Feature analysis_pET_without_SWI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import functions\n",
    "from Bio.SeqUtils.ProtParam import ProteinAnalysis\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  pET data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12216, 5)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pET = pd.read_pickle('results/pET_complete.pkl.gz')\n",
    "pET.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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></th>\n",
       "      <th>Accession</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Class</th>\n",
       "      <th>Solubility</th>\n",
       "      <th>Protein</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">pET15</th>\n",
       "      <th>2</th>\n",
       "      <td>BbCD00584211_pET15</td>\n",
       "      <td>ATGGGCCATCACCATCACCATCACAGCCATATGATTTTTGTAACTA...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>MGHHHHHHSHMIFVTKLNGDGYYLNPYHIESIEANPDTTILLMNGK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BcCD00331746_pET15</td>\n",
       "      <td>ATGGGCCATCACCATCACCATCACAGCCATATGGATAAAGAGAATC...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>MGHHHHHHSHMDKENPRVGDKYITVQKVGKKIFEAEVEILEYDAPH...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BcCD00331747_pET15</td>\n",
       "      <td>ATGGGCCATCACCATCACCATCACAGCCATATGGCACATACTACTA...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>MGHHHHHHSHMAHTTTSMEIFGSPEQVWQLIGGFNSLPDWLPYIPS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BcCD00341383_pET15</td>\n",
       "      <td>ATGGGCCATCACCATCACCATCACAGCCATATGGATAAAGAGAATC...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>MGHHHHHHSHMDKENPRVGDKYITVQKVGKKIFEAEVEILEYDAPH...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BcCD00591009_pET15</td>\n",
       "      <td>ATGGGCCATCACCATCACCATCACAGCCATATGAGTTTGAAGGGGA...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>MGHHHHHHSHMSLKGKRIGFGFTGSHCTYEEVMPHLEKLIAEGAEV...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Accession  \\\n",
       "pET15 2  BbCD00584211_pET15   \n",
       "      3  BcCD00331746_pET15   \n",
       "      4  BcCD00331747_pET15   \n",
       "      5  BcCD00341383_pET15   \n",
       "      6  BcCD00591009_pET15   \n",
       "\n",
       "                                                  Sequence  Class  Solubility  \\\n",
       "pET15 2  ATGGGCCATCACCATCACCATCACAGCCATATGATTTTTGTAACTA...      2           1   \n",
       "      3  ATGGGCCATCACCATCACCATCACAGCCATATGGATAAAGAGAATC...      1           0   \n",
       "      4  ATGGGCCATCACCATCACCATCACAGCCATATGGCACATACTACTA...      2           1   \n",
       "      5  ATGGGCCATCACCATCACCATCACAGCCATATGGATAAAGAGAATC...      1           0   \n",
       "      6  ATGGGCCATCACCATCACCATCACAGCCATATGAGTTTGAAGGGGA...      2           1   \n",
       "\n",
       "                                                   Protein  \n",
       "pET15 2  MGHHHHHHSHMIFVTKLNGDGYYLNPYHIESIEANPDTTILLMNGK...  \n",
       "      3  MGHHHHHHSHMDKENPRVGDKYITVQKVGKKIFEAEVEILEYDAPH...  \n",
       "      4  MGHHHHHHSHMAHTTTSMEIFGSPEQVWQLIGGFNSLPDWLPYIPS...  \n",
       "      5  MGHHHHHHSHMDKENPRVGDKYITVQKVGKKIFEAEVEILEYDAPH...  \n",
       "      6  MGHHHHHHSHMSLKGKRIGFGFTGSHCTYEEVMPHLEKLIAEGAEV...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pET.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature analysis\n",
    "\n",
    "pET['Analysed_seq'] = pET['Protein'].apply(ProteinAnalysis)\n",
    "pET['Molecular weight'] = pET['Analysed_seq'].apply(lambda x:x.molecular_weight())\n",
    "pET['Aromaticity'] = pET['Analysed_seq'].apply(lambda x:x.aromaticity())\n",
    "pET['Instability index'] = pET['Analysed_seq'].apply(lambda x: x.instability_index())\n",
    "pET['Isoelectric point'] = pET['Analysed_seq'].apply(lambda x:x.isoelectric_point())\n",
    "pET['Sec_str_fraction'] = pET['Analysed_seq'].apply(lambda x:x.secondary_structure_fraction())\n",
    "pET['Flexibility_scores'] = pET['Analysed_seq'].apply(lambda x:x.flexibility())\n",
    "pET['GRAVY'] = pET['Analysed_seq'].apply(lambda x:x.gravy())\n",
    "\n",
    "#Averaged flexibility\n",
    "pET['Flexibility'] = pET['Flexibility_scores'].apply(np.mean)\n",
    "\n",
    "\n",
    "\n",
    "#separate sec str in helix turn sheets\n",
