analysis/KCPG/KCPG_woAR_TT-HighGradeGlioma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for High Grade Glioma\n",
    "----- \n",
    "- Date : 17th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse) \n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutationa Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3016</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3016\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3016\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3016   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Var1     Freq\n",
       "------  -----\n",
       "CNS       613\n",
       "WLM       405\n",
       "NBL       343\n",
       "RHB       259\n",
       "OS        241\n",
       "HGG       229\n",
       "RB        221\n",
       "EWS       192\n",
       "STS       177\n",
       "GCT       139\n",
       "ACT        79\n",
       "LGG        52\n",
       "CA         35\n",
       "OST        18\n",
       "RCC         5\n",
       "GICT        2\n",
       "LMPRT       2\n",
       "PGL         2\n",
       "BCC         1\n",
       "NM          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( sort( table(df$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'known',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>290</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 290\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 290\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 290  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD    AR AR/AD   XLD \n",
       "  153    92    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       \n",
       "        Het Hom\n",
       "  AD    153   0\n",
       "  AR     92   0\n",
       "  AR/AD  43   0\n",
       "  XLD     1   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$Mode_of_Inheritance_abb , df$genotype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Mode_of_Inheritance_abb != 'AR',]\n",
    "df = droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD AR/AD   XLD \n",
       "  153    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "182"
      ],
      "text/latex": [
       "182"
      ],
      "text/markdown": [
       "182"
      ],
      "text/plain": [
       "[1] 182"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "54"
      ],
      "text/latex": [
       "54"
      ],
      "text/markdown": [
       "54"
      ],
      "text/plain": [
       "[1] 54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Tumor_Sample_Barcode'</li>\n",
       "\t<li>'Tumor_type'</li>\n",
       "\t<li>'Tumor_type2'</li>\n",
       "\t<li>'Age_of_Onset'</li>\n",
       "\t<li>'Sex'</li>\n",
       "\t<li>'Age_group'</li>\n",
       "\t<li>'sample_type'</li>\n",
       "\t<li>'sequencing_type'</li>\n",
       "\t<li>'Access_unit'</li>\n",
       "\t<li>'Status'</li>\n",
       "\t<li>'Ethnicity'</li>\n",
       "\t<li>'Race'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Tumor\\_Sample\\_Barcode'\n",
       "\\item 'Tumor\\_type'\n",
       "\\item 'Tumor\\_type2'\n",
       "\\item 'Age\\_of\\_Onset'\n",
       "\\item 'Sex'\n",
       "\\item 'Age\\_group'\n",
       "\\item 'sample\\_type'\n",
       "\\item 'sequencing\\_type'\n",
       "\\item 'Access\\_unit'\n",
       "\\item 'Status'\n",
       "\\item 'Ethnicity'\n",
       "\\item 'Race'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Tumor_Sample_Barcode'\n",
       "2. 'Tumor_type'\n",
       "3. 'Tumor_type2'\n",
       "4. 'Age_of_Onset'\n",
       "5. 'Sex'\n",
       "6. 'Age_group'\n",
       "7. 'sample_type'\n",
       "8. 'sequencing_type'\n",
       "9. 'Access_unit'\n",
       "10. 'Status'\n",
       "11. 'Ethnicity'\n",
       "12. 'Race'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Tumor_Sample_Barcode\" \"Tumor_type\"           \"Tumor_type2\"         \n",
       " [4] \"Age_of_Onset\"         \"Sex\"                  \"Age_group\"           \n",
       " [7] \"sample_type\"          \"sequencing_type\"      \"Access_unit\"         \n",
       "[10] \"Status\"               \"Ethnicity\"            \"Race\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       CCF PCGP SJLIFE\n",
       "  Adrenocortical Carcinoma               1   19      2\n",
       "  Basal Cell Carcinoma                   0    0      1\n",
       "  Carcinoma                              0    0     14\n",
       "  Central Nervous System                 1    0    322\n",
       "  Ewing's Sarcoma                        6    0     89\n",
       "  Germ Cell Tumor                        1    0     73\n",
       "  Giant Cell Tumor                       0    0      3\n",
       "  High Grade Glioma                      5   75      0\n",
       "  Low Grade Glioma                       2   15      7\n",
       "  Low Malignant Potential Renal Tumors   2    0      0\n",
       "  Neuroblastoma                          3   48    139\n",
       "  Non-Malignant Tumor                    1    0      3\n",
       "  Osteosarcoma                          14    2    113\n",
       "  Other Solid Tumor                      0    0     10\n",
       "  Paraganglioma                          0    0      1\n",
       "  Renal Cell Carcinoma                   1    0      2\n",
       "  Retinoblastoma                         0    0     98\n",
       "  Rhabdomyosarcoma                       3   29    102\n",
       "  Soft Tissue Sarcoma                    8    0     85\n",
       "  Wilms' tumor                           2    0    205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2$Tumor_type , df2$Access_unit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>f</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Central Nervous System              </td><td>323                                 </td></tr>\n",
       "\t<tr><td>Wilms' tumor                        </td><td>207                                 </td></tr>\n",
       "\t<tr><td>Neuroblastoma                       </td><td>190                                 </td></tr>\n",
       "\t<tr><td>Rhabdomyosarcoma                    </td><td>134                                 </td></tr>\n",
       "\t<tr><td>Osteosarcoma                        </td><td>129                                 </td></tr>\n",
       "\t<tr><td>Retinoblastoma                      </td><td> 98                                 </td></tr>\n",
       "\t<tr><td>Ewing's Sarcoma                     </td><td> 95                                 </td></tr>\n",
       "\t<tr><td>Soft Tissue Sarcoma                 </td><td> 93                                 </td></tr>\n",
       "\t<tr><td>High Grade Glioma                   </td><td> 80                                 </td></tr>\n",
       "\t<tr><td>Germ Cell Tumor                     </td><td> 74                                 </td></tr>\n",
       "\t<tr><td>Low Grade Glioma                    </td><td> 24                                 </td></tr>\n",
       "\t<tr><td>Adrenocortical Carcinoma            </td><td> 22                                 </td></tr>\n",
       "\t<tr><td>Carcinoma                           </td><td> 14                                 </td></tr>\n",
       "\t<tr><td>Other Solid Tumor                   </td><td> 10                                 </td></tr>\n",
       "\t<tr><td>Non-Malignant Tumor                 </td><td>  4                                 </td></tr>\n",
       "\t<tr><td>Giant Cell Tumor                    </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Renal Cell Carcinoma                </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Low Malignant Potential Renal Tumors</td><td>  2                                 </td></tr>\n",
       "\t<tr><td>Basal Cell Carcinoma                </td><td>  1                                 </td></tr>\n",
       "\t<tr><td>Paraganglioma                       </td><td>  1                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " f & n\\\\\n",
       "\\hline\n",
       "\t Central Nervous System               & 323                                 \\\\\n",
       "\t Wilms' tumor                         & 207                                 \\\\\n",
       "\t Neuroblastoma                        & 190                                 \\\\\n",
       "\t Rhabdomyosarcoma                     & 134                                 \\\\\n",
       "\t Osteosarcoma                         & 129                                 \\\\\n",
       "\t Retinoblastoma                       &  98                                 \\\\\n",
       "\t Ewing's Sarcoma                      &  95                                 \\\\\n",
       "\t Soft Tissue Sarcoma                  &  93                                 \\\\\n",
