Final Project Continued Work.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5780e9a4-a056-4d66-a6ce-ac516678e08b",
   "metadata": {},
   "source": [
    "##\n",
    "https://www.cbioportal.org/study/summary?id=breast_msk_2018"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32423155-88aa-4096-a41d-12f629df608b",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "79b1ce07-e47f-4fad-b291-ea6a836b2f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37513172-a29a-435d-8527-990c6445d801",
   "metadata": {},
   "source": [
    "## Data Processing\n",
    "Data for this study is relational"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301154db-92fd-4798-b15f-005bbfbb75fd",
   "metadata": {},
   "source": [
    "### Imports of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea0e693b-d8b1-46df-b770-e704f606867d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clinical_patients = pd.read_csv(\"breast_msk_2018/data_clinical_patient.txt\", skiprows=4, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d968922-4e2e-415e-b2b2-0eb1159c53a2",
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PATIENT_ID</th>\n",
       "      <th>METASTATIC_DZ_FUP</th>\n",
       "      <th>METASTATIC_RECURRENCE_TIME_MONTHS</th>\n",
       "      <th>PRIOR_LOCAL_RECURRENCE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>MENOPAUSAL_STATUS_AT_DIAGNOSIS</th>\n",
       "      <th>LATERALITY</th>\n",
       "      <th>T_STAGE</th>\n",
       "      <th>N_STAGE</th>\n",
       "      <th>M_STAGE</th>\n",
       "      <th>...</th>\n",
       "      <th>OVERALL_HR_STATUS_PATIENT</th>\n",
       "      <th>OVERALL_HER2_STATUS_PATIENT</th>\n",
       "      <th>HER2_STATUS_PRIMARY</th>\n",
       "      <th>VITAL_STATUS</th>\n",
       "      <th>LAST_CONTACT_DAYS_TO</th>\n",
       "      <th>TIME_TO_DEATH_MONTHS</th>\n",
       "      <th>DFS_MONTHS</th>\n",
       "      <th>DFS_EVENT</th>\n",
       "      <th>OS_MONTHS</th>\n",
       "      <th>OS_STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-0000004</td>\n",
       "      <td>Yes</td>\n",
       "      <td>446.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Pre</td>\n",
       "      <td>Left</td>\n",
       "      <td>T1c</td>\n",
       "      <td>N3a</td>\n",
       "      <td>M1</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
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       "      <td>Alive</td>\n",
       "      <td>14484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1</td>\n",
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       "      <td>31.5</td>\n",
       "      <td>0:LIVING</td>\n",
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       "      <th>1</th>\n",
       "      <td>P-0000012</td>\n",
       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Pre</td>\n",
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       "      <td>T2</td>\n",
       "      <td>N0</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
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       "      <td>Alive</td>\n",
       "      <td>22336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218.0</td>\n",
       "      <td>0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>0:LIVING</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-0000015</td>\n",
       "      <td>Yes</td>\n",
       "      <td>519.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Pre</td>\n",
       "      <td>Right</td>\n",
       "      <td>T1b</td>\n",
       "      <td>N1mi</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Deceased</td>\n",
       "      <td>16656</td>\n",
       "      <td>548.0</td>\n",
       "      <td>68.9</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1:DECEASED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-0000041</td>\n",
       "      <td>Yes</td>\n",
       "      <td>604.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Pre</td>\n",
       "      <td>Left</td>\n",
       "      <td>T1b</td>\n",
       "      <td>N0</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Deceased</td>\n",
       "      <td>19364</td>\n",
       "      <td>637.0</td>\n",
       "      <td>90.2</td>\n",
       "      <td>1</td>\n",
       "      <td>123.1</td>\n",
       "      <td>1:DECEASED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-0000057</td>\n",
       "      <td>Yes</td>\n",
       "      <td>459.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Pre</td>\n",
       "      <td>Left</td>\n",
       "      <td>TX</td>\n",
       "      <td>NX</td>\n",
       "      <td>M1</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Deceased</td>\n",
       "      <td>15871</td>\n",
       "      <td>522.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>63.6</td>\n",
       "      <td>1:DECEASED</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",
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       "      <td>...</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>P-0018917</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
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       "      <td>Right</td>\n",
       "      <td>T1c</td>\n",
       "      <td>N0</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
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       "      <td>Alive</td>\n",
       "      <td>21723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0:LIVING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>P-0018963</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Peri</td>\n",
       "      <td>Left</td>\n",
       "      <td>T1b</td>\n",
       "      <td>N0</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Alive</td>\n",
       "      <td>17610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0:LIVING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>P-0019037</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Post</td>\n",
       "      <td>Right</td>\n",
       "      <td>T1b</td>\n",
       "      <td>N0</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Alive</td>\n",
       "      <td>25027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0:LIVING</td>\n",
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       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>P-0019054</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Post</td>\n",
       "      <td>Right</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1a</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Alive</td>\n",
       "      <td>23381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0:LIVING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>P-0019073</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Post</td>\n",
       "      <td>Right</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1mi</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Alive</td>\n",
       "      <td>24691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0:LIVING</td>\n",
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       "     PATIENT_ID METASTATIC_DZ_FUP  METASTATIC_RECURRENCE_TIME_MONTHS  \\\n",
       "0     P-0000004               Yes                              446.0   \n",
       "1     P-0000012                No                                NaN   \n",
       "2     P-0000015               Yes                              519.0   \n",
       "3     P-0000041               Yes                              604.0   \n",
       "4     P-0000057               Yes                              459.0   \n",
       "...         ...               ...                                ...   \n",
       "1751  P-0018917                No                                NaN   \n",
       "1752  P-0018963                No                                NaN   \n",
       "1753  P-0019037                No                                NaN   \n",
       "1754  P-0019054                No                                NaN   \n",
       "1755  P-0019073                No                                NaN   \n",
       "\n",
       "     PRIOR_LOCAL_RECURRENCE     SEX MENOPAUSAL_STATUS_AT_DIAGNOSIS LATERALITY  \\\n",
       "0                        No  Female                            Pre       Left   \n",
       "1                        No  Female                            Pre      Right   \n",
       "2                        No  Female                            Pre      Right   \n",
       "3                        No  Female                            Pre       Left   \n",
       "4                        No  Female                            Pre       Left   \n",
       "...                     ...     ...                            ...        ...   \n",
       "1751                     No  Female                           Peri      Right   \n",
       "1752                     No  Female                           Peri       Left   \n",
       "1753                     No  Female                           Post      Right   \n",
       "1754                     No  Female                           Post      Right   \n",
       "1755                     No  Female                           Post      Right   \n",
       "\n",
       "     T_STAGE N_STAGE M_STAGE  ... OVERALL_HR_STATUS_PATIENT  \\\n",
       "0        T1c     N3a      M1  ...                  Positive   \n",
       "1         T2      N0      M0  ...                  Negative   \n",
       "2        T1b    N1mi      M0  ...                  Positive   \n",
       "3        T1b      N0      M0  ...                  Positive   \n",
       "4         TX      NX      M1  ...                  Positive   \n",
       "...      ...     ...     ...  ...                       ...   \n",
       "1751     T1c      N0      M0  ...                  Positive   \n",
       "1752     T1b      N0      M0  ...                  Positive   \n",
       "1753     T1b      N0      M0  ...                  Positive   \n",
       "1754      T2     N1a      M0  ...                  Positive   \n",
       "1755      T2    N1mi      M0  ...                  Positive   \n",
       "\n",
       "     OVERALL_HER2_STATUS_PATIENT HER2_STATUS_PRIMARY VITAL_STATUS  \\\n",
       "0                       Negative            Negative        Alive   \n",
       "1                       Negative            Negative        Alive   \n",
       "2                       Negative            Negative     Deceased   \n",
       "3                       Positive            Positive     Deceased   \n",
       "4                       Negative            Negative     Deceased   \n",
       "...                          ...                 ...          ...   \n",
       "1751                    Negative            Negative        Alive   \n",
       "1752                    Negative            Negative        Alive   \n",
       "1753                    Negative            Negative        Alive   \n",
       "1754                    Negative            Negative        Alive   \n",
       "1755                    Negative            Negative        Alive   \n",
       "\n",
       "     LAST_CONTACT_DAYS_TO  TIME_TO_DEATH_MONTHS  DFS_MONTHS  DFS_EVENT  \\\n",
       "0                   14484                   NaN         1.1          1   \n",
       "1                   22336                   NaN       218.0          0   \n",
       "2                   16656                 548.0        68.9          1   \n",
       "3                   19364                 637.0        90.2          1   \n",
       "4                   15871                 522.0         0.5          1   \n",
       "...                   ...                   ...         ...        ...   \n",
       "1751                21723                   NaN         2.9          0   \n",
       "1752                17610                   NaN         0.7          0   \n",
       "1753                25027                   NaN         1.6          0   \n",
       "1754                23381                   NaN         4.3          0   \n",
       "1755                24691                   NaN         4.5          0   \n",
       "\n",
       "      OS_MONTHS   OS_STATUS  \n",
       "0          31.5    0:LIVING  \n",
       "1         218.0    0:LIVING  \n",
       "2          98.0  1:DECEASED  \n",
       "3         123.1  1:DECEASED  \n",
       "4          63.6  1:DECEASED  \n",
       "...         ...         ...  \n",
       "1751        2.9    0:LIVING  \n",
       "1752        0.7    0:LIVING  \n",
       "1753        1.6    0:LIVING  \n",
       "1754        4.3    0:LIVING  \n",
       "1755        4.5    0:LIVING  \n",
       "\n",
       "[1756 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clinical_patients"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0fee7e-210f-4d06-a62e-a2b6c5f0c2a1",
   "metadata": {},
   "source": [
    "### Gene Mutations Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "168f6bd6-e764-4d99-8bba-0626a56170b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gene_mutations = pd.read_csv(\"breast_msk_2018/data_mutations.txt\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b95139e3-e881-4bcc-ac4b-b69bc0c46cf2",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
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       "      <th>Strand</th>\n",
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       "      <th>Variant_Classification</th>\n",
       "      <th>...</th>\n",
       "      <th>n_ref_count</th>\n",
       "      <th>n_alt_count</th>\n",
       "      <th>HGVSc</th>\n",
       "      <th>HGVSp</th>\n",
       "      <th>HGVSp_Short</th>\n",
       "      <th>Transcript_ID</th>\n",
       "      <th>RefSeq</th>\n",
       "      <th>Protein_position</th>\n",
       "      <th>Codons</th>\n",
       "      <th>Hotspot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASXL2</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>2</td>\n",
       "      <td>25976426</td>\n",
       "      <td>25976426</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>683</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000435504.4:c.1119C&gt;G</td>\n",
       "      <td>p.Phe373Leu</td>\n",
       "      <td>p.F373L</td>\n",
       "      <td>ENST00000435504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>373.0</td>\n",
       "      <td>ttC/ttG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NOTCH1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>9</td>\n",
       "      <td>139413071</td>\n",
       "      <td>139413071</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>485</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000277541.6:c.1071C&gt;G</td>\n",
       "      <td>p.Phe357Leu</td>\n",
       "      <td>p.F357L</td>\n",
       "      <td>ENST00000277541</td>\n",
       "      <td>NM_017617.3</td>\n",
       "      <td>357.0</td>\n",
       "      <td>ttC/ttG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PTEN</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>10</td>\n",
       "      <td>89624263</td>\n",
       "      <td>89624263</td>\n",
       "      <td>+</td>\n",
       "      <td>stop_gained</td>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000371953.3:c.37A&gt;T</td>\n",
       "      <td>p.Lys13Ter</td>\n",
       "      <td>p.K13*</td>\n",
       "      <td>ENST00000371953</td>\n",
       "      <td>NM_000314.4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Aaa/Taa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KDM5C</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>X</td>\n",
       "      <td>53254071</td>\n",
       "      <td>53254071</td>\n",
       "      <td>+</td>\n",
       "      <td>start_lost</td>\n",
       "      <td>Translation_Start_Site</td>\n",
       "      <td>...</td>\n",
       "      <td>387</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000375401.3:c.1A&gt;T</td>\n",
       "      <td>p.Met1?</td>\n",
       "      <td>p.M1?</td>\n",
       "      <td>ENST00000375401</td>\n",
       "      <td>NM_004187.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Atg/Ttg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CDH1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>16</td>\n",
       "      <td>68844167</td>\n",
       "      <td>68844167</td>\n",
       "      <td>+</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>Frame_Shift_Del</td>\n",
       "      <td>...</td>\n",
       "      <td>477</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000261769.5:c.755del</td>\n",
       "      <td>p.Val252GlufsTer30</td>\n",
       "      <td>p.V252Efs*30</td>\n",
       "      <td>ENST00000261769</td>\n",
       "      <td>NM_004360.3</td>\n",
       "      <td>252.0</td>\n",
       "      <td>gTa/ga</td>\n",
       "      <td>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>9309</th>\n",
       "      <td>MSH6</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>2</td>\n",
       "      <td>48027313</td>\n",
       "      <td>48027313</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>273</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000234420.5:c.2191C&gt;A</td>\n",
       "      <td>p.Gln731Lys</td>\n",
       "      <td>p.Q731K</td>\n",
       "      <td>ENST00000234420</td>\n",
       "      <td>NM_000179.2</td>\n",
       "      <td>731.0</td>\n",
       "      <td>Caa/Aaa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9310</th>\n",
       "      <td>ASXL1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>20</td>\n",
       "      <td>31021228</td>\n",
       "      <td>31021228</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000375687.4:c.1227G&gt;T</td>\n",
       "      <td>p.Lys409Asn</td>\n",
       "      <td>p.K409N</td>\n",
       "      <td>ENST00000375687</td>\n",
       "      <td>NM_015338.5</td>\n",
       "      <td>409.0</td>\n",
       "      <td>aaG/aaT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9311</th>\n",
       "      <td>TSC2</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>16</td>\n",
       "      <td>2104320</td>\n",
       "      <td>2104321</td>\n",
       "      <td>+</td>\n",
       "      <td>inframe_insertion</td>\n",
       "      <td>In_Frame_Ins</td>\n",
       "      <td>...</td>\n",
       "      <td>370</td>\n",
       "      <td>1</td>\n",
       "      <td>ENST00000219476.3:c.362_382dup</td>\n",
       "      <td>p.Ile127_Lys128insThrLeuPhePheLysValIle</td>\n",
       "      <td>p.I127_K128insTLFFKVI</td>\n",
       "      <td>ENST00000219476</td>\n",
       "      <td>NM_000548.3</td>\n",
       "      <td>121.0</td>\n",
       "      <td>gcc/gCCCTCTTCTTTAAGGTCATCAcc</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9312</th>\n",
       "      <td>HIST1H2BD</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>6</td>\n",
       "      <td>26158741</td>\n",
       "      <td>26158742</td>\n",
       "      <td>+</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>Frame_Shift_Ins</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000289316.2:c.345_346insCGTCACCAAG</td>\n",
       "      <td>p.Thr116ArgfsTer31</td>\n",
       "      <td>p.T116Rfs*31</td>\n",
       "      <td>ENST00000289316</td>\n",
       "      <td>NM_138720.2</td>\n",
       "      <td>115.0</td>\n",
       "      <td>-/CGTCACCAAG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9313</th>\n",
       "      <td>PIK3R1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>5</td>\n",
       "      <td>67591050</td>\n",
       "      <td>67591051</td>\n",
       "      <td>+</td>\n",
       "      <td>stop_gained,frameshift_variant</td>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>297</td>\n",
       "      <td>0</td>\n",
       "      <td>ENST00000274335.5:c.1643_1644delinsCTTGAAGAAGC...</td>\n",
       "      <td>p.Asp548AlafsTer2</td>\n",
       "      <td>p.D548Afs*2</td>\n",
       "      <td>ENST00000274335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>548.0</td>\n",
       "      <td>gAC/gCTTGAAGAAGCAGGCAGCTGAG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9314 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Hugo_Symbol  Entrez_Gene_Id Center NCBI_Build Chromosome  Start_Position  \\\n",
       "0          ASXL2               0  MSKCC     GRCh37          2        25976426   \n",
       "1         NOTCH1               0  MSKCC     GRCh37          9       139413071   \n",
       "2           PTEN               0  MSKCC     GRCh37         10        89624263   \n",
       "3          KDM5C               0  MSKCC     GRCh37          X        53254071   \n",
       "4           CDH1               0  MSKCC     GRCh37         16        68844167   \n",
       "...          ...             ...    ...        ...        ...             ...   \n",
       "9309        MSH6               0  MSKCC     GRCh37          2        48027313   \n",
       "9310       ASXL1               0  MSKCC     GRCh37         20        31021228   \n",
       "9311        TSC2               0  MSKCC     GRCh37         16         2104320   \n",
       "9312   HIST1H2BD               0  MSKCC     GRCh37          6        26158741   \n",
       "9313      PIK3R1               0  MSKCC     GRCh37          5        67591050   \n",
       "\n",
       "      End_Position Strand                     Consequence  \\\n",
       "0         25976426      +                missense_variant   \n",
       "1        139413071      +                missense_variant   \n",
       "2         89624263      +                     stop_gained   \n",
       "3         53254071      +                      start_lost   \n",
       "4         68844167      +              frameshift_variant   \n",
       "...            ...    ...                             ...   \n",
       "9309      48027313      +                missense_variant   \n",
       "9310      31021228      +                missense_variant   \n",
       "9311       2104321      +               inframe_insertion   \n",
       "9312      26158742      +              frameshift_variant   \n",
       "9313      67591051      +  stop_gained,frameshift_variant   \n",
       "\n",
       "      Variant_Classification  ... n_ref_count n_alt_count  \\\n",
       "0          Missense_Mutation  ...         683           0   \n",
       "1          Missense_Mutation  ...         485           0   \n",
       "2          Nonsense_Mutation  ...         147           0   \n",
       "3     Translation_Start_Site  ...         387           0   \n",
       "4            Frame_Shift_Del  ...         477           0   \n",
       "...                      ...  ...         ...         ...   \n",
       "9309       Missense_Mutation  ...         273           0   \n",
       "9310       Missense_Mutation  ...         270           0   \n",
       "9311            In_Frame_Ins  ...         370           1   \n",
       "9312         Frame_Shift_Ins  ...         136           0   \n",
       "9313       Nonsense_Mutation  ...         297           0   \n",
       "\n",
       "                                                  HGVSc  \\\n",
       "0                           ENST00000435504.4:c.1119C>G   \n",
       "1                           ENST00000277541.6:c.1071C>G   \n",
