Feature analysis_with_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",
    "\n",
    "\n",
    "#Solubility wieghted index\n",
    "weights = {'A': 0.8356471476582918,\n",
    " 'C': 0.5208088354857734,\n",
    " 'E': 0.9876987431418378,\n",
    " 'D': 0.9079044671339564,\n",
    " 'G': 0.7997168496420723,\n",
    " 'F': 0.5849790194237692,\n",
    " 'I': 0.6784124413866582,\n",
    " 'H': 0.8947913996466419,\n",
    " 'K': 0.9267104557513497,\n",
    " 'M': 0.6296623675420369,\n",
    " 'L': 0.6554221515081433,\n",
    " 'N': 0.8597433107431216,\n",
    " 'Q': 0.789434648348208,\n",
    " 'P': 0.8235328714705341,\n",
    " 'S': 0.7440908318492778,\n",
    " 'R': 0.7712466317693457,\n",
    " 'T': 0.8096922697856334,\n",
    " 'W': 0.6374678690957594,\n",
    " 'V': 0.7357837119163659,\n",
    " 'Y': 0.6112801822947587}\n",
    "\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['weights'] = pET['Protein'].apply(lambda x:functions.solubility_score(x, weights))\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",
    "pET['SWI'] = pET['weights'].apply(np.mean)\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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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' ,  \\\n",
    "          'Aromaticity', 'Helix', 'Molecular weight', 'GRAVY', 'Flexibility', 'SWI', 'Solubility']].copy()\n",
    "\n",
    "\n",
    "df1 = pET[['Sheet', 'Turn','Isoelectric point', 'Instability index', 'Helix' ]].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 'SWI' not in v:\n",
    "        df1[v] = -df1[v]\n",
    "\n",
    "\n",
    "labels = pET['Solubility'].values\n",
    "functions.make_roc(df1, labels, output=False, fname='ROC_for_pET_0_SWI.pdf', c=colors[0:5])\n",
    "\n",
    "##second plot\n",
    "\n",
    "df2 = pET[['Aromaticity', 'Molecular weight', \\\n",
    "           'GRAVY', 'Flexibility', 'SWI']].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 'SWI' not in v:\n",
    "        df2[v] = -df2[v]\n",
    "\n",
    "\n",
    "\n",
    "functions.make_roc(df2, labels, output=False, fname='ROC_for_pET_1_SWI.pdf', c=colors[5:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "functions.sns.set_context(\"paper\", font_scale=1.4)\n",
    "\n",
    "functions.corr_heatmap(df, squared=False, output=False, fname='Heatmap_for_pET_with_SWI.pdf',\\\n",
    "                       )\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlations\n"
     ]
    },
    {
     "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>Sheet</th>\n",
       "      <th>Isoelectric point</th>\n",
       "      <th>Turn</th>\n",
       "      <th>Instability index</th>\n",
       "      <th>Aromaticity</th>\n",
       "      <th>Helix</th>\n",
       "      <th>Molecular weight</th>\n",
       "      <th>GRAVY</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>SWI</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>-0.328068</td>\n",
       "      <td>-0.097355</td>\n",
       "      <td>0.070636</td>\n",
       "      <td>0.232137</td>\n",
       "      <td>-0.041549</td>\n",
       "      <td>0.145739</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>0.009231</td>\n",
       "      <td>-0.082686</td>\n",
       "      <td>-0.124552</td>\n",
       "      <td>-0.171168</td>\n",
       "      <td>-0.083467</td>\n",
       "      <td>-0.160047</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>0.010417</td>\n",
       "      <td>-0.089198</td>\n",
       "      <td>0.210953</td>\n",
       "      <td>0.182721</td>\n",
       "      <td>0.024328</td>\n",
       "      <td>-0.212443</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>-0.072715</td>\n",
       "      <td>-0.159392</td>\n",
       "      <td>0.045370</td>\n",
       "      <td>-0.143305</td>\n",
       "      <td>-0.076660</td>\n",
       "      <td>-0.172425</td>\n",
       "      <td>-0.090254</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>1.000000</td>\n",
       "      <td>0.476667</td>\n",
       "      <td>0.222074</td>\n",
       "      <td>-0.090969</td>\n",
       "      <td>-0.259083</td>\n",
       "      <td>-0.454848</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>0.476667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226637</td>\n",
       "      <td>0.470759</td>\n",
       "      <td>-0.409018</td>\n",
       "      <td>-0.457766</td>\n",
       "      <td>-0.154866</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>0.222074</td>\n",
       "      <td>0.226637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.249705</td>\n",
       "      <td>-0.037168</td>\n",
       "      <td>-0.273635</td>\n",
       "      <td>-0.162451</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>-0.090969</td>\n",
       "      <td>0.470759</td>\n",
       "      <td>0.249705</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.600141</td>\n",
       "      <td>-0.475792</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>-0.259083</td>\n",
       "      <td>-0.409018</td>\n",
       "      <td>-0.037168</td>\n",
