{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10_ctx_cc_connection_and_correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/Data16Tc/home/haichao/anaconda3/envs/SpaCon_clone/lib/python3.8/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/mnt/Data16Tc/home/haichao/anaconda3/envs/SpaCon_clone/lib/python3.8/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/mnt/Data16Tc/home/haichao/anaconda3/envs/SpaCon_clone/lib/python3.8/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/mnt/Data16Tc/home/haichao/anaconda3/envs/SpaCon_clone/lib/python3.8/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\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",
    "import anndata as ad\n",
    "import numpy as np\n",
    "import os\n",
    "import warnings\n",
    "from scipy.stats import pearsonr\n",
    "import seaborn as sns\n",
    "import re\n",
    "from allensdk.core.mouse_connectivity_cache import MouseConnectivityCache\n",
    "import umap\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx_43_regions = ['FRP', 'MOp', 'MOs', 'SSp-n', 'SSp-bfd', 'SSp-ll', 'SSp-m', 'SSp-ul', 'SSp-tr', 'SSp-un', 'SSs', 'GU', 'VISC', 'AUDd', 'AUDp', 'AUDpo', 'AUDv', 'VISal', 'VISam', 'VISl', 'VISp', 'VISpl', 'VISpm', 'VISli', 'VISpor', 'ACAd', 'ACAv', 'PL', 'ILA', 'ORBl', 'ORBm', 'ORBvl', 'AId', 'AIp', 'AIv', 'RSPagl', 'RSPd', 'RSPv', 'VISa', 'VISrl', 'TEa', 'PERI', 'ECT']\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### conn data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 11%|█         | 14/132 [00:00<00:01, 59.79it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 132/132 [00:04<00:00, 29.30it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2438"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mcc = MouseConnectivityCache(resolution=100)\n",
    "structure_tree = mcc.get_structure_tree()\n",
    "annotation = mcc.get_annotation_volume()[0]\n",
    "cc_coor = []\n",
    "for x in tqdm(range(132)):\n",
    "    for y in range(80):\n",
    "        for z in range(59):\n",
    "            point = tuple((x,y,z))\n",
    "            point_id = annotation[point]\n",
    "            # print(point_id)\n",
    "            if point_id == 0:\n",
    "                continue\n",
    "            parent = structure_tree.ancestor_ids([point_id])[0][0]\n",
    "            parent_name = structure_tree.get_structures_by_id([parent])[0]['acronym']\n",
    "            if parent_name.startswith('cc'):\n",
    "                cc_coor.append([x,y,z])\n",
    "len(cc_coor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43, 98)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cc_coor = pd.read_csv('./cc_xyz_coor_100um.csv')\n",
    "file_path = '/mnt/Data16Ta/feiyao/tracing_for_haichao/results/'\n",
    "files = os.listdir(file_path)\n",
    "files = [i for i in files if 'npy' in i]\n",
    "ctx2cc_conn_list = []\n",
    "idx = []\n",
    "for f in files:\n",
    "    ctx_region = f.split('-')[1]\n",
    "    if f.split('-')[2] != 'mean_mean':\n",
    "        ctx_region = f\"{f.split('-')[1]}-{f.split('-')[2]}\"\n",
    "    tmp = np.load(file_path + f)\n",
    "    conn_tmp = [tmp[x, y, z] for x, y, z in cc_coor]\n",
    "    ctx2cc_conn_list.append(conn_tmp)\n",
    "    idx.append(ctx_region)\n",
    "ctx2cc_conn = pd.DataFrame(data=ctx2cc_conn_list, index=idx, dtype=float)\n",
    "ctx2cc_conn = ctx2cc_conn.loc[:, (ctx2cc_conn != 0).any(axis=0)]\n",
    "ctx2cc_conn_raw = ctx2cc_conn.copy()\n",
    "ctx2cc_conn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx2cc_conn = ctx2cc_conn.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coor = [cc_coor[i] for i in ctx2cc_conn_raw.columns]\n",
    "coor = np.array(coor)\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.scatter(coor[:, 0], coor[:, 1], c=np.arange(coor.shape[0]), cmap='coolwarm')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.colorbar()\n",
    "plt.savefig('./cc/cc_id_map.pdf', format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx2cc_conn.columns = range(ctx2cc_conn.shape[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(ctx2cc_conn, figsize=(10,10))\n",
    "# sns.heatmap(ctx2cc_conn)\n",
    "plt.savefig('./cc/cc_raw_heatmap.pdf', format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ECT',\n",
       " 'SSp-un',\n",
       " 'SSp-tr',\n",
       " 'SSp-n',\n",
       " 'AIv',\n",
       " 'VISrl',\n",
       " 'AIp',\n",
       " 'PERI',\n",
       " 'SSp-ul',\n",
       " 'GU',\n",
       " 'VISp',\n",
       " 'MOs',\n",
       " 'RSPagl',\n",
       " 'MOp',\n",
       " 'VISC',\n",
       " 'SSs',\n",
       " 'SSp-ll',\n",
       " 'AId']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_drop = [col for col in ctx2cc_conn.columns if (ctx2cc_conn[col] < 0.001).all()]\n",
    "columns_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_to_drop = ctx2cc_conn[(ctx2cc_conn < 0.001).all(axis=1)].index\n",
    "len(rows_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx2cc_conn.drop(columns=columns_to_drop, inplace=True)\n",
    "ctx2cc_conn.drop(index=rows_to_drop, inplace=True)\n",
    "# ctx2cc_conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "import numpy as np\n",
    "\n",
    "# Customized rows and columns level clustering\n",
    "# Here, use the Linkage function of Scipy to generate hierarchical clustering\n",
    "row_linkage = sch.linkage(ctx2cc_conn, method='single')\n",
    "col_linkage = sch.linkage(ctx2cc_conn.T, method='single')\n",
    "\n",
    "# Draw clustermap with custom cluster\n",
    "g = sns.clustermap(ctx2cc_conn, row_linkage=row_linkage, col_linkage=col_linkage)\n",
