module_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8f4165e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data science & classics\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import os, sys\n",
    "import scipy,torch\n",
    "from scipy import sparse\n",
    "\n",
    "from sklearn.cluster import SpectralBiclustering\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from scipy.cluster import hierarchy\n",
    "\n",
    "import sklearn\n",
    "from sklearn import preprocessing\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1246049",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_optime_threshold(G,operator,candidate_thres,min_gene_num):\n",
    "    operator = operator # > or <\n",
    "\n",
    "    all_genes_in_modules = []\n",
    "    def get_overlap_old(modules):\n",
    "        total_overlap = 0\n",
    "        for m in modules:\n",
    "            total_overlap += len([n for n in m if n in all_genes_in_modules])\n",
    "        return total_overlap\n",
    "\n",
    "\n",
    "    def get_number_of_modules(threshold):\n",
    "        to_remove = []\n",
    "        for u, v, weight in G.edges(data='weight'):\n",
    "            if operator == '<':\n",
    "                if weight < threshold:\n",
    "                    to_remove.append((u, v))\n",
    "            elif operator == '>':\n",
    "                if weight > threshold:\n",
    "                    to_remove.append((u, v))\n",
    "        G_copy = G.copy()\n",
    "        G_copy.remove_edges_from(to_remove)\n",
    "        modules = [c for c in sorted(nx.strongly_connected_components(G_copy), key=len, reverse=True) if len(c) >= min_gene_num]\n",
    "        module_sizes = [len(m) for m in modules]\n",
    "        return len(module_sizes), get_overlap_old(modules)\n",
    "\n",
    "    compute_optimal_cutoff=True\n",
    "\n",
    "    if compute_optimal_cutoff:\n",
    "    #     thresholds = list(range(1,60))  # np.arange(.1, .2, )\n",
    "        thresholds = candidate_thres\n",
    "        number_of_modules = []\n",
    "        n = 0\n",
    "        for thr in thresholds:\n",
    "            n += 1\n",
    "            # print(thresholds.index(thr))\n",
    "            if n%4 == 0:\n",
    "                print(thr)\n",
    "            number_of_modules.append(get_number_of_modules(thr))\n",
    "        overlap = [i[1] for i in number_of_modules]\n",
    "        number_of_modules = [i[0] for i in number_of_modules]\n",
    "    #\n",
    "        fig = plt.figure(figsize=(14, 8))\n",
    "        # plt.subplot(2, 1, 1)\n",
    "        plt.bar(x=list(range(len(number_of_modules))), height=number_of_modules)\n",
    "        _ = plt.xticks(ticks=list(range(len(number_of_modules))), labels=['{0:.3f}'.format(i) for i in thresholds],\n",
    "                       fontsize=15)\n",
    "        plt.xlabel('Thresholds for Edge Weight', fontsize=20)\n",
    "        plt.ylabel(f'Number of Modules of Size $\\geq$ {min_gene_num}', fontsize=20)\n",
    "        print(number_of_modules)\n",
    "        plt.gca().tick_params(axis='y', which='major', labelsize=20)\n",
    "        plt.gca().tick_params(axis='x', rotation=90)\n",
    "        plt.tight_layout()\n",
    "    #     fig.savefig(os.path.join(\"./final_result\", 'module_sizes_posonly_finegrained_diffe_larger2_driver.svg'))\n",
    "        plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66420fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_SCCs(G,thres, operator,min_gene_num):\n",
    "    min_edge_weight = thres\n",
    "    G_copy = G.copy()\n",
    "    edge_weights = nx.get_edge_attributes(G,'weight')\n",
    "    if operator=='<':\n",
    "        G_copy.remove_edges_from((e for e, w in edge_weights.items() if w < min_edge_weight))\n",
    "    elif operator=='>':\n",
    "        G_copy.remove_edges_from((e for e, w in edge_weights.items() if w > min_edge_weight))\n",
    "#     print([len(c) for c in sorted(nx.strongly_connected_components(G_copy), key=len, reverse=True) if len(c) >= min_gene_num])\n",
    "#     print(len(max(nx.strongly_connected_components(G_copy), key=len)))\n",
    "    nodes_component = list(max(nx.strongly_connected_components(G_copy), key=len))\n",
    "    x = pd.DataFrame(nodes_component, columns=['Name'])\n",
    "    all_components = [list(c) for c in sorted(nx.strongly_connected_components(G_copy), key=len, reverse=True) if len(c) > 1]\n",
    "    all_components_larger = [list(c) for c in sorted(nx.strongly_connected_components(G_copy), key=len, reverse=True) if len(c) >= min_gene_num]\n",
    "#     g = [j for i in all_components_larger for j in i]\n",
    "#     print(len(all_components_larger))\n",
    "#     print(np.mean([len(i) for i in all_components_larger]))\n",
    "    return all_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1317b291",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_SCCs(weight_df,all_SCCs,min_edge_weight,drivers_gold,module_outpath,module_gene_outpath):\n",