       "      <td>-0.600141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.774244</td>\n",
       "      <td>0.280697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWI</th>\n",
       "      <td>0.145739</td>\n",
       "      <td>-0.160047</td>\n",
       "      <td>-0.212443</td>\n",
       "      <td>-0.172425</td>\n",
       "      <td>-0.454848</td>\n",
       "      <td>-0.457766</td>\n",
       "      <td>-0.273635</td>\n",
       "      <td>-0.475792</td>\n",
       "      <td>0.774244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354619</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>-0.154726</td>\n",
       "      <td>-0.154866</td>\n",
       "      <td>-0.162451</td>\n",
       "      <td>-0.170855</td>\n",
       "      <td>0.280697</td>\n",
       "      <td>0.354619</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",
       "Aromaticity       -0.328068           0.009231  0.010417          -0.072715   \n",
       "Helix             -0.097355          -0.082686 -0.089198          -0.159392   \n",
       "Molecular weight   0.070636          -0.124552  0.210953           0.045370   \n",
       "GRAVY              0.232137          -0.171168  0.182721          -0.143305   \n",
       "Flexibility       -0.041549          -0.083467  0.024328          -0.076660   \n",
       "SWI                0.145739          -0.160047 -0.212443          -0.172425   \n",
       "Solubility         0.040284          -0.065772 -0.080815          -0.090254   \n",
       "\n",
       "                   Aromaticity     Helix  Molecular weight     GRAVY  \\\n",
       "Sheet                -0.328068 -0.097355          0.070636  0.232137   \n",
       "Isoelectric point     0.009231 -0.082686         -0.124552 -0.171168   \n",
       "Turn                  0.010417 -0.089198          0.210953  0.182721   \n",
       "Instability index    -0.072715 -0.159392          0.045370 -0.143305   \n",
       "Aromaticity           1.000000  0.476667          0.222074 -0.090969   \n",
       "Helix                 0.476667  1.000000          0.226637  0.470759   \n",
       "Molecular weight      0.222074  0.226637          1.000000  0.249705   \n",
       "GRAVY                -0.090969  0.470759          0.249705  1.000000   \n",
       "Flexibility          -0.259083 -0.409018         -0.037168 -0.600141   \n",
       "SWI                  -0.454848 -0.457766         -0.273635 -0.475792   \n",
       "Solubility           -0.154726 -0.154866         -0.162451 -0.170855   \n",
       "\n",
       "                   Flexibility       SWI  Solubility  \n",
       "Sheet                -0.041549  0.145739    0.040284  \n",
       "Isoelectric point    -0.083467 -0.160047   -0.065772  \n",
       "Turn                  0.024328 -0.212443   -0.080815  \n",
       "Instability index    -0.076660 -0.172425   -0.090254  \n",
       "Aromaticity          -0.259083 -0.454848   -0.154726  \n",
       "Helix                -0.409018 -0.457766   -0.154866  \n",
       "Molecular weight     -0.037168 -0.273635   -0.162451  \n",
       "GRAVY                -0.600141 -0.475792   -0.170855  \n",
       "Flexibility           1.000000  0.774244    0.280697  \n",
       "SWI                   0.774244  1.000000    0.354619  \n",
       "Solubility            0.280697  0.354619    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Correlations')\n",
    "df_corr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bonferroni corrected p values\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>Aromaticity</th>\n",
       "      <th>Helix</th>\n",
       "      <th>Molecular weight</th>\n",
       "      <th>GRAVY</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>SWI</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>1.360251e-134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.055313e-133</td>\n",
       "      <td>1.158844e-302</td>\n",
       "      <td>2.234832e-25</td>\n",
       "      <td>2.997351e-13</td>\n",
       "      <td>2.066671e-147</td>\n",
       "      <td>2.394248e-04</td>\n",
       "      <td>3.115284e-57</td>\n",
       "      <td>4.640388e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isoelectric point</th>\n",
       "      <td>1.360251e-134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.451603e+01</td>\n",
       "      <td>7.226754e-14</td>\n",
       "      <td>1.692078e+01</td>\n",
       "      <td>3.024908e-18</td>\n",
       "      <td>1.081815e-41</td>\n",
       "      <td>3.139424e-79</td>\n",
       "      <td>1.350046e-18</td>\n",
       "      <td>3.719107e-69</td>\n",
       "      <td>1.882019e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turn</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.451603e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.450698e-14</td>\n",
       "      <td>1.372876e+01</td>\n",
       "      <td>2.870992e-21</td>\n",
       "      <td>3.466726e-121</td>\n",
       "      <td>1.878549e-90</td>\n",
       "      <td>3.941532e-01</td>\n",
