{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9_ctx_3region_DEGs_expression_in_cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/Data16Tc/home/haichao/anaconda3/envs/SpaCon_test/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "from scipy.spatial import cKDTree\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gene exp data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>totalRNA</th>\n",
       "      <th>brain_section_label</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>n_genes_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100008170567769574864159172860058606533</th>\n",
       "      <td>175</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>19.063883</td>\n",
       "      <td>33.423346</td>\n",
       "      <td>54.106801</td>\n",
       "      <td>100</td>\n",
       "      <td>175</td>\n",
       "      <td>MOB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100019036144713180485707288452329906643</th>\n",
       "      <td>373</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>44.290637</td>\n",
       "      <td>63.872748</td>\n",
       "      <td>53.902462</td>\n",
       "      <td>189</td>\n",
       "      <td>373</td>\n",
       "      <td>NDB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10002377904544842423531242460024745973</th>\n",
       "      <td>277</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>63.860525</td>\n",
       "      <td>7.998013</td>\n",
       "      <td>54.043841</td>\n",
       "      <td>126</td>\n",
       "      <td>277</td>\n",
       "      <td>RSPd2/3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100027648052649525810014127621472143070</th>\n",
       "      <td>435</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>113.480480</td>\n",
       "      <td>41.056674</td>\n",
       "      <td>54.349928</td>\n",
       "      <td>165</td>\n",
       "      <td>435</td>\n",
       "      <td>arb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100029875144524072265954931895494067096</th>\n",
       "      <td>60</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>11.572676</td>\n",
       "      <td>37.921554</td>\n",
       "      <td>54.370336</td>\n",
       "      <td>39</td>\n",
       "      <td>60</td>\n",
       "      <td>MOB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99961718914838042706216314325649765172</th>\n",
       "      <td>627</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>113.199100</td>\n",
       "      <td>23.661374</td>\n",
       "      <td>54.096322</td>\n",
       "      <td>157</td>\n",
       "      <td>627</td>\n",
       "      <td>CENT3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996242280180655885872867452807576494</th>\n",
       "      <td>157</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>86.896566</td>\n",
       "      <td>18.326938</td>\n",
       "      <td>54.027272</td>\n",
       "      <td>100</td>\n",
       "      <td>157</td>\n",
       "      <td>SCop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999921392501518309214993564089572563</th>\n",
       "      <td>97</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>21.082878</td>\n",
       "      <td>31.635078</td>\n",
       "      <td>54.090905</td>\n",
       "      <td>67</td>\n",
       "      <td>97</td>\n",
       "      <td>ORBm1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995909784199304193294645747252265238</th>\n",
       "      <td>154</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>111.605478</td>\n",
       "      <td>22.417974</td>\n",
       "      <td>54.070819</td>\n",
       "      <td>93</td>\n",
       "      <td>154</td>\n",
       "      <td>CENT3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996568769251334694329666788053033728</th>\n",
       "      <td>200</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>112.945756</td>\n",
       "      <td>31.433985</td>\n",
       "      <td>54.093405</td>\n",
       "      <td>95</td>\n",
       "      <td>200</td>\n",
       "      <td>CENT3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83602 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         totalRNA brain_section_label  \\\n",
       "100008170567769574864159172860058606533       175   Zhuang-ABCA-3.001   \n",
       "100019036144713180485707288452329906643       373   Zhuang-ABCA-3.001   \n",
       "10002377904544842423531242460024745973        277   Zhuang-ABCA-3.001   \n",
       "100027648052649525810014127621472143070       435   Zhuang-ABCA-3.001   \n",
       "100029875144524072265954931895494067096        60   Zhuang-ABCA-3.001   \n",
       "...                                           ...                 ...   \n",
       "99961718914838042706216314325649765172        627   Zhuang-ABCA-3.001   \n",
