analysis/KCPG/KCPG_woAR_TT-LowGradeGlioma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Low 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$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Low Grade Glioma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                  \n",
       "                   LGG\n",
       "  Low Grade Glioma  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$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> 2</td><td>24</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t  2 & 24\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "|  2 | 24 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,]  2   24  \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       2   NA     NA\n",
      "4:            nGenes       2   NA     NA\n",
      "5:   Frame_Shift_Del       1  0.5    0.5\n",
      "6: Missense_Mutation       1  0.5    0.5\n",
      "7:             total       2  1.0    1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Gene Summary..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hugo_Symbol Frame_Shift_Del Missense_Mutation total MutatedSamples\n",
      "1:         NF1               1                 0     1              1\n",
      "2:      PTPN11               0                 1     1              1\n",
      "   AlteredSamples\n",
      "1:              1\n",
      "2:              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> 2               </td><td> NA              </td><td> NA              </td></tr>\n",
       "\t<tr><td>nGenes           </td><td> 2               </td><td> NA              </td><td> NA              </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 1               </td><td>0.5              </td><td>0.5              </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 1               </td><td>0.5              </td><td>0.5              </td></tr>\n",
       "\t<tr><td>total            </td><td> 2               </td><td>1.0              </td><td>1.0              </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           &  2                &  NA               &  NA              \\\\\n",
       "\t nGenes            &  2                &  NA               &  NA              \\\\\n",
       "\t Frame\\_Shift\\_Del   &  1                    & 0.5                   & 0.5                  \\\\\n",
       "\t Missense\\_Mutation &  1                  & 0.5                 & 0.5                \\\\\n",
       "\t total             &  2                & 1.0               & 1.0              \\\\\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           |  2                |  NA               |  NA               |\n",
       "| nGenes            |  2                |  NA               |  NA               |\n",
       "| Frame_Shift_Del   |  1                | 0.5               | 0.5               |\n",
       "| Missense_Mutation |  1                | 0.5               | 0.5               |\n",
       "| total             |  2                | 1.0               | 1.0               |\n",
       "\n"
      ],
      "text/plain": [
       "  ID                summary Mean Median\n",
       "1 NCBI_Build        38       NA   NA   \n",
       "2 Center            NA       NA   NA   \n",
       "3 Samples            2       NA   NA   \n",
       "4 nGenes             2       NA   NA   \n",
       "5 Frame_Shift_Del    1      0.5  0.5   \n",
       "6 Missense_Mutation  1      0.5  0.5   \n",
       "7 total              2      1.0  1.0   "
      ]
     },
     "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>Missense_Mutation</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>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>PTPN11</td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Missense\\_Mutation & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1    & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t PTPN11 & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Missense_Mutation | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|\n",
       "| NF1    | 1      | 0      | 1      | 1      | 1      |\n",
       "| PTPN11 | 0      | 1      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Missense_Mutation total MutatedSamples\n",
       "1 NF1         1               0                 1     1             \n",
       "2 PTPN11      0               1                 1     1             \n",
       "  AlteredSamples\n",
       "1 1             \n",
       "2 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>2</li>\n",
       "\t<li>6</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2\n",
       "\\item 6\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2\n",
       "2. 6\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2 6"
      ]
     },
     "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   SNP   total\n",
       "---------------------  ----  ----  ------\n",
       "SJLGG010672               0     1       1\n",
       "SJLGG011283               1     0       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",
       "SJLGG010672                    1\n",
       "SJLGG011283                    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": [
       "0"
      ],
      "text/latex": [
       "0"
      ],
      "text/markdown": [
       "0"
      ],
      "text/plain": [
       "[1] 0"
      ]
     },
     "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": [
       "2"
      ],
      "text/latex": [
       "2"
      ],
      "text/markdown": [
       "2"
      ],
      "text/plain": [
       "[1] 2"
      ]
     },
     "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>Missense_Mutation</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>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>PTPN11</td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Missense\\_Mutation & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1    & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t PTPN11 & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Missense_Mutation | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|\n",