       "2                             ENST00000371953.3:c.37A>T   \n",
       "3                              ENST00000375401.3:c.1A>T   \n",
       "4                            ENST00000261769.5:c.755del   \n",
       "...                                                 ...   \n",
       "9309                        ENST00000234420.5:c.2191C>A   \n",
       "9310                        ENST00000375687.4:c.1227G>T   \n",
       "9311                     ENST00000219476.3:c.362_382dup   \n",
       "9312           ENST00000289316.2:c.345_346insCGTCACCAAG   \n",
       "9313  ENST00000274335.5:c.1643_1644delinsCTTGAAGAAGC...   \n",
       "\n",
       "                                        HGVSp            HGVSp_Short  \\\n",
       "0                                 p.Phe373Leu                p.F373L   \n",
       "1                                 p.Phe357Leu                p.F357L   \n",
       "2                                  p.Lys13Ter                 p.K13*   \n",
       "3                                     p.Met1?                  p.M1?   \n",
       "4                          p.Val252GlufsTer30           p.V252Efs*30   \n",
       "...                                       ...                    ...   \n",
       "9309                              p.Gln731Lys                p.Q731K   \n",
       "9310                              p.Lys409Asn                p.K409N   \n",
       "9311  p.Ile127_Lys128insThrLeuPhePheLysValIle  p.I127_K128insTLFFKVI   \n",
       "9312                       p.Thr116ArgfsTer31           p.T116Rfs*31   \n",
       "9313                        p.Asp548AlafsTer2            p.D548Afs*2   \n",
       "\n",
       "        Transcript_ID       RefSeq Protein_position  \\\n",
       "0     ENST00000435504          NaN            373.0   \n",
       "1     ENST00000277541  NM_017617.3            357.0   \n",
       "2     ENST00000371953  NM_000314.4             13.0   \n",
       "3     ENST00000375401  NM_004187.3              1.0   \n",
       "4     ENST00000261769  NM_004360.3            252.0   \n",
       "...               ...          ...              ...   \n",
       "9309  ENST00000234420  NM_000179.2            731.0   \n",
       "9310  ENST00000375687  NM_015338.5            409.0   \n",
       "9311  ENST00000219476  NM_000548.3            121.0   \n",
       "9312  ENST00000289316  NM_138720.2            115.0   \n",
       "9313  ENST00000274335          NaN            548.0   \n",
       "\n",
       "                            Codons Hotspot  \n",
       "0                          ttC/ttG       0  \n",
       "1                          ttC/ttG       0  \n",
       "2                          Aaa/Taa       0  \n",
       "3                          Atg/Ttg       0  \n",
       "4                           gTa/ga       0  \n",
       "...                            ...     ...  \n",
       "9309                       Caa/Aaa       0  \n",
       "9310                       aaG/aaT       0  \n",
       "9311  gcc/gCCCTCTTCTTTAAGGTCATCAcc       0  \n",
       "9312                  -/CGTCACCAAG       0  \n",
       "9313   gAC/gCTTGAAGAAGCAGGCAGCTGAG       0  \n",
       "\n",
       "[9314 rows x 45 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gene_mutations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcba267-7e3d-479a-8125-d078d39d33eb",
   "metadata": {},
   "source": [
    "### MSK-Impact \n",
    "MSK-Impact is the First Tumor-Profiling Multiplex Panel Authorized by the FDA, Setting a New Pathway to Market for Future Oncopanels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae4db20a-b695-4591-8d5d-f7d023f086f6",
   "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>SAMPLE_ID</th>\n",
       "      <th>mutations</th>\n",
       "      <th>cna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-0000004-T01-IM3</td>\n",
       "      <td>IMPACT341</td>\n",
       "      <td>IMPACT341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-0000012-T02-IM3</td>\n",
       "      <td>IMPACT341</td>\n",
       "      <td>IMPACT341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-0000015-T01-IM3</td>\n",
       "      <td>IMPACT341</td>\n",
       "      <td>IMPACT341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-0000041-T01-IM3</td>\n",
       "      <td>IMPACT341</td>\n",
       "      <td>IMPACT341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-0000057-T01-IM3</td>\n",
       "      <td>IMPACT341</td>\n",
       "      <td>IMPACT341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>P-0000075-T01-IM3</td>\n",
       "      <td>IMPACT341</td>\n",
       "      <td>IMPACT341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>P-0000244-T01-IM3</td>\n",
       "      <td>IMPACT341</td>\n",
       "      <td>IMPACT341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>P-0017564-T01-IM6</td>\n",
       "      <td>IMPACT468</td>\n",
       "      <td>IMPACT468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>P-0003591-T02-IM6</td>\n",
       "      <td>IMPACT468</td>\n",
       "      <td>IMPACT468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>P-0000215-T01-IM3</td>\n",
       "      <td>IMPACT341</td>\n",
       "      <td>IMPACT341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1918 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              SAMPLE_ID  mutations        cna\n",
       "0     P-0000004-T01-IM3  IMPACT341  IMPACT341\n",
       "1     P-0000012-T02-IM3  IMPACT341  IMPACT341\n",
       "2     P-0000015-T01-IM3  IMPACT341  IMPACT341\n",
       "3     P-0000041-T01-IM3  IMPACT341  IMPACT341\n",
       "4     P-0000057-T01-IM3  IMPACT341  IMPACT341\n",
       "...                 ...        ...        ...\n",
       "1913  P-0000075-T01-IM3  IMPACT341  IMPACT341\n",
       "1914  P-0000244-T01-IM3  IMPACT341  IMPACT341\n",
       "1915  P-0017564-T01-IM6  IMPACT468  IMPACT468\n",
       "1916  P-0003591-T02-IM6  IMPACT468  IMPACT468\n",
       "1917  P-0000215-T01-IM3  IMPACT341  IMPACT341\n",
       "\n",
       "[1918 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gene_matrix = pd.read_csv(\"breast_msk_2018/data_gene_panel_matrix.txt\", sep='\\t')\n",
    "df_gene_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f2d09f-5ba3-4f10-9de2-92928637fcf3",
   "metadata": {},
   "source": [
    "### Get Common MSK-Impact Mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97d6f743-5fe7-4db8-95c8-0696c23d3b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAMPLE_ID</th>\n",
       "      <th>cna</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutations</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IMPACT341</th>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IMPACT410</th>\n",
       "      <td>968</td>\n",
       "      <td>968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IMPACT468</th>\n",
       "      <td>518</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SAMPLE_ID  cna\n",
       "mutations                \n",
       "IMPACT341        432  432\n",
       "IMPACT410        968  968\n",
       "IMPACT468        518  518"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_gene_matrix = df_gene_matrix.groupby(['mutations']).count()\n",
    "table_gene_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ae7f7f8-2520-4080-9d92-94482770e17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ABL1'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_impact_410 = pd.read_csv(\"misc_data/data_gene_panel_impact410.txt\", skiprows=2, sep='\\t')\n",
    "genes = list(df_impact_410)\n",
    "genes[0] = genes[0].split()[-1]\n",
    "genes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48df8262-eb5d-4f64-88d2-8677f455680c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PATIENT_ID</th>\n",
       "      <th>SAMPLE_ID</th>\n",
       "      <th>SAMPLE_TYPE</th>\n",
       "      <th>SAMPLE_SITE</th>\n",
       "      <th>PRIOR_BREAST_PRIMARY</th>\n",
       "      <th>INVASIVE_CARCINOMA_DX_TIME</th>\n",
       "      <th>NGS_SAMPLE_COLLECTION_TIME</th>\n",
       "      <th>INVASIVE_CARCINOMA_DX_AGE</th>\n",
       "      <th>TUMOR_TISSUE_ORIGIN</th>\n",
       "      <th>TUMOR_SAMPLE_HISTOLOGY</th>\n",
       "      <th>...</th>\n",
       "      <th>OVERALL_HER2_STATUS</th>\n",
       "      <th>HER2_IHC_STATUS</th>\n",
       "      <th>HER2_IHC_SCORE</th>\n",
       "      <th>HER2_FISH</th>\n",
       "      <th>HER2_FISH_RATIO</th>\n",
       "      <th>ONCOTREE_CODE</th>\n",
       "      <th>CANCER_TYPE</th>\n",
       "      <th>CANCER_TYPE_DETAILED</th>\n",
       "      <th>SOMATIC_STATUS</th>\n",
       "      <th>TMB_NONSYNONYMOUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-0000004</td>\n",
       "      <td>P-0000004-T01-IM3</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Treatment Naive Primary</td>\n",
       "      <td>No</td>\n",
       "      <td>444.87</td>\n",
       "      <td>445</td>\n",
       "      <td>37</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0-1+</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-0000012</td>\n",
       "      <td>P-0000012-T02-IM3</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Treatment Naive Primary</td>\n",
       "      <td>No</td>\n",
       "      <td>516.48</td>\n",
       "      <td>517</td>\n",
       "      <td>43</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0-1+</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-0000015</td>\n",
       "      <td>P-0000015-T01-IM3</td>\n",
       "      <td>Metastasis</td>\n",
       "      <td>Liver</td>\n",
       "      <td>No</td>\n",
       "      <td>449.80</td>\n",
       "      <td>534</td>\n",
       "      <td>37</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0+</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-0000041</td>\n",
       "      <td>P-0000041-T01-IM3</td>\n",
       "      <td>Metastasis</td>\n",
       "      <td>Breast</td>\n",
       "      <td>No</td>\n",
       "      <td>513.68</td>\n",
       "      <td>618</td>\n",
       "      <td>43</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Equivocal</td>\n",
       "      <td>2+</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0.24</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-0000057</td>\n",
       "      <td>P-0000057-T01-IM3</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Post-Treatment Primary</td>\n",
       "      <td>No</td>\n",
       "      <td>458.36</td>\n",
       "      <td>489</td>\n",
       "      <td>38</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Mixed Ductal and Lobular Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0-1+</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>MDLC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Mixed Ductal and Lobular Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.166667</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>1913</th>\n",
       "      <td>P-0018917</td>\n",
       "      <td>P-0018917-T01-IM6</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Treatment Naive Primary</td>\n",
       "      <td>No</td>\n",
       "      <td>711.64</td>\n",
       "      <td>712</td>\n",
       "      <td>59</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0-1+</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>P-0018963</td>\n",
       "      <td>P-0018963-T01-IM6</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Treatment Naive Primary</td>\n",
       "      <td>No</td>\n",
       "      <td>578.55</td>\n",
       "      <td>579</td>\n",
       "      <td>48</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0-1+</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>P-0019037</td>\n",
       "      <td>P-0019037-T01-IM6</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Treatment Naive Primary</td>\n",
       "      <td>No</td>\n",
       "      <td>821.61</td>\n",
       "      <td>822</td>\n",
       "      <td>68</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Equivocal</td>\n",
       "      <td>2+</td>\n",
       "      <td>Negative</td>\n",
       "      <td>1.2</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>P-0019054</td>\n",
       "      <td>P-0019054-T01-IM6</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Treatment Naive Primary</td>\n",
       "      <td>No</td>\n",
       "      <td>764.84</td>\n",
       "      <td>767</td>\n",
       "      <td>64</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Lobular Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0-1+</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>ILC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Lobular Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>P-0019073</td>\n",
       "      <td>P-0019073-T01-IM6</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Treatment Naive Primary</td>\n",
       "      <td>No</td>\n",
       "      <td>807.70</td>\n",
       "      <td>809</td>\n",
       "      <td>67</td>\n",
       "      <td>Breast</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0-1+</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Matched</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1918 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PATIENT_ID          SAMPLE_ID SAMPLE_TYPE              SAMPLE_SITE  \\\n",
       "0     P-0000004  P-0000004-T01-IM3     Primary  Treatment Naive Primary   \n",
       "1     P-0000012  P-0000012-T02-IM3     Primary  Treatment Naive Primary   \n",
       "2     P-0000015  P-0000015-T01-IM3  Metastasis                    Liver   \n",
       "3     P-0000041  P-0000041-T01-IM3  Metastasis                   Breast   \n",
       "4     P-0000057  P-0000057-T01-IM3     Primary   Post-Treatment Primary   \n",
       "...         ...                ...         ...                      ...   \n",
       "1913  P-0018917  P-0018917-T01-IM6     Primary  Treatment Naive Primary   \n",
       "1914  P-0018963  P-0018963-T01-IM6     Primary  Treatment Naive Primary   \n",
       "1915  P-0019037  P-0019037-T01-IM6     Primary  Treatment Naive Primary   \n",
       "1916  P-0019054  P-0019054-T01-IM6     Primary  Treatment Naive Primary   \n",
       "1917  P-0019073  P-0019073-T01-IM6     Primary  Treatment Naive Primary   \n",
       "\n",
       "     PRIOR_BREAST_PRIMARY  INVASIVE_CARCINOMA_DX_TIME  \\\n",
       "0                      No                      444.87   \n",
       "1                      No                      516.48   \n",
       "2                      No                      449.80   \n",
       "3                      No                      513.68   \n",
       "4                      No                      458.36   \n",
       "...                   ...                         ...   \n",
       "1913                   No                      711.64   \n",
       "1914                   No                      578.55   \n",
       "1915                   No                      821.61   \n",
       "1916                   No                      764.84   \n",
       "1917                   No                      807.70   \n",
       "\n",
       "      NGS_SAMPLE_COLLECTION_TIME  INVASIVE_CARCINOMA_DX_AGE  \\\n",
       "0                            445                         37   \n",
       "1                            517                         43   \n",
       "2                            534                         37   \n",
       "3                            618                         43   \n",
       "4                            489                         38   \n",
       "...                          ...                        ...   \n",
       "1913                         712                         59   \n",
       "1914                         579                         48   \n",
       "1915                         822                         68   \n",
       "1916                         767                         64   \n",
       "1917                         809                         67   \n",
       "\n",
       "     TUMOR_TISSUE_ORIGIN                     TUMOR_SAMPLE_HISTOLOGY  ...  \\\n",
       "0                 Breast           Breast Invasive Ductal Carcinoma  ...   \n",
       "1                 Breast           Breast Invasive Ductal Carcinoma  ...   \n",
       "2                 Breast           Breast Invasive Ductal Carcinoma  ...   \n",
       "3                 Breast           Breast Invasive Ductal Carcinoma  ...   \n",
       "4                 Breast  Breast Mixed Ductal and Lobular Carcinoma  ...   \n",
       "...                  ...                                        ...  ...   \n",
       "1913              Breast           Breast Invasive Ductal Carcinoma  ...   \n",
       "1914              Breast           Breast Invasive Ductal Carcinoma  ...   \n",
       "1915              Breast           Breast Invasive Ductal Carcinoma  ...   \n",
       "1916              Breast          Breast Invasive Lobular Carcinoma  ...   \n",
       "1917              Breast           Breast Invasive Ductal Carcinoma  ...   \n",
       "\n",
       "     OVERALL_HER2_STATUS HER2_IHC_STATUS HER2_IHC_SCORE       HER2_FISH  \\\n",
       "0               Negative        Negative           0-1+  Not Applicable   \n",
       "1               Negative        Negative           0-1+  Not Applicable   \n",
       "2               Negative        Negative             0+  Not Applicable   \n",
       "3               Negative       Equivocal             2+        Negative   \n",
       "4               Negative        Negative           0-1+  Not Applicable   \n",
       "...                  ...             ...            ...             ...   \n",
       "1913            Negative        Negative           0-1+  Not Applicable   \n",
       "1914            Negative        Negative           0-1+  Not Applicable   \n",
       "1915            Negative       Equivocal             2+        Negative   \n",
       "1916            Negative        Negative           0-1+  Not Applicable   \n",
       "1917            Negative        Negative           0-1+  Not Applicable   \n",
       "\n",
       "     HER2_FISH_RATIO ONCOTREE_CODE    CANCER_TYPE  \\\n",
       "0     Not Applicable           IDC  Breast Cancer   \n",
       "1     Not Applicable           IDC  Breast Cancer   \n",
       "2     Not Applicable           IDC  Breast Cancer   \n",
       "3               0.24           IDC  Breast Cancer   \n",
       "4     Not Applicable          MDLC  Breast Cancer   \n",
       "...              ...           ...            ...   \n",
       "1913  Not Applicable           IDC  Breast Cancer   \n",
       "1914  Not Applicable           IDC  Breast Cancer   \n",
       "1915             1.2           IDC  Breast Cancer   \n",
       "1916  Not Applicable           ILC  Breast Cancer   \n",
       "1917  Not Applicable           IDC  Breast Cancer   \n",
       "\n",
       "                           CANCER_TYPE_DETAILED SOMATIC_STATUS  \\\n",
       "0              Breast Invasive Ductal Carcinoma        Matched   \n",
       "1              Breast Invasive Ductal Carcinoma        Matched   \n",
       "2              Breast Invasive Ductal Carcinoma        Matched   \n",
       "3              Breast Invasive Ductal Carcinoma        Matched   \n",
       "4     Breast Mixed Ductal and Lobular Carcinoma        Matched   \n",
       "...                                         ...            ...   \n",
       "1913           Breast Invasive Ductal Carcinoma        Matched   \n",
       "1914           Breast Invasive Ductal Carcinoma        Matched   \n",
       "1915           Breast Invasive Ductal Carcinoma        Matched   \n",
       "1916          Breast Invasive Lobular Carcinoma        Matched   \n",
       "1917           Breast Invasive Ductal Carcinoma        Matched   \n",
       "\n",
       "     TMB_NONSYNONYMOUS  \n",
       "0             0.133333  \n",
       "1             0.033333  \n",
       "2             0.233333  \n",
       "3             0.333333  \n",
       "4             0.166667  \n",
       "...                ...  \n",
       "1913          0.133333  \n",
       "1914          0.033333  \n",
       "1915          0.066667  \n",
       "1916          0.100000  \n",
       "1917          0.033333  \n",
       "\n",
       "[1918 rows x 35 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clinical_sample = pd.read_csv(\"breast_msk_2018/data_clinical_sample.txt\", skiprows=4, sep='\\t')\n",
    "df_clinical_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb950de6-1f34-4e45-bc72-8975aedfc248",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
       "      <th>Tumor_Sample_Barcode</th>\n",
       "      <th>Fusion</th>\n",
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       "      <th>358</th>\n",
       "      <td>DDX11L2</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0000058-T01-IM3</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>ALK</td>\n",
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       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NTRK1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0000104-T02-IM6</td>\n",
       "      <td>NTRK1-ZBTB7B fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>ZBTB7B (NM_001252406) -  NTRK1 (NM_002529) Rea...</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ZBTB7B</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0000104-T02-IM6</td>\n",
       "      <td>NTRK1-ZBTB7B fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>ZBTB7B (NM_001252406) -  NTRK1 (NM_002529) Rea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>TGFB2</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0000187-T01-IM3</td>\n",
       "      <td>TGFB2-RB1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>IL10</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0017846-T01-IM6</td>\n",
       "      <td>IL10-TNNI1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>IL10 (NM_000572) Rearrangement : chr1:g.201374...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>MRE11A</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0018120-T01-IM6</td>\n",
       "      <td>MRE11A-DISC1FP1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>MRE11A (NM_005591) Rearrangement: c.1783+13:MR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>DISC1FP1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0018120-T01-IM6</td>\n",
       "      <td>MRE11A-DISC1FP1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>MRE11A (NM_005591) Rearrangement: c.1783+13:MR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>ERBB2</td>\n",
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       "      <td>P-0018896-T01-IM6</td>\n",
       "      <td>ARHGAP42-ERBB2 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>ERBB2 (NM_004448) rearrangement: t(11;17)(q22....</td>\n",
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       "      <td>ARHGAP42-ERBB2 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>ERBB2 (NM_004448) rearrangement: t(11;17)(q22....</td>\n",
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       "  </tbody>\n",