    "plt.setp(g.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)\n",
    "plt.savefig('./cc/cc_sel_heatmap.pdf', format='pdf')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### cluster cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RSPv', 'RSPd', 'VISpm', 'VISa', 'VISam', 'SSp-bfd', 'VISpor',\n",
       "       'VISli', 'TEa', 'VISpl', 'VISl', 'AUDv', 'VISal', 'AUDpo', 'AUDp',\n",
       "       'AUDd', 'FRP', 'ORBl', 'ORBvl', 'ORBm', 'PL', 'ILA', 'SSp-m',\n",
       "       'ACAd', 'ACAv'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster import hierarchy\n",
    "col_linkage = g.dendrogram_col.linkage\n",
    "col_clusters = hierarchy.fcluster(col_linkage, t=0.5, criterion='distance')\n",
    "reordered_cols = g.dendrogram_col.reordered_ind\n",
    "col_labels = np.array(ctx2cc_conn.columns)[reordered_cols]\n",
    "col_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use UMAP for 2D dimension reduction\n",
    "reducer = umap.UMAP(n_components=2, random_state=0)\n",
    "embedding = reducer.fit_transform(ctx2cc_conn)\n",
    "\n",
    "# Classification of UMAP results\n",
    "n_clusters=3\n",
    "\n",
    "from sklearn.mixture import GaussianMixture\n",
    "# Use GMM for clustering\n",
    "gmm = GaussianMixture(n_components=n_clusters, random_state=0)\n",
    "gmm.fit(embedding)\n",
    "clusters = gmm.predict(embedding)\n",
    "\n",
    "# Convert Embedding to DataFrame for easy operation\n",
    "embedding_df = pd.DataFrame(embedding, index=ctx2cc_conn.index, columns=['UMAP1', 'UMAP2'])\n",
    "embedding_df['Cluster'] = clusters\n",
    "\n",
    "# Get discrete color board\n",
    "# color_list = plt.cm.tab10(np.linspace(0, 1, n_clusters))\n",
    "color_list = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd','#8c564b', '#17becf', '#e377c2']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "from scipy.spatial import ConvexHull\n",
    "from shapely.geometry import Polygon\n",
    "# Draw a 2D UMAP diagram with clustering results\n",
    "for cluster in range(n_clusters):\n",
    "    subset = embedding_df[embedding_df['Cluster'] == cluster]\n",
    "    points = subset[['UMAP1', 'UMAP2']].values\n",
    "    \n",
    "    # Get color\n",
    "    color = color_list[cluster]\n",
    "    \n",
    "    # Draw a scattered point map\n",
    "    ax.scatter(subset['UMAP1'], subset['UMAP2'], color=color, label=f'cc{cluster+1}', s=50, alpha=1,\n",
    "                )\n",
    "    \n",
    "    # Calculate and draw the expanded and smooth convex bag\n",
    "    if len(points) > 2:  # Ne convex bags require at least 3 points\n",
    "        hull = ConvexHull(points)\n",
    "        hull_points = points[hull.vertices]\n",
    "        \n",
    "        # Create polygon and expand\n",
    "        poly = Polygon(hull_points).buffer(0.2)\n",
    "        # Make the polygon smoother\n",
    "        smooth_poly = poly.buffer(0.2, resolution=16, join_style=1).buffer(-0.2, resolution=16, join_style=1)\n",
    "        # Get the coordinates of polygonal shape\n",
    "        x, y = smooth_poly.exterior.xy\n",
    "        # Draw a smooth polygon\n",
    "        ax.fill(x, y, alpha=0.3, fc=color, ec=color)\n",
    "\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.xlabel('UMAP1')\n",
    "plt.ylabel('UMAP2')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./cc/cc_conn_umap.pdf', format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_df['Cluster'] = embedding_df['Cluster'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_df_sel = pd.DataFrame(columns=['x', 'y', 'c'])\n",
    "coor = [cc_coor[i] for i in embedding_df.index]\n",
    "coor = np.array(coor)\n",
    "cc_df_sel[['x', 'y']] = coor[:, :2]\n",
    "cc_df_sel['c'] = embedding_df['Cluster'].values\n",
    "cc_df_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "     x   y\n",
       "0   43  33\n",
       "1   43  34\n",
       "2   43  35\n",
       "3   43  36\n",
       "4   43  37\n",
       "..  ..  ..\n",
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_df_all = pd.DataFrame(columns=['x', 'y'])\n",
    "coor = [cc_coor[i] for i in ctx2cc_conn_raw.columns]\n",
    "coor = np.array(coor)\n",
    "cc_df_all[['x', 'y']] = coor[:, :2]\n",
    "# cc_df_tmp['c'] = embedding_df['Cluster'].values\n",
    "cc_df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     x   y    c\n",
       "0   43  33  NaN\n",
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       "4   43  37    2\n",
       "..  ..  ..  ...\n",
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       "[98 rows x 3 columns]"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_df_tmp = pd.merge(cc_df_all, cc_df_sel, on=['x', 'y'], how='left')\n",
    "cc_df_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe944493fa0>"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = cc_df_sel['c'].unique()\n",
    "# Draw the scattered point for each category\n",
    "plt.scatter(cc_df_all['x'], cc_df_all['y'], color = '#D3D3D3')\n",
    "cm = ['#2ca02c', '#2377b4', '#ff7f0e']\n",
    "i=0\n",
    "for category in categories:\n",
    "    subset = cc_df_sel[cc_df_sel['c'] == category]\n",
    "    plt.scatter(subset['x'], subset['y'], label=f'cc{int(category)+1}', color=cm[i])\n",
    "    i=i+1\n",
    "plt.legend()\n",
    "# plt.savefig('./cc/cc.pdf', format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[776,\n",
       " 838,\n",
       " 905,\n",
       " 947,\n",
       " 962,\n",
       " 1009,\n",
       " 1058,\n",
       " 1915,\n",
       " 1975,\n",
       " 2027,\n",
       " 2035,\n",
       " 2088,\n",