       "| NF1    | 1      | 0      | 1      | 1      | 1      |\n",
       "| PTPN11 | 0      | 1      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Missense_Mutation total MutatedSamples\n",
       "1 NF1         1               0                 1     1             \n",
       "2 PTPN11      0               1                 1     1             \n",
       "  AlteredSamples\n",
       "1 1             \n",
       "2 1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0Lq/9GjRywWa9y4cW09cfPmzb/88ouXl5erq6uenl5cXBz3bfrY2NiwsDBes/j4\neGdn55YnysvL37p1S19ff9WqVVOmTPn06dOTJ0/at88wF/YjBL6wHyHwg/0Iu5ZZs2Zdu3bt\n/Pnzwvj/tWHDhqKiotLS0q67gzdmhAAAEm7NmjVMJjM6OlrgPb98+TIjI2PVqlVdNwUJBCEA\ngMQbOnToDz/8cOnSpdLSUgF2y2azjx8/rqKisn79egF2K3oIQgAAyXfkyBEOh7N3796OL0jG\nExkZmZOTs3PnTg0NDUH1KRYIQgAAyWdlZbVz587nz5+HhIQIpMOnT5/+9ddfY8eOXblypUA6\nFCOsLAMAQAobN25MTU2NiopSUFD4zvvprZGRkeHv729sbBweHi7s9dtEAEEIAEAKFArl7Nmz\ntbW1f/31V1VVlaenJ5Xangi4ffv2/v37e/ToER8fr6WlJfA6Ra/LJzkAALSSnJzc1atXFy5c\neO3aNU9PT97iLK1UXV29Z88e7rbyjx8/NjU1FVKdIob3CIEvvEcI/OA9wq7u1KlTXl5eDAbD\nxcVlzpw5RkZG329Po9FiYmKio6PpdLqHh8eBAwd4i2VLAAQh8IUgBH4QhBLgw4cPPj4+kZGR\nzc3NlpaWQ4YMsbW1NTIyUlVV5TZgMplFRUVZWVmpqalPnz5tampydnYOCgpycnISb+UChyAE\nvhCEwA+CUGLk5+eHhYVFRka+e/eOe0RKSkpBQaGxsbGhoYF7REVFZcqUKUuXLh0+fLjYChUm\nBCHwhSAEfhCEkicvL+/p06dv3rwpKyurra1VVFRUVVU1MzPr16/fgAED2rSzRJeDp0YBAIAw\nNTWVmIdf2gpPjQIAAKkhCAEAgNQQhAAAQGoIQgAAIDU8LAMAAASdTr99+3Z+fv6HDx/Ky8vV\n1dV79OhhZGQ0evRoAwMDcVcnXAhCAADy4nA4V65cCQsLS0xMZDKZ3IOKior19fUsFosgCAqF\nYm9vP2vWrFWrVnXv3l2sxQoL3iMEvvAeIfCD9wglw4MHDzZv3vzkyRNVVVVHR0cnJyczMzN1\ndXUZGRkOh1NVVVVSUpKcnPz48ePCwkJdXV1/f/9ly5ZJwHYTX0AQAl8IQuAHQSgBDh8+7OXl\npaCgMHv27GnTpsnJyX2ncVpaWnBwcF5enqur64ULFyTs/zuCEPhCEAI/CMIurampafHixZGR\nkYMHD966dauSklJrzmKz2efOnTt79qyFhUVcXNx/rtPdhUjaDBcAAL5v7YBQFIQAACAASURB\nVNq1kZGR06dP37VrVytTkCAIKSmpRYsW+fr65ufn//jjj7W1tUItUpQQhAAAJHL8+PHg4GA3\nN7fVq1e3427f8OHDt2zZ8vr16/nz57PZbGFUKHoIQgAAsigsLNy0aZONjc2GDRva3cnIkSPn\nzZt37dq1iIgIAdYmRghCAACy+P333xsbG728vKjUDr07t3DhQgMDA29v7/r6ekHVJkYIQgAA\nUsjJyQkPDx83bpyxsXEHu6JSqcuWLfvw4UNYWJggShMzBCEAAClER0ez2ewZM2YIpLfhw4dr\na2tfunSp3T1wOJzdu3f36dNHS0tr1qxZVVVVX7c5ceIE5f968uRJK89tPawsAwBACjExMfr6\n+iYmJgLpjUKhODk5Xbt2rbKyUlNTsx097Nu3LyAg4MiRI7q6uj4+Pm5ubk+ePKFQKC3b5Ofn\nDxw4cMuWLbwjZmZmrTy39RCEAACSj06nP3v2bMqUKQLs09HR8cqVK/fv3582bVpbz2WxWEeO\nHPH29nZ3dycIwtjY2NbWNiUlxdHRsWWz/Px8BweHL2axrTy39XBpFABA8n348IHNZuvr6wuw\nTz09PYIgioqK2nFuVlZWcXHxhAkTuB+tra2NjIxu3br1RbP8/HxTU1Majfb+/Xve2xqtPLf1\nEIQAAJKvrKyMIAh1dXUB9sm9IlpSUtLuenj7WlAoFENDw/Ly8pZtOBxOfn7+2bNn1dTUjI2N\nNTQ0Tp482cpz2wSXRgEAJB93IRjBbh8hKysrLS1No9Hace6nT58Igmi5ro2ysnJlZeUXbRgM\nhp2dXUxMjIKCwrFjxzw8PExMTLiPxnz/3DZBEAIASD5tbW2CID5//izAPmk0GovF0tHRace5\n3LkpnU6Xl5fnHqmtrTU0NGzZRlNTs7m5mffR19f3xo0bkZGRc+fO/c9z2wSXRgEAJJ+uri7x\nv/MwQamoqCAIon33Hbnx2fKyaklJCbdIfigUioWFRXl5eTvO/T4EIQCA5NPR0VFWVs7OzhZg\nnzk5OcT/vs/QVpaWlnp6evHx8dyPBQUFeXl5Y8aMadkmNjZWU1MzNzeX+5HNZj9//tzGxqY1\n57YJLo0CAEg+KpXq6uoaGxvb2NgoKysrkD4fPXqkpqbm7OzcvnrWrl27Y8cOCwsLPT299evX\nOzg4DBkyhCCIsLCwwsJCf39/FxcXVVXVuXPnbty4UU9PLzQ0tLCwcN26dd85t30wIwQAIIXJ\nkyfX19c/ffpUIL3V1tb++++/EyZMaPeypZs3b/7ll1+8vLxcXV319PTi4uK4b8THxsZyV26T\nl5e/deuWvr7+qlWrpkyZ8unTpydPnnCvxPI7t32wMS/whY15gR9szNsV0el0U1NTJSWlkydP\ndiQ2uIKDg6Oiou7fvz9s2DCBlCdGmBECAJCCoqKir69vXl5eR9495yorK4uJiZk0aZIEpCCB\nIAQAIA93d3crK6vjx4+/e/eu3Z00NDT4+/tTKJQ9e/YIrjRxQhACAJCFjIxMdHQ0lUr97bff\nqqur29EDh8PZu3dvdnZ2SEiIpaWlwCsUCwQhAACJWFhYXLhw4ePHj6tXr87Pz2/TuQwGY+vW\nrYmJiV5eXosWLRJShaKHIAQAIBdXV9cbN27U19evWbMmNjaWxWK15qyMjIzVq1enpKT4+/vv\n27dP2EWKEp4aBb7w1Cjwg6dGJUBOTs6MGTNevnzZs2fPBQsWODk58VYsa4nNZmdmZl68eDE5\nOVlNTS00NLQdmy51cghC4AtBCPwgCCUDi8U6c+bM77//XlJSIisr269fP3Nzc3V1dTU1tfr6\n+oqKipKSkpSUlOrq6m7duq1Zs8bb21tNTU3cVQseghD4QhACPwhCSVJfX3/r1q2rV6/GxsZ+\nsRhpt27dRo8ePXny5EmTJrVvce0uAUEIfCEIgR8EoaSqqan58OFDeXm5qqqqrq6utrZ2x1+9\n7/yw1igAAPwPFRUVFRUVKysrcRciUnhqFAAASA1BCAAApIYgBAAAUsM9QgAAIAiC+Pjx4927\nd0tKSj5+/KisrKynpzdw4EBra2tx1yV0CEIAALK7cuXK/v37k5OT2Wz2Fz8yNTX9+eef165d\n2717d7HUJgJ4fQL4wusTwA9en5AY+fn5CxYsePz4saam5ogRIxwdHfX09FRVVRkMRmVl5b//\n/nvv3r2cnBw9Pb2QkJAJEyaIu16hQBACXwhC4AdBKBnu3bs3bdq0+vr6BQsWTJ06VU5O7pvN\nHj16FBwcXFpaumvXri1btoi4SBHApVEAADJ6/fr1xIkTu3XrduDAgT59+nynpbOzc//+/Xfu\n3Pnrr78qKCh4enqKrEjRwIwQ+MKMEPjBjLCrq6mpsbOzq66uPnbsmIGBQWtOaW5u3rhxY1ZW\nVmJiomRsTM+D1ycAAEgnMDCwsLDQx8enlSlIEASVSvX391dUVFy7du3Xz9R0aQhCAAByqaio\nOHz4sIODw+DBg9t0oqqq6oIFC54/fx4dHd3xMjgczu7du/v06aOlpTVr1qyqqqqv29BotDVr\n1hgZGSkpKTk7OyckJHCPnzhxgvJ/PXnypN2VIAgBAMjl6tWrDAZj1qxZ7Th34sSJioqK58+f\n73gZ+/btCwgI2LRp0+nTp7Oystzc3L6+Vefp6Xn58uXAwMC4uLg+ffq4urqmpKQQBJGfnz9w\n4MBLLZiZmbW7EjwsAwBALrGxsSoqKra2tu04l0qlOjg4xMfHNzQ08HvKtDVYLNaRI0e8vb3d\n3d0JgjA2Nra1tU1JSXF0dOS1odPp586dO3PmzOzZswmCcHJySktLCw0NdXBwyM/Pd3BwmDFj\nRrsLaAkzQgAAcsnKyjI3N5eWlm7f6ZaWlnV1dYWFhR2sobi4mPdiorW1tZGR0a1bt1q2+fDh\nQ9++fUeMGMH9KCUlpaurW1ZWRhBEfn6+qakpjUZ7//59x29YYkYIAK1SUVFx7969V69evX37\ntrKysr6+Xk5OTlNTs1evXtbW1sOHD9fT0xN3jdAqJSUlpqam7T5dQ0OD20lHrkZy84z3qA6F\nQjE0NCwvL2/Zpk+fPs+fP+d9zMrKSkpK2rFjB4fDyc/PP3v27KZNm9hstqqqamBg4PLly9td\nDIIQAL6nqqoqPDw8PDz82bNn3Fs48vLyampqVCq1ubm5pqamrq6O29La2nr+/PmLFi3S1dUV\na8nwH1gsVrungwRBUKlUgiCampo6UsOnT58IglBSUuIdUVZWrqys/GZjDodz5coVd3f3gQMH\nrl69+tOnTwwGw87OLiYmRkFB4dixYx4eHiYmJmPGjGlfMQhCAPi26urqXbt2/fHHH3Q6XVdX\nd+bMmf379zczM1NXV2/ZrKam5u3bt+np6Q8fPvT29vb391+6dOnvv/+uo6Mjrsrh+7S1tbk5\n1D7cuNLX1+9IDdw/RXQ6XV5ennuktrbW0NDw65ZFRUXu7u5379718vLy9fXt1q2bvLx8c3Mz\nr4Gvr++NGzciIyMRhAAgSOfPn1+3bl1FRcWgQYNmz57dr18/CoXyzZYqKir29vb29vY///zz\n69evL168GBwcHB4evmfPnhUrVvA7C8TI1NT09evX7T69oKBARkbmm6HVetzvSSUlJVpaWtwj\nJSUlP/zwwxfN0tPTR48e3a9fv6ysrF69en2zKwqFYmFh8cVl1TbBwzIA8H8wmcylS5fOnTu3\ne/fuBw4cCAwM7N+/fyvzzMrKyt/f//jx4zo6OqtWrZoxYwaNRhN2wdBWbm5uZWVlb9++bce5\nHA7nyZMnw4cP7+CCU5aWlnp6evHx8dyPBQUFeXl5X0zpmpubp06dOnHixNu3b7dMwdjYWE1N\nzdzcXO5HNpv9/PlzGxubdheDGSEA/H80Gm3y5MlJSUkTJkxYvXp1+56P79Onz/Hjx//8888L\nFy4UFBT8888/uEzaqUydOnXLli0xMTGbNm1q67kPHz78+PHjtGnTOlgDlUpdu3btjh07LCws\n9PT01q9f7+DgMGTIEIIgwsLCCgsL/f39ExMT379/P2jQoOvXr/NO1NPTc3FxUVVVnTt37saN\nG/X09EJDQwsLC9etW9fuYrDWKPCFtUbJpr6+fuzYsY8ePfLw8Pjpp5863mFCQkJQUJC5ufmD\nBw++uLMI4rVo0aLw8PCTJ0/yu974TU1NTe7u7hwOJycnp1u3bh2sgcPh7Ny588yZM9XV1aNH\njw4ODlZTUyMIYsqUKc+ePSsuLj569OjatWu/OGv69OmXL1/Oy8vbuHHj/fv3CYIYMmTIvn37\nLC0t210JghD4QhCSCofDmTp16tWrV9evXz9p0iRBdXv//v2AgAAnJ6fExEQZGRlBdQsdVFhY\naG1traamduzYsZaPbn5fUFDQzZs3IyIi5s6dK9TyRAz3CAGAIAhi//79V69enTdvngBTkCCI\n4cOHr1y58uHDhz4+PgLsFjqoZ8+eERERHz588PHxqa6u/s/2bDb7xIkTN2/eXL16tYSlIIEZ\nIXwHZoTkkZuba2tra2FhsX//fikpwX8/9vPze/jw4ePHjx0cHATeObRbSEiIp6enlpaWl5eX\nvb09v2YlJSWHDx9OTU2dNWvWuXPnJG9mjyAEvhCE5PHjjz8mJCSEhoa2flOeNvn8+fOiRYss\nLCyePn2KFyo6lYSEhLlz51ZUVAwYMGDcuHGOjo68K6XNzc3Pnz9PSkq6ffs2QRC+vr4+Pj4S\n+b8PQQh8IQhJIjU1dfDgwbNmzfLw8BDeKFFRUcHBwdeuXZs4caLwRoF2oNFoe/fuPXLkCPdd\nF2VlZVVV1bq6us+fP7PZbCqVOm3atB07dnRkQbVODkEIfCEISWLu3LnR0dGRkZFCfbCTyWTO\nmTOnX79+SUlJwhsF2q2xsTEpKSkxMfHDhw/l5eXKysr6+vqDBg0aP368xD/xiyAEvhCEZECj\n0XR0dIYMGfLbb78Je6wTJ05ER0fn5eWZmJgIeyyA1vuPF+oPHTqUmJjI+6iiomJhYbFkyRI9\nPb2dO3dyN0j8gpSUVExMzHdOJAhi+fLlDAbjxIkTysrKvDZ//vlnZmbmgQMHWva2YcOGpUuX\nfr1vFr/jANAmcXFx9fX1Li4uIhjLxcXl8uXL165d68i7zwAC998ryxgaGq5YsYIgCA6HU1pa\nGhUV5e/vf/To0fnz53Mfs25sbPT39585c2a/fv0IguDdSuV3oqysLEEQNBotLCxsw4YN/MZl\nsVhxcXF5eXmtPA4A7ZCYmEilUvv27SuCsczMzFRUVO7cuYMghE7lv4NQXl6eN/Gys7PT0tLy\n8/PLz8+3sLDgHmQymQRBGBoafjE/+/6Jtra2d+/eHTFixIABA74e9Pbt2yEhIY2Nja08DgKX\nkZFx5MiRoqIiCwuLjRs3GhkZibsiEIq0tLRevXrxdgAQKgqFYmVllZaWJoKxAFqvzWuNci9m\nMhiMDp5oZ2enra194sSJY8eOfb1Uj4ODg7m5OZ1O9/b2bs1xEKwrV67MnDlTWlpaQ0MjMTEx\nLCzs4cOH/fv3F3ddIHjZ2dmOjo4iG87Q0PDJkyd0Oh03nqHzaEMQcjic8vLyM2fOyMvLt+k5\nWn4nLlmyZPXq1efOnXN3d//iFGVlZWVl5a/Xred3nKumpiY1NZX30dLSskePHq2vE7jYbPby\n5ct79uy5b98+NTW1wsLC9evXr1q1irusH0gSBoNBp9M1NTVFNqKWlhaHw/nw4UOblriUVFJS\nUh3ZIBcE5b+DMCcnp+WSSwoKChs2bGjN2nT/eaKSkpKHh0dQUNCIESPMzc3bWPk3vHnzpuUu\nHmFhYYJdLIok8vLyKisr58yZw10At2fPnqNGjbp+/XpFRQX3/i5IjIqKCoIgRHNdlKt79+4E\nQZSUlGhoaIhs0E5LTk6u9et8gvC04WEZgiDk5eUNDQ1buTNLa050cnIaPHjw0aNHDx482Jay\nQYi4V6q5t365mEymrKwsvrpKHu5qamw2W2QjcscSxipuAO3Wtodl2qQ1J1IolBUrVqxevTo6\nOrodQ3zB3t6+qqqK97F79+6YwbSDhoZGnz59oqOjLS0tzc3NU1NT79y54+rqiuvMkkdBQYEg\niPr6epGNWFdXRxCEoaEhZoTQeYh/Y14NDY1FixadOnWK+/ZFR1CpVO7VPOigiIgIV1dXLy8v\n7kdTU9M//vhDItcYJDl5eXl1dfXy8nKRjVheXi4lJaWnp4c/TtB5iD8ICYJwdXW9f//+s2fP\nevfuLe5agCAIwt7ePicn5+zZs+/fv7e0tFy4cGH7diqHzs/S0vL9+/ciG66wsNDY2Ljje7oC\nCFCnuFJPoVA8PT0lb2uPLk1NTW3x4sU+Pj5z5sxBCkowR0fH9+/f19TUiGCs5ubm169fDxky\nRARjQefH4XB2797dp08fLS2tWbNmtbyx9Z9tWnNu62GtUeALa42SQVxc3Pjx4729vVs+cS0k\naWlpv/zyS1hY2NKlS4U9FnR+e/fu3bZt25EjR3R1dX18fOTl5Z88efLFNXN+bVpzbushCIEv\nBCEZNDY26urqmpqaBgYGCnuswMDAu3fv4t2JzqykpOTgwYOvXr3S09NbtmyZ8KbvLBbL2NjY\n3d1927ZtBEFkZmba2tomJye3XN6BX5tBgwb957lt0ikujQKAuMjKys6dOzctLU3YdwqrqqqS\nkpImTpyIFOy0cnJyLC0tDxw4kJGRERER4ezsfPr0aSGNlZWVVVxcPGHCBO5Ha2trIyOjW7du\ntaZNa85tEwQhANl5eXlJSUmFh4cLdZQLFy40NDRs2bJFqKNAR3h5ebHZ7JCQkPDw8IsXL1pZ\nWa1Zs0ZIb9eUlZURBGFgYMD9SKFQDA0Nv3iAmV+b1pzbJghCALIzMTFZunRpYmLiy5cvhTTE\nu3fvYmJiJk+ePGjQICENAR33+PFjR0dHU1NTgiCUlJSmTJlSV1eXkZEhjLE+ffrEHYV3RFlZ\nubKysjVtWnNumyAIAYDYtWuXurp6UFBQO9bT/09NTU2BgYGysrKHDh0SeOcgQPLy8l8sKUUI\nbQU+7q73dDqdd6S2tpZ78D/btObcNkEQAgChoaHx559/lpSU7NmzR+AP0B09ejQ7O/vQoUPG\nxsaC7RkEa9y4cY8fP75582Ztbe3Lly8jIiL09fWtra2FMZaOjg5BECUlJbwjJSUlurq6rWnT\nmnPbBEEIAARBEJMmTdqyZcvDhw8PHTokwCw8c+bM9evXlyxZ8vUmM9DZ7Nu3z9raOigoaPLk\nyevWrWMymZGRkVSqUNZdsbS01NPTi4+P534sKCjIy8v74h0efm1ac26bSPv5+bX7ZJBsDQ0N\nbDabSqViyVaS+OGHH969excVFVVWVubo6NjBpbE5HE5wcHBkZOSPP/4YHh6ORds7P3l5eXd3\ndxsbG0tLyzlz5gQHB1taWgppLCkpqebm5p07d9rY2NDpdA8PD11dXT8/PwqFEhYWdvXq1VGj\nRvFrIy0tze/c9hWD9wiBL7xHSEJsNnvNmjUnTpywsrL6/ffftbW129dPdXX1rl27nj17NmvW\nrLNnz+K7FHyNw+Hs3LnzzJkz1dXVo0ePDg4O5i4WPWXKlGfPnhUXF3+nDb/j7YMgBL4QhKR1\n/PjxjRs3SklJLVq0aOrUqW1a/pDFYv3zzz9hYWF1dXX+/v7e3t7YdAk6OQQh8IUgJLMXL16s\nXLny8ePHWlpa06dPHzt2rKqq6vdPodPpiYmJly5d+vDhg62tbUhICJYVhS4BQQh8IQhJjsPh\nREVF7dixIzMzU1pa2s7Orn///ubm5vr6+srKyoqKinV1dXV1dcXFxW/fvk1PT3/+/HljY2Ov\nXr18fHwWLVokpIcsAAQOQQh8IQiBIAgOh/Pw4cPw8PC4uLiioiJ+zbS1tV1dXefOnfvDDz/g\nuRjoWhCEwBeCEL6Qn5+fmZmZl5dXWlpaX18vJyenq6trYmJiZWVlYWEh7uoA2glBCHwhCIGf\n2trahoYGGRkZFRUVcdcC0FF4mgsAAEgNQQgAAKSGIAQAAFJDEAIAAKkhCAEAgNQQhAAAQGoI\nQgAAIDUEIQAAkBqCEAAASA1BCAAApIYgBAAAUkMQAgAAqSEIAQCA1BCEAABAaghCAAAgNQQh\nAACQGoIQAABIDUEIAACkhiAEAABSQxACAACpIQgBAIDUEIQAAEBqCEIAACA1BCEAAJAaghAA\nAEgNQQgAAKSGIAQAAFJDEAIAAKkhCAEAgNQQhAAAQGoIQgAAIDUEIQAAkBqCEAAASA1BCAAA\npIYgBAAAUkMQAgAAqSEIAQCA1BCEAABAaghCAAAgNQQhAACQGoIQAABIDUEIAACkhiAEAABS\nQxACAACpIQgBAIDUEIQAAEBqCEIAACA1BCEAAJAaghAAAEgNQQgAAKSGIAQAAFJDEAIAAKkh\nCAEAgNQQhAAAQGoIQgAAIDUEIQAAkBqCEAAASA1BCAAApIYgBAAAUkMQAgAAqSEIAQCA1BCE\nAABAaghCAAAgNQQhAACQGoIQAABIDUEIAACkhiAEAABSQxACAACpIQgBAIDUEIQAAEBqCEIA\nACA1BCEAAJAaghAAAEgNQQgAAKSGIAS+8vPzMzMzi4uLxV0IdDrFxcWZmZnv3r0TdyEAAoAg\nBL7Wr18/atSo/fv3i7sQ6HT27NkzatSoTZs2ibsQAAFAEAIAAKkhCAEAgNQQhAAAQGoUDocj\n7hqgk4qNjS0tLbWwsBg+fLi4a4HO5e7du7m5ufr6+j/++KO4awHoKAQhAACQGi6NAgAAqSEI\nAQCA1BCEwFdZWVlzc7O4qwAAEC4EIXwbh8MJCgras2cPshAAJBuCEL6NQqFs2rTp7du3yEIA\nkGx4ahS+p6SkxMfHp3fv3r/++iuVShV3OdCJMJnMP//8Mzk52czMbM2aNWpqauKuCKCdMCOE\n79HT09u1axfmhfC1v/76q76+fvny5ZWVlVu3bv38+bO4KwJoJwQh/AdkIXyByWQ2NzeXlJR4\neXkNGzZs9+7d8vLyyELounBpFL6BxWLdvHkzLS1NTk5u7Nix/fv3xzVS4AkMDJSXl6dQKGvW\nrOEeqaur27ZtW319/c6dO3GNFLoczAjhS2w2e/v27dHR0b169ZKVlfX19U1MTMS8EHgWLFjw\n77//Pn36lEajcY8oKCgEBARw54U1NTXiLQ+graT9/PzEXQN0Lv/888/Tp08PHTo0cODAzMzM\nhoaG+fPns9lsdXX1wYMHX7p0SVNT08jISNxlgtgoKSkNHjz47t27GRkZQ4cO5V4hkJWVHTp0\nKEEQtra2UlL4hg1dCS6NAkEQBIfDoVAo3P8OCAiwtLScMWNGaGjoixcvduzYUVRUFBoaeujQ\nIYIgamtrlZSUxFosiAGHw7lz505mZqaGhsaoUaMMDAy4V8v19fW3bdsmJycn7gIB2g9f3Eit\nrq7u2LFjixcvnjZt2rZt2x49ekQQhKysbHV1NS8FVVRUWCxWfn5+Y2MjQRBIQRJisVgBAQFn\nzpxhsVhPnjxZv34972r5hw8fAgICGhoaxF0jQPvhqQfyKioq2rZtm4yMzJQpU7p3756cnBwY\nGDhu3DgHB4eDBw/q6uoGBQWpqKgQBFFYWKilpSUjIyPukkFEioqK5OTkevTowf149erVwsLC\n48ePKysrczicyMjIw4cPa2lp2dra7tq1y8fH59GjR6NHjxZvzQDthkujJFVUVLR161Zzc/NN\nmzZ169aNezApKenYsWMODg6Kioq3bt2aOXOmg4NDQUFBaGjoihUrRo0aJd6aQTSam5tXrlzZ\nrVu3w4cPc+/2bd261draeu7cudwGHA5nz549VVVVe/fuJXC1HLo+zAjJiJeCW7ZsaTnPGzly\npLy8/M6dO1euXLl69erLly9fvHhRTU1t1apVI0aMEGPBIEpUKtXT05NKpTY0NEhJScnJycnI\nyFRUVPAaUCiUUaNGBQYGcm8tIwWhq0MQktHdu3erq6vt7e2/vtrp4ODg5uYWHR196tQpFxcX\nJpMpLy8vliJBjPr27UsQRGBgII1G27Zt2/Dhw48ePTpmzBgrKytug7KyMl1dXd4DVgBdGl6f\nICM7O7vq6uqLFy9qaWn16tXri5+qqan9/fffzs7OqqqquC9IKiwW69SpU1paWnJycnQ63crK\nKiYmJj09ff78+SUlJREREQoKCioqKs+ePTt9+vTy5csNDQ3FXTKAACAIyYhCoQwcOPDz58+R\nkZFfZyGNRouLi5s8eTIueZENh8NJSEi4cOFCampqTU3N0KFDBw8ezM3CTZs2cTicixcvXrly\nJSMjw8PDY/jw4eKuF0Aw8LAMeXE4nD/++OPWrVtr1qxxcXHhHT979uzt27dPnz6NpdRIqLa2\n1t3dnclkHjhwgPsNqeX7glJSUh8/ftTR0ZGWlhZ3pQACgxkheX1zXpiWlhYcHLx+/XqsHUNO\n7969a2pq6tGjR1RUlL29vaqqKncdGe68cPjw4Wpqalg4BiQMgpDUvsjC6urqnTt3zpo1a/z4\n8eIuDcRDU1PTwcHByckpNzf3woULX2Shurq6iYmJuGsEEDBcGoX/f41USkpq5syZvNfFgDxY\nLNaVK1fS0tL69+8/c+ZMCoXCYrGCgoJevXq1Y8cOaWlpRUVFKpWK28YgkRCEQBD/m4WqqqpI\nQRJqamoKCAgoKCiwt7dPTk4eOnTomjVreFmYlpYmJSW1fPnyljeSASQJghD+R8t1t4FUzp49\ne/fu3UOHDqmoqISEhNy4ccPFxYWXhQkJCVpaWgMGDBB3mQDCgscCJRaLxQoNDXV1de3RoweT\nyfzP7VKRgqT14MGDH3/8UUVFpbq6+unTp5s3bz5y5AibzXZ2djYxMRk3bpy4CwQQLjz9JYHK\ny8tLS0sJgqiqqtq6devvv/8eGxv7n2exWKyIiIimpibhFwhixmKxQkJC3r9/X19f//nzZx0d\nHQaDweFwjh07Nn369KFDh7q5uSUmJu7Zs+fDhw/iLhZA6DAjlDQsA3ukMwAAEc5JREFUFuu3\n334bPXr0nDlzPD093d3d3759u2rVqv886/DhwxYWFlhKhiS4X5J0dHTs7OzWrVunpqZ27969\n8vJyV1dXgiBoNNqWLVvs7OzwdAyQAWaEkoZGo5WXl5uYmNTX16enpzs7Ozs4OGzbtu39+/f8\nTuGlIN6aIAlpaWlPT8/m5ua3b98OHTpUU1NTWlo6PT3dzs5OSkoqLy8vOTnZxsYGKQgkgfcI\nJUdRUVFDQ4OmpiZ3HdHU1FQpKamVK1d+8U7YF2chBcmmvr7+r7/+kpeX53A4LV+cp9Fo58+f\nz8vLO3v27LJly2xsbMRdKYCIIAglRHNz8+bNmx8/fuzq6mpjY3P9+vXy8vJly5ZpampKSUnx\nsnDAgAG1tbVsNpu7pwRSkITodHp4ePjbt2+9vLyGDx/O+5I0YMAAdXX1+vr6efPmDRs2TNxl\nAogOXp+QHBkZGVQqtVevXkVFRXfu3GlsbExOTg4ICDA3NycI4ut3wpCCpPX582cfHx8VFRVf\nX19ZWdkvXpz/zweMASQMglDS8PaQk5GROXr06BdZ2PKdsD///FNHRwcpSBJMJlNWVpa3TOjX\nWYgX54G0EISSpuVeAV9nYUtMJrNbt25iKRJEoKioKCIiYt26ddzL4Dt37uzWrduGDRt4WVhR\nUbFu3bqePXtysxAvzgNp4R5hF1ZaWiorK8vdLInJZIaEhBw5cqSoqGj58uVxcXHp6enDhg0b\nMmRISUnJuXPn7OzsNDQ0Wp6OXZYkGIfD8fb2fvXq1evXr52dnWVkZHr27BkREfH+/XtHR0fu\n4gkKCgo0Gi09Pf3NmzejR4/u3bu3rq6uuAsHEAMEYVfF4XB8fX0fP348dOhQKpX6xx9/ZGZm\njhkz5uXLl2lpaV5eXl9kYX19vZWVlbirBhGhUCg0Gi03N7e+vv7ff/91dnbW0tKyt7f/Igtf\nvnw5evToYcOGqauri7tkALFBEHZVFArF1taWu0ucg4NDaGjo0aNHBwwYMGLEiKSkpJSUlJZZ\n6OTkZG1tLe6SQaR69eoVGxs7bdq0Z8+efZGFeXl5ffr0yc7OjoyMnD17ds+ePcVdLIA4IQi7\nMN4ucU+fPtXQ0OCuCSknJ+fs7NwyC9XV1bmb7gKpyMrKNjU1JScn//bbb1evXm2ZhRcvXoyK\nirp3796iRYtwUxAAD8t0edynYxgMxsmTJ3nvy9fW1v7+++8sFmvbtm1aWlrirRDEpa6ubtmy\nZV5eXrq6ulu3btXT0/P19ZWXl6+pqUlPTzcxMTEyMhJ3jQDihxlhV8JkMmNiYqKiol68eGFg\nYKCsrEz877zw/v37GRkZw4YN4z4Cw50XysnJ9e3bV9xVgygUFRX5+fk1NTWZmJhIS0tzD3In\nhfHx8TNnzhw0aBBvXqioqGhsbPz1MkMA5IQg7DJKS0u3bt2ak5Njamqal5eXmZk5cuRI7iMP\n3Cy8evVqeno699kZgiDk5OTMzMzEXTWIyL59+7KysjIzM+Pi4igUirGxMfePQa9evSIiIkxM\nTCwtLQcOHMjNwhEjRvDCEgBwabRraGxs3Lhxo4GBwYYNG2RlZVksVnNzs5ycXMs2Ld8g/OJH\nIPFqamq2bt3KYDDGjRuXlJRUW1s7derU8ePHy8vLnz9/Pi0tbe/evRQKpaioKDU1ddq0aeKu\nF6ATwYywa0hMTHz69OmuXbu4CSclJUWlUhkMRnp6+ufPn7W0tCgUCu/ZGXV1dRMTE3GXDCLV\nrVs3Z2fnBw8e5OTkBAQEmJiYxMbGRkVFsVis0aNHX7x40dTUVFdXV0VFxdLSUtzFAnQumBF2\nDSdOnCgvL/f39+d+bGhouHLlypUrV/T19QsKCn744QdPT0/uj2pra7F7Dmlx54VMJnPXrl1a\nWlqPHz++ePFiRUWFgoKCmppaUFAQ91o6ALSE/Qi7Bh0dnaysrLdv3zKZzISEhJUrV16+fHnN\nmjUHDhzw8vKKj4+vrKzktkQKkpmKigp3KTUfH5+KigpnZ+fDhw+vW7dOSUmpf//+SEGAb8KM\nsGtgMplr164tKyuTkZFpamoaNmzY4sWLue9FVFZWLl269NixY3gtGrhazgt79OhBEASHw0EK\nAvCDIOwyqqurr1271tjYOGLEiJaPg168eDE+Pj40NBT/0gHP11kIAPwgCLuelrtGPHr0aN++\nfd7e3oMHDxZvVdDZcLNw7NixkyZNEnctAJ0agrCLYTKZ69atMzY2trKyevPmTXJy8pIlSyZP\nnizuuqAzwk5bAK2BIOx6Xrx4ER4eXlpaamZmNm3aNBsbG3FXBADQhSEIASQWi8W6cOHCTz/9\nJCMjI+5aADovbM0K0JWwWKybN2+mpaXJycmNHTu2f//+32l5+PBhCwsLpCDA9+E9wi6MxWJF\nREQ0NTWJuxAQETabvX379ujo6F69esnKyvr6+iYmJn6zJS8Fx48fL+IiAboczAg7F3zfh++I\ni4srLi4+fPiwoqJiaGiokZGRvb09g8Ho3r17y2ZIQYA2wYywE8H3ffi+tLS0cePGcVPwxYsX\nO3bsKCoq8vHxadkGfzYA2gpB2Inwvu/PmzdPUVGR933/i2b4l460ZGVlq6ureSmooqLCYrHy\n8/MbGxu5DfBnA6AdEISdCL7vw9c4HE5WVtazZ89YLNawYcNiY2OfPXvGTUGCIAoLC7W0tHiX\nx8+cOYM/GwBthXuEncjX3/ffvXvH/b4vKytLIAXJp6GhYe/evU+fPiUIYuXKlW5ubuPHj795\n8+b169cdHBwKCgrCw8NXrFjBW11v7ty5eIMeoK0QhJ3IsGHDAgMDdXV1g4KC8H0fCIIIDQ0t\nLS09fvw4lUrV0dFpbm5evHixqanp5cuXL168qKamtmrVqhEjRvDaIwUB2gEv1IsTk8kMDg5O\nTU11cHDw8PCQk5MLDg6+efPmzJkzud/3Q0NDV6xYMWrUKF57/EtHHkwmc86cOb/99hv3VvHl\ny5dv375dX1/v4+PTv39/JpMpLy8v7hoBJAGCUJxCQkIYDEbv3r2jo6P19fW3bdsmKyubkJBw\n+fLl0tJSNTW1pUuXtvy+D6TS2Ng4a9asMWPG6OnpXblyRV1dfebMmQ8ePPj06VNQUJC4qwOQ\nHAhCcfLz8/P19aVQKJWVlT4+PlpaWtu2bZOTk+NwOPi+DwRB3Lhx49SpU6amphMnThw+fLiU\nlNRff/2Vl5e3fft2cZcGIDkQhGLQ1NR09erV4uLikpIS3lf7L7JQvBWCuDCZzD///DM5OdnM\nzGzNmjVqamrNzc1UKpXFYklLS2dkZAQEBHh7ew8cOFDclQJIDgShGBw4cKCysrK5ufnNmzdb\ntmxx/n/t3V1IU28cB/BzNl+mZr6EL9iFzVDSfEGbI1+TUkiEDEoIyWxoFxFmEKltYSppmQqa\nRYZCV1bioMwonGiJabraxJCsrKs504pyy7WNvZz/xYFhGv1x6o51vp87H7z4XRz2Pc9znuf5\nJSXR48hCaGlp0el0QqGws7PTbDZXV1f7+Pio1eoLFy74+fm9e/dOJBIdPHiQ6TIB/inciooK\npmtgEYPBoNVqFQpFeXl5enq6s7PzrVu3QkJCtm7dShCEu7t7QkLCwsJCZGQkh4MjnuxiMBgI\ngnj06JFYLA4ODk5NTX3+/HlPT09iYuKWLVtcXV2dnZ0LCgqSk5OZrhTgX4MZoUPV1dUtLCyE\nh4cfOXKEHuns7Lxz5w5azENtba2bmxtJkkVFRfSITqcrLy/X6/X0vJDZ8gD+YZh2ONSxY8em\np6cHBgbo13+CIHJycnJzcy9fvkwfmgbWysvLUyqVcrlcq9XSIx4eHlVVVW5ubhKJRKPRMFse\nwD8MS6MOtWnTpt27d8tkMqVSmZyc7OTkRBDEzp07uVzu9+/f0WuezTw9PYVC4dOnT8fHx23P\nhouLC70WGhUVhdVygHWCpdF1Z7FY7t+/r1AoYmNjc3JySJKcm5sTi8WBgYHYFMNyy7tuzczM\niMVi+lApng0Ax8CMcH2ZTKbKysqRkZHQ0FCZTDY7OysUCul5YVdXl0KhsL37A9vQXbdGRkbi\n4uKMRuPt27cDAgKio6OFQuGDBw/GxsbwbAA4BoJwfbW3t79+/bq5uTktLe3bt289PT1fv35d\nnIXe3t58Pp/pMoEBjx8/lsvljY2NAoFgYmLCaDQePXrUarX6+vrSWejr64tnA8AB8L65Xui+\n4YODg1lZWV5eXvPz83K5vKSk5Nq1a1arNSkpic/n063Gma4UmPHbrlttbW2NjY1BQUHNzc2e\nnp5M1wjACvj8vi5MJlNRUZFcLg8MDPz58ydFUdevXz906FBycnJmZmZ/f/+VK1fUajVSkM3+\n3GUXKQjgMJgRrj2DwTA9Pa3X68PCwoqLi318fAYGBubm5vbv308QhFarLS0tjY6Oxi8dC1EU\n9fbtW51OFxsb+79dtwDAMRCEa0ClUrW2tp47d47OtqamJqPRGBoa6u3tTf/D2NhYdHQ0h8P5\n+PHjixcvjh8/jhRkoZV22QUAx0AQrpZKpZJIJGFhYbZOgXl5eWKx2GAwaDQa+k0/IiKipaXl\n8+fPSqXyxIkT9CCwzUq77AKAY+Ac4arYUrC0tHTxihZ9GiwwMLCyspJuq9Tb2/vhw4eEhITY\n2FgGCwamoMsuwIaFILTfb1PQarXSN4DgZDQshi67ABsWdo3a6bcpODQ0VFJSYjKZCIIICgqq\nqalRq9VVVVVGo5HRYoF5Li4uhYWFMplscHBQJBI1NDQkJiYGBATgJQmAcQhCOw0ODs7Pz8fE\nxCxOwbq6upSUFNuILQuHhoaYqxQYQ1HU5OTkq1evLBYLQRBZWVlSqbShoSElJYXD4YyPj3d3\nd2dnZzNdJgDbYWnUThRFtbW1dXd309v/6BQUiUTLf9d+/PiBPaIstHyPKD2OLrsAGw2C0H62\nLExPT+/v7/9tCgJ7qFQqV1dXf39/+s8bN268efOmrKyM3iNqsVjMZjOPx7NYLD09PWq1OiUl\nZceOHczWDAAEgnCVbFmYmJhYVlbGdDnAGLPZfPLkSR6P19TUxOFw/rBHNC4ujuliAeAXuHR7\nVUiSjIuL0+l0fX193t7eoaGhTFcEzOBwOMHBwUKhcPPmzfTOYalU6urqqlar6+vrNRpNfn6+\n2Wx++fJlRkYG08UCwC9woH61SJIsLCwkCOLmzZsEQdg+BQFLqFSq9vb24uLimJgYgiBqa2u1\nWm15eXlhYWFra+v27dtFIlFqaiqHw3n//v3CwgLT9QLAUlgaXRtL9s4wXQ44CEVRp06dmp6e\njoiIuHjxopub2+Lzo1wu18nJyWKxcLnc8fHxqqqq8+fPCwQCpqsGgF9gaXRt2NZIrVZrVFQU\n0+WAg5AkqdVqp6am9Hq9UqlMSkqydRMcGxtLTU2dm5s7c+bM8PCwVCrNz8/fu3cv0yUDwFKY\nEa4liqJwYzLb6HS6goKCw4cPP3z4MCgoaMm8UCKRPHv2DHtEATYyBCHAat29e3d0dPTs2bMS\niWR5FuKOPYANDjfLAKzWgQMHZmdnP336VF1dPTMzU1lZqdfrca8QwN8C3wgBVkClUlVUVJhM\nJj6fz+Vy6UEXFxeTySSTyXJycuLj47u6umzfC/ft24cVUYANDkEIsAL19fWTk5MTExNPnjwh\nSXLbtm1OTk4EQYSEhLS3t/P5/PDwcIFAQGfhnj170FwJYONDEAKswK5duxQKBYfDyczM7Ovr\nk0qlFEXx+Xx3d3eTydTb25uRkeHl5SUQCEiSjIyMZLpeAPh/2CwDsDIajUYikRgMhkuXLk1N\nTd27d29+fj47OzstLe306dMlJSW4RA3g74IgBFgxWxbW1NT4+fkNDw93dHR8+fLFw8PDx8fn\n6tWrOEUD8BdBEALYY3EW+vv7UxQ1Ojra0dERHx+fm5vLdHUAsAIIQgA7LclCAjcqAPydEIQA\n9luehQDw18GBegD7eXl5VVdX83i8kZERpmsBADthRgiwWgaDgcfjMV0FANgJQQgAAKyGpVEA\nAGA1BCEAALAaghAAAFgNQQgAAKyGIAQAAFZDEAIAAKshCAEAgNUQhAAAwGoIQgAAYDUEIQAA\nsBqCEAAAWA1BCAAArPYfqbGK7op3aLQAAAAASUVORK5CYII=",
      "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YIQAKBqBCEAQNUIQgCAqhGEAABVIwgB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\nR3v27Llw4UJ+fr63t3d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      "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAkSfgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5Ag4AAKB06Dk82RUAESbg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qMGjbIat6yhQ9SawpLC3UV5zVIy0pLT6rqWo7K1YGtx\nadHe/XvquhCAY42AAwBqVVJS0syZMw9eg2PNmjUjRowo71NDR390WPeh3XJqaHCOyLuf7hjx\nb/Nq51gdm5/2wmUv1c6x+ELfnX7j4s2L6roKgGOQgAMAaltSUlKXLl0OaExNTa2TYgAAjg01\n9QMRAAAAQK0RcAAAAACR5xYVAKhms2bNqusSAACOO2ZwAAAAAJEn4AAAAAAiT8ABAAAARJ6A\nAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNwAAAAAJEn4AAAAAAiT8ABAAAA\nRJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNwAAAAAJEn4AAAAAAiT8AB\nAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNwAAAAAJEn4AAAAAAi\nT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNwAAAAAJEn4AAA\nAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNwAAAAAJEn\n4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNwAAAA\nAJEn4AAAAAAiT8ABAACE1NTU2P9q1KhR165dn3vuuRDCmWeeGTuUJUuWVLbLtm3bEo2vvPJK\n+fiJlj179pS3vPDCC7FYbOnSpQdUUlk7QNWS67oAAACgvhgwYEBKSsqmTZvef//9m2++OTc3\n97zzzsvJyQkhzJs3b/fu3b17927RokUIIT09vbJdunfvnth0zz33XHLJJWlpaRUPUVxcPGXK\nlLlz544dO/Zw2o9JK1asWLlyZfnbPn365OTk5Ofnf/jhhzt37kxLS+vevXuLFi3KysqWL1/+\n+eefN2zYsFevXhkZGSGE5cuXp6SknHzyyXVXPtRTAg4AAOB/vPTSS4k4Y/jw4ZMnT3711Vef\neeaZxKa+ffu+8847jz322EUXXVT1LuUBx9q1a5944okHH3ywYv/du3cPHz784ENX1n7sicfj\nn3/+ecuWLZs2bZpoad68+b59++bOnZuWlnbKKaesXr168eLFgwcPXr58+bZt23r06LFmzZpV\nq1b17t17y5Yte/fu7dy5c92eAtRPblEBAAAO1KdPnxDCzp07v/QuSUlJffv2HTVq1Lp16yp2\na9GiRTwej8fj/fv3P5z2Y8/27duLioo6dOjQvHnzDh06dO3aNSUlZdWqVfv37+/Vq9dpp53W\nvHnzoqKikpKSzZs3t2zZMiMjo7S0tGnTpoWFhR9//PEZZ5xR12cA9ZSAAwAA+D8++uijiRMn\nhhAOf6bAwbvEYrGnnnqqsLDwxz/+cQ3VGVHr1q2Lx+Pz589fuHDhrFmz9uzZE4/HN27cmJ6e\nnpjTUVBQ0KBBg+Tk5DZt2qxatWrGjBmJmR1Lliw5/fTTk5NNw4dD83cDAAD4H61atSp/feKJ\nJ37nO985ml3OPvvsm2666bnnnrv99turt87oisfjpaWl7du3P/XUU1evXr1mzZoNGzZkZ2cX\nFRUl7vQpLS0tKCho1qxZCOG0005r27ZtLBZLS0tbtmxZmzZtMjIyCgoKUlNTk5L8Vg0HEnAA\nAAD/I7FiaFJSUqdOne6+++7EeqJHusu2bdvKt44aNWrSpEl33nlnTVYdJbFYLHEvTwghJydn\nzZo1sVhs7969IYQmTZqEEBIPmsnKykr0ady4cQhh06ZNRUVFubm5c+bMycvLS05O7tGjR+vW\nrevmHKC+EnAAAAD/o3zF0OraJScn58EHH3SXSrnVq1fn5eWdccYZDRo02LFjRwghMzMzEWo0\nbNgwhJBYsqTivJiCgoLVq1efc845CxYsaNas2dlnn71o0aLNmzcLOOAAAg4AAKAG3XXXXePH\nj6/4VNTj2Z49ezZs2FBcXNy4ceN169ZlZWVlZWXF4/EQwsqVK7dt27Zhw4ZWrVplZmYm+peV\nlb3//vs9evRITk7Oz89v2rTp9u3b8/Pzc3Nz6/I0oF5y4xYAAFCDGjVqNGbMmLquor7o1KlT\nixYtdu7cuW3btg4dOpx11lkhhJYtW3bo0GH//v1bt27Nzc3t2bNnef9ly5a1a9cuPT09hNCl\nS5cNGzZ89NFHnTp1qjjFA0gwgwMAAAj79u2rusP8+fMPc5fEA18rtlx22WUHtIQQZs2adcjd\nK2s/NqSmpvbt2/fg9q5du3bt2vXg9u7du5e/bteuXbt27WqwOIg4MzgAAACAyBNwAAAAAJEn\n4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNwAAAA\nAJEn4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNw\nAAAAAJEn4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACA\nyBNwAAAAAJEn4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDkCTgA\nAACAyBNwAAAAAJEn4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAAAEDk\nCTgAAACAyBNwAAAAAJEn4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIEHAAA\nAEDkCTgAAACAyBNwAAAAAJEn4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPIE\nHAAAAEDkJdd1AQAAAEfsvRs/qOsSgPrFDA4AAAAg8gQcAAAAQOQJOAAAAIDIE3AAAAAAkSfg\nAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5yXVdAABQe255cWFdl0AdWLZtaZ/nT6/rKgCg\nZpnBAQAAAESegAMAAACIPAEHAAAAEHkCDgAAACDyBBwAAABA5Ak4AAAAgMjzmFgAOI782w29\nh3bLqesqCCGEdz/dMeLf5tXOsbq26P7CZS/VzrH4Qt+dfuPizYvqugqAY5AZHAAAAEDkCTgA\nAACAyBNwAAAAAJEn4AAAAAAiT8ABAAAARJ6AAwAAAIg8AQcAAAAQecl1XQAAAMARy/3plJob\nvEXnn7x34wc1Nz5QE8zgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5Ag4AAAAg8gQcAAAA\nQOQJOAAAAIDIE3AAAAAAkSfgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5Ag4AAAAg8gQc\nAAAAQOQJOAAAAIDIE3AAAAAAkSfgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5Ag4AAAAg\n8gQcAAAAQOQJOAAAAIDIE3AAAAAAkSfgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5Ag4A\nAAAg8gQcAAAAQOQJOAAAAIDIE3AAAAAAkSfgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5\nAg4AAAAg8gQcAAAAQOQJOAAAAIDIE3AAAAAAkSfgAAAAACJPwAEAAABEnoADAAAAiDwBBwAA\nABB5Ag4AAAAg8gQcAAAAQOQJOAAAAIDIE3AAAAAAkSfgAADtSfzKAAAO40lEQVQAACJPwAEA\nAABEnoADAAAAiDwBBwAAABB5Ag4AAAAg8gQcAAAAQOQJOAAAAIDIE3AAAAAAkSfgAAAAjsq2\nbdtisVgsFnvllVfKGxMte/bsKX99gJKSkkTPF154IRaLLV26tG6qB44VyXVdAAAAcIy45557\nLrnkkrS0tENuHTRoUMOGDcvf7t+//7XXXps7d+7YsWNrq8D6YsWKFStXrix/26dPn5ycnPz8\n/A8//HDnzp1paWndu3dv0aJFWVnZ8uXLP//884YNG/bq1SsjIyOEsHz58pSUlJNPPrnuyod6\nSsABAABUj7Vr1z7xxBMPPvjgIbdOmjQpMzOz/O22bduGDx9eW6XVI/F4/PPPP2/ZsmXTpk0