analysis/KCPG/KCPG_woAR_TT-SoftTissueSarcoma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Soft Tissue Sarcoma\n",
    "----- \n",
    "- Date : 17th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse) \n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutationa Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Soft Tissue Sarcoma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     \n",
       "                      STS\n",
       "  Soft Tissue Sarcoma  93"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$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> 6</td><td>93</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t  6 & 93\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "|  6 | 93 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,]  6   93  \n",
       "[2,] 84   12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(dim(df_ss), dim(df2_ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MAF object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Summarizing..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  ID summary  Mean Median\n",
      "1:        NCBI_Build      38    NA     NA\n",
      "2:            Center      NA    NA     NA\n",
      "3:           Samples       6    NA     NA\n",
      "4:            nGenes       5    NA     NA\n",
      "5:   Frame_Shift_Ins       1 0.167    0.0\n",
      "6: Missense_Mutation       3 0.500    0.5\n",
      "7: Nonsense_Mutation       1 0.167    0.0\n",
      "8:       Splice_Site       1 0.167    0.0\n",
      "9:             total       6 1.000    1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Gene Summary..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hugo_Symbol Frame_Shift_Ins Missense_Mutation Nonsense_Mutation Splice_Site\n",
      "1:       BRCA1               1                 0                 1           0\n",
      "2:          AR               0                 1                 0           0\n",
      "3:         ATM               0                 1                 0           0\n",
      "4:         KIT               0                 1                 0           0\n",
      "5:       NTRK1               0                 0                 0           1\n",
      "   total MutatedSamples AlteredSamples\n",
      "1:     2              2              2\n",
      "2:     1              1              1\n",
      "3:     1              1              1\n",
      "4:     1              1              1\n",
      "5:     1              1              1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Checking clinical data..\n",
      "Done !\n"
     ]
    }
   ],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss, isTCGA = FALSE, verbose = T, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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> 6               </td><td>   NA            </td><td> NA              </td></tr>\n",
       "\t<tr><td>nGenes           </td><td> 5               </td><td>   NA            </td><td> NA              </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 1               </td><td>0.167            </td><td>0.0              </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 3               </td><td>0.500            </td><td>0.5              </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 1               </td><td>0.167            </td><td>0.0              </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 1               </td><td>0.167            </td><td>0.0              </td></tr>\n",
       "\t<tr><td>total            </td><td> 6               </td><td>1.000            </td><td>1.0              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " ID & summary & Mean & Median\\\\\n",
