analysis/KCPG/KCPG_woAR_TT-Wilmstumor_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Wilms' tumor \n",
    "----- \n",
    "- Date : 17th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse) \n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Muataional Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3016</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3016\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3016\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3016   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Var1     Freq\n",
       "------  -----\n",
       "CNS       613\n",
       "WLM       405\n",
       "NBL       343\n",
       "RHB       259\n",
       "OS        241\n",
       "HGG       229\n",
       "RB        221\n",
       "EWS       192\n",
       "STS       177\n",
       "GCT       139\n",
       "ACT        79\n",
       "LGG        52\n",
       "CA         35\n",
       "OST        18\n",
       "RCC         5\n",
       "GICT        2\n",
       "LMPRT       2\n",
       "PGL         2\n",
       "BCC         1\n",
       "NM          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( sort( table(df$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'known',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>290</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 290\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 290\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 290  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD    AR AR/AD   XLD \n",
       "  153    92    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       \n",
       "        Het Hom\n",
       "  AD    153   0\n",
       "  AR     92   0\n",
       "  AR/AD  43   0\n",
       "  XLD     1   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$Mode_of_Inheritance_abb , df$genotype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Mode_of_Inheritance_abb != 'AR',]\n",
    "df = droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD AR/AD   XLD \n",
       "  153    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "182"
      ],
      "text/latex": [
       "182"
      ],
      "text/markdown": [
       "182"
      ],
      "text/plain": [
       "[1] 182"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "54"
      ],
      "text/latex": [
       "54"
      ],
      "text/markdown": [
       "54"
      ],
      "text/plain": [
       "[1] 54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Tumor_Sample_Barcode'</li>\n",
       "\t<li>'Tumor_type'</li>\n",
       "\t<li>'Tumor_type2'</li>\n",
       "\t<li>'Age_of_Onset'</li>\n",
       "\t<li>'Sex'</li>\n",
       "\t<li>'Age_group'</li>\n",
       "\t<li>'sample_type'</li>\n",
       "\t<li>'sequencing_type'</li>\n",
       "\t<li>'Access_unit'</li>\n",
       "\t<li>'Status'</li>\n",
       "\t<li>'Ethnicity'</li>\n",
       "\t<li>'Race'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Tumor\\_Sample\\_Barcode'\n",
       "\\item 'Tumor\\_type'\n",
       "\\item 'Tumor\\_type2'\n",
       "\\item 'Age\\_of\\_Onset'\n",
       "\\item 'Sex'\n",
       "\\item 'Age\\_group'\n",
       "\\item 'sample\\_type'\n",
       "\\item 'sequencing\\_type'\n",
       "\\item 'Access\\_unit'\n",
       "\\item 'Status'\n",
       "\\item 'Ethnicity'\n",
       "\\item 'Race'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Tumor_Sample_Barcode'\n",
       "2. 'Tumor_type'\n",
       "3. 'Tumor_type2'\n",
       "4. 'Age_of_Onset'\n",
       "5. 'Sex'\n",
       "6. 'Age_group'\n",
       "7. 'sample_type'\n",
       "8. 'sequencing_type'\n",
       "9. 'Access_unit'\n",
       "10. 'Status'\n",
       "11. 'Ethnicity'\n",
       "12. 'Race'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Tumor_Sample_Barcode\" \"Tumor_type\"           \"Tumor_type2\"         \n",
       " [4] \"Age_of_Onset\"         \"Sex\"                  \"Age_group\"           \n",
       " [7] \"sample_type\"          \"sequencing_type\"      \"Access_unit\"         \n",
       "[10] \"Status\"               \"Ethnicity\"            \"Race\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       CCF PCGP SJLIFE\n",
       "  Adrenocortical Carcinoma               1   19      2\n",
       "  Basal Cell Carcinoma                   0    0      1\n",
       "  Carcinoma                              0    0     14\n",
       "  Central Nervous System                 1    0    322\n",
       "  Ewing's Sarcoma                        6    0     89\n",
       "  Germ Cell Tumor                        1    0     73\n",
       "  Giant Cell Tumor                       0    0      3\n",
       "  High Grade Glioma                      5   75      0\n",
       "  Low Grade Glioma                       2   15      7\n",
       "  Low Malignant Potential Renal Tumors   2    0      0\n",
       "  Neuroblastoma                          3   48    139\n",
       "  Non-Malignant Tumor                    1    0      3\n",
       "  Osteosarcoma                          14    2    113\n",
       "  Other Solid Tumor                      0    0     10\n",
       "  Paraganglioma                          0    0      1\n",
       "  Renal Cell Carcinoma                   1    0      2\n",
       "  Retinoblastoma                         0    0     98\n",
       "  Rhabdomyosarcoma                       3   29    102\n",
       "  Soft Tissue Sarcoma                    8    0     85\n",
       "  Wilms' tumor                           2    0    205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2$Tumor_type , df2$Access_unit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>f</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Central Nervous System              </td><td>323                                 </td></tr>\n",
       "\t<tr><td>Wilms' tumor                        </td><td>207                                 </td></tr>\n",
       "\t<tr><td>Neuroblastoma                       </td><td>190                                 </td></tr>\n",
       "\t<tr><td>Rhabdomyosarcoma                    </td><td>134                                 </td></tr>\n",
