Cancer_classifierL.ipynb

{
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene ID</th>\n",
       "      <th>Gene Name</th>\n",
       "      <th>22Rv1, prostate carcinoma</th>\n",
       "      <th>23132/87, gastric adenocarcinoma</th>\n",
       "      <th>253J, bladder carcinoma</th>\n",
       "      <th>253J-BV, bladder carcinoma</th>\n",
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       "      <th>5637, bladder carcinoma</th>\n",
       "      <th>59M, high grade ovarian serous adenocarcinoma</th>\n",
       "      <th>639-V, ureter urothelial carcinoma</th>\n",
       "      <th>...</th>\n",
       "      <th>WSU-DLCL2, diffuse large B-cell lymphoma</th>\n",
       "      <th>YAPC, pancreatic carcinoma</th>\n",
       "      <th>YD-10B, tongue squamous cell carcinoma</th>\n",
       "      <th>YD-15, tongue squamous cell carcinoma</th>\n",
       "      <th>YD-38, head and neck squamous cell carcinoma</th>\n",
       "      <th>YD-8, tongue squamous cell carcinoma</th>\n",
       "      <th>YH-13, glioblastoma</th>\n",
       "      <th>YKG1, glioblastoma</th>\n",
       "      <th>ZR-75-1, invasive ductal carcinoma</th>\n",
       "      <th>ZR-75-30, invasive ductal carcinoma</th>\n",
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       "      <th>0</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>45.0</td>\n",
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       "      <td>0.7</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>ENSG00000000005</td>\n",
       "      <td>TNMD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000000419</td>\n",
       "      <td>DPM1</td>\n",
       "      <td>151.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>...</td>\n",
       "      <td>113.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>120.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000000457</td>\n",
       "      <td>SCYL3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>ENSG00000000460</td>\n",
       "      <td>C1orf112</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>936</th>\n",
       "      <td>ENSG00000065600</td>\n",
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       "      <td>16.0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <th>937</th>\n",
       "      <td>ENSG00000065609</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>938</th>\n",
       "      <td>ENSG00000065613</td>\n",
       "      <td>SLK</td>\n",
       "      <td>72.0</td>\n",
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       "      <td>39.0</td>\n",
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       "      <td>81.0</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>87.0</td>\n",
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       "      <th>939</th>\n",
       "      <td>ENSG00000065615</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <th>940</th>\n",
       "      <td>ENSG00000065618</td>\n",
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       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "             Gene ID Gene Name  22Rv1, prostate carcinoma  \\\n",
       "0    ENSG00000000003    TSPAN6                        7.0   \n",
       "1    ENSG00000000005      TNMD                        NaN   \n",
       "2    ENSG00000000419      DPM1                      151.0   \n",
       "3    ENSG00000000457     SCYL3                       17.0   \n",
       "4    ENSG00000000460  C1orf112                       24.0   \n",
       "..               ...       ...                        ...   \n",
       "936  ENSG00000065600     PACC1                       21.0   \n",
       "937  ENSG00000065609    SNAP91                       16.0   \n",
       "938  ENSG00000065613       SLK                       72.0   \n",
       "939  ENSG00000065615    CYB5R4                        6.0   \n",
       "940  ENSG00000065618   COL17A1                        0.1   \n",
       "\n",
       "     23132/87, gastric adenocarcinoma  253J, bladder carcinoma  \\\n",
       "0                                10.0                     29.0   \n",
       "1                                 NaN                      0.1   \n",
       "2                               238.0                     94.0   \n",
       "3                                27.0                      5.0   \n",
       "4                                12.0                      4.0   \n",
       "..                                ...                      ...   \n",
       "936                              16.0                     28.0   \n",
       "937                               NaN                      NaN   \n",
       "938                             173.0                     39.0   \n",
       "939                              24.0                      3.0   \n",
       "940                               0.6                      0.2   \n",
       "\n",
       "     253J-BV, bladder carcinoma  42-MG-BA, glioblastoma  \\\n",
       "0                          33.0                    33.0   \n",
