DHFR_PCA_data.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "077b3903-d053-4c04-9f93-d5a627269ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import os\n",
    "import subprocess\n",
    "\n",
    "import random as random\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c89daf0-3a21-4616-b934-7cd9cace5663",
   "metadata": {},
   "source": [
    "## Analyze PCA data for E64V and R73G\n",
    "Many DHFR[3] variants had to be dropped due to mutations in the Fcy1 or linker sequences, which came in after the growth curves were ran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ad2cb65-f02f-4388-a8b4-01eb3ac0ce45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B2', 'B3', 'C2', 'C3']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_96_to_row_384 = {'A':['B', 'C'], \n",
    "                     'B':['D', 'E'],\n",
    "                     'C':['F', 'G'],\n",
    "                     'D':['H', 'I'],\n",
    "                     'E':['J', 'K'],\n",
    "                     'F':['L', 'M'],\n",
    "                     'G':['N', 'O']}\n",
    "\n",
    "col_96_to_col_384 = {'1':['2','3'],\n",
    "                     '2':['4','5'],\n",
    "                     '3':['6','7'],\n",
    "                     '4':['8','9'],\n",
    "                     '5':['10','11'],\n",
    "                     '6':['12','13'],\n",
    "                     '7':['14','15'],\n",
    "                     '8':['16','17'],\n",
    "                     '9':['18','19'],\n",
    "                     '10':['20','21'],\n",
    "                     '11':['22','23']}\n",
    "\n",
    "def get_384_wells_from_96(well):\n",
    "    row = well[0]\n",
    "    col = well[1:]\n",
    "    \n",
    "    well_list = []\n",
    "    \n",
    "    for y in row_96_to_row_384[row]:\n",
    "        for x in col_96_to_col_384[col]:\n",
    "            coord = y+x\n",
    "            \n",
    "            well_list.append(coord)\n",
    "            \n",
    "    return well_list\n",
    "\n",
    "get_384_wells_from_96('A1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8001024a-4a19-4af7-9ade-cf0bf313881d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_derivative_growth_rate(well, df, tmax=25):\n",
    "    # well : a tuple of the format (row, col)\n",
    "    # source : DataFrame where well was measured\n",
    "    # tmax : defines the interval in which the function will measure the growth rate, from t=0 to t=tmax\n",
    "    \n",
    "    df_cols = [ x for x in list(df.index) if x<=tmax]\n",
    "    # define time range\n",
    "    \n",
    "    df_data = df.loc[df_cols]\n",
    "    # select the subset of the DataFrame in the selected timerange\n",
    "    data = df_data[(well[0]+str(well[1]))]\n",
    "    # \n",
    "\n",
    "    data_diff = data.diff()*4\n",
    "    \n",
    "    get_top_5 = data_diff.nlargest(5)\n",
    "    \n",
    "    growth_rate = np.median(get_top_5)\n",
    "    std_err = np.std(get_top_5)\n",
    "    \n",
    "    return growth_rate, std_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e3b8395f-3ad2-4c04-8376-90df7076aad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_table(imported_df):\n",
    "    \n",
    "    rename_dict = {}\n",
    "\n",
    "\n",
    "    for col_name in imported_df.columns:\n",
    "        to_hours = np.float64(col_name.strip('s'))/(60*60)\n",
    "        rename_dict[col_name] = to_hours\n",
    "    \n",
    "    imported_df.rename(columns = rename_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c4c61ed-287d-4771-bfbd-a92dc8e8490b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>source_plate</th>\n",
       "      <th>source_row</th>\n",
       "      <th>source_col</th>\n",
       "      <th>dest_row</th>\n",
       "      <th>dest_col</th>\n",
       "      <th>mata</th>\n",
       "      <th>matalpha</th>\n",
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       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mata1_MATal1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>H62C_1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mata1_MATal1</td>\n",
       "      <td>D</td>\n",
