make_cs_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a5db5cf",
   "metadata": {},
   "source": [
    "## Apply CORDA2 algorithm to create cell-specific models\n",
    "\n",
    "### load and discretize expression data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3354149a",
   "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>Ensembl</th>\n",
       "      <th>GeneSymbol</th>\n",
       "      <th>MeanExpression_SchwannCells_1__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_SchwannCells_2__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_Melanocytes__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_EndothelialCells__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_SmoothMuscle__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_Fibroblasts__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_RPE__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_BCells__VoigtEtAl2019_</th>\n",
       "      <th>...</th>\n",
       "      <th>MeanExpression_mastCells__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_RETINA__WhitmoreEtAl_2014_</th>\n",
       "      <th>MeanExpression_RPE_CHOROID__WhitmoreEtAl_2014_</th>\n",
       "      <th>MeanExpression_RodPhotoreceptors__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_AmacrineCells__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_BipolarCells__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_ConePhotoreceptors__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_HorizontalCells__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_M_llerGlialCells__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_RetinalGanglionCells__LiangEtAl_2019_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000000419</td>\n",
       "      <td>DPM1</td>\n",
       "      <td>0.175932</td>\n",
       "      <td>0.229714</td>\n",
       "      <td>0.257885</td>\n",
       "      <td>0.727541</td>\n",
       "      <td>0.311105</td>\n",
       "      <td>0.333106</td>\n",
       "      <td>0.302933</td>\n",
       "      <td>0.418343</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279661</td>\n",
       "      <td>1.245000</td>\n",
       "      <td>1.417794</td>\n",
       "      <td>0.057260</td>\n",
       "      <td>0.098788</td>\n",
       "      <td>0.070328</td>\n",
       "      <td>0.048231</td>\n",
       "      <td>0.085795</td>\n",
       "      <td>0.058392</td>\n",
       "      <td>0.146947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000001036</td>\n",
       "      <td>FUCA2</td>\n",
       "      <td>0.252693</td>\n",
       "      <td>0.094937</td>\n",
       "      <td>0.158044</td>\n",
       "      <td>0.222022</td>\n",
       "      <td>0.386982</td>\n",
       "      <td>0.475236</td>\n",
       "      <td>0.512096</td>\n",
       "      <td>0.204177</td>\n",
       "      <td>...</td>\n",
       "      <td>0.542520</td>\n",
       "      <td>4.174338</td>\n",
       "      <td>41.654450</td>\n",
       "      <td>0.028375</td>\n",
       "      <td>0.027987</td>\n",
       "      <td>0.038391</td>\n",
       "      <td>0.024219</td>\n",
       "      <td>0.051119</td>\n",
       "      <td>0.033194</td>\n",
       "      <td>0.070028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000001084</td>\n",
       "      <td>GCLC</td>\n",
       "      <td>0.138687</td>\n",
       "      <td>0.175807</td>\n",
       "      <td>0.178864</td>\n",
       "      <td>0.096468</td>\n",
       "      <td>0.201367</td>\n",
       "      <td>0.212509</td>\n",
       "      <td>0.219834</td>\n",
       "      <td>0.341615</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165218</td>\n",
       "      <td>8.448317</td>\n",
       "      <td>15.988084</td>\n",
       "      <td>0.053895</td>\n",
       "      <td>0.044485</td>\n",
       "      <td>0.087505</td>\n",
       "      <td>0.176240</td>\n",
       "      <td>0.068359</td>\n",
       "      <td>0.090042</td>\n",
       "      <td>0.090069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000001630</td>\n",
       "      <td>CYP51A1</td>\n",
       "      <td>0.213246</td>\n",
       "      <td>0.082860</td>\n",
       "      <td>0.219216</td>\n",
       "      <td>0.056615</td>\n",
       "      <td>0.078965</td>\n",
       "      <td>0.077466</td>\n",
       "      <td>0.473147</td>\n",
       "      <td>0.152790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103071</td>\n",
       "      <td>36.865550</td>\n",
       "      <td>65.837700</td>\n",
       "      <td>0.433891</td>\n",
       "      <td>0.652046</td>\n",
       "      <td>0.490883</td>\n",
       "      <td>0.388910</td>\n",
       "      <td>0.539091</td>\n",
       "      <td>0.493940</td>\n",
       "      <td>0.773056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000002549</td>\n",
       "      <td>LAP3</td>\n",
       "      <td>0.407108</td>\n",
       "      <td>0.410205</td>\n",
       "      <td>0.249512</td>\n",
       "      <td>1.183100</td>\n",
       "      <td>0.440459</td>\n",
       "      <td>0.942635</td>\n",
       "      <td>0.342617</td>\n",
       "      <td>0.662582</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034478</td>\n",
       "      <td>17.282475</td>\n",
       "      <td>64.537000</td>\n",
       "      <td>0.075443</td>\n",
       "      <td>0.029530</td>\n",