       "\\hline\n",
       "\t NCBI\\_Build        & 38                  &    NA               &  NA                \\\\\n",
       "\t Center            & NA                &    NA             &  NA              \\\\\n",
       "\t Samples           &  6                &    NA             &  NA              \\\\\n",
       "\t nGenes            &  5                &    NA             &  NA              \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  1                    & 0.167                 & 0.0                  \\\\\n",
       "\t Missense\\_Mutation &  3                  & 0.500               & 0.5                \\\\\n",
       "\t Nonsense\\_Mutation &  1                  & 0.167               & 0.0                \\\\\n",
       "\t Splice\\_Site       &  1                  & 0.167               & 0.0                \\\\\n",
       "\t total             &  6                & 1.000             & 1.0              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ID | summary | Mean | Median |\n",
       "|---|---|---|---|\n",
       "| NCBI_Build        | 38                |    NA             |  NA               |\n",
       "| Center            | NA                |    NA             |  NA               |\n",
       "| Samples           |  6                |    NA             |  NA               |\n",
       "| nGenes            |  5                |    NA             |  NA               |\n",
       "| Frame_Shift_Ins   |  1                | 0.167             | 0.0               |\n",
       "| Missense_Mutation |  3                | 0.500             | 0.5               |\n",
       "| Nonsense_Mutation |  1                | 0.167             | 0.0               |\n",
       "| Splice_Site       |  1                | 0.167             | 0.0               |\n",
       "| total             |  6                | 1.000             | 1.0               |\n",
       "\n"
      ],
      "text/plain": [
       "  ID                summary Mean  Median\n",
       "1 NCBI_Build        38         NA  NA   \n",
       "2 Center            NA         NA  NA   \n",
       "3 Samples            6         NA  NA   \n",
       "4 nGenes             5         NA  NA   \n",
       "5 Frame_Shift_Ins    1      0.167 0.0   \n",
       "6 Missense_Mutation  3      0.500 0.5   \n",
       "7 Nonsense_Mutation  1      0.167 0.0   \n",
       "8 Splice_Site        1      0.167 0.0   \n",
       "9 total              6      1.000 1.0   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_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>BRCA1</td><td>1    </td><td>0    </td><td>1    </td><td>0    </td><td>2    </td><td>2    </td><td>2    </td></tr>\n",
       "\t<tr><td>AR   </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>ATM  </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>KIT  </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>NTRK1</td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t BRCA1 & 1     & 0     & 1     & 0     & 2     & 2     & 2    \\\\\n",
       "\t AR    & 0     & 1     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t ATM   & 0     & 1     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t KIT   & 0     & 1     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t NTRK1 & 0     & 0     & 0     & 1     & 1     & 1     & 1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| BRCA1 | 1     | 0     | 1     | 0     | 2     | 2     | 2     |\n",
       "| AR    | 0     | 1     | 0     | 0     | 1     | 1     | 1     |\n",
       "| ATM   | 0     | 1     | 0     | 0     | 1     | 1     | 1     |\n",