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       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>E64V_3x_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mata1_MATal2</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>R73G_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mata1_MATal1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>R73_3x_1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mata1_MATal1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>L74P_1</td>\n",
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       "      <th>...</th>\n",
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       "      <th>65</th>\n",
       "      <td>Mata2_MATal2</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>R73G_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Mata2_MATal1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>R73_3x_1</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Mata2_MATal1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Mata2_MATal1</td>\n",
       "      <td>G</td>\n",
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       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>M100W_1</td>\n",
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       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Mata2_MATal1</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>D155S_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 7 columns</p>\n",
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      "text/plain": [
       "       source_plate source_row  source_col dest_row  dest_col  mata   matalpha\n",
       "index                                                                         \n",
       "0      Mata1_MATal1          C           1        A         1  WT_1     H62C_1\n",
       "1      Mata1_MATal1          D           1        B         1  WT_1  E64V_3x_1\n",
       "2      Mata1_MATal2          D           1        C         1  WT_1     R73G_2\n",
       "3      Mata1_MATal1          E           1        D         1  WT_1   R73_3x_1\n",
       "4      Mata1_MATal1          F           1        E         1  WT_1     L74P_1\n",
       "...             ...        ...         ...      ...       ...   ...        ...\n",
       "65     Mata2_MATal2          D           1        C        11  WT_2     R73G_2\n",
       "66     Mata2_MATal1          E           1        D        11  WT_2   R73_3x_1\n",
       "67     Mata2_MATal1          F           1        E        11  WT_2     L74P_1\n",
       "68     Mata2_MATal1          G           1        F        11  WT_2    M100W_1\n",
       "69     Mata2_MATal1          H           1        G        11  WT_2    D155S_1\n",
       "\n",
       "[70 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_array = pd.read_excel('./misc_data/array_diploid_PCA_96_well.xlsx', index_col=0)\n",
    "pca_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bd924534-a85c-459b-b235-99a0efe83e0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_array['DMSO_1_well'] = 'none'\n",
    "pca_array['DMSO_2_well'] = 'none'\n",
    "pca_array['MTX_1_well'] = 'none'\n",
    "pca_array['MTX_2_well'] = 'none'\n",
    "\n",
    "for row in list(pca_array.index):\n",
    "    \n",
    "    well = pca_array.loc[row]['dest_row']+str(pca_array.loc[row]['dest_col'])\n",
    "    get_384_wells_from_96(well)\n",
    "\n",
    "    pca_array.at[row, 'DMSO_1_well'], pca_array.at[row, 'DMSO_2_well'], pca_array.at[row, 'MTX_1_well'], pca_array.at[row, 'MTX_2_well'] = get_384_wells_from_96(well)\n",
    "    \n",
    "strain_subset = pca_array[(pca_array['matalpha'].isin(['E64V_3x_1', 'R73G_2']))&(pca_array['mata']!='R53D_1')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5737c8b8-307d-4650-9735-fb0aff294255",
   "metadata": {},
   "outputs": [
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       "      <td>0.092382</td>\n",
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       "      <td>0.093386</td>\n",
       "      <td>0.093276</td>\n",
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       "      <td>0.093376</td>\n",
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       "      <td>0.094526</td>\n",
       "      <td>0.094564</td>\n",
       "      <td>0.094514</td>\n",