       "\t<tr><td>Osteosarcoma                        </td><td>129                                 </td></tr>\n",
       "\t<tr><td>Retinoblastoma                      </td><td> 98                                 </td></tr>\n",
       "\t<tr><td>Ewing's Sarcoma                     </td><td> 95                                 </td></tr>\n",
       "\t<tr><td>Soft Tissue Sarcoma                 </td><td> 93                                 </td></tr>\n",
       "\t<tr><td>High Grade Glioma                   </td><td> 80                                 </td></tr>\n",
       "\t<tr><td>Germ Cell Tumor                     </td><td> 74                                 </td></tr>\n",
       "\t<tr><td>Low Grade Glioma                    </td><td> 24                                 </td></tr>\n",
       "\t<tr><td>Adrenocortical Carcinoma            </td><td> 22                                 </td></tr>\n",
       "\t<tr><td>Carcinoma                           </td><td> 14                                 </td></tr>\n",
       "\t<tr><td>Other Solid Tumor                   </td><td> 10                                 </td></tr>\n",
       "\t<tr><td>Non-Malignant Tumor                 </td><td>  4                                 </td></tr>\n",
       "\t<tr><td>Giant Cell Tumor                    </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Renal Cell Carcinoma                </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Low Malignant Potential Renal Tumors</td><td>  2                                 </td></tr>\n",
       "\t<tr><td>Basal Cell Carcinoma                </td><td>  1                                 </td></tr>\n",
       "\t<tr><td>Paraganglioma                       </td><td>  1                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " f & n\\\\\n",
       "\\hline\n",
       "\t Central Nervous System               & 323                                 \\\\\n",
       "\t Wilms' tumor                         & 207                                 \\\\\n",
       "\t Neuroblastoma                        & 190                                 \\\\\n",
       "\t Rhabdomyosarcoma                     & 134                                 \\\\\n",
       "\t Osteosarcoma                         & 129                                 \\\\\n",
       "\t Retinoblastoma                       &  98                                 \\\\\n",
       "\t Ewing's Sarcoma                      &  95                                 \\\\\n",
       "\t Soft Tissue Sarcoma                  &  93                                 \\\\\n",
       "\t High Grade Glioma                    &  80                                 \\\\\n",
       "\t Germ Cell Tumor                      &  74                                 \\\\\n",
       "\t Low Grade Glioma                     &  24                                 \\\\\n",
       "\t Adrenocortical Carcinoma             &  22                                 \\\\\n",
       "\t Carcinoma                            &  14                                 \\\\\n",
       "\t Other Solid Tumor                    &  10                                 \\\\\n",
       "\t Non-Malignant Tumor                  &   4                                 \\\\\n",
       "\t Giant Cell Tumor                     &   3                                 \\\\\n",
       "\t Renal Cell Carcinoma                 &   3                                 \\\\\n",
       "\t Low Malignant Potential Renal Tumors &   2                                 \\\\\n",
       "\t Basal Cell Carcinoma                 &   1                                 \\\\\n",
       "\t Paraganglioma                        &   1                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| f | n |\n",
       "|---|---|\n",
       "| Central Nervous System               | 323                                  |\n",
       "| Wilms' tumor                         | 207                                  |\n",
       "| Neuroblastoma                        | 190                                  |\n",
       "| Rhabdomyosarcoma                     | 134                                  |\n",
       "| Osteosarcoma                         | 129                                  |\n",
       "| Retinoblastoma                       |  98                                  |\n",
       "| Ewing's Sarcoma                      |  95                                  |\n",
       "| Soft Tissue Sarcoma                  |  93                                  |\n",
       "| High Grade Glioma                    |  80                                  |\n",
       "| Germ Cell Tumor                      |  74                                  |\n",
       "| Low Grade Glioma                     |  24                                  |\n",
       "| Adrenocortical Carcinoma             |  22                                  |\n",
       "| Carcinoma                            |  14                                  |\n",
       "| Other Solid Tumor                    |  10                                  |\n",
       "| Non-Malignant Tumor                  |   4                                  |\n",
       "| Giant Cell Tumor                     |   3                                  |\n",
       "| Renal Cell Carcinoma                 |   3                                  |\n",
       "| Low Malignant Potential Renal Tumors |   2                                  |\n",
       "| Basal Cell Carcinoma                 |   1                                  |\n",
       "| Paraganglioma                        |   1                                  |\n",
       "\n"
      ],
      "text/plain": [
       "   f                                    n  \n",
       "1  Central Nervous System               323\n",
       "2  Wilms' tumor                         207\n",
       "3  Neuroblastoma                        190\n",
       "4  Rhabdomyosarcoma                     134\n",
       "5  Osteosarcoma                         129\n",
       "6  Retinoblastoma                        98\n",
       "7  Ewing's Sarcoma                       95\n",
