fig6_dnmt3a.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "32bd4190",
   "metadata": {},
   "source": [
    "# Using the DNMT3A subgroup to recreate the Figure 6 scatterplot from the Palma paper\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a3a0ac",
   "metadata": {},
   "source": [
    "### Importing BMatrix and BooleanNetwork code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c7e6358b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import booleanNetwork module from ./src\n",
    "import sys\n",
    "sys.path.append('./src/')\n",
    "sys.path.append('./src/BNMPy/')\n",
    "\n",
    "import booleanNetwork as bn\n",
    "import BMatrix  \n",
    "\n",
    "#importing dataframe with patient id, mutation_profiles, and clinical_data\n",
    "merged_df = pd.read_excel(\"./input_files/merged_df.xlsx\",keep_default_na=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7752634a-eea2-43ce-a718-66ebf187bf51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.23.5'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7639e9f7-0288-408c-9c39-3c7f3da4a297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.2'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9308e1a8",
   "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>labId</th>\n",
       "      <th>mutation_profiles</th>\n",
       "      <th>BM_Blast</th>\n",
       "      <th>PB_Blast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09-00705</td>\n",
       "      <td>NPM1</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10-00136</td>\n",
       "      <td>FLT3,DNMT3A</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10-00172</td>\n",
       "      <td>FLT3</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10-00507</td>\n",
       "      <td>FLT3,NPM1</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10-00542</td>\n",
       "      <td>FLT3,DNMT3A</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>80.000000</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>603</th>\n",
       "      <td>16-01262</td>\n",
       "      <td></td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>16-01270</td>\n",
       "      <td></td>\n",
       "      <td>55.694882</td>\n",
       "      <td>41.515937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>17-00072</td>\n",
       "      <td></td>\n",
       "      <td>55.694882</td>\n",
       "      <td>53.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>17-00077</td>\n",
       "      <td></td>\n",
       "      <td>90.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>17-00096</td>\n",
       "      <td></td>\n",
       "      <td>55.694882</td>\n",
       "      <td>41.515937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>608 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        labId mutation_profiles   BM_Blast   PB_Blast\n",
       "0    09-00705              NPM1  94.000000  97.000000\n",
       "1    10-00136       FLT3,DNMT3A  80.000000  19.000000\n",
       "2    10-00172              FLT3  91.000000  99.000000\n",
       "3    10-00507         FLT3,NPM1  97.000000  97.000000\n",
       "4    10-00542       FLT3,DNMT3A  87.000000  80.000000\n",
       "..        ...               ...        ...        ...\n",
       "603  16-01262                    90.000000  90.000000\n",
       "604  16-01270                    55.694882  41.515937\n",
       "605  17-00072                    55.694882  53.200000\n",
       "606  17-00077                    90.000000  74.000000\n",
       "607  17-00096                    55.694882  41.515937\n",
       "\n",
       "[608 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4814734d",
   "metadata": {},
   "source": [
    "### Getting the variables needed for the simulation (like varF and F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "332c05bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0 -1 -1]\n",
      " [ 0 -1 -1]\n",
      " [ 0 -1 -1]\n",
      " [ 3 -1 -1]\n",
      " [ 1 -1 -1]\n",
      " [ 5 -1 -1]\n",
      " [ 5 -1 -1]\n",
      " [ 5 -1 -1]\n",
      " [ 5 -1 -1]\n",
      " [ 0 -1 -1]\n",
      " [ 5 -1 -1]\n",
      " [ 0 -1 -1]\n",
      " [ 2 -1 -1]\n",
      " [ 4  3 -1]\n",
      " [ 7  3 -1]\n",
      " [ 4  9  8]\n",
      " [ 9 -1 -1]\n",
      " [ 6 -1 -1]\n",
      " [17  9 -1]]\n",