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       "      <td>0.094241</td>\n",
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       "      <td>0.094213</td>\n",
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       "      <td>0.094357</td>\n",
       "      <td>0.094339</td>\n",
       "      <td>0.094305</td>\n",
       "      <td>0.094407</td>\n",
       "      <td>0.094410</td>\n",
       "      <td>0.094409</td>\n",
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       "      <td>0.094520</td>\n",
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       "      <td>0.092794</td>\n",
       "      <td>0.092783</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.095161</td>\n",
       "      <td>0.095244</td>\n",
       "      <td>0.095131</td>\n",
       "      <td>0.095053</td>\n",
       "      <td>0.095150</td>\n",
       "      <td>0.094954</td>\n",
       "      <td>0.094992</td>\n",
       "      <td>0.094869</td>\n",
       "      <td>0.095038</td>\n",
       "      <td>0.095009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P21</th>\n",
       "      <td>0.095277</td>\n",
       "      <td>0.095425</td>\n",
       "      <td>0.095432</td>\n",
       "      <td>0.094619</td>\n",
       "      <td>0.094523</td>\n",
       "      <td>0.094555</td>\n",
       "      <td>0.094400</td>\n",
       "      <td>0.094038</td>\n",
       "      <td>0.094339</td>\n",
       "      <td>0.094204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094325</td>\n",
       "      <td>0.094303</td>\n",
       "      <td>0.094237</td>\n",
       "      <td>0.094272</td>\n",
       "      <td>0.094516</td>\n",
       "      <td>0.094240</td>\n",
       "      <td>0.094352</td>\n",
       "      <td>0.094296</td>\n",
       "      <td>0.094503</td>\n",
       "      <td>0.094396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P22</th>\n",
       "      <td>0.093804</td>\n",
       "      <td>0.093909</td>\n",
       "      <td>0.093674</td>\n",
       "      <td>0.092977</td>\n",
       "      <td>0.092891</td>\n",
       "      <td>0.092958</td>\n",
       "      <td>0.092797</td>\n",
       "      <td>0.092681</td>\n",
       "      <td>0.092839</td>\n",
       "      <td>0.092548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093531</td>\n",
       "      <td>0.093555</td>\n",
       "      <td>0.093410</td>\n",
       "      <td>0.093473</td>\n",
       "      <td>0.093591</td>\n",
       "      <td>0.093571</td>\n",
       "      <td>0.093543</td>\n",
       "      <td>0.093422</td>\n",
       "      <td>0.093506</td>\n",
       "      <td>0.093536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P23</th>\n",
       "      <td>0.096310</td>\n",
       "      <td>0.096264</td>\n",
       "      <td>0.096408</td>\n",
       "      <td>0.096167</td>\n",
       "      <td>0.096056</td>\n",
       "      <td>0.096078</td>\n",
       "      <td>0.096097</td>\n",
       "      <td>0.096006</td>\n",
       "      <td>0.096102</td>\n",
       "      <td>0.095995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096214</td>\n",
       "      <td>0.096108</td>\n",
       "      <td>0.096071</td>\n",
       "      <td>0.096237</td>\n",
       "      <td>0.096356</td>\n",
       "      <td>0.096277</td>\n",
       "      <td>0.096191</td>\n",
       "      <td>0.096072</td>\n",
       "      <td>0.096230</td>\n",
       "      <td>0.096145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P24</th>\n",
       "      <td>0.095831</td>\n",
       "      <td>0.095819</td>\n",
       "      <td>0.095976</td>\n",
       "      <td>0.095891</td>\n",
       "      <td>0.095850</td>\n",
       "      <td>0.095742</td>\n",
       "      <td>0.095734</td>\n",
       "      <td>0.095721</td>\n",
       "      <td>0.095741</td>\n",
       "      <td>0.095602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095298</td>\n",
       "      <td>0.095347</td>\n",
       "      <td>0.095305</td>\n",
       "      <td>0.095344</td>\n",
       "      <td>0.095483</td>\n",
       "      <td>0.095580</td>\n",
       "      <td>0.095330</td>\n",
       "      <td>0.095271</td>\n",
       "      <td>0.095341</td>\n",