       "      <td>0.046583</td>\n",
       "      <td>0.066031</td>\n",
       "      <td>0.070900</td>\n",
       "      <td>0.080483</td>\n",
       "      <td>0.071750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ensembl GeneSymbol  MeanExpression_SchwannCells_1__VoigtEtAl2019_  \\\n",
       "0  ENSG00000000419       DPM1                                       0.175932   \n",
       "1  ENSG00000001036      FUCA2                                       0.252693   \n",
       "2  ENSG00000001084       GCLC                                       0.138687   \n",
       "3  ENSG00000001630    CYP51A1                                       0.213246   \n",
       "4  ENSG00000002549       LAP3                                       0.407108   \n",
       "\n",
       "   MeanExpression_SchwannCells_2__VoigtEtAl2019_  \\\n",
       "0                                       0.229714   \n",
       "1                                       0.094937   \n",
       "2                                       0.175807   \n",
       "3                                       0.082860   \n",
       "4                                       0.410205   \n",
       "\n",
       "   MeanExpression_Melanocytes__VoigtEtAl2019_  \\\n",
       "0                                    0.257885   \n",
       "1                                    0.158044   \n",
       "2                                    0.178864   \n",
       "3                                    0.219216   \n",
       "4                                    0.249512   \n",
       "\n",
       "   MeanExpression_EndothelialCells__VoigtEtAl2019_  \\\n",
       "0                                         0.727541   \n",
       "1                                         0.222022   \n",
       "2                                         0.096468   \n",
       "3                                         0.056615   \n",
       "4                                         1.183100   \n",
       "\n",
       "   MeanExpression_SmoothMuscle__VoigtEtAl2019_  \\\n",
       "0                                     0.311105   \n",
       "1                                     0.386982   \n",
       "2                                     0.201367   \n",
       "3                                     0.078965   \n",
       "4                                     0.440459   \n",
       "\n",
       "   MeanExpression_Fibroblasts__VoigtEtAl2019_  \\\n",
       "0                                    0.333106   \n",
       "1                                    0.475236   \n",
       "2                                    0.212509   \n",
       "3                                    0.077466   \n",
       "4                                    0.942635   \n",
       "\n",
       "   MeanExpression_RPE__VoigtEtAl2019_  MeanExpression_BCells__VoigtEtAl2019_  \\\n",
       "0                            0.302933                               0.418343   \n",
       "1                            0.512096                               0.204177   \n",
       "2                            0.219834                               0.341615   \n",
       "3                            0.473147                               0.152790   \n",
       "4                            0.342617                               0.662582   \n",
       "\n",
       "   ...  MeanExpression_mastCells__VoigtEtAl2019_  \\\n",
       "0  ...                                  0.279661   \n",
       "1  ...                                  0.542520   \n",
       "2  ...                                  0.165218   \n",
       "3  ...                                  0.103071   \n",
       "4  ...                                  0.034478   \n",
       "\n",
       "   MeanExpression_RETINA__WhitmoreEtAl_2014_  \\\n",
       "0                                   1.245000   \n",
       "1                                   4.174338   \n",
       "2                                   8.448317   \n",
       "3                                  36.865550   \n",
       "4                                  17.282475   \n",
       "\n",
       "   MeanExpression_RPE_CHOROID__WhitmoreEtAl_2014_  \\\n",
       "0                                        1.417794   \n",
       "1                                       41.654450   \n",
       "2                                       15.988084   \n",
       "3                                       65.837700   \n",
       "4                                       64.537000   \n",
       "\n",
       "   MeanExpression_RodPhotoreceptors__LiangEtAl_2019_  \\\n",
       "0                                           0.057260   \n",
       "1                                           0.028375   \n",
       "2                                           0.053895   \n",
       "3                                           0.433891   \n",
       "4                                           0.075443   \n",
       "\n",
       "   MeanExpression_AmacrineCells__LiangEtAl_2019_  \\\n",
       "0                                       0.098788   \n",
       "1                                       0.027987   \n",
       "2                                       0.044485   \n",
       "3                                       0.652046   \n",
       "4                                       0.029530   \n",
       "\n",
       "   MeanExpression_BipolarCells__LiangEtAl_2019_  \\\n",