       " 2095,\n",
       " 2148,\n",
       " 2155,\n",
       " 2205,\n",
       " 2214,\n",
       " 2221,\n",
       " 2267,\n",
       " 2276,\n",
       " 2283,\n",
       " 2332,\n",
       " 2341,\n",
       " 2349,\n",
       " 2405,\n",
       " 181,\n",
       " 189,\n",
       " 196,\n",
       " 203,\n",
       " 291,\n",
       " 297]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th_cluster_order = []\n",
    "num = []\n",
    "for i in range(3):\n",
    "    tmp = embedding_df[embedding_df['Cluster'] == str(i)]\n",
    "    th_cluster_order = th_cluster_order + tmp.index.tolist()\n",
    "    num.append(len(tmp))\n",
    "    # print(tmp.index.tolist())\n",
    "th_cluster_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7fe944454220>"
      ]
     },
     "execution_count": 19,
     "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 = ctx2cc_conn.loc[th_cluster_order, col_labels]\n",
    "cluster_labels = ['cc1']*num[0] + ['cc2']*num[1] + ['cc3']*num[2]\n",
    "\n",
    "ctx2cc_conn['cluster'] = cluster_labels\n",
    "ctx2cc_conn_cluster_mean = ctx2cc_conn.groupby('cluster').mean()\n",
    "sns.clustermap(ctx2cc_conn_cluster_mean, figsize=(10, 3))\n",
    "# plt.savefig('./cc/ctx_cc_conn.pdf', format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx2cc_conn_cluster_mean.to_csv('./data/ctx_cc_conn_filtered.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gene exp data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "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 = sc.read_h5ad('/mnt/Data18Td/Data/haichao/merfish_raw_data_zxw3/out_cell_adata/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": 21,
   "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": 22,
   "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": 22,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>brain_section_label</th>\n",
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       "      <th>region_corr</th>\n",
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       "    <tr>\n",
       "      <th>cell_label</th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67823 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        brain_section_label          x  \\\n",
       "cell_label                                                               \n",
       "26411547786725657279968630020392489253    Zhuang-ABCA-3.023  70.979368   \n",
       "51881204226374601340923791448380690325    Zhuang-ABCA-3.023  71.048105   \n",
       "9451154036366067416684391406873704139     Zhuang-ABCA-3.023  70.906348   \n",
       "171871543800699640490603639355149752394   Zhuang-ABCA-3.023  71.407038   \n",
       "103032537075799430231760598323601697566   Zhuang-ABCA-3.023  71.756868   \n",
       "...                                                     ...        ...   \n",
       "76782871153522898745220481389975181375    Zhuang-ABCA-3.009  99.188067   \n",
       "787969273575687407995158747342318857      Zhuang-ABCA-3.009  99.457911   \n",
       "90320078306590901695895422976195202341    Zhuang-ABCA-3.009  99.669320   \n",
       "94251932557658106169391763576538731088    Zhuang-ABCA-3.009  99.532633   \n",
       "94737806914081356525582745432967518089    Zhuang-ABCA-3.009  99.422280   \n",
       "\n",
       "                                                 y          z     x_ccf  \\\n",
       "cell_label                                                                \n",
       "26411547786725657279968630020392489253   44.436003  12.009219  7.097937   \n",
       "51881204226374601340923791448380690325   43.963880  11.994586  7.104811   \n",
       "9451154036366067416684391406873704139    44.496406  12.010524  7.090635   \n",
       "171871543800699640490603639355149752394  43.722307  11.991027  7.140704   \n",
       "103032537075799430231760598323601697566  42.845081  11.969093  7.175687   \n",
       "...                                            ...        ...       ...   \n",
       "76782871153522898745220481389975181375   15.861129  38.190379  9.918807   \n",
       "787969273575687407995158747342318857     15.644515  38.208613  9.945791   \n",
       "90320078306590901695895422976195202341   15.954429  38.226878  9.966932   \n",
       "94251932557658106169391763576538731088   16.481350  38.226079  9.953263   \n",
       "94737806914081356525582745432967518089   16.628568  38.220187  9.942228   \n",
       "\n",
       "                                            y_ccf     z_ccf   region  \\\n",
       "cell_label                                                             \n",
       "26411547786725657279968630020392489253   4.443600  1.200922     TEa5   \n",
       "51881204226374601340923791448380690325   4.396388  1.199459     TEa5   \n",
       "9451154036366067416684391406873704139    4.449641  1.201052     TEa5   \n",
       "171871543800699640490603639355149752394  4.372231  1.199103     TEa5   \n",
       "103032537075799430231760598323601697566  4.284508  1.196909     TEa5   \n",
       "...                                           ...       ...      ...   \n",
       "76782871153522898745220481389975181375   1.586113  3.819038  RSPd2/3   \n",
       "787969273575687407995158747342318857     1.564451  3.820861  RSPd2/3   \n",
       "90320078306590901695895422976195202341   1.595443  3.822688    RSPd1   \n",
       "94251932557658106169391763576538731088   1.648135  3.822608    RSPd1   \n",
       "94737806914081356525582745432967518089   1.662857  3.822019  RSPd2/3   \n",
       "\n",
       "                                                 cell_type cc_ctx region_corr  \n",
