{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats, sparse\n",
    "import bottleneck\n",
    "\n",
    "def run_egad(go, nw, **kwargs):\n",
    "    \"\"\"EGAD running function\n",
    "    \n",
    "    Wrapper to lower level functions for EGAD\n",
    "\n",
    "    EGAD measures modularity of gene lists in co-expression networks. \n",
    "\n",
    "    This was translated from the MATLAB version, which does tiled Cross Validation\n",
    "    \n",
    "    The useful kwargs are:\n",
    "    int - nFold : Number of CV folds to do, default is 3, \n",
    "    int - {min,max}_count : limits for number of terms in each gene list, these are exclusive values\n",
    "\n",
    "\n",
    "    Arguments:\n",
    "        go {pd.DataFrame} -- dataframe of genes x terms of values [0,1], where 1 is included in gene lists\n",
    "        nw {pd.DataFrame} -- dataframe of co-expression network, genes x genes\n",
    "        **kwargs \n",
    "    \n",
    "    Returns:\n",
    "        pd.DataFrame -- dataframe of terms x metrics where the metrics are \n",
    "        ['AUC', 'AVG_NODE_DEGREE', 'DEGREE_NULL_AUC', 'P_Value']\n",
    "    \"\"\"\n",
    "    assert nw.shape[0] == nw.shape[1] , 'Network is not square'\n",
    "    #print(nw.index)\n",
    "    #nw.columns = nw.columns.astype(int)\n",
    "    #print(nw.columns.astype(int))\n",
    "    assert np.all(nw.index == nw.columns) , 'Network index and columns are not in the same order'\n",
    "\n",
    "    #nw_mask = nw.isna().sum(axis=1) != nw.shape[1]\n",
    "    #nw = nw.loc[nw_mask, nw_mask].astype('float')\n",
    "    #np.fill_diagonal(nw.values, 1)\n",
    "    return _runNV(go, nw, **kwargs)\n",
    "\n",
    "def _runNV(go, nw, nFold=3, min_count=20, max_count=1000):\n",
    "\n",
    "    #Make sure genes are same in go and nw\n",
    "    #go.index = go.index.map(str) \n",
    "    #nw.index = nw.index.map(str)\n",
    "    #nw.index = nw.index.str.replace('_', '')\n",
    "    #go.index = go.index.str.replace('_', '')\n",
    "    #print (nw)\n",
    "    genes_intersect = go.index.intersection(nw.index)\n",
    "\n",
    "\n",
    "    #print (genes_intersect)\n",
    "    go = go.loc[genes_intersect, :]\n",
    "    nw = nw.loc[genes_intersect, genes_intersect]\n",
    "    #print (go)\n",
    "    print (nw.shape)\n",
    "    print (go.shape)\n",
    "    sparsity = 1.0 - np.count_nonzero(go) / go.size\n",
    "    print (sparsity)\n",
    "    sparsity = 1.0 - np.count_nonzero(nw) / nw.size\n",
    "    print (sparsity)\n",
    "    #print(nw\n",
    "    #print(go\n",
    "    nw_mask = nw.isna().sum(axis=1) != nw.shape[1]\n",
    "    nw = nw.loc[nw_mask, nw_mask].astype('float')\n",
    "    np.fill_diagonal(nw.values, 1)\n",
    "    #Make sure there aren't duplicates\n",
    "    duplicates = nw.index.duplicated(keep='first')\n",
    "    nw = nw.loc[~duplicates, ~duplicates]\n",
    "\n",
    "    go = go.loc[:, (go.sum(axis=0) > min_count) & (go.sum(axis=0) < max_count)]\n",
    "    go = go.loc[~go.index.duplicated(keep='first'), :]\n",
    "    #print(go)\n",
    "\n",
    "    roc = _new_egad(go.values, nw.values, nFold)\n",
    "\n",
    "    col_names = ['AUC', 'AVG_NODE_DEGREE', 'DEGREE_NULL_AUC', 'P_Value']\n",
    "    #Put output in dataframe\n",
    "    return pd.DataFrame(dict(zip(col_names, roc)), index=go.columns)\n",
    "\n",
    "def _new_egad(go, nw, nFold):\n",
    "\n",
    "    #Build Cross validated Positive\n",
    "    x, y = np.where(go)\n",
    "    #print(x, y)\n",
    "    cvgo = {}\n",
    "    for i in np.arange(nFold):\n",
    "        a = x[i::nFold]\n",
    "        #print(a)\n",
    "        b = y[i::nFold]\n",
    "        dat = np.ones_like(a)\n",
    "        mask = sparse.coo_matrix((dat, (a, b)), shape=go.shape)\n",
    "        cvgo[i] = go - mask.toarray()\n",
    "\n",
    "    CVgo = np.concatenate(list(cvgo.values()), axis=1)\n",
    "    #print(CVgo)\n",
    "\n",
    "    sumin = np.matmul(nw.T, CVgo)\n",
    "\n",
    "    degree = np.sum(nw, axis=0)\n",
    "    #print(degree)\n",
    "    #print(degree[:, None])\n",
    "\n",
    "    predicts = sumin / degree[:, None]\n",
    "    #print(predicts)\n",
    "\n",
    "    np.place(predicts, CVgo > 0, np.nan)\n",
    "\n",
    "    #print(predicts)\n",
    "\n",
    "    #Calculate ranks of positives\n",
    "    rank_abs = lambda x: stats.rankdata(np.abs(x))\n",
    "    predicts2 = np.apply_along_axis(rank_abs, 0, predicts)\n",
    "    #print(predicts2)\n",
    "\n",
    "    #Masking Nans that were ranked (how tiedrank works in matlab)\n",
    "    predicts2[np.isnan(predicts)] = np.nan\n",
    "    #print(predicts2)\n",
    "\n",
    "    filtering = np.tile(go, nFold)\n",
    "    #print(filtering)\n",
    "\n",
    "    #negatives :filtering == 0\n",
    "    #Sets Ranks of negatives to 0\n",
    "    np.place(predicts2, filtering == 0, 0)\n",
    "\n",
    "    #Sum of ranks for each prediction\n",
    "    p = bottleneck.nansum(predicts2, axis=0)\n",
    "    n_p = np.sum(filtering, axis=0) - np.sum(CVgo, axis=0)\n",
    "\n",
    "    #Number of negatives\n",
    "    #Number of GO terms - number of postiive\n",
    "    n_n = filtering.shape[0] - np.sum(filtering, axis=0)\n",
    "\n",
    "    roc = (p / n_p - (n_p + 1) / 2) / n_n\n",
    "    U = roc * n_p * n_n\n",
    "    Z = (np.abs(U - (n_p * n_n / 2))) / np.sqrt(n_p * n_n *(n_p + n_n + 1) / 12)\n",
    "    roc = roc.reshape(nFold, go.shape[1])\n",
    "    Z = Z.reshape(nFold, go.shape[1])\n",
    "    #Stouffer Z method\n",
    "    Z = bottleneck.nansum(Z, axis=0) / np.sqrt(nFold)\n",
    "    #Calc ROC of Neighbor Voting\n",
    "    roc = bottleneck.nanmean(roc, axis=0)\n",
    "    P = stats.norm.sf(Z)\n",
    "\n",
    "    #Average degree for nodes in each go term\n",
    "    avg_degree = degree.dot(go) / np.sum(go, axis=0)\n",
    "\n",
    "    #Calc null auc for degree\n",
    "    ranks = np.tile(stats.rankdata(degree), (go.shape[1], 1)).T\n",
    "\n",
    "    np.place(ranks, go == 0, 0)\n",
    "\n",
    "    n_p = bottleneck.nansum(go, axis=0)\n",
    "    nn = go.shape[0] - n_p\n",
    "    p = bottleneck.nansum(ranks, axis=0)\n",
    "\n",
    "    roc_null = (p / n_p - ((n_p + 1) / 2)) / nn\n",
    "    #print(roc)\n",
    "    return roc, avg_degree, roc_null, P\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-05-18 21:02:42--  http://session.asuscomm.com/database.php\n",
      "Resolving session.asuscomm.com (session.asuscomm.com)... 178.55.16.55\n",
      "Connecting to session.asuscomm.com (session.asuscomm.com)|178.55.16.55|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [text/html]\n",
      "Saving to: ‘database.php’\n",
      "\n",
      "database.php            [  <=>               ]  49.98K   177KB/s    in 0.3s    \n",
      "\n",
      "2022-05-18 21:02:42 (177 KB/s) - ‘database.php’ saved [51176]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://session.asuscomm.com/database.php"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-7bb14435cf7b>:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n",
      "  df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/scType_marker_genes.csv', header=1, usecols=[0,1,2,3], skipfooter=2)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/scType_marker_genes.csv', header=1, usecols=[0,1,2,3], skipfooter=2)\n",
    "\n",
    "\n",
    "tissue_type_list = df['Tissue'].drop_duplicates().tolist()\n",
    "\n",
    "all_gene_list = df['Marker genes'].str.cat(sep=',').split(\",\")\n",
    "\n",
    "data_tissue = df.groupby(['Tissue']).apply(lambda grp: grp['Marker genes'].str.cat(sep=',').split(\",\"))\n",
    "\n",
    "all_gene_list = list(set(all_gene_list))\n",
    "\n",
    "nested_gene_marker_tissue_list = []\n",
    "\n",
    "for i in tissue_type_list:\n",
    "    gene_for_given_tissue = data[data.index ==i][0]\n",
    "    particular_gene_tissue = [ 1 if x in gene_for_given_tissue else 0 for x in all_gene_list]\n",
    "    nested_gene_marker_tissue_list.append(particular_gene_tissue)\n",
    "\n",
    "    \n",
    "\n",
    "pd.DataFrame(nested_gene_marker_tissue_list, columns = all_gene_list, index=tissue_type_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-bbf216282e9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_content_charset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf8'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# JSON default\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m#print(raw_data)   #this is data in string format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m#this would be your json data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 357\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    353\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import urllib.request\n",
    "url = \"http://session.asuscomm.com/database.php\"\n",
    "x = urllib.request.urlopen(url)\n",
    "raw_data = x.read()\n",
    "encoding = x.info().get_content_charset('utf8')  # JSON default\n",
    "#print(raw_data)   #this is data in string format\n",
    "data = json.loads(raw_data)\n",
    "print(data)   #this would be your json data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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 stable ID</th>\n",
       "      <th>Gene name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000210049</td>\n",
       "      <td>MT-TF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000211459</td>\n",
       "      <td>MT-RNR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000210077</td>\n",
       "      <td>MT-TV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000210082</td>\n",
       "      <td>MT-RNR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000209082</td>\n",
       "      <td>MT-TL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68011</th>\n",
       "      <td>ENSG00000163263</td>\n",
       "      <td>CFAP141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68012</th>\n",
       "      <td>ENSG00000143612</td>\n",
       "      <td>C1orf43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68013</th>\n",
       "      <td>ENSG00000143569</td>\n",
       "      <td>UBAP2L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68014</th>\n",
       "      <td>ENSG00000201129</td>\n",
       "      <td>SNORA58B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68015</th>\n",
       "      <td>ENSG00000143575</td>\n",
       "      <td>HAX1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68016 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene stable ID Gene name\n",
       "0      ENSG00000210049     MT-TF\n",
       "1      ENSG00000211459   MT-RNR1\n",
       "2      ENSG00000210077     MT-TV\n",
       "3      ENSG00000210082   MT-RNR2\n",
       "4      ENSG00000209082    MT-TL1\n",
       "...                ...       ...\n",
       "68011  ENSG00000163263   CFAP141\n",
       "68012  ENSG00000143612   C1orf43\n",
       "68013  ENSG00000143569    UBAP2L\n",
       "68014  ENSG00000201129  SNORA58B\n",
       "68015  ENSG00000143575      HAX1\n",
       "\n",
       "[68016 rows x 2 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_gene_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'ENSG00000282608',\n",
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       " 'ENSG00000198178',\n",
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       " 'ENSG00000243566',\n",
       " 'ENSG00000158955',\n",
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       " 'ENSG00000126337',\n",
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       " 'del',\n",
       " 'del',\n",
       " 'del',\n",
       " 'ENSG00000007062',\n",
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       " 'del',\n",
       " 'del',\n",
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       " 'ENSG00000160339',\n",
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       " 'ENSG00000119922',\n",
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       " 'ENSG00000137462',\n",
       " 'ENSG00000132693',\n",
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       " 'ENSG00000136235',\n",
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       " 'ENSG00000144648',\n",
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       " 'ENSG00000161798',\n",
       " 'ENSG00000002586',\n",
       " 'ENSG00000089199',\n",
       " 'ENSG00000050767',\n",
       " 'ENSG00000144810',\n",
       " 'ENSG00000167244',\n",
       " 'ENSG00000091138',\n",
       " 'ENSG00000133048',\n",
       " 'ENSG00000106404',\n",
       " 'ENSG00000243955',\n",
       " 'ENSG00000172164',\n",
       " 'ENSG00000160200',\n",
       " 'ENSG00000015520',\n",
       " 'ENSG00000142319',\n",
       " 'ENSG00000174059',\n",
       " 'ENSG00000110245',\n",
       " 'ENSG00000182492',\n",
       " 'ENSG00000130226',\n",
       " 'ENSG00000113494',\n",
       " 'ENSG00000173372',\n",
       " 'del',\n",
       " 'del',\n",
       " 'ENSG00000050165',\n",
       " 'ENSG00000249669',\n",
       " 'ENSG00000051523',\n",
       " 'ENSG00000104783',\n",
       " 'ENSG00000145777',\n",
       " 'ENSG00000175538',\n",
       " 'ENSG00000164081',\n",
       " 'ENSG00000120885',\n",
       " 'ENSG00000123560',\n",
       " 'ENSG00000100031',\n",
       " 'ENSG00000144668',\n",
       " 'ENSG00000010319',\n",
       " 'ENSG00000083457',\n",
       " 'ENSG00000188229',\n",
       " 'ENSG00000152778',\n",
       " 'ENSG00000198728',\n",
       " 'ENSG00000196792',\n",
       " 'del',\n",
       " 'ENSG00000145287',\n",
       " 'ENSG00000112149',\n",
       " 'del',\n",
       " 'ENSG00000162614',\n",
       " 'ENSG00000172137',\n",
       " 'ENSG00000019991',\n",
       " 'ENSG00000134627',\n",
       " 'ENSG00000128218',\n",
       " 'ENSG00000180900',\n",
       " 'ENSG00000111913',\n",
       " 'ENSG00000168903',\n",
       " 'ENSG00000114200',\n",
       " 'ENSG00000141646',\n",
       " 'ENSG00000123096',\n",
       " 'ENSG00000184481',\n",
       " 'ENSG00000196878',\n",
       " 'ENSG00000162998',\n",
       " 'ENSG00000067048',\n",
       " 'ENSG00000157992',\n",
       " 'ENSG00000187513',\n",
       " 'ENSG00000181649',\n",
       " 'ENSG00000178538',\n",
       " 'ENSG00000125872',\n",
       " 'ENSG00000177455',\n",
       " 'ENSG00000166402',\n",
       " 'ENSG00000116690',\n",
       " 'ENSG00000126460',\n",
       " 'ENSG00000162747',\n",
       " 'ENSG00000127329',\n",
       " 'ENSG00000211821',\n",
       " ...]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[dict_gene_name_to_ensg[x] if x in dict_gene_name_to_ensg.keys() else 'del' for x in all_gene_list ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hicmatrix import HiCMatrix as hm\n",
    "from hicmatrix.lib import MatrixFileHandler\n",
    "SRP_name='aggregates'\n",
    "resolution='40kbp_raw'\n",
    "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_mouse/aggregates/40kbp_raw/max/pcc/0/all_bins/KR_KR/hic_gene_corr_gw.h5'\n",
    "\n",
    "pcc_sim = hm.hiCMatrix(exp_file_path)\n",
    "all_pcc_genes = [x[3].decode() for x in pcc_sim.cut_intervals]\n",