       "9996242280180655885872867452807576494         157   Zhuang-ABCA-3.001   \n",
       "999921392501518309214993564089572563           97   Zhuang-ABCA-3.001   \n",
       "99995909784199304193294645747252265238        154   Zhuang-ABCA-3.001   \n",
       "99996568769251334694329666788053033728        200   Zhuang-ABCA-3.001   \n",
       "\n",
       "                                                  x          y          z  \\\n",
       "100008170567769574864159172860058606533   19.063883  33.423346  54.106801   \n",
       "100019036144713180485707288452329906643   44.290637  63.872748  53.902462   \n",
       "10002377904544842423531242460024745973    63.860525   7.998013  54.043841   \n",
       "100027648052649525810014127621472143070  113.480480  41.056674  54.349928   \n",
       "100029875144524072265954931895494067096   11.572676  37.921554  54.370336   \n",
       "...                                             ...        ...        ...   \n",
       "99961718914838042706216314325649765172   113.199100  23.661374  54.096322   \n",
       "9996242280180655885872867452807576494     86.896566  18.326938  54.027272   \n",
       "999921392501518309214993564089572563      21.082878  31.635078  54.090905   \n",
       "99995909784199304193294645747252265238   111.605478  22.417974  54.070819   \n",
       "99996568769251334694329666788053033728   112.945756  31.433985  54.093405   \n",
       "\n",
       "                                         n_genes_by_counts  total_counts  \\\n",
       "100008170567769574864159172860058606533                100           175   \n",
       "100019036144713180485707288452329906643                189           373   \n",
       "10002377904544842423531242460024745973                 126           277   \n",
       "100027648052649525810014127621472143070                165           435   \n",
       "100029875144524072265954931895494067096                 39            60   \n",
       "...                                                    ...           ...   \n",
       "99961718914838042706216314325649765172                 157           627   \n",
       "9996242280180655885872867452807576494                  100           157   \n",
       "999921392501518309214993564089572563                    67            97   \n",
       "99995909784199304193294645747252265238                  93           154   \n",
       "99996568769251334694329666788053033728                  95           200   \n",
       "\n",
       "                                          region  \n",
       "100008170567769574864159172860058606533      MOB  \n",
       "100019036144713180485707288452329906643      NDB  \n",
       "10002377904544842423531242460024745973   RSPd2/3  \n",
       "100027648052649525810014127621472143070      arb  \n",
       "100029875144524072265954931895494067096      MOB  \n",
       "...                                          ...  \n",
       "99961718914838042706216314325649765172     CENT3  \n",
       "9996242280180655885872867452807576494       SCop  \n",
       "999921392501518309214993564089572563       ORBm1  \n",
       "99995909784199304193294645747252265238     CENT3  \n",
       "99996568769251334694329666788053033728     CENT3  \n",
       "\n",
       "[83602 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_in = sc.read_h5ad('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/adata_processed.h5ad')\n",
    "allen_region = pd.read_csv('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/allen_region.csv')\n",
    "adata_in.obs['region'] = allen_region['region'].values\n",
    "meta = pd.read_csv('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/cell_metadata_with_cluster_annotation.csv')\n",
    "meta = meta.set_index('cell_label')\n",
    "meta = meta.loc[adata_in.obs.index.to_list()]\n",
    "adata_in.obs['cell_type'] = meta['class'].to_list()\n",
    "\n",
    "adata_out = sc.read_h5ad('/mnt/Data18Td/Data/haichao/merfish_raw_data_zxw3/out_cell_adata/adata_out_cell_distance_q0.3/after_qc/Zhuang-ABCA-3.001.h5ad')\n",
    "adata_out.obs['region'] = adata_in.obs.loc[adata_out.obs_names]['region'].values\n",
    "\n",
    "adata_in = adata_in[adata_in.obs['cell_type'].str.contains('Glut')]\n",
    "adata_out.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_com = list(set(adata_in.var_names) & set(adata_out.var_names))\n",
    "adata_in = adata_in[:, gene_com]\n",
    "adata_out = adata_out[:, gene_com]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f887404b310>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctx2cc_conn_cluster_mean = pd.read_csv('./ctx_cc_conn_filtered.csv', index_col=0)\n",
    "sns.clustermap(ctx2cc_conn_cluster_mean, cmap='coolwarm', center=0, figsize=(10, 3), linewidths=0.05, linecolor='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 67823 × 1111\n",