       "| KIT   | 0     | 1     | 0     | 0     | 1     | 1     | 1     |\n",
       "| NTRK1 | 0     | 0     | 0     | 1     | 1     | 1     | 1     |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Ins Missense_Mutation Nonsense_Mutation Splice_Site\n",
       "1 BRCA1       1               0                 1                 0          \n",
       "2 AR          0               1                 0                 0          \n",
       "3 ATM         0               1                 0                 0          \n",
       "4 KIT         0               1                 0                 0          \n",
       "5 NTRK1       0               0                 0                 1          \n",
       "  total MutatedSamples AlteredSamples\n",
       "1 2     2              2             \n",
       "2 1     1              1             \n",
       "3 1     1              1             \n",
       "4 1     1              1             \n",
       "5 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>5</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 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    INS   SNP   total\n",
       "---------------------  ----  ----  ------\n",
       "SJSTS019602               0     1       1\n",
       "SJSTS019607               1     0       1\n",
       "SJSTS019623               0     1       1\n",
       "SJSTS042132               0     1       1\n",
       "SJSTS042136               0     1       1\n",
       "SJSTS042766               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",
       "SJSTS019602                    1\n",
       "SJSTS019607                    1\n",
       "SJSTS019623                    1\n",
       "SJSTS042132                    1\n",
       "SJSTS042136                    1\n",
       "SJSTS042766                    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": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_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>BRCA1</td><td>1    </td><td>0    </td><td>1    </td><td>0    </td><td>2    </td><td>2    </td><td>2    </td></tr>\n",
       "\t<tr><td>AR   </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>ATM  </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>KIT  </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>NTRK1</td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t BRCA1 & 1     & 0     & 1     & 0     & 2     & 2     & 2    \\\\\n",
       "\t AR    & 0     & 1     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t ATM   & 0     & 1     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t KIT   & 0     & 1     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t NTRK1 & 0     & 0     & 0     & 1     & 1     & 1     & 1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| BRCA1 | 1     | 0     | 1     | 0     | 2     | 2     | 2     |\n",
       "| AR    | 0     | 1     | 0     | 0     | 1     | 1     | 1     |\n",
       "| ATM   | 0     | 1     | 0     | 0     | 1     | 1     | 1     |\n",
       "| KIT   | 0     | 1     | 0     | 0     | 1     | 1     | 1     |\n",
       "| NTRK1 | 0     | 0     | 0     | 1     | 1     | 1     | 1     |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Ins Missense_Mutation Nonsense_Mutation Splice_Site\n",
       "1 BRCA1       1               0                 1                 0          \n",
       "2 AR          0               1                 0                 0          \n",
       "3 ATM         0               1                 0                 0          \n",
       "4 KIT         0               1                 0                 0          \n",
