "“\u001b[1m\u001b[22mIgnoring unknown aesthetics: \u001b[32mfill\u001b[39m”\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Oligo\"\n",
      "   celltype regulation counts\n",
      "17    Oligo       DOWN     13\n",
      "[1] -13\n",
      "   celltype regulation counts\n",
      "16    Oligo         UP    206\n",
      "[1] 206\n",
      "  celltypes up_deg down_deg\n",
      "1     Oligo    206      -13\n",
      "  celltypes up_deg down_deg\n",
      "1     Oligo    206      -13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_text(data = up_down_df, aes(x = celltypes, y = down_deg, :\n",
      "“\u001b[1m\u001b[22mIgnoring unknown aesthetics: \u001b[32mfill\u001b[39m”\n"
     ]
    },
    {
     "data": {
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Zw1CgAAkN8ICAEAACLSXungHgUPObHtzd263Xr5aYemK73bOOKKKpcM/2+S6AYD/5p0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\n/vnuha5d7n/7102pPS190ZtzPrqq4n5ekiw/fObXv3FhxY6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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=20, repr.plot.height=15)\n",
    "\n",
    "plot.list <- list()\n",
    "for(ct in broad_cts){\n",
    "    print(ct)\n",
    "    \n",
    "    # subset sub-celltypes from each broad CT\n",
    "    ct_DEGs <- DEG_file[grep(ct, DEG_file$celltype),]\n",
    "    #print(head(ct_DEGs))\n",
    "    \n",
    "    # making dataframe for each broad celltype\n",
    "    up_down_df <- data.frame()\n",
    "    up_counts <- c()\n",
    "    down_counts <- c()\n",
    "\n",
    "    ct_down <- ct_DEGs[which(ct_DEGs$regulation == \"DOWN\"),]\n",
    "    print(ct_down)\n",
    "    down_counts <- c(down_counts, -ct_down$counts)\n",
    "    print(down_counts)\n",
    "\n",
    "    ct_up <- ct_DEGs[which(ct_DEGs$regulation == \"UP\"),]\n",
    "    print(ct_up)\n",
    "    up_counts <- c(up_counts, ct_up$counts)\n",
    "    print(up_counts)\n",
    "    \n",
    "    up_down_df <- data.frame(unique(ct_DEGs$celltype), up_counts, down_counts)\n",
    "    colnames(up_down_df) <- c(\"celltypes\", \"up_deg\", \"down_deg\")\n",
    "    print(up_down_df)\n",
    "    \n",
    "    # renaming non-neural and macrophage\n",
    "    up_down_df$celltypes[up_down_df$celltypes == 'Non-neuronal'] <- 'OPCs'\n",
    "    up_down_df$celltypes[up_down_df$celltypes == 'Macrophages'] <- 'Microglia'\n",
    "    print(up_down_df)\n",
    "\n",
    "    # plotting for each broad ct\n",
    "    plotCols <- c(\"cadetblue3\", \"coral3\", \"darkolivegreen3\", \"darkcyan\",\n",
    "        \"mediumpurple1\", \"lightgreen\", \"lightgoldenrod\", \"lightslateblue\", \n",
    "        \"mistyrose\", \"lightblue4\", \"navajowhite1\", \"magenta\", \n",
    "        \"lightsalmon\", \"mediumorchid1\", \"midnightblue\", \"lightskyblue\", \n",
    "        \"lightgoldenrodyellow\", \"black\", \"lightgrey\", \"mistyrose4\")\n",
    "    \n",
    "    # reordering GABAergic rows\n",
    "    if(ct == \"GABAergic\"){\n",
    "        up_down_df$celltypes <- factor(up_down_df$celltypes, levels = c(\"GABAergic.1\",\"GABAergic.2\",\"GABAergic.3\",\"GABAergic.4\",\"GABAergic.5\",\"GABAergic.6\",\n",
    "                         \"GABAergic.7\",\"GABAergic.8\",\"GABAergic.9\",\"GABAergic.10\",\"GABAergic.11\",\"GABAergic.12\",\n",
    "                         \"GABAergic.13\",\"GABAergic.14\",\"GABAergic.15\"))\n",
    "    }\n",
    "    up_down_df\n",
    "    \n",
    "    up.down.Plot <- ggplot(data=up_down_df, aes(x = celltypes, y = up_deg, fill = celltypes)) +\n",
    "    geom_bar(data=up_down_df, aes(x = celltypes, y = down_deg, fill = celltypes), stat=\"identity\", alpha = 0.5, width = 0.5) +\n",
    "    geom_col(position = \"stack\", width = 0.5) + coord_cartesian(ylim = c(min(up_down_df$down_deg)-200, max(up_down_df$up_deg)+200)) +\n",
    "    scale_fill_manual(values=plotCols) +\n",
    "    theme_classic() +\n",
    "    geom_text(aes(label=up_deg), hjust=0.5, vjust=-0.1, size = 11) +\n",
    "    geom_text(data=up_down_df, aes(x = celltypes, y = down_deg, fill = celltypes, label=-down_deg), hjust=0.5, vjust=1.2, size = 11) +\n",
    "    xlab(\"Cell Types\") + ylab(\"Up- & Down-regulated DEGs\") +\n",
    "    theme(plot.title = element_text(size = 50, face = \"plain\", hjust = 0.5),\n",
    "          axis.title.x = element_text(size=45, face=\"plain\"),\n",
    "          axis.title.y = element_text(size=45, face=\"plain\"),\n",
    "          axis.text.x = element_text(size=30, color=\"black\", angle = 45, hjust = 1),\n",
    "          axis.text.y = element_text(size=30, color=\"black\"),\n",
    "          legend.position = \"none\",\n",
    "          plot.margin = margin(15,15,10,15)) + # margin(t, r, b, l)\n",
    "    scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +\n",
    "    geom_hline(yintercept = 0,colour = \"black\")\n",
    "    print(up.down.Plot)\n",
    "    \n",
    "    plot.list[[ct]] <- up.down.Plot\n",
    "}\n",
    "\n",
    "pdf(paste0(OUTPATH, \"DEG_mirrorbarplot.pdf\"), width = 15, height = 12)\n",
    "for(p in plot.list){print(p)}\n",
    "#do.call(\"grid.arrange\", c(plot.list, ncol=1))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faee0785",
   "metadata": {},
   "source": [
    "# mirror barplot pt2 - combining the broad CTs that don't have any sub-cts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ac37046f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'GABAergic.6'</li><li>'Meis2'</li><li>'Glutamatergic.1'</li><li>'GABAergic.2'</li><li>'Non-neuronal'</li><li>'Oligo'</li><li>'Macrophages'</li><li>'GABAergic.4'</li><li>'Glutamatergic.2'</li><li>'GABAergic.1'</li><li>'GABAergic.3'</li><li>'GABAergic.8'</li><li>'Glutamatergic.3'</li><li>'GABAergic.7'</li><li>'GABAergic.5'</li><li>'Glutamatergic.4'</li><li>'GABAergic.10'</li><li>'GABAergic.9'</li><li>'Astrocytes'</li><li>'GABAergic.11'</li><li>'GABAergic.14'</li><li>'Glutamatergic.5'</li><li>'GABAergic.12'</li><li>'GABAergic.15'</li><li>'GABAergic.13'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'GABAergic.6'\n",
       "\\item 'Meis2'\n",
       "\\item 'Glutamatergic.1'\n",
       "\\item 'GABAergic.2'\n",
       "\\item 'Non-neuronal'\n",
       "\\item 'Oligo'\n",
       "\\item 'Macrophages'\n",
       "\\item 'GABAergic.4'\n",
       "\\item 'Glutamatergic.2'\n",
       "\\item 'GABAergic.1'\n",
       "\\item 'GABAergic.3'\n",
       "\\item 'GABAergic.8'\n",
       "\\item 'Glutamatergic.3'\n",
       "\\item 'GABAergic.7'\n",
       "\\item 'GABAergic.5'\n",
       "\\item 'Glutamatergic.4'\n",
       "\\item 'GABAergic.10'\n",
       "\\item 'GABAergic.9'\n",
       "\\item 'Astrocytes'\n",
       "\\item 'GABAergic.11'\n",
       "\\item 'GABAergic.14'\n",
       "\\item 'Glutamatergic.5'\n",
       "\\item 'GABAergic.12'\n",
       "\\item 'GABAergic.15'\n",
       "\\item 'GABAergic.13'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'GABAergic.6'\n",
       "2. 'Meis2'\n",
       "3. 'Glutamatergic.1'\n",
       "4. 'GABAergic.2'\n",
       "5. 'Non-neuronal'\n",
       "6. 'Oligo'\n",
       "7. 'Macrophages'\n",
       "8. 'GABAergic.4'\n",
       "9. 'Glutamatergic.2'\n",
       "10. 'GABAergic.1'\n",
       "11. 'GABAergic.3'\n",
       "12. 'GABAergic.8'\n",
       "13. 'Glutamatergic.3'\n",
       "14. 'GABAergic.7'\n",
       "15. 'GABAergic.5'\n",
       "16. 'Glutamatergic.4'\n",
       "17. 'GABAergic.10'\n",
       "18. 'GABAergic.9'\n",
