analysis/KCPG/KCPG_woAR_TT-EwingsSarcoma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Ewing's Sarcoma\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": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'known',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>290</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 290\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 290\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 290  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD    AR AR/AD   XLD \n",
       "  153    92    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Mode_of_Inheritance_abb != 'AR',]\n",
    "df = droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD AR/AD   XLD \n",
       "  153    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "54"
      ],
      "text/latex": [
       "54"
      ],
      "text/markdown": [
       "54"
      ],
      "text/plain": [
       "[1] 54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "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": 26,
   "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": 27,
   "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$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Ewing's Sarcoma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$Tumor_type == ttype,]\n",
    "ss =df$Tumor_Sample_Barcode %in% intersect( df$Tumor_Sample_Barcode, unique( df2_ss$Tumor_Sample_Barcode ))\n",
    "df_ss = df[ ss, ]\n",
    "df_ss = droplevels(df_ss)\n",
    "df2_ss = droplevels(df2_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ss$Hugo_Symbol=as.character(df_ss$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 \n",
       "                  EWS\n",
       "  Ewing's Sarcoma  95"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$Tumor_type , df2_ss$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setdiff( df_ss$Tumor_Sample_Barcode, df2_ss$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td> 8</td><td>95</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t  8 & 95\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "|  8 | 95 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,]  8   95  \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": 35,
   "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       8    NA     NA\n",
      "4:            nGenes       6    NA     NA\n",
      "5:   Frame_Shift_Del       3 0.375    0.0\n",
      "6: Missense_Mutation       4 0.500    0.5\n",
      "7: Nonsense_Mutation       1 0.125    0.0\n",
      "8:             total       8 1.000    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 Nonsense_Mutation total\n",
      "1:       CHEK2               2                 1                 0     3\n",
      "2:         ATM               0                 1                 0     1\n",
      "3:       BRCA1               1                 0                 0     1\n",
      "4:        GJB2               0                 1                 0     1\n",
      "5:       LZTR1               0                 0                 1     1\n",
      "6:        POLE               0                 1                 0     1\n",
      "   MutatedSamples AlteredSamples\n",
      "1:              3              3\n",
      "2:              1              1\n",
      "3:              1              1\n",
      "4:              1              1\n",
      "5:              1              1\n",
      "6:              1              1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Checking clinical data..\n",
      "Done !\n"
     ]
    }
   ],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss, isTCGA = FALSE, verbose = T, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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> 8               </td><td>   NA            </td><td> NA              </td></tr>\n",