T\nLc2bN9+3b9/cuXPT0tJOOeWU1atXL168ePDgwcuXL9+2bVuPHj3WrFmzatWq3r17b9myZe/e\nvZ07d67bU4D6yS0qAABANUhKSurbt++oUaPWrVt3OP1btGgRj8fj8Xj//v1rurZ6Zfv27UVF\nRR06dGjevHmHDh26du2akpKyatWq/fv39+rV67TTTmvevHlRUVFJScnmzZtbtmyZkZFRWlra\ntGnTwsLCjz/++IwzzqjrM4B6SsABAABUg1gs9tRTTxUWFv74xz8+ZIfmzZuXL8Dxm9/8ppbL\nqz/WrVsXj8fnz5+/cOHCWbNm7dmzJx6Pb9y4MT09PTGno6CgoEGDBsnJyW3atFm1atWMGTMS\nMzuWLFly+umnJyebhg+H5u8GAABQPc4+++ybbrrpueeeu/322w/eWnENjvbt29duafVFPB4v\nLS1t3779qaeeunr16jVr1mzYsCE7O7uoqCgnJyeEUFpaWlBQ0KxZsxDCaaed1rZt21gslpaW\ntmzZsjZt2mRkZBQUFKSmpiYl+a0aDiTgAAAAqs2oUaMmTZp05513HrzpgDU4jk+xWKxPnz6J\n1zk5OWvWrInFYnv37g0hNGnSJISQeO5MVlZWok/jxo1DCJs2bSoqKsrNzZ0zZ05eXl5ycnKP\nHj1at25dN+cA9ZXYDwAAqDY5OTkPPvjgkiVL6rqQemr16tWLFi0qLS0NIezYsSOEkJmZWVRU\nFEJITG9JrGDSqlWr8l0KCgpWr159+umnv//++82aNRs8eHBGRsbmzZvr5gSgHjODAwAAqE53\n3XXX+PHjKz4GlXJ79uzZsGFDcXFx48aN161bl5WVlZWVFY/HQwgrV67ctm3bhg0bWrVqVT7V\npays7P333+/Ro0dycnJ+fn7Tpk23b9+en5+fm5tbl6cB9ZIZHAAAQHVq1KjRmDFj6rqKeqpT\np04tWrTYuXPntm3bOnTocNZZZ4UQWrZs2aFDh/3792/dujU3N7dnz57l/ZctW9auXbv09PQQ\nQpcuXTZs2PDRRx916tSp4hQPIMEMDgAA4KgkHvhaseWyyy6r2HLA1gPMmjWrhgqrh1JTU/v2\n7Xtwe9euXbt27Xpwe/fu3ctft2vXrl27djVYHEScGRwAAABA5Ak4AAAAgMgTcAAAAACRJ+AA\nAAAAIk/AAQAAAESegAMAAACIPAEHAAAAEHkCDgAAACDyBBwAAABA5Ak4AAAAgMgTcAAAAACR\nJ+AAAAAAIk/AAQAAAESegAMAAACIPAEHAAAAEHkCDgAAACDyBBwAAABA5Ak4AAAAgMgTcAAA\nAACRJ+AAAAAAIk/AAQAAAESegAMAAACIPAEHAAAAEHkCDgAAACDyBBwAAABA5Ak4AAAAgMgT\ncAAAAACRJ+AAAAAAIk/AAQAAAESegAMAAACIPAEHAAAAEHkCDgAAACDyBBwAAABA5Ak4AAAA\ngMgTcAAAAACRJ+AAAAAAIk/AAQAAAESegAMAAACIPAEHAAAAEHkCDgAAACDyBBwAAABA5Ak4\nAAAAgMgTcAAAAACRJ+AAAAAAIk/AAQAAAESegAMAAACIPAEHAAAAEHkCDgAAACDyBBwAAABA\n5Ak4AAAAgMgTcAAAAACRJ+AAgGrz7LPPpqSkxGKxpKSkrl271nU5AADHEQEHAFSPDRs23HLL\nLcXFxU2bNg0hfPTRR+edd15dFwUAcLxIrusCAOAYMXLkyBBCenr67t27X3rppeuuu+7dd989\nZM/i4uLf//73W7durdh4wFsAAI6IgAMAqsf8+fNDCFlZWSGEa6+99rrrristLS0tLW3QoEHF\nbsXFxU2aNCkpKaliqOLi4hoq8pXF65es21VDg9eONVv3zv9kR9+TT+iQ1aSuazkqG/P21dqx\ntuzd/PTCMbV2uBqydvfaRZsW9Mo5q32z9nVdy1FZn7++rksAODYJOACgehQUFIQQ2rVrV7Fx\nw4YNB7RMnjy56nQjhDB79uyePXtWY22xWEiKxcri8Tc+3FSNw9ahN5YdIycSQmgQi9Xc4Emx\npBDCtsKtzy/9fc0dpTbN+mxGXZdQPRrEGnxxJwCOhIADAKpHPB4PITRp8n+mFRwwfSOEcOWV\nVyYnJ1eRccRisVtuuaV6a2uQFLt9wClvf3ws3AWTV7h/8+592c1SM9Ia1nUt1aDXSc1PbNqo\n5sa/+OTL/rFlSWFJYc0dotbkF+dvKdjcsnF2eqP0uq7laKU0SL20w1fruorI+3TUZTU5fI0O\nDtQIAQcAVI/GjRvv2LFj586dIYTNmzcnGlu3bn1At0aNGu3du/fgNTgSkpKSfvjDH6amplZ7\nefcO6XTvkE7VPiz1XMfmpz578XN1XQUA1AYBBwBUj759+06cOHHZsmUhhD/96U/hUNM3Eho1\nanTrrbfWanEAAMe6L/OY2GeffTYlJSUWiyUlJXXt2rXaawKAKHryySdDCHl5eSkpKffcc08I\noX///nVdFADA8eKIA44NGzbccsstxcXFTZs2DSF89NFH5513Xg0UBgAR065du3HjxjVq1Ki4\nuDgWi3Xp0mXatGl1XRQAwPHiiG9RGTlyZAghPT199+7dL7300nXXXffuu+9W7FBcXHzwfcWH\nvM0YAI4xt956q3tPAADqxBEHHPPnzw8hZGVlhRCuvfba6667rrS0tLS0NHGbcXFxcZMmTap+\n+l1xcfGXrRYAAADgEI74FpWCgoIQQrt27So2btiwIfFi8uTJVacbIYTZs2cf6UEBAAAAqhCL\nx+NHtMOJJ564Y8eOSy+9dMqUKSGEWCwWQli/fn3iMXhfOIMjFosVFBTUxNPvAACA40RJScms\nWbMyMjKSkr7MYxOqVlZWtmnTppycnGofvEZHzsvLGzBgQHKyB2Vy/DriP/2NGzfesWPHzp07\nQwibN29ONCbSjRBCo0aN9u7de/AaHAlJSUk//OEPpRsAAMCXVlJSkpWVtWvXrroupN7JzMzc\nunWrjIPj1hH/0e/bt+/EiROXLVsWQvjTn/4UQkisvlGuUaNG1lcDAABqyOzZs6Ubh7Rr167Z\ns2cPGjSorguBunHEAceTTz45ceLEvLy8lJSUxHKh/fv3r4HCAAAADiHxxAMOyYfD8eyIA452\n7dqNGzfurrvuKi4ujsVinTt3njZtWk1UBgAAcLCKC1i8/PLLHTp0qMbB16xZM2LEiJoYvHZG\nrolFSSAqvszdWbfeequbUAAAgDrXtWvXbt26VeOAFVcMrN7Ba2dkOJ6J9wAAAIDIE3AAAAAA\nkSfgAAAAACJPwAEAAABEnoADAACIkoYNGx7ydT0fPIojQ7QIOAAAgCjp2LFj//79Qwj9+/fv\n2LFjVAaP4sgQLbF4PF7XNQAAAByBsrKyzz777KSTTkpKqv6fbGtu8CiODBEi4AAAAAAiT7wH\nAAAARJ6AAwAAAIg8AQcAAAAQeQIOAAAAIPKS67oAAACAwxWPxz/44INVq1bl5+e3aNHipJNO\n6t69eywWq+u6vsDWrVvfeeed3Nzcbt26Jar94IMPVqxYcfXVVx/lyPF4vLCwsHHjxiGEjz/+\neMWKFeecc07Lli2roWiIGgEHAAAQDStXrvzqV7+6YsWKio1nnnnmSy+91Llz57qq6gu9/fbb\nX/va13bv3h1CGDp06KuvvpqSkvLHP/5x9OjRR/lQy0WLFl199dWffPLJ8OHDr7zyym9+85sh\nhIyMjNdff71fv37VUz1Eh8fEAgAA0XDhhRcuWbLkqquuys3NbdWq1ZYtWz755JMXX3yxV69e\ns2fPPsrBq5hMMXHixKMZ+eyzz/7kk0/uuOOO/fv3P/HEE5deeumECRN+9rOfHX3Acf755y9a\ntOj888+fP3/+3r17r7322i5dujz++OPdu3efO3fu0YwMUWQGBwAAEA3vvffe/fff/+Mf/7hi\nY3p6+u9+97ujH7y4uPi11147+nEOtnTp0jvuuOOhhx4KIXTr1u3aa6995JFHqmXkxYsX3333\n3f/8z/88YcKEESNGjB49ul27duvXr//DH/5QLeNDtAg4AACAaDjllFNefvnlU089tX379i1b\ntty0adMnn3wyZcqUark/5b/+679uu+223/3udzt37jz60Spq1arVX//614EDB15wwQXXXHPN\nRx999POf/7xVq1ZHP3JWVtbs2bOXLFkyZMiQCRMmtGnTZsWKFX//+9+zs7OPfnCIHLeoAAAA\n0fDqq69+/etf379/f8XGhg0bTpgw4Yorrjj68efOnXv++edX+/8i/cd//Md3vvOdEMKbb755\n0UUXxePxu++++6mnngohHOWxxo4de9dddzVo0KCkpCTR0rlz5xUrVowZM2bkyJFHXzlEi4AD\nAACIjFWrVk2ePHn16tV79uxp2rTpKaeccuWVV3bs2LFaBi8qKlq5cmX37t2rZbSKFixYMHPm\nzKuuuipRajwef+utt2bOnPmrX/3qKEeeM2fO3//+95/85CeJt7/4xS969OgxbNiw+v9kGah2\nAg4AAAAg8pLqugAAAACAoyXgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5Ag4AAAAg8gQc\nAAAAQOQJOAAAAIDIE3AAAAAAkSfgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5Ag4AAAAg\n8gQcAAAAQOQJOAAAAIDIE3AAAAAAkSfgAAAAACJPwAEAAABEnoADAAAAiDwBBwAAABB5Ag4A\nAAAg8gQcAAAAQOQJOAAAAIDI+//osSXahrRrsAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Top 2\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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b063/kftzf1FDHavHnz9aOvf3/jiqYeJFKbN2xe9uaypp4iUtsrKjet39SyoGWz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\nVAAAgOgIFQAAIDr/D1m1FhyE6WI9AAAAAElFTkSuQmCC",
      "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VIa6wEAAA5T6XwyfQjh9NNPnzlz5h43jRw5cuTIkflb2rZt27Zt2/QWAAAA/ARY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\nCgAAAADVIagAAAAAUJ3/B0Y86C3v9Fp7AAAAAElFTkSuQmCC",
      "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ml8oOP+++/ftGnTTTfd9Kc//WnkyJFDhgzp1atX4hGV8ePHN2/ePHqQJIQwdOjQ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\npUuXBx54oESJ4nlkBgAAgJMmNzd32rRpP/zhD1NTU0/oKO+9996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NHB9cXFxWHh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\nAg4AAAAg8gQcAAAAQOT9PyxeVVbHQNdWAAAAAElFTkSuQmCC",
      "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_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": 50,
   "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       2   NA     NA\n",