    "pcc_sim_matrix = pcc_sim.matrix.toarray()\n",
    "df_pcc_sim = pd.DataFrame(pcc_sim_matrix,  index=all_pcc_genes, columns = all_pcc_genes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "    pcc_sim_matrix = pcc_sim.matrix.toarray()\n",
    "    df_pcc_sim = pd.DataFrame(pcc_sim_matrix,  index=all_pcc_genes, columns = all_pcc_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 92)\n",
      "(92, 22996)\n",
      "0.9942792508337934\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "df_2d_pcc = run_egad(go_table_marker, df_pcc_sim )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4710205388227698"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d_pcc['AUC'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1621, 1621)\n",
      "(1621, 22996)\n",
      "0.9944747250520944\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "df_2d_pcc_marker = run_egad(go_table_marker, df_pcc_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>GO:2001301</th>\n",
       "      <th>GO:2001302</th>\n",
       "      <th>GO:2001303</th>\n",
       "      <th>GO:2001304</th>\n",
       "      <th>GO:2001306</th>\n",
       "      <th>GO:2001311</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetworkIDs</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000001988</th>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000003657</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000068859</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000070880</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000075270</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000090063</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000096914</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 22996 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    GO:0000002  GO:0000003  GO:0000009  GO:0000010  \\\n",
       "NetworkIDs                                                           \n",
       "ENSMUSG00000001520         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000001988         0.0         1.0         0.0         0.0   \n",
       "ENSMUSG00000003411         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000003657         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000004366         0.0         0.0         0.0         0.0   \n",
       "...                        ...         ...         ...         ...   \n",
       "ENSMUSG00000068859         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000070880         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000075270         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000090063         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000096914         0.0         0.0         0.0         0.0   \n",
       "\n",
       "                    GO:0000012  GO:0000014  GO:0000015  GO:0000016  \\\n",
       "NetworkIDs                                                           \n",
       "ENSMUSG00000001520         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000001988         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000003411         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000003657         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000004366         0.0         0.0         0.0         0.0   \n",
       "...                        ...         ...         ...         ...   \n",
       "ENSMUSG00000068859         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000070880         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000075270         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000090063         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000096914         0.0         0.0         0.0         0.0   \n",
       "\n",
       "                    GO:0000017  GO:0000018  ...  GO:2001293  GO:2001294  \\\n",
       "NetworkIDs                                  ...                           \n",
       "ENSMUSG00000001520         0.0         0.0  ...         0.0         0.0   \n",
       "ENSMUSG00000001988         0.0         0.0  ...         0.0         0.0   \n",
       "ENSMUSG00000003411         0.0         0.0  ...         0.0         0.0   \n",
       "ENSMUSG00000003657         0.0         0.0  ...         0.0         0.0   \n",
       "ENSMUSG00000004366         0.0         0.0  ...         0.0         0.0   \n",
       "...                        ...         ...  ...         ...         ...   \n",
       "ENSMUSG00000068859         0.0         0.0  ...         0.0         0.0   \n",
       "ENSMUSG00000070880         0.0         0.0  ...         0.0         0.0   \n",
       "ENSMUSG00000075270         0.0         0.0  ...         0.0         0.0   \n",
       "ENSMUSG00000090063         0.0         0.0  ...         0.0         0.0   \n",
       "ENSMUSG00000096914         0.0         0.0  ...         0.0         0.0   \n",
       "\n",
       "                    GO:2001295  GO:2001300  GO:2001301  GO:2001302  \\\n",
       "NetworkIDs                                                           \n",
       "ENSMUSG00000001520         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000001988         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000003411         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000003657         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000004366         0.0         0.0         0.0         0.0   \n",
       "...                        ...         ...         ...         ...   \n",
       "ENSMUSG00000068859         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000070880         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000075270         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000090063         0.0         0.0         0.0         0.0   \n",
       "ENSMUSG00000096914         0.0         0.0         0.0         0.0   \n",
       "\n",
       "                    GO:2001303  GO:2001304  GO:2001306  GO:2001311  \n",
       "NetworkIDs                                                          \n",
       "ENSMUSG00000001520         0.0         0.0         0.0         0.0  \n",
       "ENSMUSG00000001988         0.0         0.0         0.0         0.0  \n",
       "ENSMUSG00000003411         0.0         0.0         0.0         0.0  \n",
       "ENSMUSG00000003657         0.0         0.0         0.0         0.0  \n",
       "ENSMUSG00000004366         0.0         0.0         0.0         0.0  \n",
       "...                        ...         ...         ...         ...  \n",
       "ENSMUSG00000068859         0.0         0.0         0.0         0.0  \n",
       "ENSMUSG00000070880         0.0         0.0         0.0         0.0  \n",
       "ENSMUSG00000075270         0.0         0.0         0.0         0.0  \n",
       "ENSMUSG00000090063         0.0         0.0         0.0         0.0  \n",
       "ENSMUSG00000096914         0.0         0.0         0.0         0.0  \n",
       "\n",
       "[96 rows x 22996 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "go_table_marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5100544514893378"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d_pcc_marker['AUC'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "    from hicmatrix import HiCMatrix as hm\n",
    "    from hicmatrix.lib import MatrixFileHandler\n",
    "    \n",
    "\n",
    "\n",
    "    exp_file = hm.hiCMatrix('/grid/gillis/data/lohia/hi_c_data_processing/software/CoCoCoNet/networks/mouse_prioAggNet.h5')\n",
    "    #chr_list_1 = exp_file.getChrNames()\n",
    "    \n",
    "    exp_matrix = exp_file.matrix.toarray()\n",
    "    exp_genes_all = [x[3].decode() for x in exp_file.cut_intervals]\n",
    "    df_exp = pd.DataFrame(exp_matrix ,  index=exp_genes_all , columns = exp_genes_all)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96, 96)\n",
      "(96, 22996)\n",
      "0.9943604322490868\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "df_2d = run_egad(go_table_marker, df_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "    df = pd.read_csv(f'/grid/gillis/data/lohia/hi_c_data_processing/software/CoCoCoNet/gene2go/mouse_gene2go.csv', delim_whitespace=True)\n",
    "\n",
    "    df['val'] = 1\n",
    "\n",
    "    go_table = pd.pivot_table(df, index=['NetworkIDs'],columns=['GO_term'])\n",
    "\n",
    "    go_table = go_table.fillna(0)\n",
    "    \n",
    "    go_table = pd.DataFrame(go_table.values ,  index=go_table.index , columns = [x[1] for x in go_table.columns])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hicmatrix import HiCMatrix as hm\n",
    "from hicmatrix.lib import MatrixFileHandler\n",
    "SRP_name='aggregates'\n",
    "resolution='40kbp_raw'\n",
    "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_mouse/aggregates/10kbp_raw/max/hic_gene_inter_KR.h5'\n",
    "\n",
    "jac_sim = hm.hiCMatrix(exp_file_path)\n",
    "all_genes = [x[3].decode() for x in jac_sim.cut_intervals]\n",
    "jac_sim_matrix = jac_sim.matrix.toarray()\n",
    "df_jac_sim = pd.DataFrame(jac_sim_matrix,  index=all_genes, columns = all_genes)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 92)\n",
      "(92, 22996)\n",
      "0.9942792508337934\n",
      "0.07230623818525517\n"
     ]
    }
   ],
   "source": [
    "df_2d_jac = run_egad(go_table_marker, df_jac_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5231521483736035"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d_jac['AUC'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fffa9e65af0>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#plt.plot(data=df_2d, x='AUC', y='DEGREE_NULL_AUC')\n",
    "plt.scatter(df_2d_pcc['AUC'], df_2d_pcc['DEGREE_NULL_AUC'])\n",
    "plt.plot([0, 1], [0, 1], c='black')\n",
    "plt.axvline(x=df_2d['AUC'].mean(),c='black',ls='--')\n",
    "plt.axhline(y=df_2d['DEGREE_NULL_AUC'].mean(), c='black', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ffb77df39a0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#plt.plot(data=df_2d, x='AUC', y='DEGREE_NULL_AUC')\n",
    "plt.scatter(df_2d['AUC'], df_2d['DEGREE_NULL_AUC'])\n",
    "plt.plot([0, 1], [0, 1], c='black')\n",
    "plt.axvline(x=df_2d['AUC'].mean(),c='black',ls='--')\n",
    "plt.axhline(y=df_2d['DEGREE_NULL_AUC'].mean(), c='black', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "GO_groups = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/genomes_jlee/aug4.GOslim')\n",
    "df_slim = df_2d[df_2d.index.isin(GO_groups['go_id'].tolist())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ffb8d17e4c0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2r2+k5FPYttq6CfzHH38QHBxMeHg47dq1Y86cOdSpU8dmjKL0kITuZsyayl5QYsuKKzYhCaVA63/Pi01IYvCS/YD1Xq21dhVl3L3RWM8kJHFrw142PxyMjMApbA/e0rcOnZ6Gx5GfadjwWTw8PJg+fTp9+/bFw0MGqol/KZ3zX1YJCgwM1JGRkaZc213lTR6Q0VstiVULbx+yGku/SQr47Lkm+eKypHxZT3x9ynImIYlK3l4oBQmJKfj6eHHlWiop6f9eoTjtshYrQGUfLy4mpuR6Lu+1lkfF8sGKQyQkpVh6CyBjtcgdQx6izdiNFktCWcctyftzTD5/ioSwKSTFHKFz587MnDmT2267zUBLhTtSSu3VWgdaOiYf727EzG3crNWsfX28LMZlydXktOxlfxOSUriYmIIGLiam5ErmkNGukNDoQo07zxqvbi2Zq8xr5RQfPpvYtTOy/w6XR8Uy+If9NpM5ZPTErSVzsH3TNmsp4eoVvbgU8T1/zX+TMlf+YsGCBaxevVqSubDKUMlFKdUJ+BzwBOZorcdaOKc9MBnwAs5rrR+wW5TCEDNXWhzcsS6Dl+wnJS13urxyLTVfkrQno7X62IQkFNhM5paOJZ87kX0dyPjQzPvhYis2a+9b0E3bmul/kbh4MAkHDvDcc8/xxRdfcNNNNxm6rii9CuyhK6U8gWlAZyAAeF4pFZDnHF9gOvCk1roB8B/7hyoKYuY2bl2b+lO+bP7+QUq6xtPBy7Na+xaSc7MPsJ7MPZWyeiyLyny/wn446szX5mTrpm1SUhLvvfceLVq0IC4ujuXLl/P9999LMheGGCm5tACOa61PaK2Tge+Bp/Kc8wKwTGt9CkBrfc6+YQojzF5p8ZKVMkSa1vnisrfYhCSafvRzrun/Rks9aQbuI2kyeudF+XDUYGgnpq1bt9K4cWPGjx9Pr169OHz4ME89lfefmhDWGUno/sDpHI9jMp/LqQ5QWSm1WSm1Vyn1iqU3UkoFK6UilVKRcXFxRYtYWGX2Nm7Wkl1WHP7F+Kbg5anw9ba9z2VWzT2rDGOtfl1UZxKSGNyxLl4ehfvGkXUD9I+xj7NjyEP5fh6XL1+mf//+PPDAA6SmphIeHs6XX36Jr6+vHaMXpYGRGrql3968XZoyQHPgYcAb2KmU2qW1PpbrRVrPBmZDxiiXwocrCmLmVHZLY8IVULuqd/YwQE+lbPaIs2rO3l4eXE9NJ11nlESeu7cmgbWq8OHKQ4Zq8kkpaQVeywivKv/+XVby9squoduqx+dU0DekNWvW0LdvX2JjY6nephtpLZ7n/T2Kf6rEOuWSBMK5GUnoMUDNHI9rAGcsnHNea30VuKqU2go0Bo4hSo2uTf2J/DOeb3edyk52Gtjxv/jscwpKsL4+Xjx+T3WW7o0l695jmtYs3HWKhbtOFSqerFKPkbKLNVU7DQQyPmiuJqdmj24xkswr+3gxsksDi4n5/PnzhISE8O2331Lzjjrc9n+T4OaMCULuvHOScCwjJZc9wN1KqduVUmWBHsCKPOf8BLRTSpVRSvkALYEj9g1VuIJNv8UZSnbWXExM4dtdp4qVhLN4KlXsUk8WDflG8BT4Gp0/IWutCQ0NJSAggNDQUEaOHIn/q59nJ/MsJTXcVLiXAhO61joVGACEkZGkF2utDyml+iml+mWecwRYBxwAfiFjaOOvjgtbOKPlUbF2qVvbqxaXpnV2qaeoLqybwoV1U4r02oSklFxj5M+cOUPXrl3p0aMHtWrVYt++fXzwwQf8dcXyh5ds7C0Ky9A4dK31GmBNnudm5nk8AZhgv9CEK8kaIuhMFBT7AyYlvnh7l769eD8h30dR5vgmzv48h/TUZCZOnMibb76ZvZhWQeu9u9N2gsKxZC0XYRdGhwiWJGe4634t/gzxYVO49ucBvG9rxKdfTOe1p9rmOsfaAmO1q3rzVmh0djukti4KIlP/hV0Upjzg2GlGzkGnp3H5lx85O28A18/+TpWOA6jW42MWHknOd66l4abdmvsT8b/4fB9KUlsXtkgPXdiFtbJBXv6ZZYNhyw6QmJJeApGVvOS4k1xY+wXJZ4/hfee9VHn0dcrc6Adk9LJvH7I6X/kk73BTW2vOSG1dWCMJXdiFpbJBTgr4Y+zj2Y+t7TTkbMredIfhc3VaCpd2/sClnYvxuMEHvy6D8al/PyrP0gdZk59CQqMJCY3GUyla3VGZkxeSsmvltj4cPZRieZSMUxf5SUIXdpGVXHLWfHPKO4vU18Iytc6oSodgQ+ddP3uMC2s+J+X8n/gEPECVh4Px9Klk6LVpWucaq1/QQmJpWkstXVgkCV0YZmTERRlPZXG89oP1quV6fOWa8ydzI9JTrnFp27dcjvwJz/KVqdZtBD53tSz2+2Yt6mUtqSelpPHBikOS0EUuktBFPpYSN1DgzjsTwo5anXyzdG8sgbWqZO8I5Crl8/MrJwLg1+WdfMeu/XmAC+umkJpwlgpNOlG5/at43FDebtfOWtTLWvkla5x7Ube6E+5HErrIxdqWaeW8PArcO9PWzbqc57rSKI3Uf87ney79+lUubvqKK/vXUca3Ojf3GEO5WvfY/dqeShV4oznntnn23qxauB5J6CIXa7seWbvZmTOJF3QzL+tcVx6lkXh8N/Fh00i7msCNLZ6hUtsX8PAq55BrGVlYLGvUjIeFhcjyfuAK9ycJXeT6ql7YyTgeSmUPw3uwXjWW7o21mvwrZS5/a3SIozNJS7xEfPhsEo9swatabao9M5wbqtcp+IUlQGM9+bvyh6coPJlYVMrl3NXHVjL39fayuElFmtbZw/CW7o2lW3N/KvtYXrf8anJqxp6cFjbicGZpiQmcmfMaiUd3UKnti1T/v8+cJpkXpCR2qxLOQxJ6KWdkyr63lycfPNkg12xGS9vKJaWksem3OKLef9RiUk9J09klgE+eaUQh94kocamXz5P2z3lSL8RQxrc61Xt+jm+b51GetjfacBYluVuVcA5ScinlbH0lV2BxRiPA7UNW23y/BCtjzHNez+BeyyVO63Su7A/j4qZ5kJ5O5Yd6U7F5F5SH+d8qFPBiq9vY9FucxbKVp1Kkay2jXEopSeilnLV6dta2aYV9XSVvL5vT1jUZ09qvXk8tYsSOlRIfy4V1U7h++lfK1bqHKp3ewMv3FrPDAv5N5qO7NmL48oMWN/x4vmVNRndtVPLBCacgJZdSrqgbS1t6nZeH4mpyaoE3PGMTkrJ3/nEWOj2NS7uXcfargSSf+4Mqnd7gpuc+JmHTPOJ+HGN2eEDGh+HCXaeoPWQ1i3aftnjOpt9kr97STHropVzOSUGFmZBi6XWJyakuMZ0/r+Rzf2QspvXX73jf3Yoqj7xGmYpVAUhLumxydJZZG9XiaqOHhH1JQhdF3lg67+us1dWdlU5N4dLOxVzatRiPchXwe/I9fOq1zbeYlitRIAt3lWKS0IXdWKure1qY9GK267G/cWHtF6RcOEX5Bg9S+eE+eHrfaHZYxaZBJhOVYlJDF3ZjrR7/fMuaFuvtXp4l3xNOT75G/IYv+WvhYNKTE7mp+0j8nnjbLZJ5FplMVHopbVLPqWLFirp58+a5nnv22Wfp378/iYmJPPbYY/le07NnT3r27Mn58+fp3r17vuOvvfYazz33HKdPn+bll1/Od/ztt9+mS5cuHD16lL59++Y7Pnz4cDp06EB0dDQhISH5jo8ZM4b77ruPiIgIhg0blu/45MmTadKkCeHh4YwePTrf8VmzZlG3bl1WrlzJpEmT8h1fsGABNWvWJDQ0lBkzZuQ7vmTJEvz8/Jg/fz7z58/Pd3zNmjX4+Pgwffp0Fi9enO/45s2bAZg4cSKrVq3Kdczb25u1a9cCMGrUKDZs2JDreNWqVVm6dCkAQ4cOZefOnbmO16hRg4ULF7I8Kpb+A94g/vQxbijjSc0q3vhVuIGyVfyJa/J/XExM4cK6KeiEs1StUJaExBSup6ZRpWYdeg3+gIW7TnF+5cR8a6jc4F+Pyg/0BCDuxzH5atvlajXGt83zAPy9eCQ69Xru9t3ZgrI330H8uimkXvobjwpV8Kp0C3hk9GnK12tHxWaPk55yjXM/fJD9utRL5wDwbfsCFRp1IC3xEnHLP8n3d1ux6WOUr38/qZfjOL8q/8/2xhZP43NXS1IuxHAhbGq+45Xu64F37SYk/32C+A2z8x33vf//KFejPtdijpCw9et8x6s8HEzZm+8g6WQ01375gaa3+eY6Xhp+9wBCQkKIjo7OdbxOnTrMnp3xdxocHMyxY8dyHW/SpAmTJ08G4KWXXiImJibX8datW/PJJxk/827dunHhwoVcxx9++GFGjBgBQOfOnUlKyv2B+sQTT/DOOxmLu7Vv3568Cpv3tmzZsldrHZjvRKTkIuysa1N/Nt9bk2ivC/mOXcuxxGJqejp/X75GGQ8P7rqpAh3uzRhut/rAWfIvh1VM6WkkHt1OwuZ5lKl8K1433W54VcQylW6ydzQOdYOnB9WqyOzQ0sq0HnpgYKCOjIw05dqi5LUZu9HqCAxvL08+eaZR9qqBIXbczSjx913E/zz938W02jyPh9cNdnt/s3koqF7JO3uk0YP1qrHptzhZQteNKaWs9tClhi5KhJGldcF+S72mXb1I3E/jiFs2Gg+fStzyyqdUbt+z0Mn878Uj+XvxSLvE5AjpGnYMeYjPnmtCYnIqC3edyl6XJ2sJ3eVRsWaHKUqIlFxEiTC6tC7Y3qmnIFprrh7axMUNX5KekoRvu5e5sWU3lGfRftXz1uKdjb+vd7610HOSJXRLF0nootiM7JRT0CbSWUsGFGdiTOrlc1wIm8a1E3u54dZ6VO38Jl5+NYv8fq7gwXrVClxgTUa9lB6S0EWxGN0pJ+vPH648lG82qQcZS+sWdTkArdO5ErWWi1vmg06n8sPBVGz2uFMspuVottafzyJL6JYektBFsVjb4cjS1/ysx4OX7M+192g6kG5lL9KCpMTHcmHtF1yPOUS52k2p2mkAZSrdXKT3ckVJKWk2J27ZWpdH9iB1P5LQRbFY+zpv7XlbG0kXhk5P4/IvP5Kw/Vs8ypSl6mMhlG/4sN2n7Xvf2cKu7+cIaVrj7eWZ74PV19uLD55sYDFJyx6k7kkSuigWazc7rX3Nt0c9N/nvE1xY+znJf/8P7zqtMxbTqlCl2O9rSaWWzzjkfe1JKXIl88o+XozsYjmRZynMNyvhOmTYoiiWwi6/ay3RG+lX69RkLm79hrNfh5B65QJ+XYdy09P/dVgydxV5qy1XrhW81nxhv1kJ1yAJXRRL1nZyWVvT+ft6Z08SssTaB8CLrW7D38bNu2sxRzjz1Rtc3rmY8g0e5NagGZSv28aeTbHor++G8Nd3Qxx+HXtKSdfZ4/qtsfbBau355VGxtBm7kduHrKbN2I0ytt1JGSq5KKU6AZ8DnsAcrfVYK+fdC+wCntNaL7FblMKpFWb53YLWX887dDE9OYmErd/wz95VeN7ox03/+RDvO5pbfG/xr6yetrUbnw/Wq8a3u07lGu9v7ZuV1NtdR4EJXSnlCUwDHgFigD1KqRVa68MWzhsHhDkiUOE+bH0A5ByvnvTHPi6sm0ra5TgqNnsc3/tfweMGn0Jfz9/Xm7MJSaQXfKrbuNXChKPYhCTeCo3mh8hT/HLyYr7JW92aW/65SL3ddRgpubQAjmutT2itk4HvgacsnDcQWAqcs2N8opTp2tSf/3aoSWL4FM4tfh9Vpiw3vziWKo/0K1IyPzn2cXYMeYhKPl4OiNY5eXkoBnesazERa2DH/+ItjjRafeCsxfeTervrMJLQ/YGcGxjGZD6XTSnlDzwNzLT1RkqpYKVUpFIqMi5O9j4U+S1btozBPToQHx2Of/vnufXVLyhXo0GR3stTqexab4ILbo1XFL7eXkz4T2O6NvUv9Kxba9sHFrbeLsxjJKFbGoCQ9+N9MvCe1trmlDWt9WytdaDWOrBatWoGQxSlwV9//UX37t3p1q0bt9xyC3v27GHqpxPw8S560kjTmpDQaGoPWY1HEcenl6/XjvL12hU5hpLk7eWZa9y5p53G5Bd1I3FR8ozcFI0Bci6IUQM4k+ecQOD7zEkdfsBjSqlUrfVyewQp3JfWmq+//ppBgwaRmJjIJ598wttvv42XlxdNM88xshF1ZR8vEpJS8g3hy1LULfAqNnu8SK8zQ966dmHb7OvtZXP2qMwqdX5GEvoe4G6l1O1ALNADeCHnCVrr27P+rJSaD6ySZC4KcvLkSfr27cvPP/9M27ZtmTNnDnXr5u715b2BamllQW8vT0Z2aWBoHfXC7m+annINAA+vcoZfY6acdW1/GytceqiMpXezeHkonmhc3eZoFkngzq/AkovWOhUYQMbolSPAYq31IaVUP6VUP0cHKNxPeno6U6ZMoWHDhkRERDB16lS2bNmSL5lbUthx73kVttd67ocPcm1J5+xy1rUtlUoU8FKr2/j02Sa5/g4n/Kcxm36LszqaRbgGQ+PQtdZrgDV5nrN4A1Rr3bP4YQl39dtvv9G7d2927NhBx44dmTVrFrVq1SrUe1jrLfp6exV5xUZX4p9Z8sj7TUWRsZxuloJKJXn/Dt+y8g1HRrO4DpkpKkpESkoKY8aMoXHjxhw+fJivv/6atWvXFjqZ2/LBkw3w8ijcjcBCnu4UspJyt+b+uUYsaDKW0y3qLE4ZzeL6ZHEu4XD79u0jKCiI6OhounfvztSpU7n5ZvsvcZu3R1rJ2wulMoYsWiu0pJuzpW6RvdTqtux2bvotLl+7ct4YLWiGZ94boA/Wq5ZvfXUZzeJaJKELh0lKSuKjjz5iwoQJVKtWjWXLlvH000879JrWyjHF3Q2ppHh7eXA9NT3fB41S8GLL2xjdtVH2cwVN+LE1wxPIl+yX7o2lW3N/2WTahUlCFw6xfft2goKCOHbsGL169WLixIlUrlzZtHgGd6ybb2MNIyo06uCgiPLz9/Xm6vVUklLyL1KgNYTuOU1grSrZCdba0sUemROqrH2AxSYkWU32m36LY8eQh+zQGmEGqaELu/rnn38YMGAA7dq1Izk5mfXr1zN37lxTkzlk9NzLl7Xcf8lbRvf28sTXO2OpgAqNOpRIUvf28uTBetVs3tRNSdN8uPJQ9uOcN0BzStOaocsOWr0/4KlUsafzy+qLzkl66MJu1q5dS9++fYmJieHNN99k9OjRVKhQweywsl2ykixz9tmzNoeAjJLElUvxAHj6VLJ7PJ5Kka51dv06dM/pAl+Tc1LVpt+sL59ha5/RNK2tjlE3cgNUVl90XtJDF8V24cIFXnnlFR577DEqVKjAjh07mDx5sinJ3FbP0UiyunItlQ9XHuKt0GjKeXlwccU44pZ/4pBY07Xmj8zFwzb9Fme4HJTVpqIOJ8wa9ljU6fwF1eaFeSShiyLTWvPDDz8QEBDAokWLGD58OFFRUbRu3dqUeLJ6jrEJSWj+7TlmJUBLSSyvlHTNxcxRMRcTU2xORDIy5NHLU2WXb/K61deb4csPcufQNYW6YTt4yX6afPiz1ZE7WVTm9XPKStrFmaAlqy86Lym5iCI5e/Ys/fv3Z/ny5TRv3pyff/6Zxo0bmxpTQet25x3WaKQ/rK0kdG8vT7o19883zC//G0C1imUt1sZjE5JYuOuUgShyS0nThiZQ6cz/qezjRUJiisWJRUUpkRR2H1lRciShi0LRWvPVV18xaNAgrl+/zvjx43nrrbcoU8b8XyUjPcecSayoQxk9lcruzQbWqpL9AeFhYZ2YlHTN7+euFvoa9pKSrvEpW4ao9x+123tamqUq49Wdg/n/CoXL+OOPPwgODiY8PJz777+fL7/8kjp16pgdVrbC9hwtJSYj0rW22Mu9fcjqQkZcMgpTCrG12mIWWX3ReUlCFwVKS0tj6tSpDBs2DE9PT2bMmEFwcDAeHs51C6awPUdLM0uvJqfmujlZselj+V5na4q8PSYveSrFpGcb8/bi/UVe9jcnTca3kYKSbmFGr8jqi87Juf5FCqdz+PBh2rZtS0hICA888ACHDh2iX79+TpfMoWgrMXZt6s+OIQ/xx9jHiR75KM/dWzPXuPTy9e+nfP37sx/b+oCwNnKkzZ1VCtWO51vWpGtTf9LtkMyz5L1BbElBo1dk7Lnzkx66sCg5OZlx48YxevRoKlasyMKFC3nhhRdQdtoFx1GK23PMuz5K6uWMsd5lbqyWPdzP2vvbKkUMX36QRbtPF9jjfqnVv9P7rfX4K/t44VO2TIGbfuRV0MbOtu5ByNhz16Cs3cV3tMDAQB0ZGWnKtYVtkZGRBAUFceDAAXr06MHnn3/OTTfdZHZYJeL2IatzJfS/vhsCQPUXxvLHWPvsXtTkw58tjlLx9/XONe3e2mYeeb91WDrPGgVW22HtJrF/ZonJ2jFZKqBkKaX2aq0DLR1zvu/NwjRJSUm8++67tGzZkvPnz/PTTz+xaNGiUpPMoWSWkP3gyQaGJvUYLSFZOq+yj/Wx79bYmmwkY89dg5RcBABbtmyhd+/eHD9+nD59+jB+/Hh8fX3NDqvEWbqx6qGUXYfkFWaUiNESktGt+my1w1ZcE8KOythzFyAJvZS7fPky7733HjNnzuSOO+5gw4YNPPRQ6f0KnTep3VDGk5pVvO1eJ3b0KJGiDi20FpeMPXcNUkMvxVavXk2/fv04c+YMISEhfPTRR5QvX97ssJxK+/btAdi8ebOpcTgDI2PUhePZqqFLD70UOn/+PCEhIXz77bc0aNCAJUuW0LJlS7PDckpvv/222SE4DRl77vwkoZciWmtCQ0MZOHAgly5dYuTIkQwbNoyyZcuaHZrT6tKli9khCGGYJPRSIjY2lv79+7NixQruvfde5s6dS6NGjQp+YSl39GjGpJq6daVWLJyfJHQ3p7Vmzpw5vPPOO6SkpDBx4kRCQkLw9LS9jKzI0LdvX0Bq6MI1SEJ3Y//73//o06cPmzZton379nz55ZfcddddZoclhHAQmVjkhtLS0vj0009p1KgRe/fuZdasWWzYsEGSuRBuTnrobubXX38lKCiIX375hSeeeIIZM2ZQo0YNs8MSQpQA6aG7ieTkZD788EOaNWvGiRMnWLRoEStWrJBkLkQpIj10N/DLL78QFBTEr7/+ygsvvMDnn3+On5+f2WG5heHDh5sdghCGSUJ3YYmJiYwYMYLJkydTvXp1Vq5cyRNPPGF2WG6lQ4cOZocghGGS0F3Upk2b6N27NydOnKBv376MGzeOSpUqmR2W24mOjgagSZMmpsYhhBGGauhKqU5KqaNKqeNKqSEWjr+olDqQ+V+EUsrc7d/d2KVLlwgODuahhx5CKcWmTZuYOXOmJHMHCQkJISQkxOwwhDCkwISulPIEpgGdgQDgeaVUQJ7T/gAe0FrfA4wCZts7UAErV64kICCAuXPnMnjwYA4cOJC9eJQQQhjpobcAjmutT2itk4HvgadynqC1jtBaX8x8uAuQoRV2FBcXx/PPP8+TTz5J1apV2b17N+PHj8fHx8fs0IQQTsRIQvcHTud4HJP5nDVBwFpLB5RSwUqpSKVUZFxcnPEoSymtNd9++y3169dn6dKlfPTRR0RGRhIYaHHlTCFEKWckoVvaFdjiIupKqQfJSOjvWTqutZ6ttQ7UWgdWq1bNeJSl0OnTp+nSpQsvvfQSd911F1FRUYwYMUJWRhRCWGVklEsMUDPH4xrAmbwnKaXuAeYAnbXWF+wTXumTnp7O7Nmzeffdd0lLS+Ozzz5j4MCBspiWScaMGWN2CEIYZiSh7wHuVkrdDsQCPYAXcp6glLoNWAa8rLU+ZvcoS4nff/+dPn36sGXLFh5++GFmz57NHXfcYXZYpdp9991ndghCGFZgyUVrnQoMAMKAI8BirfUhpVQ/pVS/zNPeB6oC05VS0Uop2VuuEFJTU5kwYQL33HMP0dHRzJ07l/Xr10sydwIRERFERESYHYYQhsieoibbv38/QUFB7N27l6eeeorp06dz6623mh2WyCR7igpnY2tPUVmcyyTXr19nxIgRBAYGcvr0aRYvXsyPP/4oyVwIUWQy9d8EO3fuJCgoiCNHjvDyyy/z2WefUbVqVbPDEkK4OOmhl6CrV68SEhJCmzZtuHLlCmvWrOGbb76RZC6EsAvpoZeQ8PBw+vTpw8mTJ+nfvz+ffPIJN954o9lhCSHciCR0B0tISODtt99m3rx53H333WzdupV27dqZHZYwaPLkyWaHIIRhktAdaPny5fTv359z584xZMgQ3n//fby9vc0OSxSCLJsrXIkkdAf4+++/GThwID/88AONGzdm5cqVNG/e3OywRBGEh4cDstGFcA2S0O1Ia82CBQsICQnh6tWrfPzxxwwePBgvLy+zQxNFNHr0aEASunANktDt5NSpU/Tt25d169bRunVr5s6dS/369c0OSwhRisiwxWJKT09n2rRpNGjQgG3btvHFF1+wbds2SeZCiBInPfRiOHr0KL1792b79u088sgjzJ49m9q1a5sdlhCilJIeehGkpKQwduxYGjduzK+//spXX31FWFiYJHMhhKmkh15IUVFRBAUFERUVxTPPPMO0adO45ZZbzA5LOMisWbPMDkEIwyShG3Tt2jVGjRrFuHHj8PPzY8mSJXTr1s3ssISD1a1b1+wQhDBMEroBO3bsICgoiKNHj/J///d/fPrpp1SpUsXssEQJWLlyJQBdunQxORIhCiY1dBuuXLnCG2+8Qbt27bh27Rrr1q1j/vz5ksxLkUmTJjFp0iSzwxDCEEnoVoSFhdGwYUOmTp3KgAED+PXXX+nYsaPZYQkhhFWS0POIj4+nZ8+edOrUiXLlymWPLa9QoYLZoQkhhE2S0HNYunQpAQEBLFy4kGHDhhEdHU2bNm3MDksIIQyRm6LA2bNnGTBgAMuWLaNp06asW7dOVtkTQricUp3QtdZ8/fXXvPXWWyQlJTF27FgGDRoki2mJbAsWLDA7BCEMK7UJ/eTJkwQHB7N+/Xratm3LnDlzZMyxyKdmzZpmhyCEYaWuhp6WlsaUKVNo2LAhO3fuZNq0aWzZskWSubAoNDSU0NBQs8MQwpBS1UM/cuQIvXv3JiIigk6dOjFz5kxq1apldljCic2YMQOA5557zuRIhChYqeihp6Sk8PHHH9OkSRN+++03vvnmG9asWSPJXAjhVty+h75v3z569erF/v37+c9//sOUKVO4+eabzQ5LCCHszm176ElJSQwZMoQWLVrw999/s2zZMhYvXizJXAjhttyyh75t2zZ69+7NsWPHCAoKYsKECVSuXNnssIQQwqHcKqFfvnyZoUOHMn36dGrXrs369etlc19RLEuWLDE7BCEMc5uEvnbtWvr27UtMTAwhISGMHj2a8uXLmx2WcHF+fn5mhyCEYS5fQ79w4QKvvPIKjz32GBUqVGDHjh189tlnksyFXcyfP5/58+ebHYYQhhhK6EqpTkqpo0qp40qpIRaOK6XUF5nHDyilmtk/1Ny01ixevJj69euzaNEiRowYQVRUFK1bt3b0pUUpIglduJICSy5KKU9gGvAIEAPsUUqt0FofznFaZ+DuzP9aAjMy/98hzpw5w+uvv87y5ctp3rw54eHh3HPPPY66nBBCuAQjPfQWwHGt9QmtdTLwPfBUnnOeAr7RGXYBvkqp6naOFYA1a9YQEBDAunXrGD9+PLt27ZJkLoQQGEvo/sDpHI9jMp8r7DkopYKVUpFKqci4uLjCxgpAnTp1aN26NQcOHGDw4MGUKeM293WFEKJYjGRDZeE5XYRz0FrPBmYDBAYG5jtuxF133cXatWuL8lIhhHBrRhJ6DJBzDdEawJkinCOEy1mzZo3ZIQhhmJGSyx7gbqXU7UqpskAPYEWec1YAr2SOdmkFXNJan7VzrEKUOB8fH3x8fMwOQwhDCuyha61TlVIDgDDAE5intT6klOqXeXwmsAZ4DDgOJAKvOi5kIUrO9OnTAejfv7/JkQhRMKV1kUrZxRYYGKgjIyNNubYQRrVv3x6AzZs3mxqHEFmUUnu11oGWjrn8TFEhhBAZJKELIYSbkIQuhBBuQhK6EEK4CdNuiiql4oA/i/hyP+C8HcNxBdLm0kHaXDoUp821tNbVLB0wLaEXh1Iq0tpdXnclbS4dpM2lg6PaLCUXIYRwE5LQhRDCTbhqQp9tdgAmkDaXDtLm0sEhbXbJGroQQoj8XLWHLoQQIg9J6EII4SacOqE74+bUjmagzS9mtvWAUipCKdXYjDjtqaA25zjvXqVUmlKqe0nG5whG2qyUaq+UilZKHVJKbSnpGO3NwO92JaXUSqXU/sw2u/SqrUqpeUqpc0qpX60ct3/+0lo75X9kLNX7P+AOoCywHwjIc85jwFoydkxqBew2O+4SaPN9QOXMP3cuDW3Ocd5GMpZq7m523CXwc/YFDgO3ZT6+yey4S6DNw4BxmX+uBsQDZc2OvRhtvh9oBvxq5bjd85cz99CdanPqElJgm7XWEVrri5kPd5GxO5QrM/JzBhgILAXOlWRwDmKkzS8Ay7TWpwC01q7ebiNt1kBFpZQCKpCR0FNLNkz70VpvJaMN1tg9fzlzQrfb5tQupLDtCSLjE96VFdhmpZQ/8DQwswTjciQjP+c6QGWl1Gal1F6l1CslFp1jGGnzVKA+GdtXHgTe1Fqnl0x4prB7/jKyp6hZ7LY5tQsx3B6l1INkJPS2Do3I8Yy0eTLwntY6LaPz5vKMtLkM0Bx4GPAGdiqldmmtjzk6OAcx0uaOQDTwEHAnsF4ptU1rfdnBsZnF7vnLmRN6adyc2lB7lFL3AHOAzlrrCyUUm6MYaXMg8H1mMvcDHlNKpWqtl5dIhPZn9Hf7vNb6KnBVKbUVaAy4akI30uZXgbE6o8B8XCn1B1AP+KVkQixxds9fzlxyKY2bUxfYZqXUbcAy4GUX7q3lVGCbtda3a61ra61rA0uA/i6czMHY7/ZPQDulVBmllA/QEjhSwnHak5E2nyLjGwlKqZuBusCJEo2yZNk9fzltD12Xws2pDbb5faAqMD2zx5qqXXilOoNtditG2qy1PqKUWgccANKBOVpri8PfXIHBn/MoYL5S6iAZ5Yj3tNYuu6yuUmoR0B7wU0rFACMBL3Bc/pKp/0II4SacueQihBCiECShCyGEm5CELoQQbkISuhBCuAlJ6EII4SYkoQshhJuQhC6EEG7i/wEOnXzoTjebjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#plt.plot(data=df_2d_pcc_marker, x='AUC', y='DEGREE_NULL_AUC')\n",
    "plt.scatter(df_2d_pcc_marker['AUC'], df_2d_pcc_marker['DEGREE_NULL_AUC'])\n",
    "plt.plot([0, 1], [0, 1], c='black')\n",
    "plt.axvline(x=df_2d_pcc_marker['AUC'].mean(),c='black',ls='--')\n",
    "plt.axhline(y=df_2d_pcc_marker['DEGREE_NULL_AUC'].mean(), c='black', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ff80e128a90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#plt.plot(data=df_2d, x='AUC', y='DEGREE_NULL_AUC')\n",
    "plt.scatter(df_2d['AUC'], df_2d['DEGREE_NULL_AUC'])\n",
    "plt.plot([0, 1], [0, 1], c='black')\n",
    "plt.axvline(x=df_2d['AUC'].mean(),c='black',ls='--')\n",
    "plt.axhline(y=df_2d['DEGREE_NULL_AUC'].mean(), c='black', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>AUC</th>\n",
       "      <th>AVG_NODE_DEGREE</th>\n",
       "      <th>DEGREE_NULL_AUC</th>\n",
       "      <th>P_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GO:0000002</th>\n",
       "      <td>0.365129</td>\n",
       "      <td>19.603063</td>\n",
       "      <td>0.506772</td>\n",
       "      <td>0.017060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:0000018</th>\n",
       "      <td>0.552473</td>\n",
       "      <td>24.237166</td>\n",
       "      <td>0.580585</td>\n",
       "      <td>0.022531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:0000027</th>\n",
       "      <td>0.500107</td>\n",
       "      <td>24.461563</td>\n",
       "      <td>0.600900</td>\n",
       "      <td>0.175725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:0000028</th>\n",
       "      <td>0.432344</td>\n",
       "      <td>31.498279</td>\n",
       "      <td>0.716399</td>\n",
       "      <td>0.136796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:0000030</th>\n",
       "      <td>0.532372</td>\n",
       "      <td>18.921280</td>\n",
       "      <td>0.502340</td>\n",
       "      <td>0.272772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:2001251</th>\n",
       "      <td>0.499632</td>\n",
       "      <td>23.031182</td>\n",
       "      <td>0.567770</td>\n",
       "      <td>0.293245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:2001252</th>\n",
       "      <td>0.499031</td>\n",
       "      <td>21.837483</td>\n",
       "      <td>0.533357</td>\n",
       "      <td>0.094241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:2001257</th>\n",
       "      <td>0.522814</td>\n",
       "      <td>18.720278</td>\n",
       "      <td>0.482962</td>\n",
       "      <td>0.150789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:2001258</th>\n",
       "      <td>0.457815</td>\n",
       "      <td>18.778835</td>\n",
       "      <td>0.486479</td>\n",
       "      <td>0.036969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO:2001259</th>\n",
       "      <td>0.539032</td>\n",
       "      <td>18.103287</td>\n",
       "      <td>0.473904</td>\n",
       "      <td>0.114798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5533 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AUC  AVG_NODE_DEGREE  DEGREE_NULL_AUC   P_Value\n",
       "GO:0000002  0.365129        19.603063         0.506772  0.017060\n",
       "GO:0000018  0.552473        24.237166         0.580585  0.022531\n",
       "GO:0000027  0.500107        24.461563         0.600900  0.175725\n",
       "GO:0000028  0.432344        31.498279         0.716399  0.136796\n",
       "GO:0000030  0.532372        18.921280         0.502340  0.272772\n",
       "...              ...              ...              ...       ...\n",
       "GO:2001251  0.499632        23.031182         0.567770  0.293245\n",
       "GO:2001252  0.499031        21.837483         0.533357  0.094241\n",
       "GO:2001257  0.522814        18.720278         0.482962  0.150789\n",
       "GO:2001258  0.457815        18.778835         0.486479  0.036969\n",
       "GO:2001259  0.539032        18.103287         0.473904  0.114798\n",
       "\n",
       "[5533 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hicmatrix import HiCMatrix as hm\n",
    "from hicmatrix.lib import MatrixFileHandler\n",
    "SRP_name='aggregates'\n",
    "resolution='40kbp_raw'\n",
    "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_mouse/aggregates/40kbp_raw/max/pcc/0/all_bins/KR_KR/hic_gene_corr_gw.h5'\n",
    "\n",
    "pcc_sim = hm.hiCMatrix(exp_file_path)\n",
    "all_pcc_genes = [x[3].decode() for x in pcc_sim.cut_intervals]\n",
    "pcc_sim_matrix = pcc_sim.matrix.toarray()\n",
    "df_pcc_sim = pd.DataFrame(pcc_sim_matrix,  index=all_pcc_genes, columns = all_pcc_genes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENSMUSG00000102693</th>\n",
       "      <th>ENSMUSG00000064842</th>\n",
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       "      <th>ENSMUSG00000088894</th>\n",
       "      <th>ENSMUSG00000074733</th>\n",
       "      <th>ENSMUSG00000070263</th>\n",
       "      <th>ENSMUSG00000094649</th>\n",
       "      <th>ENSMUSG00000069475</th>\n",
       "      <th>ENSMUSG00000059326</th>\n",
       "      <th>ENSMUSG00000095993</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000102693</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571257</td>\n",
       "      <td>0.505523</td>\n",
       "      <td>0.638882</td>\n",
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       "      <td>0.442960</td>\n",
       "      <td>0.427662</td>\n",
       "      <td>0.416863</td>\n",
       "      <td>0.412963</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001455</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>-0.001243</td>\n",
       "      <td>-0.001231</td>\n",
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       "      <td>-0.000789</td>\n",
       "      <td>-0.000789</td>\n",
       "      <td>-0.000789</td>\n",
       "      <td>-0.000853</td>\n",
       "      <td>-0.000940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000064842</th>\n",
       "      <td>0.571257</td>\n",
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       "      <td>0.554578</td>\n",
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       "      <td>0.439055</td>\n",
       "      <td>0.425240</td>\n",
       "      <td>0.420280</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.001049</td>\n",
       "      <td>-0.000989</td>\n",
       "      <td>-0.001026</td>\n",
       "      <td>-0.000567</td>\n",