       "19. 'Astrocytes'\n",
       "20. 'GABAergic.11'\n",
       "21. 'GABAergic.14'\n",
       "22. 'Glutamatergic.5'\n",
       "23. 'GABAergic.12'\n",
       "24. 'GABAergic.15'\n",
       "25. 'GABAergic.13'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"GABAergic.6\"     \"Meis2\"           \"Glutamatergic.1\" \"GABAergic.2\"    \n",
       " [5] \"Non-neuronal\"    \"Oligo\"           \"Macrophages\"     \"GABAergic.4\"    \n",
       " [9] \"Glutamatergic.2\" \"GABAergic.1\"     \"GABAergic.3\"     \"GABAergic.8\"    \n",
       "[13] \"Glutamatergic.3\" \"GABAergic.7\"     \"GABAergic.5\"     \"Glutamatergic.4\"\n",
       "[17] \"GABAergic.10\"    \"GABAergic.9\"     \"Astrocytes\"      \"GABAergic.11\"   \n",
       "[21] \"GABAergic.14\"    \"Glutamatergic.5\" \"GABAergic.12\"    \"GABAergic.15\"   \n",
       "[25] \"GABAergic.13\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique(DEG_file$celltype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "7d2f4c47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       celltype regulation counts\n",
      "5         Meis2       DOWN     32\n",
      "14 Non-neuronal       DOWN     53\n",
      "17        Oligo       DOWN     13\n",
      "20  Macrophages       DOWN     17\n",
      "56   Astrocytes       DOWN     48\n",
      "[1] -32 -53 -13 -17 -48\n",
      "       celltype regulation counts\n",
      "4         Meis2         UP    175\n",
      "13 Non-neuronal         UP    396\n",
      "16        Oligo         UP    206\n",
      "19  Macrophages         UP    249\n",
      "55   Astrocytes         UP    481\n",
      "[1] 175 396 206 249 481\n",
      "     celltypes up_deg down_deg\n",
      "1        Meis2    175      -32\n",
      "2 Non-neuronal    396      -53\n",
      "3        Oligo    206      -13\n",
      "4  Macrophages    249      -17\n",
      "5   Astrocytes    481      -48\n",
      "   celltypes up_deg down_deg\n",
      "1      Meis2    175      -32\n",
      "2       OPCs    396      -53\n",
      "3      Oligo    206      -13\n",
      "4  Microglia    249      -17\n",
      "5 Astrocytes    481      -48\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>celltypes</th><th scope=col>up_deg</th><th scope=col>down_deg</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Meis2     </td><td>175</td><td>-32</td></tr>\n",
       "\t<tr><td>OPCs      </td><td>396</td><td>-53</td></tr>\n",
       "\t<tr><td>Oligo     </td><td>206</td><td>-13</td></tr>\n",
       "\t<tr><td>Microglia </td><td>249</td><td>-17</td></tr>\n",
       "\t<tr><td>Astrocytes</td><td>481</td><td>-48</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 3\n",
       "\\begin{tabular}{lll}\n",
       " celltypes & up\\_deg & down\\_deg\\\\\n",
       " <chr> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t Meis2      & 175 & -32\\\\\n",
       "\t OPCs       & 396 & -53\\\\\n",
       "\t Oligo      & 206 & -13\\\\\n",
       "\t Microglia  & 249 & -17\\\\\n",
       "\t Astrocytes & 481 & -48\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 3\n",
       "\n",
       "| celltypes &lt;chr&gt; | up_deg &lt;int&gt; | down_deg &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| Meis2      | 175 | -32 |\n",
       "| OPCs       | 396 | -53 |\n",
       "| Oligo      | 206 | -13 |\n",
       "| Microglia  | 249 | -17 |\n",
       "| Astrocytes | 481 | -48 |\n",
       "\n"
      ],
      "text/plain": [
       "  celltypes  up_deg down_deg\n",
       "1 Meis2      175    -32     \n",
       "2 OPCs       396    -53     \n",
       "3 Oligo      206    -13     \n",
       "4 Microglia  249    -17     \n",
       "5 Astrocytes 481    -48     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_text(data = up_down_df, aes(x = celltypes, y = down_deg, :\n",
      "“\u001b[1m\u001b[22mIgnoring unknown aesthetics: \u001b[32mfill\u001b[39m”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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/B1+468NLD3PhWT3nH3mNPf9M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\nXb50kXyxWbLmyFu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bDsang4JWqzlrUk0HBUEwaLbwyg0Hc/G+qanUGgWADFW6dGlRK+D8udsxMvumy5cvX8Ld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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=20, repr.plot.height=15)\n",
    "\n",
    "plot.list2 <- list()\n",
    "\n",
    "# subset sub-celltypes from each broad CT\n",
    "ct_DEGs <- DEG_file[which(DEG_file$celltype %in% c(\"Meis2\",\"Oligo\",\"Non-neuronal\",\"Macrophages\",\"Astrocytes\")),]\n",
    "#print(head(ct_DEGs))\n",
    "\n",
    "# making dataframe for each broad celltype\n",
    "up_down_df <- data.frame()\n",
    "up_counts <- c()\n",
    "down_counts <- c()\n",
    "\n",
    "ct_down <- ct_DEGs[which(ct_DEGs$regulation == \"DOWN\"),]\n",
    "print(ct_down)\n",
    "down_counts <- c(down_counts, -ct_down$counts)\n",
    "print(down_counts)\n",
    "\n",
    "ct_up <- ct_DEGs[which(ct_DEGs$regulation == \"UP\"),]\n",
    "print(ct_up)\n",
    "up_counts <- c(up_counts, ct_up$counts)\n",
    "print(up_counts)\n",
    "\n",
    "up_down_df <- data.frame(unique(ct_DEGs$celltype), up_counts, down_counts)\n",
    "colnames(up_down_df) <- c(\"celltypes\", \"up_deg\", \"down_deg\")\n",
    "print(up_down_df)\n",
    "\n",
    "# renaming non-neural and macrophage\n",
    "up_down_df$celltypes[up_down_df$celltypes == 'Non-neuronal'] <- 'OPCs'\n",
    "up_down_df$celltypes[up_down_df$celltypes == 'Macrophages'] <- 'Microglia'\n",
    "print(up_down_df)\n",
    "\n",
    "# plotting for each broad ct\n",
    "plotCols <- c(\"cadetblue3\", \"coral3\", \"darkolivegreen3\", \"darkcyan\",\n",
    "    \"mediumpurple1\", \"lightgreen\", \"lightgoldenrod\", \"lightslateblue\", \n",
    "    \"mistyrose\", \"lightblue4\", \"navajowhite1\", \"magenta\", \n",
    "    \"lightsalmon\", \"mediumorchid1\", \"midnightblue\", \"lightskyblue\", \n",
    "    \"lightgoldenrodyellow\", \"black\", \"lightgrey\", \"mistyrose4\")\n",
    "\n",
    "# reordering GABAergic rows\n",
    "if(ct == \"GABAergic\"){\n",
    "    up_down_df$celltypes <- factor(up_down_df$celltypes, levels = c(\"GABAergic.1\",\"GABAergic.2\",\"GABAergic.3\",\"GABAergic.4\",\"GABAergic.5\",\"GABAergic.6\",\n",
    "                     \"GABAergic.7\",\"GABAergic.8\",\"GABAergic.9\",\"GABAergic.10\",\"GABAergic.11\",\"GABAergic.12\",\n",
    "                     \"GABAergic.13\",\"GABAergic.14\",\"GABAergic.15\"))\n",
    "}\n",
    "up_down_df\n",
    "\n",
    "up.down.Plot <- ggplot(data=up_down_df, aes(x = celltypes, y = up_deg, fill = celltypes)) +\n",
    "geom_bar(data=up_down_df, aes(x = celltypes, y = down_deg, fill = celltypes), stat=\"identity\", alpha = 0.5, width = 0.5) +\n",
    "geom_col(position = \"stack\", width = 0.5) + coord_cartesian(ylim = c(min(up_down_df$down_deg)-200, max(up_down_df$up_deg)+200)) +\n",
    "scale_fill_manual(values=plotCols) +\n",
    "theme_classic() +\n",
    "geom_text(aes(label=up_deg), hjust=0.5, vjust=-0.1, size = 11) +\n",
    "geom_text(data=up_down_df, aes(x = celltypes, y = down_deg, fill = celltypes, label=-down_deg), hjust=0.5, vjust=1.2, size = 11) +\n",
    "xlab(\"Cell Types\") + ylab(\"Up- & Down-regulated DEGs\") +\n",
    "theme(plot.title = element_text(size = 50, face = \"plain\", hjust = 0.5),\n",
    "      axis.title.x = element_text(size=45, face=\"plain\"),\n",