       "cell_label                                                                     \n",
       "26411547786725657279968630020392489253   02 NP-CT-L6b Glut   ctx2         TEa  \n",
       "51881204226374601340923791448380690325   02 NP-CT-L6b Glut   ctx2         TEa  \n",
       "9451154036366067416684391406873704139        01 IT-ET Glut   ctx2         TEa  \n",
       "171871543800699640490603639355149752394      01 IT-ET Glut   ctx2         TEa  \n",
       "103032537075799430231760598323601697566      01 IT-ET Glut   ctx2         TEa  \n",
       "...                                                    ...    ...         ...  \n",
       "76782871153522898745220481389975181375       01 IT-ET Glut   ctx2        RSPd  \n",
       "787969273575687407995158747342318857         01 IT-ET Glut   ctx2        RSPd  \n",
       "90320078306590901695895422976195202341       01 IT-ET Glut   ctx2        RSPd  \n",
       "94251932557658106169391763576538731088       01 IT-ET Glut   ctx2        RSPd  \n",
       "94737806914081356525582745432967518089       01 IT-ET Glut   ctx2        RSPd  \n",
       "\n",
       "[67823 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ctx1 = ['SSp-m', 'ACAd', 'ACAv']\n",
    "ctx2=['RSPv','RSPd', 'VISpm', 'VISa', 'VISam', 'SSp-bfd', 'VISpor', 'VISli', 'TEa', 'VISpl', 'VISl', 'AUDv', 'VISal', 'AUDpo', 'AUDp', 'AUDd']\n",
    "ctx3 = ['FRP', 'ORBl', 'ORBvl', 'ORBm', 'PL', 'ILA']\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.obs.loc[adata_ctx.obs['region'].str.startswith(tuple(ctx4['feature'])), 'cc_ctx'] = 'ctx4'\n",
    "adata_ctx = adata_ctx[adata_ctx.obs['cc_ctx'].notna()]\n",
    "\n",
    "def split_string(s):\n",
    "    parts = re.split(r'(\\d+)', s)\n",
    "    return parts[0] if parts else ''\n",
    "adata_ctx.obs['region_corr'] = adata_ctx.obs['region'].apply(split_string)\n",
    "adata_ctx.obs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 1111)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions = adata_ctx.obs['region_corr'].values\n",
    "gene_expression = adata_ctx.X.A\n",
    "# Create a DataFrame, integrate regional information and gene expression matrix\n",
    "df = pd.DataFrame(gene_expression, columns=adata_ctx.var_names)\n",
    "df['region_corr'] = regions\n",
    "#Colonally group and calculate the average gene expression\n",
    "ctx_region_mean_expression = df.groupby('region_corr').mean()\n",
    "# Convert the result to the matrix format of the area*gene\n",
    "ctx_region_gene_matrix = ctx_region_mean_expression.values\n",
    "ctx_region_gene_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1187/1187 [00:00<00:00, 32725.57it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata_cc = adata_out[adata_out.obs['region'].str.startswith('cc')]\n",
    "tmp = adata_cc.copy()\n",
    "idx = []\n",
    "coordinate = cc_df_tmp[['x', 'y']].values\n",
    "kdtree = cKDTree(coordinate)\n",
    "for coord in tqdm(adata_cc.obs[['x', 'y']].values):\n",
    "    # Query recent neighbor indexes\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_df_tmp.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=1, c='#8f8f8f')\n",
    "\n",
    "# Draw the scattered point for each category\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=3, 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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### region conn & corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "      <th>NT_index</th>\n",
       "      <th>cc</th>\n",
       "      <th>region_corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100699349310225087355794400029769253599</th>\n",
       "      <td>289</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>67.394394</td>\n",
       "      <td>17.726380</td>\n",
       "      <td>54.104598</td>\n",
       "      <td>138</td>\n",
       "      <td>289</td>\n",
       "      <td>ccb</td>\n",
       "      <td>74</td>\n",
       "      <td>cc2</td>\n",
       "      <td>cc2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100902451446327482578212394659210886364</th>\n",
       "      <td>65</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>43.321564</td>\n",
       "      <td>36.220293</td>\n",
       "      <td>54.177884</td>\n",
       "      <td>47</td>\n",
       "      <td>65</td>\n",
       "      <td>ccg</td>\n",
       "      <td>3</td>\n",
       "      <td>cc3</td>\n",
       "      <td>cc3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101091759418520737635298400963209655403</th>\n",
       "      <td>282</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>70.727172</td>\n",
       "      <td>17.820763</td>\n",
       "      <td>54.078071</td>\n",
       "      <td>104</td>\n",
       "      <td>282</td>\n",
       "      <td>ccs</td>\n",
       "      <td>83</td>\n",
       "      <td>cc2</td>\n",
       "      <td>cc2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101177908791681271480760545204792047403</th>\n",
       "      <td>357</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>53.695160</td>\n",
       "      <td>26.428293</td>\n",
       "      <td>54.015113</td>\n",
       "      <td>124</td>\n",
       "      <td>357</td>\n",
       "      <td>ccb</td>\n",
       "      <td>43</td>\n",
       "      <td>cc1</td>\n",
       "      <td>cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101227086293358235805510357201736468429</th>\n",
       "      <td>239</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>41.673989</td>\n",
       "      <td>36.956672</td>\n",
       "      <td>54.114538</td>\n",