       "1                           NaN                     NaN   \n",
       "2                         132.0                   223.0   \n",
       "3                           5.0                     5.0   \n",
       "4                           9.0                     9.0   \n",
       "..                          ...                     ...   \n",
       "936                        30.0                    12.0   \n",
       "937                         NaN                     NaN   \n",
       "938                        58.0                    30.0   \n",
       "939                         6.0                     8.0   \n",
       "940                         1.0                     0.8   \n",
       "\n",
       "     5637, bladder carcinoma  59M, high grade ovarian serous adenocarcinoma  \\\n",
       "0                       86.0                                           23.0   \n",
       "1                        NaN                                            NaN   \n",
       "2                      222.0                                          182.0   \n",
       "3                        6.0                                            4.0   \n",
       "4                       27.0                                            9.0   \n",
       "..                       ...                                            ...   \n",
       "936                     45.0                                           30.0   \n",
       "937                      0.1                                            0.6   \n",
       "938                     42.0                                           34.0   \n",
       "939                      9.0                                           14.0   \n",
       "940                    105.0                                            0.3   \n",
       "\n",
       "     639-V, ureter urothelial carcinoma  ...  \\\n",
       "0                                  45.0  ...   \n",
       "1                                   NaN  ...   \n",
       "2                                 286.0  ...   \n",
       "3                                   5.0  ...   \n",
       "4                                  11.0  ...   \n",
       "..                                  ...  ...   \n",
       "936                                35.0  ...   \n",
       "937                                 NaN  ...   \n",
       "938                                54.0  ...   \n",
       "939                                10.0  ...   \n",
       "940                                 0.2  ...   \n",
       "\n",
       "     WSU-DLCL2, diffuse large B-cell lymphoma  YAPC, pancreatic carcinoma  \\\n",
       "0                                         0.7                        71.0   \n",
       "1                                         NaN                         NaN   \n",
       "2                                       113.0                       239.0   \n",
       "3                                        11.0                        11.0   \n",
       "4                                        19.0                        11.0   \n",
       "..                                        ...                         ...   \n",
       "936                                      26.0                         9.0   \n",
       "937                                       NaN                         NaN   \n",
       "938                                      19.0                       103.0   \n",
       "939                                      27.0                        13.0   \n",
       "940                                       NaN                         NaN   \n",
       "\n",
       "     YD-10B, tongue squamous cell carcinoma  \\\n",
       "0                                      15.0   \n",
       "1                                       NaN   \n",
       "2                                     171.0   \n",
       "3                                       5.0   \n",
       "4                                       7.0   \n",
       "..                                      ...   \n",
       "936                                    12.0   \n",
       "937                                     NaN   \n",
       "938                                    49.0   \n",
       "939                                     5.0   \n",
       "940                                     NaN   \n",
       "\n",
       "     YD-15, tongue squamous cell carcinoma  \\\n",
       "0                                     31.0   \n",
       "1                                      NaN   \n",
       "2                                    206.0   \n",
       "3                                      5.0   \n",
       "4                                     13.0   \n",
       "..                                     ...   \n",
       "936                                   13.0   \n",
       "937                                    NaN   \n",
       "938                                   81.0   \n",
       "939                                    6.0   \n",