    "    g = [{j:\"\" for j in i} for i in all_SCCs]\n",
    "    driver = weight_df.index.tolist()\n",
    "    weight_df = weight_df.to_numpy()\n",
    "    edges = []\n",
    "    for i in range(weight_df.shape[0]):\n",
    "        for j in range(weight_df.shape[1]):\n",
    "            for k in range(len(g)):\n",
    "                if g[k].get(driver[i]) is not None and g[k].get(driver[j]) is not None and weight_df[i,j]>=min_edge_weight:\n",
    "                    edges.append((driver[i],driver[j],weight_df[i,j]))\n",
    "    edges = [[s,t,str(w)] for s,t,w in edges]\n",
    "    with open(f'{module_outpath}','w') as f:\n",
    "        f.write(\"source\\ttarget\\tweight\\n\")\n",
    "        for e in edges:\n",
    "            f.write('\\t'.join(e)+'\\n')\n",
    "    attr = [(i,'1' if i in drivers_gold else '0') for i in driver] # 1 for gold driver, 0 for high score driver\n",
    "    with open(f'{module_gene_outpath}','w') as f:\n",
    "        f.write(\"gene\\tattr\\n\")\n",
    "        for one in attr:\n",
    "            f.write('\\t'.join(one)+'\\n')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b28f4730",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_random_weight():\n",
    "    random_weights = torch.zeros(len(node_names),len(node_names))\n",
    "    neighbor_weight_matrix = np.load(\"./Out/weight.npy\")\n",
    "    for i in range(random_weights.shape[1]):\n",
    "        random_weights[:,i][neighbor_weight_matrix[:,i]!=0] = torch.rand((neighbor_weight_matrix[:,i]!=0).sum()).softmax(0)\n",
    "    assert (random_weights!=0).sum()==(neighbor_weight_matrix!=0).sum()\n",
    "    \n",
    "    mean_weight = []\n",
    "    for j in range(random_weights.shape[1]):\n",
    "        mean_weight.append(random_weights[:,j][random_weights[:,j]>0].mean())\n",
    "\n",
    "    random_weights2 = torch.zeros((len(node_names),len(node_names)),requires_grad=False).numpy()\n",
    "    for j in range(len(node_names)):\n",
    "        random_weights2[:,j] = (random_weights[:,j]-mean_weight[j])/mean_weight[j]\n",
    "    random_weights2[random_weights2<=0] = 0\n",
    "    \n",
    "    return random_weights2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6afeae86",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analysis(all_SCCs):\n",
    "    contain_gold_driver_SCCs = [i for i in all_SCCs[1:] if len(set(i).intersection(set(drivers_gold)))>0]\n",
    "    print(len(contain_gold_driver_SCCs))\n",
    "    print(np.mean([len(i) for i in contain_gold_driver_SCCs]))\n",
    "    print(set(contain_gold_driver_SCCs[0]).intersection(drivers_gold))\n",
    "    DGGAT_top100_indices = DGGAT.topk(1000)[1]\n",
    "    DGGAT_top100_gene = node_names_pred[:,1][DGGAT_top100_indices].tolist()\n",
    "    print(set(contain_gold_driver_SCCs[0]).intersection(set(DGGAT_top100_gene)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c87b900",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_cancer_related_SCCs(all_SCCs,drivers_gold):\n",
    "    contain_gold_driver_SCCs = [i for i in all_SCCs[0:] if len(set(i).intersection(set(drivers_gold)))>0 and len(i)<=300]\n",
    "    return contain_gold_driver_SCCs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f27628a",
   "metadata": {},
   "outputs": [],
   "source": [
    "oncokb = pd.read_csv(f\"./data/OncoKB_cancerGeneList.tsv\", sep='\\t')[\n",
    "        'Hugo Symbol'].tolist()\n",
    "ongene = pd.read_csv(f\"./data/ongene_human.txt\", sep='\\t')[\n",
    "        'OncogeneName'].tolist()\n",
    "# load PPI edge weight\n",
    "\n",
    "data = torch.load(\"./data/CPDB_data.pkl\")\n",
    "mask_all = np.logical_or(data.mask, data.mask_te)\n",
    "test_y = [(i in oncokb) or (i in ongene) for i in data.node_names.tolist()]\n",
    "mask_all = np.logical_or(mask_all,np.array(test_y))\n",
    "node_names = data.node_names\n",
    "node_names_pred = np.array(node_names)[~mask_all]\n",
    "\n",
    "node_names = data.node_names[:,1].tolist()\n",
    "node_names_to_index = {node_names[i]:i for i in range(len(node_names))}\n",
    "\n",
    "\n",
    "drivers = [i.strip() for i in open(\"./data/796true.txt\",'r').readlines()]\n",
    "\n",
    "drivers += ongene\n",
    "drivers += oncokb\n",
    "drivers = set(drivers)\n",
    "drivers_gold = drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5caa2479",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# neighbor_weight_matrix = torch.zeros((len(node_names),len(node_names)),requires_grad=False)\n",
    "\n",
    "# n = 0\n",
    "# weight_dir = \"./train_predict_out/weight/\"\n",
    "# for file in os.listdir(weight_dir):\n",
    "#     if \"weight\" in file:\n",
    "#         n += 1\n",
    "#         print(n)\n",
    "#         PPI_weight = [i.strip().split('\\t') for i in\n",
    "#                       open(f\"{dir}/{file}\", 'r').readlines()]\n",
    "#         for source, target, weight in PPI_weight:\n",