       "8  Soft Tissue Sarcoma                   93\n",
       "9  High Grade Glioma                     80\n",
       "10 Germ Cell Tumor                       74\n",
       "11 Low Grade Glioma                      24\n",
       "12 Adrenocortical Carcinoma              22\n",
       "13 Carcinoma                             14\n",
       "14 Other Solid Tumor                     10\n",
       "15 Non-Malignant Tumor                    4\n",
       "16 Giant Cell Tumor                       3\n",
       "17 Renal Cell Carcinoma                   3\n",
       "18 Low Malignant Potential Renal Tumors   2\n",
       "19 Basal Cell Carcinoma                   1\n",
       "20 Paraganglioma                          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_count( df2$Tumor_type , sort = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       ACT BCC  CA CNS EWS GCT GICT HGG LGG\n",
       "  Adrenocortical Carcinoma              22   0   0   0   0   0    0   0   0\n",
       "  Basal Cell Carcinoma                   0   1   0   0   0   0    0   0   0\n",
       "  Carcinoma                              0   0  14   0   0   0    0   0   0\n",
       "  Central Nervous System                 0   0   0 323   0   0    0   0   0\n",
       "  Ewing's Sarcoma                        0   0   0   0  95   0    0   0   0\n",
       "  Germ Cell Tumor                        0   0   0   0   0  74    0   0   0\n",
       "  Giant Cell Tumor                       0   0   0   0   0   0    3   0   0\n",
       "  High Grade Glioma                      0   0   0   0   0   0    0  80   0\n",
       "  Low Grade Glioma                       0   0   0   0   0   0    0   0  24\n",
       "  Low Malignant Potential Renal Tumors   0   0   0   0   0   0    0   0   0\n",
       "  Neuroblastoma                          0   0   0   0   0   0    0   0   0\n",
       "  Non-Malignant Tumor                    0   0   0   0   0   0    0   0   0\n",
       "  Osteosarcoma                           0   0   0   0   0   0    0   0   0\n",
       "  Other Solid Tumor                      0   0   0   0   0   0    0   0   0\n",
       "  Paraganglioma                          0   0   0   0   0   0    0   0   0\n",
       "  Renal Cell Carcinoma                   0   0   0   0   0   0    0   0   0\n",
       "  Retinoblastoma                         0   0   0   0   0   0    0   0   0\n",
       "  Rhabdomyosarcoma                       0   0   0   0   0   0    0   0   0\n",
       "  Soft Tissue Sarcoma                    0   0   0   0   0   0    0   0   0\n",
       "  Wilms' tumor                           0   0   0   0   0   0    0   0   0\n",
       "                                      \n",
       "                                       LMPRT NBL  NM  OS OST PGL  RB RCC RHB\n",
       "  Adrenocortical Carcinoma                 0   0   0   0   0   0   0   0   0\n",
       "  Basal Cell Carcinoma                     0   0   0   0   0   0   0   0   0\n",
       "  Carcinoma                                0   0   0   0   0   0   0   0   0\n",
       "  Central Nervous System                   0   0   0   0   0   0   0   0   0\n",
       "  Ewing's Sarcoma                          0   0   0   0   0   0   0   0   0\n",
       "  Germ Cell Tumor                          0   0   0   0   0   0   0   0   0\n",
       "  Giant Cell Tumor                         0   0   0   0   0   0   0   0   0\n",
       "  High Grade Glioma                        0   0   0   0   0   0   0   0   0\n",
       "  Low Grade Glioma                         0   0   0   0   0   0   0   0   0\n",
       "  Low Malignant Potential Renal Tumors     2   0   0   0   0   0   0   0   0\n",
       "  Neuroblastoma                            0 190   0   0   0   0   0   0   0\n",
       "  Non-Malignant Tumor                      0   0   4   0   0   0   0   0   0\n",
       "  Osteosarcoma                             0   0   0 129   0   0   0   0   0\n",
       "  Other Solid Tumor                        0   0   0   0  10   0   0   0   0\n",
       "  Paraganglioma                            0   0   0   0   0   1   0   0   0\n",
       "  Renal Cell Carcinoma                     0   0   0   0   0   0   0   3   0\n",
       "  Retinoblastoma                           0   0   0   0   0   0  98   0   0\n",
       "  Rhabdomyosarcoma                         0   0   0   0   0   0   0   0 134\n",
       "  Soft Tissue Sarcoma                      0   0   0   0   0   0   0   0   0\n",
       "  Wilms' tumor                             0   0   0   0   0   0   0   0   0\n",
       "                                      \n",
       "                                       STS WLM\n",
       "  Adrenocortical Carcinoma               0   0\n",
       "  Basal Cell Carcinoma                   0   0\n",
       "  Carcinoma                              0   0\n",
       "  Central Nervous System                 0   0\n",
       "  Ewing's Sarcoma                        0   0\n",
       "  Germ Cell Tumor                        0   0\n",
       "  Giant Cell Tumor                       0   0\n",
       "  High Grade Glioma                      0   0\n",
       "  Low Grade Glioma                       0   0\n",
       "  Low Malignant Potential Renal Tumors   0   0\n",
       "  Neuroblastoma                          0   0\n",
       "  Non-Malignant Tumor                    0   0\n",
       "  Osteosarcoma                           0   0\n",
       "  Other Solid Tumor                      0   0\n",
       "  Paraganglioma                          0   0\n",
       "  Renal Cell Carcinoma                   0   0\n",
       "  Retinoblastoma                         0   0\n",
       "  Rhabdomyosarcoma                       0   0\n",
       "  Soft Tissue Sarcoma                   93   0\n",
       "  Wilms' tumor                           0 207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "table(df2$Tumor_type , df2$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Wilms' tumor\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$Tumor_type == ttype,]\n",