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       "<p>543 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "358     DDX11L2               0  MSKCC-DMP    P-0000058-T01-IM3   \n",
       "357         ALK               0  MSKCC-DMP    P-0000058-T01-IM3   \n",
       "23        NTRK1               0  MSKCC-DMP    P-0000104-T02-IM6   \n",
       "22       ZBTB7B               0  MSKCC-DMP    P-0000104-T02-IM6   \n",
       "296       TGFB2               0  MSKCC-DMP    P-0000187-T01-IM3   \n",
       "..          ...             ...        ...                  ...   \n",
       "438        IL10               0  MSKCC-DMP    P-0017846-T01-IM6   \n",
       "445      MRE11A               0  MSKCC-DMP    P-0018120-T01-IM6   \n",
       "444    DISC1FP1               0  MSKCC-DMP    P-0018120-T01-IM6   \n",
       "449       ERBB2               0  MSKCC-DMP    P-0018896-T01-IM6   \n",
       "450    ARHGAP42               0  MSKCC-DMP    P-0018896-T01-IM6   \n",
       "\n",
       "                     Fusion DNA_support RNA_support  Method    Frame  \\\n",
       "358      DDX11L2-ALK fusion         yes     unknown     NaN  unknown   \n",
       "357      DDX11L2-ALK fusion         yes     unknown     NaN  unknown   \n",
       "23      NTRK1-ZBTB7B fusion         yes     unknown     NaN  unknown   \n",
       "22      NTRK1-ZBTB7B fusion         yes     unknown     NaN  unknown   \n",
       "296        TGFB2-RB1 fusion         yes     unknown     NaN  unknown   \n",
       "..                      ...         ...         ...     ...      ...   \n",
       "438       IL10-TNNI1 fusion         yes     unknown     NaN  unknown   \n",
       "445  MRE11A-DISC1FP1 fusion         yes     unknown     NaN  unknown   \n",
       "444  MRE11A-DISC1FP1 fusion         yes     unknown     NaN  unknown   \n",
       "449   ARHGAP42-ERBB2 fusion         yes     unknown     NaN  unknown   \n",
       "450   ARHGAP42-ERBB2 fusion         yes     unknown     NaN  unknown   \n",
       "\n",
       "                                              Comments  \n",
       "358                                                NaN  \n",
       "357                                                NaN  \n",
       "23   ZBTB7B (NM_001252406) -  NTRK1 (NM_002529) Rea...  \n",
       "22   ZBTB7B (NM_001252406) -  NTRK1 (NM_002529) Rea...  \n",
       "296                                                NaN  \n",
       "..                                                 ...  \n",
       "438  IL10 (NM_000572) Rearrangement : chr1:g.201374...  \n",
       "445  MRE11A (NM_005591) Rearrangement: c.1783+13:MR...  \n",
       "444  MRE11A (NM_005591) Rearrangement: c.1783+13:MR...  \n",
       "449  ERBB2 (NM_004448) rearrangement: t(11;17)(q22....  \n",
       "450  ERBB2 (NM_004448) rearrangement: t(11;17)(q22....  \n",
       "\n",
       "[543 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fusions = pd.read_csv(\"breast_msk_2018/data_fusions.txt\", sep='\\t')\n",
    "sorted_df_fusions = df_fusions.sort_values(by='Tumor_Sample_Barcode')\n",
    "sorted_df_fusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ab4a813-a640-49c2-942d-5f73a4ac3ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_symbols = df_fusions['Hugo_Symbol']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16a7a19-64bb-4c55-b95f-506bfa70dafd",
   "metadata": {},
   "source": [
    "### Clinical Data Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f300cd1-41ea-411e-9d9e-6236b23a3cdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cases = pd.read_csv(\"breast_msk_2018/case_lists/cases_sequenced.txt\", skiprows=5, sep='\\t')\n",
    "df_cases_sequences = list(df_cases)\n",
    "df_cases_sequences[0] = df_cases_sequences[0].split()[-1]\n",
    "sampled_sequences = df_cases_sequences\n",
    "patients_sequenced = [\"-\".join(item.split(\"-\")[:2]) for item in df_cases_sequences]\n",
    "\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "30d1df35-4dc0-470d-a203-6f85e838aa5e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>PATIENT_ID</th>\n",
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       "      <td>519.0</td>\n",
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       "      <td>N1mi</td>\n",
       "      <td>M0</td>\n",
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       "      <td>Positive</td>\n",
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       "      <td>Negative</td>\n",
       "      <td>Deceased</td>\n",
       "      <td>16656</td>\n",
       "      <td>548.0</td>\n",
       "      <td>68.9</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1:DECEASED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-0000041</td>\n",
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       "      <td>604.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
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       "      <td>637.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>No</td>\n",
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       "      <td>522.0</td>\n",
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       "      <td>1:DECEASED</td>\n",
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       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Alive</td>\n",
       "      <td>25027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0:LIVING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>P-0019054</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Post</td>\n",
       "      <td>Right</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1a</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Alive</td>\n",
       "      <td>23381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0:LIVING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>P-0019073</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Post</td>\n",
       "      <td>Right</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1mi</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Alive</td>\n",
       "      <td>24691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0:LIVING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1756 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PATIENT_ID METASTATIC_DZ_FUP  METASTATIC_RECURRENCE_TIME_MONTHS  \\\n",
       "0     P-0000004               Yes                              446.0   \n",
       "1     P-0000012                No                                NaN   \n",
       "2     P-0000015               Yes                              519.0   \n",
       "3     P-0000041               Yes                              604.0   \n",
       "4     P-0000057               Yes                              459.0   \n",
       "...         ...               ...                                ...   \n",
       "1751  P-0018917                No                                NaN   \n",
       "1752  P-0018963                No                                NaN   \n",
       "1753  P-0019037                No                                NaN   \n",
       "1754  P-0019054                No                                NaN   \n",
       "1755  P-0019073                No                                NaN   \n",
       "\n",
       "     PRIOR_LOCAL_RECURRENCE     SEX MENOPAUSAL_STATUS_AT_DIAGNOSIS LATERALITY  \\\n",
       "0                        No  Female                            Pre       Left   \n",
       "1                        No  Female                            Pre      Right   \n",
       "2                        No  Female                            Pre      Right   \n",
       "3                        No  Female                            Pre       Left   \n",
       "4                        No  Female                            Pre       Left   \n",
       "...                     ...     ...                            ...        ...   \n",
       "1751                     No  Female                           Peri      Right   \n",
       "1752                     No  Female                           Peri       Left   \n",
       "1753                     No  Female                           Post      Right   \n",
       "1754                     No  Female                           Post      Right   \n",
       "1755                     No  Female                           Post      Right   \n",
       "\n",
       "     T_STAGE N_STAGE M_STAGE  ... OVERALL_HR_STATUS_PATIENT  \\\n",
       "0        T1c     N3a      M1  ...                  Positive   \n",
       "1         T2      N0      M0  ...                  Negative   \n",
       "2        T1b    N1mi      M0  ...                  Positive   \n",
       "3        T1b      N0      M0  ...                  Positive   \n",
       "4         TX      NX      M1  ...                  Positive   \n",
       "...      ...     ...     ...  ...                       ...   \n",
       "1751     T1c      N0      M0  ...                  Positive   \n",
       "1752     T1b      N0      M0  ...                  Positive   \n",
       "1753     T1b      N0      M0  ...                  Positive   \n",
       "1754      T2     N1a      M0  ...                  Positive   \n",
       "1755      T2    N1mi      M0  ...                  Positive   \n",
       "\n",
       "     OVERALL_HER2_STATUS_PATIENT HER2_STATUS_PRIMARY VITAL_STATUS  \\\n",
       "0                       Negative            Negative        Alive   \n",
       "1                       Negative            Negative        Alive   \n",
       "2                       Negative            Negative     Deceased   \n",
       "3                       Positive            Positive     Deceased   \n",
       "4                       Negative            Negative     Deceased   \n",
       "...                          ...                 ...          ...   \n",
       "1751                    Negative            Negative        Alive   \n",
       "1752                    Negative            Negative        Alive   \n",
       "1753                    Negative            Negative        Alive   \n",
       "1754                    Negative            Negative        Alive   \n",
       "1755                    Negative            Negative        Alive   \n",
       "\n",
       "     LAST_CONTACT_DAYS_TO  TIME_TO_DEATH_MONTHS  DFS_MONTHS  DFS_EVENT  \\\n",
       "0                   14484                   NaN         1.1          1   \n",
       "1                   22336                   NaN       218.0          0   \n",
       "2                   16656                 548.0        68.9          1   \n",
       "3                   19364                 637.0        90.2          1   \n",
       "4                   15871                 522.0         0.5          1   \n",
       "...                   ...                   ...         ...        ...   \n",
       "1751                21723                   NaN         2.9          0   \n",
       "1752                17610                   NaN         0.7          0   \n",
       "1753                25027                   NaN         1.6          0   \n",
       "1754                23381                   NaN         4.3          0   \n",
       "1755                24691                   NaN         4.5          0   \n",
       "\n",
       "      OS_MONTHS   OS_STATUS  \n",
       "0          31.5    0:LIVING  \n",
       "1         218.0    0:LIVING  \n",
       "2          98.0  1:DECEASED  \n",
       "3         123.1  1:DECEASED  \n",
       "4          63.6  1:DECEASED  \n",
       "...         ...         ...  \n",
       "1751        2.9    0:LIVING  \n",
       "1752        0.7    0:LIVING  \n",
       "1753        1.6    0:LIVING  \n",
       "1754        4.3    0:LIVING  \n",
       "1755        4.5    0:LIVING  \n",
       "\n",
       "[1756 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_data = df_clinical_patients[df_clinical_patients['PATIENT_ID'].isin(patients_sequenced)]\n",
    "clinical_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95985eb-1aac-4955-9a3c-68821c961831",
   "metadata": {},
   "source": [
    "### Filter by Outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d02661a5-f28a-4e55-870f-f8039660a095",
   "metadata": {},
   "outputs": [],
   "source": [
    "alive = df_clinical_patients[df_clinical_patients['OS_STATUS']==\"0:LIVING\"]\n",
    "deceased = df_clinical_patients[df_clinical_patients['OS_STATUS']==\"1:DECEASED\"]\n",
    "\n",
    "alive_patients = list(alive[\"PATIENT_ID\"])\n",
    "deceased_patients = list(deceased[\"PATIENT_ID\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ec0ae21-3e47-4520-892c-408d75e33a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
       "      <th>Tumor_Sample_Barcode</th>\n",
       "      <th>Fusion</th>\n",
       "      <th>DNA_support</th>\n",
       "      <th>RNA_support</th>\n",
       "      <th>Method</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BCAS3</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002841-T02-IM6</td>\n",
       "      <td>BRIP1-BCAS3 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>BRIP1 (NM_032043) - BCAS3 (NM_001099432) rearr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRIP1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002841-T02-IM6</td>\n",
       "      <td>BRIP1-BCAS3 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>BRIP1 (NM_032043) - BCAS3 (NM_001099432) rearr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ERMP1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002088-T02-IM5</td>\n",
       "      <td>TGFBR1-ERMP1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>TGFBR1 (NM_004612) Rearrangement : chr9:g.5801...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TGFBR1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002088-T02-IM5</td>\n",
       "      <td>TGFBR1-ERMP1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>TGFBR1 (NM_004612) Rearrangement : chr9:g.5801...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DNAH9</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0014576-T01-IM6</td>\n",
       "      <td>MAP2K4-DNAH9 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>DNAH9 (NM_001372) - MAP2K4 (NM_003010) rearran...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>PTPRS</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005613-T01-IM5</td>\n",
       "      <td>UNC5D-PTPRS fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>PTPRS (NM_002850) - UNC5D (NM_080872) rearrang...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>UNC5D</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005613-T01-IM5</td>\n",
       "      <td>UNC5D-PTPRS fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>PTPRS (NM_002850) - UNC5D (NM_080872) rearrang...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>NOTCH3</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0001308-T01-IM3</td>\n",
       "      <td>NOTCH3-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>IGF1R</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005818-T01-IM5</td>\n",
       "      <td>IGF1R-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>IGF1R (NM_000875)  Rearrangement:  c.640+53564...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>EPHA3</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005818-T01-IM5</td>\n",
       "      <td>EPHA3-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>EPHA3 (NM_005233) Rearrangement :  c.267_996du...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>543 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "0         BCAS3               0  MSKCC-DMP    P-0002841-T02-IM6   \n",
       "1         BRIP1               0  MSKCC-DMP    P-0002841-T02-IM6   \n",
       "2         ERMP1               0  MSKCC-DMP    P-0002088-T02-IM5   \n",
       "3        TGFBR1               0  MSKCC-DMP    P-0002088-T02-IM5   \n",
       "4         DNAH9               0  MSKCC-DMP    P-0014576-T01-IM6   \n",
       "..          ...             ...        ...                  ...   \n",
       "538       PTPRS               0  MSKCC-DMP    P-0005613-T01-IM5   \n",
       "539       UNC5D               0  MSKCC-DMP    P-0005613-T01-IM5   \n",
       "540      NOTCH3               0  MSKCC-DMP    P-0001308-T01-IM3   \n",
       "541       IGF1R               0  MSKCC-DMP    P-0005818-T01-IM5   \n",
       "542       EPHA3               0  MSKCC-DMP    P-0005818-T01-IM5   \n",
       "\n",
       "                  Fusion DNA_support RNA_support  Method    Frame  \\\n",
       "0     BRIP1-BCAS3 fusion         yes     unknown     NaN  unknown   \n",
       "1     BRIP1-BCAS3 fusion         yes     unknown     NaN  unknown   \n",
       "2    TGFBR1-ERMP1 fusion         yes     unknown     NaN  unknown   \n",
       "3    TGFBR1-ERMP1 fusion         yes     unknown     NaN  unknown   \n",
       "4    MAP2K4-DNAH9 fusion         yes     unknown     NaN  unknown   \n",
       "..                   ...         ...         ...     ...      ...   \n",
       "538   UNC5D-PTPRS fusion         yes     unknown     NaN  unknown   \n",
       "539   UNC5D-PTPRS fusion         yes     unknown     NaN  unknown   \n",
       "540    NOTCH3-intragenic         yes     unknown     NaN  unknown   \n",
       "541     IGF1R-intragenic         yes     unknown     NaN  unknown   \n",
       "542     EPHA3-intragenic         yes     unknown     NaN  unknown   \n",
       "\n",
       "                                              Comments  \n",
       "0    BRIP1 (NM_032043) - BCAS3 (NM_001099432) rearr...  \n",
       "1    BRIP1 (NM_032043) - BCAS3 (NM_001099432) rearr...  \n",
       "2    TGFBR1 (NM_004612) Rearrangement : chr9:g.5801...  \n",
       "3    TGFBR1 (NM_004612) Rearrangement : chr9:g.5801...  \n",
       "4    DNAH9 (NM_001372) - MAP2K4 (NM_003010) rearran...  \n",
       "..                                                 ...  \n",
       "538  PTPRS (NM_002850) - UNC5D (NM_080872) rearrang...  \n",
       "539  PTPRS (NM_002850) - UNC5D (NM_080872) rearrang...  \n",
       "540                                              None   \n",
       "541  IGF1R (NM_000875)  Rearrangement:  c.640+53564...  \n",
       "542  EPHA3 (NM_005233) Rearrangement :  c.267_996du...  \n",
       "\n",
       "[543 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_data_gene_sequences = df_fusions[df_fusions['Tumor_Sample_Barcode'].isin(sampled_sequences)]\n",
    "clinical_data_gene_sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6214abf-04ee-4d11-8b63-4eaf2cf676b5",
   "metadata": {},
   "source": [
    "### Gene Query Finder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4da5ca02-3fb6-4c5f-97fe-792aff749afa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_by_gene(dataframe_genes, hugo_symbol):\n",
    "    return dataframe_genes[dataframe_genes['Hugo_Symbol']==hugo_symbol]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14166f5e-585b-4ef2-a8ee-418660564b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_patients(dataframe):\n",
    "    temp_dataframe = dataframe.copy()\n",
    "    label = lambda row:  \"-\".join(row[\"Tumor_Sample_Barcode\"].split(\"-\")[:2])\n",
    "    temp_dataframe['PATIENT_ID'] = dataframe.apply(label, axis=\"columns\") #Apply Label\n",
    "    return temp_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8fe56dcc-bf42-42ff-bfb0-95254b288d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
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       "      <th>Method</th>\n",
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       "      <th>PATIENT_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>IGF1R</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0010464-T01-IM5</td>\n",
       "      <td>PPP1R3A-IGF1R fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>IGF1R (NM_000875) rearrangement: t(7;15)(q31.1...</td>\n",
       "      <td>P-0010464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>IGF1R</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002435-T01-IM3</td>\n",
       "      <td>ACBD6-IGF1R fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P-0002435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>IGF1R</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003779-T01-IM5</td>\n",
       "      <td>IGF1R-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>IGF1R (NM_000875) rearrangement: c.954-1844_10...</td>\n",
       "      <td>P-0003779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>IGF1R</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005818-T01-IM5</td>\n",
       "      <td>IGF1R-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>IGF1R (NM_000875)  Rearrangement:  c.640+53564...</td>\n",
       "      <td>P-0005818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "40        IGF1R               0  MSKCC-DMP    P-0010464-T01-IM5   \n",
       "268       IGF1R               0  MSKCC-DMP    P-0002435-T01-IM3   \n",
       "530       IGF1R               0  MSKCC-DMP    P-0003779-T01-IM5   \n",
       "541       IGF1R               0  MSKCC-DMP    P-0005818-T01-IM5   \n",
       "\n",
       "                   Fusion DNA_support RNA_support  Method    Frame  \\\n",
       "40   PPP1R3A-IGF1R fusion         yes     unknown     NaN  unknown   \n",
       "268    ACBD6-IGF1R fusion         yes     unknown     NaN  unknown   \n",
       "530      IGF1R-intragenic         yes     unknown     NaN  unknown   \n",
       "541      IGF1R-intragenic         yes     unknown     NaN  unknown   \n",
       "\n",
       "                                              Comments PATIENT_ID  \n",
       "40   IGF1R (NM_000875) rearrangement: t(7;15)(q31.1...  P-0010464  \n",
       "268                                                NaN  P-0002435  \n",
       "530  IGF1R (NM_000875) rearrangement: c.954-1844_10...  P-0003779  \n",
       "541  IGF1R (NM_000875)  Rearrangement:  c.640+53564...  P-0005818  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_data_with_patients = add_patients(clinical_data_gene_sequences)\n",
    "query_by_gene(clinical_data_with_patients,\"IGF1R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "45dd2ec7-1192-43d7-9ddd-46d03ccfaa60",
   "metadata": {},
   "outputs": [],
   "source": [
    "alive_patients_genes = clinical_data_with_patients[clinical_data_with_patients['PATIENT_ID'].isin(alive_patients)]\n",
    "deceased_patients_genes = clinical_data_with_patients[clinical_data_with_patients['PATIENT_ID'].isin(deceased_patients)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5a77758a-2d30-42f3-8b62-4cfba2afc6f0",
   "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>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
       "      <th>Tumor_Sample_Barcode</th>\n",
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       "      <th>PATIENT_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BCAS3</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002841-T02-IM6</td>\n",
       "      <td>BRIP1-BCAS3 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>BRIP1 (NM_032043) - BCAS3 (NM_001099432) rearr...</td>\n",
       "      <td>P-0002841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRIP1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002841-T02-IM6</td>\n",