    "#             source_index = node_names_to_index[source]\n",
    "#             target_index = node_names_to_index[target]\n",
    "#             # print(\"*\"*10)\n",
    "#             # print(source_index,'   ',target_index,'   ',float(weight))\n",
    "#             neighbor_weight_matrix[source_index,target_index] += float(weight)\n",
    "\n",
    "# index = neighbor_weight_matrix.sort(0,descending=True)[1]\n",
    "# neighbor_weight_rank_matrix = index.sort(0)[1] + 1\n",
    "# neighbor_weight_rank_matrix[neighbor_weight_matrix==0] = 0\n",
    "# neighbor_weight_rank_matrix = neighbor_weight_rank_matrix.detach()\n",
    "# neighbor_weight_rank_matrix = neighbor_weight_rank_matrix.numpy()\n",
    "\n",
    "# np.save(\"./final_result/sum_weight.npy\",neighbor_weight_matrix)\n",
    "# np.save(\"./final_result/weight_rank.npy\",neighbor_weight_rank_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4173c428",
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbor_weight_matrix = sparse.load_npz(\"./Out/weight_sparse.npz\").toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fc3f5e7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13627, 13627)\n",
      "504378\n",
      "504378\n"
     ]
    }
   ],
   "source": [
    "print(neighbor_weight_matrix.shape)\n",
    "print((neighbor_weight_matrix!=0).sum())\n",
    "print((neighbor_weight_matrix>0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9e624c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_weight = []\n",
    "for j in range(neighbor_weight_matrix.shape[1]):\n",
    "    mean_weight.append(neighbor_weight_matrix[:,j][neighbor_weight_matrix[:,j]>0].mean())\n",
    "\n",
    "\n",
    "neighbor_weight_matrix3 = torch.zeros((len(node_names),len(node_names)),requires_grad=False).numpy()\n",
    "for j in range(len(node_names)):\n",
    "    neighbor_weight_matrix3[:,j] = (neighbor_weight_matrix[:,j]-mean_weight[j])/mean_weight[j]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "62f9dc9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbor_weight_matrix3[neighbor_weight_matrix3<=0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ee08e065",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = neighbor_weight_matrix3.reshape(-1)[neighbor_weight_matrix3.reshape(-1)>0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "04b560b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x = a, # 指定绘图数据\n",
    "        bins = 40, # 指定直方图中条块的个数\n",
    "         color = 'steelblue', # 指定直方图的填充色\n",
    "         edgecolor = 'black' # 指定直方图的边框色\n",
    ")\n",
    "# plt.savefig(\"./final_result/weight_diffe.png\")\n",
    "# plt.close()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3e88b70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_df = pd.DataFrame(neighbor_weight_matrix3, index=node_names, columns=node_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e2895f96",
   "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>STIM1</th>\n",
       "      <th>TRPC1</th>\n",
       "      <th>NOS1</th>\n",
       "      <th>ATP2B4</th>\n",
       "      <th>ABCC9</th>\n",
       "      <th>KCNJ11</th>\n",
       "      <th>HADHA</th>\n",
       "      <th>HADHB</th>\n",
       "      <th>GTF2E2</th>\n",
       "      <th>GTF2E1</th>\n",
       "      <th>...</th>\n",
       "      <th>ADIG</th>\n",
       "      <th>NPPC</th>\n",
       "      <th>SIRPB2</th>\n",
       "      <th>ENPP3</th>\n",
       "      <th>TIMD4</th>\n",
       "      <th>GPR153</th>\n",
       "      <th>HDC</th>\n",
       "      <th>CSMD1</th>\n",
       "      <th>BHLHE22</th>\n",
       "      <th>PROKR1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>STIM1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.161963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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>TRPC1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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>NOS1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.099973</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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>ATP2B4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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>ABCC9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.60079</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>...</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>GPR153</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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>HDC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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>CSMD1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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>BHLHE22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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>PROKR1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>13627 rows × 13627 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         STIM1     TRPC1  NOS1    ATP2B4  ABCC9   KCNJ11  HADHA  HADHB  \\\n",