    "pET['Helix'] = pET['Sec_str_fraction'].apply(lambda x:x[0])\n",
    "pET['Turn'] = pET['Sec_str_fraction'].apply(lambda x:x[1])\n",
    "pET['Sheet'] = pET['Sec_str_fraction'].apply(lambda x:x[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flatui = [\"#d32f2f\", \"#7b1fa2\", \"#039be5\", \"#00897b\", \"#43a047\", \"#ffeb3b\",  \"#3498db\", \"#95a5a6\",  \"#34495e\", \"#2ecc71\" ]\n",
    "colors = functions.sns.color_palette(flatui)\n",
    "\n",
    "\n",
    "\n",
    "df = pET[['Sheet',  'Isoelectric point', 'Turn', 'Instability index' , 'Molecular weight', \\\n",
    "          'Aromaticity', 'Helix', 'GRAVY', 'Flexibility', 'Solubility']].copy()\n",
    "\n",
    "\n",
    "df1 = pET[['Sheet', 'Isoelectric point', 'Turn', 'Instability index' ]].copy()\n",
    "\n",
    "#making some columns negative for auc < 0.5\n",
    "for i, v in enumerate(df1.columns):\n",
    "    if 'Flexibility' not in v and 'Sheet' not in v and 'AAS' not in v:\n",
    "        df1[v] = -df1[v]\n",
    "\n",
    "\n",
    "labels = pET['Solubility'].values\n",
    "functions.make_roc(df1, labels, output=True, fname='ROC_for_pET_0.pdf', c=colors[0:4])\n",
    "\n",
    "##second plot\n",
    "\n",
    "df2 = pET[['Aromaticity', 'Helix' , 'Molecular weight', \\\n",
    "           'GRAVY', 'Flexibility']].copy()\n",
    "\n",
    "\n",
    "#making some columns negative for auc < 0.5\n",
    "for i, v in enumerate(df2.columns):\n",
    "    if 'Flexibility' not in v and 'Sheet' not in v and 'AAS' not in v:\n",
    "        df2[v] = -df2[v]\n",
    "\n",
    "\n",
    "\n",
    "functions.make_roc(df2, labels, output=False, fname='ROC_for_pET_1.pdf', c=colors[4:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "functions.corr_heatmap(df, squared=False, output=False, fname='Heatmap_for_pET_no_SWI.pdf',\\\n",
    "                       xlim=(0,9), ylim=(10,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correlations\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "\n",
    "df_corr = pd.DataFrame() # Correlation matrix\n",
    "df_p = pd.DataFrame()  # Matrix of p-values\n",
    "for x in df.columns:\n",
    "    for y in df.columns:\n",
    "        corr1 = spearmanr(df[x], df[y])\n",
    "        df_corr.loc[x,y] = corr1[0]\n",
    "        df_p.loc[x,y] = corr1[1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlations\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sheet</th>\n",
       "      <th>Isoelectric point</th>\n",
       "      <th>Turn</th>\n",
       "      <th>Instability index</th>\n",
       "      <th>Molecular weight</th>\n",
       "      <th>Aromaticity</th>\n",
       "      <th>Helix</th>\n",
       "      <th>GRAVY</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Solubility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sheet</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.222060</td>\n",
       "      <td>-0.375104</td>\n",
       "      <td>0.220627</td>\n",
       "      <td>0.070636</td>\n",
       "      <td>-0.328068</td>\n",
       "      <td>-0.097355</td>\n",
       "      <td>0.232137</td>\n",
       "      <td>-0.041549</td>\n",
       "      <td>0.040284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isoelectric point</th>\n",
       "      <td>-0.222060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010108</td>\n",
       "      <td>0.072233</td>\n",
       "      <td>-0.124552</td>\n",
       "      <td>0.009231</td>\n",
       "      <td>-0.082686</td>\n",
       "      <td>-0.171168</td>\n",
       "      <td>-0.083467</td>\n",
       "      <td>-0.065772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turn</th>\n",
       "      <td>-0.375104</td>\n",
       "      <td>-0.010108</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073050</td>\n",
       "      <td>0.210953</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>-0.089198</td>\n",
       "      <td>0.182721</td>\n",
       "      <td>0.024328</td>\n",
       "      <td>-0.080815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instability index</th>\n",
       "      <td>0.220627</td>\n",
       "      <td>0.072233</td>\n",
       "      <td>-0.073050</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045370</td>\n",
       "      <td>-0.072715</td>\n",
       "      <td>-0.159392</td>\n",
       "      <td>-0.143305</td>\n",