       "5 NTRK1       0               0                 0                 1          \n",
       "  total MutatedSamples AlteredSamples\n",
       "1 2     2              2             \n",
       "2 1     1              1             \n",
       "3 1     1              1             \n",
       "4 1     1              1             \n",
       "5 1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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51bmpqaurq6T548wXrSKplMTkxMVFJS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2tbVZWFhQqdTPhyKEIiIixBxKSGJnampq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\nAqaFU2GYRPY+AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs_mat = gs %>% dplyr::select(-c('Hugo_Symbol','total', 'AlteredSamples', 'MutatedSamples') )\n",
    "rownames(gs_mat)=gs$Hugo_Symbol\n",
    "options(repr.plot.height=4,repr.plot.width=3)\n",
    "ggballoonplot(gs_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t++Lf8CAQCAdMePH+dwlJBEJ06cUPi56g9Cmqb37t377NmzMWPG6OrqSh2fx+Nl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\nkFr7B3CNbwRS01baAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=4)\n",
    "gs %>% ggdotchart(\"Hugo_Symbol\",\"MutatedSamples\", \n",
    "                  rotate = TRUE,\n",
    "                  sorting = \"descending\",\n",
    "                  show.label = F,size = \"MutatedSamples\", \n",
    "                  ggtheme = theme_pubr() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotmafSummary\n",
    "to plot the summary of the maf file, which displays number of variants in each sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IPAUHAAAAEHkKDgAAACDyFBwAAABA5Ck4AEdsz4UAAA8jSURBVAAAgMhTcAAAAACR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",
      "text/plain": [
       "Plot with title “Top 5\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_plotmafsummary_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gUAAIiOUAEAAKIjVAAAgOgIFQAAIDpC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\nTkSuQmCC",
      "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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ABAOiYOKklJSf7+/jVq1PDy8goLC9Pr9SXZa926dRqN5urVq6YtBgAAAEAlZcqg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\nPXr0/fffq8f58ccfs7OzhRABAQFCiOjoaOPRX23atGnatOn06dM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+7ds7GxKeIKS43rzVtmX7tWqEUs+/lWWvalkYXXrFkz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ZswwLDx48+M6dO5cvXzY3N8/Ozj5y5EjR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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=7,repr.plot.width=9)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_oncoplot_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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-Soft_Tissue_Sarcoma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_oncoplot_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Transition and Transversions\n",
    "returns a list of summarized tables in various ways. Summarized data can also be visualized as a boxplot showing overall distribution of six different conversions and as a stacked barplot showing fraction of conversions in each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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       6    NA     NA\n",
       "4:            nGenes       5    NA     NA\n",
       "5:   Frame_Shift_Ins       1 0.167    0.0\n",
       "6: Missense_Mutation       3 0.500    0.5\n",
       "7: Nonsense_Mutation       1 0.167    0.0\n",
       "8:       Splice_Site       1 0.167    0.0\n",