       "    obs: 'brain_section_label', 'x', 'y', 'z', 'x_ccf', 'y_ccf', 'z_ccf', 'region', 'cell_type'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_ctx = adata_in[adata_in.obs['region'].str.startswith(tuple(ctx2cc_conn_cluster_mean.columns))]\n",
    "adata_ctx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ctx1 = ['FRP', 'ORBl', 'ORBvl', 'ORBm', 'PL', 'ILA']\n",
    "ctx2 = ['SSp-m', 'ACAd', 'ACAv']\n",
    "ctx3=['RSPv','RSPd', 'VISpm', 'VISa', 'VISam', 'SSp-bfd', 'VISpor', 'VISli', 'TEa', 'VISpl', 'VISl', 'AUDv', 'VISal', 'AUDpo', 'AUDp', 'AUDd']\n",
    "adata_ctx.obs['cc_ctx'] = None\n",
    "adata_ctx.obs.loc[adata_ctx.obs['region'].str.startswith(tuple(ctx1)), 'cc_ctx'] = 'ctx1'\n",
    "adata_ctx.obs.loc[adata_ctx.obs['region'].str.startswith(tuple(ctx2)), 'cc_ctx'] = 'ctx2'\n",
    "adata_ctx.obs.loc[adata_ctx.obs['region'].str.startswith(tuple(ctx3)), 'cc_ctx'] = 'ctx3'\n",
    "adata_ctx = adata_ctx[adata_ctx.obs['cc_ctx'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_cc = adata_out[adata_out.obs['region'].str.startswith('cc')]\n",
    "tmp = adata_cc.copy()\n",
    "idx = []\n",
    "cc_cluster_by_conn = pd.read_csv('./cc_cluster_by_conn.csv', index_col=0)\n",
    "coordinate = cc_cluster_by_conn[['x', 'y']].values\n",
    "kdtree = cKDTree(coordinate)\n",
    "for coord in tqdm(adata_cc.obs[['x', 'y']].values):\n",
    "    _, nearest_index = kdtree.query(coord)\n",
    "    idx.append(nearest_index)\n",
    "plt.figure(figsize=(9,5))\n",
    "### match the cc cluster\n",
    "adata_cc.obs['NT_index'] = idx\n",
    "adata_cc.obs['cc'] = cc_cluster_by_conn.loc[adata_cc.obs['NT_index']]['c'].values\n",
    "adata_cc = adata_cc[adata_cc.obs['cc'].notna()]\n",
    "adata_cc.obs['cc'] = 'cc'+((adata_cc.obs['cc']).astype(int)+1).astype(str)\n",
    "#### plot\n",
    "categories = adata_cc.obs['cc'].unique()\n",
    "# plt.scatter(adata_out.obs['x'], adata_out.obs['y'], s=1, c='#d3d3d3')\n",
    "plt.scatter(tmp.obs['x'], tmp.obs['y'], s=20, c='#8f8f8f')\n",
    "# plt.scatter(tmp.obs['x'], tmp.obs['y'], s=20, c='#d3d3d3')\n",
    "\n",
    "cm = ['#ff7f0e', '#2ca02c', '#2377b4']\n",
    "# cm = ['#6597b9', '#6bad6b', '#e9a161']\n",
    "i=0\n",
    "for category in categories:\n",
    "    subset = adata_cc[adata_cc.obs['cc'] == category]\n",
    "    plt.scatter(subset.obs['x'], subset.obs['y'], label=category, s=20, c=cm[i])\n",
    "    # print(f'cc_{int(category)}')\n",
    "    i=i+1\n",
    "plt.legend()\n",
    "plt.gca().invert_yaxis()\n",
    "# plt.savefig('cc_area.png', dpi=600)\n",
    "# plt.savefig('./cc/cc1_cc2_cc3.pdf', format='pdf')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DEG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.normalize_total(adata_ctx, target_sum=1e4)\n",
    "sc.pp.log1p(adata_ctx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.normalize_total(adata_cc, target_sum=1e4)\n",
    "sc.pp.log1p(adata_cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.rank_genes_groups(adata_ctx, groupby=\"cc_ctx\", reference=\"rest\", n_genes=adata_ctx.shape[1], method='wilcoxon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>score</th>\n",
       "      <th>logfoldchanges</th>\n",
       "      <th>pvals</th>\n",
       "      <th>pvals_adj</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ctx3</th>\n",
       "      <td>Nr2f2</td>\n",
       "      <td>10.998008</td>\n",
       "      <td>2.251740</td>\n",
       "      <td>3.906658e-28</td>\n",
       "      <td>2.346106e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx3</th>\n",
       "      <td>Tshz2</td>\n",
       "      <td>42.789413</td>\n",
       "      <td>1.908488</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx3</th>\n",
       "      <td>Met</td>\n",
       "      <td>15.654150</td>\n",
       "      <td>1.551293</td>\n",
       "      <td>3.112418e-55</td>\n",
       "      <td>2.659920e-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx3</th>\n",
       "      <td>Ctxn3</td>\n",
       "      <td>5.829773</td>\n",
       "      <td>1.497417</td>\n",
       "      <td>5.550263e-09</td>\n",
       "      <td>1.995580e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx3</th>\n",
       "      <td>C1ql2</td>\n",
       "      <td>4.970668</td>\n",
       "      <td>1.410147</td>\n",
       "      <td>6.672248e-07</td>\n",
       "      <td>2.193156e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx2</th>\n",
       "      <td>Scn4b</td>\n",
       "      <td>33.871655</td>\n",
       "      <td>1.264914</td>\n",
       "      <td>1.742216e-251</td>\n",
       "      <td>6.452006e-249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx2</th>\n",
       "      <td>Bmpr1b</td>\n",
       "      <td>13.590011</td>\n",