       "      <td>-0.076660</td>\n",
       "      <td>-0.090254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Molecular weight</th>\n",
       "      <td>0.070636</td>\n",
       "      <td>-0.124552</td>\n",
       "      <td>0.210953</td>\n",
       "      <td>0.045370</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.222074</td>\n",
       "      <td>0.226637</td>\n",
       "      <td>0.249705</td>\n",
       "      <td>-0.037168</td>\n",
       "      <td>-0.162451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aromaticity</th>\n",
       "      <td>-0.328068</td>\n",
       "      <td>0.009231</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>-0.072715</td>\n",
       "      <td>0.222074</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.476667</td>\n",
       "      <td>-0.090969</td>\n",
       "      <td>-0.259083</td>\n",
       "      <td>-0.154726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helix</th>\n",
       "      <td>-0.097355</td>\n",
       "      <td>-0.082686</td>\n",
       "      <td>-0.089198</td>\n",
       "      <td>-0.159392</td>\n",
       "      <td>0.226637</td>\n",
       "      <td>0.476667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.470759</td>\n",
       "      <td>-0.409018</td>\n",
       "      <td>-0.154866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRAVY</th>\n",
       "      <td>0.232137</td>\n",
       "      <td>-0.171168</td>\n",
       "      <td>0.182721</td>\n",
       "      <td>-0.143305</td>\n",
       "      <td>0.249705</td>\n",
       "      <td>-0.090969</td>\n",
       "      <td>0.470759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.600141</td>\n",
       "      <td>-0.170855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flexibility</th>\n",
       "      <td>-0.041549</td>\n",
       "      <td>-0.083467</td>\n",
       "      <td>0.024328</td>\n",
       "      <td>-0.076660</td>\n",
       "      <td>-0.037168</td>\n",
       "      <td>-0.259083</td>\n",
       "      <td>-0.409018</td>\n",
       "      <td>-0.600141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.280697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solubility</th>\n",
       "      <td>0.040284</td>\n",
       "      <td>-0.065772</td>\n",
       "      <td>-0.080815</td>\n",
       "      <td>-0.090254</td>\n",
       "      <td>-0.162451</td>\n",
       "      <td>-0.154726</td>\n",
       "      <td>-0.154866</td>\n",
       "      <td>-0.170855</td>\n",
       "      <td>0.280697</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Sheet  Isoelectric point      Turn  Instability index  \\\n",
       "Sheet              1.000000          -0.222060 -0.375104           0.220627   \n",
       "Isoelectric point -0.222060           1.000000 -0.010108           0.072233   \n",
       "Turn              -0.375104          -0.010108  1.000000          -0.073050   \n",
       "Instability index  0.220627           0.072233 -0.073050           1.000000   \n",
       "Molecular weight   0.070636          -0.124552  0.210953           0.045370   \n",
       "Aromaticity       -0.328068           0.009231  0.010417          -0.072715   \n",
       "Helix             -0.097355          -0.082686 -0.089198          -0.159392   \n",
       "GRAVY              0.232137          -0.171168  0.182721          -0.143305   \n",
       "Flexibility       -0.041549          -0.083467  0.024328          -0.076660   \n",
       "Solubility         0.040284          -0.065772 -0.080815          -0.090254   \n",
       "\n",
       "                   Molecular weight  Aromaticity     Helix     GRAVY  \\\n",
       "Sheet                      0.070636    -0.328068 -0.097355  0.232137   \n",
       "Isoelectric point         -0.124552     0.009231 -0.082686 -0.171168   \n",
       "Turn                       0.210953     0.010417 -0.089198  0.182721   \n",
       "Instability index          0.045370    -0.072715 -0.159392 -0.143305   \n",
       "Molecular weight           1.000000     0.222074  0.226637  0.249705   \n",
       "Aromaticity                0.222074     1.000000  0.476667 -0.090969   \n",
       "Helix                      0.226637     0.476667  1.000000  0.470759   \n",
       "GRAVY                      0.249705    -0.090969  0.470759  1.000000   \n",
       "Flexibility               -0.037168    -0.259083 -0.409018 -0.600141   \n",
       "Solubility                -0.162451    -0.154726 -0.154866 -0.170855   \n",
       "\n",
       "                   Flexibility  Solubility  \n",
       "Sheet                -0.041549    0.040284  \n",
       "Isoelectric point    -0.083467   -0.065772  \n",
       "Turn                  0.024328   -0.080815  \n",
       "Instability index    -0.076660   -0.090254  \n",
       "Molecular weight     -0.037168   -0.162451  \n",
       "Aromaticity          -0.259083   -0.154726  \n",
       "Helix                -0.409018   -0.154866  \n",
       "GRAVY                -0.600141   -0.170855  \n",
       "Flexibility           1.000000    0.280697  \n",
       "Solubility            0.280697    1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Correlations')\n",
    "df_corr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bonferroni corrected p values\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sheet</th>\n",
       "      <th>Isoelectric point</th>\n",
       "      <th>Turn</th>\n",
       "      <th>Instability index</th>\n",
       "      <th>Molecular weight</th>\n",
       "      <th>Aromaticity</th>\n",
       "      <th>Helix</th>\n",
       "      <th>GRAVY</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Solubility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sheet</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.112932e-134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.590711e-133</td>\n",
       "      <td>2.452378e-13</td>\n",
       "      <td>9.481450e-303</td>\n",
       "      <td>1.828499e-25</td>\n",
       "      <td>1.690913e-147</td>\n",
       "      <td>1.958930e-04</td>\n",
       "      <td>3.796681e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isoelectric point</th>\n",
       "      <td>1.112932e-134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.187675e+01</td>\n",
       "      <td>5.912798e-14</td>\n",
       "      <td>8.851210e-42</td>\n",
       "      <td>1.384428e+01</td>\n",
       "      <td>2.474925e-18</td>\n",
       "      <td>2.568619e-79</td>\n",
       "      <td>1.104583e-18</td>\n",
       "      <td>1.539834e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turn</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.187675e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.823298e-14</td>\n",
       "      <td>2.836412e-121</td>\n",
       "      <td>1.123262e+01</td>\n",
       "      <td>2.348993e-21</td>\n",
       "      <td>1.536995e-90</td>\n",
       "      <td>3.224890e-01</td>\n",
       "      <td>1.660256e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instability index</th>\n",
       "      <td>6.590711e-133</td>\n",
       "      <td>5.912798e-14</td>\n",
       "      <td>2.823298e-14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.365708e-05</td>\n",
       "      <td>3.827521e-14</td>\n",
       "      <td>1.135750e-68</td>\n",
       "      <td>2.087435e-55</td>\n",
       "      <td>9.749849e-16</td>\n",
       "      <td>7.228754e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Molecular weight</th>\n",
       "      <td>2.452378e-13</td>\n",
       "      <td>8.851210e-42</td>\n",
       "      <td>2.836412e-121</td>\n",
       "      <td>2.365708e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.068882e-134</td>\n",
       "      <td>2.004157e-140</td>\n",
       "      <td>2.287834e-171</td>\n",
       "      <td>1.787726e-03</td>\n",
       "      <td>2.324983e-71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aromaticity</th>\n",
       "      <td>9.481450e-303</td>\n",
       "      <td>1.384428e+01</td>\n",
       "      <td>1.123262e+01</td>\n",
       "      <td>3.827521e-14</td>\n",
       "      <td>1.068882e-134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.227827e-22</td>\n",
       "      <td>6.494171e-185</td>\n",
       "      <td>1.131499e-64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helix</th>\n",
       "      <td>1.828499e-25</td>\n",
       "      <td>2.474925e-18</td>\n",
       "      <td>2.348993e-21</td>\n",
       "      <td>1.135750e-68</td>\n",
       "      <td>2.004157e-140</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.616920e-65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRAVY</th>\n",