    "ss =df$Tumor_Sample_Barcode %in% intersect( df$Tumor_Sample_Barcode, unique( df2_ss$Tumor_Sample_Barcode ))\n",
    "df_ss = df[ ss, ]\n",
    "df_ss = droplevels(df_ss)\n",
    "df2_ss = droplevels(df2_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ss$Hugo_Symbol=as.character(df_ss$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              \n",
       "               WLM\n",
       "  Wilms' tumor 207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$Tumor_type , df2_ss$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setdiff( df_ss$Tumor_Sample_Barcode, df2_ss$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>22 </td><td>207</td></tr>\n",
       "\t<tr><td>84 </td><td> 12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 22  & 207\\\\\n",
       "\t 84  &  12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 22  | 207 |\n",
       "| 84  |  12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 22   207 \n",
       "[2,] 84    12 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(dim(df_ss), dim(df2_ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MAF object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss , isTCGA = FALSE, verbose = F, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>38               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Center           </td><td>NA               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Samples          </td><td>22               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td>16               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 5               </td><td>0.227            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 1               </td><td>0.045            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 9               </td><td>0.409            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 7               </td><td>0.318            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>22               </td><td>1.000            </td><td> 1               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " ID & summary & Mean & Median\\\\\n",
       "\\hline\n",
       "\t NCBI\\_Build        & 38                  &    NA               & NA                 \\\\\n",
       "\t Center            & NA                &    NA             & NA               \\\\\n",
       "\t Samples           & 22                &    NA             & NA               \\\\\n",
       "\t nGenes            & 16                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   &  5                    & 0.227                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  1                    & 0.045                 &  0                   \\\\\n",
       "\t Missense\\_Mutation &  9                  & 0.409               &  0                 \\\\\n",
       "\t Nonsense\\_Mutation &  7                  & 0.318               &  0                 \\\\\n",
       "\t total             & 22                & 1.000             &  1               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ID | summary | Mean | Median |\n",
       "|---|---|---|---|\n",
       "| NCBI_Build        | 38                |    NA             | NA                |\n",
       "| Center            | NA                |    NA             | NA                |\n",
       "| Samples           | 22                |    NA             | NA                |\n",
       "| nGenes            | 16                |    NA             | NA                |\n",
       "| Frame_Shift_Del   |  5                | 0.227             |  0                |\n",
       "| Frame_Shift_Ins   |  1                | 0.045             |  0                |\n",
       "| Missense_Mutation |  9                | 0.409             |  0                |\n",
       "| Nonsense_Mutation |  7                | 0.318             |  0                |\n",
       "| total             | 22                | 1.000             |  1                |\n",
       "\n"
      ],
      "text/plain": [
       "  ID                summary Mean  Median\n",
       "1 NCBI_Build        38         NA NA    \n",
       "2 Center            NA         NA NA    \n",
       "3 Samples           22         NA NA    \n",
       "4 nGenes            16         NA NA    \n",
       "5 Frame_Shift_Del    5      0.227  0    \n",
       "6 Frame_Shift_Ins    1      0.045  0    \n",
       "7 Missense_Mutation  9      0.409  0    \n",
       "8 Nonsense_Mutation  7      0.318  0    \n",
       "9 total             22      1.000  1    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>COL7A1</td><td>0     </td><td>0     </td><td>2     </td><td>2     </td><td>4     </td><td>4     </td><td>4     </td></tr>\n",
       "\t<tr><td>WT1   </td><td>0     </td><td>1     </td><td>0     </td><td>2     </td><td>3     </td><td>3     </td><td>3     </td></tr>\n",
       "\t<tr><td>CHEK2 </td><td>2     </td><td>0     </td><td>0     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>BRCA2 </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CDC73 </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CDH1  </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t COL7A1 & 0      & 0      & 2      & 2      & 4      & 4      & 4     \\\\\n",
       "\t WT1    & 0      & 1      & 0      & 2      & 3      & 3      & 3     \\\\\n",
       "\t CHEK2  & 2      & 0      & 0      & 0      & 2      & 2      & 2     \\\\\n",
       "\t BRCA2  & 1      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t CDC73  & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t CDH1   & 1      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| COL7A1 | 0      | 0      | 2      | 2      | 4      | 4      | 4      |\n",
       "| WT1    | 0      | 1      | 0      | 2      | 3      | 3      | 3      |\n",
       "| CHEK2  | 2      | 0      | 0      | 0      | 2      | 2      | 2      |\n",
       "| BRCA2  | 1      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| CDC73  | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| CDH1   | 1      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 COL7A1      0               0               2                \n",