      "\n",
      "\n",
      "[[ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 1  0 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 1  0 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 1  0 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 1  0 -1 -1 -1 -1 -1 -1]\n",
      " [ 1  0  0  0 -1 -1 -1 -1]\n",
      " [ 1  0  1  1 -1 -1 -1 -1]\n",
      " [ 0  0  1  0  0  0  0  0]\n",
      " [ 1  0 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1 -1 -1 -1 -1 -1 -1]\n",
      " [ 0  1  0  0 -1 -1 -1 -1]]\n"
     ]
    }
   ],
   "source": [
    "file = './input_files/dnmt3a_equations.txt' #does not have the equations at the end\n",
    "\n",
    "equations = BMatrix.get_equations(file)\n",
    "gene_dict = BMatrix.get_gene_dict(equations)\n",
    "upstream_genes = BMatrix.get_upstream_genes(equations)\n",
    "\n",
    "connectivity_matrix = BMatrix.get_connectivity_matrix(equations,upstream_genes,gene_dict)\n",
    "print(connectivity_matrix)\n",
    "print('\\n')\n",
    "\n",
    "truth_table = BMatrix.get_truth_table(equations,upstream_genes)\n",
    "print(truth_table)\n",
    "\n",
    "ngenes= len(equations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ddb859fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_equations = './input_files/calculating_equations.txt'\n",
    "\n",
    "cal_equations = BMatrix.get_equations(file_equations)\n",
    "cal_upstream_genes = BMatrix.get_cal_upstream_genes(cal_equations)\n",
    "cal_functions = BMatrix.get_cal_functions(cal_equations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04cd139e",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_mutations = './input_files/mutations.txt'\n",
    "\n",
    "mutation_dict = BMatrix.get_mutation_dict(file_mutations)\n",
    "#mutation_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9018cc6",
   "metadata": {},
   "source": [
    "### Running the simulation for all the 8 profiles from merged_df and getting the final scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05703f20",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_profiles = np.array([[profile] for profile in merged_df['mutation_profiles'].unique()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e70dabd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NPM1\n",
      "starting simulation\n",
      "getting final score\n",
      "0.60275\n",
      "FLT3,DNMT3A\n",
      "starting simulation\n",
      "getting final score\n",
      "4.81504\n",
      "FLT3\n",
      "starting simulation\n",
      "getting final score\n",
      "3.97588\n",
      "FLT3,NPM1\n",
      "starting simulation\n",
      "getting final score\n",
      "2.41684\n",
      "DNMT3A,NPM1\n",
      "starting simulation\n",
      "getting final score\n",
      "1.63099\n",
      "FLT3,DNMT3A,NPM1\n",
      "starting simulation\n",
      "getting final score\n",
      "3.62925\n",
      "DNMT3A\n",
      "starting simulation\n",
      "getting final score\n",
      "2.34628\n",
      "\n",
      "no_mutation\n",
      "starting simulation\n",
      "getting final score\n",
      "1.8547\n",
      "{'NPM1': 0.60275, 'FLT3,DNMT3A': 4.81504, 'FLT3': 3.97588, 'FLT3,NPM1': 2.41684, 'DNMT3A,NPM1': 1.63099, 'FLT3,DNMT3A,NPM1': 3.62925, 'DNMT3A': 2.34628, '': 1.8547}\n"
     ]
    }
   ],
   "source": [
    "final_scores = {}\n",
    "networks = {}\n",
    "\n",
    "for i in range(len(all_profiles)): \n",
    "    profile = all_profiles[i,0] # Gets the profile as a string ex. 'FLT3'\n",
    "    print(profile)\n",
    "    \n",
    "    mutated_connectivity_matrix, x0 = BMatrix.get_knocking_genes(profile, mutation_dict, connectivity_matrix, gene_dict)\n",
    "\n",
    "    #making the networks and running the simulation\n",
    "    print('starting simulation')\n",
    "    network = bn.BooleanNetwork(ngenes, mutated_connectivity_matrix, truth_table, x0)\n",
    "    network_traj = network.update_noise(0.05, 200000)\n",
    "    \n",
    "    #calculating the scores  \n",
    "    print('getting final score')\n",
    "    cal_range = network_traj[-100000:]\n",
    "    \n",
    "    scores_dict, final_score = BMatrix.get_calculating_scores(network_traj, cal_functions, cal_upstream_genes, gene_dict, cal_range)\n",
    "\n",
    "    print(final_score)\n",
    "    \n",