       "\t High Grade Glioma                    &  80                                 \\\\\n",
       "\t Germ Cell Tumor                      &  74                                 \\\\\n",
       "\t Low Grade Glioma                     &  24                                 \\\\\n",
       "\t Adrenocortical Carcinoma             &  22                                 \\\\\n",
       "\t Carcinoma                            &  14                                 \\\\\n",
       "\t Other Solid Tumor                    &  10                                 \\\\\n",
       "\t Non-Malignant Tumor                  &   4                                 \\\\\n",
       "\t Giant Cell Tumor                     &   3                                 \\\\\n",
       "\t Renal Cell Carcinoma                 &   3                                 \\\\\n",
       "\t Low Malignant Potential Renal Tumors &   2                                 \\\\\n",
       "\t Basal Cell Carcinoma                 &   1                                 \\\\\n",
       "\t Paraganglioma                        &   1                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| f | n |\n",
       "|---|---|\n",
       "| Central Nervous System               | 323                                  |\n",
       "| Wilms' tumor                         | 207                                  |\n",
       "| Neuroblastoma                        | 190                                  |\n",
       "| Rhabdomyosarcoma                     | 134                                  |\n",
       "| Osteosarcoma                         | 129                                  |\n",
       "| Retinoblastoma                       |  98                                  |\n",
       "| Ewing's Sarcoma                      |  95                                  |\n",
       "| Soft Tissue Sarcoma                  |  93                                  |\n",
       "| High Grade Glioma                    |  80                                  |\n",
       "| Germ Cell Tumor                      |  74                                  |\n",
       "| Low Grade Glioma                     |  24                                  |\n",
       "| Adrenocortical Carcinoma             |  22                                  |\n",
       "| Carcinoma                            |  14                                  |\n",
       "| Other Solid Tumor                    |  10                                  |\n",
       "| Non-Malignant Tumor                  |   4                                  |\n",
       "| Giant Cell Tumor                     |   3                                  |\n",
       "| Renal Cell Carcinoma                 |   3                                  |\n",
       "| Low Malignant Potential Renal Tumors |   2                                  |\n",
       "| Basal Cell Carcinoma                 |   1                                  |\n",
       "| Paraganglioma                        |   1                                  |\n",
       "\n"
      ],
      "text/plain": [
       "   f                                    n  \n",
       "1  Central Nervous System               323\n",
       "2  Wilms' tumor                         207\n",
       "3  Neuroblastoma                        190\n",
       "4  Rhabdomyosarcoma                     134\n",
       "5  Osteosarcoma                         129\n",
       "6  Retinoblastoma                        98\n",
       "7  Ewing's Sarcoma                       95\n",
       "8  Soft Tissue Sarcoma                   93\n",
       "9  High Grade Glioma                     80\n",
       "10 Germ Cell Tumor                       74\n",
       "11 Low Grade Glioma                      24\n",
       "12 Adrenocortical Carcinoma              22\n",
       "13 Carcinoma                             14\n",
       "14 Other Solid Tumor                     10\n",
       "15 Non-Malignant Tumor                    4\n",
       "16 Giant Cell Tumor                       3\n",
       "17 Renal Cell Carcinoma                   3\n",
       "18 Low Malignant Potential Renal Tumors   2\n",
       "19 Basal Cell Carcinoma                   1\n",
       "20 Paraganglioma                          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_count( df2$Tumor_type , sort = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       ACT BCC  CA CNS EWS GCT GICT HGG LGG\n",
       "  Adrenocortical Carcinoma              22   0   0   0   0   0    0   0   0\n",
       "  Basal Cell Carcinoma                   0   1   0   0   0   0    0   0   0\n",
       "  Carcinoma                              0   0  14   0   0   0    0   0   0\n",
       "  Central Nervous System                 0   0   0 323   0   0    0   0   0\n",
       "  Ewing's Sarcoma                        0   0   0   0  95   0    0   0   0\n",
       "  Germ Cell Tumor                        0   0   0   0   0  74    0   0   0\n",
       "  Giant Cell Tumor                       0   0   0   0   0   0    3   0   0\n",
       "  High Grade Glioma                      0   0   0   0   0   0    0  80   0\n",
       "  Low Grade Glioma                       0   0   0   0   0   0    0   0  24\n",
       "  Low Malignant Potential Renal Tumors   0   0   0   0   0   0    0   0   0\n",
       "  Neuroblastoma                          0   0   0   0   0   0    0   0   0\n",
       "  Non-Malignant Tumor                    0   0   0   0   0   0    0   0   0\n",
       "  Osteosarcoma                           0   0   0   0   0   0    0   0   0\n",
       "  Other Solid Tumor                      0   0   0   0   0   0    0   0   0\n",
       "  Paraganglioma                          0   0   0   0   0   0    0   0   0\n",
       "  Renal Cell Carcinoma                   0   0   0   0   0   0    0   0   0\n",
       "  Retinoblastoma                         0   0   0   0   0   0    0   0   0\n",
       "  Rhabdomyosarcoma                       0   0   0   0   0   0    0   0   0\n",
       "  Soft Tissue Sarcoma                    0   0   0   0   0   0    0   0   0\n",
       "  Wilms' tumor                           0   0   0   0   0   0    0   0   0\n",
       "                                      \n",
       "                                       LMPRT NBL  NM  OS OST PGL  RB RCC RHB\n",
       "  Adrenocortical Carcinoma                 0   0   0   0   0   0   0   0   0\n",
       "  Basal Cell Carcinoma                     0   0   0   0   0   0   0   0   0\n",
       "  Carcinoma                                0   0   0   0   0   0   0   0   0\n",
       "  Central Nervous System                   0   0   0   0   0   0   0   0   0\n",
       "  Ewing's Sarcoma                          0   0   0   0   0   0   0   0   0\n",
       "  Germ Cell Tumor                          0   0   0   0   0   0   0   0   0\n",
       "  Giant Cell Tumor                         0   0   0   0   0   0   0   0   0\n",
       "  High Grade Glioma                        0   0   0   0   0   0   0   0   0\n",
       "  Low Grade Glioma                         0   0   0   0   0   0   0   0   0\n",
       "  Low Malignant Potential Renal Tumors     2   0   0   0   0   0   0   0   0\n",
       "  Neuroblastoma                            0 190   0   0   0   0   0   0   0\n",
       "  Non-Malignant Tumor                      0   0   4   0   0   0   0   0   0\n",
       "  Osteosarcoma                             0   0   0 129   0   0   0   0   0\n",
       "  Other Solid Tumor                        0   0   0   0  10   0   0   0   0\n",
       "  Paraganglioma                            0   0   0   0   0   1   0   0   0\n",
       "  Renal Cell Carcinoma                     0   0   0   0   0   0   0   3   0\n",
       "  Retinoblastoma                           0   0   0   0   0   0  98   0   0\n",
       "  Rhabdomyosarcoma                         0   0   0   0   0   0   0   0 134\n",
       "  Soft Tissue Sarcoma                      0   0   0   0   0   0   0   0   0\n",
       "  Wilms' tumor                             0   0   0   0   0   0   0   0   0\n",
       "                                      \n",
       "                                       STS WLM\n",
       "  Adrenocortical Carcinoma               0   0\n",
       "  Basal Cell Carcinoma                   0   0\n",
       "  Carcinoma                              0   0\n",
       "  Central Nervous System                 0   0\n",
       "  Ewing's Sarcoma                        0   0\n",
       "  Germ Cell Tumor                        0   0\n",
       "  Giant Cell Tumor                       0   0\n",
       "  High Grade Glioma                      0   0\n",
       "  Low Grade Glioma                       0   0\n",