       "\t<tr><td>nGenes           </td><td> 6               </td><td>   NA            </td><td> NA              </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 3               </td><td>0.375            </td><td>0.0              </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 4               </td><td>0.500            </td><td>0.5              </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 1               </td><td>0.125            </td><td>0.0              </td></tr>\n",
       "\t<tr><td>total            </td><td> 8               </td><td>1.000            </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           &  8                &    NA             &  NA              \\\\\n",
       "\t nGenes            &  6                &    NA             &  NA              \\\\\n",
       "\t Frame\\_Shift\\_Del   &  3                    & 0.375                 & 0.0                  \\\\\n",
       "\t Missense\\_Mutation &  4                  & 0.500               & 0.5                \\\\\n",
       "\t Nonsense\\_Mutation &  1                  & 0.125               & 0.0                \\\\\n",
       "\t total             &  8                & 1.000             & 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           |  8                |    NA             |  NA               |\n",
       "| nGenes            |  6                |    NA             |  NA               |\n",
       "| Frame_Shift_Del   |  3                | 0.375             | 0.0               |\n",
       "| Missense_Mutation |  4                | 0.500             | 0.5               |\n",
       "| Nonsense_Mutation |  1                | 0.125             | 0.0               |\n",
       "| total             |  8                | 1.000             | 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            8         NA  NA   \n",
       "4 nGenes             6         NA  NA   \n",
       "5 Frame_Shift_Del    3      0.375 0.0   \n",
       "6 Missense_Mutation  4      0.500 0.5   \n",
       "7 Nonsense_Mutation  1      0.125 0.0   \n",
       "8 total              8      1.000 1.0   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Nonsense_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>CHEK2</td><td>2    </td><td>1    </td><td>0    </td><td>3    </td><td>3    </td><td>3    </td></tr>\n",
       "\t<tr><td>ATM  </td><td>0    </td><td>1    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>BRCA1</td><td>1    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>GJB2 </td><td>0    </td><td>1    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>LZTR1</td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>POLE </td><td>0    </td><td>1    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Missense\\_Mutation & Nonsense\\_Mutation & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t CHEK2 & 2     & 1     & 0     & 3     & 3     & 3    \\\\\n",
       "\t ATM   & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\t BRCA1 & 1     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t GJB2  & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\t LZTR1 & 0     & 0     & 1     & 1     & 1     & 1    \\\\\n",
       "\t POLE  & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Missense_Mutation | Nonsense_Mutation | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|\n",
       "| CHEK2 | 2     | 1     | 0     | 3     | 3     | 3     |\n",
       "| ATM   | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "| BRCA1 | 1     | 0     | 0     | 1     | 1     | 1     |\n",
       "| GJB2  | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "| LZTR1 | 0     | 0     | 1     | 1     | 1     | 1     |\n",
       "| POLE  | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Missense_Mutation Nonsense_Mutation total\n",
       "1 CHEK2       2               1                 0                 3    \n",
       "2 ATM         0               1                 0                 1    \n",
       "3 BRCA1       1               0                 0                 1    \n",