       "0                                      0.070328   \n",
       "1                                      0.038391   \n",
       "2                                      0.087505   \n",
       "3                                      0.490883   \n",
       "4                                      0.046583   \n",
       "\n",
       "   MeanExpression_ConePhotoreceptors__LiangEtAl_2019_  \\\n",
       "0                                           0.048231    \n",
       "1                                           0.024219    \n",
       "2                                           0.176240    \n",
       "3                                           0.388910    \n",
       "4                                           0.066031    \n",
       "\n",
       "   MeanExpression_HorizontalCells__LiangEtAl_2019_  \\\n",
       "0                                         0.085795   \n",
       "1                                         0.051119   \n",
       "2                                         0.068359   \n",
       "3                                         0.539091   \n",
       "4                                         0.070900   \n",
       "\n",
       "   MeanExpression_M_llerGlialCells__LiangEtAl_2019_  \\\n",
       "0                                          0.058392   \n",
       "1                                          0.033194   \n",
       "2                                          0.090042   \n",
       "3                                          0.493940   \n",
       "4                                          0.080483   \n",
       "\n",
       "   MeanExpression_RetinalGanglionCells__LiangEtAl_2019_  \n",
       "0                                           0.146947     \n",
       "1                                           0.070028     \n",
       "2                                           0.090069     \n",
       "3                                           0.773056     \n",
       "4                                           0.071750     \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# set relative path to github repo C:\\Users\\prins\\GitHub\\Human1_RPE-PR\n",
    "import os\n",
    "os.chdir(r'C:\\Users\\prins\\GitHub\\Human1_RPE-PR')\n",
    "\n",
    "# from RPE_PR data folder, read in expression data \n",
    "# (there is also another dataset in the expression data folder that can be used to create cs models)\n",
    "df = pd.read_excel(r'expression data\\expression_data.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86d12177",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ensembl</th>\n",
       "      <th>GeneSymbol</th>\n",
       "      <th>MeanExpression_SchwannCells_1__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_SchwannCells_2__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_Melanocytes__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_EndothelialCells__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_SmoothMuscle__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_Fibroblasts__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_RPE__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_BCells__VoigtEtAl2019_</th>\n",
       "      <th>...</th>\n",
       "      <th>MeanExpression_mastCells__VoigtEtAl2019_</th>\n",
       "      <th>MeanExpression_RETINA__WhitmoreEtAl_2014_</th>\n",
       "      <th>MeanExpression_RPE_CHOROID__WhitmoreEtAl_2014_</th>\n",
       "      <th>MeanExpression_RodPhotoreceptors__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_AmacrineCells__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_BipolarCells__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_ConePhotoreceptors__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_HorizontalCells__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_M_llerGlialCells__LiangEtAl_2019_</th>\n",
       "      <th>MeanExpression_RetinalGanglionCells__LiangEtAl_2019_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000000419</td>\n",
       "      <td>DPM1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000001036</td>\n",
       "      <td>FUCA2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000001084</td>\n",
       "      <td>GCLC</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000001630</td>\n",
       "      <td>CYP51A1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000002549</td>\n",
       "      <td>LAP3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           Ensembl GeneSymbol  MeanExpression_SchwannCells_1__VoigtEtAl2019_  \\\n",
       "0  ENSG00000000419       DPM1                                            2.0   \n",
       "1  ENSG00000001036      FUCA2                                            2.0   \n",
       "2  ENSG00000001084       GCLC                                            2.0   \n",
       "3  ENSG00000001630    CYP51A1                                            2.0   \n",
       "4  ENSG00000002549       LAP3                                            3.0   \n",
       "\n",
       "   MeanExpression_SchwannCells_2__VoigtEtAl2019_  \\\n",
       "0                                            2.0   \n",
       "1                                            2.0   \n",
       "2                                            2.0   \n",
       "3                                            2.0   \n",