       "STIM1      0.0  0.161963   0.0  0.000000    0.0  0.00000    0.0    0.0   \n",
       "TRPC1      0.0  0.000000   0.0  0.000000    0.0  0.00000    0.0    0.0   \n",
       "NOS1       0.0  0.000000   0.0  0.099973    0.0  0.00000    0.0    0.0   \n",
       "ATP2B4     0.0  0.000000   0.0  0.000000    0.0  0.00000    0.0    0.0   \n",
       "ABCC9      0.0  0.000000   0.0  0.000000    0.0  0.60079    0.0    0.0   \n",
       "...        ...       ...   ...       ...    ...      ...    ...    ...   \n",
       "GPR153     0.0  0.000000   0.0  0.000000    0.0  0.00000    0.0    0.0   \n",
       "HDC        0.0  0.000000   0.0  0.000000    0.0  0.00000    0.0    0.0   \n",
       "CSMD1      0.0  0.000000   0.0  0.000000    0.0  0.00000    0.0    0.0   \n",
       "BHLHE22    0.0  0.000000   0.0  0.000000    0.0  0.00000    0.0    0.0   \n",
       "PROKR1     0.0  0.000000   0.0  0.000000    0.0  0.00000    0.0    0.0   \n",
       "\n",
       "         GTF2E2  GTF2E1  ...  ADIG  NPPC  SIRPB2  ENPP3  TIMD4  GPR153  HDC  \\\n",
       "STIM1       0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "TRPC1       0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "NOS1        0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "ATP2B4      0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "ABCC9       0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "...         ...     ...  ...   ...   ...     ...    ...    ...     ...  ...   \n",
       "GPR153      0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "HDC         0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "CSMD1       0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "BHLHE22     0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "PROKR1      0.0     0.0  ...   0.0   0.0     0.0    0.0    0.0     0.0  0.0   \n",
       "\n",
       "         CSMD1  BHLHE22  PROKR1  \n",
       "STIM1      0.0      0.0     0.0  \n",
       "TRPC1      0.0      0.0     0.0  \n",
       "NOS1       0.0      0.0     0.0  \n",
       "ATP2B4     0.0      0.0     0.0  \n",
       "ABCC9      0.0      0.0     0.0  \n",
       "...        ...      ...     ...  \n",
       "GPR153     0.0      0.0     0.0  \n",
       "HDC        0.0      0.0     0.0  \n",
       "CSMD1      0.0      0.0     0.0  \n",
       "BHLHE22    0.0      0.0     0.0  \n",
       "PROKR1     0.0      0.0     0.0  \n",
       "\n",
       "[13627 rows x 13627 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "81ee0400",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drivers_cand = [i.strip() for i in open(\"./Out/genes_for_extract_cancer_SCCs.txt\",'r').readlines()]\n",
    "drivers_cand = list(set(drivers_cand).intersection(set(node_names)))\n",
    "drivers_index = [node_names_to_index[g] for g in drivers_cand]\n",
    "driver_matrix = neighbor_weight_matrix3[drivers_index][:,drivers_index]\n",
    "\n",
    "a = driver_matrix.reshape(-1)[driver_matrix.reshape(-1)>0].tolist()\n",
    "plt.hist(x = a, # 指定绘图数据\n",
    "        bins = 40, # 指定直方图中条块的个数\n",
    "         color = 'steelblue', # 指定直方图的填充色\n",
    "         edgecolor = 'black' # 指定直方图的边框色\n",
    ")\n",
    "# plt.savefig(\"./final_result/weight_diffe.png\")\n",
    "# plt.close()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a4c5c83e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>PCM1</th>\n",
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       "      <th>MKI67</th>\n",
       "      <th>...</th>\n",
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       "      <th>HADHA</th>\n",
       "      <th>CR2</th>\n",
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       "      <th>TXK</th>\n",
       "      <th>ECHS1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>HIST1H2BA</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>0.054644</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>TBC1D15</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>0.000000</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>...</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>HADHA</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>0.000000</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>CR2</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>0.000000</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>SERTAD3</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>0.000000</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>TXK</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>0.000000</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>ECHS1</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>0.000000</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>4131 rows × 4131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           PCM1  SORCS1  GNAS  HIST1H2BA  TBC1D15  POLR1A      CBX8  ITGA3  \\\n",