       "4 GJB2        0               1                 0                 1    \n",
       "5 LZTR1       0               0                 1                 1    \n",
       "6 POLE        0               1                 0                 1    \n",
       "  MutatedSamples AlteredSamples\n",
       "1 3              3             \n",
       "2 1              1             \n",
       "3 1              1             \n",
       "4 1              1             \n",
       "5 1              1             \n",
       "6 1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>6</li>\n",
       "\t<li>7</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 6\n",
       "\\item 7\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 6\n",
       "2. 7\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 6 7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples with top variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   SNP   total\n",
       "---------------------  ----  ----  ------\n",
       "SJEWS018608               0     1       1\n",
       "SJEWS018625               1     0       1\n",
       "SJEWS019196               0     1       1\n",
       "SJEWS019198               0     1       1\n",
       "SJEWS019205               1     0       1\n",
       "SJEWS041505               1     0       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJEWS018608                    1\n",
       "SJEWS018625                    1\n",
       "SJEWS019196                    1\n",
       "SJEWS019198                    1\n",
       "SJEWS019205                    1\n",
       "SJEWS041505                    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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1"
      ],
      "text/latex": [
       "1"
      ],
      "text/markdown": [
       "1"
      ],
      "text/plain": [
       "[1] 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = as.data.frame( dfx@gene.summary )  \n",
    "gf_sx = nrow( gs[ gs$MutatedSamples >= 2,] )\n",
    "gf_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_genes=length(gs$Hugo_Symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6"
      ],
      "text/latex": [
       "6"
      ],
      "text/markdown": [
       "6"
      ],
      "text/plain": [
       "[1] 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Nonsense_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>CHEK2</td><td>2    </td><td>1    </td><td>0    </td><td>3    </td><td>3    </td><td>3    </td></tr>\n",
       "\t<tr><td>ATM  </td><td>0    </td><td>1    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>BRCA1</td><td>1    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>GJB2 </td><td>0    </td><td>1    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>LZTR1</td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>POLE </td><td>0    </td><td>1    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Missense\\_Mutation & Nonsense\\_Mutation & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t CHEK2 & 2     & 1     & 0     & 3     & 3     & 3    \\\\\n",
       "\t ATM   & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\t BRCA1 & 1     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t GJB2  & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\t LZTR1 & 0     & 0     & 1     & 1     & 1     & 1    \\\\\n",
       "\t POLE  & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Missense_Mutation | Nonsense_Mutation | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|\n",
       "| CHEK2 | 2     | 1     | 0     | 3     | 3     | 3     |\n",
       "| ATM   | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "| BRCA1 | 1     | 0     | 0     | 1     | 1     | 1     |\n",
       "| GJB2  | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "| LZTR1 | 0     | 0     | 1     | 1     | 1     | 1     |\n",
       "| POLE  | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Missense_Mutation Nonsense_Mutation total\n",