       "4                                            3.0   \n",
       "\n",
       "   MeanExpression_Melanocytes__VoigtEtAl2019_  \\\n",
       "0                                         2.0   \n",
       "1                                         2.0   \n",
       "2                                         2.0   \n",
       "3                                         2.0   \n",
       "4                                         2.0   \n",
       "\n",
       "   MeanExpression_EndothelialCells__VoigtEtAl2019_  \\\n",
       "0                                              3.0   \n",
       "1                                              2.0   \n",
       "2                                              2.0   \n",
       "3                                              2.0   \n",
       "4                                              3.0   \n",
       "\n",
       "   MeanExpression_SmoothMuscle__VoigtEtAl2019_  \\\n",
       "0                                          2.0   \n",
       "1                                          3.0   \n",
       "2                                          2.0   \n",
       "3                                          2.0   \n",
       "4                                          3.0   \n",
       "\n",
       "   MeanExpression_Fibroblasts__VoigtEtAl2019_  \\\n",
       "0                                         3.0   \n",
       "1                                         3.0   \n",
       "2                                         2.0   \n",
       "3                                         2.0   \n",
       "4                                         3.0   \n",
       "\n",
       "   MeanExpression_RPE__VoigtEtAl2019_  MeanExpression_BCells__VoigtEtAl2019_  \\\n",
       "0                                 2.0                                    3.0   \n",
       "1                                 3.0                                    2.0   \n",
       "2                                 2.0                                    3.0   \n",
       "3                                 3.0                                    2.0   \n",
       "4                                 2.0                                    3.0   \n",
       "\n",
       "   ...  MeanExpression_mastCells__VoigtEtAl2019_  \\\n",
       "0  ...                                       3.0   \n",
       "1  ...                                       3.0   \n",
       "2  ...                                       2.0   \n",
       "3  ...                                       2.0   \n",
       "4  ...                                       2.0   \n",
       "\n",
       "   MeanExpression_RETINA__WhitmoreEtAl_2014_  \\\n",
       "0                                        1.0   \n",
       "1                                        2.0   \n",
       "2                                        2.0   \n",
       "3                                        3.0   \n",
       "4                                        2.0   \n",
       "\n",
       "   MeanExpression_RPE_CHOROID__WhitmoreEtAl_2014_  \\\n",
       "0                                             1.0   \n",
       "1                                             3.0   \n",
       "2                                             2.0   \n",
       "3                                             3.0   \n",
       "4                                             3.0   \n",
       "\n",
       "   MeanExpression_RodPhotoreceptors__LiangEtAl_2019_  \\\n",
       "0                                                2.0   \n",
       "1                                                2.0   \n",
       "2                                                2.0   \n",
       "3                                                3.0   \n",
       "4                                                2.0   \n",
       "\n",
       "   MeanExpression_AmacrineCells__LiangEtAl_2019_  \\\n",
       "0                                            2.0   \n",
       "1                                            2.0   \n",
       "2                                            2.0   \n",
       "3                                            3.0   \n",
       "4                                            2.0   \n",
       "\n",
       "   MeanExpression_BipolarCells__LiangEtAl_2019_  \\\n",
       "0                                           2.0   \n",
       "1                                           2.0   \n",
       "2                                           2.0   \n",
       "3                                           3.0   \n",
       "4                                           2.0   \n",
       "\n",
       "   MeanExpression_ConePhotoreceptors__LiangEtAl_2019_  \\\n",
       "0                                                2.0    \n",
       "1                                                2.0    \n",
       "2                                                2.0    \n",
       "3                                                3.0    \n",
       "4                                                2.0    \n",
       "\n",
       "   MeanExpression_HorizontalCells__LiangEtAl_2019_  \\\n",
       "0                                              2.0   \n",
       "1                                              2.0   \n",
       "2                                              2.0   \n",
       "3                                              3.0   \n",