       "2 WT1         0               1               0                \n",
       "3 CHEK2       2               0               0                \n",
       "4 BRCA2       1               0               0                \n",
       "5 CDC73       0               0               1                \n",
       "6 CDH1        1               0               0                \n",
       "  Nonsense_Mutation total MutatedSamples AlteredSamples\n",
       "1 2                 4     4              4             \n",
       "2 2                 3     3              3             \n",
       "3 0                 2     2              2             \n",
       "4 0                 1     1              1             \n",
       "5 0                 1     1              1             \n",
       "6 0                 1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>16</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 16\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 16\n",
       "2. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 16  8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples with top variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   INS   SNP   total\n",
       "---------------------  ----  ----  ----  ------\n",
       "SJWLM017957               1     0     0       1\n",
       "SJWLM018889               0     0     1       1\n",
       "SJWLM018894               0     0     1       1\n",
       "SJWLM018897               0     0     1       1\n",
       "SJWLM019663               1     0     0       1\n",
       "SJWLM019664               0     0     1       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJWLM017957                    1\n",
       "SJWLM018889                    1\n",
       "SJWLM018894                    1\n",
       "SJWLM018897                    1\n",
       "SJWLM019663                    1\n",
       "SJWLM019664                    1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variants.per.sample %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Genes with 2 or more mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3"
      ],
      "text/latex": [
       "3"
      ],
      "text/markdown": [
       "3"
      ],
      "text/plain": [
       "[1] 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = as.data.frame( dfx@gene.summary )  \n",
    "gf_sx = nrow( gs[ gs$MutatedSamples >= 2,] )\n",
    "gf_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_genes=length(gs$Hugo_Symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "16"
      ],
      "text/latex": [
       "16"
      ],
      "text/markdown": [
       "16"
      ],
      "text/plain": [
       "[1] 16"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>COL7A1</td><td>0     </td><td>0     </td><td>2     </td><td>2     </td><td>4     </td><td>4     </td><td>4     </td></tr>\n",
       "\t<tr><td>WT1   </td><td>0     </td><td>1     </td><td>0     </td><td>2     </td><td>3     </td><td>3     </td><td>3     </td></tr>\n",
       "\t<tr><td>CHEK2 </td><td>2     </td><td>0     </td><td>0     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>BRCA2 </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CDC73 </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CDH1  </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t COL7A1 & 0      & 0      & 2      & 2      & 4      & 4      & 4     \\\\\n",
       "\t WT1    & 0      & 1      & 0      & 2      & 3      & 3      & 3     \\\\\n",
       "\t CHEK2  & 2      & 0      & 0      & 0      & 2      & 2      & 2     \\\\\n",
       "\t BRCA2  & 1      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t CDC73  & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t CDH1   & 1      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| COL7A1 | 0      | 0      | 2      | 2      | 4      | 4      | 4      |\n",
       "| WT1    | 0      | 1      | 0      | 2      | 3      | 3      | 3      |\n",
       "| CHEK2  | 2      | 0      | 0      | 0      | 2      | 2      | 2      |\n",
       "| BRCA2  | 1      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| CDC73  | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| CDH1   | 1      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 COL7A1      0               0               2                \n",
       "2 WT1         0               1               0                \n",
       "3 CHEK2       2               0               0                \n",
       "4 BRCA2       1               0               0                \n",
       "5 CDC73       0               0               1                \n",
       "6 CDH1        1               0               0                \n",
       "  Nonsense_Mutation total MutatedSamples AlteredSamples\n",
       "1 2                 4     4              4             \n",
       "2 2                 3     3              3             \n",
       "3 0                 2     2              2             \n",
       "4 0                 1     1              1             \n",
       "5 0                 1     1              1             \n",
       "6 0                 1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PnYlPm9SzuWra6unjp1qru7e0REhLRZ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\nCAEAqhFCAIBqhBAAoNoDHaODYJlndbcAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=6)\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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q\nq9Vr167dvXv38OHD9+zZY2JiYmNjs379eg8Pj/Pnz/v4+CxYsMDV1dXKyqqhjvvqq68GBgZe\nvXr15MmTW7du7dSp0+XLl3NycqqsLI+mdHBwGDx4cEMFAAD1U5tVlu8VHx/fqVMnIURBQYGb\nm1t1Cy1XqrZ27dqioqLFixevWbPm4MGDKpXqk08+0Wq127dvv3r16qRJkw4dOnT8+PHevXvf\ne8S9e/cmJyePHDny6NGjf/zxh5+fn1xeXZvyVhMTk9TU1CNHjjz33HNCiJMnT1ZXv35rVAMt\nigQAAFBHkZGRQohZs2YZOhAArdqpU6eEEMOGDZPfyv8fLSsrkyRp+vTpQoiwsLDo6GghxOOP\nPy7XkZdt/uqrryRJ0mUu5E0nT5588803AwICrK2thRAjRoyQy/WrTZs27d4/qeThGEKI77//\nXt5lwYIFQoiVK1fqR7ts2TIhxKpVq/TffvXVV9W1Kf9Dt1+/fnJ9eVTIsmXLqqt/5coVOett\nZmY2YsSINWvWyM+pBFqPppuiAgAAWoZvv/32v//9rxBixowZho4FAGq1yrJUzQrNOocOHQoM\nDLS1