       "4:            nGenes       2   NA     NA\n",
       "5:   Frame_Shift_Del       1  0.5    0.5\n",
       "6: Missense_Mutation       1  0.5    0.5\n",
       "7:             total       2  1.0    1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WaJbanw0cOHDRokWJRCIW+/9V1E9/+tNf/vKXXbt2TV82AAAAALJI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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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o6O0984f+m1ZwzrHMy/LtY/UpZ/slz4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",
      "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9zw5ptv/vcvZ8+eXeAwAFDlSuVyuegZ\nAAAAKIBHRgEAAEIJQgAAgFCCEAAAIJQgBAAACCUIAQAAQglCAACAUIIQAAAglCAEAAAIJQgB\nAABCTS96AAA4I5s3bz5pZfHixZ/+9KcLGQYA6oMgBKA2vPDCC6MXBSEAnAuPjAIAAIQShAAA\nAKE8MgpAnN27d7/yyisnLT7xxBOFDAMABRKEAMQ5fPjwgQMHip4CAIrnkVEAAIBQghAAACCU\nIAQAAAglCAEAAEIJQgAAgFCCEAAAIJQgBAAACCUIAQAAQglCAACAUIIQAAAglCAEAAAIJQgB\nAABCCUIAAIBQghAAACCUIAQAAAglCAEAAEIJQgAAgFCCEAAAIJQgBAAACCUIAQAAQglCAACA\nUIIQAAAglCAEAAAIJQgBAABCCUIAAIBQghAAACBUqVwuFz0DAJze448/ftJKS0vLlVdeOYFb\nHTp06P333z9psa2tbYKTncKXv/zlyb0hAEy66UUPAAATdOTIkSNHjkzW3fbt2zdZtwKAWuGR\nUQAAgFCCEAAAIJQgBAAACCUIAQAAQglCAACAUH5lFIDasGjRoqJHAIB64xxCAACAUB4ZBQAA\nCCUIAQAAQglCAACAUIIQAAAglCAEAAAIJQgBAABCCUIAAIBQghAAACCUIAQAAAglCAEAAEIJ\nQgAAgFCCEAAAINT0ogcAgDPy+OOPFz3C2dm0aVPRIwDAafiEEAAAIJQgBAAACCUIAQAAQglC\nAACAUIIQAAAglCAEAAAI5dgJAGrDj370o6JH+D9GRkb27Nmzc+fOkZGRomcBgHMiCAGoDbNn\nzy56hIaGhoaDBw9u2bLl7bffHv+yRYsWVWYeADgXghAAzsjx48d37Nixa9eu8T8YbGpqWrdu\n3R133FGxwQBgwgQhAJxeX19fZ2fnwMDA+JctXbr0kUceaWlpqcxUAHCOBCEAjGdoaKi7u/vF\nF18c/7KWlpZHH330xhtvrMxUADApBCEAjK1cLu/du3fr1q1DQ0PjXFYqle68884HH3ywqamp\nYrMBwKQQhAAwhoGBga6urv37949/2RVXXNHR0XH11VdXZioAmFyCEAD+PyMjI7t37/71r399\n4sSJcS6bPn36fffdt3r16unT/ccUgFrlv2EA8H/19/dv2bKlv79//MuuvfbajRs3XnrppZWZ\nCgCmiCAEgIaGhoZjx47t2LGjp6dn/FMlZs2atW7duttvv71UKlVsNgCYIoIQABr279/f1dV1\n2lMlbrnllvXr18+dO7cyUwHAVBOEAEQ7w1MlLrjggg0bNlx//fWVmQoAKkMQAhDqP6dK/PKX\nvxweHh7nslKpdNddd61du9apEgDUH0EIQKKBgYHOzs6+vr7xL5s/f35HR0dra2tFhgKAShOE\nAGQZGRnp6enZtm3b+KdKzJgxY82aNatWrZo2bVrFZgOAChOEAATp7+/fvHnzwYMHx79syZIl\nGzZsuOSSSyozFQAURRACEOE/p0rs2rWrXC6Pc1lzc3N7e/uKFSucKgFAAkEIQP3bt29fV1fX\n4ODg+JctX768vb19zpw5lZkKAAonCAGoZx988EF3d3dvb+/4l1144YUbNmxoa2urzFQAUCUE\nIQD1qVwu9/b2bt269bSnStxzzz3333//zJkzKzYbAFQJQQhAHXrvvfe6urpOe6rEwoULOzo6\nFi5cWJmpAKDaCEIA6soZnipx3nnnrV279u67725sbKzYbABQbQQhAPXj7bff3rJly2lPlWhr\na3vssccuuuiiykwFAFVLEAJQD44dO7Z9+/aenp7xT5WYPXv2ww8/vGzZMqdKAECDIASgDpzh\nqRK33XZbe3t7c3NzZaYCgOonCAGoYWd4qsTFF1+8cePGJUuWVGYqAKgVghCAmnSGp0o0Njau\nXr16zZo1M2bMqNhsAFArBCEAtee9997r7Ow8cODA+Je1trZ2dHTMnz+/MlMBQM0RhADUkn//\n+989PT3bt28f/1SJmTNnPvDAAytXrnSqBACMQxACUDPO8FSJG2644dFHH503b15lpgKA2iUI\nAagN3d3du3fvHv9Uiblz565fv/7mm292qgQAnAlBCEBt6OnpGf+C1tbWhx56aNasWe+8805l\nRhrfggULih4BAE6jNP4ftQJAlXj88ceLHuHsbNq0qegRAOA0fNUeAAAglCAEAAAIJQgBAABC\nCUIAAIBQghAAACCUYycAqA0tLS1FjwAA9caxEwAAAKE8MgoAABBKEAIAAIQShAAAAKEEIQAA\nQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBK\nEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQA\nAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABA\nKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQ\nAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAA\nAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAo\nQQgAABBKEAIAAISaXvQAQLqf/vSnf/jDH879PqVS6WMf+1hzc3Nzc/Ps2bNbW1sXL1788Y9/\nvKmp6azu8/LLL//sZz8793nO3ZIlS5544okxX6q2TTvJN77xjSNHjoxev+eee9rb28/lzqcy\nNDT0ta99bfT6pk2bpuLtAKBuCEKgTpTL5aNHjx49evS9995raGh47bXXGhoaSqXSJz7xiVWr\nVrW1tZVKpaJnrDoV3rTdu3cvW7bsqquumsR7AgDnQhAC9axcLh84cODAgQOXX3752rVrb7rp\npqInqgFTt2nlcnnLli1PPvnktGnTJuuelVcul7/5zW/+9yPQ5ubmp556KnYMAGqd7xACEf76\n179u2rSpq6vrxIkTRc9SM6Zi0w4ePNjT0zNZdytEf3//mA/EZo4BQK0ThECQ55577oc//OEH\nH3xQ9CC1ZNI3bfv27f95QrVGvfzyy0WP0NBQNWMAUOsEIZDlnXfe2bRp07/+9a+iB6klk7tp\nJ06c6OrqKpfLk3K3ChsZGent7S16imoZA4A64DuEQDWaNWvW0qVLz/z6kZGRo0ePDg8PHz58\neGhoaPyL33jjjWeffXbjxo1jvnrhhRfefvvtZzHr/+OFF14Yvbhw4cIFCxZM4G6XX375WV1f\n4Kadrb6+vt7e3hUrVkzK3Spp//79g4ODRU9RLWMAUAcEIVCN5s2b9/nPf34Cf2O5XP7b3/72\nyiuv/O53vxvn/5iff/75ZcuWXXvttaNfWrBgwcTeuuEUQbh06dLPfvazE7vhWSlw0yZg69at\nbW1tc+bMmZS7VUa5XN65c2fRU1TLGADUB4+MAnWlVCpddtllq1ev/t73vvfII49Mn37KP/ba\nsWNHJQerZoVs2vDwcHd392TdrTL27t37xz/+segpqmUMAOqDIATqU2Nj48qVK5988snm5uYx\nL3j99dffeOONCk9V5Sq8ab29vfv27Zusu021t95665lnnil6imoZA4C6IQiBejZ//vwvfelL\np/rIy89yjKmSm9bV1XXs2LFJvOFUKJfLL7300tNPP/3hhx8aA4A64zuEQJ1bvHjxXXfd9dvf\n/nb0S6+//nrl56kJU7Fpra2tb7311kmLg4OD27dv/9znPjexe061Y8eO7du3b8+ePcV+mFwl\nYwBQlwQhUP/uvffe3//+96M/iXr33Xf//ve/z5s3r5Cpqtykb9pDDz30i1/84p///OdJ6z09\nPbfeeutVV1018Vknz0cffXT48OG//OUvhw4dOnTo0J/+9Kfjx4/HjgFAAkEI1L/m5ubrrrtu\nzIO8//znPwvCMU36ps2aNWv9+vU///nPT1ovl8tbtmz51re+1dhY5LcYnnnmmVdffbXwsxyq\nZAwAcvgOIRDhuuuuG3N99AdW/Nekb9rNN998/fXXj14/ePBgT0/PxO45Wd58881qyLAqGQOA\nHIIQiHDxxRePuX7aA9mTTfqmlUqlxx57bObMmaNf2rZt28DAwMRuCwBMmCAEIsydO3fMdUE4\njqnYtHnz5j3wwAOj10+cONHZ2Vkulyd8ZwBgAgQhEM1vdUzAOW7aypUrW1tbR6/39fU5CAQA\nKsyPygARPvjggzHXZ8+eXeFJasgUbVpjY2NHR8f3v//9kZGRk17aunVrW1vbnDlzzuX+E3Pv\nvfee9pPPf/zjHzt27EgYA4AcghCIcKrvp53qqUgapnLT5s+fv3r16t/85jcnrQ8PD3d3d3/h\nC184x/tPwE033XTaaw4fPjzVJVYlYwCQwyOjQIT9+/ePuV7Ih1G1Yko3bc2aNWP+aE1vb++p\n3hcAmHSCEKh/R48e7evrG/OlMb/MRsPUb9qMGTM2btw45kudnZ3Hjh0797cAAE5LEAL1b+fO\nnR999NHo9ZaWlssvv7zy89SECmzakiVLbrvtttHrg4ODHokEgMoQhECde/PNN3fv3j3mS21t\nbaVSqcLz1ISKbVp7e/uYP1Gza9eu/v7+yXoXAOBUBCFQzw4dOvSTn/zkxIkTY7465sdTVHLT\nmpubH3744dHr5XJ58+bNo3+GFACYXIIQqE/lcvn555//wQ9+cKqzE6677rprrrmmwlNVuUI2\nbdmyZW1tbaPXDx482NPTM7nvBQCcxLETQF0pl8uDg4Ovvvrqnj173n333XGuXLt2bcWmqnLF\nblqpVHrssce++93vjj7vftu2bTfddNNFF1006W8KAPyHIASq0fvvv9/V1XXm14+MjBw9evTo\n0aOHDx8+cuTIaa9ftWrV1VdffQ4DVqPa3bSLLrpo7dq1v/rVr05aP3HiRGdn51e+8hVf9QSA\nKSIIgWo0PDz83HPPTdHNlyxZ8tBDD03RzQtU05t299137927d/QPyfT19e3du3f58uVT99YA\nkMx3CIEsra2tX/ziFxsb/dvvLFRg0xobGzs6Osb8JHDr1q1DQ0NT99YAkMz/EgFBli1b9vWv\nf33Mcw44lYpt2sKFC++5557R60NDQ93d3VP97gCQySOjQITzzz///vvv/8xnPuPbaGeu8pt2\n//33v/TSS4ODgyetv/jii8uXL//kJz9ZmTEAIIcgBOrcBRdcsHLlypUrV5533nlFz1Izitq0\nmTNnbtiw4cc//vHol7q6ur7zne/4hwgAk0sQAnVl2rRpc+bMmTt