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       "      <td>-0.000496</td>\n",
       "      <td>-0.000496</td>\n",
       "      <td>-0.000529</td>\n",
       "      <td>-0.000626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000051951</th>\n",
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       "      <td>0.710465</td>\n",
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       "      <td>0.714727</td>\n",
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       "      <td>-0.000870</td>\n",
       "      <td>-0.000900</td>\n",
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       "      <td>-0.000611</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>-0.000707</td>\n",
       "      <td>-0.000704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000102851</th>\n",
       "      <td>0.638882</td>\n",
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       "      <td>0.611746</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.748751</td>\n",
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       "      <td>0.729829</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.001115</td>\n",
       "      <td>-0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000103377</th>\n",
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       "      <td>0.814532</td>\n",
       "      <td>0.794882</td>\n",
       "      <td>0.845449</td>\n",
       "      <td>0.831854</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001317</td>\n",
       "      <td>-0.001101</td>\n",
       "      <td>-0.000989</td>\n",
       "      <td>-0.000979</td>\n",
       "      <td>-0.000999</td>\n",
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       "      <td>-0.000678</td>\n",
       "      <td>-0.000678</td>\n",
       "      <td>-0.000690</td>\n",
       "      <td>-0.000806</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",
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       "      <td>...</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000070263</th>\n",
       "      <td>-0.000789</td>\n",
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       "      <td>-0.000611</td>\n",
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       "      <td>-0.000493</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>-0.000602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614652</td>\n",
       "      <td>0.644882</td>\n",
       "      <td>0.614007</td>\n",
       "      <td>0.669702</td>\n",
       "      <td>0.522178</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.537342</td>\n",
       "      <td>0.626563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000094649</th>\n",
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       "      <td>-0.000493</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>-0.000602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614652</td>\n",
       "      <td>0.644882</td>\n",
       "      <td>0.614007</td>\n",
       "      <td>0.669702</td>\n",
       "      <td>0.522178</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537342</td>\n",
       "      <td>0.626563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000069475</th>\n",
       "      <td>-0.000789</td>\n",
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       "      <td>-0.000611</td>\n",
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       "      <td>-0.000678</td>\n",
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       "      <td>-0.000493</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>-0.000602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614652</td>\n",
       "      <td>0.644882</td>\n",
       "      <td>0.614007</td>\n",
       "      <td>0.669702</td>\n",
       "      <td>0.522178</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537342</td>\n",
       "      <td>0.626563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000059326</th>\n",
       "      <td>-0.000853</td>\n",
       "      <td>-0.000529</td>\n",
       "      <td>-0.000707</td>\n",
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       "      <td>-0.000690</td>\n",
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       "      <td>-0.000612</td>\n",
       "      <td>-0.000632</td>\n",
       "      <td>-0.000558</td>\n",
       "      <td>...</td>\n",
       "      <td>0.545570</td>\n",
       "      <td>0.623842</td>\n",
       "      <td>0.515780</td>\n",
       "      <td>0.543718</td>\n",
       "      <td>0.797447</td>\n",
       "      <td>0.537342</td>\n",
       "      <td>0.537342</td>\n",
       "      <td>0.537342</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000095993</th>\n",
       "      <td>-0.000940</td>\n",
       "      <td>-0.000626</td>\n",
       "      <td>-0.000704</td>\n",
       "      <td>-0.001000</td>\n",
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       "      <td>-0.000806</td>\n",
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       "      <td>-0.000598</td>\n",
       "      <td>-0.000574</td>\n",
       "      <td>-0.000606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.427523</td>\n",
       "      <td>0.484523</td>\n",
       "      <td>0.395956</td>\n",
       "      <td>0.413000</td>\n",
       "      <td>0.581145</td>\n",
       "      <td>0.626563</td>\n",
       "      <td>0.626563</td>\n",
       "      <td>0.626563</td>\n",
       "      <td>0.700237</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50225 rows × 50225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ENSMUSG00000102693  ENSMUSG00000064842  \\\n",
       "ENSMUSG00000102693            1.000000            0.571257   \n",
       "ENSMUSG00000064842            0.571257            1.000000   \n",
       "ENSMUSG00000051951            0.505523            0.519788   \n",
       "ENSMUSG00000102851            0.638882            0.671924   \n",
       "ENSMUSG00000103377            0.537295            0.554578   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263           -0.000789           -0.000496   \n",
       "ENSMUSG00000094649           -0.000789           -0.000496   \n",
       "ENSMUSG00000069475           -0.000789           -0.000496   \n",
       "ENSMUSG00000059326           -0.000853           -0.000529   \n",
       "ENSMUSG00000095993           -0.000940           -0.000626   \n",
       "\n",
       "                    ENSMUSG00000051951  ENSMUSG00000102851  \\\n",
       "ENSMUSG00000102693            0.505523            0.638882   \n",
       "ENSMUSG00000064842            0.519788            0.671924   \n",
       "ENSMUSG00000051951            1.000000            0.611746   \n",
       "ENSMUSG00000102851            0.611746            1.000000   \n",
       "ENSMUSG00000103377            0.612794            0.867308   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263           -0.000611           -0.000760   \n",
       "ENSMUSG00000094649           -0.000611           -0.000760   \n",
       "ENSMUSG00000069475           -0.000611           -0.000760   \n",
       "ENSMUSG00000059326           -0.000707           -0.001115   \n",
       "ENSMUSG00000095993           -0.000704           -0.001000   \n",
       "\n",
       "                    ENSMUSG00000103377  ENSMUSG00000104017  \\\n",
       "ENSMUSG00000102693            0.537295            0.537295   \n",
       "ENSMUSG00000064842            0.554578            0.554578   \n",
       "ENSMUSG00000051951            0.612794            0.612794   \n",
       "ENSMUSG00000102851            0.867308            0.867308   \n",
       "ENSMUSG00000103377            1.000000            1.000000   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263           -0.000678           -0.000678   \n",
       "ENSMUSG00000094649           -0.000678           -0.000678   \n",
       "ENSMUSG00000069475           -0.000678           -0.000678   \n",
       "ENSMUSG00000059326           -0.000690           -0.000690   \n",
       "ENSMUSG00000095993           -0.000806           -0.000806   \n",
       "\n",
       "                    ENSMUSG00000103025  ENSMUSG00000089699  \\\n",
       "ENSMUSG00000102693            0.442960            0.427662   \n",
       "ENSMUSG00000064842            0.455776            0.439055   \n",
       "ENSMUSG00000051951            0.710465            0.734328   \n",
       "ENSMUSG00000102851            0.779332            0.748751   \n",
       "ENSMUSG00000103377            0.814532            0.794882   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263           -0.000522           -0.000493   \n",
       "ENSMUSG00000094649           -0.000522           -0.000493   \n",
       "ENSMUSG00000069475           -0.000522           -0.000493   \n",
       "ENSMUSG00000059326           -0.000491           -0.000612   \n",
       "ENSMUSG00000095993           -0.000655           -0.000598   \n",
       "\n",
       "                    ENSMUSG00000103201  ENSMUSG00000103147  ...  \\\n",
       "ENSMUSG00000102693            0.416863            0.412963  ...   \n",
       "ENSMUSG00000064842            0.425240            0.420280  ...   \n",
       "ENSMUSG00000051951            0.708755            0.714727  ...   \n",
       "ENSMUSG00000102851            0.741094            0.729829  ...   \n",
       "ENSMUSG00000103377            0.845449            0.831854  ...   \n",
       "...                                ...                 ...  ...   \n",
       "ENSMUSG00000070263           -0.000439           -0.000602  ...   \n",
       "ENSMUSG00000094649           -0.000439           -0.000602  ...   \n",
       "ENSMUSG00000069475           -0.000439           -0.000602  ...   \n",
       "ENSMUSG00000059326           -0.000632           -0.000558  ...   \n",
       "ENSMUSG00000095993           -0.000574           -0.000606  ...   \n",
       "\n",
       "                    ENSMUSG00000024997  ENSMUSG00000003228  \\\n",
       "ENSMUSG00000102693           -0.001455           -0.001262   \n",
       "ENSMUSG00000064842           -0.001193           -0.001049   \n",
       "ENSMUSG00000051951           -0.001094           -0.000870   \n",
       "ENSMUSG00000102851           -0.001573           -0.001405   \n",
       "ENSMUSG00000103377           -0.001317           -0.001101   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263            0.614652            0.644882   \n",
       "ENSMUSG00000094649            0.614652            0.644882   \n",
       "ENSMUSG00000069475            0.614652            0.644882   \n",
       "ENSMUSG00000059326            0.545570            0.623842   \n",
       "ENSMUSG00000095993            0.427523            0.484523   \n",
       "\n",
       "                    ENSMUSG00000096578  ENSMUSG00000088894  \\\n",
       "ENSMUSG00000102693           -0.001243           -0.001231   \n",
       "ENSMUSG00000064842           -0.000989           -0.001026   \n",
       "ENSMUSG00000051951           -0.000900           -0.000883   \n",
       "ENSMUSG00000102851           -0.001275           -0.001307   \n",
       "ENSMUSG00000103377           -0.000989           -0.000979   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263            0.614007            0.669702   \n",
       "ENSMUSG00000094649            0.614007            0.669702   \n",
       "ENSMUSG00000069475            0.614007            0.669702   \n",
       "ENSMUSG00000059326            0.515780            0.543718   \n",
       "ENSMUSG00000095993            0.395956            0.413000   \n",
       "\n",
       "                    ENSMUSG00000074733  ENSMUSG00000070263  \\\n",
       "ENSMUSG00000102693           -0.001103           -0.000789   \n",
       "ENSMUSG00000064842           -0.000567           -0.000496   \n",
       "ENSMUSG00000051951           -0.000497           -0.000611   \n",
       "ENSMUSG00000102851           -0.000594           -0.000760   \n",
       "ENSMUSG00000103377           -0.000999           -0.000678   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263            0.522178            1.000000   \n",
       "ENSMUSG00000094649            0.522178            1.000000   \n",
       "ENSMUSG00000069475            0.522178            1.000000   \n",
       "ENSMUSG00000059326            0.797447            0.537342   \n",
       "ENSMUSG00000095993            0.581145            0.626563   \n",
       "\n",
       "                    ENSMUSG00000094649  ENSMUSG00000069475  \\\n",
       "ENSMUSG00000102693           -0.000789           -0.000789   \n",
       "ENSMUSG00000064842           -0.000496           -0.000496   \n",
       "ENSMUSG00000051951           -0.000611           -0.000611   \n",
       "ENSMUSG00000102851           -0.000760           -0.000760   \n",
       "ENSMUSG00000103377           -0.000678           -0.000678   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263            1.000000            1.000000   \n",
       "ENSMUSG00000094649            1.000000            1.000000   \n",
       "ENSMUSG00000069475            1.000000            1.000000   \n",
       "ENSMUSG00000059326            0.537342            0.537342   \n",
       "ENSMUSG00000095993            0.626563            0.626563   \n",
       "\n",
       "                    ENSMUSG00000059326  ENSMUSG00000095993  \n",
       "ENSMUSG00000102693           -0.000853           -0.000940  \n",
       "ENSMUSG00000064842           -0.000529           -0.000626  \n",
       "ENSMUSG00000051951           -0.000707           -0.000704  \n",
       "ENSMUSG00000102851           -0.001115           -0.001000  \n",
       "ENSMUSG00000103377           -0.000690           -0.000806  \n",
       "...                                ...                 ...  \n",
       "ENSMUSG00000070263            0.537342            0.626563  \n",
       "ENSMUSG00000094649            0.537342            0.626563  \n",
       "ENSMUSG00000069475            0.537342            0.626563  \n",
       "ENSMUSG00000059326            1.000000            0.700237  \n",
       "ENSMUSG00000095993            0.700237            1.000000  \n",
       "\n",
       "[50225 rows x 50225 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pcc_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hicmatrix import HiCMatrix as hm\n",
    "from hicmatrix.lib import MatrixFileHandler\n",
    "SRP_name='aggregates'\n",
    "resolution='40kbp_raw'\n",
    "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_human/aggregates/100kbp_raw/max/pcc/0/all_bins/KR_KR/hic_gene_corr_gw.h5'\n",
    "\n",
    "pcc_sim = hm.hiCMatrix(exp_file_path)\n",
    "all_pcc_genes = [x[3].decode() for x in pcc_sim.cut_intervals]\n",
    "pcc_sim_matrix_human = pcc_sim.matrix.toarray()\n",
    "df_pcc_sim_human = pd.DataFrame(pcc_sim_matrix_human,  index=all_pcc_genes, columns = all_pcc_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ENSG00000119946 - neuron CNNM1 10 \n",
    "CNR1 - ENSG00000166589 16 \n",
    "CNTN1 - ENSG00000018236 12 \n",
    "\n",
    "epithilal cells\n",
    "RND3 - ENSG00000115963 2\n",
    "ASS1 - ENSG00000130707 9 \n",