       "940                                    NaN   \n",
       "\n",
       "     YD-38, head and neck squamous cell carcinoma  \\\n",
       "0                                            30.0   \n",
       "1                                             NaN   \n",
       "2                                           208.0   \n",
       "3                                             6.0   \n",
       "4                                            23.0   \n",
       "..                                            ...   \n",
       "936                                          41.0   \n",
       "937                                           NaN   \n",
       "938                                          47.0   \n",
       "939                                          16.0   \n",
       "940                                           NaN   \n",
       "\n",
       "     YD-8, tongue squamous cell carcinoma  YH-13, glioblastoma  \\\n",
       "0                                    60.0                 39.0   \n",
       "1                                     NaN                  NaN   \n",
       "2                                   252.0                136.0   \n",
       "3                                     8.0                 10.0   \n",
       "4                                     8.0                 12.0   \n",
       "..                                    ...                  ...   \n",
       "936                                  20.0                 18.0   \n",
       "937                                   NaN                  NaN   \n",
       "938                                  70.0                 40.0   \n",
       "939                                  15.0                  7.0   \n",
       "940                                   NaN                  NaN   \n",
       "\n",
       "     YKG1, glioblastoma  ZR-75-1, invasive ductal carcinoma  \\\n",
       "0                  84.0                                14.0   \n",
       "1                   NaN                                 NaN   \n",
       "2                 235.0                               374.0   \n",
       "3                  11.0                                32.0   \n",
       "4                  15.0                                20.0   \n",
       "..                  ...                                 ...   \n",
       "936                60.0                                42.0   \n",
       "937                 NaN                                 NaN   \n",
       "938                55.0                                87.0   \n",
       "939                10.0                                16.0   \n",
       "940                 NaN                                 NaN   \n",
       "\n",
       "     ZR-75-30, invasive ductal carcinoma  \n",
       "0                                   23.0  \n",
       "1                                    NaN  \n",
       "2                                  120.0  \n",
       "3                                   21.0  \n",
       "4                                   11.0  \n",
       "..                                   ...  \n",
       "936                                 11.0  \n",
       "937                                  NaN  \n",
       "938                                 58.0  \n",
       "939                                  4.0  \n",
       "940                                  NaN  \n",
       "\n",
       "[941 rows x 1021 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import the libraries for the project\n",
    "import numpy as np\n",
    "import pandas  as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import lightgbm as lgbm\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "#Load the dataset\n",
    "df=pd.read_csv(r'E-MTAB-2770-query-results.tpms.tsv',\n",
    "             sep=\"\\t\", comment='#')\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 574
    },
    "id": "pO3irI_YwGNE",
    "outputId": "f36d08d0-57af-47eb-caba-fb38e101c509"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene ID</th>\n",
       "      <th>Gene Name</th>\n",
       "      <th>22Rv1, prostate carcinoma</th>\n",
       "      <th>23132/87, gastric adenocarcinoma</th>\n",
       "      <th>253J, bladder carcinoma</th>\n",
       "      <th>253J-BV, bladder carcinoma</th>\n",
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       "      <th>639-V, ureter urothelial carcinoma</th>\n",
       "      <th>...</th>\n",
       "      <th>WSU-DLCL2, diffuse large B-cell lymphoma</th>\n",
       "      <th>YAPC, pancreatic carcinoma</th>\n",
       "      <th>YD-10B, tongue squamous cell carcinoma</th>\n",
       "      <th>YD-15, tongue squamous cell carcinoma</th>\n",
       "      <th>YD-38, head and neck squamous cell carcinoma</th>\n",
       "      <th>YD-8, tongue squamous cell carcinoma</th>\n",
       "      <th>YH-13, glioblastoma</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>23.0</td>\n",
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       "      <td>60.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>ENSG00000000005</td>\n",