       "  Low Malignant Potential Renal Tumors   0   0\n",
       "  Neuroblastoma                          0   0\n",
       "  Non-Malignant Tumor                    0   0\n",
       "  Osteosarcoma                           0   0\n",
       "  Other Solid Tumor                      0   0\n",
       "  Paraganglioma                          0   0\n",
       "  Renal Cell Carcinoma                   0   0\n",
       "  Retinoblastoma                         0   0\n",
       "  Rhabdomyosarcoma                       0   0\n",
       "  Soft Tissue Sarcoma                   93   0\n",
       "  Wilms' tumor                           0 207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "table(df2$Tumor_type , df2$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"High Grade Glioma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$Tumor_type == ttype,]\n",
    "ss =df$Tumor_Sample_Barcode %in% intersect( df$Tumor_Sample_Barcode, unique( df2_ss$Tumor_Sample_Barcode ))\n",
    "df_ss = df[ ss, ]\n",
    "df_ss = droplevels(df_ss)\n",
    "df2_ss = droplevels(df2_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ss$Hugo_Symbol=as.character(df_ss$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   \n",
       "                    HGG\n",
       "  High Grade Glioma  80"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$Tumor_type , df2_ss$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setdiff( df_ss$Tumor_Sample_Barcode, df2_ss$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td> 8</td><td>80</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t  8 & 80\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "|  8 | 80 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,]  8   80  \n",
       "[2,] 84   12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(dim(df_ss), dim(df2_ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MAF object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Summarizing..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   ID summary  Mean Median\n",
      " 1:        NCBI_Build      38    NA     NA\n",
      " 2:            Center      NA    NA     NA\n",
      " 3:           Samples       6    NA     NA\n",
      " 4:            nGenes       6    NA     NA\n",
      " 5:   Frame_Shift_Del       2 0.333      0\n",
      " 6:   Frame_Shift_Ins       1 0.167      0\n",
      " 7: Missense_Mutation       2 0.333      0\n",
      " 8: Nonsense_Mutation       2 0.333      0\n",
      " 9:       Splice_Site       1 0.167      0\n",
      "10:             total       8 1.333      1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Gene Summary..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
      "1:         NF1               0               0                 1\n",
      "2:        PMS2               0               1                 0\n",
      "3:        CDH1               0               0                 0\n",
      "4:        MSH6               1               0                 0\n",
      "5:         NF2               0               0                 1\n",
      "6:      RAD51C               1               0                 0\n",
      "   Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
      "1:                 1           0     2              2              2\n",
      "2:                 1           0     2              1              1\n",
      "3:                 0           1     1              1              1\n",
      "4:                 0           0     1              1              1\n",
      "5:                 0           0     1              1              1\n",
      "6:                 0           0     1              1              1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Checking clinical data..\n",
      "Done !\n"
     ]
    }
   ],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss, isTCGA = FALSE, verbose = T, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>38               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Center           </td><td>NA               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Samples          </td><td> 6               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td> 6               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 2               </td><td>0.333            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 1               </td><td>0.167            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 2               </td><td>0.333            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 2               </td><td>0.333            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 1               </td><td>0.167            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td> 8               </td><td>1.333            </td><td> 1               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " ID & summary & Mean & Median\\\\\n",
       "\\hline\n",
       "\t NCBI\\_Build        & 38                  &    NA               & NA                 \\\\\n",
       "\t Center            & NA                &    NA             & NA               \\\\\n",
       "\t Samples           &  6                &    NA             & NA               \\\\\n",
       "\t nGenes            &  6                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   &  2                    & 0.333                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  1                    & 0.167                 &  0                   \\\\\n",
       "\t Missense\\_Mutation &  2                  & 0.333               &  0                 \\\\\n",
       "\t Nonsense\\_Mutation &  2                  & 0.333               &  0                 \\\\\n",
       "\t Splice\\_Site       &  1                  & 0.167               &  0                 \\\\\n",
       "\t total             &  8                & 1.333             &  1               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ID | summary | Mean | Median |\n",
       "|---|---|---|---|\n",
       "| NCBI_Build        | 38                |    NA             | NA                |\n",
       "| Center            | NA                |    NA             | NA                |\n",
       "| Samples           |  6                |    NA             | NA                |\n",
       "| nGenes            |  6                |    NA             | NA                |\n",
       "| Frame_Shift_Del   |  2                | 0.333             |  0                |\n",
       "| Frame_Shift_Ins   |  1                | 0.167             |  0                |\n",
       "| Missense_Mutation |  2                | 0.333             |  0                |\n",
       "| Nonsense_Mutation |  2                | 0.333             |  0                |\n",
       "| Splice_Site       |  1                | 0.167             |  0                |\n",
       "| total             |  8                | 1.333             |  1                |\n",
       "\n"
      ],
      "text/plain": [
       "   ID                summary Mean  Median\n",
       "1  NCBI_Build        38         NA NA    \n",
       "2  Center            NA         NA NA    \n",
       "3  Samples            6         NA NA    \n",
       "4  nGenes             6         NA NA    \n",
       "5  Frame_Shift_Del    2      0.333  0    \n",
       "6  Frame_Shift_Ins    1      0.167  0    \n",
       "7  Missense_Mutation  2      0.333  0    \n",
       "8  Nonsense_Mutation  2      0.333  0    \n",
       "9  Splice_Site        1      0.167  0    \n",
       "10 total              8      1.333  1    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NF1   </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>PMS2  </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>0     </td><td>2     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CDH1  </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>MSH6  </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>NF2   </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>RAD51C</td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1    & 0      & 0      & 1      & 1      & 0      & 2      & 2      & 2     \\\\\n",
       "\t PMS2   & 0      & 1      & 0      & 1      & 0      & 2      & 1      & 1     \\\\\n",
       "\t CDH1   & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t MSH6   & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t NF2    & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t RAD51C & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| NF1    | 0      | 0      | 1      | 1      | 0      | 2      | 2      | 2      |\n",
       "| PMS2   | 0      | 1      | 0      | 1      | 0      | 2      | 1      | 1      |\n",