       "      <td>0.095292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 288 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0.000000   0.250000   0.500000   0.750000   0.999722   1.249722   \\\n",
       "A1    0.092777   0.092580   0.092675   0.092633   0.092532   0.092451   \n",
       "A2    0.093736   0.093525   0.093567   0.093525   0.093412   0.093293   \n",
       "A3    0.094562   0.094526   0.094564   0.094514   0.094382   0.094241   \n",
       "A4    0.093135   0.093020   0.093076   0.092938   0.092771   0.092657   \n",
       "A5    0.091935   0.091855   0.091865   0.091761   0.091577   0.091465   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "P20   0.093875   0.094114   0.093918   0.093438   0.093503   0.093715   \n",
       "P21   0.095277   0.095425   0.095432   0.094619   0.094523   0.094555   \n",
       "P22   0.093804   0.093909   0.093674   0.092977   0.092891   0.092958   \n",
       "P23   0.096310   0.096264   0.096408   0.096167   0.096056   0.096078   \n",
       "P24   0.095831   0.095819   0.095976   0.095891   0.095850   0.095742   \n",
       "\n",
       "     1.499722   1.749722   1.999722   2.249722   ...  69.495833  69.745833  \\\n",
       "A1    0.092504   0.092382   0.092429   0.092439  ...   0.092335   0.092315   \n",
       "A2    0.093401   0.093224   0.093323   0.093300  ...   0.093346   0.093365   \n",
       "A3    0.094362   0.094116   0.094238   0.094213  ...   0.094376   0.094357   \n",
       "A4    0.092790   0.092567   0.092647   0.092628  ...   0.092774   0.092794   \n",
       "A5    0.091561   0.091344   0.091398   0.091438  ...   0.091573   0.091563   \n",
       "..         ...        ...        ...        ...  ...        ...        ...   \n",
       "P20   0.093578   0.093130   0.093378   0.093377  ...   0.095161   0.095244   \n",
       "P21   0.094400   0.094038   0.094339   0.094204  ...   0.094325   0.094303   \n",
       "P22   0.092797   0.092681   0.092839   0.092548  ...   0.093531   0.093555   \n",
       "P23   0.096097   0.096006   0.096102   0.095995  ...   0.096214   0.096108   \n",
       "P24   0.095734   0.095721   0.095741   0.095602  ...   0.095298   0.095347   \n",
       "\n",
       "     69.995833  70.245833  70.495833  70.745833  70.995833  71.245556  \\\n",
       "A1    0.092377   0.092270   0.092320   0.092309   0.092291   0.092346   \n",
       "A2    0.093386   0.093276   0.093332   0.093376   0.093414   0.093368   \n",
       "A3    0.094339   0.094305   0.094407   0.094410   0.094409   0.094362   \n",
       "A4    0.092783   0.092796   0.092874   0.092824   0.092853   0.092806   \n",
       "A5    0.091597   0.091559   0.091570   0.091578   0.091579   0.091500   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "P20   0.095131   0.095053   0.095150   0.094954   0.094992   0.094869   \n",
       "P21   0.094237   0.094272   0.094516   0.094240   0.094352   0.094296   \n",
       "P22   0.093410   0.093473   0.093591   0.093571   0.093543   0.093422   \n",
       "P23   0.096071   0.096237   0.096356   0.096277   0.096191   0.096072   \n",
       "P24   0.095305   0.095344   0.095483   0.095580   0.095330   0.095271   \n",
       "\n",
       "     71.495556  71.745556  \n",
       "A1    0.092441   0.092393  \n",
       "A2    0.093534   0.093451  \n",
       "A3    0.094520   0.094423  \n",
       "A4    0.093007   0.092857  \n",
       "A5    0.091703   0.091629  \n",
       "..         ...        ...  \n",
       "P20   0.095038   0.095009  \n",
       "P21   0.094503   0.094396  \n",
       "P22   0.093506   0.093536  \n",
       "P23   0.096230   0.096145  \n",
       "P24   0.095341   0.095292  \n",
       "\n",
       "[384 rows x 288 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_data = pd.read_excel('./misc_data/384_DHFR_PCA.xlsx', skiprows=[0,2]+[x for x in range(387,419)], index_col=0)\n",
    "format_table(pca_data)\n",
    "pca_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e6da6bcc-6d67-4b24-8765-e16683d855da",
   "metadata": {},
   "outputs": [],
   "source": [
    "## will use t=40 for max growth rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c2415c27-e705-4f6f-af23-6cd7b8db49e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_array['DMSO_1'] = np.NaN\n",