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       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000000419</td>\n",
       "      <td>DPM1</td>\n",
       "      <td>151.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>...</td>\n",
       "      <td>113.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000000457</td>\n",
       "      <td>SCYL3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000000460</td>\n",
       "      <td>C1orf112</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
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       "      <th>936</th>\n",
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       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <th>937</th>\n",
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       "      <td>ENSG00000065613</td>\n",
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       "      <td>55.0</td>\n",
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       "      <th>939</th>\n",
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       "      <td>ENSG00000065618</td>\n",
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       "      <td>0.6</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>105.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "             Gene ID Gene Name  22Rv1, prostate carcinoma  \\\n",
       "0    ENSG00000000003    TSPAN6                        7.0   \n",
       "1    ENSG00000000005      TNMD                        NaN   \n",
       "2    ENSG00000000419      DPM1                      151.0   \n",
       "3    ENSG00000000457     SCYL3                       17.0   \n",
       "4    ENSG00000000460  C1orf112                       24.0   \n",
       "..               ...       ...                        ...   \n",
       "936  ENSG00000065600     PACC1                       21.0   \n",
       "937  ENSG00000065609    SNAP91                       16.0   \n",
       "938  ENSG00000065613       SLK                       72.0   \n",
       "939  ENSG00000065615    CYB5R4                        6.0   \n",
       "940  ENSG00000065618   COL17A1                        0.1   \n",
       "\n",
       "     23132/87, gastric adenocarcinoma  253J, bladder carcinoma  \\\n",
       "0                                10.0                     29.0   \n",
       "1                                 NaN                      0.1   \n",
       "2                               238.0                     94.0   \n",
       "3                                27.0                      5.0   \n",
       "4                                12.0                      4.0   \n",
       "..                                ...                      ...   \n",
       "936                              16.0                     28.0   \n",
       "937                               NaN                      NaN   \n",
       "938                             173.0                     39.0   \n",
       "939                              24.0                      3.0   \n",
       "940                               0.6                      0.2   \n",
       "\n",
       "     253J-BV, bladder carcinoma  42-MG-BA, glioblastoma  \\\n",
       "0                          33.0                    33.0   \n",
       "1                           NaN                     NaN   \n",
       "2                         132.0                   223.0   \n",
       "3                           5.0                     5.0   \n",
       "4                           9.0                     9.0   \n",
       "..                          ...                     ...   \n",
       "936                        30.0                    12.0   \n",
       "937                         NaN                     NaN   \n",
       "938                        58.0                    30.0   \n",
       "939                         6.0                     8.0   \n",
       "940                         1.0                     0.8   \n",
       "\n",
       "     5637, bladder carcinoma  59M, high grade ovarian serous adenocarcinoma  \\\n",
       "0                       86.0                                           23.0   \n",
       "1                        NaN                                            NaN   \n",
       "2                      222.0                                          182.0   \n",
       "3                        6.0                                            4.0   \n",
       "4                       27.0                                            9.0   \n",
       "..                       ...                                            ...   \n",
       "936                     45.0                                           30.0   \n",
       "937                      0.1                                            0.6   \n",
       "938                     42.0                                           34.0   \n",
       "939                      9.0                                           14.0   \n",