    "KRT5 -  ENSG00000186081 12 \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0009672968385351642"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pcc_sim_human['ENSG00000130707']['ENSG00000115963']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "#import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "ax = sns.histplot(df_pcc_sim_human)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_jac_corr = pd.DataFrame(jac_sim.matrix.toarray() ,  index=all_genes, columns = all_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>50225 rows × 50225 columns</p>\n",
       "</div>"
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      "text/plain": [
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       "...                                ...                 ...   \n",
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       "\n",
       "                    ENSMUSG00000074733  ENSMUSG00000070263  \\\n",
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       "...                                ...                 ...   \n",
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       "\n",
       "                    ENSMUSG00000094649  ENSMUSG00000069475  \\\n",
       "ENSMUSG00000102693           -0.000789           -0.000789   \n",
       "ENSMUSG00000064842           -0.000496           -0.000496   \n",
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       "ENSMUSG00000102851           -0.000760           -0.000760   \n",
       "ENSMUSG00000103377           -0.000678           -0.000678   \n",
       "...                                ...                 ...   \n",
       "ENSMUSG00000070263            1.000000            1.000000   \n",
       "ENSMUSG00000094649            1.000000            1.000000   \n",
       "ENSMUSG00000069475            1.000000            1.000000   \n",
       "ENSMUSG00000059326            0.537342            0.537342   \n",
       "ENSMUSG00000095993            0.626563            0.626563   \n",
       "\n",
       "                    ENSMUSG00000059326  ENSMUSG00000095993  \n",
       "ENSMUSG00000102693           -0.000853           -0.000940  \n",
       "ENSMUSG00000064842           -0.000529           -0.000626  \n",
       "ENSMUSG00000051951           -0.000707           -0.000704  \n",
       "ENSMUSG00000102851           -0.001115           -0.001000  \n",
       "ENSMUSG00000103377           -0.000690           -0.000806  \n",
       "...                                ...                 ...  \n",
       "ENSMUSG00000070263            0.537342            0.626563  \n",
       "ENSMUSG00000094649            0.537342            0.626563  \n",
       "ENSMUSG00000069475            0.537342            0.626563  \n",
       "ENSMUSG00000059326            1.000000            0.700237  \n",
       "ENSMUSG00000095993            0.700237            1.000000  \n",
       "\n",
       "[50225 rows x 50225 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jac_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_list = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/class_level_markers.csv')\n",
    "marker_list['gene'] = marker_list['gene'].str.upper()\n",
    "df_ensg_name = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/genomes_jlee/mouse_geneid_symbol.txt',sep='\\t', names=['gene_id', 'gene'])\n",
    "df_ensg_name['gene'] = df_ensg_name['gene'].str.upper()\n",
    "marker_list = marker_list.merge(df_ensg_name, right_on='gene', left_on='gene') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
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       "      <td>ENSMUSG00000032841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    group     cell_type  rank    gene  recurrence     auroc  fold_change  \\\n",
       "0     all     GABAergic     1    GAD1           7  0.941159   116.960472   \n",
       "1     all     GABAergic     2    GAD2           7  0.928440   139.811415   \n",
       "2     all     GABAergic     3   ERBB4           7  0.921449    81.717383   \n",
       "3     all     GABAergic     4  KCNIP1           7  0.916919    32.252038   \n",
       "4     all     GABAergic     5   RBMS3           7  0.902093    17.038869   \n",
       "..    ...           ...   ...     ...         ...       ...          ...   \n",
       "294   all  Non-Neuronal    96     MAG           6  0.648512   165.820081   \n",
       "295   all  Non-Neuronal    97   FNBP1           6  0.648028     5.592886   \n",
       "296   all  Non-Neuronal    98  FRMD4B           6  0.647805     9.050547   \n",
       "297   all  Non-Neuronal    99    PLLP           6  0.645540    61.884548   \n",
       "298   all  Non-Neuronal   100   PRR5L           6  0.645414   187.906185   \n",
       "\n",
       "     fold_change_detection   expression  precision  ...  population_size  \\\n",
       "0                 9.289078   820.463486   0.659089  ...     10207.000000   \n",
       "1                13.987046   659.151566   0.730005  ...     10207.000000   \n",
       "2                 5.736415  2257.167753   0.514809  ...     10207.000000   \n",
       "3                10.796420   588.571993   0.687830  ...     10207.000000   \n",
       "4                 3.607831   340.701798   0.442861  ...     10207.000000   \n",
       "..                     ...          ...        ...  ...              ...   \n",
       "294              16.747920   456.955370   0.370001  ...      8908.857143   \n",
       "295               0.955553   454.931925   0.063414  ...      8908.857143   \n",
       "296               2.335387   192.151003   0.124702  ...      8908.857143   \n",
       "297              12.642643   190.039132   0.314672  ...      8908.857143   \n",
       "298              30.603359   334.996404   0.447383  ...      8908.857143   \n",
       "\n",
       "     n_datasets   scSS  snSS  scCv2  snCv2  snCv3M  scCv3  snCv3Z  \\\n",
       "0             7   True  True   True   True    True   True    True   \n",
       "1             7   True  True   True   True    True   True    True   \n",
       "2             7   True  True   True   True    True   True    True   \n",
       "3             7   True  True   True   True    True   True    True   \n",
       "4             7   True  True   True   True    True   True    True   \n",
       "..          ...    ...   ...    ...    ...     ...    ...     ...   \n",
       "294           7  False  True   True   True    True   True    True   \n",
       "295           7  False  True   True   True    True   True    True   \n",
       "296           7  False  True   True   True    True   True    True   \n",
       "297           7  False  True   True   True    True   True    True   \n",
       "298           7  False  True   True   True    True   True    True   \n",
       "\n",
       "                gene_id  \n",
       "0    ENSMUSG00000070880  \n",
       "1    ENSMUSG00000026787  \n",
       "2    ENSMUSG00000062209  \n",
       "3    ENSMUSG00000053519  \n",
       "4    ENSMUSG00000039607  \n",
       "..                  ...  \n",
       "294  ENSMUSG00000036634  \n",
       "295  ENSMUSG00000075415  \n",
       "296  ENSMUSG00000030064  \n",
       "297  ENSMUSG00000031775  \n",
       "298  ENSMUSG00000032841  \n",
       "\n",
       "[299 rows x 21 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_list = marker_list.merge(df_ensg_name, right_on='gene', left_on='gene') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_gene_list = marker_list[marker_list['cell_type']=='GABAergic']['gene_id'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "go_table_marker = go_table[go_table.index.isin(marker_gene_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000000093</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>ENSMUSG00000000305</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>ENSMUSG00000000392</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>ENSMUSG00000116022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116076</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116258</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116470</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116510</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1748 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_type           Astro  Endo  L2/3 IT  L4/5 IT  L5 IT  L5 PT  L5/6 NP  \\\n",
       "gene_id                                                                    \n",
       "ENSMUSG00000000093    NaN   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "ENSMUSG00000000126    NaN   NaN      NaN     93.0    NaN    NaN      NaN   \n",
       "ENSMUSG00000000202   80.0   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "ENSMUSG00000000305    NaN   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "ENSMUSG00000000392    NaN   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "...                   ...   ...      ...      ...    ...    ...      ...   \n",
       "ENSMUSG00000116022    NaN   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "ENSMUSG00000116076    NaN   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "ENSMUSG00000116258    NaN   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "ENSMUSG00000116470    NaN   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "ENSMUSG00000116510    NaN   NaN      NaN      NaN    NaN    NaN      NaN   \n",
       "\n",
       "cell_type           L6 CT  L6 IT  L6 IT Car3  ...  Micro-PVM  Oligo  Peri  \\\n",
       "gene_id                                       ...                           \n",
       "ENSMUSG00000000093    NaN    NaN         NaN  ...        NaN    NaN   NaN   \n",
       "ENSMUSG00000000126    NaN    NaN         NaN  ...        NaN    NaN   NaN   \n",
       "ENSMUSG00000000202    NaN    NaN         NaN  ...        NaN    NaN   NaN   \n",
       "ENSMUSG00000000305    NaN   72.0         NaN  ...        NaN    NaN   NaN   \n",
       "ENSMUSG00000000392    NaN    NaN        21.0  ...        NaN    NaN   NaN   \n",
       "...                   ...    ...         ...  ...        ...    ...   ...   \n",
       "ENSMUSG00000116022    NaN    NaN        95.0  ...        NaN    NaN   NaN   \n",
       "ENSMUSG00000116076    NaN    NaN         NaN  ...        NaN    NaN   NaN   \n",
       "ENSMUSG00000116258    NaN    NaN         NaN  ...       67.0    NaN   NaN   \n",
       "ENSMUSG00000116470    NaN    NaN         NaN  ...       27.0    NaN   NaN   \n",
       "ENSMUSG00000116510    NaN    NaN         NaN  ...        NaN    NaN   NaN   \n",
       "\n",
       "cell_type           Pvalb   SMC  Sncg  Sst  Sst Chodl  VLMC  Vip  \n",
       "gene_id                                                           \n",
       "ENSMUSG00000000093    NaN  96.0   NaN  NaN        NaN   NaN  NaN  \n",
       "ENSMUSG00000000126    NaN   NaN   NaN  NaN        NaN   NaN  NaN  \n",
       "ENSMUSG00000000202    NaN   NaN   NaN  NaN        NaN   NaN  NaN  \n",
       "ENSMUSG00000000305    NaN   NaN   NaN  NaN        NaN   NaN  NaN  \n",
       "ENSMUSG00000000392    NaN   NaN   NaN  NaN        NaN   NaN  NaN  \n",
       "...                   ...   ...   ...  ...        ...   ...  ...  \n",
       "ENSMUSG00000116022    NaN   NaN   NaN  NaN        NaN   NaN  NaN  \n",
       "ENSMUSG00000116076    NaN   NaN   NaN  NaN        NaN   NaN  NaN  \n",
       "ENSMUSG00000116258    NaN   NaN   NaN  NaN        NaN   NaN  NaN  \n",
       "ENSMUSG00000116470    NaN   NaN   NaN  NaN        NaN   NaN  NaN  \n",
       "ENSMUSG00000116510    NaN   NaN   NaN  NaN        NaN  49.0  NaN  \n",
       "\n",
       "[1748 rows x 23 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_list.pivot(index='gene_id', columns='cell_type', values='rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_table = marker_list.pivot(index='gene_id', columns='cell_type', values='rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_table.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_list['gene_2'] = marker_list['gene_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116022</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116076</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116258</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116470</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000116510</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1748 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cell_type           Astro  Endo  L2/3 IT  L4/5 IT  L5 IT  L5 PT  L5/6 NP  \\\n",
       "gene_id                                                                    \n",
       "ENSMUSG00000000093    0.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "ENSMUSG00000000126    0.0   0.0      0.0      1.0    0.0    0.0      0.0   \n",
       "ENSMUSG00000000202    1.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "ENSMUSG00000000305    0.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "ENSMUSG00000000392    0.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "...                   ...   ...      ...      ...    ...    ...      ...   \n",
       "ENSMUSG00000116022    0.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "ENSMUSG00000116076    0.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "ENSMUSG00000116258    0.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "ENSMUSG00000116470    0.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "ENSMUSG00000116510    0.0   0.0      0.0      0.0    0.0    0.0      0.0   \n",
       "\n",
       "cell_type           L6 CT  L6 IT  L6 IT Car3  ...  Micro-PVM  Oligo  Peri  \\\n",
       "gene_id                                       ...                           \n",
       "ENSMUSG00000000093    0.0    0.0         0.0  ...        0.0    0.0   0.0   \n",
       "ENSMUSG00000000126    0.0    0.0         0.0  ...        0.0    0.0   0.0   \n",
       "ENSMUSG00000000202    0.0    0.0         0.0  ...        0.0    0.0   0.0   \n",
       "ENSMUSG00000000305    0.0    1.0         0.0  ...        0.0    0.0   0.0   \n",
       "ENSMUSG00000000392    0.0    0.0         1.0  ...        0.0    0.0   0.0   \n",
       "...                   ...    ...         ...  ...        ...    ...   ...   \n",
       "ENSMUSG00000116022    0.0    0.0         1.0  ...        0.0    0.0   0.0   \n",
       "ENSMUSG00000116076    0.0    0.0         0.0  ...        0.0    0.0   0.0   \n",
       "ENSMUSG00000116258    0.0    0.0         0.0  ...        1.0    0.0   0.0   \n",
       "ENSMUSG00000116470    0.0    0.0         0.0  ...        1.0    0.0   0.0   \n",
       "ENSMUSG00000116510    0.0    0.0         0.0  ...        0.0    0.0   0.0   \n",
       "\n",
       "cell_type           Pvalb  SMC  Sncg  Sst  Sst Chodl  VLMC  Vip  \n",
       "gene_id                                                          \n",
       "ENSMUSG00000000093    0.0  1.0   0.0  0.0        0.0   0.0  0.0  \n",
       "ENSMUSG00000000126    0.0  0.0   0.0  0.0        0.0   0.0  0.0  \n",
       "ENSMUSG00000000202    0.0  0.0   0.0  0.0        0.0   0.0  0.0  \n",
       "ENSMUSG00000000305    0.0  0.0   0.0  0.0        0.0   0.0  0.0  \n",