    "pca_array['DMSO_2'] = np.NaN\n",
    "pca_array['MTX_1'] = np.NaN\n",
    "pca_array['MTX_2'] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0c06f5d2-fad3-4da2-b243-3bb90c81d050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_plate</th>\n",
       "      <th>source_row</th>\n",
       "      <th>source_col</th>\n",
       "      <th>dest_row</th>\n",
       "      <th>dest_col</th>\n",
       "      <th>mata</th>\n",
       "      <th>matalpha</th>\n",
       "      <th>DMSO_1_well</th>\n",
       "      <th>DMSO_2_well</th>\n",
       "      <th>MTX_1_well</th>\n",
       "      <th>MTX_2_well</th>\n",
       "      <th>DMSO_1</th>\n",
       "      <th>DMSO_2</th>\n",
       "      <th>MTX_1</th>\n",
       "      <th>MTX_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mata1_MATal1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>H62C_1</td>\n",
       "      <td>B2</td>\n",
       "      <td>B3</td>\n",
       "      <td>C2</td>\n",
       "      <td>C3</td>\n",
       "      <td>0.17340</td>\n",
       "      <td>0.16520</td>\n",
       "      <td>0.07360</td>\n",
       "      <td>0.07400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mata1_MATal1</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>E64V_3x_1</td>\n",
       "      <td>D2</td>\n",
       "      <td>D3</td>\n",
       "      <td>E2</td>\n",
       "      <td>E3</td>\n",
       "      <td>0.15600</td>\n",
       "      <td>0.16304</td>\n",
       "      <td>0.09156</td>\n",
       "      <td>0.09016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mata1_MATal2</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>R73G_2</td>\n",
       "      <td>F2</td>\n",
       "      <td>F3</td>\n",
       "      <td>G2</td>\n",
       "      <td>G3</td>\n",
       "      <td>0.16000</td>\n",
       "      <td>0.16104</td>\n",
       "      <td>0.02004</td>\n",
       "      <td>0.02012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mata1_MATal1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>R73_3x_1</td>\n",
       "      <td>H2</td>\n",
       "      <td>H3</td>\n",
       "      <td>I2</td>\n",
       "      <td>I3</td>\n",
       "      <td>0.16176</td>\n",
       "      <td>0.16160</td>\n",
       "      <td>0.01388</td>\n",
       "      <td>0.01228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mata1_MATal1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>WT_1</td>\n",
       "      <td>L74P_1</td>\n",
       "      <td>J2</td>\n",
       "      <td>J3</td>\n",
       "      <td>K2</td>\n",
       "      <td>K3</td>\n",
       "      <td>0.15476</td>\n",
       "      <td>0.16260</td>\n",
       "      <td>0.01568</td>\n",
       "      <td>0.01616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Mata2_MATal2</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>R73G_2</td>\n",
       "      <td>F22</td>\n",
       "      <td>F23</td>\n",
       "      <td>G22</td>\n",
       "      <td>G23</td>\n",
       "      <td>0.16708</td>\n",
       "      <td>0.17720</td>\n",
       "      <td>0.02860</td>\n",
       "      <td>0.02852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Mata2_MATal1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>R73_3x_1</td>\n",
       "      <td>H22</td>\n",
       "      <td>H23</td>\n",
       "      <td>I22</td>\n",
       "      <td>I23</td>\n",
       "      <td>0.13960</td>\n",
       "      <td>0.15760</td>\n",
       "      <td>0.01492</td>\n",
       "      <td>0.01496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Mata2_MATal1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>L74P_1</td>\n",
       "      <td>J22</td>\n",
       "      <td>J23</td>\n",
       "      <td>K22</td>\n",
       "      <td>K23</td>\n",
       "      <td>0.16640</td>\n",
       "      <td>0.15560</td>\n",
       "      <td>0.01828</td>\n",
       "      <td>0.01620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Mata2_MATal1</td>\n",
       "      <td>G</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>M100W_1</td>\n",
       "      <td>L22</td>\n",
       "      <td>L23</td>\n",
       "      <td>M22</td>\n",
       "      <td>M23</td>\n",
       "      <td>0.16960</td>\n",