       "      <td>BRIP1-BCAS3 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>BRIP1 (NM_032043) - BCAS3 (NM_001099432) rearr...</td>\n",
       "      <td>P-0002841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ERMP1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002088-T02-IM5</td>\n",
       "      <td>TGFBR1-ERMP1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>TGFBR1 (NM_004612) Rearrangement : chr9:g.5801...</td>\n",
       "      <td>P-0002088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TGFBR1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002088-T02-IM5</td>\n",
       "      <td>TGFBR1-ERMP1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>TGFBR1 (NM_004612) Rearrangement : chr9:g.5801...</td>\n",
       "      <td>P-0002088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DNAH9</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0014576-T01-IM6</td>\n",
       "      <td>MAP2K4-DNAH9 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>DNAH9 (NM_001372) - MAP2K4 (NM_003010) rearran...</td>\n",
       "      <td>P-0014576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>APPBP2</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0006137-T01-IM5</td>\n",
       "      <td>APPBP2-RARA fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RARA (NM_000964) - APPBP2 (NM_0063) rearrangem...</td>\n",
       "      <td>P-0006137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>EPAS1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0014435-T02-IM6</td>\n",
       "      <td>EPAS1-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>EPAS1 (NM_001430) rearrangement: c.2045+133_c....</td>\n",
       "      <td>P-0014435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>NOTCH3</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0001308-T01-IM3</td>\n",
       "      <td>NOTCH3-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>None</td>\n",
       "      <td>P-0001308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>IGF1R</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005818-T01-IM5</td>\n",
       "      <td>IGF1R-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>IGF1R (NM_000875)  Rearrangement:  c.640+53564...</td>\n",
       "      <td>P-0005818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>EPHA3</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005818-T01-IM5</td>\n",
       "      <td>EPHA3-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>EPHA3 (NM_005233) Rearrangement :  c.267_996du...</td>\n",
       "      <td>P-0005818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>415 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "0         BCAS3               0  MSKCC-DMP    P-0002841-T02-IM6   \n",
       "1         BRIP1               0  MSKCC-DMP    P-0002841-T02-IM6   \n",
       "2         ERMP1               0  MSKCC-DMP    P-0002088-T02-IM5   \n",
       "3        TGFBR1               0  MSKCC-DMP    P-0002088-T02-IM5   \n",
       "4         DNAH9               0  MSKCC-DMP    P-0014576-T01-IM6   \n",
       "..          ...             ...        ...                  ...   \n",
       "534      APPBP2               0  MSKCC-DMP    P-0006137-T01-IM5   \n",
       "535       EPAS1               0  MSKCC-DMP    P-0014435-T02-IM6   \n",
       "540      NOTCH3               0  MSKCC-DMP    P-0001308-T01-IM3   \n",
       "541       IGF1R               0  MSKCC-DMP    P-0005818-T01-IM5   \n",
       "542       EPHA3               0  MSKCC-DMP    P-0005818-T01-IM5   \n",
       "\n",
       "                  Fusion DNA_support RNA_support  Method    Frame  \\\n",
       "0     BRIP1-BCAS3 fusion         yes     unknown     NaN  unknown   \n",
       "1     BRIP1-BCAS3 fusion         yes     unknown     NaN  unknown   \n",
       "2    TGFBR1-ERMP1 fusion         yes     unknown     NaN  unknown   \n",
       "3    TGFBR1-ERMP1 fusion         yes     unknown     NaN  unknown   \n",
       "4    MAP2K4-DNAH9 fusion         yes     unknown     NaN  unknown   \n",
       "..                   ...         ...         ...     ...      ...   \n",
       "534   APPBP2-RARA fusion         yes     unknown     NaN  unknown   \n",
       "535     EPAS1-intragenic         yes     unknown     NaN  unknown   \n",
       "540    NOTCH3-intragenic         yes     unknown     NaN  unknown   \n",
       "541     IGF1R-intragenic         yes     unknown     NaN  unknown   \n",
       "542     EPHA3-intragenic         yes     unknown     NaN  unknown   \n",
       "\n",
       "                                              Comments PATIENT_ID  \n",
       "0    BRIP1 (NM_032043) - BCAS3 (NM_001099432) rearr...  P-0002841  \n",
       "1    BRIP1 (NM_032043) - BCAS3 (NM_001099432) rearr...  P-0002841  \n",
       "2    TGFBR1 (NM_004612) Rearrangement : chr9:g.5801...  P-0002088  \n",
       "3    TGFBR1 (NM_004612) Rearrangement : chr9:g.5801...  P-0002088  \n",
       "4    DNAH9 (NM_001372) - MAP2K4 (NM_003010) rearran...  P-0014576  \n",
       "..                                                 ...        ...  \n",
       "534  RARA (NM_000964) - APPBP2 (NM_0063) rearrangem...  P-0006137  \n",
       "535  EPAS1 (NM_001430) rearrangement: c.2045+133_c....  P-0014435  \n",
       "540                                              None   P-0001308  \n",
       "541  IGF1R (NM_000875)  Rearrangement:  c.640+53564...  P-0005818  \n",
       "542  EPHA3 (NM_005233) Rearrangement :  c.267_996du...  P-0005818  \n",
       "\n",
       "[415 rows x 11 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alive_patients_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "75e8f2bb-3d66-49be-8f41-118a6f6d0faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RARA</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0011365-T01-IM5</td>\n",
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       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RARA (NM_000964) - COPZ2 (NM_016429) rearrange...</td>\n",
       "      <td>P-0011365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>COPZ2</td>\n",
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       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0011365-T01-IM5</td>\n",
       "      <td>COPZ2-RARA fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RARA (NM_000964) - COPZ2 (NM_016429) rearrange...</td>\n",
       "      <td>P-0011365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>MYC</td>\n",
       "      <td>0</td>\n",
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       "      <td>P-0012855-T01-IM5</td>\n",
       "      <td>MYC-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>MYC (NM_002467) rearrangement: c.1105_c.*1969d...</td>\n",
       "      <td>P-0012855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>ARID5B</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0013625-T01-IM5</td>\n",
       "      <td>ARID5B-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>ARID5B (NM_032199) rearrangement: c.734-289_c....</td>\n",
       "      <td>P-0013625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CTNNB1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0013625-T01-IM5</td>\n",
       "      <td>CTNNB1-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>CTNNB1 (NM_001904) rearrangement: c.-48-2076_c...</td>\n",
       "      <td>P-0013625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>C1orf87</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0002705-T01-IM3</td>\n",
       "      <td>C1orf87-ERBB2 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>null Note: The ERBB2-C1orf87 reciprocal transl...</td>\n",
       "      <td>P-0002705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>PTPRS</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005613-T01-IM5</td>\n",
       "      <td>ACER1-PTPRS fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>PTRPRS (NM_002850) - ACER1 (NM_133492) rearran...</td>\n",
       "      <td>P-0005613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>ACER1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005613-T01-IM5</td>\n",
       "      <td>ACER1-PTPRS fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>PTRPRS (NM_002850) - ACER1 (NM_133492) rearran...</td>\n",
       "      <td>P-0005613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>PTPRS</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005613-T01-IM5</td>\n",
       "      <td>UNC5D-PTPRS fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>PTPRS (NM_002850) - UNC5D (NM_080872) rearrang...</td>\n",
       "      <td>P-0005613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>UNC5D</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0005613-T01-IM5</td>\n",
       "      <td>UNC5D-PTPRS fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>PTPRS (NM_002850) - UNC5D (NM_080872) rearrang...</td>\n",
       "      <td>P-0005613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "24         RARA               0  MSKCC-DMP    P-0011365-T01-IM5   \n",
       "25        COPZ2               0  MSKCC-DMP    P-0011365-T01-IM5   \n",
       "38          MYC               0  MSKCC-DMP    P-0012855-T01-IM5   \n",
       "62       ARID5B               0  MSKCC-DMP    P-0013625-T01-IM5   \n",
       "63       CTNNB1               0  MSKCC-DMP    P-0013625-T01-IM5   \n",
       "..          ...             ...        ...                  ...   \n",
       "532     C1orf87               0  MSKCC-DMP    P-0002705-T01-IM3   \n",
       "536       PTPRS               0  MSKCC-DMP    P-0005613-T01-IM5   \n",
       "537       ACER1               0  MSKCC-DMP    P-0005613-T01-IM5   \n",
       "538       PTPRS               0  MSKCC-DMP    P-0005613-T01-IM5   \n",
       "539       UNC5D               0  MSKCC-DMP    P-0005613-T01-IM5   \n",
       "\n",
       "                   Fusion DNA_support RNA_support  Method    Frame  \\\n",
       "24      COPZ2-RARA fusion         yes     unknown     NaN  unknown   \n",
       "25      COPZ2-RARA fusion         yes     unknown     NaN  unknown   \n",
       "38         MYC-intragenic         yes     unknown     NaN  unknown   \n",
       "62      ARID5B-intragenic         yes     unknown     NaN  unknown   \n",
       "63      CTNNB1-intragenic         yes     unknown     NaN  unknown   \n",
       "..                    ...         ...         ...     ...      ...   \n",
       "532  C1orf87-ERBB2 fusion         yes     unknown     NaN  unknown   \n",
       "536    ACER1-PTPRS fusion         yes     unknown     NaN  unknown   \n",
       "537    ACER1-PTPRS fusion         yes     unknown     NaN  unknown   \n",
       "538    UNC5D-PTPRS fusion         yes     unknown     NaN  unknown   \n",
       "539    UNC5D-PTPRS fusion         yes     unknown     NaN  unknown   \n",
       "\n",
       "                                              Comments PATIENT_ID  \n",
       "24   RARA (NM_000964) - COPZ2 (NM_016429) rearrange...  P-0011365  \n",
       "25   RARA (NM_000964) - COPZ2 (NM_016429) rearrange...  P-0011365  \n",
       "38   MYC (NM_002467) rearrangement: c.1105_c.*1969d...  P-0012855  \n",
       "62   ARID5B (NM_032199) rearrangement: c.734-289_c....  P-0013625  \n",
       "63   CTNNB1 (NM_001904) rearrangement: c.-48-2076_c...  P-0013625  \n",
       "..                                                 ...        ...  \n",
       "532  null Note: The ERBB2-C1orf87 reciprocal transl...  P-0002705  \n",
       "536  PTRPRS (NM_002850) - ACER1 (NM_133492) rearran...  P-0005613  \n",
       "537  PTRPRS (NM_002850) - ACER1 (NM_133492) rearran...  P-0005613  \n",
       "538  PTPRS (NM_002850) - UNC5D (NM_080872) rearrang...  P-0005613  \n",
       "539  PTPRS (NM_002850) - UNC5D (NM_080872) rearrang...  P-0005613  \n",
       "\n",
       "[128 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deceased_patients_genes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a4c6ce-1eba-4e29-b764-f4a863976e2f",
   "metadata": {},
   "source": [
    "### Discovering at risk genes\n",
    "We use Set Theory to uncover Genes present in deceased patients not present in patients that survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd834f24-69bf-4479-9eef-3d59e8d4b9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C2orf67',\n",
       " 'AATF',\n",
       " 'KLHL13',\n",
       " 'C1orf87',\n",
       " 'AGBL1',\n",
       " 'KIAA1024',\n",
       " 'NOVA1',\n",
       " 'NUB1',\n",
       " 'PITPNC1',\n",
       " 'ACER1',\n",
       " 'FGFR1',\n",
       " 'ERCC2',\n",
       " 'SUFU',\n",
       " 'DIP2B',\n",
       " 'BET1',\n",
       " 'HMHA1',\n",
       " 'FGFR3',\n",
       " 'MIR1281',\n",
       " 'PDE1A',\n",
       " 'CTNNB1',\n",
       " 'HIST1H2BD',\n",
       " 'DNMT3B',\n",
       " 'TRIM8',\n",
       " 'WFDC3',\n",
       " 'BRAF',\n",
       " 'HIST1H2BC',\n",
       " 'DDR1',\n",
       " 'C6orf10',\n",
       " 'TGFB2',\n",
       " 'RNF43',\n",
       " 'AGK',\n",
       " 'ST7',\n",
       " 'SLC25A14',\n",
       " 'RIPK4',\n",
       " 'PRB2',\n",
       " 'CUL5',\n",
       " 'STK11',\n",
       " 'ARID5B',\n",
       " 'E2F3',\n",
       " 'MACROD2',\n",
       " 'ADCY9',\n",
       " 'MET',\n",
       " 'UNC5D',\n",
       " 'TIMP2',\n",
       " 'HCK',\n",
       " 'HIST1H4B',\n",
       " 'RGS9',\n",
       " 'ITPR2',\n",
       " 'HIST1H3B',\n",
       " 'AKAP8',\n",
       " 'WHSC1',\n",
       " 'ZFP64',\n",
       " 'DPP6',\n",
       " 'ZFP90',\n",
       " 'COPZ2',\n",
       " 'TET1',\n",
       " 'HEATR2']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_genes = list(set(clinical_data_with_patients[\"Hugo_Symbol\"]))\n",
    "len(all_genes)\n",
    "alive_genes = list(set(alive_patients_genes[\"Hugo_Symbol\"]))\n",
    "deceased_genes = list(set(deceased_patients_genes[\"Hugo_Symbol\"]))\n",
    "at_risk_genes = list(set(all_genes) - set(alive_genes))\n",
    "at_risk_genes\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0667a6e-3cd1-4184-a31a-ae64ff743824",
   "metadata": {},
   "source": [
    "### Example Gene (RIPK4)\n",
    ">Receptor-interacting serine/threonine-protein kinase 4 is an enzyme that in humans is encoded by the RIPK4 gene.[5][6]\n",
    "\n",
    ">The protein encoded by this gene is a serine/threonine protein kinase that interacts with protein kinase C-delta. The encoded protein can also activate NFkappaB and is required for keratinocyte differentiation. This kinase undergoes autophosphorylation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c0bf667a-bd1d-48a5-93d0-990b06b72883",
   "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>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
       "      <th>Tumor_Sample_Barcode</th>\n",
       "      <th>Fusion</th>\n",
       "      <th>DNA_support</th>\n",
       "      <th>RNA_support</th>\n",
       "      <th>Method</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Comments</th>\n",
       "      <th>PATIENT_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>RIPK4</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0000224-T01-IM3</td>\n",
       "      <td>RIPK4-TMPRSS2 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in frame</td>\n",
       "      <td>TMPRSS2 (NM_001135099) - RIPK4 (NM_012309) 297...</td>\n",
       "      <td>P-0000224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "304       RIPK4               0  MSKCC-DMP    P-0000224-T01-IM3   \n",
       "\n",
       "                   Fusion DNA_support RNA_support  Method     Frame  \\\n",
       "304  RIPK4-TMPRSS2 fusion         yes     unknown     NaN  in frame   \n",
       "\n",
       "                                              Comments PATIENT_ID  \n",
       "304  TMPRSS2 (NM_001135099) - RIPK4 (NM_012309) 297...  P-0000224  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_by_gene(clinical_data_with_patients,\"RIPK4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417b13a4-2d82-4f0c-bdef-8f3bf8228e59",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "Only one patient, could be a fringe case without further medical research. Let's see if we can get a count of each at risk gene and see what's most prevalent. The data itself isolates rows based on Fusions. Let's ignore that for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a39a28ed-8efe-4add-8dae-18047aaa90d7",
   "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>Hugo_Symbol</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>STK11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ADCY9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C2orf67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hugo_Symbol  Count\n",
       "36       STK11      3\n",
       "40       ADCY9      2\n",
       "0      C2orf67      1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_count = []\n",
    "template_dict = {\"Hugo_Symbol\":\"\",\"Count\":\"\"}\n",
    "for gene in at_risk_genes:\n",
    "    temp_dict = template_dict.copy()\n",
    "    sample = query_by_gene(clinical_data_with_patients,gene)\n",
    "    temp_dict[\"Hugo_Symbol\"] = gene\n",
    "    temp_dict[\"Count\"] = len(sample)\n",
    "    gene_count.append(temp_dict)\n",
    "    \n",
    "count_frame = pd.DataFrame(data = gene_count)\n",
    "count_frame.sort_values(by='Count', ascending=False)[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae9da73-1cb1-4935-ad94-8fb7402d9ee4",
   "metadata": {},
   "source": [
    "### Ignoring Different Fusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc326f5e-7670-4c59-a329-710ba78790ab",
   "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>Hugo_Symbol</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>STK11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C2orf67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RNF43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hugo_Symbol  Count\n",
       "36       STK11      2\n",
       "0      C2orf67      1\n",
       "29       RNF43      1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_count = []\n",
    "for gene in at_risk_genes:\n",
    "    temp_dict = template_dict.copy()\n",
    "    sample = query_by_gene(clinical_data_with_patients,gene)\n",
    "    temp_dict[\"Hugo_Symbol\"] = gene\n",
    "    temp_dict[\"Count\"] = len(set(sample[\"PATIENT_ID\"]))\n",
    "    gene_count.append(temp_dict)\n",
    "    \n",
    "count_frame = pd.DataFrame(data = gene_count)\n",
    "count_frame.sort_values(by='Count', ascending=False)[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12110095-c110-44ac-b3e9-bac381f5ae28",
   "metadata": {},
   "source": [
    "### STK11 \n",
    "\n",
    "Quick research finds this gene to be high risk for cancer\n",
    "https://www.facingourrisk.org/info/hereditary-cancer-and-genetic-testing/hereditary-cancer-genes-and-risk/genes-by-name/stk11/cancer-risk\n",
    ">If you have tested positive for a STK11 mutation, we recommend consulting with a genetics expert who can assess your personal and family history of cancer, and can help you determine the best risk management plan.\n",
    "\n",
    ">People with an inherited STK11 have a greatly increased risk of developing several types of cancer.\n",
    "\n",
    "\n",
    ">Risks for women\n",
    "Women who have an inherited mutation in STK11 have an increased lifetime risk for these cancers:\n",
    "\n",
    ">Breast cancer: The lifetime risk for a women with a STK11 mutation is about 40 - 60 percent compared to 12.5 percent for an average risk woman. \n",
    "Ovarian cancer: Women with a STK11 mutation have an increased risk for a rare type of ovarian cancer called a Sertoli cell tumor.  \n",
    "Endometrial cancer: The lifetime risk for a women with a STK11 mutation is about 10 percent compared to 3 percent for an average risk woman. \n",
    "Cervical cancer: The lifetime risk for a women with a STK11 mutation is about 10 percent compared to less than 1 percent for an average risk woman. \n",
    "\n",
    "\n",
    "https://www.nature.com/articles/6601030#:~:text=In%20carriers%20of%20LKB1%2FSTK11%20mutations%2C%20the%20risk%20of,elevated%20risks%20of%20both%20gastrointestinal%20and%20breast%20cancer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e3a7203-7080-4ab7-aad6-d5e7cb2ee3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
       "      <th>Tumor_Sample_Barcode</th>\n",
       "      <th>Fusion</th>\n",
       "      <th>DNA_support</th>\n",
       "      <th>RNA_support</th>\n",
       "      <th>Method</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Comments</th>\n",
       "      <th>PATIENT_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>STK11</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0007518-T01-IM5</td>\n",
       "      <td>STK11-SBNO2 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>SBNO2 (NM_014963) - STK11 (NM_000455)  Rearran...</td>\n",
       "      <td>P-0007518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>STK11</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0007518-T01-IM5</td>\n",
       "      <td>STK11-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>STK11 (NM_000455) Rearrangement : c.*16+129_75...</td>\n",
       "      <td>P-0007518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>STK11</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003108-T01-IM5</td>\n",
       "      <td>STK11-HMHA1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>null Note: The HMHA1 (NM_012292) - STK11 (NM_0...</td>\n",
       "      <td>P-0003108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "208       STK11               0  MSKCC-DMP    P-0007518-T01-IM5   \n",
       "209       STK11               0  MSKCC-DMP    P-0007518-T01-IM5   \n",
       "509       STK11               0  MSKCC-DMP    P-0003108-T01-IM5   \n",
       "\n",
       "                 Fusion DNA_support RNA_support  Method    Frame  \\\n",
       "208  STK11-SBNO2 fusion         yes     unknown     NaN  unknown   \n",
       "209    STK11-intragenic         yes     unknown     NaN  unknown   \n",
       "509  STK11-HMHA1 fusion         yes     unknown     NaN  unknown   \n",
       "\n",
       "                                              Comments PATIENT_ID  \n",
       "208  SBNO2 (NM_014963) - STK11 (NM_000455)  Rearran...  P-0007518  \n",
       "209  STK11 (NM_000455) Rearrangement : c.*16+129_75...  P-0007518  \n",
       "509  null Note: The HMHA1 (NM_012292) - STK11 (NM_0...  P-0003108  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_query = query_by_gene(clinical_data_with_patients,\"STK11\")\n",
    "patients_stk11 = list(patient_query[\"PATIENT_ID\"])\n",
    "patient_query"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e7e724-5ff8-4a89-8647-d0c9bbf20d2d",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "53b26ddc-1f68-4ebe-80dd-90d35633573a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
       "      <th>Tumor_Sample_Barcode</th>\n",
       "      <th>Fusion</th>\n",
       "      <th>DNA_support</th>\n",
       "      <th>RNA_support</th>\n",
       "      <th>Method</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Comments</th>\n",
       "      <th>PATIENT_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>SBNO2</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0007518-T01-IM5</td>\n",