       "      <td>1.690913e-147</td>\n",
       "      <td>2.568619e-79</td>\n",
       "      <td>1.536995e-90</td>\n",
       "      <td>2.087435e-55</td>\n",
       "      <td>2.287834e-171</td>\n",
       "      <td>3.227827e-22</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.049307e-79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flexibility</th>\n",
       "      <td>1.958930e-04</td>\n",
       "      <td>1.104583e-18</td>\n",
       "      <td>3.224890e-01</td>\n",
       "      <td>9.749849e-16</td>\n",
       "      <td>1.787726e-03</td>\n",
       "      <td>6.494171e-185</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.509464e-218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solubility</th>\n",
       "      <td>3.796681e-04</td>\n",
       "      <td>1.539834e-11</td>\n",
       "      <td>1.660256e-17</td>\n",
       "      <td>7.228754e-22</td>\n",
       "      <td>2.324983e-71</td>\n",
       "      <td>1.131499e-64</td>\n",
       "      <td>8.616920e-65</td>\n",
       "      <td>5.049307e-79</td>\n",
       "      <td>2.509464e-218</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Sheet  Isoelectric point           Turn  \\\n",
       "Sheet               0.000000e+00      1.112932e-134   0.000000e+00   \n",
       "Isoelectric point  1.112932e-134       0.000000e+00   1.187675e+01   \n",
       "Turn                0.000000e+00       1.187675e+01   0.000000e+00   \n",
       "Instability index  6.590711e-133       5.912798e-14   2.823298e-14   \n",
       "Molecular weight    2.452378e-13       8.851210e-42  2.836412e-121   \n",
       "Aromaticity        9.481450e-303       1.384428e+01   1.123262e+01   \n",
       "Helix               1.828499e-25       2.474925e-18   2.348993e-21   \n",
       "GRAVY              1.690913e-147       2.568619e-79   1.536995e-90   \n",
       "Flexibility         1.958930e-04       1.104583e-18   3.224890e-01   \n",
       "Solubility          3.796681e-04       1.539834e-11   1.660256e-17   \n",
       "\n",
       "                   Instability index  Molecular weight    Aromaticity  \\\n",
       "Sheet                  6.590711e-133      2.452378e-13  9.481450e-303   \n",
       "Isoelectric point       5.912798e-14      8.851210e-42   1.384428e+01   \n",
       "Turn                    2.823298e-14     2.836412e-121   1.123262e+01   \n",
       "Instability index       0.000000e+00      2.365708e-05   3.827521e-14   \n",
       "Molecular weight        2.365708e-05      0.000000e+00  1.068882e-134   \n",
       "Aromaticity             3.827521e-14     1.068882e-134   0.000000e+00   \n",
       "Helix                   1.135750e-68     2.004157e-140   0.000000e+00   \n",
       "GRAVY                   2.087435e-55     2.287834e-171   3.227827e-22   \n",
       "Flexibility             9.749849e-16      1.787726e-03  6.494171e-185   \n",
       "Solubility              7.228754e-22      2.324983e-71   1.131499e-64   \n",
       "\n",
       "                           Helix          GRAVY    Flexibility     Solubility  \n",
       "Sheet               1.828499e-25  1.690913e-147   1.958930e-04   3.796681e-04  \n",
       "Isoelectric point   2.474925e-18   2.568619e-79   1.104583e-18   1.539834e-11  \n",
       "Turn                2.348993e-21   1.536995e-90   3.224890e-01   1.660256e-17  \n",
       "Instability index   1.135750e-68   2.087435e-55   9.749849e-16   7.228754e-22  \n",
       "Molecular weight   2.004157e-140  2.287834e-171   1.787726e-03   2.324983e-71  \n",
       "Aromaticity         0.000000e+00   3.227827e-22  6.494171e-185   1.131499e-64  \n",
       "Helix               0.000000e+00   0.000000e+00   0.000000e+00   8.616920e-65  \n",
       "GRAVY               0.000000e+00   0.000000e+00   0.000000e+00   5.049307e-79  \n",
       "Flexibility         0.000000e+00   0.000000e+00   0.000000e+00  2.509464e-218  \n",
       "Solubility          8.616920e-65   5.049307e-79  2.509464e-218   0.000000e+00  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Bonferroni corrected p values')\n",
    "#correction for p-values\n",
    "bonferroni_ = len(df_corr)*(len(df_corr)-1)/2\n",
    "df_p*bonferroni_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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