       "| CDH1   | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| MSH6   | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| NF2    | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| RAD51C | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 NF1         0               0               1                \n",
       "2 PMS2        0               1               0                \n",
       "3 CDH1        0               0               0                \n",
       "4 MSH6        1               0               0                \n",
       "5 NF2         0               0               1                \n",
       "6 RAD51C      1               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 1                 0           2     2              2             \n",
       "2 1                 0           2     1              1             \n",
       "3 0                 1           1     1              1             \n",
       "4 0                 0           1     1              1             \n",
       "5 0                 0           1     1              1             \n",
       "6 0                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>6</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 6\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 6\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 6 9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples with top variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   INS   SNP   total\n",
       "---------------------  ----  ----  ----  ------\n",
       "SJHGG034                  1     0     1       2\n",
       "SJHGG111                  0     1     1       2\n",
       "SJHGG086                  0     1     0       1\n",
       "SJHGG087                  1     0     0       1\n",
       "SJHGG089                  0     0     1       1\n",
       "SJHGG115                  0     0     1       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJHGG034                       2\n",
       "SJHGG111                       2\n",
       "SJHGG086                       1\n",
       "SJHGG087                       1\n",
       "SJHGG089                       1\n",
       "SJHGG115                       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variants.per.sample %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Genes with 2 or more mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1"
      ],
      "text/latex": [
       "1"
      ],
      "text/markdown": [
       "1"
      ],
      "text/plain": [
       "[1] 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = as.data.frame( dfx@gene.summary )  \n",
    "gf_sx = nrow( gs[ gs$MutatedSamples >= 2,] )\n",
    "gf_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_genes=length(gs$Hugo_Symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6"
      ],
      "text/latex": [
       "6"
      ],
      "text/markdown": [
       "6"
      ],
      "text/plain": [
       "[1] 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NF1   </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>PMS2  </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>0     </td><td>2     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CDH1  </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>MSH6  </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>NF2   </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>RAD51C</td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1    & 0      & 0      & 1      & 1      & 0      & 2      & 2      & 2     \\\\\n",
       "\t PMS2   & 0      & 1      & 0      & 1      & 0      & 2      & 1      & 1     \\\\\n",
       "\t CDH1   & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t MSH6   & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t NF2    & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t RAD51C & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| NF1    | 0      | 0      | 1      | 1      | 0      | 2      | 2      | 2      |\n",
       "| PMS2   | 0      | 1      | 0      | 1      | 0      | 2      | 1      | 1      |\n",
       "| CDH1   | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| MSH6   | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| NF2    | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| RAD51C | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 NF1         0               0               1                \n",
       "2 PMS2        0               1               0                \n",
       "3 CDH1        0               0               0                \n",
       "4 MSH6        1               0               0                \n",
       "5 NF2         0               0               1                \n",
       "6 RAD51C      1               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 1                 0           2     2              2             \n",
       "2 1                 0           2     1              1             \n",
       "3 0                 1           1     1              1             \n",
       "4 0                 0           1     1              1             \n",
       "5 0                 0           1     1              1             \n",
       "6 0                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q6OUc2SSCQ/Pz8ODo7ExESmz3g3ffr0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OMgwG4/nz55cvXx46dKipqSmCIDU1NW/e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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs_mat = gs %>% dplyr::select(-c('Hugo_Symbol','total', 'AlteredSamples', 'MutatedSamples') )\n",
    "rownames(gs_mat)=gs$Hugo_Symbol\n",
    "options(repr.plot.height=4,repr.plot.width=5)\n",
    "ggballoonplot(gs_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V0LAAAbTZ06tYGLZXg8nohlj/ViexASQpYtW2ZtbV1VVdXUhQAAsNF//vMfQ0ND\nEVeyicbhcAYOHOjm5iZ1AQhCoqKisnfv3qauAgCApdTU1LZv307TtBQTpBwOR1VVdcuWLQ0p\nQDZXb4D4oqKiPDw8CCF5eXl1XbYBAMA2y5cvX716tURP4XA4NE2Hh4d7e3s3ZNc4IwQAgKa3\ncuXKH374gRAi5hwph8NRUFA4fPhwA1OQIAgBAOBLwOFwNmzYcPjwYQ0NDUKIiGlSZpOZmdn1\n69cDAgJksOuGDwEAACAT48ePz8jIWLBggaqqKtPC5XIpiqIoin8/Z319/S1btqSlpTk4OMhk\np/iMsLHhM0IAgHp9/vz5ypUrly9ffvHiRV5enoKCgpmZWdu2bYcMGdKrVy+pl5jWir23WAMA\ngC+WiorK4MGDBw8e3Aj7wtQoAACwGoIQAABYDUEIAACshiAEAABWQxACAACrIQgBAIDVEIQA\nAMBqCEIAAGA1BCEAALAaghAAAFgNQQgAAKyGIAQAAFZDEAIAAKshCAEAgNUQhAAAwGoIQgAA\nYDUEIQAAsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCEAADAaghCAABgNQQhAACw\nGoIQAABYDUEIAACshiAEAABWQxACAACrIQgBAIDVEIQAAMBqCEIAAGA1BCEAALBaowZhbm4u\nVRsjIyMXF5fw8HCapmt94k8//cT0PH/+fM2tycnJQgNyOBxDQ0NfX99r167J73ASExOnTJnS\nrl07VVVVY2NjZ2fnPXv2VFVVyW+PAAAgc01wRqilpTVMwKBBg7S0tGJjY318fDZs2FCzP03T\nR48eZX7m/1CTnp4ef0xnZ+eKiopTp071799/+fLlQvlqY2NTM4xdXFxqjhkdHU1RVG5ubs2S\nfvnlF3t7+3379hUVFTk6OhoaGt66dWv69OkODg4fP36U9HcCAABNhm5EOTk5hBA7O7uam06c\nOEEIUVJS+vDhg9Cm+Ph4QkifPn3U1NTU1NQ+fvwo1CEpKYkQ4uHhIdjI4/FOnTqlqalJCLlw\n4QK/vaqqSllZWUtLy+5/TZ06VWjYqqoqNzc3QkhOTo7Qpt9//50Qoq+vHxMTw+PxmMZXr14N\nHTqUEDJ27FgRv4RLly4xv/m8vDwR3QAAoHF8KUFI07S9vT0hJC4uTqh99uzZhJCDBw+OGjWK\nEHL06FGhDrUGIePKlSuEkJ49e/LjKiMjgxAyY8YMEXXGxMT89ttvdnZ2TCLz8voAACAASURB\nVGIJBWFOTo6KioqqqmpmZqbQE0tLS1u3bk0IycrKqmtwBCEAwBflC1osY2BgQAgpLi4WbKyq\nqgoJCVFRURkxYsTIkSOJyNnRmlxcXHr37n337l3mtJIQkpaWRgjp1KmTiGeNGjVq4cKF9+7d\nq3Xrvn37Pn/+PHPmTAsLC6FNampqc+fO7dy5c1xcnPhFiq+goGDWrFkWFhZmZmYTJ06sOWcL\nAACSUmjqAv6rqqoqJSWFw+F06dJFsP3y5ctv374dOXKkpqbm4MGD1dTUIiMjCwsLW7ZsKc6w\nFEUNHjw4Pj7+yZMnjo6O5N8gtLS0PHz48MOHD3V1dXv06DFgwAAO5//fE/ADxsfHJzIyUmjM\nW7duEUImTpxY6x4XLly4cOFCMY9aIuXl5W5ubg8fPqRpmhBy+PDhmJiYhw8famtry2N3AAAs\n0fRBWFlZmZ2dvXbt2szMzPnz55uYmAhuDQ4OJoT4+/sTQtTU1IYMGRIaGhoWFjZ16lQxxzcz\nMyOEMDOihJCnT58yA757947fx8nJKTg42NTUlHmooqLC/MDlcmsO+OzZM0JIu3btxD/G0tLS\nBQsWMD9LfRp34sSJBw8e8B/SNJ2bm7t3794ff/xRugEBAIA0SRDeu3ePoqia7WvXrl20aJFg\nS2lpaXh4uI6OjqenJ9MycuTI0NDQo0ePih+EzIzrq1evmIfMGaGdnd3atWvbt2+fkZGxcuXK\n06dPf/fdd1FRUbUWJojH42VlZenp6amqqopZACGkvLx879694vev1f3794VauFxucnJyA4cF\nAGC5JghCLS0twWsVeDxeTk5OcnLyH3/8MXjwYFtbW/6mM2fOlJaW+vv7KysrMy3M7Oj169dz\nc3P5J3CivXnzhhBibGzMPFy6dOn8+fMHDBigoKBACLG1tQ0NDe3Vq1d0dPTt27cdHBxEj8bh\ncFq1avXu3buqqipmhEbDPwQ+Ho9XsxEAACTSBItlLC0twwWcPXs2KSnp0KFD+fn5P/zwg2BP\nZl507969/Kv9NDQ0Pn36RNN0SEiImLtjlqq2bduWeejs7Dxo0CDBDONyucwyHDHPrqytrauq\nqrKzs+va3ezZszdt2iTYqKury1+exF81KqmhQ4eqqKjwP8tkfiF+fn7SjQYAAIwvZdXo+PHj\nW7VqJbhQ8+3bt1FRUWpqat//Ly8vL0LIsWPHxBz54sWLhJAOHTqI6KOrq0sIqa6uFmfArl27\nEkKYCx9rOn/+/I4dO7KyssQsT3xWVlZ///23lpYW81BNTW337t3ffPONzHcEAMAqTb9YhkFR\nlIGBwZMnT6qrq5klKidOnKiqqho7duy+ffsEe3769MnAwODevXtPnz5t37696GGvX78eFxdn\nZ2fHLBnNysrq2bPnwIEDha7BYK5EFJyVFeH777/fuXPn+vXrv/vuO0NDQ8FNlZWVf/75JyHE\n3d1dnKEk5evrO2DAgDt37lRVVfXs2VPMpbMAACDCl3JGSAjh8Xg8Hq+wsJB5yMyLBgQECHVT\nU1Pz8fEh9Z0U0jR9/vz5YcOGEUJWrVrFrIKxsLBo27ZtSEjImTNn+D2vXbu2f/9+Gxsb5or+\nevXo0WP27NnFxcX29vaxsbH0v/dv+/Dhw9SpU5OSkpycnJhbzMiDpqamm5ubh4cHUhAAQCa+\nlDNCQoiOjg4hJC0tTV9fPzMzMz4+3tDQ0NXVtWbPcePGHTly5NixYz///DN/nWdSUhL/A7Pi\n4uL79+8zy2SWLFnCX3RKCNm3b1+fPn2GDx/er18/CwuLzMzMGzdu6OjoHDx4UElJScxSN2/e\n/OnTpwMHDri4uOjr63ft2vXjx48pKSklJSVWVlZHjhyp9boLAAD4An1BZ4S9evUihMyfP5/8\ne/uYsWPH1roy093dXU9PLy0tLTExkd/45s2bsH9FR0fTND1s2LCYmJg1a9YIXhTRtWvXO3fu\njBs37sWLF6Ghoe/fv58yZcrjx4979uwpfqkKCgr79++PiYkZPXq0oqLi9evXc3Jy7Ozsfvvt\nt0ePHjF3WQMAgGaBouv45iOQk6ioKA8PD0JIXl6e0EeMAADQ+L6gM0IAAIDGhyAEAABWQxAC\nAACrIQgBAIDVEIQAAMBqCEIAAGA1BCEAALAaghAAAFgNQQgAAKyGIAQAAFZDEAIAAKvV8+0T\nr1+/Fn8s3DkTAACanXqC0MjISPyxcP9uAABoduoJwu+//75x6gAAAGgS9QThvn37GqcOAACA\nJiHNN9Snp6c/e/bsxYsXxsbG1tbW1tbWgt98CwAA0IxIFoQPHz4MDAyMiooSbHR1df3jjz9s\nbGxkWhgAAEBjkCAI8/LyXF1dCwoK3NzcXF1djYyM8vPzr1y5cvnyZVdX14cPHxoYGMivUAAA\nAHmQIAiXLl1aUFBw5MiRgIAAfmNQUFBwcHBAQMDSpUvxgSIAADQ7ElxQn5CQ4OjoKJiCDH9/\n/z59+sTHx8u0MAAAgMYgQRBmZWW1a9eu1k1WVlbZ2dkyKgkAAKDxSBCE1tbWSUlJPB5PqJ2m\n6cTExPbt28u0MAAAgMYgQRC6ubk9evRo8eLFFRUV/MbKysply5Y9ePDA1dVVDuUBAADIFyX+\nfdFKS0t79OiRlpZmZmbWr18/IyOj169f//PPPy9evLCyskpKSlJXV5drrV+HqKgoDw8PQkhe\nXh7uzgoA0OQkWDWqrq5+8+bN1atX7969++jRo0yjsrLy3Llzly9fjhQEAIDmSIIzQr7q6uqc\nnJzc3FwTExMzMzMFBWluT8NaOCMEAPiiSJxh+fn5ISEhz549y8/Pt7Cw6Ny586hRo1RVVeVR\nHAAAgLxJFoRbt25dsmRJaWmpYOPSpUu3b98+fPhwmRYGAADQGCRYNXrx4sV58+ZRFLVs2bL4\n+Pjs7Oy4uLigoKD379+PGTMmMTFRflUCAADIiQRnhL/99huXyz1//ryzszPT0rp1a0dHRw8P\nD3d3919++SU8PFw+RQIAAMiLBGeESUlJvXv35qcgn4uLS58+fRISEmRaGAAAQGOQIAi1tbX1\n9fVr3aSnp6esrCyjkgAAABqPBEHYt2/f+Pj4oqIiofbCwsLY2Ng+ffrItDAAAIDGIEEQrly5\nkqIoLy+vpKQkfuPt27cHDx5cWVm5dOlSOZQHAAAgX/UslunZs6fgQyUlpRs3bvTo0UNXV9fI\nyCgvL+/du3eEEAcHh4iIiE6dOsmxUgAAADmoJwhzc3OFWvhfQ19QUKCoqMg8zMrKSk5Olkd9\nAAAAclVPEL5+/bpx6gAAAGgSEnxGCAAA8PWR7BZrt27dio6OLigoqHXrhg0blJSUZFEVAABA\nI5EgCA8ePDhp0iQRHTZt2tTgeuRo06ZNP/zwg4mJyePHjzU1NYW2zp49e8eOHf/880/fvn0J\nIampqR07dqxrqHbt2qWnp8u3XAAAaBQSBOH69estLCx+//33jh071vrVS83i+5hevny5ZMmS\n7du3i9O5ZcuWTC4KEfz6pE+fPv3666/nzp179uyZjo5Ot27dli5d6uDgILOKAQBAniSIrpyc\nnEOHDvn4+Mivmsaxc+dOf3//3r1719vTxsZG9A1UKyoqevfu/eDBg/bt2/v4+Lx9+zYiIuLc\nuXP79u37/vvvZVcyAADIiwSLZTp27Kimpia/UhrHlClTaJqeMmVKRUVFw0fbsmXLgwcP/P39\nHz16dPjw4YsXL967d69FixZz5szJy8tr+Pg1paSkbN269bfffrt37548xgcAYBsJgnDcuHFb\ntmypqqqSXzWNwMnJacqUKSkpKRs2bGj4aBcvXiSEbNiwgT8tbGtrO3PmzLKyshs3bjR8fCG/\n/vqrra3tvHnzAgMD7e3tFyxYIPNdAACwjQRTowsWLLh9+7adnV1AQIChoSFFUUIdAgICZFqb\nvGzYsOHcuXOrVq0aOXJk+/btGzLU+/fv7ezsjI2NBRuZ8+bCwkJ+C4/Hy8rKYn6W+kzxzp07\nixcvpmmaeUjT9ObNm11dXb28vKQbEAAAiERBmJSUdPXq1bdv3/7444+1dmguQaitrb1t27aR\nI0dOnTr16tWrHE6dp8UpKSl+fn5CjXp6ert27WJ+rnk/nYqKihMnThBCBD+D/PDhQ7t27RpY\n9uXLl/kpyOBwOJGRkQhCAICGkCAIf/zxx0+fPi1evLhTp07NYoGoCL6+vkOHDj137tyBAwcm\nT55cV7eCgoKwsDChRnNz87r6v3371tfXNyUlZeLEiba2tjIrlxBCSElJiVALRVHFxcWy3QsA\nANtIkGd3797dtGnT9OnT5VdNo6EoaseOHVevXv3hhx+8vLwEL4cQ5OzsfO3aNXEGLCsr2759\n+7p1696/fz958uSdO3cKblVXV9+zZw/z8+PHj//44w8paq55SUZ1dbU4a18BAEAECRbLmJub\nW1payq+URmZmZrZu3boPHz7MnTu3gUPFxMR06dLlxx9/tLKyunLlyp9//qmoqCjYQVlZeeq/\nBg8eLN1ehg0bxjyXy+Uy07n29vYiTmcBAEAcEgTh8OHDQ0ND5VdK45sxY8Y333wTGhp67tw5\nqQdZt26du7t7aWnp6dOnb9261b9/fxlWKIiiqDNnzuzYsWPw4MEDBw7cuHHj9evXhRIXAAAk\nJcHU6E8//TRhwoTJkydPmDCh1lWjze58kcvl7t27t0ePHjNnznR1dZVihOPHjy9ZssTd3f3o\n0aN6enoyr1CIgoLCzJkzZ86cKe8dAQCwhwRByF8gs3///lo7CK1pbBa6dOmyaNGiNWvWHD16\nVNLn0jS9Zs0aJSWlQ4cONUIKAgCAPEgQhAsXLpRfHU1o2bJloaGhaWlpkj7x5cuXjx49UldX\nHzFiRM2tK1as8PT0lEWBAAAgRxIE4Rf+5RJSU1FR2bt3r4uLi6RPfP78OSGktLQ0ISGh5ta6\nvqwKAAC+KJT485lTpkzx9/d3cnIScQU61CsqKsrDw4MQkpeXV9dlGwAA0GgkiLR9+/b179/f\n3Nz8xx9/TE5Obo6fCAIAAAiRIAj/+eefWbNmVVRUbNy4sXv37jY2NmvXrs3MzJRfcQAAAPIm\nwdQoo6qq6tq1a8ePHz916tT79+8JIY6Ojv7+/iNHjsTKSXFgahQA4IsicRDyVVRUXL58+fjx\n4+Hh4R8/fuRyuR4eHv7+/t7e3hoaGrKt8muCIAQA+KJIv+yFpmkul6uqqqqqqkoIqa6ujoiI\n8Pf3NzMz27ZtGz5BBACAZkHiIPz48ePJkyfHjRunr68/aNCgP//8s2XLlkuXLk1MTMzJyVm3\nbp2CgsLcuXNDQkLkUS4AAIBsSXAd4cGDB0+fPh0VFVVeXk4IsbW19fX19fX17dSpE79PUFDQ\nsGHDOnXqdOHChTFjxsi+XgAAAJmSIAgnTZpECOnVqxeTf3V906yxsbGBgUHHjh1lUyAAAIA8\nSRCEW7ZsGTFihJmZmehuWlpar1+/blhVAAAAjUSCIJw3b5786gAAAGgS4gZhZWVlYWGh4HL/\nmJiY8PBwbW1tb29ve3t7+ZQHAAAgX/WvGi0oKPDz82vRokVgYCC/cf369e7u7tu3b1+9enXv\n3r137NghzyIBAADkpZ4gLCsrc3BwCAsLs7Cw+Oabb5jGtLS0xYsXDxw48MqVK8xX8c2bNy89\nPV3+1QIAAMhYPVOjO3fuzMjImD9//qZNm/hfOnHgwAFCyL59+5iFMx06dHBwcNi0adPu3bvl\nXS4AAIBs1XNGePr0aX19/bVr1wp+9VJkZCQhxNTUlHnYq1cvKyurxMRE+VUJAAAgJ/UEYUZG\nhp2dnYqKCr/lzZs39+/f79SpE0VR/EYLCwt8DQUAADRH9QRhUVGRlpaWYEtMTAwhxN3dXbCx\nsrLy8+fPMi8OAABA3uoJwrZt26alpQm2hIWFkf8NQpqm09PT27ZtK4/6AAAA5KqeIOzZs2di\nYuLVq1eZhxkZGadPn1ZUVHR2dub3iYyMzM3NtbGxkWOZAAAA8lFPEC5dulRBQcHLy+unn346\ncODAqFGjeDyej4+PpqYm0yE2NnbKlCmEkB9++EHuxQIAAMhaPZdPWFpa/v3335MnT161ahXT\nYmBgsGHDBkLIw4cPBwwYkJ+fTwhZunRpt27d5F0rAACAzNV/i7XRo0c7OjpGR0c/efLEzMws\nICBAR0eHEFJaWkpRlKen58yZM4cOHSr/UgEAAGSPkvqr5GmaFryCgq+qqsrGxiY1NbVhhX21\noqKiPDw8CCF5eXmC924FAIAmIfE31PPVmoKMp0+fSj0sAABAY5I+CAEAAL4CCEIAAGA1BCEA\nALAaghAAAFgNQQgAAKyGIAQAAFZDEAIAAKshCAEAgNUQhAAAwGoIQgAAYLX6b7pdU3p6+rNn\nz168eGFsbGxtbW1tbS14uzUOh7Nx40bZVQgAACBHkgXhw4cPAwMDo6KiBBtdXV3/+OMP/hfz\ncjicwMBAmRUIAAAgTxIEYV5enqura0FBgZubm6urq5GRUX5+/pUrVy5fvuzq6vrw4UMDAwP5\nFQoAACAPEgTh0qVLCwoKjhw5EhAQwG8MCgoKDg4OCAhYunTpvn375FAhAACAHEmwWCYhIcHR\n0VEwBRn+/v59+vSJj4+XaWGirF+/nqIoiqJCQkJq7XDjxg2mw/Tp0wXbnz9/PmnSJFtbW3V1\ndRMTEycnp82bN3/+/FmwT3h4eM0n8nl6elIUlZycLKtjAQCApiVBEGZlZbVr167WTVZWVtnZ\n2TIqSQJ1BWFoaGjNxhMnTnTs2PHgwYNcLtfb27tLly6pqakLFizo3LlzYWFhQ8qIiopyd3fX\n1tbW1dUdMGDAzZs3GzIaAAA0JgmC0NraOikpicfjCbXTNJ2YmNi+fXuZFlY/DQ2NiIiIkpIS\noXYejxcWFqahoSHYmJOTM3nyZEVFxZiYmMTExGPHjkVGRmZnZ//nP//JyMio6/xPHHv27PHw\n8EhISPjmm2/69OkTGxvbr1+/y5cvSz0gAAA0JgmC0M3N7dGjR4sXL66oqOA3VlZWLlu27MGD\nB66urnIoTxRvb+/y8vKzZ88KtcfHx798+dLb21uwMTo6uqSkZMGCBYJ1qqqqbtq0ydra+ty5\nc5WVlVLUUFhYOH/+/Hbt2j169CgyMvLcuXO3b99WUlKaMWMGTdNSDCiOysrK8vJyOQ0OAMA2\nEgThypUrra2tN2zYYGlp6e/vHxgYGBAQYGlpuXbtWisrqxUrVsityNoNGjSoRYsWJ06cEGoP\nDQ2lKMrX11ew8cmTJ4SQNm3aCHXmcrlz5szx8/OTbnZ03759ZWVlGzduNDc3Z1q6deu2e/fu\nMWPGFBUVSTGgaE+fPvXw8FBTU1NTU+vTp09iYqLMdwEAwDYSrBpVV1e/efPm6tWrd+/effTo\nUaZRWVl57ty5y5cvV1dXl0+FdVJRUfH29g4NDf3w4YO2tjbTyMyL9uvXz9jYWLCzqakpIWTn\nzp39+/cXisPZs2fPnj1buhqOHz/eokULLy8vwcYJEyZIN5poHz58cHd3f/XqFTM7fevWLVdX\n10ePHjGHBgAA0pHsgvqWLVtu3rx506ZNOTk5ubm5JiYmZmZmCgrS3J5GJkaPHh0cHHz27Nlv\nv/2Wabl161Zubu6iRYuEeo4fP37t2rX37t2ztLT08fHx8/Nzc3PT09Ora+SoqCg/P7+a7UlJ\nSYIPX7x4YWZmlpubu3jx4vj4+NLS0m7dui1evNjNzU2wW0lJyYgRI5ifpV6YExoampuby3/I\n4/GKiooOHDjw008/STcgAAAQiYKwqqqK/7OpqSn/RKSqqorL5QreZa3RDBw4UEtLKyQkhB+E\n/HlRoVWsOjo6CQkJgYGBZ86cCQsLCwsLI4R069bNw8Pj+++/t7KyEho5MzMzMzNT9N7Lysre\nvXunoaHh4OCgoqLSq1evkpKSa9euxcTE7NixY+bMmfyelZWV0dHRDTxYZnZXEJfLffz4cQOH\nBQBgOQk+I1Ssm5qaWtu2bfv27bt9+/bS0lL5lStEWVl5+PDhUVFR7969I//Oizo5ORkZGdXs\nbG5uHhoa+vbt2zNnzsyZM6djx47Jycm//vpr+/btf/31V6HO06ZNo2vj4eHB7/P+/XtCyIsX\nL7y9vdPT00+ePHnp0qX79+9raWkFBga+evWK35PD4bT9V621iaPmtSvV1dV1XdACAABikiAI\n582b16lTJ/Lvn3VHR0crKysul0sIGTBggIODQ3Fx8Zw5czp37iyPdSJ1GT16dFVVVXh4OCEk\nISEhJydn5MiRQn1omr5169bDhw8JIVpaWt7e3lu3bn38+DGPx8vOznZ3dw8KCrpy5Yqku9bT\n06MoSllZedu2bUpKSkxjhw4dZsyYUVZWFhcXx++pra39/F9//fWXdEfq4+Ojo6PD4fz3JeNw\nOCoqKuPHj5duNAAAYEgQhP7+/unp6cOGDUtPT3/+/HlcXFxaWlp2dvbo0aNTU1O3bt16//79\nkydPvnjxYunSpfKrWIibm5uOjg5zZT0zL8r/NI6PoigfH58BAwbUbG/duvXixYsJIadPn5Z0\n14qKinp6eqampqqqqoLtzFnaixcvJB1QNGNj4/Pnz/NncU1MTE6dOtWhQwfZ7gUAgG0kCMJV\nq1aZmpqGhIQIrro0MTE5cuQIRVGLFi1iPpwbPXr0P//8I4dSa6ekpDRixIiYmJi3b9+ePHnS\n2dm51rlHW1vb/Pz869ev19yUk5NDCOGvO5WIvb19VlYWM0fKx3xux5w9y5ajo+OTJ08yMjKe\nPXuWlZU1aNAgme8CAIBtJAjC+Ph4R0dHZWVloXZFRcXevXvHxMQwD7t27fr8+XOZFSiG0aNH\nV1dXBwUF1TovymCWrowbN+7SpUuC7Xfu3AkKCuJyuUOHDpVi13PmzKmurp4+fXpZWRl/wN27\ndxsZGfXp00eKAetFUVSbNm0sLS35c6QAANAQEqwa1dTUzMrKqnVTVlYW/9ZrmZmZQrc3k7f+\n/fu3atXqwIEDtc6LMry9vZcsWbJ27VpPT8/WrVtbWloqKChkZWWlpaVxOJytW7f26tVLil0P\nHDhw4MCBJ06ciI+P79OnT2Fh4ZUrVxQUFPbv36+pqdmwwwIAgMYgwVmFk5PTjRs3jhw5ItR+\n7Nix2NhYJycnQkhxcXFUVFSXLl1kWWN9FBQUmPvIODs7Gxoa1tVtzZo1iYmJY8eO1dLSSkhI\nuHfvXosWLaZOnZqamjpr1izpdk1R1JkzZ1avXt2mTZuIiIj09HRfX9/79+9j0hIAoLmgxL8l\n5ps3b3r27JmTk+Ps7Ozi4mJoaJifnx8bG3v16lU9Pb3ExERFRcXu3bvn5eVFREQgCeoSFRXF\nXIORl5cnIrYBAKBxSDA1qq+vHxsbu3TpUuYUkN/u5eW1ceNGU1PTrKwsJSWlgwcPIgUBAKC5\nkOCMkO/169epqanZ2dlGRkYdO3Y0MzNj2nk8HvN1uLIu8quCM0IAgC+KNLcJNTQ0rPUvONYx\nAgBAsyNBEFpYWIjY6urqeuDAgYaWAwAA0LgkCEKhiyJomn7z5k1BQQEhxMnJqeZ9qwEAAL58\nEgTho0ePajZmZmYuWLDgwYMHkyZNkl1VAAAAjaShn+q1adMmJCREVVX1hx9+kElBAAAAjUkG\ny1uUlJQGDhwYERHR8KEAAAAamWzWeRYWFvJvtgkAANCMSPAZ4efPn2ttjIiIOHbsmK2treyq\nAgAAaCQSBKHQt+4JCQoKanAxAAAAjU2CIGRubF2Tnp7eyJEjXV1dZVQSAABA45EgCE+ePCm/\nOgAAAJoEbooGAACsVs8ZYXJysvhjdevWrWHFAAAANLZ6grB79+7ijyXFF1kAAAA0rXqCkPnC\nIEFJSUlv3ryp2Q4AANAc1ROEkZGRQi1+fn5hYWE12wEAAJojLJYBAABWQxACAACrIQgBAIDV\nEIQAAMBqCEIAAGA1BCEAALBaPZdPTJ8+XaglMTGx1nZCyO7du2VVFgAAQOOgRN8OhqIo8cfC\nnWXEERUVxdyOIC8vz9DQsKnLAQBgu3rOCE+fPt04dQAAADSJeoJw+PDhjVMHAABAk8BiGQAA\nYDUEIQAAsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCEAADAaghCAABgNQQhAACw\nGoIQAABYrdkHYWJi4pQpU9q1a6eqqmpsbOzs7Lxnz56qqirBPsnJydT/4nA4hoaGvr6+165d\nExowPDycoqhav2eKEOLp6UlRVHJyspwOBwAAGlkzDkKapn/55Rd7e/t9+/YVFRU5OjoaGhre\nunVr+vTpDg4OHz9+FOqvp6c37F/Ozs4VFRWnTp3q37//8uXLZfIFUtHR0RRF5ebmNnwoAABo\nNPV8+8SXbMuWLT///LO+vv6xY8f69+/PfHViXl7etGnTzp07N3Xq1KNHjwr279GjR3h4OP8h\nTdPh4eETJ05cvXp17969Bw8e3JBiqqur169f35ARAACgSTTXM8Lc3NwlS5aoqqomJCS4urry\nv0DYyMjo+PHjrVu3PnbsWHZ2togRKIry8fFhovHnn3+W+qTwypUrv//+u4ODQ0xMjHQjSCQi\nImLevHkzZ848efIkvgkZAKDhmusZ4b59+z5//rxw4UILCwuhTWpqanPnzj148GBcXJy5ubno\ncVxcXHr37h0fHx8fH+/o6ChFJaNGjSosLJTiiVKYPn36nj17mNTftWuXt7c384lm4+wdAOCr\n1FzPCG/dukUImThxYq1bFy5c+OjRo3HjxtU7DkVRzKTokydPpKskNze3rKysrKzM09Ozrj6V\nlZXR/0pKSpJuRzExMXv27CGE0DTNnAuePXs2ODhYutEAAIDRXM8Inz17Rghp165dw4cyMzMj\nhGRkZAg2RkVF+fn51excM8ZUVFSYH7hcbl27KCkpGTBgQAPrvH79ulALh8OJjY0NCAho4MgA\nAGzWLIOQx+NlZWXp6empqqo2fDQDAwNCyKtXrwQbMzMzMzMzGz44df8V2wAAHKNJREFUAAB8\n4Zrl1CiHw2nVqtX79++FrheUzps3bwghxsbGgo3Tpk2ja+Ph4SHFLlq0aHH5X1IvLnV2dhZq\n4fF4Li4u0o0GAACMZhmEhBBra+uqqqq61oXm5OTMnj1706ZN4gyVk5NDCGnbtq0s6/tfioqK\n7v/q3r27dIO4urrOnDmTEMLcE4AQ4uPjI87noAAAIEJzDcKuXbsSQk6cOFHr1vPnz+/YsSMr\nK0ucoS5evEgI6dChg+yqk5cdO3ZERkYyl0+EhYWFhYVhySgAQAM1y88ICSHff//9zp07169f\n/9133xkaGgpuqqys/PPPPwkh7u7u9Y5z/fr1uLg4Ozs76a6daHweHh7STc8CAECtmusZYY8e\nPWbPnl1cXGxvbx8bG8u/tPzDhw9Tp05NSkpycnIaOnSoiBFomj5//vywYcMIIatWrcKpFQAA\nOzXXM0JCyObNmz99+nTgwAEXFxd9ff2uXbt+/PgxJSWlpKTEysrqyJEjQtczJCUl8a+IKC4u\nvn//PrNMZsmSJSIuAQQAgK9bMw5CBQWF/fv3+/v7792798aNG9evX9fT07Ozsxs6dOjs2bOV\nlJSE+r958yYsLIz5maKoVq1aDRs2bO7cua6uro1eOwAAfCko3K+ykUVFRTEf8uXl5Ql9ugkA\nAI2vuX5GCAAAIBMIQgAAYDUEIQAAsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCE\nAADAaghCAABgNQQhAACwGoIQAABYDUEIAACshiAEAABWQxACAACrIQgBAIDVEIQAAMBqCEIA\nAGA1BCEAALAaghAAAFgNQQgAAKyGIAQAAFZDEAIAAKshCAEAgNUQhAAAwGoIQgAAYDUEIQAA\nsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCEAADAaghCAABgNQQhAACwGoIQAABY\nDUEIAACshiAEAABWa65BmJ6eTtWgq6vbu3fvPXv2VFdX83uuX7+e2RoSElLrUDdu3GA6TJ8+\nXbD9+fPnkyZNsrW1VVdXNzExcXJy2rx58+fPn+V7YAAA0LiaaxAydHR0hv1r6NChFhYWCQkJ\n06dPDwgIqNm5riAMDQ2t2XjixImOHTsePHiQy+V6e3t36dIlNTV1wYIFnTt3LiwsFOz56dOn\nn3/+uUePHi1atGjdurW3t3dCQoJMjg4AABoD3Tw9e/aMENKnTx+h9jt37rRo0YIQEhMTw7Ss\nW7eOEKKhoaGsrFxcXCzUv7q62sTERENDgxAybdo0pvHFixctWrRQU1PjD0LT9KdPn/7zn/8Q\nQvz8/PiN5eXlXbt2JYS0b99+/Pjxnp6eXC6XELJv3766Kr906RLzm8/Ly2vIbwAAAGSieZ8R\n1tSzZ89Zs2YRQuLi4gTbvb29y8vLz549K9Q/Pj7+5cuX3t7ego3R0dElJSULFixwdXXlN6qq\nqm7atMna2vrcuXOVlZVM45YtWx48eODv7//o0aPDhw9fvHjx3r17LVq0mDNnTl5enjwOkMfj\nPX/+PC0traqqSh7jAwCwzdcWhISQ1q1bE0Jyc3MFGwcNGtSiRYsTJ04IdQ4NDaUoytfXV7Dx\nyZMnhJA2bdoIdeZyuXPmzPHz8+PPjl68eJEQsmHDBgUFBabF1tZ25syZZWVlN27ckNkh/evG\njRvt27e3tLRs3769ubn5+fPnZb4LAAC2+QqDkDkXtLa2FmxUUVHx9vaOjIz88OEDv5HH44WF\nhfXr18/Y2Fiws6mpKSFk586dmZmZQoPPnj3777//NjQ0ZB6+f//ezs5O6OlqamqEEKGPEhvu\n5cuXXl5eGRkZzMPXr1/7+fk9fvxYtnsBAGCbrycIeTxebm7uypUrg4ODlZWVhWY7CSGjR4+u\nqKgQnB29detWbm7uyJEjhXqOHz/ewMDg3r17lpaWfn5+x48ff/v2ba07TU5Ovnv3rmBLRUUF\nc97Zu3dvfuOHDx/a/WvixInSHeDp06eLiop4PB7/eCsqKoKDg6UbDQAAGM07COPi4vjXTnC5\nXDMzsxUrVigqKm7dutXKykqo88CBA7W0tATXjtY6L0oI0dHRSUhI8PPz43K5YWFhY8eO1dfX\n7969e1BQELNIpy5v3751d3dPSUmZOHGira0tv53H42X8S+rPDvnngnwcDuf58+fSjQYAAAyF\npi6gQXR0dJycnPgPlZSUOnXq5OfnZ2NjU7OzsrLy8OHDg4OD3717p6ury8yLOjk5GRkZZWdn\nC3U2NzcPDQ0tKiqKjY2Njo6Ojo5OTk5OTk7esGHDunXrFi1aJNS/rKxs+/bt69ate//+/eTJ\nk3fu3Cm4VVFR0d3dnfm5sLAwKSlJioPt1KmTUEt1dXXNRgAAkExTL1uVUl2XT9TEXD4RGhpK\n03RERAQhZP/+/TRN37x5kxCyfft2mqbj4+PJv5dP8Hi8+Pj4Bw8eCI3D4/Gys7MHDBhABK7N\nYERHR7dr144Q0qtXrytXroiuR+rLJz58+NC6dWsO578n8RwOR0dH5+XLlxINAgAAQpr31Kik\n3NzcdHR0mNlRZl50xIgRQn0oivLx8WECT6i9devWixcvJoScPn2a375u3Tp3d/fS0tLTp0/f\nunWrf//+cipeS0srJibG09NTUVFRQUGhb9++V65cEVqnAwAAkmJXECopKY0YMSImJubt27cn\nT550dnY2MjKq2c3W1jY/P//69es1N+Xk5BBCtLW1mYfHjx9fsmSJu7v7gwcPhg8fTlGUXOu3\ntLS8cOHCp0+fSktLY2Nju3XrJtfdAQCwAbuCkBAyevTo6urqoKCgnJycmutFGTNnziSEjBs3\njj+Nybhz505QUBCXyx06dCghhKbpNWvWKCkpHTp0SE9PrxGKZygoKCgpKTXa7gAAvm7Ne7GM\nFPr379+qVasDBw7UOi/K8Pb2XrJkydq1az09PVu3bm1paamgoJCVlZWWlsbhcLZu3dqrVy9C\nyMuXLx89eqSurl7rOCtWrPD09JTvwQAAQIOxLggVFBR8fX337Nnj7OzMvy6+pjVr1vj5+W3c\nuPHRo0cJCQkqKioWFhZTp04NDAzkX5jBXLpQWlpa6122CwoK5HQIAAAgQxRN001dA7tERUV5\neHgQQvLy8kQkMQAANA7WfUYIAAAgCEEIAACshiAEAABWQxACAACrIQgBAIDVEIQAAMBqCEIA\nAGA1BCEAALAaghAAAFgNQQgAAKyGIAQAAFZDEAIAAKshCAEAgNUQhAAAwGoIQgAAYDUEIQAA\nsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCEAADAagpNXQB7FRUVKSsrN3UVAABf\nP21tbYqi6tqKIGwyHTp0aOoSAABYobS0VE1Nra6tFE3TjVkNVFZWZmZmtm/fvqkLAQBgC9FB\niDPCxqaoqGhhYXH58mXpnv7w4cMFCxYQQpYsWdK/f3+Zlvb1279///HjxwkhZ86cEfG/Amo1\nbdq0jIwMGxubzZs3N3Utzczbt2/HjRtHCJk0adLYsf/X3r0HNXG1DQB/lpAgk3ALGsQAAUUZ\ny7Vc6thqREBEsdRLVG4OSlVUphWoF7S29dIRK1hosaO17Vi0VgvepirKGECpN8Sx6AyjqAUx\nIiIKCgoESPb947xfvrwJ5mLlus/vr3D27Obsw9l9krN7NlF93ZwB5tSpU1lZWQCwe/fuUaNG\nvdlGdF+HwkTYBzgcTkhIyBuvS154enq+8UYYq7CwkLwIDAy0tLTs28YMODweDwCsra2x4xnr\n4cOH5MWoUaMwesaqqqoiL8aNG+fj49MTb4F3jSKEEGI0TIQIIYQYDW+WGWC6urpaWloAgMvl\nqoZJkYHa2tra29tB373UqFvNzc0KhcLU1NTCwqKv2zLAKJXKFy9eAIC5ufmQIUP6ujkDjFwu\nb21tBQBLS0sWi9UTb4GJECGEEKPh0ChCCCFGw0SIEEKI0TARIoQQYjRMhP2IVCqlKEo15UiH\nrq6ujIyMwMBAKysrX1/fpKSkp0+fGlVhkDE8dK2trV999ZWvr6+FhYWTk1NERERpaal6BQ8P\nD0pLYGBgTzW9HzA8enqDw7SOB4ZF7/nz59pxU4mNjSXVGNL39B6D2nr8jEej/qGrqys4OBgA\nZDKZ3srk4RR2dnazZs0iT2sbM2ZMU1OT4RUGE8NDJ5fLvby8AMDNzW3BggVhYWHkJrSff/5Z\ntSkzMzMrKyu//7V06dKe34++YXj0DAkOozoebXD0mpub/bpDnpOSlJREM6bv6T0Gu9XTZzxM\nhH2vsLBwx44dfn5+5KOJ3vPR1atXAcDPz6+5uZmmaaVSmZCQAADbt283sMKgYWzovvnmGwCI\niYnp7OwkJeXl5RYWFubm5o8ePaJpmjzDYvny5T3e9H7A2OjpDQ5zOh5tfPS0KRSK0NDQkSNH\nPn/+nGZM39N7DGrrhTMeJsK+Z2trq/4dXe8RtWjRIgCQSqWqkpaWFjMzM1dXV6VSaUiFQcPY\n0JFRptraWvXCtWvXAkBubi5N02fOnAGA7OzsHmx0v2Fs9PQGhzkdjzY+etoyMjLYbHZZWRn5\nkyF9T+8xqK0Xznh4jbDvPXz4sK2tra2tLSwszJD6RUVFlpaWYrFYVcLj8YKCgu7duyeTyQyp\nMGgYG7qmpiY/P78RI0aoF5Knbz979gwA7ty5AwCurq779u1bvXp1WlpaQUGBUqnsgbb3PWOj\npzc4zOl4YHz0NNy8eXPdunWbN2/29/cnJQzpe3qPQW29cMbDh273PdWTJgx5aAJN03V1da6u\nrmw2W73c2dkZAOrq6hwdHXVXcHJyeksN73tGhQ4AysvLNUo6Ojpyc3MBYPz48QBQWVkJADEx\nMY2Njao6YrH4wIEDDg4Ob6XN/Yex0dMdHL09czB1PDA+eupomk5KShIKhUlJSapChvQ9vceg\nht454+E3wgGmsbGxo6NDY1gGAIYOHQoAdXV1eiv0TjsHhIaGhpCQkIqKioULF3p7e8P/fSr3\n8/MrKytrbm4uLy+fNWtWSUnJokWLaMY/g0l3cLDjGe7EiRPFxcXbtm1Tf9waM/ue9jGooXfO\neJgIBxhySGg/J5OUd3R06K3QG63s99ra2tLT093c3P7666/Fixfv2bOHlH/++ef5+fn5+fn+\n/v4WFhbe3t55eXm+vr5SqZRckGcy3cHBjmcghUKxZs0ab2/vefPmqZczre+97hjU0DtnPEyE\nA4ytrS2bzdYeTCfDKUKhUG+F3mlnf1ZYWOjp6blmzZrRo0cXFRX99NNPqkGVSZMmTZs2zdT0\n/y8ZsFisuXPnQndDOkyjOzjY8Qx0+vTpysrKhIQEjXM3o/qejmNQQ++c8TARDjAURdnb29fW\n1ioUCvVyMp9XKBTqrdCbre2H0tLSQkJCXr16dezYsStXrkyePFnvKnw+HwA04okIVXCw4xlo\n586d5ubm5Afr9RqUfc+oY7B3zniYCAeewMDA58+fX758WVXS3t5eVFTk7Ozs6OhoSAXGOnTo\n0Pr160NCQm7evDlz5kyNj+T3798fOnSo9hnq77//BoBuL2AwhyHBwY6n17179woKCubMmWNl\nZaVezpy+p/sY7FZvnPEMmWOBekd4eDhoTUhqb2+vrq5+8OCBquTKlSsAEBwcTMbHaZretGkT\nqM0e1Vth8DEkdEql0sPDg8PhaMxhUhcQEGBiYnL8+HFVSXFxMZvN9vDwkMvlPdT4Pmdgx9Mb\nHAZ2PNrg6BFkOvm+ffu0t8OEvmfIMdgnZzxMhP1It0cU+ZhjZ2enKlEqleTKgY+PT0pKCpnG\n5ObmRp5PYUiFwceQ0JEZRVwud1x3Tp8+TdP0jRs3eDweAEycOHHBggUTJkwAABsbG9Ws50HJ\nwI6nNzgM7Hi0wdEjyFy3mpoa7e0woe8Zcgz2yRkPE2E/YvgRJZfLt23bNnHiRAsLCx8fn5Ur\nVzY0NBhVYZAxJHTnzp3TMTSyf/9+Uu3WrVvR0dEikWjIkCHu7u5Lliypq6vr7f3pXYZ3PL3B\nYVrHo42JXlNTE4vFcnZ2ft2mBn3fM+QY7JMzHv5CPUIIIUbDm2UQQggxGiZChBBCjIaJECGE\nEKNhIkQIIcRomAgRQggxGiZChBBCjIaJECGEEKNhIkQIIcRomAgRQggxGiZChN6abdu2URRF\nUdQff/zRbYULFy6QCsuWLevltr2BpKQkiqLu3bunKvnnn3/i4+O9vb25XK5QKBSLxZmZme3t\n7X3YSGLx4sUURT1+/LivG4IGJEyECL19r0uEeXl5xm5KKpVSFJWVldWjqxgiNzd37Nixe/fu\nZbFYERERnp6et2/fTklJcXd31/5ZVIQGEEyECL1lPB4vPz+/paVFo1ypVB45coT8wsCAI5PJ\nFi9ezGazCwsLr1+/fvDgwTNnztTU1CQlJVVVVQ2IL7gIvQ4mQoTesoiICLlc/ueff2qUX758\nuba2NiIiok9a9S9JpdKWlpaUlJSgoCBVobm5eUZGxpgxY06cONHZ2dmHzUPo38BEiNBbNm3a\nNAsLi9zcXI3yvLw8iqLmzJmjXiiRSCiK0rjM5uHh4ezsDABhYWFTpkwBgOTkZIqirl27RipU\nVlbGxcW5uLiYmZnZ29tPmTKloKCALHrdKkqlMisrSywWW1paOjg4xMTEVFRUqL/p/fv3o6Oj\nR44cKRAIIiIizp49q7701q1bAODi4qKxUywW65NPPpFIJOqjozqaBwCrVq2iKEqhUGzcuNHH\nx8fGxiY0NPT8+fM0TWdmZgYEBHC53LFjx+bk5KhWcXZ2lkgkMpksKiqKz+cLBILw8PCSkpLX\n/xP07C9N00ePHhWLxcOGDbO0tAwICPj111/xp3iY61//whRC6L/S0tIAIC8vLyYmhsPhNDU1\nqRYpFAoHBwexWEx+bi0hIYGUk7zY1tamvh13d3eRSETT9MmTJz/99FMAkEgku3btqq+vp2n6\nwYMHw4YNoygqKCho4cKF/v7+pqamJiYmRUVFr1uls7Nz6tSpAODq6hobGzt58mSKorhcLlmF\npumysjJra2sA8PLykkgkzs7OJiYmXl5eAHD37l2apr/77jsA8PPzq6qq0h0E3c2jafqzzz4D\ngJiYmCVLlpw9e/aHH37gcrk8Hm/u3LnBwcEnTpw4dOiQSCQCgCtXrpBVRCLRhAkThELh1KlT\nf/nll/T0dFdXV1NT099++41U+PjjjwFA9et9evc3OzsbAHg8XlhYWExMjK2tLQDs3LnzDf7p\naBDARIjQW6NKhGRcNCcnR7Xo4sWLAJCdnW1UIqRpmnwzy8zMVC3dunUrAOzevVtVIpVKAWDZ\nsmWvW2XXrl0AsHz58s7OTlJy7tw5DocjEona2tqUSuX777+vngk6Ojri4uLIZ2WSCBsbG+3s\n7ADAxMRkzpw5Bw8efPLkSbdB0Ns8kgjj4+NVFdatW0dysKp5R44cAYAtW7aQP0lenDdvnkKh\nICXPnj1zcXERCAQvX76ktRKh7v2laVooFAoEgmfPnpGlMpmMw+F4eHh0u0do0MOhUYTevtDQ\nUCsrK/V7R7sdF30z48aN27VrV2xsrKrE09MTABoaGl63Snp6ukAgyMzMNDU1JSWTJk1KTEys\nqam5fPnyjRs3Ll26FBQUlJiYSJay2ezvv/+efEckbGxsSktLJRIJi8U6cuRIVFSUQCB49913\nU1NT7969+wbNUyVaAHjnnXcAICYmRtU8UvL06VP1VbZs2WJi8t9TFp/PX7Vq1ZMnT7SHoPXu\nr0KhePz4MYvFMjc3J0sdHBwuXry4Z8+e1wUQDW6mfd0AhAYhMzOzmTNnHjhwoLGxkc/nk/tF\nxWKxvb19TU3Nv9x4UFAQuWNFLpffunXr6tWrBw8e1FG/ra2tqqrK3d19//796uXNzc0AUFFR\nMWzYMACYPn26+lJLS8sPPvjg1KlTqhKRSJSXl/fixYvz589LpVKpVFpeXl5eXr59+/a0tLS1\na9ca1Tx7e3vVa5KutEvU2djYjBkzRr1k3LhxAFBZWWns/k6ePHnatGknT5708vJauHDhlClT\nfHx8/P39tRuJGAITIUI9Yv78+Tk5OcePH4+Pjy8tLZXJZKpUoRet866N5ubmzZs3FxQU3L59\nu6urSyQS+fr66qj/4MEDAKioqFiyZIn20paWlq6uLgAYMWKExiKhUKhqT2lpKZfL9fT0tLKy\nioiIIPe+0jRNplWkpqYGBASQ/Gdg8yiK0luijmRr7ZKHDx8au78AcODAgS1btuTk5GzYsGHD\nhg08Hm/GjBkbN250c3PT0QY0WOHQKEI9Ijg42MbGhoyOknHR2bNnG7iujkFOAIiMjNyxY8fo\n0aP37t1bX19///7933//XUf94cOHA8Ds2bO7vTqybt06R0dHAHj06JHGiqoHtVAUNWvWLHIz\nqjqKopycnMgVvmPHjr1Z8wz06NEjjc8HMpkMAAQCgbH7CwCWlpbp6emPHz8uKyv79ttvvb29\nDx06NH78+Pr6+n/fVDTgYCJEqEdwOJzZs2cXFhY2NDQcPnx40qRJ6kN/GtSnT1RXV+tIhE+f\nPj19+rRYLD569GhsbCxJAyQlvI6VldXw4cPLyso0pvoVFxdnZGQ8efKEfA06c+aM+tJXr16R\n+3oIb2/v+vr6bmcskHcnFxTfoHkGevnyZWlpqXrJyZMnAUD7O5ze/a2urv76668LCgpMTEz8\n/f2Tk5MvXLiwYsWKpqYm3VMy0GCFiRChnjJ//nyFQpGamiqTyebOndttHTK+l5+fT/7s6Ogg\nN1VqkMvl5AWHwwGA9vZ21dcjuVyempoK/5tN1VcBgMTERJlM9uWXXyqVSlJSU1MTHR2dlZXF\n5/Pd3d3FYrFUKv3xxx/JUoVCsXr1avV8vGLFCgCIjo5WnxEIAGVlZampqSwW68MPPzSqeW9g\n5cqVjY2N5HVJSUl2djafz4+MjNSuqXt/TUxMvvjii+Tk5NbWVrKUpmlyBZF8OUaM0yP3oiLE\nSKrpE+TPzs7OoUOHAgBFUao7+zWmT5CpBWw2Oy4uLiUlxcPDw8XFRSwWq6ZPXLp0CQBcXV1T\nU1Nrampomp4xYwYA+Pn5JScnL1u2TCgUBgQEODg4cDicuLi4bldpbW0lN4N4eHjEx8fPnz+f\nx+OR56WRd7l27ZqNjQ0A+Pr6RkVFkdtSSG4j0ydoml6/fj05aTg5OQUFBYWGhpJqJiYm6jPw\n9DaPZHrVZmmaJnfT7N+/X1VC7kRduXIl+VMkEtnZ2Tk4OAgEAolEEhgYSOYm7t27l1TQmD6h\ne3+VSuVHH30EAC4uLvHx8ZGRkU5OTgAQGhqqmm6BGAUTIUJvjUYipGk6ISEBAAIDA1UlGomQ\npunDhw/7+vqam5vz+fzY2Ni6urqtW7cuWrSILO3o6IiMjDQ3N7e2tr5x4wZN042NjYmJiY6O\njlwud/z48WlpaR0dHSUlJcHBweHh4d2uQtO0XC7ftGnTe++9x+VyHRwcJBKJahFRXV0dGRk5\ncuRIPp8/ffr0/Pz8s2fPhoeH19bWqupcv349KirK09OTy+Xa2tr6+fktXbr0zp076tvR27w3\nS4Te3t51dXXR0dEikcjW1nbq1KnFxcWq+hqJUO/+Njc3b9q0aezYsTwez8rKysfHJz09nUxJ\nRAxE0fhUIYRQP0PTdFdXF03TZKzV2dnZ2tq6vLy8r9uFBiecPoEQ6ncoimKz2X3dCsQUeLMM\nQgghRsNEiBBCiNHwGiFCCCFGw2+ECCGEGA0TIUIIIUbDRIgQQojRMBEihBBiNEyECCGEGA0T\nIUIIIUbDRIgQQojRMBEihBBiNEyECCGEGA0TIUIIIUb7D22K78u3k1BdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=4)\n",
    "gs %>% ggdotchart(\"Hugo_Symbol\",\"MutatedSamples\", \n",
    "                  rotate = TRUE,\n",
    "                  sorting = \"descending\",\n",
    "                  show.label = F,size = \"MutatedSamples\", \n",
    "                  ggtheme = theme_pubr() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotmafSummary\n",
    "to plot the summary of the maf file, which displays number of variants in each sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FVVNXjw4IP2qoi6hoaGRCLR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\nAIDIE3AAAAAAkSfgAAAAACLv/wO39yoc9tMarAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Top 6\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_plotmafsummary_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kQ1ABAAAAIB2CCgAAAADp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\nIb1BQQamZrYAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Variant Classification summary”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=5)\n",
    "plotmafSummary(maf = dfx, rmOutlier = FALSE ,dashboard = F,addStat = 'median', textSize = 2, top = gf_sx, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oncoplots or waterfall plots\n",
    "- Adding Clinical Data on oncoplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Oncoplots with the total genes from the gene summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVqxo3Vq1dX2s24oYyMjKysrBEjRkRFRV25cmXlypX79u07e/bsX+dQs2L2gI2N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\nhPK3YPW+fCGEMsXRa6+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/p5Vu7Zs+et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DvXr1pMFAbWxsits5XvqZc82aNTt27Lh2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\nTp3q6+vr4uLi7+8fFRWlVqvb29uNixkMhpMnTy5evDgkJMTFx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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=7,repr.plot.width=9)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_oncoplot_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=6)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_oncoplot_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Transition and Transversions\n",
    "returns a list of summarized tables in various ways. Summarized data can also be visualized as a boxplot showing overall distribution of six different conversions and as a stacked barplot showing fraction of conversions in each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class  MAF \n",
       "                   ID summary  Mean Median\n",
       " 1:        NCBI_Build      38    NA     NA\n",
       " 2:            Center      NA    NA     NA\n",
       " 3:           Samples       6    NA     NA\n",
       " 4:            nGenes       6    NA     NA\n",
       " 5:   Frame_Shift_Del       2 0.333      0\n",
       " 6:   Frame_Shift_Ins       1 0.167      0\n",
       " 7: Missense_Mutation       2 0.333      0\n",
       " 8: Nonsense_Mutation       2 0.333      0\n",
       " 9:       Splice_Site       1 0.167      0\n",
       "10:             total       8 1.333      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ADAj1XUHUlV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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=13,repr.plot.height=6)\n",
    "dfx.titv = titv(maf = dfx, plot = F, useSyn = T  )\n",
    "plotTiTv(res = dfx.titv ,showBarcodes = F, textSize=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gjQAAAAgAICNAAAAKCAAA0AAAAoIEAD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\nENAAABAQ0AAAEBDQAAAQENAAABD4Xz2jcZ3SeJeuAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=8)\n",
    "set.seed(42)\n",
    "geneCloud(input = dfx, min=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=8, width=8)\n",
    "set.seed(42)\n",
    "geneCloud(input = dfx, min=0)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lt7i8XufPHV9twDizu+c1/tOfMaP3nsNla5AGxNBCEAocje+mzurP1qjt9jCx0/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\nPO7MZ+95PnvP81EUpXZqqj5yt8yJe9UcM2eE8w8njrp/8VjHN+8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TWz0KADj4CUIApqPLLrusUCi0ehQAcJAThABMR7NmzbrkkktaPQoA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\nIChBCAAAEJQgBAAACEoQAgAABPX/AceIeQ8WB2slAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "           min.freq =1,\n",
    "           max.words=50, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.35, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_genecloud_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Drug Interactions\n",
    "- plot shows potential druggable gene categories along with upto top 5 genes involved in them. \n",
    "- One can also extract information on drug-gene interactions. \n",
    "- [check this documentation](https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html#98_drug-gene_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “Druggable categories”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=6, repr.plot.width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_drugInteractions_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oncogenic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pathway alteration fractions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pathway  N n_affected_genes fraction_affected\n",
      " 1:      Hippo 38                1        0.02631579\n",
      " 2:    RTK-RAS 85                1        0.01176471\n",
      " 3: Cell_Cycle 15                0        0.00000000\n",
      " 4:        MYC 13                0        0.00000000\n",
      " 5:      NOTCH 71                0        0.00000000\n",
      " 6:       NRF2  3                0        0.00000000\n",
      " 7:       PI3K 29                0        0.00000000\n",
      " 8:   TGF-Beta  7                0        0.00000000\n",
      " 9:       TP53  6                0        0.00000000\n",
      "10:        WNT 68                0        0.00000000\n"
     ]
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "op = OncogenicPathways(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mg9y0JLS2vs2LE7duz45ZdfuD/piUhHR+fkyZMHDx7cv3//qVOnvvvuu++++45h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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-High_Grade_Glioma_OncogenicPathway_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_OncogenicPathway_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "\n",