       "ENSMUSG00000000392    0.0  0.0   0.0  0.0        0.0   0.0  0.0  \n",
       "...                   ...  ...   ...  ...        ...   ...  ...  \n",
       "ENSMUSG00000116022    0.0  0.0   0.0  0.0        0.0   0.0  0.0  \n",
       "ENSMUSG00000116076    0.0  0.0   0.0  0.0        0.0   0.0  0.0  \n",
       "ENSMUSG00000116258    0.0  0.0   0.0  0.0        0.0   0.0  0.0  \n",
       "ENSMUSG00000116470    0.0  0.0   0.0  0.0        0.0   0.0  0.0  \n",
       "ENSMUSG00000116510    0.0  0.0   0.0  0.0        0.0   1.0  0.0  \n",
       "\n",
       "[1748 rows x 23 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_table.sum(axis=1).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_table[marker_table != 0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1686, 1686)\n",
      "(1686, 23)\n",
      "0.9434473154881634\n",
      "0.00015267734141610756\n"
     ]
    }
   ],
   "source": [
    "df_2d_marker = run_egad(marker_table, df_jac_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'seaborn'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-102-a84c0541e888>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'"
     ]
    }
   ],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ffb8f0990d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#plt.plot(data=df_2d_marker, x='AUC', y='DEGREE_NULL_AUC')\n",
    "plt.scatter(df_2d_marker['AUC'], df_2d_marker['DEGREE_NULL_AUC'])\n",
    "plt.plot([0, 1], [0, 1], c='black')\n",
    "plt.axvline(x=df_2d_marker['AUC'].mean(),c='black',ls='--')\n",
    "plt.axhline(y=df_2d_marker['DEGREE_NULL_AUC'].mean(), c='black', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>AUC</th>\n",
       "      <th>AVG_NODE_DEGREE</th>\n",
       "      <th>DEGREE_NULL_AUC</th>\n",
       "      <th>P_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astro</th>\n",
       "      <td>0.503210</td>\n",
       "      <td>1.913791</td>\n",
       "      <td>0.478775</td>\n",
       "      <td>0.391881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Endo</th>\n",
       "      <td>0.515818</td>\n",
       "      <td>2.170890</td>\n",
       "      <td>0.574170</td>\n",
       "      <td>0.296932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L2/3 IT</th>\n",
       "      <td>0.521518</td>\n",
       "      <td>1.774665</td>\n",
       "      <td>0.424600</td>\n",
       "      <td>0.161082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L4/5 IT</th>\n",
       "      <td>0.544023</td>\n",
       "      <td>1.944930</td>\n",
       "      <td>0.503752</td>\n",
       "      <td>0.068288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L5 IT</th>\n",
       "      <td>0.623693</td>\n",
       "      <td>1.917719</td>\n",
       "      <td>0.488662</td>\n",
       "      <td>0.000173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L5 PT</th>\n",
       "      <td>0.505324</td>\n",
       "      <td>2.007771</td>\n",
       "      <td>0.522082</td>\n",
       "      <td>0.010282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L5/6 NP</th>\n",
       "      <td>0.481446</td>\n",
       "      <td>1.958254</td>\n",
       "      <td>0.493898</td>\n",
       "      <td>0.193746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L6 CT</th>\n",
       "      <td>0.530166</td>\n",
       "      <td>1.927822</td>\n",
       "      <td>0.486792</td>\n",
       "      <td>0.164214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L6 IT</th>\n",
       "      <td>0.527801</td>\n",
       "      <td>1.974229</td>\n",
       "      <td>0.513981</td>\n",
       "      <td>0.172077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L6 IT Car3</th>\n",
       "      <td>0.594622</td>\n",
       "      <td>1.767828</td>\n",
       "      <td>0.429782</td>\n",
       "      <td>0.000774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L6b</th>\n",
       "      <td>0.594225</td>\n",
       "      <td>2.099337</td>\n",
       "      <td>0.536406</td>\n",
       "      <td>0.001011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lamp5</th>\n",
       "      <td>0.544632</td>\n",
       "      <td>1.842110</td>\n",
       "      <td>0.450468</td>\n",
       "      <td>0.014267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meis2</th>\n",
       "      <td>0.572895</td>\n",
       "      <td>1.788634</td>\n",
       "      <td>0.424824</td>\n",
       "      <td>0.007227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Micro-PVM</th>\n",
       "      <td>0.483450</td>\n",
       "      <td>2.146425</td>\n",
       "      <td>0.558534</td>\n",
       "      <td>0.173389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oligo</th>\n",
       "      <td>0.447767</td>\n",
       "      <td>2.050176</td>\n",
       "      <td>0.527428</td>\n",
       "      <td>0.041854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peri</th>\n",
       "      <td>0.482623</td>\n",
       "      <td>2.131128</td>\n",
       "      <td>0.561921</td>\n",
       "      <td>0.069004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pvalb</th>\n",
       "      <td>0.491573</td>\n",
       "      <td>1.997302</td>\n",
       "      <td>0.509245</td>\n",
       "      <td>0.350954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMC</th>\n",
       "      <td>0.537113</td>\n",
       "      <td>2.071364</td>\n",
       "      <td>0.536003</td>\n",
       "      <td>0.105072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sncg</th>\n",
       "      <td>0.538548</td>\n",
       "      <td>1.833858</td>\n",
       "      <td>0.462546</td>\n",
       "      <td>0.098128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sst</th>\n",
       "      <td>0.637208</td>\n",
       "      <td>1.852387</td>\n",
       "      <td>0.447168</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sst Chodl</th>\n",
       "      <td>0.571757</td>\n",
       "      <td>1.904202</td>\n",
       "      <td>0.476954</td>\n",
       "      <td>0.006663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VLMC</th>\n",
       "      <td>0.514868</td>\n",
       "      <td>1.818941</td>\n",
       "      <td>0.443888</td>\n",
       "      <td>0.314480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vip</th>\n",
       "      <td>0.536460</td>\n",
       "      <td>1.886273</td>\n",
       "      <td>0.484717</td>\n",
       "      <td>0.116352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AUC  AVG_NODE_DEGREE  DEGREE_NULL_AUC   P_Value\n",
       "cell_type                                                       \n",
       "Astro       0.503210         1.913791         0.478775  0.391881\n",
       "Endo        0.515818         2.170890         0.574170  0.296932\n",
       "L2/3 IT     0.521518         1.774665         0.424600  0.161082\n",
       "L4/5 IT     0.544023         1.944930         0.503752  0.068288\n",
       "L5 IT       0.623693         1.917719         0.488662  0.000173\n",
       "L5 PT       0.505324         2.007771         0.522082  0.010282\n",
       "L5/6 NP     0.481446         1.958254         0.493898  0.193746\n",
       "L6 CT       0.530166         1.927822         0.486792  0.164214\n",
       "L6 IT       0.527801         1.974229         0.513981  0.172077\n",
       "L6 IT Car3  0.594622         1.767828         0.429782  0.000774\n",
       "L6b         0.594225         2.099337         0.536406  0.001011\n",
       "Lamp5       0.544632         1.842110         0.450468  0.014267\n",
       "Meis2       0.572895         1.788634         0.424824  0.007227\n",
       "Micro-PVM   0.483450         2.146425         0.558534  0.173389\n",
       "Oligo       0.447767         2.050176         0.527428  0.041854\n",
       "Peri        0.482623         2.131128         0.561921  0.069004\n",
       "Pvalb       0.491573         1.997302         0.509245  0.350954\n",
       "SMC         0.537113         2.071364         0.536003  0.105072\n",
       "Sncg        0.538548         1.833858         0.462546  0.098128\n",
       "Sst         0.637208         1.852387         0.447168  0.000003\n",
       "Sst Chodl   0.571757         1.904202         0.476954  0.006663\n",
       "VLMC        0.514868         1.818941         0.443888  0.314480\n",
       "Vip         0.536460         1.886273         0.484717  0.116352"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d_pcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "[1686 rows x 156467 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
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       "non-gene                           0.0                 0.0  ...       0.0   \n",
       "non-gene                           0.0                 0.0  ...       0.0   \n",
       "non-gene                           0.0                 0.0  ...       0.0   \n",
       "non-gene                           0.0                 0.0  ...       0.0   \n",
       "non-gene                           0.0                 0.0  ...       0.0   \n",
       "\n",
       "                    non-gene  non-gene  non-gene  non-gene  non-gene  \\\n",
       "ENSMUSG00000102693       0.0       0.0       0.0       0.0       0.0   \n",
       "ENSMUSG00000064842       0.0       0.0       0.0       0.0       0.0   \n",
       "ENSMUSG00000051951       0.0       0.0       0.0       0.0       0.0   \n",
       "ENSMUSG00000102851       0.0       0.0       0.0       0.0       0.0   \n",
       "ENSMUSG00000103377       0.0       0.0       0.0       0.0       0.0   \n",
       "...                      ...       ...       ...       ...       ...   \n",
       "non-gene                 0.0       0.0       0.0       0.0       0.0   \n",
       "non-gene                 0.0       0.0       0.0       0.0       0.0   \n",
       "non-gene                 0.0       0.0       0.0       0.0       0.0   \n",
       "non-gene                 0.0       0.0       0.0       0.0       0.0   \n",
       "non-gene                 0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "                    non-gene  non-gene  non-gene  non-gene  \n",
       "ENSMUSG00000102693       0.0       0.0       0.0       0.0  \n",
       "ENSMUSG00000064842       0.0       0.0       0.0       0.0  \n",
       "ENSMUSG00000051951       0.0       0.0       0.0       0.0  \n",
       "ENSMUSG00000102851       0.0       0.0       0.0       0.0  \n",
       "ENSMUSG00000103377       0.0       0.0       0.0       0.0  \n",
       "...                      ...       ...       ...       ...  \n",
       "non-gene                 0.0       0.0       0.0       0.0  \n",
       "non-gene                 0.0       0.0       0.0       0.0  \n",
       "non-gene                 0.0       0.0       0.0       0.0  \n",
       "non-gene                 0.0       0.0       0.0       0.0  \n",
       "non-gene                 0.0       0.0       0.0       0.0  \n",
       "\n",
       "[156467 rows x 156467 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jac_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_jac_sim_subset_matrix = df_jac_sim[df_jac_sim.index.isin(marker_table.index)]\n",
    "df_jac_sim_subset_genes =  df_jac_sim_subset_matrix.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from hicmatrix import HiCMatrix as hm\n",
    "from hicmatrix.lib import MatrixFileHandler\n",
    "from scipy.sparse import csr_matrix, dia_matrix, triu, tril, coo_matrix\n",
    "import scipy.stats as stats\n",
    "import os.path\n",
    "\n",
    "    \n",
    "def calc_auc(exp_file, jac_sim, outfile, auc_type, given_top_percentile=1 ,jac_sim_species='a', exp_file_species='a', ortho_genes=\"ortho_genes\"):\n",
    "    \n",
    "        exp_genes_all = [x[3].decode() for x in exp_file.cut_intervals]\n",
    "        exp_matrix = exp_file.matrix\n",
    "        \n",
    "        \n",
    "        all_genes_all = [x[3].decode() for x in jac_sim.cut_intervals]\n",
    "        \n",
    "        entire_matrix = jac_sim.matrix\n",
    "        entire_matrix = entire_matrix.astype('float32')\n",
    "        \n",
    "        \n",
    "    \n",
    "        if jac_sim_species != exp_file_species:\n",
    "            df_1_1 = pd.read_csv(ortho_genes)\n",
    "            df_1_1 = df_1_1.drop_duplicates(subset=[jac_sim_species], keep=False)\n",
    "            df_1_1 = df_1_1.drop_duplicates(subset=[exp_file_species], keep=False)\n",
    "            jac_sim_species_ortho_subset = list (set(all_genes_all) & set(df_1_1[jac_sim_species].tolist()))\n",
    "            df_1_1 = df_1_1[df_1_1[jac_sim_species].isin(jac_sim_species_ortho_subset)]\n",
    "            exp_file_species_ortho_subset = list (set(exp_genes_all) & set(df_1_1[exp_file_species].tolist()))\n",
    "            df_1_1 = df_1_1[df_1_1[exp_file_species].isin(exp_file_species_ortho_subset)]\n",
    "            subset_genes_exp_file = df_1_1[exp_file_species].tolist()\n",
    "            subset_genes_jac_sim = df_1_1[jac_sim_species].tolist()\n",
    "            \n",
    "        else:\n",
    "            subset_genes_exp_file = list (set(all_genes_all) & set(exp_genes_all))\n",
    "            subset_genes_jac_sim  = list (set(all_genes_all) & set(exp_genes_all))\n",
    "            \n",
    "        exp_genes_index_list = [i for i, value in enumerate(exp_genes_all) if value in subset_genes_exp_file]\n",
    "        exp_genes = [value for i, value in enumerate(exp_genes_all) if value in subset_genes_exp_file]\n",
    "        exp_matrix  = csr_matrix(exp_matrix)[exp_genes_index_list, :][:, exp_genes_index_list]\n",
    "        \n",
    "        updated_cut_intervals = [exp_file.cut_intervals[x] for x in exp_genes_index_list]\n",
    "        exp_file.update_matrix(exp_matrix,  updated_cut_intervals)\n",
    "   \n",
    "        exp_matrix = exp_matrix.toarray()\n",
    "    \n",
    "        \n",
    "\n",
    "        rank_abs = lambda x: stats.rankdata(x, method='ordinal')\n",
    "        exp_matrix = np.apply_along_axis(rank_abs, 1, exp_matrix)\n",
    "        \n",
    "        exp_matrix = exp_matrix * 1.0\n",
    "        \n",
    "        exp_matrix = exp_matrix.astype('float32')\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        chr_list_2 = exp_file.getChrNames()\n",
    "\n",
    "        \n",
    "        all_gene_index_list = [i for i, value in enumerate(all_genes_all) if value in subset_genes_jac_sim]\n",
    "        all_genes = [value for i, value in enumerate(all_genes_all) if value in subset_genes_jac_sim]\n",
    "        \n",
    "        entire_matrix  = csr_matrix(entire_matrix)[all_gene_index_list, :][:, all_gene_index_list]\n",
    "        \n",
    "        updated_cut_intervals = [jac_sim.cut_intervals[x] for x in all_gene_index_list]\n",
    "        jac_sim.update_matrix(entire_matrix, updated_cut_intervals)\n",
    "        \n",
    "        entire_matrix = entire_matrix.toarray()\n",
    "        entire_matrix = entire_matrix.astype('float32')\n",
    "        \n",
    "        chr_list_1 = jac_sim.getChrNames()\n",
    "\n",
    "        max_value = entire_matrix.max() + 1 + exp_matrix.max()\n",
    "        max_value = max_value.astype('float32')\n",
