analysis/KCPG/KCPG_woAR_TT-GermCellTumor_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Germ Cell Tumor\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$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Germ Cell Tumor\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 \n",
       "                  GCT\n",
       "  Germ Cell Tumor  74"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$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> 5</td><td>74</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t  5 & 74\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "|  5 | 74 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,]  5   74  \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       4   NA     NA\n",
      "4:            nGenes       5   NA     NA\n",
      "5:   Frame_Shift_Del       2 0.50    0.5\n",
      "6: Missense_Mutation       3 0.75    1.0\n",
      "7:             total       5 1.25    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:      BMPR1A               0                 1     1              1\n",
      "2:       CHEK2               1                 0     1              1\n",
      "3:         MPL               0                 1     1              1\n",
      "4:       PALB2               1                 0     1              1\n",
      "5:        POLE               0                 1     1              1\n",
      "   AlteredSamples\n",
      "1:              1\n",
      "2:              1\n",
      "3:              1\n",
      "4:              1\n",
      "5:              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> 4               </td><td>  NA             </td><td> NA              </td></tr>\n",
       "\t<tr><td>nGenes           </td><td> 5               </td><td>  NA             </td><td> NA              </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 2               </td><td>0.50             </td><td>0.5              </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 3               </td><td>0.75             </td><td>1.0              </td></tr>\n",
       "\t<tr><td>total            </td><td> 5               </td><td>1.25             </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           &  4                &   NA              &  NA              \\\\\n",
       "\t nGenes            &  5                &   NA              &  NA              \\\\\n",
       "\t Frame\\_Shift\\_Del   &  2                    & 0.50                  & 0.5                  \\\\\n",
       "\t Missense\\_Mutation &  3                  & 0.75                & 1.0                \\\\\n",
       "\t total             &  5                & 1.25              & 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           |  4                |   NA              |  NA               |\n",
       "| nGenes            |  5                |   NA              |  NA               |\n",
       "| Frame_Shift_Del   |  2                | 0.50              | 0.5               |\n",
       "| Missense_Mutation |  3                | 0.75              | 1.0               |\n",
       "| total             |  5                | 1.25              | 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            4        NA  NA   \n",
       "4 nGenes             5        NA  NA   \n",
       "5 Frame_Shift_Del    2      0.50 0.5   \n",
       "6 Missense_Mutation  3      0.75 1.0   \n",
       "7 total              5      1.25 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>BMPR1A</td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CHEK2 </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>MPL   </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>PALB2 </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>POLE  </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 BMPR1A & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t CHEK2  & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t MPL    & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t PALB2  & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t POLE   & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Missense_Mutation | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|\n",
       "| BMPR1A | 0      | 1      | 1      | 1      | 1      |\n",
       "| CHEK2  | 1      | 0      | 1      | 1      | 1      |\n",
       "| MPL    | 0      | 1      | 1      | 1      | 1      |\n",
       "| PALB2  | 1      | 0      | 1      | 1      | 1      |\n",
       "| POLE   | 0      | 1      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Missense_Mutation total MutatedSamples\n",
       "1 BMPR1A      0               1                 1     1             \n",
       "2 CHEK2       1               0                 1     1             \n",
       "3 MPL         0               1                 1     1             \n",
       "4 PALB2       1               0                 1     1             \n",
       "5 POLE        0               1                 1     1             \n",
       "  AlteredSamples\n",
       "1 1             \n",
       "2 1             \n",
       "3 1             \n",
       "4 1             \n",
       "5 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>5</li>\n",