       "PCM1        0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "SORCS1      0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "GNAS        0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "HIST1H2BA   0.0     0.0   0.0        0.0      0.0     0.0  0.054644    0.0   \n",
       "TBC1D15     0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "...         ...     ...   ...        ...      ...     ...       ...    ...   \n",
       "HADHA       0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "CR2         0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "SERTAD3     0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "TXK         0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "ECHS1       0.0     0.0   0.0        0.0      0.0     0.0  0.000000    0.0   \n",
       "\n",
       "           MDK  MKI67  ...  ZNRF3  INTU  CDK5R2  MYH8  PDE4D  HADHA  CR2  \\\n",
       "PCM1       0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "SORCS1     0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "GNAS       0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "HIST1H2BA  0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "TBC1D15    0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "...        ...    ...  ...    ...   ...     ...   ...    ...    ...  ...   \n",
       "HADHA      0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "CR2        0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "SERTAD3    0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "TXK        0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "ECHS1      0.0    0.0  ...    0.0   0.0     0.0   0.0    0.0    0.0  0.0   \n",
       "\n",
       "           SERTAD3  TXK  ECHS1  \n",
       "PCM1           0.0  0.0    0.0  \n",
       "SORCS1         0.0  0.0    0.0  \n",
       "GNAS           0.0  0.0    0.0  \n",
       "HIST1H2BA      0.0  0.0    0.0  \n",
       "TBC1D15        0.0  0.0    0.0  \n",
       "...            ...  ...    ...  \n",
       "HADHA          0.0  0.0    0.0  \n",
       "CR2            0.0  0.0    0.0  \n",
       "SERTAD3        0.0  0.0    0.0  \n",
       "TXK            0.0  0.0    0.0  \n",
       "ECHS1          0.0  0.0    0.0  \n",
       "\n",
       "[4131 rows x 4131 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_df_driver = pd.DataFrame(driver_matrix, index=[node_names[i] for i in drivers_index], columns=[node_names[i] for i in drivers_index])\n",
    "G_driver = nx.from_pandas_adjacency(weight_df_driver,create_using=nx.MultiDiGraph)\n",
    "G_driver.remove_edges_from(list(nx.selfloop_edges(G_driver)))\n",
    "weight_df_driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8b60be9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16000000000000003\n",
      "0.24000000000000002\n",
      "0.32000000000000006\n",
      "0.4\n",
      "0.4800000000000001\n",
      "[22, 24, 25, 32, 35, 33, 42, 52, 38, 26, 19, 11, 7, 4, 2, 1, 0, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_optime_threshold(G_driver,'<',np.arange(.1, .5, 0.02),2) # G,operator,candidate_thres,min_gene_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9ad4bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_edge_weight = 0.24\n",
    "all_SCCs = find_SCCs(G_driver,min_edge_weight,'<',2) # G,thres, operator,min_gene_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "84db285c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[213, 32, 13, 8, 5, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n",
      "['CRNKL1', 'LSM1', 'SART3', 'PSME4', 'SNRPN', 'LSM4', 'LSM3', 'CCNL1', 'SNRPG', 'ZRSR2', 'LIN28B', 'PSMA1', 'THOC5', 'PRCC', 'TOE1', 'SNRPE', 'LSM5', 'KRT13', 'RSRC1', 'PAXBP1', 'SNRPF', 'DIS3L', 'EXOSC6', 'CD2BP2', 'PLRG1', 'UPF2', 'SNRNP40', 'LIN28A', 'SF3A2', 'U2AF1', 'DDX23', 'AQR']\n"
     ]
    }
   ],
   "source": [
    "print([len(i) for i in all_SCCs])\n",
    "print(all_SCCs[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "247f2fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for i in range(len(all_SCCs)):\n",
    "    with open(f'./module_gene/module_{i}_{len(all_SCCs[i])}genes.txt','w') as f:\n",
    "        f.write('\\n'.join(all_SCCs[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "42861698",
   "metadata": {},
   "outputs": [],
   "source": [
    "write_SCCs(weight_df_driver,\n",
    "                       all_SCCs,0.24,drivers_gold,\n",
    "           './final_result/modules_subMean_DivMean_threshold0.24_driver.txt',\n",
    "           './final_result/modules_geneAttr_subMean_DivMean_threshold0.24_driver.txt') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "717ac4d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.16196282, 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbor_weight_matrix3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "d48b030e",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = torch.tensor(neighbor_weight_matrix3).sort(0,descending=True)[1]\n",
