1_plot_domain_clinvar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('Output Datasets/PTEN_MCF10A_annotated_091919.csv', index_col=False)"
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>variant</th>\n",
       "      <th>MCF10A score</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "      <th>ClinVar</th>\n",
       "      <th>significance</th>\n",
       "      <th>agg condition</th>\n",
       "      <th>COSMIC</th>\n",
       "      <th>COSMIC cancer type</th>\n",
       "      <th>COSMIC tissue type</th>\n",
       "      <th>gnomAD</th>\n",
       "      <th>AA position</th>\n",
       "      <th>domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>K6E</td>\n",
       "      <td>0.3311</td>\n",
       "      <td>0.06073</td>\n",
       "      <td>0.03506</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>['Carcinoma', 'Glioma']</td>\n",
       "      <td>['Endometrium', 'Stomach', 'Central nervous sy...</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>PBD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>K6I</td>\n",
       "      <td>0.4544</td>\n",
       "      <td>0.04539</td>\n",
       "      <td>0.02621</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>['Glioma']</td>\n",
       "      <td>['Central nervous system']</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>PBD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>R15S</td>\n",
       "      <td>0.3870</td>\n",
       "      <td>0.05989</td>\n",
       "      <td>0.03458</td>\n",
       "      <td>yes</td>\n",
       "      <td>Likely pathogenic</td>\n",
       "      <td>Hereditary cancer-predisposing syndrome</td>\n",
       "      <td>yes</td>\n",
       "      <td>['Carcinoma', 'Glioma']</td>\n",
       "      <td>['Thyroid', 'Large intestine', 'Central nervou...</td>\n",
       "      <td>no</td>\n",
       "      <td>15</td>\n",
       "      <td>phosphatase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>M35V</td>\n",
       "      <td>0.3606</td>\n",
       "      <td>0.08549</td>\n",
       "      <td>0.04936</td>\n",
       "      <td>yes</td>\n",
       "      <td>Likely pathogenic</td>\n",
       "      <td>PTEN hamartoma tumor syndrome, not provided, I...</td>\n",
       "      <td>yes</td>\n",
       "      <td>['Carcinoma', 'Glioma']</td>\n",
       "      <td>['Biliary tract', 'Large intestine', 'Central ...</td>\n",
       "      <td>no</td>\n",
       "      <td>35</td>\n",
       "      <td>phosphatase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>P38H</td>\n",
       "      <td>0.3521</td>\n",
       "      <td>0.10320</td>\n",
       "      <td>0.05958</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>['Malignant melanoma']</td>\n",
       "      <td>['NS']</td>\n",
       "      <td>no</td>\n",
       "      <td>38</td>\n",
       "      <td>phosphatase</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 variant  MCF10A score  std_dev  std_err ClinVar  \\\n",
       "0           0     K6E        0.3311  0.06073  0.03506      no   \n",
       "1           1     K6I        0.4544  0.04539  0.02621      no   \n",
       "2           2    R15S        0.3870  0.05989  0.03458     yes   \n",
       "3           3    M35V        0.3606  0.08549  0.04936     yes   \n",
       "4           4    P38H        0.3521  0.10320  0.05958      no   \n",
       "\n",
       "        significance                                      agg condition  \\\n",
       "0                NaN                                                NaN   \n",
       "1                NaN                                                NaN   \n",
       "2  Likely pathogenic            Hereditary cancer-predisposing syndrome   \n",
       "3  Likely pathogenic  PTEN hamartoma tumor syndrome, not provided, I...   \n",
       "4                NaN                                                NaN   \n",
       "\n",
       "  COSMIC       COSMIC cancer type  \\\n",
       "0    yes  ['Carcinoma', 'Glioma']   \n",
       "1    yes               ['Glioma']   \n",
       "2    yes  ['Carcinoma', 'Glioma']   \n",
       "3    yes  ['Carcinoma', 'Glioma']   \n",
       "4    yes   ['Malignant melanoma']   \n",
       "\n",
       "                                  COSMIC tissue type gnomAD  AA position  \\\n",
       "0  ['Endometrium', 'Stomach', 'Central nervous sy...     no            6   \n",
       "1                         ['Central nervous system']     no            6   \n",
       "2  ['Thyroid', 'Large intestine', 'Central nervou...     no           15   \n",
       "3  ['Biliary tract', 'Large intestine', 'Central ...     no           35   \n",
       "4                                             ['NS']     no           38   \n",
       "\n",
       "        domain  \n",
       "0          PBD  \n",
       "1          PBD  \n",
       "2  phosphatase  \n",
       "3  phosphatase  \n",
       "4  phosphatase  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# now start plotting colormaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "domain          category\n",