    "      axis.title.y = element_text(size=45, face=\"plain\"),\n",
    "      axis.text.x = element_text(size=30, color=\"black\", angle = 45, hjust = 1),\n",
    "      axis.text.y = element_text(size=30, color=\"black\"),\n",
    "      legend.position = \"none\",\n",
    "      plot.margin = margin(15,15,10,15)) + # margin(t, r, b, l)\n",
    "scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +\n",
    "geom_hline(yintercept = 0,colour = \"black\")\n",
    "print(up.down.Plot)\n",
    "\n",
    "plot.list2[[ct]] <- up.down.Plot\n",
    "\n",
    "pdf(paste0(OUTPATH, \"DEG_mirrorbarplot_combined.pdf\"), width = 15, height = 12)\n",
    "for(p in plot.list2){print(p)}\n",
    "#do.call(\"grid.arrange\", c(plot.list, ncol=1))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db707ca8",
   "metadata": {},
   "source": [
    "# volcano plot\n",
    "3 plots total: GABA, Gluta, everything else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "95fd8d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.1.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.10.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.11.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.12.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.13.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.14.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.15.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.2.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.3.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.4.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.5.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.6.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.7.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.8.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.9.txt'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.1.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.10.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.11.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.12.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.13.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.14.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.15.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.2.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.3.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.4.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.5.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.6.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.7.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.8.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//GABAergic.9.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.1.txt'\n",
       "2. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.10.txt'\n",
       "3. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.11.txt'\n",
       "4. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.12.txt'\n",
       "5. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.13.txt'\n",
       "6. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.14.txt'\n",
       "7. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.15.txt'\n",
       "8. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.2.txt'\n",
       "9. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.3.txt'\n",
       "10. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.4.txt'\n",
       "11. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.5.txt'\n",
       "12. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.6.txt'\n",
       "13. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.7.txt'\n",
       "14. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.8.txt'\n",
       "15. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.9.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.1.txt\" \n",
       " [2] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.10.txt\"\n",
       " [3] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.11.txt\"\n",
       " [4] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.12.txt\"\n",
       " [5] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.13.txt\"\n",
       " [6] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.14.txt\"\n",
       " [7] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.15.txt\"\n",
       " [8] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.2.txt\" \n",
       " [9] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.3.txt\" \n",
       "[10] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.4.txt\" \n",
       "[11] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.5.txt\" \n",
       "[12] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.6.txt\" \n",
       "[13] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.7.txt\" \n",
       "[14] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.8.txt\" \n",
       "[15] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.9.txt\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaba_DEGs <- list.files(paste0(DATA, \"sub_ct/\"), pattern = \"GABA\", full.names = T)\n",
    "gaba_DEGs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "cf69d9d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>p_val</th><th scope=col>avg_log2FC</th><th scope=col>pct.1</th><th scope=col>pct.2</th><th scope=col>p_val_adj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Ube3a</th><td>1.250787e-236</td><td>-1.1787724</td><td>0.905</td><td>0.988</td><td>2.918087e-232</td></tr>\n",
       "\t<tr><th scope=row>Malat1</th><td>1.571203e-233</td><td>-0.9431463</td><td>1.000</td><td>1.000</td><td>3.665615e-229</td></tr>\n",
       "\t<tr><th scope=row>AY036118</th><td>1.085238e-222</td><td>-1.1566483</td><td>0.908</td><td>0.990</td><td>2.531860e-218</td></tr>\n",
       "\t<tr><th scope=row>Meg3</th><td>2.586236e-201</td><td> 0.7045479</td><td>1.000</td><td>1.000</td><td>6.033688e-197</td></tr>\n",
       "\t<tr><th scope=row>Atp6v0b</th><td>2.757750e-163</td><td>-0.9556613</td><td>0.710</td><td>0.933</td><td>6.433830e-159</td></tr>\n",
       "\t<tr><th scope=row>Kcnq1ot1</th><td>2.187058e-129</td><td> 0.6613646</td><td>1.000</td><td>0.961</td><td>5.102405e-125</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & p\\_val & avg\\_log2FC & pct.1 & pct.2 & p\\_val\\_adj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tUbe3a & 1.250787e-236 & -1.1787724 & 0.905 & 0.988 & 2.918087e-232\\\\\n",
       "\tMalat1 & 1.571203e-233 & -0.9431463 & 1.000 & 1.000 & 3.665615e-229\\\\\n",
       "\tAY036118 & 1.085238e-222 & -1.1566483 & 0.908 & 0.990 & 2.531860e-218\\\\\n",
       "\tMeg3 & 2.586236e-201 &  0.7045479 & 1.000 & 1.000 & 6.033688e-197\\\\\n",
       "\tAtp6v0b & 2.757750e-163 & -0.9556613 & 0.710 & 0.933 & 6.433830e-159\\\\\n",
       "\tKcnq1ot1 & 2.187058e-129 &  0.6613646 & 1.000 & 0.961 & 5.102405e-125\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | p_val &lt;dbl&gt; | avg_log2FC &lt;dbl&gt; | pct.1 &lt;dbl&gt; | pct.2 &lt;dbl&gt; | p_val_adj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Ube3a | 1.250787e-236 | -1.1787724 | 0.905 | 0.988 | 2.918087e-232 |\n",
       "| Malat1 | 1.571203e-233 | -0.9431463 | 1.000 | 1.000 | 3.665615e-229 |\n",
       "| AY036118 | 1.085238e-222 | -1.1566483 | 0.908 | 0.990 | 2.531860e-218 |\n",
       "| Meg3 | 2.586236e-201 |  0.7045479 | 1.000 | 1.000 | 6.033688e-197 |\n",