tZ08efLIkSPfe++9KqvJyyEtW7ZMf4yGWq2WV8HQPTPb1NT03n3l5yHoNpmZmdXcprzk\nh647usarq29jY5OSkrJkyZLt27eHh4eHh4fv3LlTftwV0EqwKhgAAKibkJCQ27dvv/nmm489\n9pihYwGA+7jvCs1y7uO+Cy3L1bp27SqEsLKyGj16dFBQUGFhYU5Ojkql8vb2FkKEhoZKkqTR\naOTHPlbSpUsXIcSBAwckSdJqtbo1B6trs7oeVVe/ujWq637OAGPFCA4AAFA38fHxhg4BAGqr\nS5cu1a3QbGFhIYQICQmZO3duDQst61d77rnnlixZsmjRoqysrOTk5J07d06cOHH69OnTpk17\n5513tm3bdvv27fz8/NjY2HsjmTBhgoODw6+//jpq1KiCgoLjx4/L5dW1WV2Pqqtf3RrVDXYq\ngWaPERwAAAAAWqwaVmieO3du586dFy9enJeXV8NCy/rVXFxcwsPDu3fvvmrVqqioqH/9618b\nNmwQQlhbWx86dGjw4MHR0dHm5uavv/66q6urvJaHjpWV1W+//TZw4MDo6Og2bdrMmTNHLq+u\nzepUV7+6Naob6cQCzZBCqv5hqwAAAACABpGfn3/9+nVbW1v54Sb//ve/Q0JCtm7dKj+XCsCD\nY4oKAAAAADS6Cxcu+Pv729nZffXVV9nZ2evXr1er1SxmBDQgRnAAAAAAQFPYvn378uXLk5OT\nLSws+vTp89FHHw0YMMDQQQEtBwkOAAAAAABg9FhyBgAAAAAAGD0SHAAAAAAAwOjVP8Hxww8/\nKBSKxMTEBowGAAAAAACgHur8FJXy8vLQ0NCoqKiQkJDGCAgAAAAAAKCu6pzguHPnzqRJk2qo\noNVqU1JSiouL7y3Pz88fOnSomRnPpgUAAAAAAA2pzrkGe3t7+cErQ4cOPXLkSKWtWq12+PDh\n95br2NnZ3bx5kxwHAAAAAABoQA2caLh8+XIN2Q0hRF5e3tGjR4cPH16b1l4Zsq72h7bSlpgK\njX7J/HUTLdqrK4rLlszYXKlyuaKNqaSpVL+G8kU/PVtRXFZRUrpi1i7aaf7tlCva/GdDcEVJ\nqRBCvynaafHtmEqad9aNk9/qmqKd5tyOqdDMXzdRv6ka6pcq2ojqfXl0Vg1bAQAA0OI1cIKj\nU6dOAQEBNec4HBwcatla6KMdan/oqWdPDsxM1y858vTpPh+9kBt/fmDR6UqVdwdddL9m7X+m\nciTVld+MTc6NP39px+GBfy33uHO2yFx9U+UmaldeXTsNFc8Gv/4P3c6tdB5qKG/sfhmqHY87\nZy9ssrm047AQQr+pup7n3UEXG7WdhupXc2unrp/DDX79L2za3yDtPHQ798jT/5Hf6pqqx/Vy\nv2Z95OlTldppbvGcsehqpylwv3ujUv2ayxsvnvq143/G4cjTp/WbqqH+Ge8cURMSHAAAAK2a\nQp5vUg/yFJUzZ8707NlTv7zKNTjS09OnTp0qv05MTPTx8anNITwWhNY+HnOtpt/Vy5Z37+pK\nPpg1pMPIvpqy8tnvjLYoM9WV37YrP9c116xC4ZdkX8vyDR8d0ZSVZ/4adWvBe7pypaakbVmW\nVmGSo+ygMTGrTblv+gm5nc8iljRIPB9tP6krv2VpHe/seu95qKF893cvNGy/mkk7bcuyXNIv\nZP4adbegeHXcqnqf53Ndc49Pi2vYdvKXLmuQfjW3dlbsS5bL6/E5jHd2vbA4sEHaMddqDvSz\nvVtQLISQL1n9rpdZhWKLOkRuRw6p+cTz8//9IYQoNrHIMmtvIrQdym+aiwpd/fuWv/Fq94aN\n5wHb8Uuyf7PnPLlwddyqmuuLGp145kzNFQAAANCyNXyCo0pnz57VVWukBMe9Li17XH7Rd73v\ng7Qj9H5vvuLq/iDtuF3NbJCQdPE01ClqqH41k3ZEI5zqhmqnmZyiBm+noT6KD9iOaLgf/Ab/\nKWuoeOo0d+9eunkczeRHowGbIsEBAADQypkYOgAAAAAAAIAHRYIDAAAAAAAYvfovMhoREdFw\nYQAAAAAAANQfIzgAAGhgP/zwg0KhSExMNHQgAAAArUgDPyYWAIBWq7y8PDQ0NCoqKiQkxNCx\nAAAAtDokOAAAaBh37tyZNGlSbWpW+Uh1uTw/P3/o0KFmZtygAQAA6obfnwAAaBj29vbyw9fl\nJ6lXV02r1Q4fPryGCnZ2djdv3qxfjqNcU/5z2k/FdyunThrWhdtpUdciH+nwmGfbLo16IHuV\n/eOe42pfv2TPnorLGY0XjxDibkpK2aHDyuHDzLt3b9QDKZRKq6emKaysGvUoAAC0JCQ4AABo\nUpcvX64huyGEyMvLO3r06PDhw+vR+L700BUxS+obWt3sTf+1CY7ibtPRz7F3bWpWXLiQ8+Ls\nxo5HVrzzpyY4ilRebvNyE/UIMEZ91/saOoS/2P1OnqFD+NMyz/e/PDrL0FEATY0EBwAATapT\np04BAQE15zgcHBzq13jh3QIhhKnC1NnapX4t1EZJRcmdsnxbpVplpmqkQ0iS9lrhNSFE4d3C\nWu6iLfj/NU1dXRVmpo0VWHGxNi/PxM5OYWnZSIcQQmiuXZfu3pUKChrvEAAAtDwkOAAAaFIm\nJiaHDh26dw2O9PT0qVOn6uo8yCHaq9r/PCnsQVowuOK7RUN+HFi/fR12bDfr6N6w8TSxrMCg\nu0lJho4CAAAjw2NiAQBoaiYmJt7e3v5/1aNHD0PHBaD1Sk5ODg4O9vDwsLW17dev3/fff6/V\nanVbs7OzX3zxxZ49e9rY2DzyyCMff/xxRUWFbquzs7NCoUhLS9Nv8Pnnn1fc49ixY7oKe/fu\nlQu3bNlybzw8b7t5unz58r59++QFp4QQeXl50dHR+/btCw8Pz8zMFEJotdqkpKT9+/cfPnw4\nPz9frpacnHzx4kWDBY3WhBEcAAAAQKsWERExZsyY0tJSe3t7R0fHEydOzJw5MzU1denSpUKI\nM2fOBAYGZmVl2dradurUKTY2Njo6+tdffz148KBSqayuTR8fn6CgIN3bI0eOlJWVtW/fXley\nadMm+cWWLVumTZsmv+Z5282TVqu9c+dOVlZWWlpa+/btFQqFEKKwsDA6OtrGxqZLly4ZGRkJ\nCQlOTk5paWm3bt3y8/NLT09PS0vz9/fPzs4uKiry8vIydCfQKjCCAwAAAGi9Kioq5syZU1pa\nOm/ePPkv2BMnTqhUquXLl9+8eVOr1b744otZWVkvvfTSrVu3EhMTL1269PDDDx87dmzVqlU1\nNPvaa6/t+5/XXnuttLT0rbfe8vb2lrcWFRXt3r1brVar1eqwsLC8vP+/PKf8vO1PPvmkvLy8\n0XuOWsvNzT127Nj58+clSbKzs5MLr127ptFo+vXr16VLFxcXF1NTU1NT06ysLEdHR7VardFo\nrK2tS0pKUlNTe/XqZdj40XqQ4AAAoIFFRERIktSzZ09DBwIA95eUlJSYmOjs7LxkyRJ5ASB/\nf//vvvtu6dKlBQUFp06dio6OdnNz+/zzz83NzYUQrq6uX331lRDiyy+/rE37RUVFL730Uq9e\nvT744ANd4e7du4uLiydOnDhu3Ljy8vJdu3bJ5fLztiVJCggIaPiuor7s7e3Hjh0r5yl0CQ5r\na2shxJUrV65evXrlyhUfHx9TU1NXV9e0tLTw8HCVSuXp6RkfH+/r61u/B58D9cBHDQAAAGi9\nUlJShBB+fn5t2rTRFT755JPyi/Xr1wshBg4cqD8bpV+/fkql8sqVK3l5ebo/d6uzePHiS5cu\n7du3T799eX7KlClTNBrNhg0btmzZMnPmzIbrExqFPNBGd8VVKpWpqem5c+eEEJaWlvLzv7p1\n6+bm5qZQKFQqVVJSkqurq1qtLi4utrCweMD1s4HaIMEBAAAAtF7ygpGmpjU9XFlec0GfXF9/\nqdEq3bx58/PPPx84cOCoUaP0C/fv329nZzdy5EhJkmxsbMLDw7Ozsx0dHevZBzSJ27dvK5VK\nCwsLIURubm5MTIy7u7uXl9fVq1fluUvyFCRLS0shxI0bN8rKyjw8PCIjI/Pz883MzPz8/Dp0\n6GDgPqClI4sGAAAAtF7dunUTQpw9e1aj0egKt23b9tFHH6WkpPj4+Aghjh8/fvfuXd3W+Pj4\n4uJiFxcXe3v7mhvftm1bRUXF9OnT9VMk27Zt02g0pqamkyZNCg4ONjMz02g0O3bsaOCOoUFp\nNJqCggLd8I3MzEytVtutWzdzc3N5dI/+CJ3i4uILFy74+vomJCTY2toGBgaq1eqsrCzDhI7W\nhAQHAAAA0Hr5+Ph4eXllZGR8+umn8miOjIyM2bN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aoVCcO3fuH//4x8cff6xf+c6dO5MmTaohkl1pxa0AACAASURBVOpaa+geNwpJkq5c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\n9/v9/t///vfbtm0rLS29kqXMZnOgffPNN7/44otCiJKSkpKSE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      "text/plain": [
       "Plot with title “Top 16\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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_plotmafsummary_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KOwsHDo0KEdOnRYuXLlLjdaWFg4duzYhx56aNCgQWvXrl23bl2rVq3y8/Mrav/0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\nHVEBAAB2AudEvPNUT5klAAAAAElFTkSuQmCC",
      "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5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\n27Z1dXXl5eU1NjY2NTUpFIqCggKZMgQAAAAA6+H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QH68UHx/PheS4SSIb7vnz55mZmWx78eLFgYGBT58+PXny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\nAAAAAAA4PAIcAAAAAADA4f0fRI8Mi4fw/mwAAAAASUVORK5CYII=",