       "      <td>115</td>\n",
       "      <td>239</td>\n",
       "      <td>ccg</td>\n",
       "      <td>4</td>\n",
       "      <td>cc3</td>\n",
       "      <td>cc3</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96560296481515876710645847659207426556</th>\n",
       "      <td>58</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>43.282826</td>\n",
       "      <td>35.133529</td>\n",
       "      <td>54.189423</td>\n",
       "      <td>39</td>\n",
       "      <td>58</td>\n",
       "      <td>ccg</td>\n",
       "      <td>2</td>\n",
       "      <td>cc3</td>\n",
       "      <td>cc3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96578052950946015015587401781979033053</th>\n",
       "      <td>101</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>72.815062</td>\n",
       "      <td>15.920409</td>\n",
       "      <td>54.055571</td>\n",
       "      <td>59</td>\n",
       "      <td>101</td>\n",
       "      <td>ccs</td>\n",
       "      <td>88</td>\n",
       "      <td>cc2</td>\n",
       "      <td>cc2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96728852215317096694357846973382843058</th>\n",
       "      <td>100</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>41.845540</td>\n",
       "      <td>34.861770</td>\n",
       "      <td>54.158130</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>ccg</td>\n",
       "      <td>2</td>\n",
       "      <td>cc3</td>\n",
       "      <td>cc3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97838821125536355149234993233022684525</th>\n",
       "      <td>133</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>70.380641</td>\n",
       "      <td>18.437770</td>\n",
       "      <td>54.082850</td>\n",
       "      <td>83</td>\n",
       "      <td>133</td>\n",
       "      <td>ccb</td>\n",
       "      <td>80</td>\n",
       "      <td>cc2</td>\n",
       "      <td>cc2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98228718176791721432106544380776578140</th>\n",
       "      <td>115</td>\n",
       "      <td>Zhuang-ABCA-3.001</td>\n",
       "      <td>42.396503</td>\n",
       "      <td>36.154184</td>\n",
       "      <td>54.151896</td>\n",
       "      <td>68</td>\n",
       "      <td>115</td>\n",
       "      <td>ccg</td>\n",
       "      <td>3</td>\n",
       "      <td>cc3</td>\n",
       "      <td>cc3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         totalRNA brain_section_label  \\\n",
       "100699349310225087355794400029769253599       289   Zhuang-ABCA-3.001   \n",
       "100902451446327482578212394659210886364        65   Zhuang-ABCA-3.001   \n",
       "101091759418520737635298400963209655403       282   Zhuang-ABCA-3.001   \n",
       "101177908791681271480760545204792047403       357   Zhuang-ABCA-3.001   \n",
       "101227086293358235805510357201736468429       239   Zhuang-ABCA-3.001   \n",
       "...                                           ...                 ...   \n",
       "96560296481515876710645847659207426556         58   Zhuang-ABCA-3.001   \n",
       "96578052950946015015587401781979033053        101   Zhuang-ABCA-3.001   \n",
       "96728852215317096694357846973382843058        100   Zhuang-ABCA-3.001   \n",
       "97838821125536355149234993233022684525        133   Zhuang-ABCA-3.001   \n",
       "98228718176791721432106544380776578140        115   Zhuang-ABCA-3.001   \n",
       "\n",
       "                                                 x          y          z  \\\n",
       "100699349310225087355794400029769253599  67.394394  17.726380  54.104598   \n",
       "100902451446327482578212394659210886364  43.321564  36.220293  54.177884   \n",
       "101091759418520737635298400963209655403  70.727172  17.820763  54.078071   \n",
       "101177908791681271480760545204792047403  53.695160  26.428293  54.015113   \n",
       "101227086293358235805510357201736468429  41.673989  36.956672  54.114538   \n",
       "...                                            ...        ...        ...   \n",
       "96560296481515876710645847659207426556   43.282826  35.133529  54.189423   \n",
       "96578052950946015015587401781979033053   72.815062  15.920409  54.055571   \n",
       "96728852215317096694357846973382843058   41.845540  34.861770  54.158130   \n",
       "97838821125536355149234993233022684525   70.380641  18.437770  54.082850   \n",
       "98228718176791721432106544380776578140   42.396503  36.154184  54.151896   \n",
       "\n",
       "                                         n_genes_by_counts  total_counts  \\\n",
       "100699349310225087355794400029769253599                138           289   \n",
       "100902451446327482578212394659210886364                 47            65   \n",
       "101091759418520737635298400963209655403                104           282   \n",
       "101177908791681271480760545204792047403                124           357   \n",
       "101227086293358235805510357201736468429                115           239   \n",
       "...                                                    ...           ...   \n",
       "96560296481515876710645847659207426556                  39            58   \n",
       "96578052950946015015587401781979033053                  59           101   \n",
       "96728852215317096694357846973382843058                  69           100   \n",
       "97838821125536355149234993233022684525                  83           133   \n",
       "98228718176791721432106544380776578140                  68           115   \n",
       "\n",
       "                                        region  NT_index   cc region_corr  \n",