       "| Atp6v0b | 2.757750e-163 | -0.9556613 | 0.710 | 0.933 | 6.433830e-159 |\n",
       "| Kcnq1ot1 | 2.187058e-129 |  0.6613646 | 1.000 | 0.961 | 5.102405e-125 |\n",
       "\n"
      ],
      "text/plain": [
       "         p_val         avg_log2FC pct.1 pct.2 p_val_adj    \n",
       "Ube3a    1.250787e-236 -1.1787724 0.905 0.988 2.918087e-232\n",
       "Malat1   1.571203e-233 -0.9431463 1.000 1.000 3.665615e-229\n",
       "AY036118 1.085238e-222 -1.1566483 0.908 0.990 2.531860e-218\n",
       "Meg3     2.586236e-201  0.7045479 1.000 1.000 6.033688e-197\n",
       "Atp6v0b  2.757750e-163 -0.9556613 0.710 0.933 6.433830e-159\n",
       "Kcnq1ot1 2.187058e-129  0.6613646 1.000 0.961 5.102405e-125"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testing <- read.table('/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//GABAergic.1.txt')\n",
    "head(testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "6409e2c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"GABAergic.1\"\n",
      "[1] \"GABAergic.10\"\n",
      "[1] \"GABAergic.11\"\n",
      "[1] \"GABAergic.12\"\n",
      "[1] \"GABAergic.13\"\n",
      "[1] \"GABAergic.14\"\n",
      "[1] \"GABAergic.15\"\n",
      "[1] \"GABAergic.2\"\n",
      "[1] \"GABAergic.3\"\n",
      "[1] \"GABAergic.4\"\n",
      "[1] \"GABAergic.5\"\n",
      "[1] \"GABAergic.6\"\n",
      "[1] \"GABAergic.7\"\n",
      "[1] \"GABAergic.8\"\n",
      "[1] \"GABAergic.9\"\n",
      "[1] 9431    6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>p_val</th><th scope=col>avg_log2FC</th><th scope=col>pct.1</th><th scope=col>pct.2</th><th scope=col>p_val_adj</th><th scope=col>celltype</th><th scope=col>diffexpressed</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Ube3a</th><td>1.250787e-236</td><td>-1.1787724</td><td>0.905</td><td>0.988</td><td>2.918087e-232</td><td>GABAergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Malat1</th><td>1.571203e-233</td><td>-0.9431463</td><td>1.000</td><td>1.000</td><td>3.665615e-229</td><td>GABAergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>AY036118</th><td>1.085238e-222</td><td>-1.1566483</td><td>0.908</td><td>0.990</td><td>2.531860e-218</td><td>GABAergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Meg3</th><td>2.586236e-201</td><td> 0.7045479</td><td>1.000</td><td>1.000</td><td>6.033688e-197</td><td>GABAergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Atp6v0b</th><td>2.757750e-163</td><td>-0.9556613</td><td>0.710</td><td>0.933</td><td>6.433830e-159</td><td>GABAergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Kcnq1ot1</th><td>2.187058e-129</td><td> 0.6613646</td><td>1.000</td><td>0.961</td><td>5.102405e-125</td><td>GABAergic.1</td><td>DE</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & p\\_val & avg\\_log2FC & pct.1 & pct.2 & p\\_val\\_adj & celltype & diffexpressed\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tUbe3a & 1.250787e-236 & -1.1787724 & 0.905 & 0.988 & 2.918087e-232 & GABAergic.1 & DE\\\\\n",
       "\tMalat1 & 1.571203e-233 & -0.9431463 & 1.000 & 1.000 & 3.665615e-229 & GABAergic.1 & DE\\\\\n",
       "\tAY036118 & 1.085238e-222 & -1.1566483 & 0.908 & 0.990 & 2.531860e-218 & GABAergic.1 & DE\\\\\n",
       "\tMeg3 & 2.586236e-201 &  0.7045479 & 1.000 & 1.000 & 6.033688e-197 & GABAergic.1 & DE\\\\\n",
       "\tAtp6v0b & 2.757750e-163 & -0.9556613 & 0.710 & 0.933 & 6.433830e-159 & GABAergic.1 & DE\\\\\n",
       "\tKcnq1ot1 & 2.187058e-129 &  0.6613646 & 1.000 & 0.961 & 5.102405e-125 & GABAergic.1 & DE\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | p_val &lt;dbl&gt; | avg_log2FC &lt;dbl&gt; | pct.1 &lt;dbl&gt; | pct.2 &lt;dbl&gt; | p_val_adj &lt;dbl&gt; | celltype &lt;chr&gt; | diffexpressed &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| Ube3a | 1.250787e-236 | -1.1787724 | 0.905 | 0.988 | 2.918087e-232 | GABAergic.1 | DE |\n",
       "| Malat1 | 1.571203e-233 | -0.9431463 | 1.000 | 1.000 | 3.665615e-229 | GABAergic.1 | DE |\n",
       "| AY036118 | 1.085238e-222 | -1.1566483 | 0.908 | 0.990 | 2.531860e-218 | GABAergic.1 | DE |\n",
       "| Meg3 | 2.586236e-201 |  0.7045479 | 1.000 | 1.000 | 6.033688e-197 | GABAergic.1 | DE |\n",
       "| Atp6v0b | 2.757750e-163 | -0.9556613 | 0.710 | 0.933 | 6.433830e-159 | GABAergic.1 | DE |\n",
       "| Kcnq1ot1 | 2.187058e-129 |  0.6613646 | 1.000 | 0.961 | 5.102405e-125 | GABAergic.1 | DE |\n",
       "\n"
      ],
      "text/plain": [
       "         p_val         avg_log2FC pct.1 pct.2 p_val_adj     celltype   \n",
       "Ube3a    1.250787e-236 -1.1787724 0.905 0.988 2.918087e-232 GABAergic.1\n",
       "Malat1   1.571203e-233 -0.9431463 1.000 1.000 3.665615e-229 GABAergic.1\n",
       "AY036118 1.085238e-222 -1.1566483 0.908 0.990 2.531860e-218 GABAergic.1\n",
       "Meg3     2.586236e-201  0.7045479 1.000 1.000 6.033688e-197 GABAergic.1\n",
       "Atp6v0b  2.757750e-163 -0.9556613 0.710 0.933 6.433830e-159 GABAergic.1\n",
       "Kcnq1ot1 2.187058e-129  0.6613646 1.000 0.961 5.102405e-125 GABAergic.1\n",
       "         diffexpressed\n",
       "Ube3a    DE           \n",
       "Malat1   DE           \n",
       "AY036118 DE           \n",
       "Meg3     DE           \n",
       "Atp6v0b  DE           \n",
       "Kcnq1ot1 DE           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D8SjaZlg8A8ZlOTy7mmpMBlNMz07gAAAAAAYM5DQAgAAADzn6Qo3zl4kDGmptaE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5Z7I7wykVNoP/Qdz4KQAAAAAAME9QQQgA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\nz3PbFy4TV6x7oalHUtTBJ2DjwjV5nRdLG46nWxQyAKz6g78Qb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07Bkq/fWVIakfozvjQNQpUbfAoAAPKLc1dyQRzacDWZz7mMxsdmV7nN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\n9tz7iU/kWcF3GM5ZIJBrzdgJ6+oqbh0+xlh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X0x0+WzCmkmKOkHFz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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "big_mtx <- c()\n",
    "for(file in gaba_DEGs){\n",
    "    celltype <- gsub(\".txt\",\"\",str_split(file, pattern = \"//\")[[1]][2])\n",
    "    print(celltype)\n",
    "    \n",
    "    ct_file <- read.table(file)\n",
    "    ct_file[[\"celltype\"]] <- celltype\n",
    "    big_mtx <- rbind(big_mtx, ct_file)\n",
    "}\n",
    "print(dim(big_mtx))\n",
    "\n",
    "# adding DE column\n",
    "big_mtx$diffexpressed <- \"NO\"\n",
    "big_mtx$diffexpressed[which((big_mtx$avg_log2FC > 0.25 | big_mtx$avg_log2FC < -0.25) & (big_mtx$p_val_adj < 0.05))] <- \"DE\"\n",
    "\n",
    "head(big_mtx)\n",
    "\n",
    "plot_colors <- c(\"cadetblue3\", \"coral3\", \"darkolivegreen3\", \"darkcyan\",\n",
    "    \"mediumpurple1\", \"lightgreen\", \"lightgoldenrod\", \"lightslateblue\", \n",
    "    \"mistyrose\", \"lightblue4\", \"navajowhite1\", \"magenta\", \n",
    "    \"lightsalmon\", \"mediumorchid1\", \"midnightblue\", \"lightskyblue\", \n",
    "    \"lightgoldenrodyellow\", \"black\", \"lightgrey\", \"mistyrose4\")\n",
    "\n",
    "big_mtx$celltype <- factor(big_mtx$celltype, levels = c(\"GABAergic.1\",\"GABAergic.2\",\"GABAergic.3\",\"GABAergic.4\",\"GABAergic.5\",\"GABAergic.6\",\n",
    "                 \"GABAergic.7\",\"GABAergic.8\",\"GABAergic.9\",\"GABAergic.10\",\"GABAergic.11\",\"GABAergic.12\",\n",
    "                 \"GABAergic.13\",\"GABAergic.14\",\"GABAergic.15\"))\n",
    "\n",
    "gplot <- ggplot(data=big_mtx, aes(x=avg_log2FC, y=-log10(p_val_adj), col = celltype)) +\n",