      "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",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_oncoplot_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=6, width=12)\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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zbV\nS167du3dd981zvnoo4+U6Iafn19oaGirVq3kZlpaWlxcXJUaDh06lJWV5ejo6OTkpGTOmTMn\nKytLrVa7ubkpi31mZ2cnJibK9Lp165ToRkhIyLhx43r06CE3586du3379lqd7/Dhw2WivLz8\nvffe69y5c2Bg4MSJE+Pj47Ozs2sYuXDt2rVly5apVCo3NzdlBdaCgoLPPvushubmz5+flpbm\n5uYmN8ePH5+WlhYVFTV//vzU1FSl2KeffpqWlqaEWh5cbm7u+vXr1Wq1u7u7RvOvv1jcunVr\n1qxZSplFixYp0Q1fX9/IyEhlYM7OnTuNSwIAAFg6AhwA8OjT6/Vl91K95IULF5ycnFavXp2b\nm/vjjz8Ko4+DzJkzJycnZ/v27ZcuXXruuedkphxKUMWcOXOuX79++fLlP/3pT0pm165dz549\nm5+ff+TIEWWVCjmmo7y8fNq0aTLno48++uc//xkXF7dv374JEybIzGnTptVqPkV0dLQS45Cy\ns7MXL178xhtvBAYGurm5TZgw4erVq/c8dsCAARcvXszPzz9x4kTr1q1lZnp6eg3NdenSRavV\n2tjYyM327dtrtVpXV9cuXboEBQUpxZ588kmtVmtnZ2f6ifyuiIiIK1euyDE1Xbt2lZlpaWlK\nga1bt8rEuHHjjhw5kpCQkJGRoXzOdsOGDfXYGQAAgIZFgAMA8B/Wr18fFRXl7e3duXPnysrK\nadOmxcXFxcXFvf3227JARUWFEhwpKiqqcriLi8vMmTPVarVarR41apSSP3PmTHd3dyGEt7e3\nVqs1Pjw3N/f06dNCiCZNmihfb1WpVDNmzJDprKysX3/91fRT0Gg0SUlJycnJERERzZs3r7L3\n4sWLX331lY+Pj7JUh7FvvvlGxjU6duwYFRUlM/Py8kxv/aGxtrb+5ptvWrZsKYRwcXFRbpC8\nmNK5c+dkIjU1NTEx8datW0KI8ePHb9y4cePGjV988YWyNioAAIClYw0OAHj0JSYmRkREmFKy\nTZs2AQEByqZarR47dqwQ4tSpUxs2bMjJyTly5MihQ4eqxzUUHh4eyiQU4/UdjKdmVFn34fjx\n4zJRUVFRPR4hnThxwtfX15RTkFQq1dChQ4cOHarX6w8fPpyVlZWZmZmampqbmysLFBUVTZky\nRVnAQmrRooWMwkiurq4yodfrTW/6oenYsaO9vb2yqcw5qqioUDJ79ux55MgRIUReXt7LL7+s\n0WiCg4OfffbZZ599NjAwUKVSPeQ+AwAAmA8BDgDAvxkvnCGdOHHijTfe2Llzp3Gmra3tnTt3\n7lmDsnSFifnCaJRBDU6cOPG7Ze5Jo9EEBAQEBASMHj1aCJGenj58+HC5BGlqaqrBYDB+ya/S\nyUb+/m9KbxcuXKjT6datWydHauj1+p07d+7cufP999/v1q1bfHz8U0899ZC6CwAAYGYEOAAA\n/1blJbm8vDw8PPzo0aNCCCcnpxEjRoSGhgYHB3/22WeLFi2qr0aV5TmbN2++ZcuWe5YxHlhR\ns9zc3EmTJsn07Nmzg4ODjfdqtdqpU6fKJT9u3ryp0+mUBUEeSU5OTj/++OO5c+cSExOTk5N/\n+eUXZXxHTk7OoEGD8vLylO/dAgAAWDQCHACA+8rOzpbRDSFEZmZmx44dZVqZ6FEvvLy8ZOLm\nzZs+Pj73m6ViInt7+23btsm0t7d3lQCHEOLGjRsyYf3/7d1NSGpbFMDxo0giNnjRxECsgZJH\nqKC60ItmCdEg5NDXICGowWsUUZOCJhGEEA2jQFAqiCKIkKhBQRESBEVBTYQCAymSExY8+nLy\nBpt7iHuvvhtmdS7/32i5ObrXGQmLvdcymwsLC3PZ64tLpVKxWEzEg4ODQ0NDt7e3m5ubwWBw\nb29PkqT7+/vd3d3fvL4EAADwxdFkFACQUTKZ1OKHhwcRrK+vaxWEd+HxeGw2m4inpqa09dPT\nU1mW3W53poagv+RwOCoqKkQ8PT09MjJyc3MjPj49PS0sLGhbeL3ej7yEohVWPoyqqn9/J47G\nFBcX+/1+bTqvJEnvO9UFAADgE3GCAwCQkTZ5VJKk6urq2trax8dHMdtVeJcZHBaLZWJioqen\nR5Kk8fHxaDTqdrvPz8+j0ajo9KEoyusepf9rcnKyublZ5BYIBAKBQFFRkcViSSaTWrtQk8k0\nNjaWe/LZFRQUmM1mMXRmdnb2+fm5ra3t50YneeJ0Ol0ul2hf4vP5qqqqnE6nqqonJydaeq8H\n2QIAAOgaJzgAABmVlZV1dXWJ+OXlZX9///j42OVydXR0iMVYLJZOp3PfqLu7u7OzU8Q7Ozsz\nMzNbW1uiuuH1eufm5t70a01NTcvLyyUlJdpKKpW6urrSqht2u31tba2mpib3zLMzGo319fUi\nPjg46OvrE/1NP4bBYIhEIqKekk6nDw8Pl5aWtre3VVUVD4RCoQ+rtgAAAOQbJzgA4HP4/vn2\n793zm75S+Jc5T8lkYjAYgsGgLMvz8/OJREKW5cbGxtHR0aOjo4uLC/FMJBJpbW3NcSOj0bi4\nuKgoysrKytnZWTwet9lsHo+nt7fX5/OZTG/+t2pvb29paQmHwxsbG/F4/PLy0mg0lpaWOhwO\nRVH8fv8Po2rzJxQKDQwMRKPRu7s7q9WaZZpMPoizMOFweHV1NZFIXF9fW61Wu91eV1fX39//\n+oQOAACA3hne5XQxAAAAAADAJ+KKCgAAAAAA0D0KHAAAAAAAQPcocAAAdGB4eNjwexoaGj47\n2Wz+mBcBAAD4amgyCgDQgcrKSm3MSnbl5eX5TiYXf8yLAAAAfDU0GQUAAAAAALrHFRUAAAAA\nAKB7FDgAAAAAAIDuUeAAAAAAAAC6R4EDAAAAAADo3n9yHpuVF13YiAAAAABJRU5ErkJggg==",
      "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_oncoplot_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=6, width=12)\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": 49,
   "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      22    NA     NA\n",
       "4:            nGenes      16    NA     NA\n",
       "5:   Frame_Shift_Del       5 0.227      0\n",
       "6:   Frame_Shift_Ins       1 0.045      0\n",
       "7: Missense_Mutation       9 0.409      0\n",
       "8: Nonsense_Mutation       7 0.318      0\n",
       "9:             total      22 1.000      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G1NTUTJgwITVYXFx8//33px0YAAAAgIuRzmlBLS0t8+bN+/SnPz1n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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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SONj5fH3264WHzjj9q78rusPL5769Z8aG+rb2kV0K2OYgFYlwQ0dEVlQPf351u3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\ngEGgAQBgEGgAABgCMVu7u/kU+pQAAAAASUVORK5CYII=",
      "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6b28jyxWKwkMT77VCOmltXM7f0Z+ip1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\nnnz44YcjP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LFiw449dvuummUaNG5Y6n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\ngFCCEAAAIJQgBAAACPV/TiOjhTVFX6UAAAAASUVORK5CYII=",
      "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 >=2]\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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=2]\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": 54,
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_drugInteractions_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oncogenic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pathway alteration fractions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pathway  N n_affected_genes fraction_affected\n",
      " 1:    RTK-RAS 85                2        0.02352941\n",
      " 2:       TP53  6                1        0.16666667\n",
      " 3:      NOTCH 71                1        0.01408451\n",
      " 4: Cell_Cycle 15                0        0.00000000\n",
      " 5:      Hippo 38                0        0.00000000\n",
      " 6:        MYC 13                0        0.00000000\n",
      " 7:       NRF2  3                0        0.00000000\n",
      " 8:       PI3K 29                0        0.00000000\n",
      " 9:   TGF-Beta  7                0        0.00000000\n",
      "10:        WNT 68                0        0.00000000\n"
     ]
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "op = OncogenicPathways(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oeHR/1ygUDw4MEDY2Nj7lSZmZm6uroO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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Wilms_tumor_OncogenicPathway_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_OncogenicPathway_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "\n",
    "svg(out_svg, height=9, width=12)\n",