    "    final_scores.update({profile: final_score})\n",
    "    \n",
    "print(final_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c47c9c8",
   "metadata": {},
   "source": [
    "## Creating the PB and BM dataframes for the scatterplots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960e78cc",
   "metadata": {},
   "source": [
    "#### Grouping the patients with the same profiles together and getting their average scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9293032",
   "metadata": {},
   "outputs": [],
   "source": [
    "PB_Blast = []\n",
    "BM_Blast = []\n",
    "Network_Scores = []\n",
    "\n",
    "for i in range(len(all_profiles)):\n",
    "    mutation_group = all_profiles[i,0] # Gets the profile as a string ex. 'FLT3'\n",
    "\n",
    "    group_df = merged_df[merged_df['mutation_profiles'] == mutation_group]\n",
    "\n",
    "    #network scores\n",
    "    score = final_scores[mutation_group]\n",
    "    Network_Scores.append(score)\n",
    "    \n",
    "    #PB Blast\n",
    "    PB = group_df['PB_Blast'].mean()\n",
    "    PB_Blast.append(PB)\n",
    "    \n",
    "    #BM Blast\n",
    "    BM = group_df['BM_Blast'].mean()\n",
    "    BM_Blast.append(BM)\n",
    "    \n",
    "Mutation_Profiles = [profile[0] for profile in all_profiles]\n",
    "Mutation_Profiles = ['WT' if item == '' else item for item in Mutation_Profiles]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0da593ef",
   "metadata": {},
   "source": [
    "#### Plotting the scatterplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b2d0e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Regression Plot: PB_Blast vs. Network_Scores')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PB_v_Network = pd.DataFrame({\n",
    "    'Mutation_Profiles': Mutation_Profiles,\n",
    "    'PB_Blast': PB_Blast,\n",
    "    'Network_Scores': Network_Scores\n",
    "})\n",
    "\n",
    "plt.scatter(PB_v_Network['PB_Blast'], PB_v_Network['Network_Scores'])\n",
    "\n",
    "for x, y, label in zip(PB_v_Network['PB_Blast'], PB_v_Network['Network_Scores'], PB_v_Network['Mutation_Profiles']):\n",
    "    plt.text(x, y, label, ha='right', va='bottom')\n",
    "\n",
    "plt.xlabel('PB Blast %')\n",
    "plt.ylabel('Network Score')\n",
    "plt.title('PB Blast % vs Network Score for Mutation Profiles')\n",
    "plt.show()\n",
    "\n",
    "sns.regplot(x='PB_Blast', y='Network_Scores', data=PB_v_Network, fit_reg=True)\n",
    "plt.title('Regression Plot: PB_Blast vs. Network_Scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26be8a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Regression Plot: BM_Blast vs. Network_Scores')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BM_v_Network = pd.DataFrame({\n",
    "    'BM_Blast': BM_Blast,\n",
    "    'Network_Scores': Network_Scores,\n",
    "    'Mutation_Profiles': Mutation_Profiles  # Assuming Mutation_Profiles is a list\n",
    "})\n",
    "\n",
    "plt.scatter(BM_v_Network['BM_Blast'], BM_v_Network['Network_Scores'])\n",
    "\n",
    "for x, y, label in zip(BM_v_Network['BM_Blast'], BM_v_Network['Network_Scores'], BM_v_Network['Mutation_Profiles']):\n",
    "    plt.text(x, y, label, ha='right', va='bottom')\n",
    "\n",
    "plt.xlabel('BM Blast %')\n",
    "plt.ylabel('Network Score')\n",
    "plt.title('BM Blast % vs Network Score for Mutation Profiles')\n",
    "plt.show()\n",
    "\n",
    "sns.regplot(x='BM_Blast', y='Network_Scores', data=BM_v_Network, fit_reg=True)\n",
    "plt.title('Regression Plot: BM_Blast vs. Network_Scores')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a2c037",
   "metadata": {},
   "source": [
    "#### Getting the pearson correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2183b4ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Pearson correlation for PB vs Network: 0.40510285374298105\n",
      "Pearson correlation for BM vs Network: 0.3317927995860926\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "pearson_PB, _ = pearsonr(PB_Blast, Network_Scores)\n",
    "pearson_BM, _ = pearsonr(BM_Blast, Network_Scores)\n",
    "\n",
    "print(\"\\nPearson correlation for PB vs Network:\", pearson_PB)\n",
    "print(\"Pearson correlation for BM vs Network:\", pearson_BM)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}