    "neighbor_weight_rank_matrix = index.sort(0)[1] + 1\n",
    "neighbor_weight_rank_matrix[neighbor_weight_matrix3==0] = 0\n",
    "neighbor_weight_rank_matrix = neighbor_weight_rank_matrix.detach()\n",
    "neighbor_weight_rank_matrix = neighbor_weight_rank_matrix.numpy()\n",
    "weight_matrix = pd.DataFrame(neighbor_weight_rank_matrix, index=node_names, columns=node_names)\n",
    "G = nx.from_pandas_adjacency(weight_matrix,create_using=nx.MultiDiGraph)\n",
    "G.remove_edges_from(list(nx.selfloop_edges(G)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "703867ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "8\n",
      "12\n",
      "16\n",
      "20\n",
      "24\n",
      "28\n",
      "32\n",
      "36\n",
      "40\n",
      "44\n",
      "48\n",
      "52\n",
      "56\n",
      "[301, 708, 870, 401, 268, 180, 148, 121, 98, 80, 71, 65, 58, 51, 45, 43, 43, 40, 39, 39, 39, 39, 37, 36, 35, 35, 34, 35, 34, 34, 33, 33, 33, 33, 33, 33, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_optime_threshold(G,'>',list(range(1,60)),2) # G,operator,candidate_thres,min_gene_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "04750835",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_edge_weight = 3\n",
    "all_SCCs = find_SCCs(G,min_edge_weight,'>',2) # G,thres, operator,min_gene_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "b261b13a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151\n",
      "3.0927152317880795\n",
      "{'JAK2', 'DTX1', 'PLAUR', 'IGF2', 'DKK3', 'E2F3', 'TIMP1', 'IGFBP1', 'CRP'}\n",
      "{'FBLN1', 'DEFA1', 'SRA1', 'E2F3'}\n"
     ]
    }
   ],
   "source": [
    "analysis(all_SCCs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91b8be9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# random_weights  = generate_random_weight()\n",
    "random_weights = np.load('./final_result/weights_random.npy')\n",
    "print(random_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79cbce9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_random = torch.tensor(random_weights).sort(0,descending=True)[1]\n",
    "random_weight_rank_matrix = index_random.sort(0)[1] + 1\n",
    "random_weight_rank_matrix[random_weights==0] = 0\n",
    "random_weight_rank_matrix = random_weight_rank_matrix.detach()\n",
    "random_weight_rank_matrix = random_weight_rank_matrix.numpy()\n",
    "random_weight_matrix = pd.DataFrame(random_weight_rank_matrix, index=node_names, columns=node_names)\n",
    "G_random = nx.from_pandas_adjacency(random_weight_matrix,create_using=nx.MultiDiGraph)\n",
    "G_random.remove_edges_from(list(nx.selfloop_edges(G_random)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8db330fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "8\n",
      "12\n",
      "16\n",
      "20\n",
      "24\n",
      "28\n",
      "32\n",
      "36\n",
      "40\n",
      "44\n",
      "48\n",
      "52\n",
      "56\n",
      "[221, 286, 251, 176, 124, 82, 66, 56, 45, 39, 38, 29, 25, 24, 23, 22, 22, 20, 20, 16, 16, 16, 16, 16, 16, 16, 15, 15, 15, 14, 14, 14, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 11, 10]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_optime_threshold(G_random,'>',list(range(1,60)),2) # G,operator,candidate_thres,min_gene_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47ae1e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_edge_weight = 2\n",
    "all_SCCs_random = find_SCCs(G_random,min_edge_weight,'>',2) # G,thres, operator,min_gene_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "040427dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41\n",
      "2.1707317073170733\n",
      "{'GNAS'}\n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "analysis(all_SCCs_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fd370f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.01023979, 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbor_weight_matrix3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a6e60a97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.56\n",
      "0.6400000000000001\n",
      "0.7200000000000002\n",
      "0.8000000000000003\n",
      "0.8800000000000003\n",
      "0.9600000000000004\n",
      "1.0400000000000005\n",
      "1.1200000000000006\n",
      "1.2000000000000006\n",
      "1.2800000000000007\n",
      "1.3600000000000008\n",
      "1.4400000000000008\n",
      "1.520000000000001\n",
      "1.600000000000001\n",
      "1.680000000000001\n",
      "1.7600000000000011\n",
      "1.8400000000000012\n",
      "1.9200000000000013\n",
      "[294, 289, 283, 304, 300, 297, 295, 308, 315, 325, 416, 422, 422, 420, 434, 441, 451, 459, 472, 456, 485, 495, 498, 502, 482, 481, 469, 489, 475, 467, 455, 454, 445, 424, 418, 407, 402, 389, 377, 370, 363, 351, 346, 344, 331, 316, 301, 284, 275, 273, 265, 260, 250, 242, 232, 221, 214, 213, 207, 205, 201, 194, 187, 182, 180, 178, 174, 165, 161, 156, 151, 145, 139, 129, 126]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight_matrix = pd.DataFrame(neighbor_weight_matrix3, index=node_names, columns=node_names)\n",