       "\t<li>6</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 6\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 6\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 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",
       "SJGCT018639               1     1       2\n",
       "SJGCT018635               0     1       1\n",
       "SJGCT019228               1     0       1\n",
       "SJGCT042505               0     1       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJGCT018639                    2\n",
       "SJGCT018635                    1\n",
       "SJGCT019228                    1\n",
       "SJGCT042505                    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": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "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>BMPR1A</td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CHEK2 </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>MPL   </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>PALB2 </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>POLE  </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 BMPR1A & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t CHEK2  & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t MPL    & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t PALB2  & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t POLE   & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Missense_Mutation | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|\n",
       "| BMPR1A | 0      | 1      | 1      | 1      | 1      |\n",
       "| CHEK2  | 1      | 0      | 1      | 1      | 1      |\n",
       "| MPL    | 0      | 1      | 1      | 1      | 1      |\n",
       "| PALB2  | 1      | 0      | 1      | 1      | 1      |\n",
       "| POLE   | 0      | 1      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Missense_Mutation total MutatedSamples\n",
       "1 BMPR1A      0               1                 1     1             \n",
       "2 CHEK2       1               0                 1     1             \n",
       "3 MPL         0               1                 1     1             \n",
       "4 PALB2       1               0                 1     1             \n",
       "5 POLE        0               1                 1     1             \n",
       "  AlteredSamples\n",
       "1 1             \n",
       "2 1             \n",
       "3 1             \n",
       "4 1             \n",
       "5 1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XH9OHbs2LE/XIAIIUSlUn19fXV1deXk5BwcHL59+9b79v6BIORR+/fvFxMTO3Hi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\nRK5CYII=",
      "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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Wn6+++/19TUPHr0KFIQOoA1a9YcOXKktrZWwnmBHA7H0NBw2bJlii4MQO5aCEKR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\nRU5ErkJggg==",
      "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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hF4BQcAAFBOunXrNnPmzLy8vP79+yfSjfDPKzW6dOmSSDdCCNWqVbviiitCCM8/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\n4YsvvujatWvDhg2fffbZBx98MISwc+fO5cuXjx8/fvr06ccff/y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      "text/plain": [
       "Plot with title “Top 5\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-Germ_Cell_Tumor_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_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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54RQshuuUe7w4pTGRmrP/589dyF1csb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",
      "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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ysrq3PnzoWFhcnJyQcOHPjjjz/8/f2bNGli9NMZOXLkzp07t2zZcu/evVOnTs2a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\nrFOnziObvXfv3q5du4QQr7zySpcuXZR4s2XLlur9BoARVD2otGj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CUlRW6Nj49/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6Oztbbjp//vz3338vhNiwYcPJkydlojh3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\nQHCQqAAAAAAAgOAgUQEAAAAAAMH5HwttiO8twE0YAAAAAElFTkSuQmCC",
      "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-Germ_Cell_Tumor_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_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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1dffXUIobS0dP369SGE0aNHZ2Rk7N69e8aMGXPnzl21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\nAABA7Ak4AAAAgNj7/wH5wrH5QFOiXAAAAABJRU5ErkJggg==",
      "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-Germ_Cell_Tumor_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_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       4   NA     NA\n",
       "4:            nGenes       5   NA     NA\n",
       "5:   Frame_Shift_Del       2 0.50    0.5\n",
       "6: Missense_Mutation       3 0.75    1.0\n",
       "7:             total       5 1.25    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": {