       "1 CHEK2       2               1                 0                 3    \n",
       "2 ATM         0               1                 0                 1    \n",
       "3 BRCA1       1               0                 0                 1    \n",
       "4 GJB2        0               1                 0                 1    \n",
       "5 LZTR1       0               0                 1                 1    \n",
       "6 POLE        0               1                 0                 1    \n",
       "  MutatedSamples AlteredSamples\n",
       "1 3              3             \n",
       "2 1              1             \n",
       "3 1              1             \n",
       "4 1              1             \n",
       "5 1              1             \n",
       "6 1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cIx8ZjUwgEAaG1tXbFiRU5Ojo2NjZub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\nBoUDACA2KBwAALFB4QAAiO3/AaJ6CBFDfUrCAAAAAElFTkSuQmCC",
      "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=3)\n",
    "ggballoonplot(gs_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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90UJIQIB5oK1dTUnDp1ihDSv39/YePbt28NDAya2AwAAKijoKCg+SlICOHz+bm5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\nQjrtfzFYgFiFcipEAAAAAElFTkSuQmCC",
      "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L/I5/O1trbGxcUFZLYAup6D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\nAAAAAABI7/8BWb48YRGYX4kAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Top 6\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QAAIDpCBQAAiI5QAQAAoiNUAACA6AgV\nAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAA\niI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIj\nVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUA\nACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACI\njlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNU\nAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAA\nIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiO\nUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QA\nAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAg\nOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5Q\nAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAA\ngOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOvUJlbvvvjs3NzeVSmVkZPTt2/eAzwQA\nABzm6hwqH3300dVXX11aWtqsWbMQwltvvfW5z33uIAwGAAAcvuocKmPGjAkhNG/efMuWLf/1\nX/8VQnj55ZcP/FwAAMBhLKuuF1i4cGEIoW3btiGEyy677PLLLy8vLy8vL8/MzExvUFpaes89\n96xbt67qpXb7EgAAoAZ1DpXt27eHELp06VJ18aOPPkqvlJaWNm3atKysrIY9lJaW1vVKAQCA\nw0qdX/qVJEkIoWnTplUXK59OmTVrVs2VEkJ4/vnn63qlAADAYSWVDo/a69Kly4cffnjqqae+\n+OKLa9eu7dChQ/hHvYRaPKOSSqW2b9+el5e3P0MDAACfbnV+6dcpp5zyyCOPLF26NITw+9//\nPlR5OiWEkJOTs23btj3fo5KWkZHxve99T6UAAAA1q/MzKitXrjzyyCNDCDk5Oel3mwwaNGje\nvHkHZToAAOCwVOf3qHTp0mXq1KnpSkmlUn369JkzZ87BmAwAADhs1fkZFQAAgIOtzs+oAAAA\nHGxCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiO\nUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QA\nAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAg\nOkIFAACIjlABAACiI1QAAIDoCBUAACA6Bz1Uxo8fn0qlbrzxxsqV++67L5VKDR06tJZ7KC4u\nTqVSRx999MEY729/+9uwYcO6devWokWLE0888d57762oqAghrFmzJpVK9evX7wBe124HMmfO\nnI4dO2ZlZb388ssH8BgP6s0FAACHxkEPleHDh4cQHn/88cqVOXPmhBAuvfTSWu4hlUq1bt26\nZcuW9RugW7duqVRq69ate37rueee+8xnPjNz5sxt27a1a9fulVdeueqqq8aNG1e/K9qn3Q7k\nZz/72erVq7/2ta916tTpAB7jfu4KAABicNBDpV+/fn369Fm6dOl7770XQigrK5s7d25+fv6Q\nIUNCCDNmzDj22GPz8/Pbt2//1a9+dfPmzSGE5cuXp1KpU0455b777jvyyCNXr169fv36kpKS\n9A73epH333+/W7duQ4cOnThxYpcuXdq1azd+/PgQwsKFC9MXfPbZZ3fs2FF1sLKysuuuu27H\njh1jx45du3bt8uXLX3nllfz8/J/85Cfr1q3b7Sj2eqUhhNmzZ/fv379JkyadO3e+9tprt2zZ\nUsN6SUlJ5YEsXLjwww8/DCF84QtfyM3Nrfqt9OFceOGFbdq0ad++/de//vUNGzbUMMZux7jb\nrtauXXvllVd26tSpZcuWgwYNeuWVV2q4uXaz16NYuHBhKpVK//hCCOPGjUulUv/xH/+xdOnS\nbt26XXbZZaNHj27VqtVxxx334osvjhs3rkOHDh06dJg8eXIIoTbb1PKsOOmkk1Kp1AMPPJC+\nyJlnnplKpRYvXlzb8xIAgMglB9+ECRNCCFOmTEmS5IUXXgghDB8+PEmSpUuXZmZmFhYWXnHF\nFccff3wIYezYsUmSvPPOOyGE9u3bFxQUFBUVLVu2LIRQVFS0z4tkZWW1bNny9NNPT6VSIYT5\n8+ent0lbuXJl1an++te/hhA6dOiwc+fOysWHHnroxz/+8d///vfVq1fv80pXrFiRm5tbUFAw\natSoU089NYQwcuTIGtar7rPqYHPmzKn6rZ07dx511FEhhAsvvPDkk08OIQwbNqyGMXY7xqq7\n2rVr13HHHRdC+PznP3/JJZekUqnc3Nz333+/upur6u1T3VG89NJLIYTzzz8/vdktt9wSQpg6\ndeprr72WnqFbt27pK83IyGjWrNnAgQNTqVQqlXrvvfdqs00tz4o777wzhHDZZZclSbJt27ac\nnJxu3bpVVFQc+NMXAICGcChC5c033wwhnHPOOUmS3HzzzSGEmTNnJknyzDPPjBgx4ve//32S\nJAsWLAghDB48OPnHr6S5ubnLly9P/u/v9zVfJCcnZ926dUmSfOMb3wghTJo0KUmSrl27hhC2\nbNmy21TTp08PIZx11ll7nbk2V5p+PdvAgQPXr1+/ZcuWUaNGTZgwoYb1qvtMkiQdIe+8885u\n35oxY0a6UpIkKS0t7dy5c2Zm5tatW6sbY7djrLqrxx57LD1JeXl5kiS33npr+vf+Gm6uStUd\nRc2h0rJly9LS0u3bt+fn54cQXnjhhSRJBg8eHEKYO3dubbap5VnxySefZGVltWrVqqys7Mkn\nnwwhfO9736vLWQkAQNSy9v2cy37r27dvUVHRs88+u23bttmzZzdv3vycc84JIZx55pmFhYUP\nPvjgr3/961dffTWEkH4je1qfPn169Oix265qvsjxxx/fpk2bEEJRUVEIYbfXeu0mSZIQQmZm\n5j7nr+5KTzjhhKZNm86fP799+/YDBw4cNmzYN7/5zRrWa+ntt98OIZx00kkhhOzs7JUrV9bm\n2Pdq6dKlIYQhQ4ZkZGSk/2X8+PF/+9vf0t+t+eaq31GccMIJ2dnZ2dnZLVq0KCkpOfHEE0MI\n7dq1qzptzdvU8qxo3br1F7/4xblz5y5atOjPf/5zCGHYsGH7HA8AgMbiEH088fDhw3fu3PnA\nAw+8/vrrQ4cOTf8p/ZlnnhkwYMC0adN69uxZ9WPB0nJzc/fcT80XSf86HkJIv5apZr169Qoh\nvPnmm+Xl5ZWL06dPv/3229MvNtvnlXbq1GnZsmWjR48uLCycN2/eNddcc+6559awXkvpeVq0\naFGnY9+rdIxV3hrpfykrK0t/WfPNVfNRpPcc9oilyn3u9cvabFP7syL9OQ1z5sz585//3LFj\nx/QzVAAAfDoculAJIaQ/UGvEiBHpxVmzZlVUVEycOHHatGnpP+3vUz0uklb5i3WloqKi3r17\nr1ix4s4770x/d8WKFaNHj/7BD36Qk5NTmyt94oknpkyZcuWVV65evXrOnDkFBQXz5s3bsGFD\ndeu1HDX9ycLpVz2Vl5dfdNFFp59+eklJyT6Pfc9j7NOnT3rO9LfSr+bq27dvbcao7ijSqZB+\nWiZJkmeffbaWx1VLtf8RDx06NCsr68EHH3z99dcvuuiivUYRAACN1KF46VcIoXfv3scee+wb\nb7zRsmXL9LsRQgitW7cOIfz0pz9dtGjRI488Eqr8sb869bhIXl5eCGHy5Mljxoxp2rRp5Xp2\ndvaUKVPOP//8G2+8cdq0aR06dHj11VdLSkquvvrq7t27r1mzZp9XWlJSMnHixAceeODiiy8u\nLi7etGlThw4dCgsLq1v/+OOPa3NbDR06tHPnztOnT9+1a9cnn3yyYMGCL3/5y/n5+TUce9Vj\nrLqr8847r1+/fs8+++ygQYOOOOKIhx56KC8v7/rrr9+1a9c+x6juKLKzs5s0afLuu+8OGDBg\n27ZtlS8kO1Bq/yNu1arV4MGD05927XVfAACfMofuj9DpJ1Uuuuiiyucrrr/++sGDB69atWrx\n4sUPP/xw165dP/jgg/Xr19ewk3pcZMyYMd27d//hD39YXFy827e+9KUvLVq06IILLigpKfnr\nX//au3fvX/3qV1OmTKnllQ4bNuwXv/hFQUHB3Xff/cc//nHQoEGPP/54RkZGdeu1vKHy8/Pn\nzZt37rnnPvfcc8uWLfvmN795zz331Hzs1R1jVlbW008/ffnlly9btmz27NlnnHHGggUL0u+8\n36fqjqJ58+a/+93vunXr9s477xQVFd10002dOnWqWoD7qU4/4vRJ1bZt29NOO+1ADQAAQAxS\ne75eCBqLF1544Qtf+MLIkSPvvvvuhp4FAIADycv6aazuueeea6+9NoRw5ZVXNvQsAAAcYJ5R\nobE64YQTNmzYcOmll06cOLE2n/MGAEAjIlQAAIDoeOkXAAAQHaECAABER6gAAADRESoAAEB0\nhAoAABAdoQIAAERHqAAAANERKgAAQHSECgAAEB2hAgAAREeoAAAA0REqAABAdIQKAAAQnX2H\nyn333ZdKpZYsWXIIpgEAAAghZFX3jdLS0ieeeOLFF1+cPHnyoRwIAACg2lDZvHnzRRddVJtd\nVFRULFu2bPv27Xuub9q06YwzzsjKqvZaAAAA9lRtQrRp0yZJkhDCGWecMX/+/Oo2q6ioGDRo\nUA0bFBYWrlu37lPTKh9//PH69esbeop6qqioeOedd8rKyhp6kHpKkmTVqlWdOnVKpVINPUt9\nZGVl9ezZMyOjsb4xLDMz86ijjmqk9+UkSd57772dO3c29CD11KjvvI39nhsa/523efPmnTt3\nbugpAOpsf3/n+OCDD2qolBBCcXHx888/P2jQoP28ohiUlZXdPOHmFRs/aOhB6mnb5m0r3l7R\n0FPU367Ssq2btjYraJad0yh/Vw4hHNnryKYtmjb0FPWUn9Vk5KUjhwwZ0tCD1Mfrr79++79P\nLt5e2tCD1NO2LZtX/v1vDT1FPZXtKt26ubhZi8Ks7JyGnqX+uvTo3bR5i4aeop7atcj72U9u\nP+KIIxp6EIC62d9f+Lp27Tpw4MCaW6Vt27b7eS2RKC8v37R909JkSbOj8xt6lnpJQlankJQ3\n9Bj1lRmyMoubZRc21kpJZYb1bdesb5x/U67YmeS8lb9jx46GHqSeSkpKVm7Y/l5ut6xmrRt6\nlnppmuzK6xkqGue9N0lytm0ob9qqvNE+oxIyMj8obB9Co5x/1/qVJ6Q2lJSUNPQgAHW2v7/z\nZWRkPPPMM3u+R+Xdd98dPnx45Tb7eS1RyS7Iatarsf5RvNkxjXVyGlb5tvJdbzX0EPstp03X\n7LZdG3qKesrt0q+hR6BRSsp3hZ0bGnoKgPo4AH+czsjI6NOnz26LeXl5+79nAADg8PSpeq4D\nAAD4dBAqAABAdPb90q/nnnvu4I8BAADwvzyjAgAAREeoAAAA0REqAABAdIQKAAAQHaECAABE\nR6gAAADRESoAAEB0hAoAABAdoQIAAERHqAAAANERKgAAQHSECgAAEB2hAgAAREeoAAAA0REq\nAABAdIQKAAAQHaECAABER6gAAADRESoAAEB0hAoAABAdoQIAAERHqAAAANERKgAAQHSECgAA\nEB2hAgAAREeoAAAA0REqAABAdIQKAAAQHaECAABER6gAAADRESoAAEB0hAoAABAdoQIAAERH\nqAAAANERKgAAQHSECgAAEB2hAgAAREeoAAAA0REqAABAdIQKAAAQHaECAP6CauEAABX8SURB\nVABER6gAAADRESoAAEB0hAoAABAdoQIAAERHqAAAANERKgAAQHSECgAAEB2hAgAAREeoAAAA\n0REqAABAdIQKAAAQHaECAABER6gAAADRESoAAEB0hAoAABAdoQIAAERHqAAAANERKgAAQHSE\nCgAAEB2hAgAAREeoAAAA0REqAABAdLIaeoBGZvWHH63/aOPWd7Y39CBwSCVlSdMdzRp6CgDg\nMOIZFQAAIDpCBQAAiI5QAQAAoiNUAACA6Hgzfd0c0bnjqh4rWn++sKEHgUOqfFv5ricaeggA\n4HDiGRUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAg\nOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5Q\nAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAA\ngOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6\nQgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlAB\nAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA\n6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKJTU6jcfffdubm5qVQq\nIyOjb9++h2wmAADgMFdtqHz00UdXX311aWlps2bNQghvvfXW5z73uUM4GAAAcPjKqu4bY8aM\nCSE0b9588+bNDz744OWXX/7yyy/vdcvS0tJ77rln3bp1VRd3+xKgYRV//FHx+w9m5DVr6EHg\nkCrfsv6Tdk0aeor98thjjz3++OMNPQU0gCFDhlxwwQUNPUVDqjZUFi5cGEJo27ZtCOGyyy67\n/PLLy8vLy8vLMzMzq25WWlratGnTsrKyGq6jtLT0AE3L/klCyYc7kvKkoeeoryTs/GRXbpvs\nkGroSeollZnK75zXSIenoSUVJVtD0kjvvElFaUlGTn5ovGd/KpWR36wRz08Dqaio+OCDD8rL\nyxt6kHqqqKjYsGFDq1atMjIa61uaMzMzu3bt2njnp9pQ2b59ewihS5cuVRc/+uij3VZmzZpV\nc6WEEJ5//vnPfOYz+zFkRFIhVbpi18drNzT0IPWxa2vZlne3NfQU9ZeUVuzavCu7RXYqp7E+\n4jQ/qml2s2rvdFFLkpYZrRt6iP2SkREqNn5UkWqUv2smZbvKthc39BT1VVFRUVaakZUTGvPv\nCllNClNZ2Q09Rb1UVKTad23oIepv7ty5999///Llyxt6kPrYtGnTRx991NBT1F9FRcWuXbuy\ns7Mb9S/6f//73wsKChp6inpq0qSJZ1T2LkmSEELTpk2rLu72dEoI4ctf/nJWVlYNrZJKpa6+\n+ur9GzIWOTk5g08bfMK6Exp6kHpKkmRt0drG+6edJEmKi4sLCwtTjfN3zczMzPbt2zfS4UMI\nWVlZjfdDNY466qhT+x3dIrWzoQepv5KSFknjfEYlSZKdO3emP5qloWepp1QqlZ+f39BT1N9n\n+vXq0KFDQ09RT02bNs3Ly8vKapR/4kk/F1FRUdHQg9RTkiQlJSX5+fmN986bkZHReH9tCCE0\n6keeA6Lae36TJk02bNiwcePGEMLatWvTix07dtxts5ycnG3btu35HpW0jIyM733ve3l5eQdu\n4IaUSqVGjhzZ0FMAddahQ4eLLrooJyenoQeBBjBkyJAmTRrr21ROO+20jRs3eo8Kh6dTTz21\noUdoYKnq/kR3ySWXPPLIIwUFBcXFxZMmTbrhhhsyMzP3+SovAACA/VdtqKxcufLII48MIeTk\n5KTfDT9o0KB58+Yd0ukAAIDDUrXvjurSpcvUqVPTlZJKpfr06TNnzpxDORkAAHDYqvYZFQAA\ngIbSiD9vDgAA+LQSKgAAQHSECgAAEB2hAgAAREeoAAAA0REqAABAdIQKAAAQHaECAABER6gA\nAADRESoAAEB0hAoAABAdoQIAAERHqAAAANERKgAAQHSECgAAEB2hAgAAREeoAAAA0REqAABA\ndIQKAAAQHaECAABER6gAAADRESoAAEB0hAoAABAdoQIAAERHqAAAANERKgAAQHSECgAAEB2h\nAgAAREeoAAAA0REqAABAdIQKAAAQHaECAABER6gAAADRESoAAEB0hAoAABAdoQIAAERHqAAA\nANERKgAAQHSECgAAEB2hAgAAREeoAAAA0REqAABAdIQKAAAQHaECAABER6gAAADRESoAAEB0\nhAoAABAdoQIAAERHqAAAANERKgAAQHSECgAAEB2hAgAAREeoAAAA0REqAABAdIQKAAAQHaEC\nAABER6gAAADRESoAAEB0hAoAABAdoQIAAERHqAAAANERKgAAQHSECgAAEB2hAgAAREeoAAAA\n0REqAABAdIQKAAAQHaECAABER6gAAADRESpwKGRlZbVq1aq0tLTq4rhx41Kp1C9+8YvXX389\nlUq98MILDTVe7dV8IDVcsHfv3o8//njNO6/cphHdIA3ulFNOSf1fbdq0acB5duzYceutt/bq\n1Ss/P79Hjx5jx47dtGlTCGHr1q2pVGrNmjW7bV/1Zz1z5szu3buPHz8+1O6EqXrs7du3v/DC\nC5cvX17zRT755JNUKlVcXFz/Izw85OXlffGLX0ySpHLlgQce6N27dwOOdEB4BIPGJauhB4Ao\nlJeXz549e8eOHfvcMi8v77zzzsvMzKzrVWzduvXpp58+//zz018mSTJjxozs7OwQQqdOnSZN\nmtS9e/e67rNB1HAgB0rjukFqcAjOqxDCd77znTFjxlR+mZHxv3+B2rhxY5IkrVq1qsdu62fs\n2LFPPfXUlClT+vTps3z58u9///vvvvvuzJkzq9u+6s/6zjvvHDt27KhRo2p/dTfccMN1112X\nJMn7778/derU/v37v/rqq0cfffQBOJKI1f68Cvtxaj3zzDP33nvvVVddVfcBo+YRDBqTBEiS\nxx57rPb3mscee6yu+8/MzDzvvPO+8pWvVK789a9/bdu2bf/+/adMmbJly5YQwurVq5Mkufvu\nu4866qi8vLz+/fsvXLgwvfFeF1euXDl06NDCwsIjjjjilltu2bVrV5Ik6V0tXrz4vPPO69ix\n45lnnrlq1aq67qTeB7Jy5coQQuVOvve971199dVJkpx22mmpVKpJkyY/+9nPkiRZsGDBgAED\n8vLy2rRpM2rUqPT2VbepeoO8/fbbgwcPbt68ea9evX7+859XVFTUfJhROdjnVZIkJ5988oQJ\nE3ZbTN8+jz32WG5u7rRp05JqbvOSkpIQwpNPPnnWWWe1a9fuxhtvXLBgwZe+9KU2bdoMHTp0\nx44dSR3PkPLy8hYtWsyaNatyJf2n5c2bN6dH+tOf/vSZz3ymSZMmgwYNSv98K3/WZ599dkZG\nRn5+/oQJE3Y7YWo49ttuu63yy4qKikGDBlWenHudfN26deEf/dZ41em8qt+plZub+8///M8t\nW7Zcs2ZNeuX+++8/5phj0v9e13ulR7BG+ggGDU6oQJIkyfTp00MI7c9pc9S1XWr4p/05bUII\n06dPr+v+MzMzf/e737Vo0aKkpCS9csstt4wePfrkk0+uGir/7//9v1Qq9cADD7z99tuXXXZZ\nUVFRkiR7Xdy1a1e/fv1Gjhz57rvvzp8//+ijj7755puTf/z377LLLtuxY8fWrVv79++f/ntz\nnXZS7wOp7j/zSZIcc8wx6d+WKioqOnbsOGrUqLfffnvu3LlNmzZ98MEHd9um8gbZuXNnt27d\nvvGNbyxfvvzRRx8tLCy8//77azjM2KTPqxYnDW079F9r+KfFSUPrd14lNYbKySef/Oijj27c\nuLG62zwdKtdcc01FRcXLL78cQhg8ePCOHTvWrFmTl5f3hz/8oa5nSFlZWW5u7ve///3y8vL0\nSnl5+cqVK0tLS9Mjffazn126dOnixYt79+49duzYpMrPOkmS/v37z5gxI33BypOh5mOvGipJ\nkjz00EOtW7dOqj+3Px2hkj6vRjZrPrVl65r/Gdmsef1Ordzc3Oeff/6MM8649NJL0yuVoVLX\ne6VHsMb7CAYNzku/4H817ZHf8sSCg7TzXr16HXXUUU8++eTQoUOTJJkxY8avf/3rxYsXV91m\n+fLlzZo1+6d/+qfmzZvfddddr7zySnWLzz333Lp16371q19lZWV17979Jz/5yY9+9KM77rgj\nvZ+RI0fm5ubm5uZecMEFb7zxRv12sj8HUoOdO3d+//vfv+qqqwoKCnr27HnKKad8+OGH1W08\ne/bsbdu2/fKXv8zNze3Ro8d3v/vdqVOnXnnlldUdZpxyO/Zu0vu0g7f/H/3oRz/60Y8qv3z6\n6adPOeWUEMItt9xywQUXhBB27NhRw21+9dVXp1KpAQMGpP88nJub2759+wEDBnz88cd1PUMy\nMzNvuummH/7wh48//vgll1wyePDgk08+uXPnziGEnTt3pkft06dPCGHYsGH7fD9JPfTo0WP9\n+vWbNm1atGhRPc7txuWz2TlD8vMP3v5TqdTdd9993HHHzZ49+7zzzqtcr+u90iNYY38EgwYk\nVODQGTFixMMPPzx06NA33nhj06ZNX/jCF3bbYODAgZ06derevfvw4cOHDRt27rnnVrf49ttv\nf/LJJx06dEhfcNeuXelXFKS/rHyNfm5ubg17rm4nqVRqPw+kBnl5ed/61rcWLFjwyiuvPP/8\n888888w555xT3cbLly8//vjjK4/ixBNP/OUvf1n53T0P8/C023tU2rdvX15eHkLo169feqXm\n2zz9DpZUKpWbm1v5bpb0TVqPM2TChAlDhgyZOXPm3Llzb7/99pYtW44fP/76669Pf7fqSAfo\n6P+PysFqvoNQSz179hw/fvzo0aOXLFlSuVjXe6VHMI9gUG9CBf7Xtr+X7OcGNRs+fPjtt9++\nbdu26dOnX3zxxXu+vbVVq1ZLliyZNWvW9OnTL7zwws9+9rNPPfXUXhebN29+wgknpP+suKc9\n3xhaj53sz4Gk7fXNvhs3bhw4cGAIYdiwYTfffHPN72Hd7XeOzMzMsrKyyi8P7PtfD56dH/1t\nPzeoWWFhYbdu3aqubN26NYSQ/48/t9fpNq+qrmfI22+/PXv27DFjxvTv3/+OO+4oLy9fvnz5\nqaeeetxxxw0YMCActD6p9O6777Zu3bqgoKDe53YjsnhX6QHZpmZjx4596KGHxo0bd+KJJ6ZX\n6nqv9AhW+WUjfQSDBiRUIIR//Eq39slPar9xPRx11FF9+/Z94oknZsyYMW3atD03mD9//qpV\nqy6//PKLL754zZo1Rx555CuvvFJeXr7nYq9evd56661NmzYVFBSEEGbOnLl48eLbb7+9uqve\n657rupNaHsiWLVtatmwZQliyZMmen2f61FNPffzxxytWrMjJyQkhpD+Ltjo9e/b893//99LS\n0vTGixYt6tmz5z7Hi0f6VNn88h9rv/HBUKfbvKq6niG7du264YYbLr744vTLvTIzM3v16tW6\ndev3338/HSoHVZIk06ZNS79Iqd7ndqOQPlWmbd1Sp+3rJzs7+ze/+c3nPve5yk9jq+u98oA8\n+HgEg8OTUIEQQjj33HMfffTRWn6MbPpVB/UzYsSIW2+9dfPmzZ///Of3/O7atWtHjRrVqlWr\nfv36/fnPfw4hHHXUUS+88MKei0cccUT6XaQTJ0589913r7nmmptuuqmG693rnuu6k30eSJs2\nbbKzsydOnDh27Ng//vGPr7/+euV/5tP/D41du3bl5OQUFxcvWbKkTZs2991334IFC4qKinbu\n3Jmbm1u5TeUOzz333Ly8vO985zs33XTT0qVL77zzzrvuuquWE8bgkJ1XNavuNt/nBU866aQ6\nnSF9+vQZNGjQeeedd9ttt/Xp02fz5s3/+Z//WVxcfNZZZ9V15sqToea/Om/atOnDDz9MkmTF\nihVTp079y1/+8uqrr9Zj8sal9udVOBCn1oknnnjdddfdddddvXr1CnW/Vx6QBx+PYHCYaoh3\n8MNhJzMz86WXXkqSZMWKFSGEb3/72+n13T71q7y8/IYbbmjXrl1ubu6xxx47c+bMJEn2upgk\nyerVqy+88MKCgoIjjjjiBz/4Qfpzlqp+hlKSJL/61a9+85vf1HUn9T6QJEnuvffejh07tmrV\n6utf//pvf/vbO+64I73Bbbfd1qxZs0mTJpWVlY0cObJ58+ZdunQZO3bso48+WlRU9PTTT1fd\npupRLFu27Itf/GLz5s179OgxadKkqh/uuedhHoZq+NSvytunuts8/alfK1euTG/WqVOnZ599\nNv3vV1999aJFi5K6nyEbNmwYO3Zsjx49cnNzO3fuPHz48DfffHPPkW677bYRI0Yk1X/qV+XJ\nUPOxV/63rG3bthdccMHbb79d+d29Tv7p+NSvQyA3N3fBggWVX27ZsqVr166VH09cp3ulRzCP\nYFBvqcSbCwEAgMhk7HsTAACAQ8t7VID/4y9/+cuPf/zjPdcLCwvvvffeQz4O0TmUZ4izkbpy\nzsCniZd+AQAA0fHSLwAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QA\nAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAg\nOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5Q\nAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAA\ngOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6\nQgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlAB\nAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA\n6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpC\nBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEA\nAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIFAACIjlABAACiI1QAAIDo\nCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAAoiNUAACA6AgVAAAgOkIF\nAACIjlABAACiI1QAAIDoCBUAACA6QgUAAIiOUAEAAKIjVAAAgOgIFQAAIDpCBQAAiI5QAQAA\noiNUAACA6Px/TaZR6B9V5HcAAAAASUVORK5CYII=",
      "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OWJw8PDbW1to6OjbW1thRA2NjaLFi0KCQmpXbt2QUMyMjIGDBhgbW2dkZFhwEoA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\nAACgdf4fCg8epwpq1igAAAAASUVORK5CYII=",
      "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=6)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_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": 63,
   "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       8    NA     NA\n",
       "4:            nGenes       6    NA     NA\n",
       "5:   Frame_Shift_Del       3 0.375    0.0\n",