       "100699349310225087355794400029769253599    ccb        74  cc2         cc2  \n",
       "100902451446327482578212394659210886364    ccg         3  cc3         cc3  \n",
       "101091759418520737635298400963209655403    ccs        83  cc2         cc2  \n",
       "101177908791681271480760545204792047403    ccb        43  cc1         cc1  \n",
       "101227086293358235805510357201736468429    ccg         4  cc3         cc3  \n",
       "...                                        ...       ...  ...         ...  \n",
       "96560296481515876710645847659207426556     ccg         2  cc3         cc3  \n",
       "96578052950946015015587401781979033053     ccs        88  cc2         cc2  \n",
       "96728852215317096694357846973382843058     ccg         2  cc3         cc3  \n",
       "97838821125536355149234993233022684525     ccb        80  cc2         cc2  \n",
       "98228718176791721432106544380776578140     ccg         3  cc3         cc3  \n",
       "\n",
       "[498 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_cc.obs['region_corr'] = adata_cc.obs['cc']\n",
    "adata_cc.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 68321 × 1111\n",
       "    obs: 'brain_section_label', 'x', 'y', 'z', 'region', 'region_corr'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_cc_ctx = ad.concat([adata_ctx, adata_cc])\n",
    "adata_cc_ctx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 68321 × 1111\n",
       "    obs: 'brain_section_label', 'x', 'y', 'z', 'region', 'region_corr'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.pp.normalize_total(adata_cc_ctx, target_sum=1e4)\n",
    "sc.pp.log1p(adata_cc_ctx)\n",
    "adata_cc_ctx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### conn & corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 1111)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions = adata_cc_ctx.obs['region_corr'].values\n",
    "gene_expression = adata_cc_ctx.X.A\n",
    "\n",
    "# Create a DataFrame to integrate regional information and gene expression matrix together\n",
    "df = pd.DataFrame(gene_expression, columns=adata_cc_ctx.var_names)\n",
    "df['region_corr'] = regions\n",
    "\n",
    "# Calculate the average gene expression by regional grouping and calculate the average gene\n",
    "region_mean_expression = df.groupby('region_corr').mean()\n",
    "# Convert the result to the matrix format of the regional*gene\n",
    "region_gene_matrix = region_mean_expression.values\n",
    "region_gene_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_all = np.corrcoef(region_gene_matrix)\n",
    "corr_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cc1', 'cc2', 'cc3']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc = [f'cc{i+1}' for i in range(n_clusters)]\n",
    "cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>cc1</th>\n",
       "      <th>cc2</th>\n",
       "      <th>cc3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACAd</th>\n",
       "      <td>0.278914</td>\n",
       "      <td>0.262625</td>\n",
       "      <td>0.301169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAv</th>\n",
       "      <td>0.281494</td>\n",
       "      <td>0.265369</td>\n",
       "      <td>0.304893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDd</th>\n",
       "      <td>0.288976</td>\n",
       "      <td>0.261093</td>\n",
       "      <td>0.312110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDp</th>\n",
       "      <td>0.298039</td>\n",
       "      <td>0.275037</td>\n",
       "      <td>0.320481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDpo</th>\n",
       "      <td>0.289891</td>\n",
       "      <td>0.262205</td>\n",
       "      <td>0.313013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDv</th>\n",
       "      <td>0.298164</td>\n",
       "      <td>0.277583</td>\n",
       "      <td>0.321607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRP</th>\n",
       "      <td>0.277721</td>\n",
       "      <td>0.246925</td>\n",
       "      <td>0.296658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILA</th>\n",
       "      <td>0.247419</td>\n",
       "      <td>0.234853</td>\n",
       "      <td>0.275192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORBl</th>\n",
       "      <td>0.289135</td>\n",
       "      <td>0.269547</td>\n",
       "      <td>0.309606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORBm</th>\n",
       "      <td>0.269378</td>\n",
       "      <td>0.250819</td>\n",
       "      <td>0.295708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORBvl</th>\n",
       "      <td>0.281068</td>\n",
       "      <td>0.254882</td>\n",
       "      <td>0.303450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>0.261306</td>\n",
       "      <td>0.244187</td>\n",
       "      <td>0.287497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSPd</th>\n",
       "      <td>0.292823</td>\n",
       "      <td>0.274363</td>\n",
       "      <td>0.312567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSPv</th>\n",
       "      <td>0.283483</td>\n",
       "      <td>0.282744</td>\n",
       "      <td>0.301130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSp-bfd</th>\n",
       "      <td>0.298980</td>\n",
       "      <td>0.278068</td>\n",
       "      <td>0.317007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSp-m</th>\n",
       "      <td>0.300344</td>\n",
       "      <td>0.277375</td>\n",
       "      <td>0.317821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEa</th>\n",
       "      <td>0.286117</td>\n",