       "940                    105.0                                            0.3   \n",
       "\n",
       "     639-V, ureter urothelial carcinoma  ...  \\\n",
       "0                                  45.0  ...   \n",
       "1                                   NaN  ...   \n",
       "2                                 286.0  ...   \n",
       "3                                   5.0  ...   \n",
       "4                                  11.0  ...   \n",
       "..                                  ...  ...   \n",
       "936                                35.0  ...   \n",
       "937                                 NaN  ...   \n",
       "938                                54.0  ...   \n",
       "939                                10.0  ...   \n",
       "940                                 0.2  ...   \n",
       "\n",
       "     WSU-DLCL2, diffuse large B-cell lymphoma  YAPC, pancreatic carcinoma  \\\n",
       "0                                         0.7                        71.0   \n",
       "1                                         NaN                         NaN   \n",
       "2                                       113.0                       239.0   \n",
       "3                                        11.0                        11.0   \n",
       "4                                        19.0                        11.0   \n",
       "..                                        ...                         ...   \n",
       "936                                      26.0                         9.0   \n",
       "937                                       NaN                         NaN   \n",
       "938                                      19.0                       103.0   \n",
       "939                                      27.0                        13.0   \n",
       "940                                       NaN                         NaN   \n",
       "\n",
       "     YD-10B, tongue squamous cell carcinoma  \\\n",
       "0                                      15.0   \n",
       "1                                       NaN   \n",
       "2                                     171.0   \n",
       "3                                       5.0   \n",
       "4                                       7.0   \n",
       "..                                      ...   \n",
       "936                                    12.0   \n",
       "937                                     NaN   \n",
       "938                                    49.0   \n",
       "939                                     5.0   \n",
       "940                                     NaN   \n",
       "\n",
       "     YD-15, tongue squamous cell carcinoma  \\\n",
       "0                                     31.0   \n",
       "1                                      NaN   \n",
       "2                                    206.0   \n",
       "3                                      5.0   \n",
       "4                                     13.0   \n",
       "..                                     ...   \n",
       "936                                   13.0   \n",
       "937                                    NaN   \n",
       "938                                   81.0   \n",
       "939                                    6.0   \n",
       "940                                    NaN   \n",
       "\n",
       "     YD-38, head and neck squamous cell carcinoma  \\\n",
       "0                                            30.0   \n",
       "1                                             NaN   \n",
       "2                                           208.0   \n",
       "3                                             6.0   \n",
       "4                                            23.0   \n",
       "..                                            ...   \n",
       "936                                          41.0   \n",
       "937                                           NaN   \n",
       "938                                          47.0   \n",
       "939                                          16.0   \n",
       "940                                           NaN   \n",
       "\n",
       "     YD-8, tongue squamous cell carcinoma  YH-13, glioblastoma  \\\n",
       "0                                    60.0                 39.0   \n",
       "1                                     NaN                  NaN   \n",
       "2                                   252.0                136.0   \n",
       "3                                     8.0                 10.0   \n",
       "4                                     8.0                 12.0   \n",
       "..                                    ...                  ...   \n",
       "936                                  20.0                 18.0   \n",
       "937                                   NaN                  NaN   \n",
       "938                                  70.0                 40.0   \n",
       "939                                  15.0                  7.0   \n",