       "6: Missense_Mutation       4 0.500    0.5\n",
       "7: Nonsense_Mutation       1 0.125    0.0\n",
       "8:             total       8 1.000    1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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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": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M8z2Zpk8PzTb7gztn768Iwe1wicIQQ0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\nTkSuQmCC",
      "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ccn40tfOXvosL6tvTMrK/36z8/6z3uX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\n5OR06aU5drt37164cGF5eXnyoVgsdvPNN5955pnHfpUDBw4kD7a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2ND326NYSB0yeXDHj+ilLls667fYbFiycPtxh//X3f9za0lHiPGVl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",
      "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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “Druggable categories”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=6, repr.plot.width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_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": 71,
   "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                2        0.33333333\n",
      " 2:        WNT 68                1        0.01470588\n",
      " 3: Cell_Cycle 15                0        0.00000000\n",
      " 4:      Hippo 38                0        0.00000000\n",
      " 5:        MYC 13                0        0.00000000\n",
      " 6:      NOTCH 71                0        0.00000000\n",
      " 7:       NRF2  3                0        0.00000000\n",
      " 8:       PI3K 29                0        0.00000000\n",
      " 9:    RTK-RAS 85                0        0.00000000\n",
      "10:   TGF-Beta  7                0        0.00000000\n"
     ]
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "op = OncogenicPathways(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AQhRPfu3YUQ/v7+ubm5x44d0x8tKiqKiYlxc3NzcXFRrzYAABozJQPH22+/7eTk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\nAABAdggcAAAAILv/AZeUjhY2SWR7AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Ewings_Sarcoma_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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAADwCAIAAACfVArnAAAABmJLR0QA/wD/AP+gvaeTAAAX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\nXwtTbsTmKGHKAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “WNT 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": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 77,
   "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": 78,
   "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": 79,
   "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": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Ewings_Sarcoma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Ewings_Sarcoma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Ewings_Sarcoma\""
      ]
     },
     "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Ewings_Sarcoma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Ewings_Sarcoma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Ewings_Sarcoma_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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Ewings_Sarcoma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Ewings_Sarcoma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Ewings_Sarcoma_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": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Ewings_Sarcoma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Ewings\\_Sarcoma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Ewings_Sarcoma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Ewings_Sarcoma_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": 84,
   "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": 85,
   "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"
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