       "      <td>STK11-SBNO2 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>SBNO2 (NM_014963) - STK11 (NM_000455)  Rearran...</td>\n",
       "      <td>P-0007518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>STK11</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0007518-T01-IM5</td>\n",
       "      <td>STK11-SBNO2 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>SBNO2 (NM_014963) - STK11 (NM_000455)  Rearran...</td>\n",
       "      <td>P-0007518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>STK11</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0007518-T01-IM5</td>\n",
       "      <td>STK11-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>STK11 (NM_000455) Rearrangement : c.*16+129_75...</td>\n",
       "      <td>P-0007518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>BRCA2</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003108-T01-IM5</td>\n",
       "      <td>BRCA2-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>null Note: The BRCA2 (NM_000059) intragenic re...</td>\n",
       "      <td>P-0003108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>HMHA1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003108-T01-IM5</td>\n",
       "      <td>STK11-HMHA1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>null Note: The HMHA1 (NM_012292) - STK11 (NM_0...</td>\n",
       "      <td>P-0003108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>STK11</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003108-T01-IM5</td>\n",
       "      <td>STK11-HMHA1 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>null Note: The HMHA1 (NM_012292) - STK11 (NM_0...</td>\n",
       "      <td>P-0003108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>AXL</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003108-T01-IM5</td>\n",
       "      <td>AXL-intragenic</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>null Note: The AXL (NM_021913) rearrangement e...</td>\n",
       "      <td>P-0003108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>KLHL13</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003108-T01-IM5</td>\n",
       "      <td>RB1-KLHL13 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>null Note: The RB1 (NM_000321) rearrangement e...</td>\n",
       "      <td>P-0003108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>RB1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003108-T01-IM5</td>\n",
       "      <td>RB1-KLHL13 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>null Note: The RB1 (NM_000321) rearrangement e...</td>\n",
       "      <td>P-0003108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "207       SBNO2               0  MSKCC-DMP    P-0007518-T01-IM5   \n",
       "208       STK11               0  MSKCC-DMP    P-0007518-T01-IM5   \n",
       "209       STK11               0  MSKCC-DMP    P-0007518-T01-IM5   \n",
       "507       BRCA2               0  MSKCC-DMP    P-0003108-T01-IM5   \n",
       "508       HMHA1               0  MSKCC-DMP    P-0003108-T01-IM5   \n",
       "509       STK11               0  MSKCC-DMP    P-0003108-T01-IM5   \n",
       "510         AXL               0  MSKCC-DMP    P-0003108-T01-IM5   \n",
       "511      KLHL13               0  MSKCC-DMP    P-0003108-T01-IM5   \n",
       "512         RB1               0  MSKCC-DMP    P-0003108-T01-IM5   \n",
       "\n",
       "                 Fusion DNA_support RNA_support  Method    Frame  \\\n",
       "207  STK11-SBNO2 fusion         yes     unknown     NaN  unknown   \n",
       "208  STK11-SBNO2 fusion         yes     unknown     NaN  unknown   \n",
       "209    STK11-intragenic         yes     unknown     NaN  unknown   \n",
       "507    BRCA2-intragenic         yes     unknown     NaN  unknown   \n",
       "508  STK11-HMHA1 fusion         yes     unknown     NaN  unknown   \n",
       "509  STK11-HMHA1 fusion         yes     unknown     NaN  unknown   \n",
       "510      AXL-intragenic         yes     unknown     NaN  unknown   \n",
       "511   RB1-KLHL13 fusion         yes     unknown     NaN  unknown   \n",
       "512   RB1-KLHL13 fusion         yes     unknown     NaN  unknown   \n",
       "\n",
       "                                              Comments PATIENT_ID  \n",
       "207  SBNO2 (NM_014963) - STK11 (NM_000455)  Rearran...  P-0007518  \n",
       "208  SBNO2 (NM_014963) - STK11 (NM_000455)  Rearran...  P-0007518  \n",
       "209  STK11 (NM_000455) Rearrangement : c.*16+129_75...  P-0007518  \n",
       "507  null Note: The BRCA2 (NM_000059) intragenic re...  P-0003108  \n",
       "508  null Note: The HMHA1 (NM_012292) - STK11 (NM_0...  P-0003108  \n",
       "509  null Note: The HMHA1 (NM_012292) - STK11 (NM_0...  P-0003108  \n",
       "510  null Note: The AXL (NM_021913) rearrangement e...  P-0003108  \n",
       "511  null Note: The RB1 (NM_000321) rearrangement e...  P-0003108  \n",
       "512  null Note: The RB1 (NM_000321) rearrangement e...  P-0003108  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_data_with_patients[clinical_data_with_patients['PATIENT_ID'].isin(patients_stk11)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d7c2b3fe-b51a-4d38-b42a-c0b723e71133",
   "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>PATIENT_ID</th>\n",
       "      <th>METASTATIC_DZ_FUP</th>\n",
       "      <th>METASTATIC_RECURRENCE_TIME_MONTHS</th>\n",
       "      <th>PRIOR_LOCAL_RECURRENCE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>MENOPAUSAL_STATUS_AT_DIAGNOSIS</th>\n",
       "      <th>LATERALITY</th>\n",
       "      <th>T_STAGE</th>\n",
       "      <th>N_STAGE</th>\n",
       "      <th>M_STAGE</th>\n",
       "      <th>...</th>\n",
       "      <th>OVERALL_HR_STATUS_PATIENT</th>\n",
       "      <th>OVERALL_HER2_STATUS_PATIENT</th>\n",
       "      <th>HER2_STATUS_PRIMARY</th>\n",
       "      <th>VITAL_STATUS</th>\n",
       "      <th>LAST_CONTACT_DAYS_TO</th>\n",
       "      <th>TIME_TO_DEATH_MONTHS</th>\n",
       "      <th>DFS_MONTHS</th>\n",
       "      <th>DFS_EVENT</th>\n",
       "      <th>OS_MONTHS</th>\n",
       "      <th>OS_STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>P-0003108</td>\n",
       "      <td>Yes</td>\n",
       "      <td>504.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Pre</td>\n",
       "      <td>Left</td>\n",
       "      <td>T1c</td>\n",
       "      <td>N0</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Deceased</td>\n",
       "      <td>15886</td>\n",
       "      <td>523.0</td>\n",
       "      <td>60.3</td>\n",
       "      <td>1</td>\n",
       "      <td>78.9</td>\n",
       "      <td>1:DECEASED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>P-0007518</td>\n",
       "      <td>Yes</td>\n",
       "      <td>843.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Post</td>\n",
       "      <td>Right</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1mi</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Deceased</td>\n",
       "      <td>25741</td>\n",
       "      <td>847.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>1:DECEASED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PATIENT_ID METASTATIC_DZ_FUP  METASTATIC_RECURRENCE_TIME_MONTHS  \\\n",
       "413  P-0003108               Yes                              504.0   \n",
       "873  P-0007518               Yes                              843.0   \n",
       "\n",
       "    PRIOR_LOCAL_RECURRENCE     SEX MENOPAUSAL_STATUS_AT_DIAGNOSIS LATERALITY  \\\n",
       "413                     No  Female                            Pre       Left   \n",
       "873                     No  Female                           Post      Right   \n",
       "\n",
       "    T_STAGE N_STAGE M_STAGE  ... OVERALL_HR_STATUS_PATIENT  \\\n",
       "413     T1c      N0      M0  ...                  Positive   \n",
       "873      T2    N1mi      M0  ...                  Positive   \n",
       "\n",
       "    OVERALL_HER2_STATUS_PATIENT HER2_STATUS_PRIMARY VITAL_STATUS  \\\n",
       "413                    Negative            Negative     Deceased   \n",
       "873                    Negative            Negative     Deceased   \n",
       "\n",
       "    LAST_CONTACT_DAYS_TO  TIME_TO_DEATH_MONTHS  DFS_MONTHS  DFS_EVENT  \\\n",
       "413                15886                 523.0        60.3          1   \n",
       "873                25741                 847.0        12.5          1   \n",
       "\n",
       "     OS_MONTHS   OS_STATUS  \n",
       "413       78.9  1:DECEASED  \n",
       "873       16.7  1:DECEASED  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clinical_patients[df_clinical_patients['PATIENT_ID'].isin(patients_stk11)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d0fa02-c94c-4d1c-bb15-2293cda22991",
   "metadata": {},
   "source": [
    "### ADCY9\n",
    ">The ADCY9 genetic variants are associated with glioma susceptibility and patient prognosis\n",
    "\n",
    "https://www.sciencedirect.com/science/article/abs/pii/S0888754320320656#:~:text=Studies%20have%20shown%20that%20ADCY9%20gene%20polymorphism%20is,the%20effect%20of%20ADCY9%20genetic%20variants%20on%20glioma.\n",
    "\n",
    "\n",
    ">After adjusting for age and gender, we found a significant relationship between heterozygous genotypes of rs2531995 and HCC risk (OR = 1.34, 95% CI = 1.01–1.77, p = 0.045). ADCY9 rs2230742 had a strong relationship with lower risk of HCC in allele (p = 0.006), co-dominant (p = 0.023), dominant (p = 0.010), and additive (p = 0.006) models. Stratified analysis showed that rs879620 increased HCC risk and rs2230742 was associated with lower risk of HCC in the individuals aged 55 or younger, rs2531992 significantly decreased HCC risk in the elder group (age > 55). For women, rs2230742 and rs2230741 were significantly associated with HCC risk in multiple models (p < 0.05). FDR analysis showed that rs2230742 could protect individuals from HCC risk in the allele model (FDR-p = 0.030). In addition, haplotype analysis indicated that Crs879620Ars2230742Ars2230741 haplotype was a protective factor for HCC (OR = 0.67, 95% CI = 0.50–0.89, p = 0.007, FDR-p = 0.028).\n",
    "https://www.frontiersin.org/articles/10.3389/fonc.2020.01450/full#:~:text=Background%3A%20Adenylyl%20cyclase%20type%209%20%28ADCY9%29%20modulates%20signal,ADCY9%20gene%20polymorphisms%20were%20associated%20with%20cancer%20development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "52777d93-27ab-4c4d-821e-f4b81cbc260a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
       "      <th>Tumor_Sample_Barcode</th>\n",
       "      <th>Fusion</th>\n",
       "      <th>DNA_support</th>\n",
       "      <th>RNA_support</th>\n",
       "      <th>Method</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Comments</th>\n",
       "      <th>PATIENT_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>ADCY9</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003762-T02-IM5</td>\n",
       "      <td>ADCY9-CDK12 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>ADCY9 (NM_001116) - CDK12 (NM_016507) rearrang...</td>\n",
       "      <td>P-0003762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>ADCY9</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC-DMP</td>\n",
       "      <td>P-0003762-T01-IM5</td>\n",
       "      <td>ADCY9-CDK12 fusion</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>t(16;17)(p13.3;q12)(chr16:g.4033430::chr17:g.3...</td>\n",
       "      <td>P-0003762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Entrez_Gene_Id     Center Tumor_Sample_Barcode  \\\n",
       "261       ADCY9               0  MSKCC-DMP    P-0003762-T02-IM5   \n",
       "504       ADCY9               0  MSKCC-DMP    P-0003762-T01-IM5   \n",
       "\n",
       "                 Fusion DNA_support RNA_support  Method    Frame  \\\n",
       "261  ADCY9-CDK12 fusion         yes     unknown     NaN  unknown   \n",
       "504  ADCY9-CDK12 fusion         yes     unknown     NaN  unknown   \n",
       "\n",
       "                                              Comments PATIENT_ID  \n",
       "261  ADCY9 (NM_001116) - CDK12 (NM_016507) rearrang...  P-0003762  \n",
       "504  t(16;17)(p13.3;q12)(chr16:g.4033430::chr17:g.3...  P-0003762  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_query_ADCY9 = query_by_gene(clinical_data_with_patients,\"ADCY9\")\n",
    "patients_ADCY9 = list(patient_query_ADCY9[\"PATIENT_ID\"])\n",
    "patient_query_ADCY9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "79c3fa52-9a86-421a-b236-0521dd0a5162",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PATIENT_ID</th>\n",
       "      <th>METASTATIC_DZ_FUP</th>\n",
       "      <th>METASTATIC_RECURRENCE_TIME_MONTHS</th>\n",
       "      <th>PRIOR_LOCAL_RECURRENCE</th>\n",
       "      <th>SEX</th>\n",
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       "      <th>LATERALITY</th>\n",
       "      <th>T_STAGE</th>\n",
       "      <th>N_STAGE</th>\n",
       "      <th>M_STAGE</th>\n",
       "      <th>...</th>\n",
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       "      <th>OVERALL_HER2_STATUS_PATIENT</th>\n",
       "      <th>HER2_STATUS_PRIMARY</th>\n",
       "      <th>VITAL_STATUS</th>\n",
       "      <th>LAST_CONTACT_DAYS_TO</th>\n",
       "      <th>TIME_TO_DEATH_MONTHS</th>\n",
       "      <th>DFS_MONTHS</th>\n",
       "      <th>DFS_EVENT</th>\n",
       "      <th>OS_MONTHS</th>\n",
       "      <th>OS_STATUS</th>\n",
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       "      <th>486</th>\n",
       "      <td>P-0003762</td>\n",
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       "      <td>549.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>Pre</td>\n",
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       "      <td>T3</td>\n",
       "      <td>N0</td>\n",
       "      <td>M0</td>\n",
       "      <td>...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Deceased</td>\n",
       "      <td>17339</td>\n",
       "      <td>570.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>1:DECEASED</td>\n",
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       "  </tbody>\n",
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       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PATIENT_ID METASTATIC_DZ_FUP  METASTATIC_RECURRENCE_TIME_MONTHS  \\\n",
       "486  P-0003762               Yes                              549.0   \n",
       "\n",
       "    PRIOR_LOCAL_RECURRENCE     SEX MENOPAUSAL_STATUS_AT_DIAGNOSIS LATERALITY  \\\n",
       "486                     No  Female                            Pre       Left   \n",
       "\n",
       "    T_STAGE N_STAGE M_STAGE  ... OVERALL_HR_STATUS_PATIENT  \\\n",
       "486      T3      N0      M0  ...                  Negative   \n",
       "\n",
       "    OVERALL_HER2_STATUS_PATIENT HER2_STATUS_PRIMARY VITAL_STATUS  \\\n",
       "486                    Negative            Negative     Deceased   \n",
       "\n",
       "    LAST_CONTACT_DAYS_TO  TIME_TO_DEATH_MONTHS  DFS_MONTHS  DFS_EVENT  \\\n",
       "486                17339                 570.0         6.0          1   \n",
       "\n",
       "     OS_MONTHS   OS_STATUS  \n",
       "486       27.4  1:DECEASED  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clinical_patients[df_clinical_patients['PATIENT_ID'].isin(patients_ADCY9)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eaafe47-592f-4d69-b124-ff47bb312a4f",
   "metadata": {},
   "source": [
    "## Genes found in surviving Pateints\n",
    "These genes were found only in surviving patients. With the introduction of immunotherapy techniques and Natural Killers, let's see if any are related."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1a488c5f-e3b4-406d-bfea-29317361d8b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABCA8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NUP93</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SFRS6</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>APPBP2</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>FLT1</td>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>ERG</td>\n",
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       "      <td>CSF3R</td>\n",
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       "    <tr>\n",
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       "      <td>ANKRD11</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>FZD10</td>\n",
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       "      <th>267</th>\n",
       "      <td>DLAT</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>268 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol  Count\n",
       "0         ABCA8      1\n",
       "1         NUP93      1\n",
       "2         SFRS6      1\n",
       "3        APPBP2      1\n",
       "4          FLT1      1\n",
       "..          ...    ...\n",
       "263         ERG      2\n",
       "264       CSF3R      1\n",
       "265     ANKRD11      2\n",
       "266       FZD10      1\n",
       "267        DLAT      1\n",
       "\n",
       "[268 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_genes = list(set(all_genes) - set(deceased_genes))\n",
    "\n",
    "positive_gene_count = []\n",
    "for gene in positive_genes:\n",
    "    temp_dict = template_dict.copy()\n",
    "    sample = query_by_gene(clinical_data_with_patients,gene)\n",
    "    temp_dict[\"Hugo_Symbol\"] = gene\n",
    "    temp_dict[\"Count\"] = len(set(sample[\"PATIENT_ID\"]))\n",
    "    positive_gene_count.append(temp_dict)\n",
    "    \n",
    "count_frame_positive = pd.DataFrame(data = positive_gene_count)\n",
    "count_frame_positive.sort_values(by='Count', ascending=False)[:3]\n",
    "count_frame_positive"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d8a191-1611-41c6-acbe-056eafd42b7f",
   "metadata": {},
   "source": [
    "### Machine Learning\n",
    "For this next section, we'll be implementing Random Forests to try and find gene interaction networks that can predict outcome. For this to work, we must aggregate based on genes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bfeb17e7-26b8-4dd1-916b-558dc949eb92",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mutation_present(dataframe_genes, patient_id, gene):\n",
    "    query_result = query_by_gene(dataframe_genes, gene)\n",
    "    result = len(query_result[query_result['PATIENT_ID']== patient_id]) > 0\n",
    "    return 0 if result == 0 else 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "747dcf81-bb96-4d21-b4c2-948a81ba4138",
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_gene_network = pd.DataFrame()\n",
    "patients_gene_network[\"PATIENT_ID\"] = df_clinical_patients[\"PATIENT_ID\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f243770a-5abc-4d3c-aa87-1a42d4fecbc4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fac80e18-2561-42f3-af05-8a61e9ce035e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       BCAS3\n",
       "1       BRIP1\n",
       "2       ERMP1\n",
       "3      TGFBR1\n",
       "4       DNAH9\n",
       "        ...  \n",
       "538     PTPRS\n",
       "539     UNC5D\n",
       "540    NOTCH3\n",
       "541     IGF1R\n",
       "542     EPHA3\n",
       "Name: Hugo_Symbol, Length: 543, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_data_with_patients[\"Hugo_Symbol\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd13b519-f1e7-4b99-a223-204021e3d2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_genes_mapped = list(set(clinical_data_with_patients[\"Hugo_Symbol\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6c8d1c16-82ce-41f4-84a4-97f184bd6777",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_gene_map(dataframe_genes, all_genes):\n",
    "    gene_map = {}\n",
    "    for gene in all_genes:\n",
    "        patient_list = list(dataframe_genes[dataframe_genes['Hugo_Symbol']==gene][\"PATIENT_ID\"])\n",
    "        gene_map[gene] = patient_list\n",
    "    return gene_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6ae80b2-c5b3-4e86-886f-124c15da7ba5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "972cdd45-e347-48f7-9794-187be0069098",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_map = create_gene_map(clinical_data_with_patients, all_genes_mapped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab3b368a-7d6f-4ecb-b45d-45606dcc50aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "826bf164-e0f2-4398-8958-23388754c004",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_patient_gene_network(dataframe, mapped_genes, gene):\n",
    "    temp_dataframe = dataframe.copy()\n",
    "    patients = mapped_genes[gene]\n",
    "    truth_value = lambda row: row[\"PATIENT_ID\"] in patients\n",
    "    label = lambda row:  True if truth_value(row) else False\n",
    "    temp_dataframe[gene] = temp_dataframe.apply(label, axis=\"columns\") #Apply Label\n",
    "    return temp_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "657ba2c4-2c15-4a1a-9f9a-353c04d81942",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9a1a63e3-8a9a-4b30-a04e-90e7c29d441b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>P-0019037</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>P-0019054</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>P-0019073</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1756 rows × 374 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PATIENT_ID  APPBP2  WDR72   ABL1  PTPRS   TERT   MDC1  CD79B  LOC150197  \\\n",
       "0     P-0000004   False  False  False  False  False  False  False      False   \n",
       "1     P-0000012   False  False  False  False  False  False  False      False   \n",
       "2     P-0000015   False  False  False  False  False  False  False      False   \n",
       "3     P-0000041   False  False  False  False  False  False  False      False   \n",
       "4     P-0000057   False  False  False  False  False  False  False      False   \n",
       "...         ...     ...    ...    ...    ...    ...    ...    ...        ...   \n",
       "1751  P-0018917   False  False  False  False  False  False  False      False   \n",
       "1752  P-0018963   False  False  False  False  False  False  False      False   \n",
       "1753  P-0019037   False  False  False  False  False  False  False      False   \n",
       "1754  P-0019054   False  False  False  False  False  False  False      False   \n",
       "1755  P-0019073   False  False  False  False  False  False  False      False   \n",
       "\n",
       "      PPM1D  ...  NRIP1  EPHA3    MET  UNC5D   ROS1  TIMP2  GPBP1  EIF4A2  \\\n",
       "0     False  ...  False  False  False  False  False  False  False   False   \n",
       "1     False  ...  False  False  False  False  False  False  False   False   \n",
       "2     False  ...  False  False  False  False  False  False  False   False   \n",
       "3     False  ...  False  False  False  False  False  False  False   False   \n",
       "4     False  ...  False  False  False  False  False  False  False   False   \n",