       "      <td>6.092023e-123</td>\n",
       "      <td>2.029201e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instability index</th>\n",
       "      <td>8.055313e-133</td>\n",
       "      <td>7.226754e-14</td>\n",
       "      <td>3.450698e-14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.678082e-14</td>\n",
       "      <td>1.388138e-68</td>\n",
       "      <td>2.891421e-05</td>\n",
       "      <td>2.551309e-55</td>\n",
       "      <td>1.191648e-15</td>\n",
       "      <td>2.058951e-80</td>\n",
       "      <td>8.835143e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aromaticity</th>\n",
       "      <td>1.158844e-302</td>\n",
       "      <td>1.692078e+01</td>\n",
       "      <td>1.372876e+01</td>\n",
       "      <td>4.678082e-14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.306411e-134</td>\n",
       "      <td>3.945122e-22</td>\n",
       "      <td>7.937320e-185</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.382944e-64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helix</th>\n",
       "      <td>2.234832e-25</td>\n",
       "      <td>3.024908e-18</td>\n",
       "      <td>2.870992e-21</td>\n",
       "      <td>1.388138e-68</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.449525e-140</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.053179e-64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Molecular weight</th>\n",
       "      <td>2.997351e-13</td>\n",
       "      <td>1.081815e-41</td>\n",
       "      <td>3.466726e-121</td>\n",
       "      <td>2.891421e-05</td>\n",
       "      <td>1.306411e-134</td>\n",
       "      <td>2.449525e-140</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.796242e-171</td>\n",
       "      <td>2.184998e-03</td>\n",
       "      <td>5.553875e-207</td>\n",
       "      <td>2.841645e-71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRAVY</th>\n",
       "      <td>2.066671e-147</td>\n",
       "      <td>3.139424e-79</td>\n",
       "      <td>1.878549e-90</td>\n",
       "      <td>2.551309e-55</td>\n",
       "      <td>3.945122e-22</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.796242e-171</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.171375e-79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flexibility</th>\n",
       "      <td>2.394248e-04</td>\n",
       "      <td>1.350046e-18</td>\n",
       "      <td>3.941532e-01</td>\n",
       "      <td>1.191648e-15</td>\n",
       "      <td>7.937320e-185</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.184998e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.067123e-218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWI</th>\n",
       "      <td>3.115284e-57</td>\n",
       "      <td>3.719107e-69</td>\n",
       "      <td>6.092023e-123</td>\n",
       "      <td>2.058951e-80</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.553875e-207</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solubility</th>\n",
       "      <td>4.640388e-04</td>\n",
       "      <td>1.882019e-11</td>\n",
       "      <td>2.029201e-17</td>\n",
       "      <td>8.835143e-22</td>\n",
       "      <td>1.382944e-64</td>\n",
       "      <td>1.053179e-64</td>\n",
       "      <td>2.841645e-71</td>\n",
       "      <td>6.171375e-79</td>\n",
       "      <td>3.067123e-218</td>\n",
       "      <td>0.000000e+00</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      1.360251e-134   0.000000e+00   \n",
       "Isoelectric point  1.360251e-134       0.000000e+00   1.451603e+01   \n",
       "Turn                0.000000e+00       1.451603e+01   0.000000e+00   \n",
       "Instability index  8.055313e-133       7.226754e-14   3.450698e-14   \n",
       "Aromaticity        1.158844e-302       1.692078e+01   1.372876e+01   \n",
       "Helix               2.234832e-25       3.024908e-18   2.870992e-21   \n",
       "Molecular weight    2.997351e-13       1.081815e-41  3.466726e-121   \n",
       "GRAVY              2.066671e-147       3.139424e-79   1.878549e-90   \n",
       "Flexibility         2.394248e-04       1.350046e-18   3.941532e-01   \n",
       "SWI                 3.115284e-57       3.719107e-69  6.092023e-123   \n",
       "Solubility          4.640388e-04       1.882019e-11   2.029201e-17   \n",
       "\n",
       "                   Instability index    Aromaticity          Helix  \\\n",
       "Sheet                  8.055313e-133  1.158844e-302   2.234832e-25   \n",
       "Isoelectric point       7.226754e-14   1.692078e+01   3.024908e-18   \n",
       "Turn                    3.450698e-14   1.372876e+01   2.870992e-21   \n",
       "Instability index       0.000000e+00   4.678082e-14   1.388138e-68   \n",
       "Aromaticity             4.678082e-14   0.000000e+00   0.000000e+00   \n",
       "Helix                   1.388138e-68   0.000000e+00   0.000000e+00   \n",
       "Molecular weight        2.891421e-05  1.306411e-134  2.449525e-140   \n",
       "GRAVY                   2.551309e-55   3.945122e-22   0.000000e+00   \n",