      "significance    category\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# converting column dtype from obj to cat\n",
    "df1_cat = df1.loc[:, ['domain', 'significance']]\n",
    "\n",
    "for col in df1_cat.columns:\n",
    "    df1_cat[col] = df1_cat[col].astype('category')\n",
    "\n",
    "print(df1_cat.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in df1_cat.columns:\n",
    "    df1_cat[col] = df1_cat[col].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_cat['variant'] = df1['variant']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>significance</th>\n",
       "      <th>variant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>K6E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>K6I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>R15S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>M35V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>P38H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>G44D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>A79T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>D92N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>H93Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>H93R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>I101F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>I101T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>N117S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>H123Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>H123Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>C124S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>C124R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>A126D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>G127R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>G129R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>G129E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>R130L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>R130P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>R130Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>T131I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>G132D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>V133I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>M134I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>I135V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>Y138L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>R142W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Y155C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>T167N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>Q171E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>R173H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "      <td>Y176C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "      <td>Y180H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>M198I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>N228S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>T232A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>P246L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>N262S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>D268E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>D326N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>K342N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>P354Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>T382S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    domain  significance variant\n",
       "0        2            -1     K6E\n",
       "1        2            -1     K6I\n",
       "2        3             2    R15S\n",
       "3        3             2    M35V\n",
       "4        3            -1    P38H\n",
       "5        3             3    G44D\n",
       "6        3             1    A79T\n",
       "7        3            -1    D92N\n",
       "8        3            -1    H93Q\n",
       "9        3             5    H93R\n",
       "10       3            -1   I101F\n",
       "11       3            -1   I101T\n",
       "12       3             5   N117S\n",
       "13       3             4   H123Y\n",
       "14       3             3   H123Q\n",
       "15       3            -1   C124S\n",
       "16       3             3   C124R\n",
       "17       3            -1   A126D\n",
       "18       3             2   G127R\n",
       "19       3             3   G129R\n",
       "20       3             3   G129E\n",
       "21       3             4   R130L\n",
       "22       3             0   R130P\n",
       "23       3             3   R130Q\n",
       "24       3             3   T131I\n",
       "25       3             2   G132D\n",
       "26       3            -1   V133I\n",
       "27       3             2   M134I\n",
       "28       3             4   I135V\n",
       "29       3            -1   Y138L\n",
       "30       3             5   R142W\n",
       "31       3             0   Y155C\n",
       "32       3             3   T167N\n",
       "33       3            -1   Q171E\n",
       "34       3             4   R173H\n",
       "35      -1             5   Y176C\n",
       "36      -1             5   Y180H\n",