       "...     ...  ...    ...    ...    ...    ...    ...    ...    ...     ...   \n",
       "1751  False  ...  False  False  False  False  False  False  False   False   \n",
       "1752  False  ...  False  False  False  False  False  False  False   False   \n",
       "1753  False  ...  False  False  False  False  False  False  False   False   \n",
       "1754  False  ...  False  False  False  False  False  False  False   False   \n",
       "1755  False  ...  False  False  False  False  False  False  False   False   \n",
       "\n",
       "        ERG  CSF3R  \n",
       "0     False  False  \n",
       "1     False  False  \n",
       "2     False  False  \n",
       "3     False  False  \n",
       "4     False  False  \n",
       "...     ...    ...  \n",
       "1751  False  False  \n",
       "1752  False  False  \n",
       "1753  False  False  \n",
       "1754  False  False  \n",
       "1755  False  False  \n",
       "\n",
       "[1756 rows x 374 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "for gene in all_genes_mapped:\n",
    "    patients_gene_network = add_patient_gene_network(patients_gene_network, gene_map, gene)\n",
    "patients_gene_network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3433b14d-739e-4c35-9079-06ca5f105194",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5a49e4b0-dc8e-4fb9-a058-0cef13b260c0",
   "metadata": {},
   "source": [
    "### Confirming Gene Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "13ab7564-2b5d-498c-b22c-79c0cd482ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1364    True\n",
       "Name: DNAH9, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_gene_network[patients_gene_network['PATIENT_ID']==\"P-0014576\"][\"DNAH9\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "69a54624-9d0e-4ace-8c4e-e752c4bd8a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_class(dataframe, alive_patients_list):\n",
    "    temp_dataframe = dataframe.copy()\n",
    "    truth_value = lambda row: row[\"PATIENT_ID\"] in alive_patients_list\n",
    "    label = lambda row:  1 if truth_value(row) else 0\n",
    "    temp_dataframe[\"Survived\"] = temp_dataframe.apply(label, axis=\"columns\") #Apply Label\n",
    "    return temp_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a2815b92-b03a-4026-bd92-3d96ab546fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "46ca3f85-9e04-4016-992f-62a7582a25c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_gene_network = add_class(patients_gene_network, alive_patients)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "020bae6b-ba4d-459c-9a7e-d2249c335d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>PATIENT_ID</th>\n",
       "      <th>APPBP2</th>\n",
       "      <th>WDR72</th>\n",
       "      <th>ABL1</th>\n",
       "      <th>PTPRS</th>\n",
       "      <th>TERT</th>\n",
       "      <th>MDC1</th>\n",
       "      <th>CD79B</th>\n",
       "      <th>LOC150197</th>\n",
       "      <th>PPM1D</th>\n",
       "      <th>...</th>\n",
       "      <th>EPHA3</th>\n",
       "      <th>MET</th>\n",
       "      <th>UNC5D</th>\n",
       "      <th>ROS1</th>\n",
       "      <th>TIMP2</th>\n",
       "      <th>GPBP1</th>\n",
       "      <th>EIF4A2</th>\n",
       "      <th>ERG</th>\n",
       "      <th>CSF3R</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-0000004</td>\n",
       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>P-0000015</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-0000041</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>P-0018917</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>P-0019037</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1756 rows × 375 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PATIENT_ID  APPBP2  WDR72   ABL1  PTPRS   TERT   MDC1  CD79B  LOC150197  \\\n",
       "0     P-0000004   False  False  False  False  False  False  False      False   \n",
       "1     P-0000012   False  False  False  False  False  False  False      False   \n",
       "2     P-0000015   False  False  False  False  False  False  False      False   \n",
       "3     P-0000041   False  False  False  False  False  False  False      False   \n",
       "4     P-0000057   False  False  False  False  False  False  False      False   \n",
       "...         ...     ...    ...    ...    ...    ...    ...    ...        ...   \n",
       "1751  P-0018917   False  False  False  False  False  False  False      False   \n",
       "1752  P-0018963   False  False  False  False  False  False  False      False   \n",
       "1753  P-0019037   False  False  False  False  False  False  False      False   \n",
       "1754  P-0019054   False  False  False  False  False  False  False      False   \n",
       "1755  P-0019073   False  False  False  False  False  False  False      False   \n",
       "\n",
       "      PPM1D  ...  EPHA3    MET  UNC5D   ROS1  TIMP2  GPBP1  EIF4A2    ERG  \\\n",
       "0     False  ...  False  False  False  False  False  False   False  False   \n",
       "1     False  ...  False  False  False  False  False  False   False  False   \n",
       "2     False  ...  False  False  False  False  False  False   False  False   \n",
       "3     False  ...  False  False  False  False  False  False   False  False   \n",
       "4     False  ...  False  False  False  False  False  False   False  False   \n",
       "...     ...  ...    ...    ...    ...    ...    ...    ...     ...    ...   \n",
       "1751  False  ...  False  False  False  False  False  False   False  False   \n",
       "1752  False  ...  False  False  False  False  False  False   False  False   \n",
       "1753  False  ...  False  False  False  False  False  False   False  False   \n",
       "1754  False  ...  False  False  False  False  False  False   False  False   \n",
       "1755  False  ...  False  False  False  False  False  False   False  False   \n",
       "\n",
       "      CSF3R  Survived  \n",
       "0     False         1  \n",
       "1     False         1  \n",
       "2     False         0  \n",
       "3     False         0  \n",
       "4     False         0  \n",
       "...     ...       ...  \n",
       "1751  False         1  \n",
       "1752  False         1  \n",
       "1753  False         1  \n",
       "1754  False         1  \n",
       "1755  False         1  \n",
       "\n",
       "[1756 rows x 375 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_gene_network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6e6c8d13-48f2-4740-b69c-f83595b61488",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_table_of_analysis(dataframe, real_label_column_name, test_label_column_name, positive_result, negative_result):\n",
    "    row_count = dataframe.shape[0]\n",
    "    true_positive = dataframe.query('`{0}` == `{1}` and `{1}` == \"{2}\"'.format(real_label_column_name, test_label_column_name, positive_result))\n",
    "    false_positive = dataframe.query('`{0}` == \"{3}\" and `{1}` == \"{2}\"'.format(real_label_column_name, test_label_column_name, positive_result, negative_result))\n",
    "    true_negative = dataframe.query('`{0}` == `{1}` and `{0}` == \"{2}\"'.format(real_label_column_name, test_label_column_name, negative_result))\n",
    "    false_negative = dataframe.query('`{0}` == \"{2}\" and `{1}` == \"{3}\"'.format(real_label_column_name, test_label_column_name, positive_result, negative_result))\n",
    "\n",
    "    true_positive_r = true_positive.shape[0]\n",
    "    false_positive_r = false_positive.shape[0]\n",
    "    true_negative_r = true_negative.shape[0] \n",
    "    false_negative_r = false_negative.shape[0]\n",
    "    accuracy = float((true_positive_r + true_negative_r)) / float(row_count)\n",
    "\n",
    "    tpr = float(true_positive_r)/float((true_positive_r + false_negative_r))\n",
    "    tnr = float(true_negative_r)/float((true_negative_r + false_positive_r))\n",
    "    accuracy = float((true_positive_r + true_negative_r)) / float(row_count)\n",
    "\n",
    "    d = {\"TP\":[true_positive_r],\"FP\":[false_positive_r],\"TN\":[true_negative_r],\"FN\":[false_negative_r], \"Accuracy\": [accuracy], \"TPR\":[tpr], \"TNR\": [tnr]}\n",
    "\n",
    "    result_table = pd.DataFrame(data=d)\n",
    "\n",
    "    #print(result_table)\n",
    "    return result_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "71ab1c27-f838-46cd-80d3-3eedf1fb0bd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_headers = list(patients_gene_network)[1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0b848cf6-6986-419d-a98a-2c4fe5037f01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 1\n",
      " 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0\n",
      " 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 0 1 0 1 1 1 1 0 1 0 1 1\n",
      " 0 1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1\n",
      " 1 1 0 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1\n",
      " 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1\n",
      " 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0\n",
      " 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 1 0 1 0 1 1 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1\n",
      " 0 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1\n",
      " 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1\n",
      " 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 1 0\n",
      " 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1\n",
      " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 1 1 0 0 1\n",
      " 0 1 0 0 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1\n",
      " 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0\n",
      " 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1\n",
      " 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1 1\n",
      " 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0\n",
      " 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 0\n",
      " 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 1\n",
      " 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1\n",
      " 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0\n",
      " 0 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(patients_gene_network, test_size=0.5, random_state=42, shuffle=True)\n",
    "\n",
    "X_train = train[x_headers].to_numpy()\n",
    "y_train = train['Survived'].to_numpy()\n",
    "X_test = test[x_headers].to_numpy()\n",
    "Y_test = test['Survived'].to_numpy()\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1d556ba2-f20d-4156-aeec-c9702ecb7bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_accuracy = -1\n",
    "df4 = pd.DataFrame(data = X_test, columns = x_headers)\n",
    "d = {\"Number of Trees\": [np.nan], \"Depth\":[np.nan], \"TP\":[np.nan],\"FP\":[np.nan],\"TN\":[np.nan],\"FN\":[np.nan], \"Accuracy\": [np.nan], \"TPR\":[np.nan], \"TNR\": [np.nan]}\n",
    "\n",
    "table_of_results = pd.DataFrame(data=d)\n",
    "\n",
    "    \n",
    "for total_trees in range(1,11):\n",
    "    for depth in range(1,6):\n",
    "        temp_dataframe = df4.copy()\n",
    "        clf_model = RandomForestClassifier(max_depth=depth, n_estimators=total_trees, random_state=0) \n",
    "        clf_model.fit(X_train,y_train)\n",
    "\n",
    "        y_predicted = clf_model.predict(X_test)\n",
    "        \n",
    "\n",
    "        temp_dataframe[\"Class\"] = Y_test\n",
    "        temp_dataframe[\"Predicted\"] = y_predicted\n",
    "        #print(\"Number of Trees - \", total_trees)\n",
    "        #print(\"Overall Depth - \", depth)\n",
    "        table_accuracy = create_table_of_analysis(temp_dataframe, \"Class\", \"Predicted\", 1, 0)\n",
    "        table_accuracy[\"Number of Trees\"] = total_trees\n",
    "        table_accuracy[\"Depth\"] = depth\n",
    "        accuracy = table_accuracy.iloc[0][\"Accuracy\"]\n",
    "        table_of_results = pd.concat([table_of_results,table_accuracy])\n",
    "        \n",
    "        if accuracy > max_accuracy:\n",
    "            maximum_table = table_accuracy\n",
    "            optimal_features = {\"Total Trees\" : total_trees, \"Depth\": depth}\n",
    "            maximum_model = clf_model\n",
    "            maximum_confusion_matrix = confusion_matrix(Y_test, y_predicted)\n",
    "            max_accuracy = accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b9adeea6-6f1e-4b23-8ff7-93028aa7ef7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Number of Trees  Depth     TP     FP   TN   FN  Accuracy       TPR  TNR\n",
      "0              1.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              1.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              1.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              1.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              1.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              2.0    1.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
      "0              2.0    2.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
      "0              2.0    3.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
      "0              2.0    4.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
      "0              2.0    5.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
      "0              3.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              3.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              3.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              3.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              3.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              4.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              4.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              4.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              4.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              4.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              5.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              5.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              5.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              5.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              5.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              6.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              6.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              6.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              6.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              6.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              7.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              7.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              7.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              7.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              7.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              8.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              8.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              8.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              8.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              8.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              9.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              9.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              9.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              9.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0              9.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0             10.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0             10.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0             10.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0             10.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
      "0             10.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n"
     ]
    }
   ],
   "source": [
    "table_of_results = table_of_results.dropna()\n",
    "columns_entries = list(table_of_results)\n",
    "print(table_of_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "53573d27-2032-4f9c-ac99-1e810c117a64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    TP   FP  TN  FN  Accuracy  TPR  TNR  Number of Trees  Depth\n",
      "0  691  187   0   0  0.787016  1.0  0.0                1      1\n"
     ]
    }
   ],
   "source": [
    "print(maximum_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e9aab996-4385-4eaa-8cd8-a513b20f1e0d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.998553</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.785877</td>\n",
       "      <td>0.998553</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.787016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Trees  Depth     TP     FP   TN   FN  Accuracy       TPR  TNR\n",
       "0              1.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              1.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              1.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              1.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              1.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              2.0    1.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
       "0              2.0    2.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
       "0              2.0    3.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
       "0              2.0    4.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
       "0              2.0    5.0  690.0  187.0  0.0  1.0  0.785877  0.998553  0.0\n",
       "0              3.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              3.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              3.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              3.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              3.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              4.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              4.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              4.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              4.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              4.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              5.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              5.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              5.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              5.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              5.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              6.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              6.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              6.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              6.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              6.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              7.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              7.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              7.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              7.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              7.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              8.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              8.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              8.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              8.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              8.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              9.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              9.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              9.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              9.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0              9.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0             10.0    1.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0             10.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0             10.0    3.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0             10.0    4.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0\n",
       "0             10.0    5.0  691.0  187.0  0.0  0.0  0.787016  1.000000  0.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_of_results = table_of_results.dropna()\n",
    "columns_entries = list(table_of_results)\n",
    "table_of_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6f8383e6-c8f7-4c39-bf91-8ccfe33387a3",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    " \n",
    "# syntax for 3-D projection\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "ax.scatter(table_of_results[\"Number of Trees\"], table_of_results[\"Depth\"], table_of_results[\"Accuracy\"])\n",
    "\n",
    "ax.set_xlabel(columns_entries[0])\n",
    "ax.set_ylabel(columns_entries[1])\n",
    "ax.set_zlabel(columns_entries[6])\n",
    "\n",
    "ax.set_title('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "add344a7-b02c-45ef-b065-d777ce388e58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=1, n_estimators=1, random_state=0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maximum_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a7556fb9-5dc3-4bbf-a44d-da3cd5ccc6a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- RNF43 <= 0.50\n",
      "|   |--- class: 1.0\n",
      "|--- RNF43 >  0.50\n",
      "|   |--- class: 0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "tree_1 = maximum_model.estimators_[0]\n",
    "text_representation = tree.export_text(tree_1, feature_names= x_headers)\n",
    "print(text_representation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c3cf7e-a211-4e37-8dad-eccb6a4c3674",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "Although randomized, the Random Forest always tends to take an at Risk Gene or Positive Gene which is not found in either of the classes. This makes sense since this decision node's information is maximized by this decision, although the classification here isn't meaningful. Let's remove all positive and at risks genes from the analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfe9ef40-859e-49cc-aa99-c4bc58d6afa4",
   "metadata": {},
   "source": [
    "## Genes of Interest\n",
    "We implement Set Theory to remove all at risk and positive genes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "45186536-cb1d-4e2b-a627-7ee531a05002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RUNX1',\n",
       " 'ETV6',\n",
       " 'KDM6A',\n",
       " 'PAX5',\n",
       " 'FH',\n",
       " 'CDH1',\n",
       " 'SOX9',\n",
       " 'PTEN',\n",
       " 'STAG2',\n",
       " 'RB1',\n",
       " 'SF3B1',\n",
       " 'IDH2',\n",
       " 'PTPRS',\n",
       " 'FGFR2',\n",
       " 'ERBB2',\n",
       " 'NOTCH2',\n",
       " 'SBNO2',\n",
       " 'RARA',\n",
       " 'RAD51C',\n",