       "4                                              2.0   \n",
       "\n",
       "   MeanExpression_M_llerGlialCells__LiangEtAl_2019_  \\\n",
       "0                                               2.0   \n",
       "1                                               2.0   \n",
       "2                                               2.0   \n",
       "3                                               3.0   \n",
       "4                                               2.0   \n",
       "\n",
       "   MeanExpression_RetinalGanglionCells__LiangEtAl_2019_  \n",
       "0                                                2.0     \n",
       "1                                                2.0     \n",
       "2                                                2.0     \n",
       "3                                                3.0     \n",
       "4                                                2.0     \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "conditions = df.columns[2:]\n",
    "df_discretized = df.copy(deep=True)\n",
    "\n",
    "for c in conditions:\n",
    "    q25 = df_discretized[c].quantile(0.25)\n",
    "    q75 = df_discretized[c].quantile(0.75)\n",
    "\n",
    "    df_discretized.loc[df_discretized[c] < 0.00001, c] = -1  # negative confidence (NC)\n",
    "    df_discretized.loc[(df_discretized[c] <= q25) & (df_discretized[c] >  0.00001), c] = -10  # 1 'LC'\n",
    "    df_discretized.loc[(df_discretized[c] <= q75) & (df_discretized[c] > q25), c] = -20  # 2 'MC'\n",
    "    df_discretized.loc[df_discretized[c] > q75, c] = -30  # 3 'HC'\n",
    "    \n",
    "\n",
    "    df_discretized.loc[df_discretized[c] == -10, c] = 1\n",
    "    df_discretized.loc[df_discretized[c] == -20, c] = 2\n",
    "    df_discretized.loc[df_discretized[c] == -30, c] = 3\n",
    "\n",
    "# Replace NA values with 0\n",
    "df_discretized.fillna(0, inplace=True)\n",
    "\n",
    "# Display the head of the modified DataFrame\n",
    "df_discretized.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01745f98",
   "metadata": {},
   "source": [
    "### load Human1 template model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7bfef9da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Name</strong></td>\n",
       "                <td>HumanGEM</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>2b252aab820</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of metabolites</strong></td>\n",
       "                <td>8499</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of reactions</strong></td>\n",
       "                <td>13085</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of genes</strong></td>\n",
       "                <td>2897</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of groups</strong></td>\n",
       "                <td>149</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Objective expression</strong></td>\n",
       "                <td>1.0*MAR13082 - 1.0*MAR13082_reverse_11d67</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Compartments</strong></td>\n",
       "                <td>Cytosol, Extracellular, Lysosome, Endoplasmic reticulum, Mitochondria, Peroxisome, Golgi apparatus, Nucleus, Inner mitochondria</td>\n",
       "            </tr>\n",
       "          </table>"
      ],
      "text/plain": [
       "<Model HumanGEM at 0x2b252aab820>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from cobra.io import read_sbml_model\n",
    "mod = read_sbml_model(r'models\\template\\Human-GEM_28062023.xml')\n",
    "mod"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc806e0b",
   "metadata": {},
   "source": [
    "### Map expression to reactions to create cell-specific models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6397b017",
   "metadata": {},
   "outputs": [],
   "source": [
    "from corda import reaction_confidence\n",
    "from cobra import Configuration\n",
    "from corda import CORDA\n",
    "from cobra.io import write_sbml_model\n",
    "import datetime\n",
    "from pathlib import Path\n",
    "\n",
    "config = Configuration()\n",
    "config.solver = \"glpk\"\n",
    "now = datetime.datetime.now()\n",
    "date = now.strftime(\"%Y%m%d\")\n",
    "\n",
    "# create folder if it does not exist Path().cwd() / \"models\" / \"cs_mods\" / \"date\"\n",
    "folder_path = Path().cwd() / \"models\" / \"cs_mods\" / date\n",
    "folder_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "i=0\n",
    "models={} # empty dict to place new models in\n",
    "for c in conditions:\n",
    "    mod_c = mod.copy() # make deep copy of basis model 'mod' (Human1) for every cell-specific model\n",
    "    mod_c.id = c\n",
    "    gene_conf_dict = dict([(i,x) for i, x in zip(df_discretized.Ensembl, df_discretized[c])]) # gene confidences dictionary\n",
    "    rxn_conf_dict = {rxn.id:reaction_confidence(rxn, gene_conf_dict) for rxn in mod.reactions} # reaction confidence dictionary\n",
    "    mod_CORDA = CORDA(mod, rxn_conf_dict) \n",
    "    mod_CORDA.build()\n",
    "    print(c)\n",
    "    print(mod_CORDA)\n",
    "    mod_c = mod_CORDA.cobra_model()\n",
    "    models[i] = mod_c\n",
    "    model_name = f\"{c}.xml\"\n",
    "    write_sbml_model(mod_c, folder_path / model_name)\n",
    "    i = i + 1"
   ]
  }
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