    "G = nx.from_pandas_adjacency(weight_matrix,create_using=nx.MultiDiGraph)\n",
    "G.remove_edges_from(list(nx.selfloop_edges(G)))\n",
    "find_optime_threshold(G,'<',np.arange(.5, 2, 0.02),2) # G,operator,candidate_thres,min_gene_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "978465b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_edge_weight = 0.960\n",
    "all_SCCs = find_SCCs(G,0.960,'<',2) # G,thres, operator,min_gene_num\n",
    "all_SCCs_cancer_related = extract_cancer_related_SCCs(all_SCCs,drivers_gold)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c2c923d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n",
      "2.8863636363636362\n",
      "{'ESCO2', 'S100A4', 'CBX8'}\n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "analysis(all_SCCs_cancer_related)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1019098b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "print([len(set(i).intersection(drivers_gold)) for i in all_SCCs_cancer_related])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6aca0bec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PHF2', 'HIST1H2BE', 'USP37', 'CBX8', 'ESCO2', 'ZNF362', 'HIST1H2BF', 'CIZ1', 'S100A4']\n"
     ]
    }
   ],
   "source": [
    "print(all_SCCs_cancer_related[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e5ef6402",
   "metadata": {},
   "outputs": [],
   "source": [
    "write_SCCs(pd.DataFrame(neighbor_weight_matrix3, \n",
    "                        index=node_names, columns=node_names),\n",
    "                       all_SCCs_cancer_related,0.960,drivers_gold,\n",
    "           './final_result/modules_subMean_DivMean_threshold0.960.txt',\n",
    "           './final_result/modules_geneAttr_subMean_DivMean_threshold0.960.txt') #weight_df,all_SCCs,min_edge_weight,drivers_gold,module_outpath,module_gene_outpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "60097240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16000000000000003\n",
      "0.24000000000000002\n",
      "0.32000000000000006\n",
      "0.4\n",
      "0.4800000000000001\n",
      "[11, 13, 14, 14, 17, 21, 22, 22, 22, 21, 20, 20, 18, 19, 24, 25, 24, 26, 30, 26]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight_matrix_random = pd.DataFrame(random_weights, index=node_names, columns=node_names)\n",
    "G_random = nx.from_pandas_adjacency(weight_matrix_random,create_using=nx.MultiDiGraph)\n",
    "G_random.remove_edges_from(list(nx.selfloop_edges(G_random)))\n",
    "find_optime_threshold(G_random,'<',np.arange(.1, .5, 0.02),2) # G,operator,candidate_thres,min_gene_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "03c4ec42",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_edge_weight = 0.46\n",
    "all_SCCs_random = find_SCCs(G_random,0.46,'<',2) # G,thres, operator,min_gene_num\n",
    "all_SCCs_random_cancer_related = extract_cancer_related_SCCs(all_SCCs_random,drivers_gold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "312d5c9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "2.0\n",
      "{'ROBO1'}\n",
      "set()\n",
      "[['ARPP19', 'ROBO1'], ['CNTN2', 'L1CAM'], ['CCL11', 'CCL2'], ['IKZF4', 'IKZF2'], ['SLC34A2', 'SLC30A3'], ['HOXD9', 'MEIS1']]\n"
     ]
    }
   ],
   "source": [
    "analysis(all_SCCs_random)\n",
    "print(all_SCCs_random_cancer_related)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ed5b930a",
   "metadata": {},
   "outputs": [],
   "source": [
    "write_SCCs(weight_matrix_random,all_SCCs_random_cancer_related,0.46,drivers_gold,'./final_result/modules_random.txt','./final_result/modules_geneAttr_random.txt') #weight_df,all_SCCs,min_edge_weight,drivers_gold,module_outpath,module_gene_outpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "0d9e0907",
   "metadata": {},
   "outputs": [],
   "source": [
    "def weight_distribution():\n",
    "    drivers_gold = [i.strip() for i in open(\"./data/796true.txt\",'r').readlines()]\n",
    "    non_driver_gold = [i.strip() for i in open(\"./data/2187false.txt\",'r').readlines()]\n",
    "    neighbor_weight_matrix = np.load(\"./Out/weight.npy\")\n",
    "    mean_weight = []\n",
    "    for j in range(neighbor_weight_matrix.shape[1]):\n",
    "        mean_weight.append(neighbor_weight_matrix[:,j][neighbor_weight_matrix[:,j]>0].mean())\n",
    "    neighbor_weight_matrix2 = torch.zeros((len(node_names),len(node_names)),requires_grad=False).numpy()\n",
    "    for j in range(len(node_names)):\n",
    "        neighbor_weight_matrix2[:,j] = (neighbor_weight_matrix[:,j]-mean_weight[j])/mean_weight[j]\n",
    "    neighbor_weight_matrix2[neighbor_weight_matrix2<=0] = 0\n",
    "#     print(neighbor_weight_matrix2)\n",
    "    drivers_gold_index = [node_names_to_index[i] for i in drivers_gold]\n",
    "    non_drivers_gold_index = [node_names_to_index[i] for i in non_driver_gold]\n",