    "        geom_point(size = 3.8) + theme_classic() +\n",
    "        ggtitle(\"GABAergic\") + scale_color_manual(\"Cell Type\", values=plot_colors) +\n",
    "        theme(plot.title = element_text(color=\"black\", size=40, hjust=0.5),\n",
    "                axis.title.y = element_text(size = 25),\n",
    "                axis.title.x = element_text(size = 25),\n",
    "                axis.text = element_text(size = 25),\n",
    "                #legend.position = \"none\") +\n",
    "                legend.title = element_text(size = 25),\n",
    "                legend.text = element_text(size = 20),\n",
    "                legend.key.width = unit(0.9, \"cm\"),\n",
    "                legend.key.height = unit(0.75, \"cm\")) + \n",
    "        theme(aspect.ratio = 1) +\n",
    "        geom_point(data=big_mtx[(big_mtx$diffexpressed == \"NO\"),], aes(x=avg_log2FC, y=-log10(p_val_adj)), col = \"gray95\", size = 4) +\n",
    "        guides(col = guide_legend(ncol = 1))\n",
    "gplot\n",
    "\n",
    "pdf(paste0(OUTPATH, \"GABA_volcano.pdf\"), width = 12, height = 10)\n",
    "gplot\n",
    "dev.off()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07f65078",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105ffe1f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "2e78f2e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.1.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.2.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.3.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.4.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.5.txt'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//Glutamatergic.1.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//Glutamatergic.2.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//Glutamatergic.3.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//Glutamatergic.4.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/sub\\_ct//Glutamatergic.5.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.1.txt'\n",
       "2. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.2.txt'\n",
       "3. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.3.txt'\n",
       "4. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.4.txt'\n",
       "5. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.5.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.1.txt\"\n",
       "[2] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.2.txt\"\n",
       "[3] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.3.txt\"\n",
       "[4] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.4.txt\"\n",
       "[5] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/sub_ct//Glutamatergic.5.txt\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gluta_DEGs <- list.files(paste0(DATA, \"sub_ct/\"), pattern = \"Glutamatergic\", full.names = T)\n",
    "gluta_DEGs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "e9d884c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Glutamatergic.1\"\n",
      "[1] \"Glutamatergic.2\"\n",
      "[1] \"Glutamatergic.3\"\n",
      "[1] \"Glutamatergic.4\"\n",
      "[1] \"Glutamatergic.5\"\n",
      "[1] 4540    6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>p_val</th><th scope=col>avg_log2FC</th><th scope=col>pct.1</th><th scope=col>pct.2</th><th scope=col>p_val_adj</th><th scope=col>celltype</th><th scope=col>diffexpressed</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Malat1</th><td>7.220629e-200</td><td>-1.087393</td><td>1.000</td><td>1.000</td><td>1.684573e-195</td><td>Glutamatergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Ube3a</th><td>2.206559e-189</td><td>-1.347811</td><td>0.960</td><td>0.998</td><td>5.147902e-185</td><td>Glutamatergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Nrg3os</th><td>1.309095e-165</td><td>-1.469942</td><td>0.360</td><td>0.904</td><td>3.054119e-161</td><td>Glutamatergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Gm20642</th><td>5.985421e-157</td><td>-1.366354</td><td>0.247</td><td>0.838</td><td>1.396399e-152</td><td>Glutamatergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>AY036118</th><td>6.094492e-156</td><td>-1.147903</td><td>0.956</td><td>0.996</td><td>1.421845e-151</td><td>Glutamatergic.1</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Atp6v0b</th><td>1.787607e-139</td><td>-1.003257</td><td>0.883</td><td>0.988</td><td>4.170487e-135</td><td>Glutamatergic.1</td><td>DE</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & p\\_val & avg\\_log2FC & pct.1 & pct.2 & p\\_val\\_adj & celltype & diffexpressed\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tMalat1 & 7.220629e-200 & -1.087393 & 1.000 & 1.000 & 1.684573e-195 & Glutamatergic.1 & DE\\\\\n",
       "\tUbe3a & 2.206559e-189 & -1.347811 & 0.960 & 0.998 & 5.147902e-185 & Glutamatergic.1 & DE\\\\\n",
       "\tNrg3os & 1.309095e-165 & -1.469942 & 0.360 & 0.904 & 3.054119e-161 & Glutamatergic.1 & DE\\\\\n",
       "\tGm20642 & 5.985421e-157 & -1.366354 & 0.247 & 0.838 & 1.396399e-152 & Glutamatergic.1 & DE\\\\\n",
       "\tAY036118 & 6.094492e-156 & -1.147903 & 0.956 & 0.996 & 1.421845e-151 & Glutamatergic.1 & DE\\\\\n",
       "\tAtp6v0b & 1.787607e-139 & -1.003257 & 0.883 & 0.988 & 4.170487e-135 & Glutamatergic.1 & DE\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | p_val &lt;dbl&gt; | avg_log2FC &lt;dbl&gt; | pct.1 &lt;dbl&gt; | pct.2 &lt;dbl&gt; | p_val_adj &lt;dbl&gt; | celltype &lt;chr&gt; | diffexpressed &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| Malat1 | 7.220629e-200 | -1.087393 | 1.000 | 1.000 | 1.684573e-195 | Glutamatergic.1 | DE |\n",
       "| Ube3a | 2.206559e-189 | -1.347811 | 0.960 | 0.998 | 5.147902e-185 | Glutamatergic.1 | DE |\n",
       "| Nrg3os | 1.309095e-165 | -1.469942 | 0.360 | 0.904 | 3.054119e-161 | Glutamatergic.1 | DE |\n",
       "| Gm20642 | 5.985421e-157 | -1.366354 | 0.247 | 0.838 | 1.396399e-152 | Glutamatergic.1 | DE |\n",
       "| AY036118 | 6.094492e-156 | -1.147903 | 0.956 | 0.996 | 1.421845e-151 | Glutamatergic.1 | DE |\n",
       "| Atp6v0b | 1.787607e-139 | -1.003257 | 0.883 | 0.988 | 4.170487e-135 | Glutamatergic.1 | DE |\n",
       "\n"
      ],
      "text/plain": [
       "         p_val         avg_log2FC pct.1 pct.2 p_val_adj     celltype       \n",
       "Malat1   7.220629e-200 -1.087393  1.000 1.000 1.684573e-195 Glutamatergic.1\n",
       "Ube3a    2.206559e-189 -1.347811  0.960 0.998 5.147902e-185 Glutamatergic.1\n",
       "Nrg3os   1.309095e-165 -1.469942  0.360 0.904 3.054119e-161 Glutamatergic.1\n",
       "Gm20642  5.985421e-157 -1.366354  0.247 0.838 1.396399e-152 Glutamatergic.1\n",
       "AY036118 6.094492e-156 -1.147903  0.956 0.996 1.421845e-151 Glutamatergic.1\n",
       "Atp6v0b  1.787607e-139 -1.003257  0.883 0.988 4.170487e-135 Glutamatergic.1\n",
       "         diffexpressed\n",
       "Malat1   DE           \n",
       "Ube3a    DE           \n",
       "Nrg3os   DE           \n",
       "Gm20642  DE           \n",
       "AY036118 DE           \n",