       "37       1            -1   M198I\n",
       "38       1             5   N228S\n",
       "39       1            -1   T232A\n",
       "40       1             3   P246L\n",
       "41       1            -1   N262S\n",
       "42       1            -1   D268E\n",
       "43       1            -1   D326N\n",
       "44       1             5   K342N\n",
       "45       0             5   P354Q\n",
       "46       0            -1   T382S"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def heatmap(df, col_1, col_2, title, cmap):\n",
    "    # col_1 is the list of variants, col_2 is the data to plot\n",
    "    # first process the data to horizontal\n",
    "    df_toplot = df[[col_1, col_2]].transpose()\n",
    "    df_toplot.columns = df_toplot.iloc[0]\n",
    "    df_toplot = df_toplot.drop(df_toplot.index[0])\n",
    "    df_toplot = df_toplot[df_toplot.columns].astype(float)\n",
    "    print(df_toplot)\n",
    "    \n",
    "    # now plot the data\n",
    "    fig = plt.figure(figsize=(7,5), dpi=200)\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.imshow(df_toplot, cmap=cmap)\n",
    "    \n",
    "    # set the ticks...\n",
    "    ax.set_xticks(np.arange(len(df_toplot.columns)))\n",
    "    ax.set_yticks(np.arange(0))\n",
    "    \n",
    "    # label the ticks\n",
    "    ax.set_xticklabels(df_toplot.columns, fontsize=6, rotation=90)\n",
    " \n",
    "    # set white grids\n",
    "    ax.set_xticks(np.arange(len(df_toplot.columns)+1)-0.5, minor=True)\n",
    "    ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=0.4)\n",
    "    ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "    \n",
    "    #set the title\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variant  K6E  K6I  R15S  M35V  P38H  G44D  A79T  D92N  H93Q  H93R  ...  M198I  \\\n",
      "domain   2.0  2.0   3.0   3.0   3.0   3.0   3.0   3.0   3.0   3.0  ...    1.0   \n",
      "\n",
      "variant  N228S  T232A  P246L  N262S  D268E  D326N  K342N  P354Q  T382S  \n",
      "domain     1.0    1.0    1.0    1.0    1.0    1.0    1.0    0.0    0.0  \n",
      "\n",
      "[1 rows x 47 columns]\n"
     ]
    },
    {
     "data": {
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dKeYoGG/OzP+vDxOvqaNhM5uLcFjdIHuCUVXGZ/6v8j3aySwSv1m84fKnV+yf+f9kd1/T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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# specify custom colormaps\n",
    "from matplotlib import colors\n",
    "cmap = colors.ListedColormap(\n",
    "    ['goldenrod','lightcoral','mediumturquoise','steelblue','orchid'])\n",
    "#bounds= np.arange(5)\n",
    "#norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "heatmap(df1_cat, 'variant', 'domain', 'PTEN variant by domain', cmap)\n",
    "#mpl.rcParams['pdf.fonttype'] = 42\n",
    "#plt.savefig('Output PDFs/PTEN_variant_domain.pdf', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 5, 9, 9, 5, 10, 2, 5, 5, 2, 5, 5, 2, 9, 10, 5, 10, 5, 9, 10, 10, 9, 0, 10, 10, 9, 5, 9, 9, 5, 2, 0, 10, 5, 9, 2, 2, 5, 2, 5, 10, 5, 5, 5, 2, 2, 5]\n"
     ]
    }
   ],
   "source": [
    "# need to make adjustments to cat encoding of ClinVar sig\n",
    "\n",
    "new_ClinVar_sig = []\n",
    "sig = df1['significance']\n",
    "\n",
    "for i in range(len(sig)):\n",
    "    if str(sig[i]) == 'nan':\n",
    "        new_ClinVar_sig.append(5)\n",
    "    elif 'Conflicting' in str(sig[i]):\n",
    "        new_ClinVar_sig.append(0)\n",
    "    elif 'Uncertain' in (sig[i]):\n",
    "        new_ClinVar_sig.append(2)\n",
    "    elif 'Likely pathogenic' in (sig[i]):\n",
    "        new_ClinVar_sig.append(9)\n",
    "    elif 'Pathogenic' in (sig[i]):\n",
    "        new_ClinVar_sig.append(10)\n",
    "    else:\n",
    "        new_ClinVar_sig.append(2)\n",
    "\n",
    "#if df1['ClinVar significance'][0] == 'N/A':\n",
    "#    df1_cat.loc[0, 'ClinVar significance'] = 0\n",
    "\n",
    "print(new_ClinVar_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_cat['ClinVar significance'] = new_ClinVar_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variant               K6E  K6I  R15S  M35V  P38H  G44D  A79T  D92N  H93Q  \\\n",
      "ClinVar significance  5.0  5.0   9.0   9.0   5.0  10.0   2.0   5.0   5.0   \n",
      "\n",
      "variant               H93R  ...  M198I  N228S  T232A  P246L  N262S  D268E  \\\n",
      "ClinVar significance   2.0  ...    5.0    2.0    5.0   10.0    5.0    5.0   \n",
      "\n",
      "variant               D326N  K342N  P354Q  T382S  \n",
      "ClinVar significance    5.0    2.0    2.0    5.0  \n",
      "\n",
      "[1 rows x 47 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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eT4JSXj5D9FcrtFLEmNlcRHn/oOy7k9rwori7L19Gfi/pZHzVQt5GhIJma2A8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\n4W/8wlAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap(df1_cat, 'variant', 'ClinVar significance', 'ClinVar significance', 'coolwarm')\n",
    "#mpl.rcParams['pdf.fonttype'] = 42\n",
    "#plt.savefig('Output PDFs/PTEN_ClinVar_significance.pdf', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}