       "Flexibility             1.191648e-15  7.937320e-185   0.000000e+00   \n",
       "SWI                     2.058951e-80   0.000000e+00   0.000000e+00   \n",
       "Solubility              8.835143e-22   1.382944e-64   1.053179e-64   \n",
       "\n",
       "                   Molecular weight          GRAVY    Flexibility  \\\n",
       "Sheet                  2.997351e-13  2.066671e-147   2.394248e-04   \n",
       "Isoelectric point      1.081815e-41   3.139424e-79   1.350046e-18   \n",
       "Turn                  3.466726e-121   1.878549e-90   3.941532e-01   \n",
       "Instability index      2.891421e-05   2.551309e-55   1.191648e-15   \n",
       "Aromaticity           1.306411e-134   3.945122e-22  7.937320e-185   \n",
       "Helix                 2.449525e-140   0.000000e+00   0.000000e+00   \n",
       "Molecular weight       0.000000e+00  2.796242e-171   2.184998e-03   \n",
       "GRAVY                 2.796242e-171   0.000000e+00   0.000000e+00   \n",
       "Flexibility            2.184998e-03   0.000000e+00   0.000000e+00   \n",
       "SWI                   5.553875e-207   0.000000e+00   0.000000e+00   \n",
       "Solubility             2.841645e-71   6.171375e-79  3.067123e-218   \n",
       "\n",
       "                             SWI     Solubility  \n",
       "Sheet               3.115284e-57   4.640388e-04  \n",
       "Isoelectric point   3.719107e-69   1.882019e-11  \n",
       "Turn               6.092023e-123   2.029201e-17  \n",
       "Instability index   2.058951e-80   8.835143e-22  \n",
       "Aromaticity         0.000000e+00   1.382944e-64  \n",
       "Helix               0.000000e+00   1.053179e-64  \n",
       "Molecular weight   5.553875e-207   2.841645e-71  \n",
       "GRAVY               0.000000e+00   6.171375e-79  \n",
       "Flexibility         0.000000e+00  3.067123e-218  \n",
       "SWI                 0.000000e+00   0.000000e+00  \n",
       "Solubility          0.000000e+00   0.000000e+00  "
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pET.to_pickle('results/pET_all_analysis.pkl.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# E. coli data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3198, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For ecoli data\n",
    "ecoli_w3110 = pd.read_pickle('results/ecoli.pkl.gz', compression='infer')\n",
    "ecoli_w3110.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Accession</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>ECK number</th>\n",
       "      <th>JW</th>\n",
       "      <th>Solubility(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JW0002 thrB ECK0003</td>\n",
       "      <td>MRGSHHHHHHTDPALRAMVKVYAPASSANMSVGFDVLGAAVTPVDG...</td>\n",
       "      <td>ECK0003</td>\n",
       "      <td>JW0002</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JW0003 thrC ECK0004</td>\n",
       "      <td>MRGSHHHHHHTDPALRAMKLYNLKDHNEQVSFAQAVTQGLGKNQGL...</td>\n",
       "      <td>ECK0004</td>\n",
       "      <td>JW0003</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JW0004 yaaX ECK0005</td>\n",
       "      <td>MRGSHHHHHHTDPALRAMKKMQSIVLALSLVLVAPMAAQAAEITLV...</td>\n",
       "      <td>ECK0005</td>\n",
       "      <td>JW0004</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JW0005 yaaA ECK0006</td>\n",
       "      <td>MRGSHHHHHHTDPALRAMLILISPAKTLDYQSPLTTTRYTLPELLD...</td>\n",
       "      <td>ECK0006</td>\n",
       "      <td>JW0005</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JW0007 talB ECK0008</td>\n",
       "      <td>MRGSHHHHHHTDPALRAMTDKLTSLRQYTTVVADTGDIAAMKLYQP...</td>\n",
       "      <td>ECK0008</td>\n",
       "      <td>JW0007</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Accession                                           Sequence  \\\n",
       "2  JW0002 thrB ECK0003  MRGSHHHHHHTDPALRAMVKVYAPASSANMSVGFDVLGAAVTPVDG...   \n",
       "3  JW0003 thrC ECK0004  MRGSHHHHHHTDPALRAMKLYNLKDHNEQVSFAQAVTQGLGKNQGL...   \n",
       "4  JW0004 yaaX ECK0005  MRGSHHHHHHTDPALRAMKKMQSIVLALSLVLVAPMAAQAAEITLV...   \n",
       "5  JW0005 yaaA ECK0006  MRGSHHHHHHTDPALRAMLILISPAKTLDYQSPLTTTRYTLPELLD...   \n",
       "7  JW0007 talB ECK0008  MRGSHHHHHHTDPALRAMTDKLTSLRQYTTVVADTGDIAAMKLYQP...   \n",
       "\n",
       "  ECK number      JW  Solubility(%)  \n",
       "2    ECK0003  JW0002           32.0  \n",
       "3    ECK0004  JW0003           18.0  \n",
       "4    ECK0005  JW0004           78.0  \n",
       "5    ECK0006  JW0005            7.0  \n",
       "7    ECK0008  JW0007           85.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecoli_w3110.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecoli_w3110['Analysed_seq'] = ecoli_w3110['Sequence'].apply(ProteinAnalysis)\n",
    "\n",
    "ecoli_w3110['Molecular weight'] = ecoli_w3110['Analysed_seq'].apply(lambda x:x.molecular_weight())\n",
    "ecoli_w3110['Aromaticity'] = ecoli_w3110['Analysed_seq'].apply(lambda x:x.aromaticity())\n",
    "ecoli_w3110['Instability index'] = ecoli_w3110['Analysed_seq'].apply(lambda x:x.instability_index())\n",