       "Atp6v0b  DE           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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6O53udd3fIBAAAAANjwCAgBrFQi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\n6Gz6k4eOzaWDc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bhfP/e+iQ\nyr3WFM4f7+k56deYRb3NMEFo+IsvOrbsoDOlgbMYo7z0teTOttY/2v4/d+144P1bv3Vl419u\narjZe9VNqnriCYLOYim/9Y6ZN8pKVuMyucgz9s3bGZv/T6eip4l32FW1DGSkGJjvUmt6dGjo\n/v/WuAy15ERcjGmpUOQ8MzGmsvHps4Ojx/2hRa5sXTEKOiIKpzOLCEE5EXH+q+5B8Twtr4RF\n6IvEjk4GV3sVs9kjAabxV9weLVh1bFKSpr++prriI5s3lpjNRJS0u158/2e6L7yS6/RExBkj\nxkgQpt729Q533Se/UPepv2E6faFWsnz0gtlprlS/SysjVmzbsKxLAgAAAAAAAFgH/0UNALCm\nHBwb8yW1VX091tOzpbh4mdZzftBZbRv/9v+xd9/xcZTXwvjPM7Ozvautei+25d67aaZjYwgE\nEhOSwM1Nu7+Um/fmDbkJyRvyJnkJaTckIbkXAsEGAhhswBgbDDbu3ZYsy+q9be9t5vn9sbIs\nS6vdGRVLss/3Dz7S7DPPnF3v7Io5c87z8573/tmzfSsfCBBC+stZKAVKZWqNZdMj6es3DEkH\n5jz8L76684G2pkQlbIQBoIVff0Kmu6JBqNR6DkIYDWdOWbXQU3Uy7gBfvjyik3DbjWuGInOv\n135ob/rtm9SFZZKCESPqcUndhQoC7/PK9Ek6qfb6g592Sl6tcJpSyVgAiI62RpICuMPhE722\nZZa0cY0LTVefdvYCmTqLD/aThQPSdxlFz8z4NLIr/n+k1Kj/t/n6Fre3zul2BMOhOx/qvGVj\ndtN5Y1czddmJjJOnputmL9TPXsjIRawLO2UUmJad7XpT5GAKNN+4bELjQQghhBBCCCGEMEGI\nEELSVEksB2QJqerrm6BgriWEZS13fzbtprvdZ456as5E7DYgIE9J182cp5+ziFGqhu/CKJSl\n3/9F42+e9NadB0LipAkJYeRcwb/+h37u4iGPpKiLCGEoFVvaRamQqik1rbip593XAx0tMKwm\nzJsvFzlV/4QM8Wdz2uaIbd8HwxOENBr1NdaG+3qowHMGk6a4gtVoJc0/JCEqBmEYMUfZ39lD\npec3VCwb4HkCZBT7TiKzUgEAeoXcG4kmHRwXIaTK5sQEIQKAiCDUuTxT8AwIqfRqr0tSYCGV\ntE+kBNLVyiFbCECBXlugH3SIuZXjdbjJMiP9tpre96N8kEKS7x1CiIozlqSsuypxIYQQQggh\nhBC6fmGCECEkFgV4t6HhlQsXPmlv7/H7WULydLpbCgoemTlzSWbmZEd39fRJLB+kAFIrDq9n\nrFpjWn6DafkNIsfL9MbSJ57u2729++2Xo17PFY8RYly8KvvBLysysobvqJBpLbqZXe7zkOxa\n7cB0+cYlhGGK/r//vPCf3xCCwSHJxbCBBYHGlsUSKWxgAcLus8cHb4y47N3bXrYf2MMHLtf0\nEIbVz1mUed8j6sJSkZOzGi2nM0roMkqIIiObsIkWWQQAgdIam4REAiGQpdE8XlnKMczh7r53\nm9ph+qxZaFYqjAo5AOTrNF3ewOhSm5TSdq9vvEND05IjGOanZL9ZR1q2qbddfGEjZRhnyjh8\n7xMAhYzN149brnEqk7Oa5XmP7Wv6AySuISUEgKzM/yrLcFcxOoQQQgghhBBC1yNMECKERKlz\nOB55773DXV0MIQKlABABqHM46hyOZ0+dur+8/C+33GJSDi0CuCZJfZpE+i5IEiKTpd++KW39\nBm/tOX9TXcRpY1UaRbpFP2exTJ+oj+jczPu73D9Jcq02dgggeaYlRlUuACgsOaVPPN346x+F\nHdbBu1IZIYRIyiAJcgIUwrZeoDTWPdV1+mjzH5/ig4EhEVGBd5095jpzNHPj5zI3bRaz8iIA\nGBavsO7dKTodRw2LViQd5IlEA7yEWjpKweoPcgwDAMssafk67a6Wjnqne0hMhBA69dKGYZ7n\nKWUJmZ1qOtQ1+jrgCC+EeF6RLPmKrnmDF9ubUjqKK4uqD4scTAnpyyqKxKvqlooCrMxMZ0Wv\nzDfd5ZuWLo44j3e8BDR+LTUhDAFmef7jFt2sqx8eQgghhBBCCKHrDSYIEULJHe7quu311z2h\nEAAIgy7iD/z0em3tyZ6ejx98MEenm4wAr6oyk0nSeJ7ScrN5goJBAwjL6mbO082cJ36XNE3p\nzIw7zve8m2RmwihZ/eKcR2K/RlwO99ljMqMp4nPTcHhgmMwnAFAACVe6ZV4BAEAQqCAQlnWd\nPNz42x9TOkK+UhCAQNe2f/B+X87mr4qZP/22+2wfvy8y8cbI5Om3bEg6zBeJiJltsJDARwUq\nYwgAZGpUj84scYcjDS6PIxgSKOjksjyd5tmztVMuPQjgjURP99kXpqfk67SlRn29yzO6LCZD\nSCxFiq5zatkU/cO7L7vYnpFr6u0gIrouE0prFt009oMSAilK5aqsjLFMEojylFI1J/mFjbjs\nzhOH+prqXFZrQKEKGtP4GfMy8osqzAa9fAJL9yrSbzWoso+2/d0d7IxVC1KgQAhQAKAmVd7S\n3C+makomLgCEEEIIIYQQQmjAFL1OgRCaOto9nnvefNMTDie4akgBmlyue7ZtO/jww8qpegF0\nvKzJydFynC8SEZ8ouKu4eAIDQmOwIOuz4ai33vZJ/CUMAQCIUqa/qeQ/VJwRKO3Z+UbXGy8K\noSAwzMAyhLF19TRW4qqQVgej6ooAAKszEJYN23qb//hzSgkkuEBPAQB6d23TlM00LV2bdH5l\nVm76nZ/p2fGqmGAsmz7PmVOTx8xKPsE5lpFd2XlVL+fmp13Omk/B1GAMIXDKal+YngIAG4vz\nnj1T6+cjo0gR6jkZc93USKEETEq5jGGiU6/LKCXk1LpN6954luVp0prjpllLrFmFYzwiIaCR\ncZsriuWs5Nw5BaiyOk732etdntiLyTJMkV47J9U0L82c9FyLuBxd/3zBum8XCAIFUBCioABA\nYc/r3Xllf1l5V/mMWbfkZaom7I+ZTF3lPTN+0eE+0+465Qp2hKJepUxvVOXmGhZk6isl3WWC\nEEIIIYQQQgiNxTV+HR8hNHb/sW+fNRhMWjcjUHqqt/cPp059b/HiqxPYZJGz7DcWLPjFkSNi\nBjOELLFYll5PazROL4Qwy/P/JU1TerLr1VDEA8AAoUBjOT8BgBSYli/K+VwsO9j8l6ftn+7u\n33PQJf5YpzhtrQ9WysVe2hVAaecVDp4Qoi2rBICu1/8uhINicmWEYdq3PGdcuJKIuH6d9Zkv\nhjrbnCcOJh5mXnWL5a4HxQSuV3Acy0R48RkOkpqsxS4B0HGca1A55hRBKTQ5PX+trrs1LytP\np3l0VvGL5+s9EcldIsvMhokID007HMOUGHW1dtcUTIo7UzKPrH942a6XWcpTYcQAuwpmnF6V\nvNQ4qTyd9sGyQoP0Wj17MLT1YlOn1x+7MyO2kReEeqenzune19HzUHlhhnrE9qf+5vr6p38Y\nddpjvxK4Yk3UjNa6tI7fH7/5wWcdix6ZWZymmqj24ISwOYYFOYYFEzQ/QgghhBBCCCEkBvvk\nk09OdgwITQ8ej+eZZ54BgEceeaSoqGiyw7lKml2uxz/4QGRXPULIyZ6e7yxadM3XyiyyWLbU\n1CQtIiSEyAj55z33XA+dV6c1s7qwPO0WkyqXY+Uco1RxBrO6oChl1ZLcL5Sl3cSxSgDoeuPF\nvg/eSjAJGxKiOjaYLhOVIySQucersPMAkHnfZkVqRsuff0VF1hVRKgT8mpIKpSUn+XEIMS5d\nA3zUV1dDgF4RGyEAwMi4zPsfzXn4X0Sua8gQ0uH1WwMhUaECAMDijNQiQ5JToDcQ6PIFxM95\nNblC4RO9NgCYnWqel2b2RKK9fgmhEoCNxXm6iWxaiKYRg1weeztNQV5jWk/BDHN3i9LvJYQZ\nKO4lDAOU8qysZvHNp9dspGPul7syK/2zZYVK6atydvkCz5276AyP+PkT4KMn++wFeq1RIR/+\naLiv++L/+XbU4xmpbpkAEKDZjVUdqTknosy8tJRRFDgihBBCCCGEEELTBVYQIoQSebuhQfya\nW5RSWyBwoKNjbW7uhEY16YwKxY5Nm9Zs3eqNRIQRXh+GEArw3Pr1S7B8cFLxAb/79FHP+dMR\nhw2AcsYU7Yw5hvlLWbV28DAZoygwLS8wLY87SbCzrXv71qTHytjv9eVxYT2bNEdouBDU14eA\nIarsAtOSNa5TR4SolLX9CHGfPWGYt1TUWIbJeuBLKWtu7d31lvPkwYitL7ZdkZZhXLQq7daN\n8pR0CYcGWJmVft7uFDlYxjBLLMk7l85PSzneM0WzJrEz/MO2Lp7SW/KyHigtuCk3829VF91h\nUf9k89NTsjTqCY0QTSO5Ok2uTt3m8U92IPE5UjM/+sy/ZbdcWGdvE1rrI047kctVmbn6uYt3\np5fU8kn7jyZBgGRpVbcXJL+5YTh/NPrShYYgzycIgVKI8sI/LjR8c+4Mw7AcYfOffhn1eRM/\nB0IpBVi8e8v7lu/vaGr7bNlYm6kihBBCCCGEEEJTFiYIEUKJnO3rYwgZKQcW15m+vms+QQgA\nc9PSjm3efO9bb5232Ya8RLFfDQrFi7ffjqsPTiIqCL3vv9n91j94vx8IAUJi3eSsH+9klcqM\nuz+bcdcDRNyKej3vvComU84Gaf6brub7DBF9osoYXWM4+wMvEMJw8oKv/gcQErb1iHxS/QgJ\n93VJ2kNhyc79wtdzv/B1IRyKetwyvYHh4lTYiFGg185JNZ21OsQMviHHohdRPFeg15abDbV2\n1+hCugoIwCft3QV6bYFe+0FLp8jsYKZGfU/Rtf95iESyB0Mv1WNSukoAACAASURBVDT2BqZo\nsWyMQEh74YwDc5d8eVbp4O03eP0Xzl4Y29yEZYiSZf989kKQF7ScLFOjmmE2Fhl0YuqX97Z1\nu0PhpB/EFCAQ5Xe1dj5QWjB4u/vMMe/FalFRUqoI+kvOfFrF3didY7GM3LAUIYQQQgghhBCa\n1jBBiBBKpNfvl9ottNc/RQsjxl2ZyXTmC1/4e3X181VVhzo7B3KEpSbTZysqvr1woUGhmNwI\nr2dCMND0X0+5Th/t75xJKQxK8fGhUOc/X/BUnSz69pOsWhuMuPwRByGMmjMpZEObYVJBcB7/\nVGThjNzBF29x2e7OtmYFKAGg0F9NSAEIMBGadsSfcszPAMMolIXf+pEqrwgAhJCEjp2x2STv\ncgkjV8hT0ka374B7i/NtwVCHd8STPfbU56Sa1uZYxExY63BZA8ExRjWhKAABsqOxzReNBqN8\n4sGxp19uMjxQVsCNuR8jujY4Q+E/n73ol1QrPEkohUaXp9cfTFdfXoQvW6u+KS9rT2snGalB\nZyKxnWhUoA0uT2yTLQAtbt+hrr4cnWZDUW7iQtsQzx/psYo/7tk++6352YPXOLQf2ksIQ6m4\nTs6E5F08dWHRjeesDkseJggRQgghhBBCCF2bMEGIEErEqFBIvQ5ovJ6yYjKG+fLs2V+ePdsX\nibR5PBFByNJoUlR4MXGyUdr8p1+6zhyL/Rx3AAC46s/t3/E9V6XaHeoeeMSgyik2ry5PWy9j\n+gvsAi31vJS0N+vnM3fYl//+v+rrdnS0HQhyISYKcregaQkb6iOMPwoA+nmLcj7/VYUlO7YL\nZzRJe4IEOJNZ2i7jSs4yj80q29bQctbqIEDolfkCAgCErMvOuDkvK+kdBhTgg5aOfR09ZMqv\nXUqB2oJi87K5Ou3mGcVT/Smhq0Wg9B8XGv3R6Ng6dF5VFxyuwQlCAFiXY4nwwr6O7rg5wthG\ng5wLCxAYmgeN87zppf90eH1/OXfxgdKCWSnGkYKpc3p4kau0Xpq81u4a3N/YW31abHYQACjV\nOfuUAW+L2yv+oAghhBBCCCGE0PSCCUKEUCKlJpOk/qIAUGaezLzFZNFwXMV1+cSnJtu+D5wn\nDiYe48uTt92p41VuEvIM3u4Kdpzs2FrTu3Nt0bfSNKVAactfn5EaAB/w6bmMhbO/srDyXzw1\nZ92njwS72/mgRzbLpMorMi5cESscHKAuqpB2AEHQFEvcZbzJWebBssKllrSDXb0Xne4I33/l\nXSWTzTQbVmdnpKmUiWeI+aita19HDwCIX+506qMAbR5vs9tbqNcmH42uA6f77F2+6VReTwBs\nwaFFvQRgfX5Wnl6zs7kjVvJLCBAASgkFKmOZ1VkZa7IzZAzT5vH1+AN1Tne1zZm04pBS4EF4\nta7pMXlZnk4Td0yfxL6sBMgVrVwpjbhEdUUeTOl1u4z4zY4QQgghhBBC6JqFCUKEUCJ3FhX9\n+MAB8eMVLHtTXt7ExYNQUlQQul5/gTCECiNelHaXKNru1vePH3LtmlIACEbdH1z82Y3F/66q\ndQVaG6XGwMoVhGUBAAjRzZyrmzk38XhlVq4qpyDY2UqHlchQAqE0WVjPChxwXkHZF2WDlDCM\nYeHK4fNQgHqn+4Ld1RsI+qNRlUyWrlJWmA2lRv0ElbLF1uTjKXWHI4FoVMtxWk7GiK4FbPF4\n97Z1japj4VRHCBzp7sMEIYo52mOdXu9zQiAwQivdCpOhzKhv9fjqnW5nKBwWBL1cnqfTlBn1\nSln/8qt5Og0B2N7YNrzCOC5KQaDwen3Lt+bNiPsB4ovwUl9AXyR6xe8MAQkliP27MNgiGCGE\nEEIIIYTQtQsThAihRBZmZKzMzh68wF5ij82Zo+G45OMQmjC+uvNhhy3BgJCZ7bhDB3BpdcB4\nYp3oPmn63exDKcAQGDnXOBwhZKB3qHiZ9z3S+LufDt4iyIl1kdoxVxVVXQ6UCFTTGikNzxu+\njmCrx7e9sS1WpUQIUApAoNnlOdLdl6FW3VOUWzBhySqWEJNCblLIpe64q6UTiMjlHacZSqHW\n4aaJ3mXoehGIRts9vun1NqcUNNyI/5vAEBK7OWDE3QHeaW6nUk5uCtQWCJ6xOuanxSnaU8vY\nuFOxfDStvSG9vU7tcxFeCGj11szC7rxyXq5UD46fEM6YErb2iA4HAMCvNeZL/1hDCCGEEEII\nIYSmC0wQIoSS+M0NN6zcsoUmawDIEJKiUv1o+fKrFhhCcfkuVice0LNGK8iSZ20oCBE+2Jja\nmC0lOwgAlFLD/GWSdgEA46KVpuU3OA7tjf0asMhaNhh4zdDiFcoQb778FKkJdWxZkPVZQvoH\nnOy1bWtoHajU6T9ZL/3e4w/8rerinDTzPUW5SpaVGtsEcYXCrW7v9MqaSBLmeW8kosN7Jq57\nzlBk2r3PKYBFPfr1dDu8vnaPT+peBEi17YoEoS8SrbG7unz+Dm+c2QouHJ91eJfS7wEAShgA\nIJQWnzsU5eS1C9alb/rc4MH