       "940                                   NaN                  NaN   \n",
       "\n",
       "     YKG1, glioblastoma  ZR-75-1, invasive ductal carcinoma  \\\n",
       "0                  84.0                                14.0   \n",
       "1                   NaN                                 NaN   \n",
       "2                 235.0                               374.0   \n",
       "3                  11.0                                32.0   \n",
       "4                  15.0                                20.0   \n",
       "..                  ...                                 ...   \n",
       "936                60.0                                42.0   \n",
       "937                 NaN                                 NaN   \n",
       "938                55.0                                87.0   \n",
       "939                10.0                                16.0   \n",
       "940                 NaN                                 NaN   \n",
       "\n",
       "     ZR-75-30, invasive ductal carcinoma  \n",
       "0                                   23.0  \n",
       "1                                    NaN  \n",
       "2                                  120.0  \n",
       "3                                   21.0  \n",
       "4                                   11.0  \n",
       "..                                   ...  \n",
       "936                                 11.0  \n",
       "937                                  NaN  \n",
       "938                                 58.0  \n",
       "939                                  4.0  \n",
       "940                                  NaN  \n",
       "\n",
       "[941 rows x 1013 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove normal cell lines\n",
    "df=df.loc[:,~df.columns.str.contains('normal', case=False)]\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "flfxP2tByQRy"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VU9xtzDNFAmS"
   },
   "outputs": [],
   "source": [
    "#Define the targets for prediction (cancer samples)\n",
    "target=list(df.columns[2:])\n",
    "\n",
    "#A function to engineer the labels  list\n",
    "# The 2 labels of interest are 'leukemia' and 'other cancers'\n",
    "def eng_labels(x):\n",
    "   labs=[]\n",
    "   for s in x:\n",
    "         if 'leukemia' in s:\n",
    "             labs.append('leukemia')\n",
    "         else: \n",
    "             labs.append('other cancers')\n",
    "   return(labs)\n",
    "\n",
    "#Engineer the labels\n",
    "labs=eng_labels(target)\n",
    "labs=['Gene']+labs\n",
    "labs=pd.Series(labs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "6JOp5WZNIIY6",
    "outputId": "99b2aac8-e91b-4d71-b491-ebc0f62343fb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-17f06c39-14ea-4109-ab08-03147aa23e62\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Gene</th>\n",
       "      <th>TSPAN6</th>\n",
       "      <th>TNMD</th>\n",
       "      <th>DPM1</th>\n",
       "      <th>SCYL3</th>\n",
       "      <th>C1orf112</th>\n",
       "      <th>FGR</th>\n",
       "      <th>CFH</th>\n",
       "      <th>FUCA2</th>\n",
       "      <th>GCLC</th>\n",
       "      <th>NFYA</th>\n",
       "      <th>...</th>\n",
       "      <th>NDUFB4</th>\n",
       "      <th>SPEN</th>\n",
       "      <th>MYLK</th>\n",
       "      <th>ZC3H15</th>\n",
       "      <th>MAP2K4</th>\n",
       "      <th>PACC1</th>\n",
       "      <th>SNAP91</th>\n",
       "      <th>SLK</th>\n",
       "      <th>CYB5R4</th>\n",
       "      <th>COL17A1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>156.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>325.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>101.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>179.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>94.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>76.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>141.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>155.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>223.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>99.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>222.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>...</td>\n",
       "      <td>168.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>34.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>...</td>\n",
       "      <td>109.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>109.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>157.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leukemia</th>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>152.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>165.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>295.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>123.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>258.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>...</td>\n",
