analysis/KCPG/KCPG_woAR_TT-Rhabdomyosarcoma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Rhabdomyosarcoma\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$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Rhabdomyosarcoma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                  \n",
       "                   RHB\n",
       "  Rhabdomyosarcoma 134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$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>11 </td><td>134</td></tr>\n",
       "\t<tr><td>84 </td><td> 12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 11  & 134\\\\\n",
       "\t 84  &  12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 11  | 134 |\n",
       "| 84  |  12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 11   134 \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>11               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td>10               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 2               </td><td>0.182            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 2               </td><td>0.182            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 3               </td><td>0.273            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 2               </td><td>0.182            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 2               </td><td>0.182            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>11               </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           & 11                &    NA             & NA               \\\\\n",
       "\t nGenes            & 10                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   &  2                    & 0.182                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  2                    & 0.182                 &  0                   \\\\\n",
       "\t Missense\\_Mutation &  3                  & 0.273               &  0                 \\\\\n",
       "\t Nonsense\\_Mutation &  2                  & 0.182               &  0                 \\\\\n",
       "\t Splice\\_Site       &  2                  & 0.182               &  0                 \\\\\n",
       "\t total             & 11                & 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           | 11                |    NA             | NA                |\n",
       "| nGenes            | 10                |    NA             | NA                |\n",
       "| Frame_Shift_Del   |  2                | 0.182             |  0                |\n",
       "| Frame_Shift_Ins   |  2                | 0.182             |  0                |\n",
       "| Missense_Mutation |  3                | 0.273             |  0                |\n",
       "| Nonsense_Mutation |  2                | 0.182             |  0                |\n",
       "| Splice_Site       |  2                | 0.182             |  0                |\n",
       "| total             | 11                | 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           11         NA NA    \n",
       "4  nGenes            10         NA NA    \n",
       "5  Frame_Shift_Del    2      0.182  0    \n",
       "6  Frame_Shift_Ins    2      0.182  0    \n",
       "7  Missense_Mutation  3      0.273  0    \n",
       "8  Nonsense_Mutation  2      0.182  0    \n",
       "9  Splice_Site        2      0.182  0    \n",
       "10 total             11      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>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NF1   </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>ATM   </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>ATR   </td><td>0     </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>BRCA2 </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CHEK2 </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>COL7A1</td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1    & 0      & 0      & 1      & 1      & 0      & 2      & 2      & 2     \\\\\n",
       "\t ATM    & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t ATR    & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t BRCA2  & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t CHEK2  & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t COL7A1 & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| NF1    | 0      | 0      | 1      | 1      | 0      | 2      | 2      | 2      |\n",
       "| ATM    | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| ATR    | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| BRCA2  | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| CHEK2  | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| COL7A1 | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 NF1         0               0               1                \n",
       "2 ATM         0               0               1                \n",
       "3 ATR         0               0               0                \n",
       "4 BRCA2       0               0               0                \n",
       "5 CHEK2       1               0               0                \n",
       "6 COL7A1      1               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 1                 0           2     2              2             \n",
       "2 0                 0           1     1              1             \n",
       "3 1                 0           1     1              1             \n",