    "svg(out_svg, height=9, width=12)\n",
    "op\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ttvl5aWdujQIdm1du3aK6+8snHjxgcP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\nAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Hippo pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=2)\n",
    "for( i in 1:length( op$data$Pathway ) ){\n",
    "    pway = op$data$Pathway[i]\n",
    "    PlotOncogenicPathways(dfx,pway, fullPathway = F, removeNonMutated = T)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=8)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[10], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[9], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=9)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = F, removeNonMutated = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-High_Grade_Glioma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-High_Grade_Glioma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-High_Grade_Glioma\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)) )\n",
    "paste0(\"Saving MAF file to : \", out_file)\n",
    "maftools::write.mafSummary(dfx, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-High_Grade_Glioma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-High_Grade_Glioma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-High_Grade_Glioma_drugInteractions.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions.tsv')\n",
    "paste0(\"Saving drug Interactions data to : \", out_file)\n",
    "write.table(dfx_drug, out_file, sep=\"\\t\", row.names = F, quote = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-High_Grade_Glioma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-High_Grade_Glioma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-High_Grade_Glioma_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_mutation_data.tsv\")\n",
    "paste0(\"Saving mutation data to : \", out_file)\n",
    "write.table(df_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-High_Grade_Glioma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-High\\_Grade\\_Glioma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-High_Grade_Glioma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-High_Grade_Glioma_clinical_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_clinical_data.tsv\")\n",
    "paste0(\"Saving clinical data to : \", out_file)\n",
    "write.table(df2_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               _                           \n",
       "platform       x86_64-redhat-linux-gnu     \n",
       "arch           x86_64                      \n",
       "os             linux-gnu                   \n",
       "system         x86_64, linux-gnu           \n",
       "status                                     \n",
       "major          3                           \n",
       "minor          5.0                         \n",
       "year           2018                        \n",
       "month          04                          \n",
       "day            23                          \n",
       "svn rev        74626                       \n",
       "language       R                           \n",
       "version.string R version 3.5.0 (2018-04-23)\n",
       "nickname       Joy in Playing              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.0 (2018-04-23)\n",
       "Platform: x86_64-redhat-linux-gnu (64-bit)\n",
       "Running under: Fedora 24 (Workstation Edition)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  grid      stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] wordcloud_2.6       RColorBrewer_1.1-2  knitr_1.22         \n",
       " [4] corrplot_0.84       maftools_1.8.0      Biobase_2.42.0     \n",
       " [7] BiocGenerics_0.28.0 vcd_1.4-4           ggpubr_0.2         \n",
       "[10] magrittr_1.5        xtable_1.8-4        mixOmics_6.6.2     \n",
       "[13] MASS_7.3-51.1       factoextra_1.0.5    FactoMineR_1.41    \n",
       "[16] DescTools_0.99.27   gtools_3.8.1        broom_0.5.1        \n",
       "[19] d3heatmap_0.6.1.2   pheatmap_1.0.12     reshape2_1.4.3     \n",
       "[22] mosaic_1.4.0        Matrix_1.2-15       mosaicData_0.17.0  \n",
       "[25] ggformula_0.9.0     ggstance_0.3.1      lattice_0.20-38    \n",
       "[28] xda_0.2             forcats_0.3.0       stringr_1.4.0      \n",
       "[31] dplyr_0.8.1         purrr_0.3.2         readr_1.3.1        \n",
       "[34] tidyr_0.8.3         tibble_2.1.1        ggplot2_3.1.1.9000 \n",
       "[37] tidyverse_1.2.1    \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] readxl_1.2.0                uuid_0.1-2                 \n",
       "  [3] backports_1.1.4             circlize_0.4.5             \n",
       "  [5] NMF_0.21.0                  plyr_1.8.4                 \n",
       "  [7] igraph_1.2.4.1              repr_0.19.2                \n",
       "  [9] lazyeval_0.2.2              splines_3.5.0              \n",
       " [11] BiocParallel_1.16.6         GenomeInfoDb_1.18.2        \n",
       " [13] gridBase_0.4-7              digest_0.6.18              \n",
       " [15] foreach_1.4.4               htmltools_0.3.6            \n",
       " [17] BSgenome_1.50.0             cluster_2.0.7-1            \n",
       " [19] doParallel_1.0.14           mosaicCore_0.6.0           \n",
       " [21] ComplexHeatmap_1.20.0       Biostrings_2.50.2          \n",
       " [23] modelr_0.1.2                matrixStats_0.54.0         \n",
       " [25] rARPACK_0.11-0              colorspace_1.4-1           \n",
       " [27] rvest_0.3.2                 ggrepel_0.8.1              \n",
       " [29] xfun_0.5                    haven_2.1.0                \n",
       " [31] crayon_1.3.4                RCurl_1.95-4.11            \n",
       " [33] jsonlite_1.6                survival_2.43-3            \n",
       " [35] zoo_1.8-4                   iterators_1.0.10           \n",
       " [37] glue_1.3.1                  registry_0.5               \n",
       " [39] gtable_0.3.0                zlibbioc_1.28.0            \n",
       " [41] XVector_0.22.0              DelayedArray_0.8.0         \n",
       " [43] GetoptLong_0.1.7            shape_1.4.4                \n",
       " [45] scales_1.0.0                mvtnorm_1.0-8              \n",
       " [47] rngtools_1.3.1              bibtex_0.4.2               \n",
       " [49] Rcpp_1.0.1                  mclust_5.4.2               \n",
       " [51] flashClust_1.01-2           foreign_0.8-71             \n",
       " [53] stats4_3.5.0                htmlwidgets_1.3            \n",
       " [55] httr_1.4.0                  XML_3.98-1.16              \n",
       " [57] pkgconfig_2.0.2             manipulate_1.0.1           \n",
       " [59] labeling_0.3                tidyselect_0.2.5           \n",
       " [61] rlang_0.3.4                 munsell_0.5.0              \n",
       " [63] cellranger_1.1.0            tools_3.5.0                \n",
       " [65] cli_1.1.0                   generics_0.0.2             \n",
       " [67] evaluate_0.13               ggdendro_0.1-20            \n",
       " [69] nlme_3.1-137                leaps_3.0                  \n",
       " [71] xml2_1.2.0                  compiler_3.5.0             \n",
       " [73] rstudioapi_0.10             png_0.1-7                  \n",
       " [75] stringi_1.4.3               highr_0.8                  \n",
       " [77] RSpectra_0.13-1             IRdisplay_0.7.0            \n",
       " [79] pillar_1.4.0                lmtest_0.9-36              \n",
       " [81] GlobalOptions_0.1.0         cowplot_0.9.4              \n",
       " [83] data.table_1.12.2           bitops_1.0-6               \n",
       " [85] corpcor_1.6.9               rtracklayer_1.42.1         \n",
       " [87] GenomicRanges_1.34.0        R6_2.4.0                   \n",
       " [89] gridExtra_2.3               IRanges_2.16.0             \n",
       " [91] codetools_0.2-16            boot_1.3-20                \n",
       " [93] assertthat_0.2.1            SummarizedExperiment_1.12.0\n",
       " [95] pkgmaker_0.27               rjson_0.2.20               \n",
       " [97] withr_2.1.2                 GenomicAlignments_1.18.1   \n",
       " [99] Rsamtools_1.34.1            S4Vectors_0.20.1           \n",
       "[101] GenomeInfoDbData_1.2.0      expm_0.999-3               \n",
       "[103] hms_0.4.2                   IRkernel_0.8.15            \n",
       "[105] Cairo_1.5-9                 pbdZMQ_0.3-3               \n",
       "[107] scatterplot3d_0.3-41        lubridate_1.7.4            \n",
       "[109] base64enc_0.1-3             ellipse_0.4.1              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
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