       "      <td>134.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>274.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>103.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>142.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>130.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>141.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>474.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>142.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other cancers</th>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>129.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 941 columns</p>\n",
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      "text/plain": [
       "Gene          TSPAN6 TNMD   DPM1 SCYL3 C1orf112  FGR   CFH  FUCA2   GCLC  \\\n",
       "other cancers    7.0  NaN  151.0  17.0     24.0  NaN   0.1  100.0  124.0   \n",
       "other cancers   10.0  NaN  238.0  27.0     12.0  NaN   0.4  325.0  101.0   \n",
       "other cancers   29.0  0.1   94.0   5.0      4.0  NaN   0.3   76.0   16.0   \n",
       "other cancers   33.0  NaN  132.0   5.0      9.0  NaN   0.1   60.0   20.0   \n",
       "other cancers   33.0  NaN  223.0   5.0      9.0  NaN   0.4   99.0    8.0   \n",
       "other cancers   86.0  NaN  222.0   6.0     27.0  0.3   2.0  138.0   11.0   \n",
       "other cancers   23.0  NaN  182.0   4.0      9.0  NaN   9.0  123.0   38.0   \n",
       "other cancers   45.0  NaN  286.0   5.0     11.0  NaN  15.0  151.0   23.0   \n",
       "other cancers   85.0  NaN  195.0   4.0     14.0  NaN   0.3  109.0   20.0   \n",
       "leukemia         0.2  NaN  101.0  11.0     22.0  0.1   0.2    0.4   19.0   \n",
       "other cancers   13.0  NaN  165.0   7.0     16.0  NaN   2.0  155.0   20.0   \n",
       "other cancers    9.0  NaN  295.0   6.0     17.0  NaN   8.0  222.0   18.0   \n",
       "other cancers   47.0  NaN  258.0   8.0     12.0  0.8   5.0  155.0   37.0   \n",
       "other cancers   27.0  NaN  260.0   4.0     10.0  NaN  16.0   33.0    9.0   \n",
       "other cancers   37.0  NaN  274.0   4.0     11.0  NaN   0.4  103.0   10.0   \n",
       "other cancers    8.0  NaN  209.0  16.0     14.0  NaN  22.0  193.0    7.0   \n",
       "other cancers    0.1  NaN  238.0   4.0     10.0  NaN   2.0   81.0   17.0   \n",
       "other cancers   25.0  NaN  474.0  11.0     24.0  0.1   0.7   51.0   24.0   \n",
       "other cancers   26.0  NaN  163.0   5.0      7.0  0.1  58.0   97.0   33.0   \n",
       "other cancers   39.0  NaN  135.0   7.0     14.0  0.2   1.0   18.0   13.0   \n",
       "\n",
       "Gene            NFYA  ... NDUFB4  SPEN  MYLK ZC3H15 MAP2K4 PACC1 SNAP91  \\\n",
       "other cancers   95.0  ...  156.0  76.0   1.0  139.0   32.0  21.0   16.0   \n",
       "other cancers   48.0  ...  101.0  42.0   0.3  179.0   32.0  16.0    NaN   \n",
       "other cancers   17.0  ...  141.0  14.0  26.0   68.0   16.0  28.0    NaN   \n",
       "other cancers   35.0  ...  155.0  25.0  10.0  107.0   21.0  30.0    NaN   \n",
       "other cancers   42.0  ...   97.0  43.0   2.0  176.0   15.0  12.0    NaN   \n",
       "other cancers   43.0  ...  168.0  44.0   4.0  132.0   16.0  45.0    0.1   \n",
       "other cancers   47.0  ...   74.0  35.0   9.0  150.0   15.0  30.0    0.6   \n",
       "other cancers   75.0  ...  109.0  37.0   1.0  154.0   34.0  35.0    NaN   \n",
       "other cancers   51.0  ...  157.0  26.0   1.0   91.0   25.0  36.0    0.1   \n",
       "leukemia       102.0  ...  152.0  51.0  43.0  150.0   25.0  55.0    NaN   \n",
       "other cancers   40.0  ...  165.0  26.0  11.0  154.0   20.0  16.0    NaN   \n",
       "other cancers   38.0  ...  123.0  40.0  16.0  222.0   35.0  30.0    NaN   \n",
       "other cancers   47.0  ...  134.0  54.0  32.0  104.0   16.0  27.0    0.7   \n",
       "other cancers   71.0  ...  169.0  33.0  37.0  134.0   23.0  21.0    0.8   \n",
       "other cancers   45.0  ...  142.0  47.0   1.0  144.0   28.0  22.0    0.1   \n",
       "other cancers   56.0  ...  130.0  32.0   0.5  141.0   32.0  11.0    NaN   \n",
       "other cancers   34.0  ...   85.0  32.0   6.0  131.0   31.0   7.0    NaN   \n",
       "other cancers   62.0  ...  142.0  46.0   3.0  153.0   22.0  30.0    NaN   \n",
       "other cancers   19.0  ...  100.0  15.0  21.0  120.0   28.0  39.0    NaN   \n",
       "other cancers   60.0  ...  129.0  59.0   1.0  118.0   29.0  17.0    0.1   \n",
       "\n",
       "Gene             SLK CYB5R4 COL17A1  \n",
       "other cancers   72.0    6.0     0.1  \n",
       "other cancers  173.0   24.0     0.6  \n",
       "other cancers   39.0    3.0     0.2  \n",
       "other cancers   58.0    6.0     1.0  \n",
       "other cancers   30.0    8.0     0.8  \n",
       "other cancers   42.0    9.0   105.0  \n",
       "other cancers   34.0   14.0     0.3  \n",
       "other cancers   54.0   10.0     0.2  \n",
       "other cancers   23.0    7.0     0.7  \n",
       "leukemia        12.0    4.0     0.1  \n",
       "other cancers   63.0   10.0     1.0  \n",
       "other cancers   60.0   15.0     0.6  \n",