       "      <td>0.16864</td>\n",
       "      <td>0.02200</td>\n",
       "      <td>0.02236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Mata2_MATal1</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>11</td>\n",
       "      <td>WT_2</td>\n",
       "      <td>D155S_1</td>\n",
       "      <td>N22</td>\n",
       "      <td>N23</td>\n",
       "      <td>O22</td>\n",
       "      <td>O23</td>\n",
       "      <td>0.16160</td>\n",
       "      <td>0.16600</td>\n",
       "      <td>0.03252</td>\n",
       "      <td>0.03352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       source_plate source_row  source_col dest_row  dest_col  mata  \\\n",
       "index                                                                 \n",
       "0      Mata1_MATal1          C           1        A         1  WT_1   \n",
       "1      Mata1_MATal1          D           1        B         1  WT_1   \n",
       "2      Mata1_MATal2          D           1        C         1  WT_1   \n",
       "3      Mata1_MATal1          E           1        D         1  WT_1   \n",
       "4      Mata1_MATal1          F           1        E         1  WT_1   \n",
       "...             ...        ...         ...      ...       ...   ...   \n",
       "65     Mata2_MATal2          D           1        C        11  WT_2   \n",
       "66     Mata2_MATal1          E           1        D        11  WT_2   \n",
       "67     Mata2_MATal1          F           1        E        11  WT_2   \n",
       "68     Mata2_MATal1          G           1        F        11  WT_2   \n",
       "69     Mata2_MATal1          H           1        G        11  WT_2   \n",
       "\n",
       "        matalpha DMSO_1_well DMSO_2_well MTX_1_well MTX_2_well   DMSO_1  \\\n",
       "index                                                                     \n",
       "0         H62C_1          B2          B3         C2         C3  0.17340   \n",
       "1      E64V_3x_1          D2          D3         E2         E3  0.15600   \n",
       "2         R73G_2          F2          F3         G2         G3  0.16000   \n",
       "3       R73_3x_1          H2          H3         I2         I3  0.16176   \n",
       "4         L74P_1          J2          J3         K2         K3  0.15476   \n",
       "...          ...         ...         ...        ...        ...      ...   \n",
       "65        R73G_2         F22         F23        G22        G23  0.16708   \n",
       "66      R73_3x_1         H22         H23        I22        I23  0.13960   \n",
       "67        L74P_1         J22         J23        K22        K23  0.16640   \n",
       "68       M100W_1         L22         L23        M22        M23  0.16960   \n",
       "69       D155S_1         N22         N23        O22        O23  0.16160   \n",
       "\n",
       "        DMSO_2    MTX_1    MTX_2  \n",
       "index                             \n",
       "0      0.16520  0.07360  0.07400  \n",
       "1      0.16304  0.09156  0.09016  \n",
       "2      0.16104  0.02004  0.02012  \n",
       "3      0.16160  0.01388  0.01228  \n",
       "4      0.16260  0.01568  0.01616  \n",
       "...        ...      ...      ...  \n",
       "65     0.17720  0.02860  0.02852  \n",
       "66     0.15760  0.01492  0.01496  \n",
       "67     0.15560  0.01828  0.01620  \n",
       "68     0.16864  0.02200  0.02236  \n",
       "69     0.16600  0.03252  0.03352  \n",
       "\n",
       "[70 rows x 15 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for row in list(pca_array.index):\n",
    "    \n",
    "    dmso_1_well = pca_array.loc[row]['DMSO_1_well']\n",
    "    pca_array.at[row, 'DMSO_1'] = get_derivative_growth_rate(tuple([dmso_1_well[0], int(dmso_1_well[1:])]), pca_data.T, tmax=40)[0]\n",
    "    \n",
    "    dmso_2_well = pca_array.loc[row]['DMSO_2_well']\n",
    "    pca_array.at[row, 'DMSO_2'] = get_derivative_growth_rate(tuple([dmso_2_well[0], int(dmso_2_well[1:])]), pca_data.T, tmax=40)[0]\n",
    "    \n",
    "    \n",
    "    mtx_1_well = pca_array.loc[row]['MTX_1_well']\n",
    "    pca_array.at[row, 'MTX_1'] = get_derivative_growth_rate(tuple([mtx_1_well[0], int(mtx_1_well[1:])]), pca_data.T, tmax=40)[0]\n",