    "\n",
    "        if auc_type == 'inter_only':\n",
    "        \n",
    "            for chrom in chr_list_1:\n",
    "\n",
    "                start, end = jac_sim.getChrBinRange(chrom)\n",
    "                \n",
    "                entire_matrix[start: end, start: end] = max_value \n",
    "                \n",
    "\n",
    "                \n",
    "\n",
    "                \n",
    "        \n",
    "        elif auc_type == 'intra_only':\n",
    "\n",
    "\n",
    "            for chrom in chr_list_2:\n",
    "\n",
    "                start, end = exp_file.getChrBinRange(chrom)\n",
    "                \n",
    "                coordinate_matrix[start: end, start: end] = max_value\n",
    "                \n",
    "            inter_coodinates = np.where(coordinate_matrix != max_value)\n",
    "            x_inter = inter_coodinates[0].tolist()\n",
    "            y_inter = inter_coodinates[1].tolist()\n",
    "            exp_matrix[x_inter, y_inter] = max_value\n",
    "            np.fill_diagonal(exp_matrix , max_value)\n",
    "            #exp_matrix.setdiag(max_value, k=0)\n",
    "\n",
    "\n",
    "        else:\n",
    "\n",
    "            np.fill_diagonal(entire_matrix , max_value)\n",
    "\n",
    "            np.fill_diagonal(exp_matrix , max_value)\n",
    "            #exp_matrix.setdiag(max_value, k=0)\n",
    "            #entire_matrix.setdiag(max_value, k=0)\n",
    "\n",
    "\n",
    "        df_jac = pd.DataFrame(entire_matrix ,  index=all_genes, columns = all_genes)\n",
    "        df_exp = pd.DataFrame(exp_matrix ,  index=exp_genes, columns = exp_genes)\n",
    "\n",
    "\n",
    "            df_jac_subset = df_jac.loc[subset_genes_jac_sim, subset_genes_jac_sim]\n",
    "\n",
    "\n",
    "        coodinates_1 = np.where(df_jac_subset == max_value)\n",
    "\n",
    "\n",
    "        x_coo = coodinates_1[0].tolist()\n",
    "        y_coo = coodinates_1[1].tolist() \n",
    "\n",
    "        df_jac_subset.to_numpy()[x_coo, y_coo] = max_value\n",
    "\n",
    "\n",
    "            \n",
    "        rank_abs = lambda x: stats.rankdata(x)\n",
    "        predicts2 = np.apply_along_axis(rank_abs, 1, df_jac_subset)\n",
    "        predicts2 = predicts2.astype('float')\n",
    "        \n",
    "        predicts2[x_coo, y_coo] = np.nan\n",
    "        \n",
    "        top_percentile = 100 - given_top_percentile\n",
    "                \n",
    "        exp_upper = lambda x: np.where(x < np.percentile(x[~np.isnan(x)] , top_percentile), 0, 1.0) if x[~np.isnan(x)].shape[0] > 0 else np.zeros(x.shape[0])\n",
    "        tp = np.apply_along_axis(exp_upper, 1, df_exp_subset)\n",
    "        tp[x_coo, y_coo] = np.nan\n",
    "        exp_upper = lambda x: np.where(x >= np.percentile(x[~np.isnan(x)] , top_percentile), 0, 1.0) if x[~np.isnan(x)].shape[0] > 0 else np.zeros(x.shape[0])\n",
    "        tn = np.apply_along_axis(exp_upper, 1, df_exp_subset)\n",
    "        tn[x_coo, y_coo] = np.nan\n",
    "        pos_rank_sum = tp * predicts2\n",
    "        auc_array = (((np.nansum(pos_rank_sum, axis=1) / (np.nansum(tp, axis=1)) )- (np.nansum(tp, axis=1) + 1)/2)) / np.nansum(tn, axis=1)\n",
    "        \n",
    "        df = pd.DataFrame(columns = ['gene_id_jac_sim', 'chrom_jac_sim', 'gene_id_exp_file', 'chrom_exp_file', 'auc'])  \n",
    "        df['auc'] = auc_array.tolist()\n",
    "        df['gene_id_exp_file'] = subset_genes_exp_file\n",
    "        df['gene_id_jac_sim'] = subset_genes_jac_sim\n",
    "        \n",
    "        gene_list = [x[3].decode() for x in jac_sim.cut_intervals]\n",
    "        _chrom_list = [x[0] for x in jac_sim.cut_intervals]\n",
    "        df['chrom_jac_sim'] = [_chrom_list[gene_list.index(x)] for x in df['gene_id_jac_sim']]\n",
    "\n",
    "        gene_list = [x[3].decode() for x in exp_file.cut_intervals]\n",
    "        _chrom_list = [x[0] for x in exp_file.cut_intervals]\n",
    "        df['chrom_exp_file'] = [_chrom_list[gene_list.index(x)] for x in df['gene_id_exp_file']]\n",
    "        df.to_csv(outfile, sep='\\t', index=False)\n",
    "        \n",
    "        \n",
    "if __name__ == '__main__':\n",
    "\n",
    "    \n",
    "    import argparse\n",
    "    parser = argparse.ArgumentParser()\n",
    "\n",
    "    parser.add_argument('--outfile', default='tss',\n",
    "                            help='tss or tss_max')\n",
    "    parser.add_argument('--exp_file_path', default='tss',\n",
    "                            help='tss or tss_max')\n",
    "    parser.add_argument('--jac_sim_path', default='tss',\n",
    "                            help='tss or tss_max')\n",
    "    parser.add_argument('--auc_type', default='intra_only',\n",
    "                            help='tss or tss_max')\n",
    "    parser.add_argument('--jac_sim_species', default='a',\n",
    "                            help='tss or tss_max')\n",
    "    parser.add_argument('--exp_file_species', default='a',\n",
    "                            help='tss or tss_max')\n",
    "    parser.add_argument('--ortho_genes', default='b',\n",
    "                            help='tss or tss_max')\n",
    "    parser.add_argument('--given_top_percentile', default=1,\n",
    "                            help='tss or tss_max')\n",
    "    \n",
    "\n",
    "\n",
    "    args = parser.parse_args()\n",
    "    \n",
    "    if not os.path.isfile(args.outfile):\n",
    "\n",
    "    \n",
    "        jac_sim = hm.hiCMatrix(args.jac_sim_path)\n",
    "        exp_file = hm.hiCMatrix(args.exp_file_path)\n",
    "\n",
    "        print (args.jac_sim_path)\n",
    "        print (args.exp_file_path)\n",
    "\n",
    "        calc_auc(exp_file, jac_sim, args.outfile, args.auc_type, given_top_percentile=int(args.given_top_percentile), jac_sim_species=args.jac_sim_species, exp_file_species=args.exp_file_species, ortho_genes=args.ortho_genes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'UMAP' from 'umap' (/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/umap/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-c09c1b15e4f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mumap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mUMAP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#import plotly.express as px\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mreducer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUMAP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'random'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'UMAP' from 'umap' (/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/umap/__init__.py)"
     ]
    }
   ],
   "source": [
    "from umap import UMAP\n",
    "#import plotly.express as px\n",
    "reducer = UMAP(n_components=2, init='random', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
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       "      <th>cell_type</th>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSMUSG00000029212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Non-Neuronal</td>\n",
       "      <td>Astro</td>\n",
       "      <td>5</td>\n",
       "      <td>TSPAN7</td>\n",
       "      <td>6</td>\n",
       "      <td>0.956186</td>\n",
       "      <td>12.682771</td>\n",
       "      <td>2.162795</td>\n",
       "      <td>2402.956782</td>\n",
       "      <td>0.507171</td>\n",
       "      <td>...</td>\n",
       "      <td>2775.142857</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSMUSG00000058254</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",
       "      <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>2263</th>\n",
       "      <td>Non-Neuronal</td>\n",
       "      <td>VLMC</td>\n",
       "      <td>95</td>\n",
       "      <td>GM30624</td>\n",
       "      <td>2</td>\n",
       "      <td>0.631826</td>\n",
       "      <td>17.677736</td>\n",
       "      <td>9.874028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.384595</td>\n",
       "      <td>...</td>\n",
       "      <td>473.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>ENSMUSG00000112430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>Non-Neuronal</td>\n",
       "      <td>VLMC</td>\n",
       "      <td>96</td>\n",
       "      <td>SH3PXD2A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.630981</td>\n",
       "      <td>2.410348</td>\n",
       "      <td>2.116879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.127298</td>\n",
       "      <td>...</td>\n",
       "      <td>473.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSMUSG00000053617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2265</th>\n",
       "      <td>Non-Neuronal</td>\n",
       "      <td>VLMC</td>\n",
       "      <td>97</td>\n",
       "      <td>NXN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.629492</td>\n",
       "      <td>0.582957</td>\n",
       "      <td>0.256502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054695</td>\n",
       "      <td>...</td>\n",
       "      <td>473.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSMUSG00000020844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2266</th>\n",
       "      <td>Non-Neuronal</td>\n",
       "      <td>VLMC</td>\n",
       "      <td>98</td>\n",
       "      <td>PRDM6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.625416</td>\n",
       "      <td>13.189692</td>\n",
       "      <td>12.174497</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>473.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSMUSG00000069378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>Non-Neuronal</td>\n",
       "      <td>VLMC</td>\n",
       "      <td>100</td>\n",
       "      <td>SNED1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.613429</td>\n",
       "      <td>1.294267</td>\n",
       "      <td>0.901668</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>473.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSMUSG00000047793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2268 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             group cell_type  rank      gene  recurrence     auroc  \\\n",
       "0     Non-Neuronal     Astro     1    SLC1A2           6  0.995769   \n",
       "1     Non-Neuronal     Astro     2       NTM           6  0.978977   \n",
       "2     Non-Neuronal     Astro     3    SLC1A3           6  0.972470   \n",
       "3     Non-Neuronal     Astro     4    GABRB1           6  0.958026   \n",
       "4     Non-Neuronal     Astro     5    TSPAN7           6  0.956186   \n",
       "...            ...       ...   ...       ...         ...       ...   \n",
       "2263  Non-Neuronal      VLMC    95   GM30624           2  0.631826   \n",
       "2264  Non-Neuronal      VLMC    96  SH3PXD2A           2  0.630981   \n",
       "2265  Non-Neuronal      VLMC    97       NXN           2  0.629492   \n",
       "2266  Non-Neuronal      VLMC    98     PRDM6           2  0.625416   \n",
       "2267  Non-Neuronal      VLMC   100     SNED1           2  0.613429   \n",
       "\n",
       "      fold_change  fold_change_detection   expression  precision  ...  \\\n",
       "0       79.327970               3.925241  6011.876920   0.627385  ...   \n",
       "1       18.671875               3.094278  3039.715310   0.591128  ...   \n",
       "2       16.164421               3.715663  3039.491778   0.620450  ...   \n",
       "3       48.232032               5.003564  1849.913350   0.683010  ...   \n",
       "4       12.682771               2.162795  2402.956782   0.507171  ...   \n",
       "...           ...                    ...          ...        ...  ...   \n",
       "2263    17.677736               9.874028     0.000000   0.384595  ...   \n",
       "2264     2.410348               2.116879     0.000000   0.127298  ...   \n",
       "2265     0.582957               0.256502     0.000000   0.054695  ...   \n",
       "2266    13.189692              12.174497     0.000000        NaN  ...   \n",
       "2267     1.294267               0.901668     0.000000        NaN  ...   \n",
       "\n",
       "      population_size  n_datasets   scSS  snSS  scCv2  snCv2 snCv3M  scCv3  \\\n",
       "0         2775.142857           7  False  True   True   True   True   True   \n",
       "1         2775.142857           7  False  True   True   True   True   True   \n",
       "2         2775.142857           7  False  True   True   True   True   True   \n",
       "3         2775.142857           7  False  True   True   True   True   True   \n",
       "4         2775.142857           7  False  True   True   True   True   True   \n",
       "...               ...         ...    ...   ...    ...    ...    ...    ...   \n",
       "2263       473.000000           5  False   NaN   True  False   True    NaN   \n",
       "2264       473.000000           5  False   NaN  False  False   True    NaN   \n",
       "2265       473.000000           5  False   NaN  False  False   True    NaN   \n",
       "2266       473.000000           5  False   NaN  False  False   True    NaN   \n",
       "2267       473.000000           5  False   NaN  False  False   True    NaN   \n",
       "\n",
       "     snCv3Z             gene_id  \n",
       "0      True  ENSMUSG00000005089  \n",
       "1      True  ENSMUSG00000059974  \n",
       "2      True  ENSMUSG00000005360  \n",
       "3      True  ENSMUSG00000029212  \n",
       "4      True  ENSMUSG00000058254  \n",
       "...     ...                 ...  \n",
       "2263  False  ENSMUSG00000112430  \n",
       "2264   True  ENSMUSG00000053617  \n",
       "2265   True  ENSMUSG00000020844  \n",
       "2266   True  ENSMUSG00000069378  \n",
       "2267   True  ENSMUSG00000047793  \n",
       "\n",
       "[2268 rows x 21 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_list[['gene_id', 'cell_type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENSMUSG00000102693</th>\n",
       "      <th>ENSMUSG00000064842</th>\n",
       "      <th>ENSMUSG00000051951</th>\n",
       "      <th>ENSMUSG00000102851</th>\n",
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       "      <th>ENSMUSG00000103147</th>\n",
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       "      <th>ENSMUSG00000059326</th>\n",
       "      <th>ENSMUSG00000095993</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>cell_type</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.178146</td>\n",
       "      <td>0.166713</td>\n",
       "      <td>0.160793</td>\n",
       "      <td>0.201759</td>\n",
       "      <td>0.209873</td>\n",
       "      <td>0.209873</td>\n",
       "      <td>0.165755</td>\n",
       "      <td>0.159554</td>\n",
       "      <td>0.159754</td>\n",
       "      <td>0.163636</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000373</td>\n",
       "      <td>-0.000444</td>\n",
       "      <td>-0.000516</td>\n",
       "      <td>-0.000346</td>\n",
       "      <td>-0.000346</td>\n",
       "      <td>-0.000346</td>\n",
       "      <td>-0.000409</td>\n",
       "      <td>-0.000384</td>\n",