    "op\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HNXG7RmAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ACKIhACAAAAgKIIhAAAAACgKAIhAAAA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\nWfkAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ElLS6tevXp8uWYFlI69+ALC2PHHH5+M\nt7mjT7nWGSt9p9lWrVqV/tQAALAHSim9BQ4q5oorrhg/fnwURUccccRnn322u5ez2xQWFu6/\n//4bNmyIomj06NGlTycCAMAeyBlC2GUKCwsLCgricffu3XfvYgAA4D9yUxnYBfLz8zdv3nzN\nNdfEP+9x4oknlr46EQAA9kyCEHaBZs2aJTcXjaLop+7yCgAAexRbRmEXGzRoUKdOnXb3KgAA\n4D9zUxnYBR588MHVq1dnZGRkZma2bdvWzw8CAPCrIAgBAAACZcsoAABAoAQhAABAoAQhAABA\noAQhAABAoAQhAABAoAQhAABAoAQhAABAoAQhAABAoAQhAABAoCrt7gX8Oix/9dXV8+bt7lUA\nAAC/VsfdfvvuXsIOCMIyWT1vXu6kSbt7FQAAwK/VnhmEtowCAAAEShACAAAEShACAAAEShAC\nAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShAC\nAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShAC\nAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShAC\nAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShAC\nAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShAC\nAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEShAC\nAAAEShACAAAEShACAAAEShACAAAEShACAAAEShACAAAEagdBuHHjxuuuu65p06ZVq1Zt2rTp\n008/vc2Enj17Dhs2LCsrKyUlZevWrfHByy+/vGnTpsmcAQMGnH/++VEUtWnT5rHHHtvmFQ49\n9NADDjig9JGvv/46PT397LPP/vkfCQAAgLLYNgiLiorOOuusTZs2TZs27csvv7zpppv69u2b\nk5NTes78+fPbtGlT+si33347YcKEdevWxQ83btw4YcKEwYMHFxYWfvLJJ61bty49ed26dV9+\n+eXatWs3b96cHBw1atTWrVvjl83Ly+vSpUtGRsbxxx+fm5tb+rnDhw+/4447kofZ2dmZmZk1\natRo3rz522+/Xd6Dr7322nHHHVejRo3TTz/9q6++Kt83BwAA8Cu3bRDOnj07Ly9v3LhxjRs3\nrlu3bp8+fU455ZT8/Pxkwvfff7906dJtgnDcuHFnnXVWMm3ChAn169c/7bTTlixZEkVRixYt\nSk9esGDBwQcffNBBB3399dfxkby8vJdffrlp06atW7dev359ZmZm165dc3Nz69evP2zYsHjO\n9OnTr7rqqpEjRx533HHxkRUrVpx33nnDhg1bsWJFq1atBg8eXK6Db7zxxtChQx955JHc3Nz0\n9PQbbrhh13yjAAAAvxLbBmFxcfHKlSuffPLJ5PTdG2+8kTRYFEULFy7cZ599DjnkkORIQUHB\nI488MmLEiMLCwoKCgpKSkkceeWTw4MGpqakLFixo0aJFenp66bdYsGBBmzZtGjRokJyUGz16\n9MCBA5ctW9amTZsnn3wyMzOzb9++tWvX7tGjR3xysqSkZNq0aevXry8uLj7mmGPiZ02ZMiUz\nM/PMM8+sVatW7969999//3IdvPXWWx944IE2bdrUqVOnf//+n3766S79YgEAAPZ02wbhSSed\ndPPNNw8ZMqRWrVqdOnWaNGlSSUlJ6QnxftGUlJTkyIQJE9q2bXv00UdXrVo1Pz9/7ty5ubm5\nv/3tb6MdbS6ND7Zu3bpBgwZ5eXlRFOXl5U2ZMuX0009PT09v1KhRVlbWZZddFs/csGFDw4YN\noyhKSUnJysrq169fkyZN9ttvv/ivjRo1Wrhw4fLly5ctW/bwww/fe++9ZT9YXFy8cOHCI488\nMn6pxYsXl05cAACAEGwbhGlpaSNGjFi7du2MGTNKSkouvvjihx9+uPSE+fPnt23bNnlYUlIy\nZsyYoUOHRlGUkZGxbt268ePH9+nTZ5999on+1X7bvEVciQ0bNozPEI4aNerqq6/Oyclp3br1\n6tWrv/nmm44dO8Yzc3NzmzRpkjwxOzv72GOPTR7ut99+K1euPOywwxo3blxcXNyqVauyH0xN\nTT3ggAPGjRv33XffTZ48+a677jriiCN+7ncJAADwq/JvQfjdd9/FkVa5cuWTTz552rRpp556\n6uuvv/7WW28l1/vNmzev9Em/N998MzU19dRTT42iKCMjY8WKFRMnThw4cGAURUVFRR9//PE2\nQbhp06bPP/882TKal5c3derUQYMGxZWYm5vbqFGjSpUqxZP/8pe/JHEYRVF2dnayeXXmzJkX\nXnjhSy+9VFBQkJOT8+6777711ltlPxhF0eOPPz558uQWLVq8+eabhx56aPPmzXf1dwsAALBH\n+7cgHD9+/JAhQ5KHlSpVatasWYsWLX7zm98sX748iqJPP/30k08+OeGEE5I5Y8aMue666+Id\npPvuu+9DDz3Uvn37Zs2aRVG0dOnSH374IT5Hl1i0aFHt2rXr1asXnyGMTw9Wr149Ppe4YsWK\njIyMeObixYvXrl177rnnJs+dO3duEoRPPfXUFVdccdppp6WlpTVp0qRx48ZpaWllP7hmzZrW\nrVt/9tlnK1euHDx48PLly7t27bpLv1gAAIA93b8F4ZFHHvnWW2+98847P/zwwzfffHPPPfe8\n8sorQ4cOLS4unjVrVn5+/vDhwzt16tSoUaPkKUuWLOnevXs8zsjIeP3116+66qr44YIFCw47\n7LC99tpr67+UlJQsWLAg3nHaoEGDjz/++NVXXx04cGB8RV+bNm0OOeSQefPmLVq0aNmyZb17\n977tttuSs4Vr1qzJy8s7+uij44c1a9acNWvWqlWrNm3a9Mc//nH9+vUdOnQo+8GsrKzOnTvn\n5+fn5OT06tXr3nvvrVmz5i/8VQMAAOxZ/i0IO3fuPHz48L59++67776ZmZk5OTnZ2dn16tV7\n6KGH7r///kMPPbRSpUrPPPNM6acMHjy4SpUq8TgjI+Pggw/u3Llz/HD+/PlLly6tXEpOTk5y\nVWHDhg3XrFkzZMiQ6tWrL1u2bMuWLYcffvgJJ5wwZMiQk046qUuXLgMGDOjbt2/yRnPnzm3Z\nsuXee+8dPxw+fHj16tUPP/zw/fff/4033nj99derVatW9oODBw+uUaNGgwYNunbteuONN/br\n1++X+4oBAAD2TCnb3ESUHcq+/fbcSZN29yoAAIBfq55LluzuJezAtncZBQAAIBCCEAAAIFCC\nEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCC\nEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCC\nEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCC\nEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCC\nEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCC\nEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCC\nEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFCCEAAAIFApJSUlu3sN\nAAAA7AbOEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARK\nEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARK\nEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARK\nEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARK\nEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAARK\nEAIAAARKEAIAAARKEAIAAARKEAIAAARKEAIAAATq/wE4Y1zg8ryypAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “NOTCH 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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 62,
   "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": 63,
   "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": 64,
   "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": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Wilms_tumor'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Wilms_tumor'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Wilms_tumor\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)) )\n",
    "paste0(\"Saving MAF file to : \", out_file)\n",
    "maftools::write.mafSummary(dfx, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Wilms_tumor_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Wilms_tumor_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Wilms_tumor_drugInteractions.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions.tsv')\n",
    "paste0(\"Saving drug Interactions data to : \", out_file)\n",
    "write.table(dfx_drug, out_file, sep=\"\\t\", row.names = F, quote = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Wilms_tumor_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Wilms_tumor_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Wilms_tumor_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_mutation_data.tsv\")\n",
    "paste0(\"Saving mutation data to : \", out_file)\n",
    "write.table(df_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Wilms_tumor_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Wilms\\_tumor\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Wilms_tumor_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Wilms_tumor_clinical_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_clinical_data.tsv\")\n",
    "paste0(\"Saving clinical data to : \", out_file)\n",
    "write.table(df2_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 70,
   "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",
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       " [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              "
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