       " 'STAT3',\n",
       " 'ACBD6',\n",
       " 'MDC1',\n",
       " 'JAK2',\n",
       " 'PPM1D',\n",
       " 'SMARCA4',\n",
       " 'NOTCH3',\n",
       " 'EP300',\n",
       " 'IGF1R',\n",
       " 'STAT5B',\n",
       " 'BCAS3',\n",
       " 'ARID2',\n",
       " 'MLL3',\n",
       " 'BRCA2',\n",
       " 'CARD11',\n",
       " 'PIK3R1',\n",
       " 'NOTCH4',\n",
       " 'CDK12',\n",
       " 'ARID1B',\n",
       " 'NOTCH1',\n",
       " 'BRIP1',\n",
       " 'AXL',\n",
       " 'ARID1A',\n",
       " 'TMPRSS2',\n",
       " 'ZFHX3',\n",
       " 'PDE1C',\n",
       " 'NFKBIA',\n",
       " 'MYC',\n",
       " 'NF1']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curious_genes = list(set(all_genes) - set(positive_genes) - set(at_risk_genes))\n",
    "curious_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9292f7b7-94dd-42d2-a840-599583b014ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n"
     ]
    }
   ],
   "source": [
    "print(len(curious_genes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0727058f-7b1a-4106-9fd2-6137cb23c563",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(patients_gene_network, test_size=0.5, random_state=42, shuffle=True)\n",
    "\n",
    "X_train = train[curious_genes].to_numpy()\n",
    "y_train = train['Survived'].to_numpy()\n",
    "X_test = test[curious_genes].to_numpy()\n",
    "Y_test = test['Survived'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "862ed730-450b-47c0-bf5e-12789fd5ffb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_accuracy = -1\n",
    "df4 = pd.DataFrame(data = X_test, columns = curious_genes)\n",
    "d = {\"Number of Trees\": [np.nan], \"Depth\":[np.nan], \"TP\":[np.nan],\"FP\":[np.nan],\"TN\":[np.nan],\"FN\":[np.nan], \"Accuracy\": [np.nan], \"TPR\":[np.nan], \"TNR\": [np.nan]}\n",
    "\n",
    "table_of_results = pd.DataFrame(data=d)\n",
    "\n",
    "    \n",
    "for total_trees in range(2,11):\n",
    "    for depth in range(2,6):\n",
    "        temp_dataframe = df4.copy()\n",
    "        clf_model = RandomForestClassifier(max_depth=depth, n_estimators=total_trees, random_state=0) \n",
    "        clf_model.fit(X_train,y_train)\n",
    "\n",
    "        y_predicted = clf_model.predict(X_test)\n",
    "        \n",
    "\n",
    "        temp_dataframe[\"Class\"] = Y_test\n",
    "        temp_dataframe[\"Predicted\"] = y_predicted\n",
    "        #print(\"Number of Trees - \", total_trees)\n",
    "        #print(\"Overall Depth - \", depth)\n",
    "        table_accuracy = create_table_of_analysis(temp_dataframe, \"Class\", \"Predicted\", 1, 0)\n",
    "        table_accuracy[\"Number of Trees\"] = total_trees\n",
    "        table_accuracy[\"Depth\"] = depth\n",
    "        accuracy = table_accuracy.iloc[0][\"Accuracy\"]\n",
    "        table_of_results = pd.concat([table_of_results,table_accuracy])\n",
    "        \n",
    "        if accuracy > max_accuracy:\n",
    "            maximum_table = table_accuracy\n",
    "            optimal_features = {\"Total Trees\" : total_trees, \"Depth\": depth}\n",
    "            maximum_model = clf_model\n",
    "            maximum_confusion_matrix = confusion_matrix(Y_test, y_predicted)\n",
    "            max_accuracy = accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "175addfb-3ef3-48cb-86ff-2ed712081b77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Number of Trees  Depth     TP     FP   TN   FN  Accuracy       TPR  \\\n",
      "0              2.0    2.0  689.0  187.0  0.0  2.0  0.784738  0.997106   \n",
      "0              2.0    3.0  689.0  187.0  0.0  2.0  0.784738  0.997106   \n",
      "0              2.0    4.0  689.0  187.0  0.0  2.0  0.784738  0.997106   \n",
      "0              2.0    5.0  687.0  187.0  0.0  4.0  0.782460  0.994211   \n",
      "0              3.0    2.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              3.0    3.0  690.0  186.0  1.0  1.0  0.787016  0.998553   \n",
      "0              3.0    4.0  690.0  186.0  1.0  1.0  0.787016  0.998553   \n",
      "0              3.0    5.0  689.0  186.0  1.0  2.0  0.785877  0.997106   \n",
      "0              4.0    2.0  687.0  186.0  1.0  4.0  0.783599  0.994211   \n",
      "0              4.0    3.0  687.0  186.0  1.0  4.0  0.783599  0.994211   \n",
      "0              4.0    4.0  687.0  186.0  1.0  4.0  0.783599  0.994211   \n",
      "0              4.0    5.0  686.0  186.0  1.0  5.0  0.782460  0.992764   \n",
      "0              5.0    2.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              5.0    3.0  690.0  186.0  1.0  1.0  0.787016  0.998553   \n",
      "0              5.0    4.0  690.0  186.0  1.0  1.0  0.787016  0.998553   \n",
      "0              5.0    5.0  687.0  186.0  1.0  4.0  0.783599  0.994211   \n",
      "0              6.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000   \n",
      "0              6.0    3.0  691.0  186.0  1.0  0.0  0.788155  1.000000   \n",
      "0              6.0    4.0  691.0  186.0  1.0  0.0  0.788155  1.000000   \n",
      "0              6.0    5.0  689.0  186.0  1.0  2.0  0.785877  0.997106   \n",
      "0              7.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000   \n",
      "0              7.0    3.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              7.0    4.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              7.0    5.0  689.0  187.0  0.0  2.0  0.784738  0.997106   \n",
      "0              8.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000   \n",
      "0              8.0    3.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              8.0    4.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              8.0    5.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              9.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000   \n",
      "0              9.0    3.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              9.0    4.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0              9.0    5.0  689.0  187.0  0.0  2.0  0.784738  0.997106   \n",
      "0             10.0    2.0  691.0  187.0  0.0  0.0  0.787016  1.000000   \n",
      "0             10.0    3.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0             10.0    4.0  690.0  187.0  0.0  1.0  0.785877  0.998553   \n",
      "0             10.0    5.0  688.0  187.0  0.0  3.0  0.783599  0.995658   \n",
      "\n",
      "        TNR  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.000000  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.000000  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.005348  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n",
      "0  0.000000  \n"
     ]
    }
   ],
   "source": [
    "table_of_results = table_of_results.dropna()\n",
    "columns_entries = list(table_of_results)\n",
    "print(table_of_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0988e50b-954a-4829-8ba9-e7d98bafa7c0",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    " \n",
    "# syntax for 3-D projection\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "ax.scatter(table_of_results[\"Number of Trees\"], table_of_results[\"Depth\"], table_of_results[\"Accuracy\"])\n",
    "\n",
    "ax.set_xlabel(columns_entries[0])\n",
    "ax.set_ylabel(columns_entries[1])\n",
    "ax.set_zlabel(columns_entries[6])\n",
    "\n",
    "ax.set_title('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3df15a63-d93a-4906-bd2f-4021e59d62ef",
   "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>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>TPR</th>\n",
       "      <th>TNR</th>\n",
       "      <th>Number of Trees</th>\n",
       "      <th>Depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>691</td>\n",
       "      <td>186</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.788155</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005348</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TP   FP  TN  FN  Accuracy  TPR       TNR  Number of Trees  Depth\n",
       "0  691  186   1   0  0.788155  1.0  0.005348                6      3"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maximum_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "85b91cf3-1571-485a-b881-639f3541a4b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- RARA <= 0.50\n",
      "|   |--- SF3B1 <= 0.50\n",
      "|   |   |--- NOTCH4 <= 0.50\n",
      "|   |   |   |--- class: 1.0\n",
      "|   |   |--- NOTCH4 >  0.50\n",
      "|   |   |   |--- class: 0.0\n",
      "|   |--- SF3B1 >  0.50\n",
      "|   |   |--- class: 0.0\n",
      "|--- RARA >  0.50\n",
      "|   |--- class: 0.0\n",
      "\n",
      "=======\n",
      "|--- RARA <= 0.50\n",
      "|   |--- ARID2 <= 0.50\n",
      "|   |   |--- ACBD6 <= 0.50\n",
      "|   |   |   |--- class: 1.0\n",
      "|   |   |--- ACBD6 >  0.50\n",
      "|   |   |   |--- class: 0.0\n",
      "|   |--- ARID2 >  0.50\n",
      "|   |   |--- class: 0.0\n",
      "|--- RARA >  0.50\n",
      "|   |--- class: 0.0\n",
      "\n",
      "=======\n",
      "|--- IGF1R <= 0.50\n",
      "|   |--- EP300 <= 0.50\n",
      "|   |   |--- NOTCH4 <= 0.50\n",
      "|   |   |   |--- class: 1.0\n",
      "|   |   |--- NOTCH4 >  0.50\n",
      "|   |   |   |--- class: 0.0\n",
      "|   |--- EP300 >  0.50\n",
      "|   |   |--- class: 0.0\n",
      "|--- IGF1R >  0.50\n",
      "|   |--- ACBD6 <= 0.50\n",
      "|   |   |--- class: 1.0\n",
      "|   |--- ACBD6 >  0.50\n",
      "|   |   |--- class: 0.0\n",
      "\n",
      "=======\n",
      "|--- BRCA2 <= 0.50\n",
      "|   |--- ARID2 <= 0.50\n",
      "|   |   |--- STAT3 <= 0.50\n",
      "|   |   |   |--- class: 1.0\n",
      "|   |   |--- STAT3 >  0.50\n",
      "|   |   |   |--- class: 0.0\n",
      "|   |--- ARID2 >  0.50\n",
      "|   |   |--- class: 0.0\n",
      "|--- BRCA2 >  0.50\n",
      "|   |--- class: 0.0\n",
      "\n",
      "=======\n",
      "|--- NOTCH4 <= 0.50\n",
      "|   |--- PAX5 <= 0.50\n",
      "|   |   |--- PIK3R1 <= 0.50\n",
      "|   |   |   |--- class: 1.0\n",
      "|   |   |--- PIK3R1 >  0.50\n",
      "|   |   |   |--- class: 0.0\n",
      "|   |--- PAX5 >  0.50\n",
      "|   |   |--- class: 1.0\n",
      "|--- NOTCH4 >  0.50\n",
      "|   |--- class: 0.0\n",
      "\n",
      "=======\n",
      "|--- STAT3 <= 0.50\n",
      "|   |--- BRIP1 <= 0.50\n",
      "|   |   |--- MDC1 <= 0.50\n",
      "|   |   |   |--- class: 1.0\n",
      "|   |   |--- MDC1 >  0.50\n",
      "|   |   |   |--- class: 0.0\n",
      "|   |--- BRIP1 >  0.50\n",
      "|   |   |--- class: 0.0\n",
      "|--- STAT3 >  0.50\n",
      "|   |--- class: 0.0\n",
      "\n",
      "=======\n"
     ]
    }
   ],
   "source": [
    "for tree_i in maximum_model.estimators_:\n",
    "    text_representation = tree.export_text(tree_i, feature_names=curious_genes)\n",
    "    print(text_representation)\n",
    "    print(\"=======\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6861144-b7f5-4dd5-b766-effdfcf1869b",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "We examine one run of this data. Since Random Tree Ensemble is non-deterministic and features randomized selections of Feature Sets and Sample Data, there is a very slim chance that two runs will produce the same model\n",
    "\n",
    "RAD51C mutation according to Memorial Sloan Kettering Cancer Center increases your risk of both ovarian cancer and maybe breast cancer. This gene is located on chromosome 17 and is involved in DNA repair. Class 0 is deceased and class 1 is survived. According to this decision tree the mutation predicts survival, but the absence of the mutation relies on outside influences of genes to neutralize the risk factor. While this research is in no way medical (I am not a medical researcher), according to the algorithm, there are interaction networks across multiple structures. This is a major discovery if true, but let's examine the data more.\n",
    "\n",
    "STAT3 has also been shown in approximately 40% of breast cancers (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801660/#:~:text=STAT3%2C%20one%20of%20the%20seven%20members%20of%20the,not%20display%20amplification%20of%20HER2%2Fneu%20receptor%20%281%2C%202%29.)\n",
    "\n",
    "ARID2 has been shown in several cancers\n",
    "\n",
    "The genes of interest are found in research to be highly prevalent in Cancer.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "895bad05-f6a2-4b78-b751-ec9286d0dcbc",
   "metadata": {},
   "source": [
    "### Further Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "01cd559f-0de7-4010-b571-2abc787b48c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test) == maximum_table[\"TN\"] + maximum_table[\"FN\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b6526c0d-5561-497b-9292-f198b8ee4c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.21298405, 0.78701595])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = np.histogram(Y_test, bins=[-np.inf] + [0,1] + [np.inf])[0]/Y_test.size\n",
    "freq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06af5d30-e8d2-4935-9247-a23a9633fbe0",
   "metadata": {},
   "source": [
    "The accuracy of the model, however, is not as high as it looks. The above boolean function shows that while it did not classify each class as survive, it primarily predicted only one class. Examing the histogram above shows that the accuracy is close to equivalent of the distribution of survive and not survive. This prediction model is essentially predicting survival in almost all of the cases and the accuracy is misleading."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3e5c714-daff-48db-a725-366c66102ba8",
   "metadata": {},
   "source": [
    "### Decision Tree Genes\n",
    "We use a RegEx to help find all genes in the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1bfec6fd-a587-4cbb-a62b-d9644068b134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SF3B1',\n",
       " 'PIK3R1',\n",
       " 'NOTCH4',\n",
       " 'EP300',\n",
       " 'IGF1R',\n",
       " 'BRIP1',\n",
       " 'ARID2',\n",
       " 'PAX5',\n",
       " 'RARA',\n",
       " 'STAT3',\n",
       " 'ACBD6',\n",
       " 'MDC1',\n",
       " 'BRCA2']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = \"\"\n",
    "import re\n",
    "pattern = '([A-Z])\\w+[0-9]*'\n",
    "genes_of_interest = []\n",
    "geneExpression = re.compile(pattern)\n",
    "\n",
    "for tree_i in maximum_model.estimators_:\n",
    "    text_representation = tree.export_text(tree_i, feature_names=curious_genes)\n",
    "    \n",
    "    lines = text_representation.split(\"\\n\")\n",
    "    for line in lines:\n",
    "        mo = geneExpression.search(line)\n",
    "        if mo:\n",
    "            genes_of_interest.append(mo.group())\n",
    "    \n",
    "genes_of_interest = list(set(genes_of_interest))\n",
    "genes_of_interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6d64a6a4-53c3-40d1-a6a5-1533c6252530",
   "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>SF3B1</th>\n",
       "      <th>PIK3R1</th>\n",
       "      <th>NOTCH4</th>\n",
       "      <th>EP300</th>\n",
       "      <th>IGF1R</th>\n",
       "      <th>BRIP1</th>\n",
       "      <th>ARID2</th>\n",
       "      <th>PAX5</th>\n",
       "      <th>RARA</th>\n",
       "      <th>STAT3</th>\n",
       "      <th>ACBD6</th>\n",
       "      <th>MDC1</th>\n",
       "      <th>BRCA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.006188</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.004380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.006188</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.005645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SF3B1    PIK3R1    NOTCH4     EP300     IGF1R     BRIP1     ARID2  \\\n",
       "0   0.000000  0.003578  0.003578  0.004380  0.002529  0.003578  0.005645   \n",
       "1   0.003578  0.000000  0.003578  0.004380  0.002529  0.003578  0.005645   \n",
       "2   0.003578  0.003578  0.000000  0.004380  0.002529  0.003578  0.005645   \n",
       "3   0.004380  0.004380  0.004380  0.000000  0.003572  0.004380  0.006188   \n",
       "4   0.002529  0.002529  0.002529  0.003572  0.000000  0.002529  0.005041   \n",
       "5   0.003578  0.003578  0.003578  0.004380  0.002529  0.000000  0.005645   \n",
       "6   0.005645  0.005645  0.005645  0.006188  0.005041  0.005645  0.000000   \n",
       "7   0.002529  0.002529  0.002529  0.003572  0.000000  0.002529  0.005041   \n",
       "8   0.003578  0.003578  0.003578  0.004380  0.002529  0.003578  0.005645   \n",
       "9   0.003578  0.003578  0.003578  0.004380  0.002529  0.003578  0.005645   \n",
       "10  0.002529  0.002529  0.002529  0.003572  0.000000  0.002529  0.005041   \n",
       "11  0.002529  0.002529  0.002529  0.003572  0.000000  0.002529  0.005041   \n",
       "12  0.003578  0.003578  0.003578  0.004380  0.002529  0.003578  0.005645   \n",
       "\n",
       "        PAX5      RARA     STAT3     ACBD6      MDC1     BRCA2  \n",
       "0   0.002529  0.003578  0.003578  0.002529  0.002529  0.003578  \n",
       "1   0.002529  0.003578  0.003578  0.002529  0.002529  0.003578  \n",
       "2   0.002529  0.003578  0.003578  0.002529  0.002529  0.003578  \n",
       "3   0.003572  0.004380  0.004380  0.003572  0.003572  0.004380  \n",
       "4   0.000000  0.002529  0.002529  0.000000  0.000000  0.002529  \n",
       "5   0.002529  0.003578  0.003578  0.002529  0.002529  0.003578  \n",
       "6   0.005041  0.005645  0.005645  0.005041  0.005041  0.005645  \n",
       "7   0.000000  0.002529  0.002529  0.000000  0.000000  0.002529  \n",
       "8   0.002529  0.000000  0.003578  0.002529  0.002529  0.003578  \n",
       "9   0.002529  0.003578  0.000000  0.002529  0.002529  0.003578  \n",
       "10  0.000000  0.002529  0.002529  0.000000  0.000000  0.002529  \n",
       "11  0.000000  0.002529  0.002529  0.000000  0.000000  0.002529  \n",
       "12  0.002529  0.003578  0.003578  0.002529  0.002529  0.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "assert(scipy.__version__ > \"1.7\")\n",
    "\n",
    "#print(train_naive_bayes)\n",
    "\n",
    "def converter(column):\n",
    "    return [2 if item else 1 for item in column]\n",
    "\n",
    "\n",
    "nominal_convert = pd.DataFrame(data = df4, columns = genes_of_interest)\n",
    "\n",
    "nominal_convert = nominal_convert.apply(lambda x: converter(x))\n",
    "#nominal_convert = train_naive_bayes[genes_of_interest]\n",
    "\n",
    "\n",
    "from scipy.stats.contingency import association\n",
    "\n",
    "association_data = []\n",
    "\n",
    "for gene_i in genes_of_interest:\n",
    "    data_row = []\n",
    "    for gene_j in genes_of_interest:\n",
    "        \n",
    "        column_1 = nominal_convert[gene_i].to_numpy()\n",
    "        column_2 = nominal_convert[gene_j].to_numpy()\n",
    "        \n",
    "        two_links = []\n",
    "        for row_i in range(len(column_1)):\n",
    "            two_links_temp = [column_1[row_i],column_2[row_i]]\n",
    "            two_links.append(two_links_temp)\n",
    "        \n",
    "        data_row.append(association(two_links, method=\"tschuprow\"))\n",
    "    association_data.append(data_row)\n",
    "    \n",
    "\n",
    "df_association = pd.DataFrame(data = association_data, columns = genes_of_interest)\n",
    "df_association"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "60114978-699c-4773-9e78-20d673e0ebad",
   "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>SF3B1</th>\n",
       "      <th>PIK3R1</th>\n",
       "      <th>NOTCH4</th>\n",
       "      <th>EP300</th>\n",
       "      <th>IGF1R</th>\n",
       "      <th>BRIP1</th>\n",
       "      <th>ARID2</th>\n",
       "      <th>PAX5</th>\n",
       "      <th>RARA</th>\n",
       "      <th>STAT3</th>\n",
       "      <th>ACBD6</th>\n",
       "      <th>MDC1</th>\n",
       "      <th>BRCA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019440</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.033672</td>\n",
       "      <td>0.019440</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.019440</td>\n",
       "      <td>0.019440</td>\n",
       "      <td>0.023837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.033672</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>0.030717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.030717</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.013762</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SF3B1    PIK3R1    NOTCH4     EP300     IGF1R     BRIP1     ARID2  \\\n",
       "0   0.000000  0.019474  0.019474  0.023837  0.013762  0.019474  0.030717   \n",
       "1   0.019474  0.000000  0.019474  0.023837  0.013762  0.019474  0.030717   \n",
       "2   0.019474  0.019474  0.000000  0.023837  0.013762  0.019474  0.030717   \n",
       "3   0.023837  0.023837  0.023837  0.000000  0.019440  0.023837  0.033672   \n",
       "4   0.013762  0.013762  0.013762  0.019440  0.000000  0.013762  0.027430   \n",
       "5   0.019474  0.019474  0.019474  0.023837  0.013762  0.000000  0.030717   \n",
       "6   0.030717  0.030717  0.030717  0.033672  0.027430  0.030717  0.000000   \n",
       "7   0.013762  0.013762  0.013762  0.019440  0.000000  0.013762  0.027430   \n",
       "8   0.019474  0.019474  0.019474  0.023837  0.013762  0.019474  0.030717   \n",
       "9   0.019474  0.019474  0.019474  0.023837  0.013762  0.019474  0.030717   \n",
       "10  0.013762  0.013762  0.013762  0.019440  0.000000  0.013762  0.027430   \n",
       "11  0.013762  0.013762  0.013762  0.019440  0.000000  0.013762  0.027430   \n",
       "12  0.019474  0.019474  0.019474  0.023837  0.013762  0.019474  0.030717   \n",
       "\n",
       "        PAX5      RARA     STAT3     ACBD6      MDC1     BRCA2  \n",
       "0   0.013762  0.019474  0.019474  0.013762  0.013762  0.019474  \n",
       "1   0.013762  0.019474  0.019474  0.013762  0.013762  0.019474  \n",
       "2   0.013762  0.019474  0.019474  0.013762  0.013762  0.019474  \n",
       "3   0.019440  0.023837  0.023837  0.019440  0.019440  0.023837  \n",
       "4   0.000000  0.013762  0.013762  0.000000  0.000000  0.013762  \n",
       "5   0.013762  0.019474  0.019474  0.013762  0.013762  0.019474  \n",
       "6   0.027430  0.030717  0.030717  0.027430  0.027430  0.030717  \n",
       "7   0.000000  0.013762  0.013762  0.000000  0.000000  0.013762  \n",
       "8   0.013762  0.000000  0.019474  0.013762  0.013762  0.019474  \n",
       "9   0.013762  0.019474  0.000000  0.013762  0.013762  0.019474  \n",
       "10  0.000000  0.013762  0.013762  0.000000  0.000000  0.013762  \n",
       "11  0.000000  0.013762  0.013762  0.000000  0.000000  0.013762  \n",
       "12  0.013762  0.019474  0.019474  0.013762  0.013762  0.000000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "assert(scipy.__version__ > \"1.7\")\n",
    "\n",
    "\n",
    "def converter(column):\n",
    "    return [2 if item == 1 else 1 for item in column]\n",
    "\n",
    "nominal_convert = pd.DataFrame(data = df4, columns = genes_of_interest)\n",
    "\n",
    "nominal_convert = nominal_convert.apply(lambda x: converter(x))\n",