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EkJOT8/3vf//KK68MIWzZsiWzEQEAAADIBukUSclTkAoLC0MIixcv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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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zcevKI8qxIAzjfhVl8AxLhV5Wt+fd0fFxZWh6n/98x/36NX/7IwoyhM/SOm5KTl\n3rni68pLdc7jP9j73VGNfvG3u9rDPy8A8Y0ADSDmVOZOe/SaX37y4s8qD/oet7y0SQ+v/+5n\nLvmPFHuKhd0ixl1Wvuods9+pvPTbw796rfXV81sGvC5PECVAACY4AjSAWJRkS3rfgg88eeOz\nH1700dy0vBB7K8+u+I9ld/z1nTVrKtZZMj3El09f/Dnl+dt+8X9tx5fPP/Ay9E3HAUwEBGgA\nsSs7NfvWxbc9865tD659eHXFWt2q5dy0vDUV6x5a+8if3/HXm+a+Oz05PUzzRIxzpGTetfJe\n5Ybfrf0t39r9wLn/SYAGEAyb3x/spqoAEF1DvqHDHYf2n9nX2N3Q1NPQ2t/i8g64hlxDPm92\nak5uWu7IfxdmFF5QtGjx5Ium5U7nkBQAgOUI0AAAAIAGSjgAAAAADQRoAAAAQAMBGgAAANBA\ngAYAAAA0EKABAAAADQRoAAAAQAMBGgAAANBAgAYAAAA0EKABAAAADcnRngAAQETE5/N1dHS0\ntbV1d3c7nc6+vj6v1+v1eoeHh1NSUpKTk9PS0rKzs3NzcydNmlRSUpKXlxftKQPABMVR3gDi\nUk1NTUNDgyVdpb0lIyOjsLCwpKRk8uTJKSkplnQ+pqGhocbGxuPHjzc3N3u93uBf6HA4ysvL\nZ82aVVZWZrPZLJzSzp07Dx06NKoxPT39lltusXCUYDz55JOtra0RHlREMjMz3/ve90Z+XADx\nghVoABOd2+12u90jv25qahIRm81WWlpaXV1dUVFhbTY9n8vl2rdv37Fjxzwez/hefuzYsWPH\njjkcjoULFy5YsCBioR8AJjgCNACM5vf7m5ubm5ub8/LyLr744qqqKmv793g8e/bsqa2tHR4e\nDr03l8u1e/fu/fv3L1q0qLq62m7n4RYACC8CNACYcjqdzz333Lx585YvX56cbM0bZn19/Usv\nveRyuSzp7Ry327179+4333xz9erVkydPtrZzAMD5WKgAgDHU1tZu3rx5cHAwxH6Gh4f/8Y9/\nbN261fL0fE5XV9fmzZv37dsXpv4BAMIKNAAEo7Ozs6am5rrrrht3gYTL5aqpqWlvbw/y/oyM\njMzMTIfDkZqaOjAw0N/f73K5gqyW3r17d0dHx9q1a61aNQcAnI/3VgCJIy0tbfr06cHf7/P5\nRp4gdDqdYy4wt7a27tixY/Xq1eOYmMvleuqpp7q7uwPflpubO3369KqqqiFKCeoAACAASURB\nVPz8fGVSHxoaamlpqaura2xsDDzhuro6t9t95ZVXJl6GzszMrKioCOsQqampYe0fQLxLtDdW\nABNZVlbW+AKuiDidzqampkOHDvX19Zndc+TIkVmzZpWWlmr1HEx6njt37oIFCwoKCgJ3lZyc\nXFFRUVFR4fP5WlpaXn/99ebmZrObT58+vWXLlquvvjopKUlrwjFu0qRJ4/5bBgBLUAMNACIi\neXl51dXVN99884oVKwIkztdee02r2+Hh4ZqamgDpubCwcNOmTatXrx4zPZ/PbrdPmTLluuuu\nu/zyyx0Oh9ltzc3NO3bs0JowAGBMBGgA+D92u33hwoWbNm1KT09X3tDc3NzS0hJ8h9u3bzer\ne7bZbCtWrHjHO95RXFw8nrmKiMiMGTP+5V/+Zf78+WY3HD169I033hh3/wAAIwI0AIxWUFCw\nceNGs+cFjx8/HmQ/J06cOHbsmPKS3W7fuHHjwoULQz+oJSUlZeXKlUuWLDG7YdeuXU6nM8RR\nAADnEKABQKGkpGThwoXKS6dPnw6mh8HBwZdeekl5KTk5+aqrrqqsrBz//AyWLFly6aWXKi/5\nfL4XX3zR7/dbOBwATGQEaABQu/DCC5VbWPT09AR40PCcV155RblRhs1mu+qqq8rLyy2Y4j+r\nrq5eunSp8lJbW9vRo0ctHxEAJiYCNACopaWlmcXctra2wK91Op1mxRuLFy8uKysLdXImFi1a\nZNb53r17LTk5HABAgAYAU2YBemBgIPAL9+zZoyyZKCgoCFCsHDqbzbZmzZqUlBTjpb6+viNH\njoRvaACYOAjQAGAqJydH2R74EJO+vr76+npju91uX7du3bjPMgxSdna2WTH0wYMHqYQGgNAR\noAHAVEZGhrI9cICura1V5tQ5c+bk5+dbM7OA5s2bl5eXZ2zv6enR2oMPAKBEgAYAbV6v1+yS\n3+83e1zPbFuPcFiwYIGyvba2NmJzAIBERYAGAFNmtc5mK9Mi0t7e7nK5jO3l5eWTJk2ybGZj\nmT17dmpqqrG9qanJ5/NFbBoAkJAI0ABgqqenR9ludk6hiDQ2NirbI7n8LCIpKSmzZ882tnu9\n3tbW1kjOBAASDwEaAEyZnZnicDjMXtLU1GRsTE1NraiosGxawVEGaDGZIQAgeARoAFDzeDyn\nTp1SXioqKlK2e73erq4uY3tJSUnoR3brys/PV+5n197eHuGZAECCIUADgNq+ffuUDwtmZmaa\nVTN3dHQo998oLS21eHJBsNvtxcXFxnazSQIAgkSABgCF9vb2gwcPKi8FKMbo6OhQtpeUlFgz\nLU3KcYeGhpxOZ+QnAwAJgwANAKN1dXVt2bLF7OBrs9piEent7TU2JiUlmZV8hJtZcDd7OBIA\nEIzkaE8AAGKI3+8/cuTIrl27hoaGlDeUl5cHWE5WBujMzMxwnz5oJjc3V9munGe86Orq2r59\ne/j6LygomD9/fvj6B5AACNAAICLS29vb2Nh46NChwKuzS5YsCXC1v7/f2JiWlhbq5MbLbLu9\nvr6+CM/EQv39/WE9DmbatGkEaACBEaABJI6+vj6ttUm/3+/xeAYHB51Op/L0k1Gqq6snT54c\n4AaPx2NsDLBpdLglJycnJSUZa1ECH0UOAAiMAA0gcbjd7vCtTZaVlS1dujTwPcrCjyiuQI+M\nbvxsYFbeDQAIBg8RAsDYioqKNmzYMGYpszJAR3EF2mx0swpvAEAwWIEGgDHMnDlz9erVyclx\n+YaZlJRkbGQFGgBCEZf/HgBAZDgcjiVLlsybNy/I+5OTk41l0NEtOHa73cbGOP0wAAAxgvdQ\nAFDIzMxcuHDhggULtLKm8uboBmjl6HEdoMvLy6+55ppozwLAhBbH76EAYBW73Z6RkZGRkeFw\nOEpKSqZOnZqfnz+OflJTU42NUQzQPp9PuTFISkpK5CcDAAmDAA0gcRQUFNx4441RnEB2drbx\nNG9lEUVkmGX3rKysCM8EABIJu3AAgGWUwbS/vz9au16YHZiSnZ0d4ZkAQCIhQAOAZXJycoyN\nPp+vra0t8pMRkZaWFmU7ARoAQkGABgDLFBUVKdvNgmy4NTc3GxttNltBQUHkJwMACYMADQCW\nKSgoUB620traGvnJ+Hw+5bgFBQVxvQsHAEQdARoALJOUlKRchG5ra4t8GXRHR4fX6zW2l5SU\nRHgmAJBgCNAAYKXKykpj4/Dw8LFjxyI8k9raWmW7coYAgOARoAHAStOmTVO2Hzx40O/3R2wa\nLpfrzTffNLanpqaWlpZGbBoAkJAI0ABgpby8POUhLN3d3Y2NjRGbxqFDh3w+n7G9qqpKWaUN\nAAgeb6MAYLEFCxYo2/fv3x+ZCXg8nsOHDysvzZ8/PzJzAIAERoAGAIvNnDlTeaZ3W1vbwYMH\nIzCB7du3K0/wLikpKSwsjMAEACCxEaABwGIpKSkXXHCB8tIrr7zS2dkZ1tGPHDly4sQJ5aUl\nS5aEdWgAmCAI0ABgverq6oyMDGO7z+fbtm1b+La0czqdO3fuVF4qLy+fMmVKmMYFgAmFAA0A\n1ktJSbnkkkuUl5xO57PPPqvcoTlE3d3dzzzzjDKd2+325cuXWz4iAExMBGgACIu5c+eWl5cr\nL50+ffqpp54aGBiwcLj29vbNmzf39vYqr15yySWTJk2ycDgAmMgI0AAQLmvWrElLS1Ne6ujo\n2Lx5s9PptGSghoaGv/71r4ODg8qrpaWl1dXVlgwEABACNACET2Zm5saNG832Xe7p6Xn88cd3\n7txpFnyD0dXVtWXLlpqaGrO66uzs7CuuuMJms417CADAKMnRngAAJLLS0tI1a9Y8//zzyqs+\nn+/QoUPHjh276KKL5syZY7ZcrdTb27t///7a2toABxympaVdddVV6enp2vMGAJgjQANAeM2a\nNcvv97/44otmSdfj8bz88suvvPJKaWlpZWVlZWVlTk6O8k6/33/mzJnGxsbGxsazZ88GHjcj\nI+Paa6+1qvTZ6/Vu377dkq7Ol5qaumzZMq2XdHV1hWMmRnPnzi0qKorAQADiDgEaAMJu9uzZ\nycnJzz///PDwsNk9fr+/ubm5ubl5165dycnJDocjIyPD4XCkpKS43W6XyzUwMOByuZQHdBvl\n5ORcffXVubm5Vv0WhoeHa2trrertnIyMDN0A3d/fH46ZGJWVlRGgASgRoAEgEqqqqnJzc7du\n3drT0zPmzUNDQz09PcHcqTRt2rS1a9cqT0MEAISOhwgBIEIKCgpuuOGG+fPnh2+ItLS0VatW\nbdy4kfQMAOHDCjQARE5qaurKlSvnzJmzc+fOtrY2C3u22+2zZ8++5JJLlCcgAgAsRIAGgEgr\nKip6+9vf3tLSsn///qamphB7S0lJmT179qJFi7KysiyZHgAgMAI0AERHaWlpaWlpf3//iRMn\n6urqzpw5E2BDOqOUlJSysrKZM2dWVlYmJ/NmDgCRY9N6vwYAhInX621raztz5kx3d3dPT09f\nX9/Q0JDX6x0eHk5OTk5JSUlJScnJycnJycnLy5s8eXJBQYHZES0AgLAiQAMAAAAaWL0AAAAA\nNBCgAQAAAA0EaAAAAEADARoAAADQQIAGAAAANBCgAQAAAA0EaAAAAEADARoAAADQQIAGAAAA\nNBCgAQAAAA0EaAAAAEADARoAAADQkBztCQCAtkcffXR4eNjs6vz581euXBnuOTQ1NW3ZsiXA\nDdddd11ZWdmY/dTU1DQ0NFgypbS3ZGRkFBYWlpSUTJ48OSUlJZQ+d+7ceejQoVGN6enpt9xy\nSyjdAkBcI0ADSDR1dXUrVqyw28P7DVtdXV1Y+x8Ht9vtdrtHft3U1CQiNputtLS0urq6oqLC\nZrNFdXYAkDgI0AASzeDgYEtLy5QpU8I3hM/ns2rZOKz8fn9zc3Nzc3NeXt7FF19cVVUV7RkB\nQCKgBhpAAjpx4kRY+z916pTH4wnrENZyOp3PPffc9u3bh4aGoj0XAIh7BGgACai+vt7n84Wv\n/xis3whGbW3t5s2bBwcHoz0RAIhvBGgACcjtdp8+fTpMncdL/YZSZ2dnTU1NWD9dAEDCowYa\nQGKqq6urqKgIR88RqN9IS0ubPn168Pf7fL6RJwidTueYC8ytra07duxYvXp1aHMEgImLAA0g\nMdXX169atSoce3FEoH4jKytr3AHX6XQ2NTUdOnSor6/P7J4jR47MmjWrtLR0vBMEgAmNEg4A\nicnj8Zw6dcrybn0+X2Njo+XdWigvL6+6uvrmm29esWJFUlKS2W2vvfZaJGcFAImEAA0gQRjD\nYjj24jh9+vS5vZYDDB11drt94cKFmzZtSk9PV97Q3Nzc0tIS4VkBQGIgQANIEMaK58bGxgAH\nFo6PsX4jJycnJyfH2lGsUlBQsHHjRrM6luPHj0d4PgCQGAjQABKE8ZQQj8dz8uRJC4dQ7r8R\n46eTlJSULFy4UHkpfBuVAEBiI0ADSBAlJSUOh2NUo7UP/CnrN2I8QIvIhRdemJyseGS8p6cn\nwIOGAAAzBGgACcJmsxmzbGNjo4Vn7ynrNwoLC63qP0zS0tLKy8uVl9ra2iI8GQBIAARoAIlj\nxowZo1q8Xq9VVRzxWL9xjlmAHhgYiPBMACABEKABJI7i4uLMzMxRjVbtxdHc3Gys3zBG9thk\n9pgjx3oDwDgQoAEkDmUVR1NTkyVVHMr6jYKCgtB7joCMjAxlOwEaAMaBAA0goRiXhIeGhpqa\nmkLs1ufz1dfXj2qMl/qNALxeb7SnAADxhwANIKEUFxdnZWWNagy9iiOu6zfEvNbZbGUaABAA\nARpAolFWcYS41BrX9Rsi0tPTo2w3O6cQABAAARpAojEuDA8PDzc2No67Q+X+G3G0/CzmZ6YY\nd84GAIyJAA0g0RQVFWVnZ49qDOVElebmZuPDdnFUAO3xeE6dOqW8VFRUFOHJAEACIEADSEDG\n5eGmpiaPxzO+3uK9fmPfvn3KCpbMzMxJkyZFfj4AEO8I0AASkHF52Ofzja+KI97rN9rb2w8e\nPKi8VFFREeHJAEBiIEADSECFhYXGo0PGtxdHXNdvdHV1bdmyZXh4WHl19uzZEZ4PACQGAjSA\nxGRcJD516pRxK7oxGes3cnNzY79+w+/319bWPvHEE2ZHpZSXl5eUlER4VgCQGJKjPQEACIsZ\nM2a8/vrr57eMFGPMmTMn+E6U9Rsxvvzc29vb2Nh46NAhs63rRixZsiRiUwKABEOABpCY8vPz\n8/LynE7n+Y11dXVaAbqlpSUq9Rt9fX3bt28P/n6/3+/xeAYHB51Op8vlGvP+6urqyZMnhzBB\nAJjQCNAAElZVVdXevXvPbzl9+vTg4GDwp4dEq37D7XbX1taGqfOysrKlS5eGqXMAmAiogQaQ\nsIxl0MqSDDM+n6++vn5UY4zXb4ypqKhow4YNdjtv/gAwfryHAkhYkyZNMu5zHPxeHNGq3wif\nmTNnvu1tb+P4bgAIEQEaQCIz5t3m5uaBgYFgXhun+28oORyOVatWrV+/PjmZyj0ACBUBGkAi\nM1Zx+P1+Y2GGUcLUb2RmZi5btuzmm2+eN29etOcCAAmCpQgAiSwvLy8/P//s2bPnN9bV1c2f\nPz/wC1tbW+OxfsNut2dkZGRkZDgcjpKSkqlTp+bn50d7UgCQaAjQABJcVVXVqADd3Nzscrkc\nDkeAV0W3fqOgoODGG2+MzFgAAF2UcABIcMYqDhEJXMWhLPOI/eVnAEBkEKABJDjlynHgvTha\nWlqMDxoqgzgAYAIiQANIfMbs29ra2t/fb3a/sn6DYmIAwAgCNIDEp6y+MKviUNZvsPwMADiH\nAA0g8eXk5BQWFo5qNKviUNZvUAANADiHAA1gQjAuIbe1tfX19RnvpH4DABAYARrAhKBcQjZm\nZeo3AABjIkADmBCys7OLi4tHNRoDNPUbAIAxEaABTBTGHNze3t7b23t+C/UbAIAxEaABTBRj\nVnH4/f6GhoZRN1C/AQAYhQANYKLIysqaPHnyqMbz9+JobW11uVyjbqB+AwAwCgEawARiTMMd\nHR09PT0jvzbWb+Tl5VG/AQAYhQANYAJRLiePLEIr999g+RkAYESABjCBZGZmlpSUjGocWXim\nfgMAECQCNICJxZiJOzs7nU4n9RsAgCARoAFMLGZVHNRvAACCRIAGMLE4HI7S0tJRjQcOHKB+\nAwAQpORoTwAAIm3GjBktLS3nt3i93lH3UL8RgNfr3b59u+XdpqamLlu2zPJuAcByBGgAE870\n6dNfeuklv98f4B6WnwMYHh6ura21vNuMjAwCNIC4QAkHgAknIyPDWMUxCgEaAGCGAA1gIgp8\nQDf1GwCAAAjQACai6dOn22w2s6ssPwMAAiBAA5iI0tPTp0yZYnaVAA0ACIAADWCCMkvJ1G8A\nAAKzBX4OHQAAAMD5WIEGAAAANBCgAQAAAA0EaAAAAEADARoAAADQQIAGAAAANBCgAQAAAA0E\naAAAAEADARoAAADQQIAGAAAANBCgAQAAAA3J0Z4AgAD+LnLcin7sIukiaSLpIjkipSKlItlW\n9PyoyLD51fkiK60YJbAmkS0Bb7hOpCyIfmpEGiyZkEjaW//JECkUKRGZLJIScrevirxuwez+\nSapImkiqSLpIgUixSLFIltWjxAW/yO9E+sxvuFkkJ3LTARDDCNDAROATcYm4RESkReSoiIhk\niywQmSuSGrZx60RWhP+brrow9z8ObhH3W79uEhERm0ipSLVIhYgtavNS8Ih43vr16bd+MUlk\nvsiscP5sxKC2gOlZRE6IXBihuQCIbQRoYMLqFXlZ5DWRBSJLRJLCMMSgSIvIlDD0fI7PumXj\nsPKLNIs0i+SJXCxSFe35BNYl8pLIbpELRC6KVLHfb0T63/p1usgtERn0fG+OdQMBGsD/ogYa\nmOC8IvtEnhDpCk//J8LT7TmnzltAjQtOkedEtosMRXsmY/KK7BX5S9h+Ns535rz0HBW+IL7K\nOCtyNhJzARDzCNAAROSsyJ9FToah53oRXxi6PScG6zeCUSuyWWQw2tMIRofIEyLtYR6lIcz9\nj+nkeVU3AVjyTAKAuEeABjBiWGSrSIfV3brPq6y1XLzUbyh1itSE+dOFVYZEtoh0h61/fwwE\n0zHrN0aE+xsVAPGBGmgg7qTqVND63nqgrV+kd6ybh0SeEXmXSHpIExytTqTC0g7PiUD9RprI\ndJ37z/2BO4NYYG4V2SGyevyzExGxicwZ1wuHRTwibpHeIMonBkW2iLwrPMsuJ4P44Qwrr0hj\ncHf2irSJTA7vdADEPAI0EHcc441cgyJnRI6L1JsX4A6I7LF677l6kVXhCV4RqN/ICiHgOkWa\nRA4F3NvhiMgskdLxDiEi9pAjuIi4RFpFagN+XdAtUiuyIOSxjPaHoU8t9QF3YxzlBAEaACUc\nwMSRLlIhsk7kPSKzzW+rFXFaOq5H5JSlHY7wBb1qGC15ItUiN4usCLjJyWuRm5Eph0iVyLUi\n7xDJNb9tr4jX6qHfFGmxuk9dygISh8gkVfuJOCm8ARBGBGhgAkoXWSuyxuSqX+RwaP0bw2I4\nKkdPq576CsdmfCGyiywU2WReGNMcAwnynCKRG0SKTa4OWF10fkbkJUs7HIcBk3X3mSIzTO5v\nDu+MAMQ8AjQwYc0RWWJyqSG0no0Vz406X5EHyVi/kRPDB8UViGw0f8uN+iN050sR2Wge9y3c\nqqVO5G8xsAvhCRG/qn2W+cMGMfX3BSAKCNDARLZIJFPV3hfafrfG2OGxeo885f4bMX46SYnI\nQpNL4duoZHwcItUml06ZxM3gDYnUiTwl8lwMpGcx2X9jkkiBSJ5IvupqQxg+EAKIJwRoYCJL\nFplncimUszNKRByGRmsf+FPWb8R4gBaRC00e3e4Z6xDpyJtv8g/EoP5UvSLtIkdFXhZ5WuSX\nIs/FTNVKt8gZVfust36h/KHyvHVCO4AJil04gAmuXGSPqt0VQp82kSqRQ//c2CgyZN17jrJ+\no9CizsMnTaTcpEKmTSQrwrMJKFWkwCRcDohkB9fJSyKNsffZ4HxmxRgz3/rFDJP/g5zQ3N8Q\nQEJhBRqY4JT7DEhoAVpUT195raviiMf6jXPKTdoHIjqLoBSZtAd/gGJbbKdnMQnQZed9mMkV\nKVDd0xgb9ScAooMADUxwKSY7V4RY51qsqq62ai+OZlX9hnLDhBhk9phjDB7rnWHSHoNZf3zO\nmByvOPOf/6fys9lwPJ+CCSBUBGgASmmhvdymih1N5ge4aFHWbyiXCWOQWSqNwQBt9jNgi+gs\nwkj5+GCS4UfX7MsNjvUGJi4CNDDBDZnsJ6DcnUOLcUl4yIpHr3wi9YbGeKnfCMDyA0pCZ3Zc\nSGpEZxEuPpMEXGn4DZpVcZyKyY89ACKBhwiBCc7s0MHQH8grFskylMCeCDnsxnX9hpjXP5it\nTEeRWToMfqoXBlHvMRClsxibTeY2U9U4Q6TT0OgXqROZb/G8AMQDAjQwwSl3IE4XybOi8yqR\nA//c0iTiFUkJoc+4rt8QkR6TdrODS6Ko36RduTWyUjD7VDijFKCVjw+miUxVtVeJ7DbphAAN\nTESUcAATmU/kqKp9hkVvDsaF4WGRxhA6VO6/EUfLz2J+Zopx5+yoa1U15oX2+SdGDKkKgcT8\nJ99sk8TWmN9mBEBYEKCBieywqoTDJrLAov6LVBsGh3KiSrOqriCOCqA9IqdMLpntGRctXSK9\nqvY4+tMOoMmk6FxZvzGCRwkB/B8CNDBhNYnsUrXPsah+Y4RxebgphA10471+Y59Jbss035A7\nWl5XNdrOO6Ivrin338gWKTF/iVmANjuKBUAiI0ADE9CwyKsiz6o2e84RudTSsYyxwzfeKo54\nr99oFzlocqkiohMZ20mThdU5IrmRnov13CZn+gT+bGBWxdEZ2rn3AOISARqYOHwiHSKviPxO\n5HVVek4XudLqTcoKVUeHjO9b77iu3+gS2WKyY6CIzI7oXMbQLPKc6sfDIXJxFKZjvTqTHfoC\n1G+MMPu0RhUHMOGwCwcQd1wi24O+2S/iFfGIuES6zHf2FZEskavDU0gww1APcErErX9Wi7F+\nw2yD3pjiFzkissv8EJnygJUDkeQS2SdySHXJLrIhJp90HAdl/UZREJVLVSKvqNqPJ8pHCwDB\nIkADcccjUmt1n9NFVod8+qAZY4AeKcaYo9OJsn4jxpefe0UaRQ6Zb103YkmEpjOaT8Qj4hXp\nEekQaRE5aXKEe7LIxphJ+SHqM9ldJJja7myRIpEzhvYekXaR4lCnBiB+EKCBCS5HZJXIlHAO\nkS+SZ9juo04zQLdEqX6jT2e9X0T8Ih6RQRGniCuI+6tFJo9zav9rWOQnofUQWI7I5bG3Sci4\nKZ/5swVdTD9DFaBF5AQBGphQCNDABNcj8oZIUpjXF6tE9v5zy2mRQZ3TQ6JVv+EOw3r/OWUi\nS8PWuSXmiVyaEBs/n6MM0OVBH684XeRlVfsJkUtFbOOfF4C4wkOEABpEnhR52vxY79AZl/eU\nJRlmfKpjL2K8fmNMRSIbYvhNeIrIO0VWJVZ6PityVtUe/N582SYrzS6RlnFOCkAcitn3bgAR\ndkrkcZG9JlWwIZqkejwx+L0LolW/ET4zRd4Wk8d3n+MUaTZ/8DFOKZefk0Wm6XTChtAACNAA\n/o9PZI/IVpPDPkJkjB3NIgPBvTZO999QcoisElkf8xV0/SI7RR4zP3s87vhNMu50zb8LswBd\nZ75NIYBEE+Pv4ACM8kRuGtcLvSL9Ir0ip0WazAs2GkS2ilxl9QfsGSKv/XOLX6ReZP5YL0yY\n+o1MkYUiC+LqjbdX5G8iS0UWR3smoWsT6VO1656tmCVSLNJuaB85p71yPFMDEG/i6H0cQIhS\nRPJE8kQqRC4VaRTZbXKI2imRHSKrLR09TyTfUIFaF0SAbo3P+g27SIZIhohDpERkqkh+eAay\nae5ncj6fiFtkQORswGqN3SJJIheMd5QYodz+OUOkTL+rGaoALSLHCdDABEGABiasSpEpIv8w\n+V77iMgskVJLR6wyBOhmEddYx3NEt36jQOTGSI01PnYrPur4RNpFDqn+tEe8IlIennN2IsNn\n8lubOa5vWqaL7FK1N4p4E+uxSwBqBGhgIksWWW+eLV4WeYelw80Q2WNorBdZYP4Sf6LUb8Q4\nu0iJSIlIi8hW1ZK/T+Q1kQ1RmJo1Toq4Ve0HRQ5aN8qQSGMQR4IDiHs8RAhgtUiWqv2MSKel\nAylXjgPvxdGietAwyDMvMA6lIm8TSVJdalAF63ihrN8IB/biACYEAjSAVJELTS5ZngaM2bdV\npN/8fmX9RpiKiTFiksgyVbtPpDnSc7GGV6QxUmOdiuePGQCCRYAGICKzTQo3lU9KhUJZfWEs\n0hihrN9g+TkC5oqkqtot/3mIjPoIbjCn3DQGQKIhQAMQkSST5wU7rB4oR6TQ0GhWxaGs36AA\nOgKSTf6clXu2xL4Il1VQxQEkPgI0gBFFqkZvGM6iMy4hm23QS/1GFCkPrFY+hxfjXBE/C6Yl\nYFUSgETALhwARmSYtHusfqOoEnnF0FgnUv3PLdRvRJdyo8B4DNB1JqfTLxCZGnLnr5p8S3PC\n8PMMIKEQoAGMiNi7QbbqIDdjgKZ+I7rSVI3xGKDN9t9YZLL5jJZukwB9nAANJDZKOACMMNs6\nID0MYxlzcLtI7z+3UL8RXcqHCN0mq7kxq1vkjKq91Ir0LOaf6DpEuq3oH0CMIkADGKGsQk4L\nz7uEMnacn5j9Ig2GG6jfiCTl37tfxBvpiYTE7Hk+q846cZif1smjhEAiI0ADGNGiaswMz1hZ\nIpMNjefvxdEq4jLcQP1GJJmtNHsiOotQKVOsXWS6dUOYfa4jQAOJjAANQER6TA4dLAvbiMY0\n3CHS89avjfUbedRvRJbPpN3yXVnC54xJHUW5pYVJ00Vsqnaz8mgAiYAADUBE9pu0V4RtROVy\n8sgitHL/DZafI8xsBTqOArTZ44NW1W+MyDD/nMkiNJCwCNAAWkSOllrbyAAAHnNJREFUqNqT\nzes7Q5cpUmJoHFl4pn4jFsT7CrTP5ICeZJFKq8cyq+I4EW/PXAIIFgEamOCcIttM/pmfH+a9\n7YyZuFPESf1GbIuXAN2s2gZRRCpNTq0PxXSTf0z7RVqtHgtATCBAAxNZo8hfVMu9IpIisjjM\no5tVcVC/EQvM/nVQptIYFO79N86XJjJFcxoA4hsBGpiAPCL1Ik+KPGu+qUJ1eHaAPp9yC7AD\n1G/EhiST9q6IzmKchlQfw0QkNWxl/WZVHPXmxTAA4hgnEQJxxyWyfVwvHBYZFHGJdI5VmjlF\n5MJxDaFrhmH7POM2w9RvREWKiE31cxIXAbrJZL/qqrAtG00Tsauy8qDIKSvODAcQWwjQQNzx\niNSGs/8ckQ2R+npqushLY6V5lp+jwi6Sfd7Ggue0i/hi/tvLyOy/cb5Ukamq039E5DgBGkg8\nMf4mCCDC8kWuFUmL1HAZQWz0QYCOlhxV44DIsUhPRI9b5KSqPcCpgZYw+0FtiJ8nLwEEiwAN\n4JwqkU0i2ZEdNPAB3dRvRJHZ9savi7gjOhE9dSZlxzNNTjyxSqXJl7pDIo3hHBdAFBCgAYhI\nrsjlIhuiUdZldpDbCJafo8is8KBXZLNIX0TnosGsfiPwR7XQpZj/iSl3pAYQxwjQwARXLLJW\n5F3hjxdm0s23ABMCdFTli2SZXHKK7I7oXILVZ7L1cq5IUfhHN/txbYrtNXsA2niIEJhQUkUy\nRRwiWSJlIhXh36suGFUip1Tt1G9E3VSRwyaXYvOMPbN9lyPz+XCqSIpqAxCfSL3I3IjMAUAk\n2Pz+2HwTBAAAAGIRJRwAAACABgI0AAAAoIEADQAAAGggQAMAAAAaCNAAAACABgI0AAAAoIEA\nDQAAAGggQAMAAAAaCNAAAACABgI0AAAAoIEADQAAAGggQAMAAAAaCNAAAACABgI0AAAAoIEA\nDQAAAGhIjvYEgMTxkV/t2Xq4LfR+bDbJSU/Jc6TkZaTmZ6ZWl+deXJl/0dS8zLTI/R/W5/ev\nfOj5ZueA2Q3/+Py6qfkOawe9+/+3d+/ReZV1osd3mvulaZsmbVN6b2lL6YUC9sK1KijQIiBH\nHXCQ0ZEjqOsc5rLGceboco2jDmuODiozjo661INw1KOLBijFchMq2FKgaem9Jb2maZM2adMk\nzfU9f/ScDvbdb5Inl0KSz+cv3e+b/e5FNHyz89vP88TWH/++4pyDo/Ky3vjS9X37QZ27/xcb\nH9t4qPfnyUhPG5WXNSova2Re5pTR+YunFi2aWjRhZF5aWu/PDcA7SUDDu04iEZ1oaj3R1Lov\naoyi6PkdR6MoGpaWtmRa0T1XT7t2Zsmw/k+wDftqO6nnKIoeL6/83Htn9PdlDGht7Ynq+ubq\n+uYoitZXHP/lhgNRFE0YlfuJpVPueM+k4Tl+/AIMVH6Cw8DQkUi8vOfYy3uOzRhT8JfXz7xp\nbmm/flyXt2DLBHSPHKxt+vqqbd9+dtcnlky+/7qZ2Rl9PEfX3pHYXlX/6t7jG/YdP3zi9Imm\n1rrG1hNNrVkZw4oLskbnZxcXZC+cOPLKGcUXjy9MH+ZmOEBPCGgYYHYfPfXZn79+5+JJX14+\nJyczvT8+orW9Y9Xmw52/Z8eR+u1V9bPHDe+PCxj0Gprbvve7Pc/vOPqdP1k4c2zf/DOsqGn4\nj7VvrdxY2dDclvxqa3tHQ3PbvmONURT9dmtV9HQ0IjfzA3PG3Xft9Gkl+X1yAQBDh4cIYUB6\nZN3+27738vGGlv44+Qs7q+saW7t8W1l5ZX98+tCxvar+5ofW/m5ndS/PU1HT8Nmfv/6+b73w\nyLr9sfUc60RT669eO/D+f3nh84++vuvoqV5eA8CQIqBhoNp2+OS9D7/W2t7R52de2b1H6MrK\nDyUSff7hQ0tzW8d9P39t86ETPT7Dmq1Hbn5o7ao3D/fse5FIRE9sOrziuy89+up+302AbjLC\nAf1rRG7mDReP6/772zoSJ5ta65pa91Sf6vIG8/q9x7+8css3Pjyvd9f4Rxqa27q5lsjB2qY3\nDtReOmlUH376u1ZBdsbyed2dOz/7TTx8oulgbWfPYkZR1NjS/smfvPrb+68pys8KuqREInrw\n2Z3ffnZX0FfFam7r+OJvNr+y59gDt8/P7Z+5IIDBREBD/xo/MveB2+f34AsTieitmlPPbj/6\n05f3Hkq9IMajr+6/deH4xVNH9+Ia/8jqLVXNbd29q11WXjlEAnpMYXbPvo+1jS3lB06UlR9a\n9WbV6db22PfUnGr+l2d2fvWWuUFnfmT9vj6p57PKyiub2zr+7c5LPVwI0DkBDe9SaWnR9JKC\n6SUFn7py6sN/2Pf1p7a1pOjaB5/Z9eg9fRbQj22MmWweMzx7RG5m8qTs45sq/8fyORl6K7VR\neVnLZpUsm1XypRUt//jktl+/fjD2bY+s33/30ikzxhR087TlB+q+8vjWTt4wY0zBVTOK544f\nUZSfNSI38+Tp1ppTzZsPnfjdzuozjxLGenpL1ddWbfvyijndvAyAoUlAw7tdxrC0P7tiyuKp\nRXf+cF1tY8xQxytvHVtfcXzR1KLef1bNqebf765JPn7rJRcU5GR8a83Oc44fO9Xyyp5jV19Y\n3PuPHvRG5WV98yMLFk8t+ptfb0p+tb0j8fC6fV+5+eLunOp0a/t9j7yeavx9ybTRX/jg7IWT\nRia/9JHLJkZRtGFf7YPP7Fwb942OoujHv69YOHHkzQvGd+dKAIYmDxHCwHBRaeEP7rosMz3+\n/7N9snNeFEVl5ZUdcY+S3bbwglQTwCvL++ajh4iPXj7x/vdfGPvSmq1HuvkY369eO5hqm5sv\nfHD2o59eElvPZ10+edTDf774mx9ZkGoZxG+s3p5q2gSASEDDAPKeKUWfvGJK7Esv7znWJx+x\nMm5+Y+bY4bPHFU4vKYhd9Xn1mwEz00RRdO+100tH5CQfP1TXtONIfZdf3taR+MGLb8W+9A8f\nmnvfsund3Kfy9ksn/OozS/OzYv4OWVnXlLynOgBnCWgYSD67bEZeVsxdw73HGjrfebs7Kmoa\nyg/WJR+/beEFZ5ps+byYP+ufam47s9k43ZSTmX7HokmxL+3qRkA/tfnwgdqYIebr54y9a8nk\noCuZd8GIf73z0tjg/uHaivYOy9oBxBPQMJCMzMu8akZJ7Euv7avt5clXptgY5Zb/Pw67Yn78\nFEdZ3H1rOnF1im9i9anmLr/2ybhNIjPTh33t1nndvPf8dstmlZwZjD7H8YaW1/f39n9RAIOV\ngIYBJtUTe91pr04kEvH7pyyZNnr8yNwz/3lqcf5FpYXJ73l2+5FT3d4AjyiKZo6NX23j6Mku\nvokdicS6iuPJxz+0YPyY4dk9u5i/un5mVkbMvwt+273lwAGGIAENA8zkorzY47ELdHTfpkN1\nFTUNycdvW3jB2//rirhHCZvbOp7eUtWbTx9q8rIyYps19gnOt9t55FTsN/rji+NnQrpjbGHO\nVTNifit7pY8G6wEGHwENA0xxihuNxxtae3Pa2McHszKG3Tj3j7ZRvCnFWhxlKcY/SCV22qIw\nN7Pzr/rDWzFROzwnY8GEzpbd6NJ1s8cmHzxSf7o35wQYxAQ0DBJNLT0fomjrSMQW8HWzxxbm\n/FHSTS3OnxM3xbF2d02XG49zVlNre+zSJbGrc7zdzqMxTxlePrmol3sHTivJTz5Yc6q5zXOE\nAHEENAwwx1LMOhfl93AENoqiV/bU1MSd9pz5jTNiHyVs70jEPtxGrLdqzt3T8YyLx4/o/Aur\n62O+TZ2v+twdY4bHhHsiEfmlCCCWgIYBZv/x+H2YRxdk9ficsdt3j8jNXDYrZrGIVFMcsc8g\nEit2F8Ci/Kwut/KO/T1n4qj4sfjuq2+On//JjhvUBsAPRxhgXtoVvwNzSUEP70Cfbm1fHfcI\n4Ir542M3PpwyOn9u3I3SDftqD/V6LeqhoK0j8csNB5KPr5hfmtHVJEbsHeguBz+69FZ1zPOj\n6cPShufEbLMCgB+OMJCcPN36UtzNyyiK5k/o4q//qTyz7WhD3CJ0sfMbZyyfX/pm5Ynk449v\nqrz3muk9u4yh4+E/7EsO1mFpaXcvndLl104clVec9JvSxBQLs3Rf7EjJ2MKcYT1YWRpgCBDQ\nMJB873d7YmN3XGHOhWNi9tnujtjRi4mj8i6bNCrVlyyfV/rA6u1xpxLQXXhu+9GvPrk1+fjH\nLp84vaSL+Y0oih69Z0mfX1IiEb0Y92eNyyan/B8AwBBnhAMGjI0H6n68tiL2pWtnlvTsXmFd\nY+vzO2M24j67fXesSUV58y6IueG97fDJXUfjH4+jpa3jf/52xz3/a0PyFtlTRuf//fKL3pGr\niqLopV3V5QditnB/z+Si838xAAOCO9AwMOw8Uv+pn74au/ZZFEW3XzahZ6ddtflwW3vMUmW3\nXpJyfuOM5fNLNx+KmeIoKz/0V9fP6tnFDEqt7R3bq+qf3HT4128cjJ1gLsrP+sFdlxVkvzM/\njROJ6NvP7Uo+npGedtO8ccnHAYgENLz7dSQS//vVA//wxNbTre2xb7j6wuJFU3p4s/CxuPmN\nBRNGxi4M/HbL55X+01MxUxxl5ZV/ed2swTc6W13f/HePbe7mm9vbEw0t7aeaW4+ebN55tD72\nV5Qzxo/M/eknF13Y1eIb/ednf9j72r7a5OM3XFyaPGwNwBkCGt6lEonoYF3js9uO/uTlvXuP\nxSyScNZfXDezZx9RWde0fu/x5OO3pn588KyJo/IWTBhZfvDcP/3vO9a46WDdgom9XZn43ab+\ndNsj6/b37TlvnDvunz48f0RXuw/2nyc3H/7K41uSj6elRfdeO+38Xw/AQCGgoX8dPtHU/TuX\nURR1dCRONrXVNbXsqW44crLrvZTvuXrapamf9uvcyrjdB9OHpd0ct1VKsuXzS5MD+sxpB19A\n960po/O/dtvcK6cXv4PXsHZ3zf2/2JiIuzn+8cWTY1cqBOAMAQ39q66xtc/vXJ51xfTRX7hh\ndo+/PHb9jasvLO7m3+5vmlv69VXbko8/sany72+6qJebSw9u+443/OyVfdkZ6ZdNGnX+x13a\nOhIPPb/7u8/tSn6cMYqi8SNz/9oUO0CnrMIBA9WCCSMfuuPSLrfeSGV7Vf32qvrk47dd0t3n\nESeMyo2903y0vnldRcxkCGclEtHTW6r+y7+/fPdP1u+pPq/rllTUNNz+7y8/+MzO2HrOyUz/\nwV2Xjcx7x6ZKAAYEAQ0D0i2XjP/Ff11SlN/z7btjbz/nZaVfP2ds90+yIsW23mXltvXulhd3\nVn/wwRe/+9zujthZij7V1Nr+ned23fSdl2IXrYuiKH1Y2rc+usDwBkCXBDQMMGOGZ3/9tnkP\nfnRhTmZ6j0/SkUjEDkDfcPG4vKyA0y5PEdCrNle1pFhxj3O0dSS+uWbHZx5+raElZoucPtGR\nSDy28dD7vvnCt9bsbEqxlktm+rDvffzSm+Z2a/wdYIgzAw0DRumInLuvmHL30im5vUjnMzbs\nq62sa0o+3sn23bHGj8xdOGnkG/vPvaN58nTr73ZVX39RwM3sd7lpJfnP/eWyHnxhQ0tb1YnT\nB2ubfr+75tntR1MNbKzZeuTeh1//8d2XZ6b38X2NDftqv/rE1tjHPc8alZf10J0L39mHGgEG\nEAEN71IZ6WnF+dnFBdklw7MXTSl67+ySWWML++qBs9jln4sLspeGJ9SKeeOTAzqKorLyysEU\n0D2Wn5UxvaRgeknBtTNLvnjjRc9sP/LPT+/YeSRm+vylXdVfXrnlGx+e11cffaC28YHV25/Y\ndLjzt10+edR371hYOiK3rz4XYNAT0NC/LiotfOq/Xf1OX8UfaW3vWLU5JqpuuWR8Dx5JvGne\nuK8+uTX5+JqtRxpa2vKz/JD5T2lp0fUXjb16RvHf/HpTWdwIzaOv7r9t4QWLpvZ2D+2m1vaH\nnt/9gxffam3vbJAmNzP98++d8ZlrpmekWzIFIIB/t8GQ88LO6rrG1uTjP1pb8aO1FX31Kadb\n25/ZevSWS8b31QkHjZzM9G9/bGF7R+LJuF9j/nHV1rLPXdWb86/ZduQrZVsOxY3ovN2Nc8d9\nafmc8SPdeAYIJqBhyIldf6NfPqj8kICOlZYWPXD7/I0H6pIzd9PBE1sPn5xTWtiD0x6qa/pK\n2ZY12450/rbLJo/62xtmv6en278DYBUOGFoamtvWbO0isPrKizuraxtbzs9nDTgF2RmfWzYj\n9qWe/YbzxKbKDz74Yuf1PGNMwX/cdfn/+cwV6hmgNwQ0DC2rt1Q1n68F5to6Ek9trjo/nzUQ\n3X7ZhPzsmD8DbjxwIug8jS3tf/ubTZ9/9I1TzSkXwhtXmPPA7fNX//drrp8z9vzvfQgwyBjh\ngKHlsY0xz671n5XllXcunnQ+P3EAyc4YtmhK0fM7jp5z/M3KE4lE1M3MrTp5+hM/Xh+7rMcZ\nhTmZn33v9D9bOqU3C4cD8HYCGoaQ6vrm3++uOZ+fuH7vscMnTpeOyDmfHzqALJgwIjmgG5rb\nmtvau9O7e481/OmP1h2sTfm84J2LJn3hhtkjcm3NDdCXBDQMIY9vqozdMvrupVPeN3tML0/+\nz0/veLPy3NmDRCJ6YlPlPVdP6+XJB6vRBdmxx0+ebusyoHceqf/4j9ZV1zfHvjqpKO+B2+cv\nnTa6t5cIQBIBDUPIyhTzG/deO633+2jsqT6VHNBRFJWVC+iUUm0qmYj7Peft6hpbP/XTV2Pr\nOS0t+tSVU//6A7N6v2MlALE8RAhDRUVNQ+x+zoumFvXJLnTL55XGju1uPnSioqah9+cflOqa\n4lcpGZ0ff2f6jI5E4v5fvhE7uTE8J+ORTy/50vI56hmg/whoGCpSLY5264IL+uT8YwtzFk2J\nHxhYGbfrHlEUHYqL4JF5mZ1vDfj9F996YUd18vExw7N/9ZmlxjYA+puAhiEhkYiv2IxhaTfM\nHddXn3Lz/NLY4ys3HupqJGGI+kPF8eSD4wo7+4PA8YaWh57bnXx8anH+b+67cva4nuzAAkAQ\nAQ1DwqZDdbFzFNfMLCnKz+qrT7lxbumwuDGOipqGLYfD1jYeCvYda9xedTL5+BXTO7uF/K8v\n7G5oOXe956yMYT/8xOUTRtmXG+B8ENAwJKR6fPCWPprfOGN0QVaq+Cs7v+tPDwjff3FP7I35\na2eWpPqSmlPNP3tlX/Lxv7hu5vSSgj68NgA6IaBh8GvrSJTFzW/kZKZfN6e3q9edY8X88bHH\ny8rjV9AbstZXHP/FhgPJx3Mz0xdPTbnP9lNvVrW2n7uR5JzSQuucAJxPAhoGv1f21NScilnv\n7Po5Y/Oz+ngtyxsuHpcxLGaKo+rk6Q17a/v2swaut6obPvfo6+0dMb9R3LVkcicrQK/afDj5\n4MfeMzH2nzkA/URAw+CXavvuWxbE3y3ujZF5mVddWBx/GeXxy4AMKYlEtGbbkVv+bW3sEs75\nWRn3Xjs91dceb2hZF/fQ4bKZffxnBAA6J6BhkDvd2r56S1Xy8cKczE5mbXvj5hRTHE9trmpr\nH7pTHPWn21ZvqfrI91++52cb6k+f+xTgGfdcM62TZzpf21+bPAYztTh/8ui8vrxQALpiJ0IY\n5J7ZdrShOSbXls8rzUzvl1+hPzBnXGb65uRR3drGlhd3VYfuGd7Q0vZ3j23uu6v7f4ZnZ3zx\nxou6//7q+uaeXUZza0dtY8vRk83bqk7GzmycddWM4s+/d0Ynb9haGbNkR0VNw5QvPtmDC+vS\nldOLf/7pxf1xZoCBTkDDIJdq/5QPXdL38xtnDM/JWDarZM3WI8kvlZVXhgZ0S1vHI+v299Gl\n/afiguyggK4/3dYfl3HW5NF5D91xaeejzFsPxwQ0AOefEQ4YzOoaW5/feTT5+NjCnEVTUi71\n0Huppjh+u7WqqbW9/z53gJo9rvDnf75kZF5m52+LXTQagPNPQMNgtmrz4dix45vnj0/vz3Ub\n3n/RmNilJBpb2p/dFnNneihbPq/0N/dd0eUeKIlEVHXi9Pm5JAA6J6BhMHss1fxGP6y/8Xb5\nWRnvmxU/qhG7o/jQNLU4/7t/svChOy7Ny0q5bt1ZjS1tzW3njpUD8I4wAw2DVmVd0/q9Maue\nTS3On3fBiP7+9BULSle9GbNo8fM7jp5oah2R28W4wiCWlhYtmDDyTxdPvnXhBd1fv7nmVEu/\nXhUA3SegYdBKda/3QwvGp/X/thvvmzUmPyujoeXcBUDa2hOrt1R97PKJ/X4F7xqFOZljC7PH\nFuaUjshdOn30spklnaxVl8qxhph1owF4R6QlbK4LAADdZgYaAAACCGgAAAggoAEAIICABgCA\nAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAgg\noAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIa\nAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEA\nIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAAC\nCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICA\nBgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgA\nAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCA\nAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAgg\noAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIa\nAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEA\nIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAAC\nCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICA\nBgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgA\nAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCA\nAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAgg\noAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIa\nAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEA\nIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAAC\nCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICA\nBgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgA\nAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCA\nAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAgg\noAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIa\nAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEA\nIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAAC\nCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICA\nBgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgA\nAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCA\nAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAggoAEAIICABgCAAAIaAAACCGgAAAgg\noAEAIICABgCAAP8Xyee02vNCMRIAAAAASUVORK5CYII=",
      "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-Germ_Cell_Tumor_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_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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sZVNMAySxACnCAIAYBUGjRoUKOVI0eOxOPxjAwTlUgkysvLGy0OHDgwI8MAZJwg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\nBEoQAgAABEoQAgAABEoQAgAABOr/AY09So5nU1lNAAAAAElFTkSuQmCC",
      "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-Germ_Cell_Tumor_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_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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/HES11aWlrjxg1POumgEML06YsrvDpgQNcQwuTJC9577/Ps7MzKNxnFffvbByei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MmDBBP1JXV5eQkJCWlnbgwAGDwdC55QEA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\ngg==",
      "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-Germ_Cell_Tumor_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_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:       TP53  6                1         0.1666667\n",
      " 2: Cell_Cycle 15                0         0.0000000\n",
      " 3:      Hippo 38                0         0.0000000\n",
      " 4:        MYC 13                0         0.0000000\n",
      " 5:      NOTCH 71                0         0.0000000\n",
      " 6:       NRF2  3                0         0.0000000\n",
      " 7:       PI3K 29                0         0.0000000\n",
      " 8:    RTK-RAS 85                0         0.0000000\n",
      " 9:   TGF-Beta  7                0         0.0000000\n",
      "10:        WNT 68                0         0.0000000\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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gUR6erqhoWFsfLGxkapVJqUlBQREWFra4tbXgF6BR4Ch/qpy/379+/fv7+Nyo6O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\nBy9czygosVeIAAAAAElFTkSuQmCC",
      "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-Germ_Cell_Tumor_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Germ_Cell_Tumor_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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CAEAACIVP26XsDpofypp/Zs2FDXqwAA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\niJQgBAAAiJQgBAAAiNR/AeVUXYTxaqGhAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “TP53 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-Germ_Cell_Tumor'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Germ_Cell_Tumor'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Germ_Cell_Tumor\""
      ]
     },
     "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-Germ_Cell_Tumor_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Germ_Cell_Tumor_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Germ_Cell_Tumor_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-Germ_Cell_Tumor_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Germ_Cell_Tumor_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Germ_Cell_Tumor_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-Germ_Cell_Tumor_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Germ\\_Cell\\_Tumor\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Germ_Cell_Tumor_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Germ_Cell_Tumor_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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.0 (2018-04-23)\n",
       "Platform: x86_64-redhat-linux-gnu (64-bit)\n",