    "ecoli_w3110['Isoelectric point'] = ecoli_w3110['Analysed_seq'].apply(lambda x:x.isoelectric_point())\n",
    "ecoli_w3110['Sec_str_fraction'] = ecoli_w3110['Analysed_seq'].apply(lambda x:x.secondary_structure_fraction())\n",
    "ecoli_w3110['Flex'] = ecoli_w3110['Analysed_seq'].apply(lambda x:x.flexibility())\n",
    "ecoli_w3110['GRAVY'] = ecoli_w3110['Analysed_seq'].apply(lambda x:x.gravy())\n",
    "\n",
    "\n",
    "ecoli_w3110['weights'] = ecoli_w3110['Sequence'].apply(lambda x:functions.solubility_score(x, weights))\n",
    "ecoli_w3110['SWI'] = ecoli_w3110['weights'].apply(np.mean)\n",
    "\n",
    "\n",
    "\n",
    "ecoli_w3110['Flexibility'] = ecoli_w3110['Flex'].apply(np.mean)\n",
    "#separate sec str in helix turn sheets\n",
    "ecoli_w3110['Helix'] = ecoli_w3110['Sec_str_fraction'].apply(lambda x:x[0])\n",
    "ecoli_w3110['Turn'] = ecoli_w3110['Sec_str_fraction'].apply(lambda x:x[1])\n",
    "ecoli_w3110['Sheet'] = ecoli_w3110['Sec_str_fraction'].apply(lambda x:x[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = ecoli_w3110[['Sheet', 'Instability index', 'Turn', 'Isoelectric point', \\\n",
    "                 'GRAVY', 'Aromaticity', 'Helix',  \\\n",
    "                  'Molecular weight',  'Flexibility', 'SWI',\\\n",
    "                 'Solubility(%)']]\n",
    "\n",
    "functions.corr_heatmap(d, squared=False, output=False,\\\n",
    "                       fname='Heatmap_for_Ecoli_features.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correlations\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "\n",
    "df_corr_ecoli = pd.DataFrame() # Correlation matrix\n",
    "df_p_ecoli = pd.DataFrame()  # Matrix of p-values\n",
    "for x in d.columns:\n",
    "    for y in d.columns:\n",
    "        corr1 = spearmanr(d[x], d[y])\n",
    "        df_corr_ecoli.loc[x,y] = corr1[0]\n",
    "        df_p_ecoli.loc[x,y] = corr1[1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlations\n"
     ]
    },
    {
     "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>Sheet</th>\n",
       "      <th>Instability index</th>\n",
       "      <th>Turn</th>\n",
       "      <th>Isoelectric point</th>\n",
       "      <th>GRAVY</th>\n",
       "      <th>Aromaticity</th>\n",
       "      <th>Helix</th>\n",
       "      <th>Molecular weight</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>SWI</th>\n",
       "      <th>Solubility(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sheet</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.146456</td>\n",
       "      <td>-0.403058</td>\n",
       "      <td>-0.156209</td>\n",
       "      <td>0.309439</td>\n",
       "      <td>-0.326514</td>\n",
       "      <td>0.027550</td>\n",
       "      <td>0.059391</td>\n",
       "      <td>-0.112851</td>\n",
       "      <td>0.083016</td>\n",
       "      <td>0.027396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instability index</th>\n",
       "      <td>0.146456</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.134142</td>\n",
       "      <td>0.015775</td>\n",
       "      <td>-0.169240</td>\n",
       "      <td>0.015088</td>\n",
       "      <td>0.054600</td>\n",
       "      <td>0.069486</td>\n",
       "      <td>-0.057118</td>\n",
       "      <td>-0.158018</td>\n",
       "      <td>-0.099881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turn</th>\n",
       "      <td>-0.403058</td>\n",
       "      <td>-0.134142</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018457</td>\n",
       "      <td>0.096262</td>\n",
       "      <td>0.047374</td>\n",
       "      <td>-0.103803</td>\n",
       "      <td>0.158678</td>\n",
       "      <td>0.126597</td>\n",
       "      <td>-0.134466</td>\n",
       "      <td>-0.122414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isoelectric point</th>\n",
       "      <td>-0.156209</td>\n",
       "      <td>0.015775</td>\n",
       "      <td>-0.018457</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013594</td>\n",
       "      <td>0.005004</td>\n",
       "      <td>0.028062</td>\n",
       "      <td>-0.353713</td>\n",
       "      <td>-0.207243</td>\n",
       "      <td>-0.264909</td>\n",
       "      <td>-0.192862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRAVY</th>\n",
       "      <td>0.309439</td>\n",
       "      <td>-0.169240</td>\n",
       "      <td>0.096262</td>\n",
       "      <td>-0.013594</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.107626</td>\n",
       "      <td>0.487898</td>\n",
       "      <td>0.118364</td>\n",
       "      <td>-0.612059</td>\n",
       "      <td>-0.453011</td>\n",
       "      <td>-0.208084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aromaticity</th>\n",
       "      <td>-0.326514</td>\n",
       "      <td>0.015088</td>\n",
       "      <td>0.047374</td>\n",
       "      <td>0.005004</td>\n",
       "      <td>-0.107626</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468139</td>\n",
       "      <td>0.179587</td>\n",
       "      <td>-0.272430</td>\n",
