Example.ipynb

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    "# Example\n",
    "\n",
    "The following notebook leads you through how to run the clock on a CSV file containing beta values for every probe contained within the EPIC array. The tool will automatically select the appropriate probes used in the model and run the analysis.\n",
    "\n",
    "(Note the clock uses a smaller number of probes and the CSV file in this example just contains those probes if you wish to create a smaller CSV file to run this example) \n",
    "\n",
    "The first step is to load the clock functions using the following import function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import run_clock"
   ]
  },
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   "source": [
    "Then to actually run the clock we just need to supply a path and filename of the CSV file"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = run_clock.run_clock(\"test_data/beta_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Predicted Passage Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D01</td>\n",
       "      <td>17.85484236967554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A02</td>\n",
       "      <td>16.301581821775326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E03</td>\n",
       "      <td>19.220405642149018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A04</td>\n",
       "      <td>19.254180243969017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C04</td>\n",
       "      <td>18.391262474385165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>G04</td>\n",
       "      <td>19.85380325649158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A06</td>\n",
       "      <td>17.63664343465122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F06</td>\n",
       "      <td>15.849810834641282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>H06</td>\n",
       "      <td>15.931068069884663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sample ID Predicted Passage Number\n",
       "0       D01        17.85484236967554\n",
       "1       A02       16.301581821775326\n",
       "2       E03       19.220405642149018\n",
       "3       A04       19.254180243969017\n",
       "4       C04       18.391262474385165\n",
       "5       G04        19.85380325649158\n",
       "6       A06        17.63664343465122\n",
       "7       F06       15.849810834641282\n",
       "8       H06       15.931068069884663"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(results, index=[\"Sample ID\",\"Predicted Passage Number\"]).T"
   ]
  }
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