       "      <td>1.168321</td>\n",
       "      <td>4.590144e-42</td>\n",
       "      <td>8.792501e-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx2</th>\n",
       "      <td>Adgrl2</td>\n",
       "      <td>57.814980</td>\n",
       "      <td>1.156904</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx2</th>\n",
       "      <td>Blnk</td>\n",
       "      <td>15.697517</td>\n",
       "      <td>1.049282</td>\n",
       "      <td>1.572777e-55</td>\n",
       "      <td>5.139282e-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx2</th>\n",
       "      <td>Plekha2</td>\n",
       "      <td>13.359410</td>\n",
       "      <td>1.018079</td>\n",
       "      <td>1.043885e-40</td>\n",
       "      <td>1.840883e-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx1</th>\n",
       "      <td>Crym</td>\n",
       "      <td>70.281998</td>\n",
       "      <td>3.123753</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx1</th>\n",
       "      <td>Col6a1</td>\n",
       "      <td>49.039230</td>\n",
       "      <td>2.726512</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx1</th>\n",
       "      <td>Col12a1</td>\n",
       "      <td>45.270657</td>\n",
       "      <td>2.506889</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx1</th>\n",
       "      <td>Tnfrsf8</td>\n",
       "      <td>15.637980</td>\n",
       "      <td>2.191909</td>\n",
       "      <td>4.012542e-55</td>\n",
       "      <td>3.277893e-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctx1</th>\n",
       "      <td>Prss12</td>\n",
       "      <td>57.639507</td>\n",
       "      <td>2.105650</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene      score  logfoldchanges          pvals      pvals_adj\n",
       "region                                                                  \n",
       "ctx3      Nr2f2  10.998008        2.251740   3.906658e-28   2.346106e-27\n",
       "ctx3      Tshz2  42.789413        1.908488   0.000000e+00   0.000000e+00\n",
       "ctx3        Met  15.654150        1.551293   3.112418e-55   2.659920e-54\n",
       "ctx3      Ctxn3   5.829773        1.497417   5.550263e-09   1.995580e-08\n",
       "ctx3      C1ql2   4.970668        1.410147   6.672248e-07   2.193156e-06\n",
       "ctx2      Scn4b  33.871655        1.264914  1.742216e-251  6.452006e-249\n",
       "ctx2     Bmpr1b  13.590011        1.168321   4.590144e-42   8.792501e-41\n",
       "ctx2     Adgrl2  57.814980        1.156904   0.000000e+00   0.000000e+00\n",
       "ctx2       Blnk  15.697517        1.049282   1.572777e-55   5.139282e-54\n",
       "ctx2    Plekha2  13.359410        1.018079   1.043885e-40   1.840883e-39\n",
       "ctx1       Crym  70.281998        3.123753   0.000000e+00   0.000000e+00\n",
       "ctx1     Col6a1  49.039230        2.726512   0.000000e+00   0.000000e+00\n",
       "ctx1    Col12a1  45.270657        2.506889   0.000000e+00   0.000000e+00\n",
       "ctx1    Tnfrsf8  15.637980        2.191909   4.012542e-55   3.277893e-54\n",
       "ctx1     Prss12  57.639507        2.105650   0.000000e+00   0.000000e+00"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions = adata_ctx.obs['cc_ctx'].unique()\n",
    "top_genes_df = pd.DataFrame(columns=['region', 'gene', 'score', 'logfoldchanges', 'pvals', 'pvals_adj'])\n",
    "for region in regions:\n",
    "    markers_df = sc.get.rank_genes_groups_df(adata_ctx, group=region)\n",
    "    markers_df = markers_df.sort_values(by='logfoldchanges', ascending=False)\n",
    "    top_genes = markers_df.head(5)\n",
    "    top_genes['region'] = region\n",
    "    top_genes = top_genes[['region', 'names', 'scores', 'logfoldchanges', 'pvals', 'pvals_adj']]\n",
    "    top_genes.columns = ['region', 'gene', 'score', 'logfoldchanges', 'pvals', 'pvals_adj']\n",
    "    top_genes_df = pd.concat([top_genes_df, top_genes], ignore_index=True)\n",
    "top_genes_df = top_genes_df.set_index('region')\n",
    "top_genes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(16, 4))\n",
    "# top_genes_df_filt = top_genes_df[top_genes_df['logfoldchanges'] > 1]\n",
    "cc = ['ctx1', 'ctx2', 'ctx3']\n",
    "for i, cc_area in enumerate(cc):\n",
    "    gene_data = adata_cc[:, top_genes_df.loc[cc_area]['gene']].X.A.mean(axis=1)\n",
    "    group_data = adata_cc.obs['cc']\n",
    "    df = pd.DataFrame({\n",
    "        'Expression': gene_data,\n",
    "        'subregion': group_data\n",
    "    })\n",
    "    ax = axes[i]\n",
    "    sns.barplot(x='subregion', y='Expression', data=df, ax = ax, palette=['#3a923a', '#3274a1', '#e1812c'], order=['cc3', 'cc1', 'cc2'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SpaCon_clone",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  },
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