       "Running under: Fedora 24 (Workstation Edition)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  grid      stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] wordcloud_2.6       RColorBrewer_1.1-2  knitr_1.22         \n",
       " [4] corrplot_0.84       maftools_1.8.0      Biobase_2.42.0     \n",
       " [7] BiocGenerics_0.28.0 vcd_1.4-4           ggpubr_0.2         \n",
       "[10] magrittr_1.5        xtable_1.8-4        mixOmics_6.6.2     \n",
       "[13] MASS_7.3-51.1       factoextra_1.0.5    FactoMineR_1.41    \n",
       "[16] DescTools_0.99.27   gtools_3.8.1        broom_0.5.1        \n",
       "[19] d3heatmap_0.6.1.2   pheatmap_1.0.12     reshape2_1.4.3     \n",
       "[22] mosaic_1.4.0        Matrix_1.2-15       mosaicData_0.17.0  \n",
       "[25] ggformula_0.9.0     ggstance_0.3.1      lattice_0.20-38    \n",
       "[28] xda_0.2             forcats_0.3.0       stringr_1.4.0      \n",
       "[31] dplyr_0.8.1         purrr_0.3.2         readr_1.3.1        \n",
       "[34] tidyr_0.8.3         tibble_2.1.1        ggplot2_3.1.1.9000 \n",
       "[37] tidyverse_1.2.1    \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] readxl_1.2.0                uuid_0.1-2                 \n",
       "  [3] backports_1.1.4             circlize_0.4.5             \n",
       "  [5] NMF_0.21.0                  plyr_1.8.4                 \n",
       "  [7] igraph_1.2.4.1              repr_0.19.2                \n",
       "  [9] lazyeval_0.2.2              splines_3.5.0              \n",
       " [11] BiocParallel_1.16.6         GenomeInfoDb_1.18.2        \n",
       " [13] gridBase_0.4-7              digest_0.6.18              \n",
       " [15] foreach_1.4.4               htmltools_0.3.6            \n",
       " [17] BSgenome_1.50.0             cluster_2.0.7-1            \n",
       " [19] doParallel_1.0.14           mosaicCore_0.6.0           \n",
       " [21] ComplexHeatmap_1.20.0       Biostrings_2.50.2          \n",
       " [23] modelr_0.1.2                matrixStats_0.54.0         \n",
       " [25] rARPACK_0.11-0              colorspace_1.4-1           \n",
       " [27] rvest_0.3.2                 ggrepel_0.8.1              \n",
       " [29] xfun_0.5                    haven_2.1.0                \n",
       " [31] crayon_1.3.4                RCurl_1.95-4.11            \n",
       " [33] jsonlite_1.6                survival_2.43-3            \n",
       " [35] zoo_1.8-4                   iterators_1.0.10           \n",
       " [37] glue_1.3.1                  registry_0.5               \n",
       " [39] gtable_0.3.0                zlibbioc_1.28.0            \n",
       " [41] XVector_0.22.0              DelayedArray_0.8.0         \n",
       " [43] GetoptLong_0.1.7            shape_1.4.4                \n",
       " [45] scales_1.0.0                mvtnorm_1.0-8              \n",
       " [47] rngtools_1.3.1              bibtex_0.4.2               \n",
       " [49] Rcpp_1.0.1                  mclust_5.4.2               \n",
       " [51] flashClust_1.01-2           foreign_0.8-71             \n",
       " [53] stats4_3.5.0                htmlwidgets_1.3            \n",
       " [55] httr_1.4.0                  XML_3.98-1.16              \n",
       " [57] pkgconfig_2.0.2             manipulate_1.0.1           \n",
       " [59] labeling_0.3                tidyselect_0.2.5           \n",
       " [61] rlang_0.3.4                 munsell_0.5.0              \n",
       " [63] cellranger_1.1.0            tools_3.5.0                \n",
       " [65] cli_1.1.0                   generics_0.0.2             \n",
       " [67] evaluate_0.13               ggdendro_0.1-20            \n",
       " [69] nlme_3.1-137                leaps_3.0                  \n",
       " [71] xml2_1.2.0                  compiler_3.5.0             \n",
       " [73] rstudioapi_0.10             png_0.1-7                  \n",
       " [75] stringi_1.4.3               highr_0.8                  \n",
       " [77] RSpectra_0.13-1             IRdisplay_0.7.0            \n",
       " [79] pillar_1.4.0                lmtest_0.9-36              \n",
       " [81] GlobalOptions_0.1.0         cowplot_0.9.4              \n",
       " [83] data.table_1.12.2           bitops_1.0-6               \n",
       " [85] corpcor_1.6.9               rtracklayer_1.42.1         \n",
       " [87] GenomicRanges_1.34.0        R6_2.4.0                   \n",
       " [89] gridExtra_2.3               IRanges_2.16.0             \n",
       " [91] codetools_0.2-16            boot_1.3-20                \n",
       " [93] assertthat_0.2.1            SummarizedExperiment_1.12.0\n",
       " [95] pkgmaker_0.27               rjson_0.2.20               \n",
       " [97] withr_2.1.2                 GenomicAlignments_1.18.1   \n",
       " [99] Rsamtools_1.34.1            S4Vectors_0.20.1           \n",
       "[101] GenomeInfoDbData_1.2.0      expm_0.999-3               \n",
       "[103] hms_0.4.2                   IRkernel_0.8.15            \n",
       "[105] Cairo_1.5-9                 pbdZMQ_0.3-3               \n",
       "[107] scatterplot3d_0.3-41        lubridate_1.7.4            \n",
       "[109] base64enc_0.1-3             ellipse_0.4.1              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
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