       "      <td>-0.464074</td>\n",
       "      <td>-0.328931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helix</th>\n",
       "      <td>0.027550</td>\n",
       "      <td>0.054600</td>\n",
       "      <td>-0.103803</td>\n",
       "      <td>0.028062</td>\n",
       "      <td>0.487898</td>\n",
       "      <td>0.468139</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.260863</td>\n",
       "      <td>-0.557496</td>\n",
       "      <td>-0.602545</td>\n",
       "      <td>-0.364232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Molecular weight</th>\n",
       "      <td>0.059391</td>\n",
       "      <td>0.069486</td>\n",
       "      <td>0.158678</td>\n",
       "      <td>-0.353713</td>\n",
       "      <td>0.118364</td>\n",
       "      <td>0.179587</td>\n",
       "      <td>0.260863</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.117507</td>\n",
       "      <td>-0.040323</td>\n",
       "      <td>-0.357656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flexibility</th>\n",
       "      <td>-0.112851</td>\n",
       "      <td>-0.057118</td>\n",
       "      <td>0.126597</td>\n",
       "      <td>-0.207243</td>\n",
       "      <td>-0.612059</td>\n",
       "      <td>-0.272430</td>\n",
       "      <td>-0.557496</td>\n",
       "      <td>0.117507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.780143</td>\n",
       "      <td>0.372535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWI</th>\n",
       "      <td>0.083016</td>\n",
       "      <td>-0.158018</td>\n",
       "      <td>-0.134466</td>\n",
       "      <td>-0.264909</td>\n",
       "      <td>-0.453011</td>\n",
       "      <td>-0.464074</td>\n",
       "      <td>-0.602545</td>\n",
       "      <td>-0.040323</td>\n",
       "      <td>0.780143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.503647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solubility(%)</th>\n",
       "      <td>0.027396</td>\n",
       "      <td>-0.099881</td>\n",
       "      <td>-0.122414</td>\n",
       "      <td>-0.192862</td>\n",
       "      <td>-0.208084</td>\n",
       "      <td>-0.328931</td>\n",
       "      <td>-0.364232</td>\n",
       "      <td>-0.357656</td>\n",
       "      <td>0.372535</td>\n",
       "      <td>0.503647</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Sheet  Instability index      Turn  Isoelectric point  \\\n",
       "Sheet              1.000000           0.146456 -0.403058          -0.156209   \n",
       "Instability index  0.146456           1.000000 -0.134142           0.015775   \n",
       "Turn              -0.403058          -0.134142  1.000000          -0.018457   \n",
       "Isoelectric point -0.156209           0.015775 -0.018457           1.000000   \n",
       "GRAVY              0.309439          -0.169240  0.096262          -0.013594   \n",
       "Aromaticity       -0.326514           0.015088  0.047374           0.005004   \n",
       "Helix              0.027550           0.054600 -0.103803           0.028062   \n",
       "Molecular weight   0.059391           0.069486  0.158678          -0.353713   \n",
       "Flexibility       -0.112851          -0.057118  0.126597          -0.207243   \n",
       "SWI                0.083016          -0.158018 -0.134466          -0.264909   \n",
       "Solubility(%)      0.027396          -0.099881 -0.122414          -0.192862   \n",
       "\n",
       "                      GRAVY  Aromaticity     Helix  Molecular weight  \\\n",
       "Sheet              0.309439    -0.326514  0.027550          0.059391   \n",
       "Instability index -0.169240     0.015088  0.054600          0.069486   \n",
       "Turn               0.096262     0.047374 -0.103803          0.158678   \n",
       "Isoelectric point -0.013594     0.005004  0.028062         -0.353713   \n",
       "GRAVY              1.000000    -0.107626  0.487898          0.118364   \n",
       "Aromaticity       -0.107626     1.000000  0.468139          0.179587   \n",
       "Helix              0.487898     0.468139  1.000000          0.260863   \n",
       "Molecular weight   0.118364     0.179587  0.260863          1.000000   \n",
       "Flexibility       -0.612059    -0.272430 -0.557496          0.117507   \n",
       "SWI               -0.453011    -0.464074 -0.602545         -0.040323   \n",
       "Solubility(%)     -0.208084    -0.328931 -0.364232         -0.357656   \n",
       "\n",
       "                   Flexibility       SWI  Solubility(%)  \n",
       "Sheet                -0.112851  0.083016       0.027396  \n",
       "Instability index    -0.057118 -0.158018      -0.099881  \n",
       "Turn                  0.126597 -0.134466      -0.122414  \n",
       "Isoelectric point    -0.207243 -0.264909      -0.192862  \n",
       "GRAVY                -0.612059 -0.453011      -0.208084  \n",
       "Aromaticity          -0.272430 -0.464074      -0.328931  \n",
       "Helix                -0.557496 -0.602545      -0.364232  \n",
       "Molecular weight      0.117507 -0.040323      -0.357656  \n",