6ygXWT94XmbKkQFyplrBCVWLUSX0WCCGEEEIIIYTQdIFtcxBC\nSSy2WJ5bvx4AErQNZAhRsOxbGzemq9VXMTSE4og47QkeDRtZT5H4ihDqLJdH1dJqwBiZPOWG\nOyTtAgBASP7j3zHMWwoAgUyu6UETrx7hO5oAAD3f8+6h1r/GNtQ53bHsYIJL3xTgTJ/9l8er\njvdYJcc2MZqv6exgzD8vNgdH6NOIrh/ecGSyQ5CMIaTCbBz17hfs7lHUzlKgbe7+RGAgGt3R\n1PaL4+e2NbQc7u7r8AUGV+MyAr/ow38u/Oh1hd8b20KoQKgQ60Iqi4ZnHflA99+/inrdA7uY\nV90svqCRAG0rX8AyzOxUXIMQIYQQQgghhNA1CxOECKHkHq2s3LZhg04uBwByZZowljXM1+sP\nPvzwiqysyYkPoUGI7HJxfNjIOmcq+5aqrYtUrgoFr2a8hRL7xRHwFigk7WFed5vr1OGOLc+1\nvfCHrjdfcp0+KkTCYnZk5Iqi7/wk/aFHWjcaKJO8N2WDbd9F64cRQXijrjlxdnBAmBe2NbRu\nb2ybCpk51zTMmkjV4PL86VytPxpNPhRdu9rjVb9NZYSQeWlmg3z0xa+2YBBEr0U6mC8aFSjt\nCwSfPVN7pKtvoL25QK9YynDe/rfza08AAIm7LiEFAAjVVTf+5knK95992orZhnlLxURFCfHr\nDPWVy1dmpo3lRUAIIYQQQgghhKY4bDGKEBJlQ0lJ4+OPP33s2Naammb35VvyZ6emPlpZ+dV5\n8xRTpm8hugqEcCjQ2hh1OQjHyVMtyqzcyY7ostjifN58ee8qTSDjiq85QoHzSC7nChulvLcJ\nse7Z3v9j/2VqYNVqy90Ppd+2iSTrNkkYpm8+F+0WdWGdEHK687WeYJknIj7/RAHgSHefXs6t\ny7GI3mtCXCeL89kCoS21TV+eVXqdPF80XJPHO9khSECAaGXs+rwx3fEz6sJZlmH80ejz1fXu\n8Ih9WTObawqrjyadilLw1lb1vPOaZcPDsS15j3+n9odfDzttiUoJCaEMe+TWz+eajDeP7UVA\nCCGEEEIIIYSmOEwQIoTEMiuVP1+9+uerV3d6vW0ej4Jlc3W6FNXo1yi6mgRKj3V3n7NarX6/\nTi4vN5tX5+RgUnMUgu0tXdtecp08PLgqTp6SlrLu9ozb72OUk/9+0M6a13WD1j5fNbywhBII\n6yWWzlPgVaPM7Awcnw8EOl79b+fxA0XfeZIzJGpYR6lQZ91LIG5RzPDBNBT11toPEVIqqn7w\nEgLwYVtXZYopVSWtODIY5TmWYS+V4Pib651H9/nqayIOO6NQyFMz9HMWG5eslmlFrdqlV1wX\npTkUaJPLc87qmJNqmuxY0OSwBUKTHYJYhICCYT8/o0Q3qso5RzDsjoQJEDk7miYlhIBJIX+7\noc0dDif4RKs8sosSQsR96PXseDVt/UZWpQYAzmAq/eHTDb/+UbCzNe5gChCVKw/evjmtvPKB\nsgJ2VEWQCCGEEEIIIYTQdIEJQoSQZFlabZZWO9lRiBURhL+cOfPzw4e7fFc0edNw3FfnzfvB\n0qUmpXKyYptmKO3e8UrX6y/AsHaWYZu1640XbR+9W/Sdn6gLyyYrwJgz/B77fBXASBVqEi/4\nEpD5BAnj416zphQAfI219b/8QfmPf8soRnzL2fxNoahHSnhMKHSR0hIJu8Syj5Tu6+jeVJKf\ndHCQ5492W6tszm6/nxcoATAouJkyWrTn9cCpwwBACIm9HwItjc7jBzq2/sVy7+aM2+9L2sqv\nUK8TmQqd7gjA/s4eTBBetyKClM+QSZWtVT9YWmhWSrt1wB+N7u/oOWN1uEKi2imPhFLI0mrO\n9NkSjNE7evS2bvFz8sGA+8wx07K1sV8VGVkVP/1Dzzuvde98nYauSNxShmkpW9C++o61M2bM\nT0/B3CBCCCGEEEIIoWseJggRQteyHr//3m3bDnV1Da9l8EUiTx87tqWmZse99y7IyJiE4Kab\n9i1/6d35JsRP6VAACLvsF3/6ndIfPq0prrjq0fVrsh+os+4d3zk5BxFfrZIIpYG2po6tf819\n9JsjDfGF+yTOKciJczSxAFTbXBuLKZMwjXfO6ni7sS0QjQ78s1MA0tFqeud//AFPbM+BbDGl\nAgDwgWDHlud8decLv/6DwetBDqeXc4UGXbPbzYKXUoYHNb1G245SgC6v3x2O6HE9s+vSNHpb\n35KbJTU7eLrP/lZD67gkQRlCktb1p3S2SJyVeGvPDSQIAYBRqjLv/0LGPZ/1nD/taaqzWfuC\ncmU4xaKYOX9pevq9WvU0+vdCCCGEEEIIIYTGAhOECKFrljscXvfKK7V2OwCMdOWy2+db+8or\nhz/3uVmpqVcztmnHcejj3p1vAiQs+BIohWjjr3808//9D6uZhBpTgUZPdr4CwIz8Dy4ZBdnF\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7l55ZYtX9+zJyL+8uL1QVNcUfGzP+pm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rz5vXi9eM/Iz0jFYaPfN8n+f77H+5qOhg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\nMjTtncc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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "big_mtx <- c()\n",
    "for(file in gluta_DEGs){\n",
    "    celltype <- gsub(\".txt\",\"\",str_split(file, pattern = \"//\")[[1]][2])\n",
    "    print(celltype)\n",
    "    \n",
    "    ct_file <- read.table(file)\n",
    "    ct_file[[\"celltype\"]] <- celltype\n",
    "    big_mtx <- rbind(big_mtx, ct_file)\n",
    "}\n",
    "print(dim(big_mtx))\n",
    "\n",
    "# adding DE column\n",
    "big_mtx$diffexpressed <- \"NO\"\n",
    "big_mtx$diffexpressed[which((big_mtx$avg_log2FC > 0.25 | big_mtx$avg_log2FC < -0.25) & (big_mtx$p_val_adj < 0.05))] <- \"DE\"\n",
    "\n",
    "head(big_mtx)\n",
    "\n",
    "plot_colors <- c(\"cadetblue3\", \"coral3\", \"darkolivegreen3\", \"darkcyan\",\n",
    "    \"mediumpurple1\", \"lightgreen\", \"lightgoldenrod\", \"lightslateblue\", \n",
    "    \"mistyrose\", \"lightblue4\", \"navajowhite1\", \"magenta\", \n",
    "    \"lightsalmon\", \"mediumorchid1\", \"midnightblue\", \"lightskyblue\", \n",
    "    \"lightgoldenrodyellow\", \"black\", \"lightgrey\", \"mistyrose4\")\n",
    "\n",
    "#big_mtx$celltype <- factor(big_mtx$celltype, levels = c(\"GABAergic.1\",\"GABAergic.2\",\"GABAergic.3\",\"GABAergic.4\",\"GABAergic.5\",\"GABAergic.6\",\n",
    "#                 \"GABAergic.7\",\"GABAergic.8\",\"GABAergic.9\",\"GABAergic.10\",\"GABAergic.11\",\"GABAergic.12\",\n",
    "#                 \"GABAergic.13\",\"GABAergic.14\",\"GABAergic.15\"))\n",
    "\n",
    "gplot <- ggplot(data=big_mtx, aes(x=avg_log2FC, y=-log10(p_val_adj), col = celltype)) +\n",
    "        geom_point(size = 3.8) + theme_classic() +\n",
    "        ggtitle(\"Glutamatergic\") + scale_color_manual(\"Cell Type\", values=plot_colors) +\n",
    "        theme(plot.title = element_text(color=\"black\", size=40, hjust=0.5),\n",
    "                axis.title.y = element_text(size = 25),\n",
    "                axis.title.x = element_text(size = 25),\n",
    "                axis.text = element_text(size = 25),\n",
    "                #legend.position = \"none\") +\n",
    "                legend.title = element_text(size = 25),\n",
    "                legend.text = element_text(size = 20),\n",
    "                legend.key.width = unit(0.9, \"cm\"),\n",
    "                legend.key.height = unit(0.75, \"cm\")) + \n",
    "        theme(aspect.ratio = 1) +\n",
    "        geom_point(data=big_mtx[(big_mtx$diffexpressed == \"NO\"),], aes(x=avg_log2FC, y=-log10(p_val_adj)), col = \"gray95\", size = 4) +\n",
    "        guides(col = guide_legend(ncol = 1))\n",
    "gplot\n",
    "\n",
    "pdf(paste0(OUTPATH, \"Glutamatergic_volcano.pdf\"), width = 12, height = 10)\n",
    "gplot\n",
    "dev.off()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6124ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48199860",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "db73db5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Astrocytes.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Macrophages.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Meis2.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Non-neuronal.txt'</li><li>'/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Oligo.txt'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/broad\\_ct//Astrocytes.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/broad\\_ct//Macrophages.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/broad\\_ct//Meis2.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/broad\\_ct//Non-neuronal.txt'\n",
       "\\item '/projects/ps-epigen/users/cmiciano/ASAP\\_Aaron/outputs/find\\_marks/230613/default\\_thres/broad\\_ct//Oligo.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Astrocytes.txt'\n",
       "2. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Macrophages.txt'\n",
       "3. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Meis2.txt'\n",
       "4. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Non-neuronal.txt'\n",
       "5. '/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Oligo.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Astrocytes.txt\"  \n",
       "[2] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Macrophages.txt\" \n",
       "[3] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Meis2.txt\"       \n",
       "[4] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Non-neuronal.txt\"\n",
       "[5] \"/projects/ps-epigen/users/cmiciano/ASAP_Aaron/outputs/find_marks/230613/default_thres/broad_ct//Oligo.txt\"       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other_DEGs <- list.files(paste0(DATA, \"broad_ct/\"), pattern = \".txt\", full.names = T)\n",