    "    drivers_gold_weight = neighbor_weight_matrix2[drivers_gold_index]\n",
    "    non_drivers_gold_weight = neighbor_weight_matrix2[non_drivers_gold_index]\n",
    "    drivers_gold_weight = drivers_gold_weight[drivers_gold_weight>0].reshape(-1).tolist()\n",
    "    non_drivers_gold_weight = non_drivers_gold_weight[non_drivers_gold_weight>0].reshape(-1).tolist()\n",
    "    plt.hist(x = drivers_gold_weight, # 指定绘图数据\n",
    "        bins = 50, # 指定直方图中条块的个数\n",
    "         color = 'steelblue', # 指定直方图的填充色\n",
    "         edgecolor = 'black' # 指定直方图的边框色\n",
    "    )\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    plt.hist(x = non_drivers_gold_weight, # 指定绘图数据\n",
    "        bins = 50, # 指定直方图中条块的个数\n",
    "         color = 'steelblue', # 指定直方图的填充色\n",
    "         edgecolor = 'black' # 指定直方图的边框色\n",
    "    )\n",
    "    plt.show()\n",
    "    print(np.mean(drivers_gold_weight))\n",
    "    print(np.mean(non_drivers_gold_weight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "cf414da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11473012823793767\n",
      "0.2223228977819852\n"
     ]
    }
   ],
   "source": [
    "weight_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2cb8d34d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TRA2A', 'NXF1', 'FHOD3', 'HERC2', 'ALDOA', 'RAD52', 'WRN', 'MON2', 'PTPRN2', 'FHOD1', 'NCOA6', 'HNF1A', 'GATA5', 'LIMK1', 'ROCK1', 'GINS3', 'GINS4', 'CEP162', 'OFD1', 'PSMC6', 'BASP1', 'INTU', 'SBDS', 'RASSF2', 'KPNA3', 'KSR2', 'CDC37', 'FLNB', 'NUP210', 'RBPJ', 'NOTCH4', 'TANK', 'KPNA2', 'ZBTB17', 'KLF5', 'PDE3A', 'CSDE1', 'MPHOSPH6', 'EXOSC3', 'FOLH1', 'FABP7', 'PPIL3', 'GOT1', 'MTAP', 'GLO1', 'GMPS', 'MAP2K3', 'YWHAG', 'TUBB', 'MAP2K5', 'MAPK7', 'MAP2K6', 'MAP3K2', 'MAPK8IP1', 'MITF', 'TFEB', 'TNFRSF11A', 'VAV3', 'TCF7', 'RUNX3', 'H3F3A', 'TH', 'SNAI1', 'TRIM5', 'PKM', 'HLA-DRB5', 'EHHADH', 'POC1A', 'CEP63', 'PRDM9', 'CCDC14', 'PEX5', 'TRIM54', 'CEP192', 'CEP152', 'PCM1', 'SCP2', 'HMGCL', 'POC5', 'SIM2', 'MATR3', 'POLD1', 'HSPH1', 'KRT13', 'PAXBP1', 'PRCC', 'PSMA1', 'DDX23', 'LSM3', 'AQR', 'RSRC1', 'SNRPN', 'EXOSC6', 'LSM1', 'U2AF1', 'CCNL1', 'THOC5', 'SNRPG', 'LIN28B', 'LSM5', 'CRNKL1', 'CD2BP2', 'LSM4', 'SART3', 'LIN28A', 'DIS3L', 'TOE1', 'SNRPF', 'SF3A2', 'PLRG1', 'PSME4', 'SNRNP40', 'UPF2', 'SNRPE', 'ZRSR2', 'HDLBP', 'CTCF', 'COPE', 'PSAT1', 'PLEC', 'NCAPG', 'RFC4', 'CHTF18', 'HIF3A', 'EGLN2', 'EPAS1', 'MTA2', 'CHD5', 'GCM1', 'PRDM1', 'SENP1', 'DHX9', 'MAP2', 'TWIST2', 'ETS2', 'DCTN1', 'DSTYK', 'CDH13', 'TCERG1', 'ARID5B', 'EGR1', 'MUC16', 'MCM5', 'BCLAF1', 'DHX30', 'PPP1CC', 'PA2G4', 'PIH1D1', 'HADHA', 'NFKB2', 'PFKP', 'GAPDH', 'SIK2', 'PABPC4', 'NKRF', 'PPIB', 'AGO1', 'PDCD6IP', 'NR3C1', 'CDC73', 'RC3H1', 'DHX15', 'BCL2L1', 'STUB1', 'KMT5A', 'CCNA1', 'ILK', 'ELAVL1', 'POLR2A', 'MTHFD1', 'MAP3K1', 'IGF2BP1', 'TPT1', 'PPIA', 'EIF4G2', 'FLII', 'SHMT2', 'PPP5C', 'HLA-B', 'RPL23A', 'PIK3R1', 'CDK9', 'MCL1', 'TOP2A', 'HNRNPH2', 'PSMC5', 'MAP3K3', 'RPS9', 'EEF1D', 'IARS', 'RARS', 'CRY2', 'RUVBL1', 'EEF2', 'AHSA1', 'RPS6KB2', 'DDX1', 'YAP1', 'NEFM', 'CAPZA1', 'PSMD2', 'IRF5', 'TUBB2A', 'TUBB2B', 'GBF1', 'PTPN6', 'SS18', 'HDAC4', 'RICTOR', 'RBM14', 'RPL22', 'TXN', 'BCL2A1', 'ENO1', 'MLLT3', 'HSPA1L', 'SMARCA4', 'SRSF1', 'GSK3A', 'IGF2BP3', 'PHB', 'KRT17', 'VIM', 'SMURF1', 'RPS3A', 'CRTC2', 'MAP3K14', 'PABPC1', 'LSM14A', 'NR3C2', 'TPM3', 'G6PD', 'RPLP0', 'SMARCC1', 'CREB1', 'EIF4B', 'AGO2', 'SRRM2', 'NFATC3', 'HNRNPA2B1', 'CAMK1D', 'ITGA4', 'YBX1', 'RXRA', 'RNF31', 'CAD', 'HSPA9', 'VCAM1', 'MSH6', 'TIPIN', 'MGMT', 'NCL', 'RPL4', 'EPRS', 'RPS27A', 'TNFAIP3', 'IRF7', 'SHC1', 'DCUN1D1', 'KRAS', 'RBL1', 'RIPK3', 'TUBB4A', 'THRA', 'SFN', 'BECN1', 'EEF1A2', 'RFC3', 'TES', 'ASXL2', 'WEE1', 'HNRNPL', 'TELO2', 'PRDX1', 'CHEK1', 'CFL1', 'PAN2', 'HDAC6', 'TPM4', 'FUS', 'PCBP1', 'RAD18', 'RPL10A', 'KRT9', 'FOS', 'RPS15A', 'PMAIP1', 'SIK3', 'MAP3K7', 'CASP8', 'WDR48', 'EIF3F', 'DDX39B', 'TXNIP', 'PSMD1', 'HSPA2', 'CEBPB', 'PGAM1', 'HSPA4', 'PHGDH', 'TOP1', 'ANXA1', 'MEF2D', 'SUPT6H', 'IRF3', 'ATAD3A', 'RASSF1', 'DARS', 'RPL10', 'FANCI', 'RPS4X', 'UBASH3B', 'SNRNP200', 'PCNA', 'KRT10', 'KRT1', 'CSNK1A1', 'TCF7L2', 'PSMC3', 'VARS', 'RAD21', 'PPP1R15A', 'PFKL', 'PSMA3', 'MCM3', 'BRINP1', 'EPB41L2', 'TAB1', 'SIRT6', 'RPAP1', 'RPL24', 'MARS', 'ARNT', 'RUVBL2', 'INPPL1', 'TFE3', 'BRSK1', 'TAB2', 'PAK1', 'HUWE1', 'HDAC5', 'LARS', 'PSEN1', 'FOXK1', 'DDX3X', 'RPS12', 'CCT6A', 'CALD1', 'SMAD7', 'CCNE1', 'TARDBP', 'CSNK1E', 'TUBB6', 'GARS', 'NEDD8', 'PKP3', 'TRIM25', 'CNTLN', 'GOPC', 'SUCLA2', 'SDHB', 'CREBBP', 'PLAGL1', 'PNKD', 'GON4L', 'GEMIN5', 'ZFP36L2', 'CDKL2', 'CDK14']\n"
     ]
    }
   ],
   "source": [
    "module_dir = \"./module_gene/\"\n",
    "genes = []\n",
    "for file in os.listdir(module_dir):\n",
    "    genes += [line.strip() for line in open(f\"{module_dir}/{file}\",'r').readlines()]\n",
    "# print(genes)"
   ]
  }
 ],
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