       "Flexibility           1.000000  0.780143       0.372535  \n",
       "SWI                   0.780143  1.000000       0.503647  \n",
       "Solubility(%)         0.372535  0.503647       1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Correlations')\n",
    "df_corr_ecoli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bonferroni corrected p values\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sheet</th>\n",
       "      <th>Instability index</th>\n",
       "      <th>Turn</th>\n",
       "      <th>Isoelectric point</th>\n",
       "      <th>GRAVY</th>\n",
       "      <th>Aromaticity</th>\n",
       "      <th>Helix</th>\n",
       "      <th>Molecular weight</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>SWI</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>4.681568e-15</td>\n",
       "      <td>1.779996e-123</td>\n",
       "      <td>3.518390e-17</td>\n",
       "      <td>3.507685e-70</td>\n",
       "      <td>1.373213e-78</td>\n",
       "      <td>6.562002e+00</td>\n",
       "      <td>4.282383e-02</td>\n",
       "      <td>8.557785e-09</td>\n",
       "      <td>1.424718e-04</td>\n",
       "      <td>6.676983e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instability index</th>\n",
       "      <td>4.681568e-15</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.424148e-12</td>\n",
       "      <td>2.048794e+01</td>\n",
       "      <td>3.083184e-20</td>\n",
       "      <td>2.165327e+01</td>\n",
       "      <td>1.105340e-01</td>\n",
       "      <td>4.620172e-03</td>\n",
       "      <td>6.773707e-02</td>\n",
       "      <td>1.371299e-17</td>\n",
       "      <td>8.310253e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turn</th>\n",
       "      <td>1.779996e-123</td>\n",
       "      <td>1.424148e-12</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.632029e+01</td>\n",
       "      <td>2.705314e-06</td>\n",
       "      <td>4.055300e-01</td>\n",
       "      <td>2.206153e-07</td>\n",
       "      <td>9.694803e-18</td>\n",
       "      <td>3.687836e-11</td>\n",
       "      <td>1.233143e-12</td>\n",
       "      <td>2.066478e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isoelectric point</th>\n",
       "      <td>3.518390e-17</td>\n",
       "      <td>2.048794e+01</td>\n",
       "      <td>1.632029e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.432149e+01</td>\n",
       "      <td>4.274910e+01</td>\n",
       "      <td>6.192663e+00</td>\n",
       "      <td>3.797751e-93</td>\n",
       "      <td>1.271375e-30</td>\n",
       "      <td>9.308670e-51</td>\n",
       "      <td>1.971823e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRAVY</th>\n",
       "      <td>3.507685e-70</td>\n",
       "      <td>3.083184e-20</td>\n",
       "      <td>2.705314e-06</td>\n",
       "      <td>2.432149e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.772332e-08</td>\n",
       "      <td>3.346652e-189</td>\n",
       "      <td>1.037239e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.748186e-160</td>\n",
       "      <td>7.066585e-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aromaticity</th>\n",
       "      <td>1.373213e-78</td>\n",
       "      <td>2.165327e+01</td>\n",
       "      <td>4.055300e-01</td>\n",
       "      <td>4.274910e+01</td>\n",
       "      <td>5.772332e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.456892e-172</td>\n",
       "      <td>7.588180e-23</td>\n",
       "      <td>8.586503e-54</td>\n",
       "      <td>7.984424e-169</td>\n",
       "      <td>7.988737e-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helix</th>\n",
       "      <td>6.562002e+00</td>\n",
       "      <td>1.105340e-01</td>\n",
       "      <td>2.206153e-07</td>\n",
       "      <td>6.192663e+00</td>\n",
       "      <td>3.346652e-189</td>\n",
       "      <td>3.456892e-172</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.642880e-49</td>\n",
       "      <td>6.568243e-259</td>\n",
       "      <td>1.144394e-313</td>\n",
       "      <td>3.552435e-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Molecular weight</th>\n",
       "      <td>4.282383e-02</td>\n",
       "      <td>4.620172e-03</td>\n",
       "      <td>9.694803e-18</td>\n",
       "      <td>3.797751e-93</td>\n",
       "      <td>1.037239e-09</td>\n",
       "      <td>7.588180e-23</td>\n",
       "      <td>3.642880e-49</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.449571e-09</td>\n",
       "      <td>1.242351e+00</td>\n",
       "      <td>2.220367e-95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flexibility</th>\n",
       "      <td>8.557785e-09</td>\n",
       "      <td>6.773707e-02</td>\n",
       "      <td>3.687836e-11</td>\n",
       "      <td>1.271375e-30</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.586503e-54</td>\n",
       "      <td>6.568243e-259</td>\n",
       "      <td>1.449571e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.249430e-104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWI</th>\n",