       "4 0                 1           1     1              1             \n",
       "5 0                 0           1     1              1             \n",
       "6 0                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>10</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 10\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 10\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 10  9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples with top variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   INS   SNP   total\n",
       "---------------------  ----  ----  ----  ------\n",
       "SJRHB019581               0     0     1       1\n",
       "SJRHB019584               0     1     0       1\n",
       "SJRHB019594               0     0     1       1\n",
       "SJRHB019599               0     0     1       1\n",
       "SJRHB019834               1     0     0       1\n",
       "SJRHB020                  1     0     0       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJRHB019581                    1\n",
       "SJRHB019584                    1\n",
       "SJRHB019594                    1\n",
       "SJRHB019599                    1\n",
       "SJRHB019834                    1\n",
       "SJRHB020                       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variants.per.sample %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Genes with 2 or more mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1"
      ],
      "text/latex": [
       "1"
      ],
      "text/markdown": [
       "1"
      ],
      "text/plain": [
       "[1] 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = as.data.frame( dfx@gene.summary )  \n",
    "gf_sx = nrow( gs[ gs$MutatedSamples >= 2,] )\n",
    "gf_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_genes=length(gs$Hugo_Symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "10"
      ],
      "text/latex": [
       "10"
      ],
      "text/markdown": [
       "10"
      ],
      "text/plain": [
       "[1] 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NF1   </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>ATM   </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>ATR   </td><td>0     </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>BRCA2 </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CHEK2 </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>COL7A1</td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1    & 0      & 0      & 1      & 1      & 0      & 2      & 2      & 2     \\\\\n",
       "\t ATM    & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t ATR    & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t BRCA2  & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t CHEK2  & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t COL7A1 & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| NF1    | 0      | 0      | 1      | 1      | 0      | 2      | 2      | 2      |\n",
       "| ATM    | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| ATR    | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| BRCA2  | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| CHEK2  | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| COL7A1 | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 NF1         0               0               1                \n",
       "2 ATM         0               0               1                \n",
       "3 ATR         0               0               0                \n",
       "4 BRCA2       0               0               0                \n",
       "5 CHEK2       1               0               0                \n",
       "6 COL7A1      1               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 1                 0           2     2              2             \n",
       "2 0                 0           1     1              1             \n",
       "3 1                 0           1     1              1             \n",
       "4 0                 1           1     1              1             \n",
       "5 0                 0           1     1              1             \n",
       "6 0                 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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7dVLFq0qFu3bkz+SVhezQ/H+j8umbSu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\nXkMQAgAAr/0/46YOEBYdO0MAAAAASUVORK5CYII=",
      "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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3Xq/X6/V6w8LC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\nBBwAAAAAAED2/j+tzergpFzfEAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Top 10\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_plotmafsummary_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VXbt2rZOTkxAiODh44cKF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\nAGBHVAAAgB1RAQAAdgJ1Ncf5AdqHHAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Variant Classification summary”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=5)\n",
    "plotmafSummary(maf = dfx, rmOutlier = FALSE ,dashboard = F,addStat = 'median', textSize = 2, top = gf_sx, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oncoplots or waterfall plots\n",