    "    \n",
    "    mtx_2_well = pca_array.loc[row]['MTX_2_well']\n",
    "    pca_array.at[row, 'MTX_2'] = get_derivative_growth_rate(tuple([mtx_2_well[0], int(mtx_2_well[1:])]), pca_data.T, tmax=40)[0]\n",
    "\n",
    "pca_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "571959f4-6241-4509-a035-4acd815d596d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_array['DMSO_mean'] = (pca_array['DMSO_1']+pca_array['DMSO_2'])/2\n",
    "pca_array['MTX_mean'] = (pca_array['MTX_1']+pca_array['MTX_2'])/2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e8c097f-04b7-432c-85cb-a53b4b23bf2a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d876828f-4063-48a5-bbb9-6b28ebd7e425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.826625386996904, pvalue=2.3456553098215367e-05)\n",
      "SpearmanrResult(correlation=0.9932887229730295, pvalue=2.0262802685416723e-16)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), facecolor='white')\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "plt.scatter(strain_subset['DMSO_1'], strain_subset['DMSO_2'], color='grey')\n",
    "print(stats.spearmanr(strain_subset['DMSO_1'], strain_subset['DMSO_2']))\n",
    "\n",
    "vals = stats.spearmanr(strain_subset['DMSO_1'], strain_subset['DMSO_2'])\n",
    "plt.text(0.075,0.172, r'$\\rho$='+str(np.round(vals[0], 3)), fontsize=14)\n",
    "plt.text(0.075,0.165, 'pval='+str(np.format_float_scientific(vals[1],3)), fontsize=14)\n",
    "\n",
    "\n",
    "\n",
    "plt.scatter(strain_subset['MTX_1'], strain_subset['MTX_2'], color='k')\n",
    "print(stats.spearmanr(strain_subset['MTX_1'], strain_subset['MTX_2']))\n",
    "\n",
    "\n",
    "vals = stats.spearmanr(strain_subset['MTX_1'], strain_subset['MTX_2'])\n",
    "plt.text(0.075,0.055, r'$\\rho$='+str(np.round(vals[0], 3)), fontsize=14)\n",
    "plt.text(0.075,0.048, 'pval='+str(np.format_float_scientific(vals[1],3)), fontsize=14)\n",
    "\n",
    "plt.legend(['DMSO', 'MTX'], fontsize=16)\n",
    "plt.xlim(0,0.20)\n",
    "plt.ylim(0,0.20)\n",
    "\n",
    "plt.xlabel('R1 max growth rate (OD/h)', fontsize=16)\n",
    "plt.ylabel('R2 max growth rate (OD/h)', fontsize=16)\n",
    "plt.xticks(fontsize=12.5)\n",
    "plt.yticks(fontsize=12.5)\n",
    "\n",
    "\n",
    "plt.plot([0,0.20], [0,0.20], 'k--')\n",
    "plt.axvline(0.035)\n",
    "plt.axhline(0.035)\n",
    "\n",
    "#################################\n",
    "plt.subplot(122)\n",
    "\n",
    "pca_heatmap_array = strain_subset.pivot_table(index='mata', columns='matalpha', values='MTX_mean')\n",
    "rows = ['WT_1', 'WT_2', 'H62C_1', 'E64V_1', 'R73G_2', 'L74P_1', 'Y79S_2', 'M100W_1', 'D155S_1']\n",
    "g = sns.heatmap(pca_heatmap_array.loc[rows], cmap='magma', cbar_kws=dict(label='Growth rate (OD/h)'))\n",
    "\n",
    "g.figure.axes[-1].yaxis.label.set_size(16)\n",
    "cbar = g.collections[0].colorbar\n",
    "cbar.ax.tick_params(labelsize=13)\n",
    "\n",
    "\n",
    "\n",
    "cbar_kws=dict(label='Growth rate (OD/h)')\n",
    "\n",
    "plt.xticks([0.5,1.5], ['E64V', 'R73G'], fontsize=16)\n",
    "plt.xlabel('DHFR[3]-Fcy1', fontsize=16)\n",
    "\n",
    "plt.yticks([0.5,1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5], ['WT 1', 'WT 2', 'H62C', 'E64V', 'R73G', 'L74P', 'Y79S', 'M100W', 'D155S'], fontsize=16)\n",
    "plt.ylabel('DHFR[1,2]-Fcy1', fontsize=16)\n",
    "\n",
    "\n",
    "ax1.text(-0.04, 0.205, 'a', fontsize=24)\n",
    "ax1.text(0.21, 0.205, 'b', fontsize=24)\n",
    "\n",
    "plt.plot([0.5], [4.5], '*', color='steelblue', ms=18)\n",
    "plt.plot([0.5], [5.5], '*', color='steelblue', ms=18)\n",
    "plt.plot([0.5], [6.5], '*', color='steelblue', ms=18)\n",
    "plt.plot([0.5], [7.5], '*', color='steelblue', ms=18)\n",
    "\n",
    "plt.plot([1.5], [3.5], '*', color='steelblue', ms=18)\n",
    "plt.plot([1.5], [8.5], '*', color='steelblue', ms=18)\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('./figures/PCA_data.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78ee4dc8-7029-4114-ab21-cd04d44fac65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ee81b37-28ab-4b3d-acbf-1b52e48d6db0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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