       "other cancers   34.0    7.0     0.7  \n",
       "other cancers   23.0   13.0     3.0  \n",
       "other cancers   49.0    9.0     6.0  \n",
       "other cancers   48.0    5.0     1.0  \n",
       "other cancers   64.0   12.0   612.0  \n",
       "other cancers   68.0    7.0     0.4  \n",
       "other cancers   18.0    8.0     0.5  \n",
       "other cancers   57.0    7.0     0.1  \n",
       "\n",
       "[20 rows x 941 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Engineer the data\n",
    "data=df.iloc[:,1:]   \n",
    "data.columns=labs\n",
    "data=data.transpose()\n",
    "data.columns=data.loc['Gene',:]\n",
    "data=data.iloc[1:,:]\n",
    "pd.set_option('display.max_rows', 20)\n",
    "pd.set_option('display.max_columns', 20)\n",
    "data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rdSDWOgU0TTa"
   },
   "source": [
    "# New Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "A7vTYsHodWrG",
    "outputId": "911ef3b7-cbf3-4726-ed85-eefda39e1cb4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d974077c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Visualize SCYL3 gene expression across the sample\n",
    "sns.histplot(data['SCYL3'], kde=True) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "SfC-kwgNmKGp",
    "outputId": "a654eaf6-bf6e-4d3f-8a11-8ad03d8b0ff9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e438ca400>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data['DPM1'], kde=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "wyPdwZtQmPey",
    "outputId": "3716b1d0-5f4f-4a6b-869a-3e95ba41dff0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e43360550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data['MSMO1'], kde=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p8Mn5757a4y7"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "#Data preprocessing\n",
    "data_x=data                       \n",
    "data_x=np.array(data_x)\n",
    "le = LabelEncoder()\n",
    "le.fit(data.index)\n",
    "data_y=le.transform(data.index) \n",
    "data_y=pd.Series(data_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1ZT2okLtFFZY"
   },
   "outputs": [],
   "source": [
    "#Train/Test split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(\n",
    " data_x, data_y, test_size=0.33, random_state=87)\n",
    "\n",
    "#Converting the data to np.arrays\n",
    "#This is needed to facilitate the training process\n",
    "xtrain=np.array(xtrain, dtype=np.float32)\n",
    "ytrain=np.array(ytrain, dtype=np.float32)\n",
    "\n",
    "\n",
    "xtrain, ytrain=shuffle(xtrain, ytrain)\n",
    "xtest, ytest=shuffle(xtest, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i3Nj8WfYNDsf"
   },
   "outputs": [],
   "source": [
    "#These are the parameters to use for tuning the training process\n",
    "p={ 'boosting':'dart', 'learning_rate':0.01, 'objective':'binary', 'max_depth':6\n",
    "   , 'num_leaves':102, 'min_data_in_leaf':40, 'bagging_fraction':1,'device_type':'cpu',\n",
    "   'feature_fraction':1, 'verbose':0, 'bagging_freq':7, 'extra_trees':'true','cegb_tradeoff':5,\n",
    "  'max_bin':100, 'min_data_in_bin':6,'n_estimators':15750}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1vRp6Fl4-9aS"
   },
   "outputs": [],
   "source": [
    "#Train the classifier and name is lgbmodel\n",
    "lgbmodel=LGBMClassifier(**p).fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FOxJMPljLAL9",
    "outputId": "e8df3346-a970-4fc7-9d1c-17eec892219f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9880239520958084"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Make predictions and calculate accuracy\n",
    "predictions = lgbmodel.predict(xtest)\n",
    "rpreds = [round(i) for i in predictions]\n",
    "accuracy = accuracy_score(ytest, rpreds)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pk5Ew8RWcBXD"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "nEKh7uCatmWG",
    "outputId": "0d0a65e8-9e08-48ad-e4b4-dd7fd3fddba6"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make the confusion matrix\n",
    "cm = confusion_matrix(ytest, predictions)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yiUg4wQ5FZYa"
   },
   "outputs": [],
   "source": [
    "#Extract the feature importances\n",
    "importances=lgbmodel.feature_importances_\n",
    "importances=pd.Series(importances)\n",
    "importances.index=data.columns\n",
    "importances= importances.sort_values(ascending=False)\n",
    "importances[0:29].to_csv(r'important_genes.csv', sep='\\t', header='true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 471
    },
    "id": "R8Zp163ITlP_",
    "outputId": "ab327287-84c6-485b-bbe3-1798afecb98a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 29 artists>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Data visualization of the feature importances\n",
    "plt.figure(figsize=(28,20))\n",
    "plt.bar(importances[0:29].index, importances[0:29], width=0.7, bottom=None, align='center', data=None)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}