       "      <td>0.262562</td>\n",
       "      <td>0.312671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISa</th>\n",
       "      <td>0.290089</td>\n",
       "      <td>0.264966</td>\n",
       "      <td>0.312611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISal</th>\n",
       "      <td>0.288796</td>\n",
       "      <td>0.260408</td>\n",
       "      <td>0.309858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISam</th>\n",
       "      <td>0.297986</td>\n",
       "      <td>0.274052</td>\n",
       "      <td>0.324184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISl</th>\n",
       "      <td>0.291718</td>\n",
       "      <td>0.268541</td>\n",
       "      <td>0.318078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISli</th>\n",
       "      <td>0.274898</td>\n",
       "      <td>0.244883</td>\n",
       "      <td>0.294951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISpl</th>\n",
       "      <td>0.289472</td>\n",
       "      <td>0.265645</td>\n",
       "      <td>0.313973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISpm</th>\n",
       "      <td>0.299108</td>\n",
       "      <td>0.274570</td>\n",
       "      <td>0.323666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISpor</th>\n",
       "      <td>0.274229</td>\n",
       "      <td>0.245647</td>\n",
       "      <td>0.298697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cc1       cc2       cc3\n",
       "ACAd     0.278914  0.262625  0.301169\n",
       "ACAv     0.281494  0.265369  0.304893\n",
       "AUDd     0.288976  0.261093  0.312110\n",
       "AUDp     0.298039  0.275037  0.320481\n",
       "AUDpo    0.289891  0.262205  0.313013\n",
       "AUDv     0.298164  0.277583  0.321607\n",
       "FRP      0.277721  0.246925  0.296658\n",
       "ILA      0.247419  0.234853  0.275192\n",
       "ORBl     0.289135  0.269547  0.309606\n",
       "ORBm     0.269378  0.250819  0.295708\n",
       "ORBvl    0.281068  0.254882  0.303450\n",
       "PL       0.261306  0.244187  0.287497\n",
       "RSPd     0.292823  0.274363  0.312567\n",
       "RSPv     0.283483  0.282744  0.301130\n",
       "SSp-bfd  0.298980  0.278068  0.317007\n",
       "SSp-m    0.300344  0.277375  0.317821\n",
       "TEa      0.286117  0.262562  0.312671\n",
       "VISa     0.290089  0.264966  0.312611\n",
       "VISal    0.288796  0.260408  0.309858\n",
       "VISam    0.297986  0.274052  0.324184\n",
       "VISl     0.291718  0.268541  0.318078\n",
       "VISli    0.274898  0.244883  0.294951\n",
       "VISpl    0.289472  0.265645  0.313973\n",
       "VISpm    0.299108  0.274570  0.323666\n",
       "VISpor   0.274229  0.245647  0.298697"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_subregions = adata_cc_ctx[adata_cc_ctx.obs['region'].str.startswith('cc')].obs['region_corr'].unique()\n",
    "ctx_subregions = adata_cc_ctx[adata_cc_ctx.obs['region'].str.startswith(tuple(ctx_43_regions))].obs['region_corr'].unique()\n",
    "\n",
    "region_labels = region_mean_expression.index\n",
    "ctx_indices = [i for i, label in enumerate(region_labels) if label in ctx_subregions]\n",
    "cc_indices = [i for i, label in enumerate(region_labels) if label in cc_subregions]\n",
    "corr = corr_all[np.ix_(ctx_indices, cc_indices)]\n",
    "\n",
    "ctx_regions_df = [region_labels[i] for i in ctx_indices]\n",
    "cc_regions_df = [region_labels[i] for i in cc_indices]\n",
    "corr_df = pd.DataFrame(corr, index=ctx_regions_df, columns=cc_regions_df)\n",
    "corr_df.columns = cc\n",
    "corr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>cluster</th>\n",
       "      <th>cc1</th>\n",
       "      <th>cc2</th>\n",
       "      <th>cc3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACAd</th>\n",
       "      <td>0.000454</td>\n",
       "      <td>3.466430e-05</td>\n",
       "      <td>9.106244e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAv</th>\n",
       "      <td>0.000533</td>\n",
       "      <td>2.746207e-05</td>\n",
       "      <td>9.169748e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDd</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>6.115146e-04</td>\n",
       "      <td>1.393698e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDp</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>5.699260e-04</td>\n",
       "      <td>4.200837e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDpo</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>5.697359e-04</td>\n",
       "      <td>4.588037e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDv</th>\n",
       "      <td>0.000007</td>\n",
       "      <td>4.365286e-04</td>\n",
       "      <td>8.039079e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRP</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>3.140131e-07</td>\n",
       "      <td>1.314226e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILA</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>1.089208e-06</td>\n",
       "      <td>7.981067e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORBl</th>\n",
       "      <td>0.000029</td>\n",
       "      <td>6.969948e-07</td>\n",
       "      <td>1.154751e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORBm</th>\n",
       "      <td>0.000014</td>\n",
       "      <td>8.764364e-07</td>\n",
       "      <td>1.038650e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORBvl</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>8.880653e-07</td>\n",
       "      <td>1.177964e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>0.000038</td>\n",