    "#nominal_convert = train_naive_bayes[genes_of_interest]\n",
    "\n",
    "\n",
    "from scipy.stats.contingency import association\n",
    "\n",
    "association_data = []\n",
    "\n",
    "for gene_i in genes_of_interest:\n",
    "    data_row = []\n",
    "    for gene_j in genes_of_interest:\n",
    "        \n",
    "        column_1 = nominal_convert[gene_i].to_numpy()\n",
    "        column_2 = nominal_convert[gene_j].to_numpy()\n",
    "        \n",
    "        two_links = []\n",
    "        for row_i in range(len(column_1)):\n",
    "            two_links_temp = [column_1[row_i],column_2[row_i]]\n",
    "            two_links.append(two_links_temp)\n",
    "        \n",
    "        data_row.append(association(two_links))\n",
    "    association_data.append(data_row)\n",
    "    \n",
    "\n",
    "df_association = pd.DataFrame(data = association_data, columns = genes_of_interest)\n",
    "df_association"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf58619-11ae-482c-9da9-408e132ee1f0",
   "metadata": {},
   "source": [
    "### Association Algorithm\n",
    "For Nominal Association, I used Association from Scipy.\n",
    "\n",
    "The default is Cramer's V\n",
    "\n",
    ">Cramer’s V, Tschuprow’s T and Pearson’s Contingency Coefficient, all measure the degree to which two nominal or ordinal variables are related, or the level of their association. This differs from correlation, although many often mistakenly consider them equivalent. Correlation measures in what way two variables are related, whereas, association measures how related the variables are. As such, association does not subsume independent variables, and is rather a test of independence. A value of 1.0 indicates perfect association, and 0.0 means the variables have no association.\n",
    "\n",
    "Cramer's V:\n",
    ">φc is the intercorrelation of two discrete variables[2] and may be used with variables having two or more levels. φc is a symmetrical measure: it does not matter which variable we place in the columns and which in the rows. Also, the order of rows/columns doesn't matter, so φc may be used with nominal data types or higher (notably, ordered or numerical).\n",
    "https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V\n",
    "\n",
    "I also used Tschuprow's T\n",
    ">In statistics, Tschuprow's T is a measure of association between two nominal variables, giving a value between 0 and 1 (inclusive). It is closely related to Cramér's V, coinciding with it for square contingency tables. It was published by Alexander Tschuprow (alternative spelling: Chuprov) in 1939.[1]\n",
    "\n",
    "Contingency Tables are defined as follows\n",
    ">In statistics, a contingency table (also known as a cross tabulation or crosstab) is a type of table in a matrix format that displays the (multivariate) frequency distribution of the variables. They are heavily used in survey research, business intelligence, engineering, and scientific research. They provide a basic picture of the interrelation between two variables and can help find interactions between them. The term contingency table was first used by Karl Pearson in \"On the Theory of Contingency and Its Relation to Association and Normal Correlation\",[1] part of the Drapers' Company Research Memoirs Biometric Series I published in 1904.\n",
    "\n",
    "https://en.wikipedia.org/wiki/Contingency_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c7d604-f456-4037-8dfd-2b2c6d117eef",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "We examine one run of this data. Since Random Tree Ensemble is non-deterministic and features randomized selections of Feature Sets and Sample Data, there is a very slim chance that two runs will produce the same model\n",
    "\n",
    "ETV6\n",
    ">The ETV6 gene provides instructions for producing a protein that functions as a transcription factor\n",
    "\n",
    "ARID2\n",
    ">AT rich interactive domain 2 (ARID; RFX-like) (ARID2) is a gene that encodes a protein that functions in a chromatin remodeling complex to promote gene transcription"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16426e18-d56a-4047-9cdc-9ea0529fdbeb",
   "metadata": {},
   "source": [
    "### Neural Network with Logistic Neuron Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "09db0047-47a1-45fa-8927-aaf9078437f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    TP   FP  TN  FN  Accuracy       TPR       TNR\n",
      "0  683  186   1   8  0.779043  0.988423  0.005348\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "df4 = pd.DataFrame(data = X_test, columns = curious_genes)\n",
    "\n",
    "clf = MLPClassifier(random_state=1, max_iter=300, activation='logistic').fit(X_train, y_train)\n",
    "\n",
    "\n",
    "\n",
    "probability_predictions = clf.predict_proba(X_test)\n",
    "\n",
    "\n",
    "temp_dataframe = df4.copy()\n",
    "\n",
    "\n",
    "y_predicted = clf.predict(X_test)\n",
    "\n",
    "\n",
    "temp_dataframe[\"Class\"] = Y_test\n",
    "temp_dataframe[\"Predicted\"] = y_predicted\n",
    "#print(\"Number of Trees - \", total_trees)\n",
    "#print(\"Overall Depth - \", depth)\n",
    "table_accuracy = create_table_of_analysis(temp_dataframe, \"Class\", \"Predicted\", 1, 0)\n",
    "print(table_accuracy)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a0a9d220-209a-4ccf-b3ca-8786a5ad1230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.21298405, 0.78701595])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114a9809-69b6-4263-8938-5d69ee92a45f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f5364c6c-1c42-4ed5-b337-8da2aff3c09b",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "Again, the Machine Learning Algorithm leans on predicting positives. Which as the above histogram shows, will produce results of around 78.7% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fcf998c5-c87c-4f8a-ad6f-d41c7f5fd866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    TP   FP  TN  FN  Accuracy       TPR      TNR\n",
      "0  682  183   4   9  0.781321  0.986975  0.02139\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "naive_bayes = GaussianNB()\n",
    "\n",
    "df4 = pd.DataFrame(data = X_test, columns = curious_genes)\n",
    "\n",
    "\n",
    "train_naive_bayes, test_naive_bayes = train_test_split(patients_gene_network, test_size=0.5, random_state=42, shuffle=True)\n",
    "\n",
    "X_train_naive_bayes = train_naive_bayes[genes_of_interest].to_numpy()\n",
    "y_train_naive_bayes = train_naive_bayes['Survived'].to_numpy()\n",
    "X_test_naive_bayes = test_naive_bayes[genes_of_interest].to_numpy()\n",
    "Y_test_naive_bayes = test_naive_bayes['Survived'].to_numpy()\n",
    "\n",
    "naive_bayes.fit(X_train_naive_bayes , y_train_naive_bayes)\n",
    "\n",
    " \n",
    "#Predict on test data\n",
    "y_predicted = naive_bayes.predict(X_test_naive_bayes)\n",
    "\n",
    "temp_dataframe = df4.copy()\n",
    "\n",
    "\n",
    "y_predicted = naive_bayes.predict(X_test_naive_bayes)\n",
    "\n",
    "\n",
    "temp_dataframe[\"Class\"] = Y_test\n",
    "temp_dataframe[\"Predicted\"] = y_predicted\n",
    "#print(\"Number of Trees - \", total_trees)\n",
    "#print(\"Overall Depth - \", depth)\n",
    "table_accuracy = create_table_of_analysis(temp_dataframe, \"Class\", \"Predicted\", 1, 0)\n",
    "\n",
    "print(table_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4fbfc2-6848-44f8-9b02-bc87fb59eb0f",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "Perhaps we can attempt to half the data into equal parts of each class to allow both classes to have predicitons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c37299d5-eebd-4d30-873b-d8505eb1a39b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = patients_gene_network['Survived'] == 1\n",
    "\n",
    "df_survived = patients_gene_network[mask]\n",
    " \n",
    "# invert the boolean values\n",
    "df_deceased = patients_gene_network[~mask]\n",
    "\n",
    "space_of_deceased = freq[1]\n",
    "\n",
    "range_end = int(len(df_survived) * space_of_deceased)\n",
    "\n",
    "df_survived = df_survived.iloc[:range_end] \n",
    "\n",
    "new_df = df_survived.append(df_deceased)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "dcd0ac3d-9b65-452c-9495-62f4b32f7400",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    TP   FP  TN  FN  Accuracy       TPR       TNR\n",
      "0  158  175   3   1  0.477745  0.993711  0.016854\n"
     ]
    }
   ],
   "source": [
    "naive_bayes = GaussianNB()\n",
    "\n",
    "\n",
    "train_naive_bayes, test_naive_bayes = train_test_split(new_df, test_size=0.5, random_state=42, shuffle=True)\n",
    "\n",
    "X_train_naive_bayes = train_naive_bayes[genes_of_interest].to_numpy()\n",
    "y_train_naive_bayes = train_naive_bayes['Survived'].to_numpy()\n",
    "X_test_naive_bayes = test_naive_bayes[genes_of_interest].to_numpy()\n",
    "Y_test_naive_bayes = test_naive_bayes['Survived'].to_numpy()\n",
    "\n",
    "naive_bayes.fit(X_train_naive_bayes , y_train_naive_bayes)\n",
    "\n",
    " \n",
    "#Predict on test data\n",
    "y_predicted = naive_bayes.predict(X_test_naive_bayes)\n",
    "\n",
    "temp_dataframe = test_naive_bayes.copy()\n",
    "\n",
    "\n",
    "\n",
    "temp_dataframe[\"Class\"] = Y_test_naive_bayes\n",
    "temp_dataframe[\"Predicted\"] = y_predicted\n",
    "#print(\"Number of Trees - \", total_trees)\n",
    "#print(\"Overall Depth - \", depth)\n",
    "table_accuracy = create_table_of_analysis(temp_dataframe, \"Class\", \"Predicted\", 1, 0)\n",
    "\n",
    "print(table_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "05ba9537-5a4a-412b-842d-68833c698d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.52818991, 0.47181009])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_new_data = np.histogram(Y_test_naive_bayes, bins=[-np.inf] + [0,1] + [np.inf])[0]/Y_test_naive_bayes.size\n",
    "freq_new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d5d5d5d2-9a61-4b00-888a-43a7d2d9a524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "674\n",
      "RandomForestClassifier(max_depth=2, n_estimators=2, random_state=0)\n"
     ]
    }
   ],
   "source": [
    "max_accuracy = -1\n",
    "d = {\"Number of Trees\": [np.nan], \"Depth\":[np.nan], \"TP\":[np.nan],\"FP\":[np.nan],\"TN\":[np.nan],\"FN\":[np.nan], \"Accuracy\": [np.nan], \"TPR\":[np.nan], \"TNR\": [np.nan]}\n",
    "\n",
    "table_of_results = pd.DataFrame(data=d)\n",
    "\n",
    "print(len(new_df))\n",
    "train_dt, test_dt = train_test_split(new_df, test_size=0.5, random_state=42, shuffle=True)\n",
    "\n",
    "X_train_dt = train_dt[curious_genes].to_numpy()\n",
    "y_train_dt = train_dt['Survived'].to_numpy()\n",
    "X_test_dt = test_dt[curious_genes].to_numpy()\n",
    "Y_test_dt = test_dt['Survived'].to_numpy()\n",
    "\n",
    "df4 = pd.DataFrame(data = X_test_dt, columns = curious_genes)\n",
    "\n",
    "    \n",
    "for total_trees in range(2,11):\n",
    "    for depth in range(2,6):\n",
    "        temp_dataframe = df4.copy()\n",
    "        clf_model = RandomForestClassifier(max_depth=depth, n_estimators=total_trees, random_state=0) \n",
    "        clf_model.fit(X_train_dt,y_train_dt)\n",
    "\n",
    "        y_predicted = clf_model.predict(X_test_dt)\n",
    "        \n",
    "        \n",
    "\n",
    "        temp_dataframe[\"Class\"] = Y_test_dt\n",
    "        temp_dataframe[\"Predicted\"] = y_predicted\n",
    "        #print(\"Number of Trees - \", total_trees)\n",
    "        #print(\"Overall Depth - \", depth)\n",
    "        table_accuracy = create_table_of_analysis(temp_dataframe, \"Class\", \"Predicted\", 1, 0)\n",
    "        table_accuracy[\"Number of Trees\"] = total_trees\n",
    "        table_accuracy[\"Depth\"] = depth\n",
    "        accuracy = table_accuracy.iloc[0][\"Accuracy\"]\n",
    "        table_of_results = pd.concat([table_of_results,table_accuracy])\n",
    "        \n",
    "        if accuracy > max_accuracy:\n",
    "            maximum_table = table_accuracy\n",
    "            optimal_features = {\"Total Trees\" : total_trees, \"Depth\": depth}\n",
    "            maximum_model = clf_model\n",
    "            maximum_confusion_matrix = confusion_matrix(Y_test_dt, y_predicted)\n",
    "            max_accuracy = accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "35d519c0-b415-454e-9e32-80ffe82961a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- RAD51C <= 0.50\n",
      "|   |--- FGFR2 <= 0.50\n",
      "|   |   |--- class: 0.0\n",
      "|   |--- FGFR2 >  0.50\n",
      "|   |   |--- class: 0.0\n",
      "|--- RAD51C >  0.50\n",
      "|   |--- PPM1D <= 0.50\n",
      "|   |   |--- class: 0.0\n",
      "|   |--- PPM1D >  0.50\n",
      "|   |   |--- class: 1.0\n",
      "\n",
      "=======\n",
      "|--- IGF1R <= 0.50\n",
      "|   |--- BCAS3 <= 0.50\n",
      "|   |   |--- class: 0.0\n",
      "|   |--- BCAS3 >  0.50\n",
      "|   |   |--- class: 0.0\n",
      "|--- IGF1R >  0.50\n",
      "|   |--- class: 0.0\n",
      "\n",
      "=======\n"
     ]
    }
   ],
   "source": [
    "for tree_i in maximum_model.estimators_:\n",
    "    text_representation = tree.export_text(tree_i, feature_names=curious_genes)\n",
    "    print(text_representation)\n",
    "    print(\"=======\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b7ac78e7-433b-4180-8a91-89c4bf422000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PPM1D', 'FGFR2', 'IGF1R', 'BCAS3', 'RAD51C']"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = \"\"\n",
    "import re\n",
    "pattern = '([A-Z])\\w+[0-9]*'\n",
    "genes_of_interest = []\n",
    "geneExpression = re.compile(pattern)\n",
    "\n",
    "for tree_i in maximum_model.estimators_:\n",
    "    text_representation = tree.export_text(tree_i, feature_names=curious_genes)\n",
    "    \n",
    "    lines = text_representation.split(\"\\n\")\n",
    "    for line in lines:\n",
    "        mo = geneExpression.search(line)\n",
    "        if mo:\n",
    "            genes_of_interest.append(mo.group())\n",
    "    \n",
    "genes_of_interest = list(set(genes_of_interest))\n",
    "genes_of_interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7bc0343e-eb21-4edb-b030-4625af9f7198",
   "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>PPM1D</th>\n",
       "      <th>FGFR2</th>\n",
       "      <th>IGF1R</th>\n",
       "      <th>BCAS3</th>\n",
       "      <th>RAD51C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.005179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.007335</td>\n",
       "      <td>0.007335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.005179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.007335</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.007335</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.007335</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PPM1D     FGFR2     IGF1R     BCAS3    RAD51C\n",
       "0  0.000000  0.005179  0.000000  0.005179  0.005179\n",
       "1  0.005179  0.000000  0.005179  0.007335  0.007335\n",
       "2  0.000000  0.005179  0.000000  0.005179  0.005179\n",
       "3  0.005179  0.007335  0.005179  0.000000  0.007335\n",
       "4  0.005179  0.007335  0.005179  0.007335  0.000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "assert(scipy.__version__ > \"1.7\")\n",
    "\n",
    "#print(train_naive_bayes)\n",
    "\n",
    "def converter(column):\n",
    "    return [2 if item else 1 for item in column]\n",
    "\n",
    "\n",
    "nominal_convert = pd.DataFrame(data = df4, columns = genes_of_interest)\n",
    "\n",
    "nominal_convert = nominal_convert.apply(lambda x: converter(x))\n",
    "#nominal_convert = train_naive_bayes[genes_of_interest]\n",
    "\n",
    "\n",
    "from scipy.stats.contingency import association\n",
    "\n",
    "association_data = []\n",
    "\n",
    "for gene_i in genes_of_interest:\n",
    "    data_row = []\n",
    "    for gene_j in genes_of_interest:\n",
    "        \n",
    "        column_1 = nominal_convert[gene_i].to_numpy()\n",
    "        column_2 = nominal_convert[gene_j].to_numpy()\n",
    "        \n",
    "        two_links = []\n",
    "        for row_i in range(len(column_1)):\n",
    "            two_links_temp = [column_1[row_i],column_2[row_i]]\n",
    "            two_links.append(two_links_temp)\n",
    "        \n",
    "        data_row.append(association(two_links, method=\"tschuprow\"))\n",
    "    association_data.append(data_row)\n",
    "    \n",
    "\n",
    "df_association = pd.DataFrame(data = association_data, columns = genes_of_interest)\n",
    "df_association"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "0e796ece-a158-485d-ad9d-2210973b89bd",
   "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>PPM1D</th>\n",
       "      <th>FGFR2</th>\n",
       "      <th>IGF1R</th>\n",
       "      <th>BCAS3</th>\n",
       "      <th>RAD51C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.022173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.031404</td>\n",
       "      <td>0.031404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.022173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.031404</td>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.031404</td>\n",
       "      <td>0.022173</td>\n",
       "      <td>0.031404</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PPM1D     FGFR2     IGF1R     BCAS3    RAD51C\n",
       "0  0.000000  0.022173  0.000000  0.022173  0.022173\n",
       "1  0.022173  0.000000  0.022173  0.031404  0.031404\n",
       "2  0.000000  0.022173  0.000000  0.022173  0.022173\n",
       "3  0.022173  0.031404  0.022173  0.000000  0.031404\n",
       "4  0.022173  0.031404  0.022173  0.031404  0.000000"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "assert(scipy.__version__ > \"1.7\")\n",
    "\n",
    "\n",
    "def converter(column):\n",
    "    return [2 if item == 1 else 1 for item in column]\n",
    "\n",
    "nominal_convert = pd.DataFrame(data = df4, columns = genes_of_interest)\n",
    "\n",
    "nominal_convert = nominal_convert.apply(lambda x: converter(x))\n",
    "#nominal_convert = train_naive_bayes[genes_of_interest]\n",
    "\n",
    "\n",
    "from scipy.stats.contingency import association\n",
    "\n",
    "association_data = []\n",
    "\n",
    "for gene_i in genes_of_interest:\n",
    "    data_row = []\n",
    "    for gene_j in genes_of_interest:\n",
    "        \n",
    "        column_1 = nominal_convert[gene_i].to_numpy()\n",
    "        column_2 = nominal_convert[gene_j].to_numpy()\n",
    "        \n",
    "        two_links = []\n",
    "        for row_i in range(len(column_1)):\n",
    "            two_links_temp = [column_1[row_i],column_2[row_i]]\n",
    "            two_links.append(two_links_temp)\n",
    "        \n",
    "        data_row.append(association(two_links))\n",
    "    association_data.append(data_row)\n",
    "    \n",
    "\n",
    "df_association = pd.DataFrame(data = association_data, columns = genes_of_interest)\n",
    "df_association"
   ]
  },
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   "source": [
    "## Analysis\n",
    "\n",
    "Highest Association appears to be FGFR2 and BCAS3 as well as FGFR2 and RAD51C. I've added Chromosome locations as well.\n",
    "\n",
    "### FGFR2\n",
    ">The protein encoded by this gene is a member of the fibroblast growth factor receptor family, where amino acid sequence is highly conserved between members and throughout evolution. FGFR family members differ from one another in their ligand affinities and tissue distribution. A full-length representative protein consists of an extracellular region, composed of three immunoglobulin-like domains, a single hydrophobic membrane-spanning segment and a cytoplasmic tyrosine kinase domain. The extracellular portion of the protein interacts with fibroblast growth factors, setting in motion a cascade of downstream signals, ultimately influencing mitogenesis and differentiation. This particular family member is a high-affinity receptor for acidic, basic and/or keratinocyte growth factor, depending on the isoform. Mutations in this gene are associated with Crouzon syndrome, Pfeiffer syndrome, Craniosynostosis, Apert syndrome, Jackson-Weiss syndrome, Beare-Stevenson cutis gyrata syndrome, Saethre-Chotzen syndrome, and syndromic craniosynostosis. Multiple alternatively spliced transcript variants encoding different isoforms have been noted for this gene. [provided by RefSeq, Jan 2009]\n",
    "\n",
    "Found on Chromosome 10 | Long arm 26.13\n",
    "\n",
    "### BCAS3\n",
    ">The BCAS3 gene is regulated by estrogen receptor alpha (ER-α).[7] The PELP1 protein acts as a transcriptional coactivator of estrogen receptor induced BCAS3 gene expression. In addition BCAS3 possesses histone acetyltransferase activity and itself appears to act as a coactivator of ER-α.[8] Furthermore, BCAS3 requires PELP1 to function as a coactivator in ER-α. Hence BCAS3 apparently is involved in a positive feedback loop leading to ER-α mediated signal amplification.\n",
    "\n",
    "Found on Chromosome 17 | Long arm 23\n",
    "\n",
    "### RAD51C\n",
    "\n",
    "This has come up in research before and was found as a risk factor for several cancers and is associated with DNA repair\n",
    "\n",
    ">This gene is a member of the RAD51 family. RAD51 family members are highly similar to bacterial RecA and Saccharomyces cerevisiae Rad51 and are known to be involved in the homologous recombination and repair of DNA. \n",
    "\n",
    ">This gene is a member of the RAD51 family. RAD51 family members are highly similar to bacterial RecA and Saccharomyces cerevisiae Rad51 and are known to be involved in the homologous recombination and repair of DNA. This protein can interact with other RAD51 paralogs and is reported to be important for Holliday junction resolution. Mutations in this gene are associated with Fanconi anemia-like syndrome. This gene is one of four localized to a region of chromosome 17q23 where amplification occurs frequently in breast tumors. Overexpression of the four genes during amplification has been observed and suggests a possible role in tumor progression. Alternative splicing results in multiple transcript variants. [provided by RefSeq, Jul 2013]\n",
    "https://www.ncbi.nlm.nih.gov/gene/5889\n",
    "\n",
    "Found on Chromosome 17 | Long arm 23"
   ]
  },
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    "## Ending Notes\n",
    "A lot of the mutations examined appear to be associated with gene transcription, healthy tissue development, and DNA repair. This all makes sense in the context of what cancer is. The association matrix might provide insights of genes that mutate together and might require some tweaks to the target genes to allow for selection of genes of clinical interest. Otherwise, running this whole notebook again should allow for a randomized selection of genes that can be examined. There are limitations with this study, but I hope the data engineering and data science steps taken in this study helps."
   ]
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