37ty5c6+55ppPfepTV155\npU8Fx1dVm9bW1rZ8+fLe3t6T1gcGBrZv375u3bpCpgKAeiUIgWo0f/78b3/720VPUWPqZtPa\n29tfe+214eHhk9Z7enqWLVu2YMGCQqYCgLrkR2UAqC5z5sxpb28fvT4yMrJ58+aRkZHKjwQA\n9UoQAlB1VqxYsWTJktHr/f39u3fvrvw8AFCvBCEAVadUKm3YsGHGjBmjX9q2bdvAwEDlRwKA\nuiQIAahGl1xyyZo1a0avHz9+/H/+53/K5XLlRwKA+iMIAahSq1atmj9//uj1ffv27d27t/Lz\nAED9EYQAVKlp06Z1dHSMeQDG1q1bh4aGKj8SANQZQQhA9Wptbb3rrrtGrw8NDXV3d1d+HgCo\nM4IQgKq2du3aCy64YPT6iy++2NfXV/l5AKCeCEIAqlpTU9OGDRvGfKmzs/P48eMVngcA6okg\nBKDaXX/99bfccsvo9YGBgR07dlR+HgCoG4IQgBqwfv36WbNmjV7ftWvXwYMHKz8PANQHQQhA\nDZg7d+66detGr4+MjGzZsmVkZKTyIwFAHRCEANSG22+//Zprrhm9/vbbb+/Zs6fy8wBAHRCE\nANSGUqnU0dExffr00S/t3Lmz8vMAQB0QhADUjEsvvfS+++4bve6RUQCYGEEIQC1ZvXr1FVdc\nUfQUAFAnBCEAtWT69OkbN24slUpFDwIA9UAQAlBjFi1adOeddxY9BQDUA0EIQO158MEHW1pa\nip4CAGqeIASg9jQ1NT366KNFTwEANU8QAlCTbrzxxqVLlxY9BQDUNkEIQJnJAukAAAlBSURB\nVK165JFHmpqaip4CAGqYIASgVrW0tKxbt67oKQCghglCAGrYHXfcsWjRoqKnAIBaVSqXy0XP\nAAAAQAF8QggAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEI\nAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAA\nhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEE\nIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgA\nABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACE\nEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQh\nAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAA\nEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQS\nhAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEA\nAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQ\nShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKE\nAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAA\nQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBK\nEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQA\nAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABA\nKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQ\nAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAA\nAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAo\nQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShAC\nAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChBCAAAEEoQAgAAhBKEAAAA\noQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIAAIQShAAAAKEEIQAAQChB\nCAAAEEoQAgAAhBKEAAAAoQQhAABAKEEIAAAQShACAACEEoQAAAChBCEAAEAoQQgAABBKEAIA\n/G/7dSAAAAAAIMjfepDLIoApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQA\nAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEE\nAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkh\nBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIAp\nIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACA\nKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAA\ngCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIA\nAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJC\nAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgS\nQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACY\nEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAA\nmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQA\nAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEE\nAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkh\nBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIAp\nIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACA\nKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAA\ngCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIA\nAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJC\nAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgS\nQgAAgCkhBAAAmBJCAACAKSEEAACYEkIAAIApIQQAAJgSQgAAgCkhBAAAmAoaPf5hX536RQAA\nAABJRU5ErkJggg==",
      "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_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": 56,
   "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_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": 58,
   "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:    RTK-RAS 85                2        0.02352941\n",
      " 2: Cell_Cycle 15                0        0.00000000\n",
      " 3:      Hippo 38                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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NcoKipiT1lqMG1oa2v/8ssvrT1eAOB5nuf42i5/tER5efmuXbtOnz794MGD/Px8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\nrcrnAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_Grade_Glioma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Low_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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5YtM09SzRyzu3WGEBo3bnz22Wdn9tgv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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": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 64,
   "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": 65,
   "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": 66,
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Low_Grade_Glioma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Low_Grade_Glioma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Low_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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Low_Grade_Glioma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Low_Grade_Glioma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Low_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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Low_Grade_Glioma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Low_Grade_Glioma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Low_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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Low_Grade_Glioma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Low\\_Grade\\_Glioma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Low_Grade_Glioma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Low_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": 71,
   "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": 72,
   "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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