       "      <td>ENSMUSG00000025902</td>\n",
       "      <td>Endo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.108031</td>\n",
       "      <td>0.097523</td>\n",
       "      <td>0.081969</td>\n",
       "      <td>0.118587</td>\n",
       "      <td>0.119869</td>\n",
       "      <td>0.119869</td>\n",
       "      <td>0.095599</td>\n",
       "      <td>0.094466</td>\n",
       "      <td>0.094876</td>\n",
       "      <td>0.093490</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000413</td>\n",
       "      <td>-0.000351</td>\n",
       "      <td>-0.000355</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>-0.000428</td>\n",
       "      <td>-0.000219</td>\n",
       "      <td>ENSMUSG00000002459</td>\n",
       "      <td>Astro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.124528</td>\n",
       "      <td>0.118970</td>\n",
       "      <td>0.095882</td>\n",
       "      <td>0.140469</td>\n",
       "      <td>0.133804</td>\n",
       "      <td>0.133804</td>\n",
       "      <td>0.106576</td>\n",
       "      <td>0.102504</td>\n",
       "      <td>0.102056</td>\n",
       "      <td>0.103876</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001213</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-0.001116</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.001023</td>\n",
       "      <td>-0.000859</td>\n",
       "      <td>ENSMUSG00000025905</td>\n",
       "      <td>L5 IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.124528</td>\n",
       "      <td>0.118970</td>\n",
       "      <td>0.095882</td>\n",
       "      <td>0.140469</td>\n",
       "      <td>0.133804</td>\n",
       "      <td>0.133804</td>\n",
       "      <td>0.106576</td>\n",
       "      <td>0.102504</td>\n",
       "      <td>0.102056</td>\n",
       "      <td>0.103876</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001213</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-0.001116</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.001023</td>\n",
       "      <td>-0.000859</td>\n",
       "      <td>ENSMUSG00000025905</td>\n",
       "      <td>L6 IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.124528</td>\n",
       "      <td>0.118970</td>\n",
       "      <td>0.095882</td>\n",
       "      <td>0.140469</td>\n",
       "      <td>0.133804</td>\n",
       "      <td>0.133804</td>\n",
       "      <td>0.106576</td>\n",
       "      <td>0.102504</td>\n",
       "      <td>0.102056</td>\n",
       "      <td>0.103876</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001213</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-0.001116</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.001023</td>\n",
       "      <td>-0.000859</td>\n",
       "      <td>ENSMUSG00000025905</td>\n",
       "      <td>L6 IT Car3</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>-0.001659</td>\n",
       "      <td>-0.001450</td>\n",
       "      <td>-0.001149</td>\n",
       "      <td>-0.001612</td>\n",
       "      <td>-0.001151</td>\n",
       "      <td>-0.001151</td>\n",
       "      <td>-0.000950</td>\n",
       "      <td>-0.000977</td>\n",
       "      <td>-0.000920</td>\n",
       "      <td>-0.000996</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032368</td>\n",
       "      <td>0.031494</td>\n",
       "      <td>0.028345</td>\n",
       "      <td>0.032037</td>\n",
       "      <td>0.032037</td>\n",
       "      <td>0.032037</td>\n",
       "      <td>0.028804</td>\n",
       "      <td>0.028606</td>\n",
       "      <td>ENSMUSG00000033717</td>\n",
       "      <td>L6b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>-0.002251</td>\n",
       "      <td>-0.002021</td>\n",
       "      <td>-0.001267</td>\n",
       "      <td>-0.002103</td>\n",
       "      <td>-0.002146</td>\n",
       "      <td>-0.002146</td>\n",
       "      <td>-0.001643</td>\n",
       "      <td>-0.001495</td>\n",
       "      <td>-0.001412</td>\n",
       "      <td>-0.001449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045809</td>\n",
       "      <td>0.044804</td>\n",
       "      <td>0.041711</td>\n",
       "      <td>0.045228</td>\n",
       "      <td>0.045228</td>\n",
       "      <td>0.045228</td>\n",
       "      <td>0.040445</td>\n",
       "      <td>0.039271</td>\n",
       "      <td>ENSMUSG00000025085</td>\n",
       "      <td>Endo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>-0.001260</td>\n",
       "      <td>-0.000996</td>\n",
       "      <td>-0.000322</td>\n",
       "      <td>-0.001172</td>\n",
       "      <td>-0.000955</td>\n",
       "      <td>-0.000955</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>-0.000767</td>\n",
       "      <td>-0.000767</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050967</td>\n",
       "      <td>0.050358</td>\n",
       "      <td>0.046030</td>\n",
       "      <td>0.049895</td>\n",
       "      <td>0.049895</td>\n",
       "      <td>0.049895</td>\n",
       "      <td>0.043535</td>\n",
       "      <td>0.043980</td>\n",
       "      <td>ENSMUSG00000025089</td>\n",
       "      <td>L6 IT Car3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>-0.001703</td>\n",
       "      <td>-0.001529</td>\n",
       "      <td>-0.001257</td>\n",
       "      <td>-0.001613</td>\n",
       "      <td>-0.001380</td>\n",
       "      <td>-0.001380</td>\n",
       "      <td>-0.001215</td>\n",
       "      <td>-0.001186</td>\n",
       "      <td>-0.001096</td>\n",
       "      <td>-0.001132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129176</td>\n",
       "      <td>0.126605</td>\n",
       "      <td>0.114628</td>\n",
       "      <td>0.122967</td>\n",
       "      <td>0.122967</td>\n",
       "      <td>0.122967</td>\n",
       "      <td>0.105943</td>\n",
       "      <td>0.103637</td>\n",
       "      <td>ENSMUSG00000087095</td>\n",
       "      <td>L2/3 IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>-0.001687</td>\n",
       "      <td>-0.001485</td>\n",
       "      <td>-0.001246</td>\n",
       "      <td>-0.001549</td>\n",
       "      <td>-0.001315</td>\n",
       "      <td>-0.001315</td>\n",
       "      <td>-0.001153</td>\n",
       "      <td>-0.001147</td>\n",
       "      <td>-0.001077</td>\n",
       "      <td>-0.001097</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131176</td>\n",
       "      <td>0.128906</td>\n",
       "      <td>0.118243</td>\n",
       "      <td>0.125312</td>\n",
       "      <td>0.125312</td>\n",
       "      <td>0.125312</td>\n",
       "      <td>0.108840</td>\n",
       "      <td>0.107101</td>\n",
       "      <td>ENSMUSG00000043969</td>\n",
       "      <td>L2/3 IT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2193 rows × 50227 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ENSMUSG00000102693  ENSMUSG00000064842  ENSMUSG00000051951  \\\n",
       "0               0.178146            0.166713            0.160793   \n",
       "1               0.108031            0.097523            0.081969   \n",
       "2               0.124528            0.118970            0.095882   \n",
       "3               0.124528            0.118970            0.095882   \n",
       "4               0.124528            0.118970            0.095882   \n",
       "...                  ...                 ...                 ...   \n",
       "2188           -0.001659           -0.001450           -0.001149   \n",
       "2189           -0.002251           -0.002021           -0.001267   \n",
       "2190           -0.001260           -0.000996           -0.000322   \n",
       "2191           -0.001703           -0.001529           -0.001257   \n",
       "2192           -0.001687           -0.001485           -0.001246   \n",
       "\n",
       "      ENSMUSG00000102851  ENSMUSG00000103377  ENSMUSG00000104017  \\\n",
       "0               0.201759            0.209873            0.209873   \n",
       "1               0.118587            0.119869            0.119869   \n",
       "2               0.140469            0.133804            0.133804   \n",
       "3               0.140469            0.133804            0.133804   \n",
       "4               0.140469            0.133804            0.133804   \n",
       "...                  ...                 ...                 ...   \n",
       "2188           -0.001612           -0.001151           -0.001151   \n",
       "2189           -0.002103           -0.002146           -0.002146   \n",
       "2190           -0.001172           -0.000955           -0.000955   \n",
       "2191           -0.001613           -0.001380           -0.001380   \n",
       "2192           -0.001549           -0.001315           -0.001315   \n",
       "\n",
       "      ENSMUSG00000103025  ENSMUSG00000089699  ENSMUSG00000103201  \\\n",
       "0               0.165755            0.159554            0.159754   \n",
       "1               0.095599            0.094466            0.094876   \n",
       "2               0.106576            0.102504            0.102056   \n",
       "3               0.106576            0.102504            0.102056   \n",
       "4               0.106576            0.102504            0.102056   \n",
       "...                  ...                 ...                 ...   \n",
       "2188           -0.000950           -0.000977           -0.000920   \n",
       "2189           -0.001643           -0.001495           -0.001412   \n",
       "2190           -0.000665           -0.000767           -0.000767   \n",
       "2191           -0.001215           -0.001186           -0.001096   \n",
       "2192           -0.001153           -0.001147           -0.001077   \n",
       "\n",
       "      ENSMUSG00000103147  ...  ENSMUSG00000096578  ENSMUSG00000088894  \\\n",
       "0               0.163636  ...           -0.000373           -0.000444   \n",
       "1               0.093490  ...           -0.000413           -0.000351   \n",
       "2               0.103876  ...           -0.001213           -0.001144   \n",
       "3               0.103876  ...           -0.001213           -0.001144   \n",
       "4               0.103876  ...           -0.001213           -0.001144   \n",
       "...                  ...  ...                 ...                 ...   \n",
       "2188           -0.000996  ...            0.032368            0.031494   \n",
       "2189           -0.001449  ...            0.045809            0.044804   \n",
       "2190            0.000069  ...            0.050967            0.050358   \n",
       "2191           -0.001132  ...            0.129176            0.126605   \n",
       "2192           -0.001097  ...            0.131176            0.128906   \n",
       "\n",
       "      ENSMUSG00000074733  ENSMUSG00000070263  ENSMUSG00000094649  \\\n",
       "0              -0.000516           -0.000346           -0.000346   \n",
       "1              -0.000355            0.000059            0.000059   \n",
       "2              -0.001116           -0.000780           -0.000780   \n",
       "3              -0.001116           -0.000780           -0.000780   \n",
       "4              -0.001116           -0.000780           -0.000780   \n",
       "...                  ...                 ...                 ...   \n",
       "2188            0.028345            0.032037            0.032037   \n",
       "2189            0.041711            0.045228            0.045228   \n",
       "2190            0.046030            0.049895            0.049895   \n",
       "2191            0.114628            0.122967            0.122967   \n",
       "2192            0.118243            0.125312            0.125312   \n",
       "\n",
       "      ENSMUSG00000069475  ENSMUSG00000059326  ENSMUSG00000095993  \\\n",
       "0              -0.000346           -0.000409           -0.000384   \n",
       "1               0.000059           -0.000428           -0.000219   \n",
       "2              -0.000780           -0.001023           -0.000859   \n",
       "3              -0.000780           -0.001023           -0.000859   \n",
       "4              -0.000780           -0.001023           -0.000859   \n",
       "...                  ...                 ...                 ...   \n",
       "2188            0.032037            0.028804            0.028606   \n",
       "2189            0.045228            0.040445            0.039271   \n",
       "2190            0.049895            0.043535            0.043980   \n",
       "2191            0.122967            0.105943            0.103637   \n",
       "2192            0.125312            0.108840            0.107101   \n",
       "\n",
       "                 gene_id   cell_type  \n",
       "0     ENSMUSG00000025902        Endo  \n",
       "1     ENSMUSG00000002459       Astro  \n",
       "2     ENSMUSG00000025905       L5 IT  \n",
       "3     ENSMUSG00000025905       L6 IT  \n",
       "4     ENSMUSG00000025905  L6 IT Car3  \n",
       "...                  ...         ...  \n",
       "2188  ENSMUSG00000033717         L6b  \n",
       "2189  ENSMUSG00000025085        Endo  \n",
       "2190  ENSMUSG00000025089  L6 IT Car3  \n",
       "2191  ENSMUSG00000087095     L2/3 IT  \n",
       "2192  ENSMUSG00000043969     L2/3 IT  \n",
       "\n",
       "[2193 rows x 50227 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pcc_sim.merge(marker_list[['gene_id', 'cell_type']], left_on=df_pcc_sim.index, right_on='gene_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00,  1.00000000e+00],\n",
       "       [ 5.71256919e-01,  5.71256919e-01],\n",
       "       [ 5.05523164e-01,  5.05523164e-01],\n",
       "       ...,\n",
       "       [-7.89479182e-04, -7.89479182e-04],\n",
       "       [-8.52762882e-04, -8.52762882e-04],\n",
       "       [-9.40203123e-04, -9.40203123e-04]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pcc_sim[\n",
    "    [\n",
    "        \"ENSMUSG00000102693\",\n",
    "        \"ENSMUSG00000102693\",\n",
    "    ]\n",
    "].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00,  5.71256919e-01,  5.05523164e-01, ...,\n",
       "        -7.89479182e-04, -8.52762882e-04, -9.40203123e-04],\n",
       "       [ 5.71256919e-01,  1.00000000e+00,  5.19787869e-01, ...,\n",
       "        -4.96218317e-04, -5.28529783e-04, -6.26098922e-04],\n",
       "       [ 5.05523164e-01,  5.19787869e-01,  1.00000000e+00, ...,\n",
       "        -6.11435339e-04, -7.07052226e-04, -7.03668866e-04],\n",
       "       ...,\n",
       "       [-7.89479182e-04, -4.96218317e-04, -6.11435339e-04, ...,\n",
       "         1.00000000e+00,  5.37342233e-01,  6.26562922e-01],\n",
       "       [-8.52762882e-04, -5.28529783e-04, -7.07052226e-04, ...,\n",
       "         5.37342233e-01,  1.00000000e+00,  7.00236598e-01],\n",
       "       [-9.40203123e-04, -6.26098922e-04, -7.03668866e-04, ...,\n",
       "         6.26562922e-01,  7.00236598e-01,  1.00000000e+00]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pcc_sim.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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