       "      <td>1.424718e-04</td>\n",
       "      <td>1.371299e-17</td>\n",
       "      <td>1.233143e-12</td>\n",
       "      <td>9.308670e-51</td>\n",
       "      <td>6.748186e-160</td>\n",
       "      <td>7.984424e-169</td>\n",
       "      <td>1.144394e-313</td>\n",
       "      <td>1.242351e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.381320e-203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solubility(%)</th>\n",
       "      <td>6.676983e+00</td>\n",
       "      <td>8.310253e-07</td>\n",
       "      <td>2.066478e-10</td>\n",
       "      <td>1.971823e-26</td>\n",
       "      <td>7.066585e-31</td>\n",
       "      <td>7.988737e-80</td>\n",
       "      <td>3.552435e-99</td>\n",
       "      <td>2.220367e-95</td>\n",
       "      <td>4.249430e-104</td>\n",
       "      <td>1.381320e-203</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Sheet  Instability index           Turn  \\\n",
       "Sheet               0.000000e+00       4.681568e-15  1.779996e-123   \n",
       "Instability index   4.681568e-15       0.000000e+00   1.424148e-12   \n",
       "Turn               1.779996e-123       1.424148e-12   0.000000e+00   \n",
       "Isoelectric point   3.518390e-17       2.048794e+01   1.632029e+01   \n",
       "GRAVY               3.507685e-70       3.083184e-20   2.705314e-06   \n",
       "Aromaticity         1.373213e-78       2.165327e+01   4.055300e-01   \n",
       "Helix               6.562002e+00       1.105340e-01   2.206153e-07   \n",
       "Molecular weight    4.282383e-02       4.620172e-03   9.694803e-18   \n",
       "Flexibility         8.557785e-09       6.773707e-02   3.687836e-11   \n",
       "SWI                 1.424718e-04       1.371299e-17   1.233143e-12   \n",
       "Solubility(%)       6.676983e+00       8.310253e-07   2.066478e-10   \n",
       "\n",
       "                   Isoelectric point          GRAVY    Aromaticity  \\\n",
       "Sheet                   3.518390e-17   3.507685e-70   1.373213e-78   \n",
       "Instability index       2.048794e+01   3.083184e-20   2.165327e+01   \n",
       "Turn                    1.632029e+01   2.705314e-06   4.055300e-01   \n",
       "Isoelectric point       0.000000e+00   2.432149e+01   4.274910e+01   \n",
       "GRAVY                   2.432149e+01   0.000000e+00   5.772332e-08   \n",
       "Aromaticity             4.274910e+01   5.772332e-08   0.000000e+00   \n",
       "Helix                   6.192663e+00  3.346652e-189  3.456892e-172   \n",
       "Molecular weight        3.797751e-93   1.037239e-09   7.588180e-23   \n",
       "Flexibility             1.271375e-30   0.000000e+00   8.586503e-54   \n",
       "SWI                     9.308670e-51  6.748186e-160  7.984424e-169   \n",
       "Solubility(%)           1.971823e-26   7.066585e-31   7.988737e-80   \n",
       "\n",
       "                           Helix  Molecular weight    Flexibility  \\\n",
       "Sheet               6.562002e+00      4.282383e-02   8.557785e-09   \n",
       "Instability index   1.105340e-01      4.620172e-03   6.773707e-02   \n",
       "Turn                2.206153e-07      9.694803e-18   3.687836e-11   \n",
       "Isoelectric point   6.192663e+00      3.797751e-93   1.271375e-30   \n",
       "GRAVY              3.346652e-189      1.037239e-09   0.000000e+00   \n",
       "Aromaticity        3.456892e-172      7.588180e-23   8.586503e-54   \n",
       "Helix               0.000000e+00      3.642880e-49  6.568243e-259   \n",
       "Molecular weight    3.642880e-49      0.000000e+00   1.449571e-09   \n",
       "Flexibility        6.568243e-259      1.449571e-09   0.000000e+00   \n",
       "SWI                1.144394e-313      1.242351e+00   0.000000e+00   \n",
       "Solubility(%)       3.552435e-99      2.220367e-95  4.249430e-104   \n",
       "\n",
       "                             SWI  Solubility(%)  \n",
       "Sheet               1.424718e-04   6.676983e+00  \n",
       "Instability index   1.371299e-17   8.310253e-07  \n",
       "Turn                1.233143e-12   2.066478e-10  \n",
       "Isoelectric point   9.308670e-51   1.971823e-26  \n",
       "GRAVY              6.748186e-160   7.066585e-31  \n",
       "Aromaticity        7.984424e-169   7.988737e-80  \n",
       "Helix              1.144394e-313   3.552435e-99  \n",
       "Molecular weight    1.242351e+00   2.220367e-95  \n",
       "Flexibility         0.000000e+00  4.249430e-104  \n",
       "SWI                 0.000000e+00  1.381320e-203  \n",
       "Solubility(%)      1.381320e-203   0.000000e+00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Bonferroni corrected p values')\n",
    "\n",
    "#correction for p-values\n",
    "bonferroni_ = len(df_corr_ecoli)*(len(df_corr_ecoli)-1)/2\n",
    "df_p_ecoli*bonferroni_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecoli_w3110.to_pickle('results/ecoli_all_analysis.pkl.gz')"
   ]
  },
  {
   "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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