       "      <td>1.144148e-06</td>\n",
       "      <td>7.018622e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSPd</th>\n",
       "      <td>0.000019</td>\n",
       "      <td>7.062989e-04</td>\n",
       "      <td>1.888608e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSPv</th>\n",
       "      <td>0.000049</td>\n",
       "      <td>8.105875e-04</td>\n",
       "      <td>3.025978e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSp-bfd</th>\n",
       "      <td>0.000019</td>\n",
       "      <td>4.317370e-04</td>\n",
       "      <td>3.731170e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSp-m</th>\n",
       "      <td>0.000799</td>\n",
       "      <td>2.815791e-06</td>\n",
       "      <td>1.012444e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEa</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>3.333817e-04</td>\n",
       "      <td>1.138775e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISa</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>7.333210e-04</td>\n",
       "      <td>5.921385e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISal</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>4.756454e-04</td>\n",
       "      <td>1.858051e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISam</th>\n",
       "      <td>0.000006</td>\n",
       "      <td>8.601038e-04</td>\n",
       "      <td>5.310773e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISl</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>3.263364e-04</td>\n",
       "      <td>6.685498e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISli</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>4.238643e-04</td>\n",
       "      <td>9.831281e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISpl</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>2.921813e-04</td>\n",
       "      <td>1.066889e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISpm</th>\n",
       "      <td>0.000006</td>\n",
       "      <td>5.447638e-04</td>\n",
       "      <td>4.145406e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISpor</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>4.092621e-04</td>\n",
       "      <td>1.611255e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster       cc1           cc2           cc3\n",
       "ACAd     0.000454  3.466430e-05  9.106244e-05\n",
       "ACAv     0.000533  2.746207e-05  9.169748e-05\n",
       "AUDd     0.000002  6.115146e-04  1.393698e-06\n",
       "AUDp     0.000003  5.699260e-04  4.200837e-06\n",
       "AUDpo    0.000003  5.697359e-04  4.588037e-06\n",
       "AUDv     0.000007  4.365286e-04  8.039079e-06\n",
       "FRP      0.000018  3.140131e-07  1.314226e-03\n",
       "ILA      0.000013  1.089208e-06  7.981067e-04\n",
       "ORBl     0.000029  6.969948e-07  1.154751e-03\n",
       "ORBm     0.000014  8.764364e-07  1.038650e-03\n",
       "ORBvl    0.000018  8.880653e-07  1.177964e-03\n",
       "PL       0.000038  1.144148e-06  7.018622e-04\n",
       "RSPd     0.000019  7.062989e-04  1.888608e-06\n",
       "RSPv     0.000049  8.105875e-04  3.025978e-06\n",
       "SSp-bfd  0.000019  4.317370e-04  3.731170e-07\n",
       "SSp-m    0.000799  2.815791e-06  1.012444e-05\n",
       "TEa      0.000010  3.333817e-04  1.138775e-05\n",
       "VISa     0.000005  7.333210e-04  5.921385e-07\n",
       "VISal    0.000003  4.756454e-04  1.858051e-06\n",
       "VISam    0.000006  8.601038e-04  5.310773e-07\n",
       "VISl     0.000002  3.263364e-04  6.685498e-06\n",
       "VISli    0.000002  4.238643e-04  9.831281e-06\n",
       "VISpl    0.000002  2.921813e-04  1.066889e-05\n",
       "VISpm    0.000006  5.447638e-04  4.145406e-07\n",
       "VISpor   0.000003  4.092621e-04  1.611255e-05"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctx2cc_conn_cluster_mean = pd.read_csv('./data/ctx_cc_conn_filtered.csv')\n",
    "ctx2cc_conn_cluster_mean = ctx2cc_conn_cluster_mean.transpose()\n",
    "ctx2cc_conn_cluster_mean = ctx2cc_conn_cluster_mean.loc[corr_df.index]\n",
    "ctx2cc_conn_cluster_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1_long = corr_df.reset_index().melt(id_vars='index', var_name='cc', value_name='Correlation')\n",
    "df2_long = ctx2cc_conn_cluster_mean.reset_index().melt(id_vars='index', var_name='cc', value_name='Connection_strength')\n",
    "# Merge two long format dataframe\n",
    "df_merged = pd.merge(df1_long, df2_long, on=['index', 'cc'])\n",
    "df_merged = df_merged[df_merged['Connection_strength'] > 0.0001]\n",
    "# Calculate the number of phase relationships and P value\n",
    "correlation, p_value = pearsonr(df_merged['Correlation'], df_merged['Connection_strength'])\n",
    "\n",
    "# Draw chart\n",
    "plt.figure(figsize=(4.5, 4))\n",
    "sns.regplot(data=df_merged, x='Connection_strength', y='Correlation',\n",
    "            scatter_kws={'s': 30, 'alpha': 1},\n",
    "            line_kws={'linewidth': 0.5, 'color': '#D8383A', 'linestyle': '-'})\n",
    "\n",
    "# Set different colors according to CC1, CC2, CC3\n",
    "colors = ['#2377b4', '#ff7f0e', '#2ca02c']\n",
    "\n",
    "for i, c in enumerate(cc):\n",
    "    subset = df_merged[df_merged['cc'] == c]  \n",
    "    plt.scatter(subset['Connection_strength'], subset['Correlation'], s=30, color=colors[i], label=c)\n",
    "\n",
    "plt.title(f'r = {correlation:.3f}, p = {p_value:.2e}', fontweight='bold')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.show()\n",
    "# plt.savefig('cc_ctx_corr.pdf', format='pdf')"
   ]
  }
 ],
 "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}