    "- Adding Clinical Data on oncoplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Oncoplots with the total genes from the gene summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wsLs7GxYabqELZ7927artGt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\nbt2irRvx8fFFRUX79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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=6)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=6)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class  MAF \n",
       "                   ID summary  Mean Median\n",
       " 1:        NCBI_Build      38    NA     NA\n",
       " 2:            Center      NA    NA     NA\n",
       " 3:           Samples      11    NA     NA\n",
       " 4:            nGenes      10    NA     NA\n",
       " 5:   Frame_Shift_Del       2 0.182      0\n",
       " 6:   Frame_Shift_Ins       2 0.182      0\n",
       " 7: Missense_Mutation       3 0.273      0\n",
       " 8: Nonsense_Mutation       2 0.182      0\n",
       " 9:       Splice_Site       2 0.182      0\n",
       "10:             total      11 1.000      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vq0IIt99+++njlZWVmzZtuvzyy1v/2a2pqSmE0KlTp/OsFovFBg4c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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=13,repr.plot.height=6)\n",
    "dfx.titv = titv(maf = dfx, plot = F, useSyn = T  )\n",
    "plotTiTv(res = dfx.titv ,showBarcodes = F, textSize=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OpBgAYAAUFx7fQzfc9vWm4vL3Mw3rHMfRvLci9WHBuMPt3veUbodP/I2IhOfbVm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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=8)\n",
    "set.seed(42)\n",
    "geneCloud(input = dfx, min=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k//1sVNFx4x9gdhUH6uu/jI2sK9z5ZWbBwnDSZiLe87ftqXzg+Sk/wfuFhbJn3K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Cqkf+so5s49WWEvOI+Q4FNh6MLIxs+9fN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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "           min.freq =1,\n",
    "           max.words=50, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.35, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_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 >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Drug Interactions\n",
    "- plot shows potential druggable gene categories along with upto top 5 genes involved in them. \n",
    "- One can also extract information on drug-gene interactions. \n",
    "- [check this documentation](https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html#98_drug-gene_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_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": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pathway alteration fractions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pathway  N n_affected_genes fraction_affected\n",
      " 1:       TP53  6                3        0.50000000\n",
      " 2:    RTK-RAS 85                1        0.01176471\n",
      " 3: Cell_Cycle 15                0        0.00000000\n",
      " 4:      Hippo 38                0        0.00000000\n",
      " 5:        MYC 13                0        0.00000000\n",
      " 6:      NOTCH 71                0        0.00000000\n",
      " 7:       NRF2  3                0        0.00000000\n",
      " 8:       PI3K 29                0        0.00000000\n",
      " 9:   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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f7+/omJiWKxWF1dnS2USCQxMTEjR45c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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Rhabdomyosarcoma_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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAADwCAIAAACfVArnAAAABmJLR0QA/wD/AP+gvaeTAAAb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\nQAgAAJBS/x/59R+cd5kSlgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vq12rZtW1BQUGhoaPPmzadMmaKuSSqKEhsbW15ebjKZ/P391aleffXVDRs2+Pn5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\nExh6jyUAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=2)\n",
    "for( i in 1:length( op$data$Pathway ) ){\n",
    "    pway = op$data$Pathway[i]\n",
    "    PlotOncogenicPathways(dfx,pway, fullPathway = F, removeNonMutated = T)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "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": 67,
   "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": 68,
   "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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Rhabdomyosarcoma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Rhabdomyosarcoma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Rhabdomyosarcoma\""
      ]
     },
     "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Rhabdomyosarcoma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Rhabdomyosarcoma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Rhabdomyosarcoma_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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Rhabdomyosarcoma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Rhabdomyosarcoma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Rhabdomyosarcoma_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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Rhabdomyosarcoma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Rhabdomyosarcoma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Rhabdomyosarcoma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Rhabdomyosarcoma_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": 73,