    "other_DEGs <- other_DEGs[-c(2, 3, 4)]\n",
    "other_DEGs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1af9f3a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Astrocytes\"\n",
      "[1] \"Macrophages\"\n",
      "[1] \"Meis2\"\n",
      "[1] \"Non-neuronal\"\n",
      "[1] \"Oligo\"\n",
      "[1] 1670    6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>p_val</th><th scope=col>avg_log2FC</th><th scope=col>pct.1</th><th scope=col>pct.2</th><th scope=col>p_val_adj</th><th scope=col>celltype</th><th scope=col>diffexpressed</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Malat1</th><td>1.369949e-16</td><td>-1.0767772</td><td>1.000</td><td>1.000</td><td>3.196091e-12</td><td>Astrocytes</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Gm42418</th><td>2.716989e-08</td><td> 1.1450988</td><td>1.000</td><td>0.954</td><td>6.338735e-04</td><td>Astrocytes</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Apoe</th><td>6.305670e-08</td><td>-1.2547158</td><td>0.847</td><td>0.928</td><td>1.471113e-03</td><td>Astrocytes</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Maml2</th><td>6.390487e-08</td><td> 0.7305912</td><td>0.915</td><td>0.686</td><td>1.490901e-03</td><td>Astrocytes</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Shroom3</th><td>1.339824e-07</td><td> 0.5952793</td><td>0.407</td><td>0.113</td><td>3.125808e-03</td><td>Astrocytes</td><td>DE</td></tr>\n",
       "\t<tr><th scope=row>Stk3</th><td>1.522663e-07</td><td> 0.4565056</td><td>0.627</td><td>0.258</td><td>3.552373e-03</td><td>Astrocytes</td><td>DE</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & p\\_val & avg\\_log2FC & pct.1 & pct.2 & p\\_val\\_adj & celltype & diffexpressed\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tMalat1 & 1.369949e-16 & -1.0767772 & 1.000 & 1.000 & 3.196091e-12 & Astrocytes & DE\\\\\n",
       "\tGm42418 & 2.716989e-08 &  1.1450988 & 1.000 & 0.954 & 6.338735e-04 & Astrocytes & DE\\\\\n",
       "\tApoe & 6.305670e-08 & -1.2547158 & 0.847 & 0.928 & 1.471113e-03 & Astrocytes & DE\\\\\n",
       "\tMaml2 & 6.390487e-08 &  0.7305912 & 0.915 & 0.686 & 1.490901e-03 & Astrocytes & DE\\\\\n",
       "\tShroom3 & 1.339824e-07 &  0.5952793 & 0.407 & 0.113 & 3.125808e-03 & Astrocytes & DE\\\\\n",
       "\tStk3 & 1.522663e-07 &  0.4565056 & 0.627 & 0.258 & 3.552373e-03 & Astrocytes & DE\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | p_val &lt;dbl&gt; | avg_log2FC &lt;dbl&gt; | pct.1 &lt;dbl&gt; | pct.2 &lt;dbl&gt; | p_val_adj &lt;dbl&gt; | celltype &lt;chr&gt; | diffexpressed &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| Malat1 | 1.369949e-16 | -1.0767772 | 1.000 | 1.000 | 3.196091e-12 | Astrocytes | DE |\n",
       "| Gm42418 | 2.716989e-08 |  1.1450988 | 1.000 | 0.954 | 6.338735e-04 | Astrocytes | DE |\n",
       "| Apoe | 6.305670e-08 | -1.2547158 | 0.847 | 0.928 | 1.471113e-03 | Astrocytes | DE |\n",
       "| Maml2 | 6.390487e-08 |  0.7305912 | 0.915 | 0.686 | 1.490901e-03 | Astrocytes | DE |\n",
       "| Shroom3 | 1.339824e-07 |  0.5952793 | 0.407 | 0.113 | 3.125808e-03 | Astrocytes | DE |\n",
       "| Stk3 | 1.522663e-07 |  0.4565056 | 0.627 | 0.258 | 3.552373e-03 | Astrocytes | DE |\n",
       "\n"
      ],
      "text/plain": [
       "        p_val        avg_log2FC pct.1 pct.2 p_val_adj    celltype  \n",
       "Malat1  1.369949e-16 -1.0767772 1.000 1.000 3.196091e-12 Astrocytes\n",
       "Gm42418 2.716989e-08  1.1450988 1.000 0.954 6.338735e-04 Astrocytes\n",
       "Apoe    6.305670e-08 -1.2547158 0.847 0.928 1.471113e-03 Astrocytes\n",
       "Maml2   6.390487e-08  0.7305912 0.915 0.686 1.490901e-03 Astrocytes\n",
       "Shroom3 1.339824e-07  0.5952793 0.407 0.113 3.125808e-03 Astrocytes\n",
       "Stk3    1.522663e-07  0.4565056 0.627 0.258 3.552373e-03 Astrocytes\n",
       "        diffexpressed\n",
       "Malat1  DE           \n",
       "Gm42418 DE           \n",
       "Apoe    DE           \n",
       "Maml2   DE           \n",
       "Shroom3 DE           \n",
       "Stk3    DE           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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zkiID4J1p1wAAIABJREFUfRe/+kc1v/nl4vseSVIP/ScOu50ycO5UMjoB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\nJAKHAAAgAElEQVTy59LR96j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+u3DLoOG/qi0wavGuoXIVe72DpEZwevdWXsiUK0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\nbVcSIYTQNCmNWdfese+Zh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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "big_mtx <- c()\n",
    "for(file in other_DEGs){\n",
    "    celltype <- gsub(\".txt\",\"\",str_split(file, pattern = \"//\")[[1]][2])\n",
    "    print(celltype)\n",
    "    \n",
    "    ct_file <- read.table(file)\n",
    "    ct_file[[\"celltype\"]] <- celltype\n",
    "    big_mtx <- rbind(big_mtx, ct_file)\n",
    "}\n",
    "print(dim(big_mtx))\n",
    "\n",
    "# adding DE column\n",
    "big_mtx$diffexpressed <- \"NO\"\n",
    "big_mtx$diffexpressed[which((big_mtx$avg_log2FC > 0.25 | big_mtx$avg_log2FC < -0.25) & (big_mtx$p_val_adj < 0.05))] <- \"DE\"\n",
    "\n",
    "head(big_mtx)\n",
    "\n",
    "plot_colors <- c(\"cadetblue3\", \"coral3\", \"darkolivegreen3\", \"darkcyan\",\n",
    "    \"mediumpurple1\", \"lightgreen\", \"lightgoldenrod\", \"lightslateblue\", \n",
    "    \"mistyrose\", \"lightblue4\", \"navajowhite1\", \"magenta\", \n",
    "    \"lightsalmon\", \"mediumorchid1\", \"midnightblue\", \"lightskyblue\", \n",
    "    \"lightgoldenrodyellow\", \"black\", \"lightgrey\", \"mistyrose4\")\n",
    "\n",
    "#big_mtx$celltype <- factor(big_mtx$celltype, levels = c(\"GABAergic.1\",\"GABAergic.2\",\"GABAergic.3\",\"GABAergic.4\",\"GABAergic.5\",\"GABAergic.6\",\n",
    "#                 \"GABAergic.7\",\"GABAergic.8\",\"GABAergic.9\",\"GABAergic.10\",\"GABAergic.11\",\"GABAergic.12\",\n",
    "#                 \"GABAergic.13\",\"GABAergic.14\",\"GABAergic.15\"))\n",
    "\n",
    "gplot <- ggplot(data=big_mtx, aes(x=avg_log2FC, y=-log10(p_val_adj), col = celltype)) +\n",
    "        geom_point(size = 3.8) + theme_classic() +\n",
    "        ggtitle(\"\") + scale_color_manual(\"Cell Type\", values=plot_colors) +\n",
    "        theme(plot.title = element_text(color=\"black\", size=40, hjust=0.5),\n",
    "                axis.title.y = element_text(size = 25),\n",
    "                axis.title.x = element_text(size = 25),\n",
    "                axis.text = element_text(size = 25),\n",
    "                #legend.position = \"none\") +\n",
    "                legend.title = element_text(size = 25),\n",
    "                legend.text = element_text(size = 20),\n",
    "                legend.key.width = unit(0.9, \"cm\"),\n",
    "                legend.key.height = unit(0.75, \"cm\")) + \n",
    "        theme(aspect.ratio = 1) +\n",
    "        geom_point(data=big_mtx[(big_mtx$diffexpressed == \"NO\"),], aes(x=avg_log2FC, y=-log10(p_val_adj)), col = \"gray95\", size = 4) +\n",
    "        guides(col = guide_legend(ncol = 1))\n",
    "gplot\n",
    "\n",
    "pdf(paste0(OUTPATH, \"Other_volcano.pdf\"), width = 12, height = 10)\n",
    "gplot\n",
    "dev.off()\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R [conda env:Rupdate]",
   "language": "R",
   "name": "conda-env-Rupdate-r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}