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.0 (2018-04-23)\n",
       "Platform: x86_64-redhat-linux-gnu (64-bit)\n",
       "Running under: Fedora 24 (Workstation Edition)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  grid      stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] wordcloud_2.6       RColorBrewer_1.1-2  knitr_1.22         \n",
       " [4] corrplot_0.84       maftools_1.8.0      Biobase_2.42.0     \n",
       " [7] BiocGenerics_0.28.0 vcd_1.4-4           ggpubr_0.2         \n",
       "[10] magrittr_1.5        xtable_1.8-4        mixOmics_6.6.2     \n",
       "[13] MASS_7.3-51.1       factoextra_1.0.5    FactoMineR_1.41    \n",
       "[16] DescTools_0.99.27   gtools_3.8.1        broom_0.5.1        \n",
       "[19] d3heatmap_0.6.1.2   pheatmap_1.0.12     reshape2_1.4.3     \n",
       "[22] mosaic_1.4.0        Matrix_1.2-15       mosaicData_0.17.0  \n",
       "[25] ggformula_0.9.0     ggstance_0.3.1      lattice_0.20-38    \n",
       "[28] xda_0.2             forcats_0.3.0       stringr_1.4.0      \n",
       "[31] dplyr_0.8.1         purrr_0.3.2         readr_1.3.1        \n",
       "[34] tidyr_0.8.3         tibble_2.1.1        ggplot2_3.1.1.9000 \n",
       "[37] tidyverse_1.2.1    \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] readxl_1.2.0                uuid_0.1-2                 \n",
       "  [3] backports_1.1.4             circlize_0.4.5             \n",
       "  [5] NMF_0.21.0                  plyr_1.8.4                 \n",
       "  [7] igraph_1.2.4.1              repr_0.19.2                \n",
       "  [9] lazyeval_0.2.2              splines_3.5.0              \n",
       " [11] BiocParallel_1.16.6         GenomeInfoDb_1.18.2        \n",
       " [13] gridBase_0.4-7              digest_0.6.18              \n",
       " [15] foreach_1.4.4               htmltools_0.3.6            \n",
       " [17] BSgenome_1.50.0             cluster_2.0.7-1            \n",
       " [19] doParallel_1.0.14           mosaicCore_0.6.0           \n",
       " [21] ComplexHeatmap_1.20.0       Biostrings_2.50.2          \n",
       " [23] modelr_0.1.2                matrixStats_0.54.0         \n",
       " [25] rARPACK_0.11-0              colorspace_1.4-1           \n",
       " [27] rvest_0.3.2                 ggrepel_0.8.1              \n",
       " [29] xfun_0.5                    haven_2.1.0                \n",
       " [31] crayon_1.3.4                RCurl_1.95-4.11            \n",
       " [33] jsonlite_1.6                survival_2.43-3            \n",
       " [35] zoo_1.8-4                   iterators_1.0.10           \n",
       " [37] glue_1.3.1                  registry_0.5               \n",
       " [39] gtable_0.3.0                zlibbioc_1.28.0            \n",
       " [41] XVector_0.22.0              DelayedArray_0.8.0         \n",
       " [43] GetoptLong_0.1.7            shape_1.4.4                \n",
       " [45] scales_1.0.0                mvtnorm_1.0-8              \n",
       " [47] rngtools_1.3.1              bibtex_0.4.2               \n",
       " [49] Rcpp_1.0.1                  mclust_5.4.2               \n",
       " [51] flashClust_1.01-2           foreign_0.8-71             \n",
       " [53] stats4_3.5.0                htmlwidgets_1.3            \n",
       " [55] httr_1.4.0                  XML_3.98-1.16              \n",
       " [57] pkgconfig_2.0.2             manipulate_1.0.1           \n",
       " [59] labeling_0.3                tidyselect_0.2.5           \n",
       " [61] rlang_0.3.4                 munsell_0.5.0              \n",
       " [63] cellranger_1.1.0            tools_3.5.0                \n",
       " [65] cli_1.1.0                   generics_0.0.2             \n",
       " [67] evaluate_0.13               ggdendro_0.1-20            \n",
       " [69] nlme_3.1-137                leaps_3.0                  \n",
       " [71] xml2_1.2.0                  compiler_3.5.0             \n",
       " [73] rstudioapi_0.10             png_0.1-7                  \n",
       " [75] stringi_1.4.3               highr_0.8                  \n",
       " [77] RSpectra_0.13-1             IRdisplay_0.7.0            \n",
       " [79] pillar_1.4.0                lmtest_0.9-36              \n",
       " [81] GlobalOptions_0.1.0         cowplot_0.9.4              \n",
       " [83] data.table_1.12.2           bitops_1.0-6               \n",
       " [85] corpcor_1.6.9               rtracklayer_1.42.1         \n",
       " [87] GenomicRanges_1.34.0        R6_2.4.0                   \n",
       " [89] gridExtra_2.3               IRanges_2.16.0             \n",
       " [91] codetools_0.2-16            boot_1.3-20                \n",
       " [93] assertthat_0.2.1            SummarizedExperiment_1.12.0\n",
       " [95] pkgmaker_0.27               rjson_0.2.20               \n",
       " [97] withr_2.1.2                 GenomicAlignments_1.18.1   \n",
       " [99] Rsamtools_1.34.1            S4Vectors_0.20.1           \n",
       "[101] GenomeInfoDbData_1.2.0      expm_0.999-3               \n",
       "[103] hms_0.4.2                   IRkernel_0.8.15            \n",
       "[105] Cairo_1.5-9                 pbdZMQ_0.3-3               \n",
       "[107] scatterplot3d_0.3-41        lubridate_1.7.4            \n",
       "[109] base64enc_0.1-3             ellipse_0.4.1              "
      ]
     },
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