       "9:             total       6 1.000    1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZOXPmhBDuv//+b37zmz/96U+//e1vjxgxYt++fdGXShZJqR9rSyko\nKKiqqpo6dWpRUVFBQUFxcfHMmTOrq6sLCgrSCAwAAADAuUunSFq1atXMmTNbWlpCCMePH//h\nD3+Ymtq+ffujjz4afan/+I//aGlpqaqqOnWqa9euCxcufPPNN48dO1ZbW/vQQw8VFhamkRYA\nAACAjEinSFq4cGEIoVu3bl/72td+97vfHTlyJIRw++23jxs3LoSwcuXKzEYEAAAAIBekUyRt\n3rw5hHDnnXfedNNNq1evDiHk5eV997vfvemmm0IIu3fvzmxEAAAAAHJBOkVS8hSk4uLiEMKq\nVatCCFdffXX37t27dOkSQjh69GhGEwIAAACQE9Ipknr16hVCeOWVV3bu3Pn666+HEK677roQ\nQl1dXWoWAAAAgE+ZdIqkL37xiyGEefPmDR48OHnk9vXXX//cc8/967/+awhh4MCBmY0IAAAA\nQC5Ip0j6p3/6p/z8/PDhW2xDhw4dOnToL3/5y7fffjuE8K1vfSuzEQEAAADIBekUSUOHDq2q\nqho+fHh+fv7nP//5n/zkJ3l5ecmpGTNmfOUrX8loQgAAAAByQn56Hxs1atSGDRsSiUQs9v+q\nqL//+7//9re/3b9//8xlAwAAACCHpFkkJaVapBDC8OHDzzkMAAAAALkr/SKpubl5586dR44c\nOXWqpKTkHCIBAAAAkIvSKZJOnDjxwAMPzJ8///jx46d9IPlTbgAAAAB8mqRTJC1YsGDOnDkZ\njwIAAABALkunSFq6dGnyom/fvoMGDYrH4xmNBAAAAEAuSqdI2rVrVwhhzJgxL7zwghYJAAAA\noI2IffwjpxgyZEgIoby8XIsEAAAA0HakUyT99V//dQihtrY202EAAAAAyF3pvNp20003rVq1\navny5XPmzLnuuus6dOjwkQcGDBiQiWwAAAAA5JA0i6SampoQwuzZs2fPnn3qAy0tLeeaCwAA\nAIAck86rbQAAAAC0QensSCotLe3Ro0fGowAAAACQy9Ipkh599NGM5wAAAAAgx3m1DQAAAIBI\n0tmRlPTBBx88//zzmzdv3rFjR58+fYYMGXL99de3a9cug+EAAAAAyB1pFkmvvvrqlClTamtr\nTx4cPHjw0qVLhw8fnolgAAAAAOSWdF5tO3To0Lhx4z7SIoUQamtrr7/++sOHD2ciGAAAAAC5\nJZ0i6V/+5V/2798fQhg5cuTPfvazqqqqf//3fy8rKwsh7Nu3b86cORnOCAAAAEAOSOfVtpdf\nfjmEMHr06FWrVsXj8eTgxIkTr7322rVr1/72t7/NZEAAAAAAckM6O5K2bNkSQvj617+eapFC\nCPF4fOLEialZAAAAAD5l0imSCgsLQwhvv/32R8YPHToUQrjgggvOPRYAAAAAuSadIqm4uDiE\nsGDBgq1bt6YGt23bNn/+/NQsAAAAAJ8y6ZyRdOutt65aterAgQODBw8eM2bMJZdcsnv37jVr\n1pw4cSKE8PWvfz3TIQEAAADIvnSKpMmTJz/xxBOrVq1qbm5etWrVyVPXXHPN3/3d32UoGwAA\nAAA5JJ1X2/Ly8p5//vkHH3ywU6dOqcGOHTt+73vf+8UvfpGXl5e5eAAAAADkinR2JIUQ2rVr\nd++9995zzz27du3auXNnnz59vvCFL5z8I24AAAAAfMqkWSQlxePxvn379u3bN1NpAAAAAMhZ\nUYukX/3qVyGEgoKC8vLy3/72t60/XFZWdq65AAAAAMgxUYuksWPHhhD69Omzc+fOL33pS60/\n3NLScq65AAAAAMgx6Ry2DQAAAEAbFHVH0re+9a0QQteuXUMIEydOPI+JAAAAAMhJUYukH/3o\nR6nrysrK8xMGAAAAgNyVzqtt27dv37p166njzc3NW7duffPNN885FQAAAAA5J+qOpJNVVFTs\n2bPn1BO1GxoaBg4c2Llz5yNHjmQiGwAAAAA55CyKpGnTpiUv3nnnnZNvkxKJRE1NTQjh+PHj\nmYsHAAAAQK44iyJp0aJFrdym9OrV65wSAQAAAJCT0jkjqXUzZszI+JoAAAAAZN1Z7Eh69tln\nkxdTpkw5dOhQ6jYlHo9ffvnll112WcbSAQAAAJAzzqJI+upXv5q8WL58+cGDB1O3AAAAALQF\n6fxqW2VlZQihpaXlnXfeaW5uPnnqwIEDlZWVc+bMyUw6AAAAAHJGOmcktbS0zJ07t0ePHhdd\ndFH3/23QoEErV67MeEoAAAAAsi6dHUlPPvnk3XffnfEoAAAAAOSydHYkLVmyJHkxePDgEELH\njh2vvvrqTp06hRD69et32223ZTAfAAAAADkinSJp27ZtIYS7775706ZNX/7yl5ubm6uqqnbu\n3Dls2LB333130qRJEdfZsGFD3imWLVuWnE0kEvPnz7/88svbt2/fo0ePKVOmHDx4MI20AAAA\nAGREOkXSoUOHQgiXXXZZCOEv/uIv3n///Zqamm7duv3DP/zDgQMHi66JiAAAEs1JREFUZs2a\nFXGdLVu2tDJ73333zZgxY9u2bYlEYt++fY8//vi1117b1NSURmAAAAAAzl06RVKPHj1CCH/8\n4x/Dh2+3rV69OoTw3nvvhRDWrVsXcZ1kkfTYY4+9eZLrrrsuhPD+++8vXrw4hDBr1qz33nvv\nN7/5TSwWq6mpqaqqSiMwAAAAAOcuncO2hw4d+tZbbz3yyCMXXHDBxIkTQwgLFy48fPjwk08+\nGUI4fPhwxHWSRdKoUaOKioo+MvXiiy++++67IYQ777wzPz+/rKzsyiuv3LBhw89//vOxY8ee\nacFEIlFXV3fs2LFTp3bs2BExFQAAAACnlU6RNHPmzOeff/7EiRMvvfTS9773vUsvvfS///u/\nFyxYkJwdMWJExHWSRdKiRYueeuqplpaW0tLShx9+OFkqJXufwsLC5O6nEEJRUdGGDRvq6+vP\ntFoikRgzZkyU/VCJRCJiQgAAAABS0nm17Utf+tIzzzxzySWXhBDy8vIWLVrUrl275NTFF1/8\n0EMPRVmkqakp2QotXry4sbHxwIEDK1euHD58+N69e8OHxzB17tw59fwFF1wQWt3utGvXrohv\n1SW/AgAAAICzkk6RFEK44YYbdu3a9fTTT4cQxo0b98YbbyxdunT58uWbN28eOnRolBX2799f\nVlZWXl5eVVXV2Ni4cePGwsLChoaGuXPnnvb5vLy8EEIsdsbAffr0KS8vj/LVPXv2jPIYAAAA\nACdL59W27du3nzhxYsCAAV27dk2O9OvXr1+/fs3NzW+++eahQ4f69+//sYv07t177dq1qduS\nkpLx48dXVla+9tprIYQuXbqEEJLHJCUdOXIk+akzLRiLxdasWdPKGUk333xz6skIfyUAAAAA\n/0s6RVJFRcWePXtaWlo+Mt7Q0DBw4MDOnTsnS5/WVVdXr1+//sILL5w8eXJypKmpKYTQqVOn\nEELfvn1DCEePHt2zZ0+vXr1CCFu3bg0hJK/PJBaLDRw48LRTHTp0+NhIAAAAALTiLIqkadOm\nJS/eeeedk2+TEolETU1NCOH48eNRVtuxY8f06dPj8Xg8Hr/mmmuqq6tXrFgRQigrKwshjBkz\nprCw8MiRI3PmzLnvvvteeuml119/PYQwYcKE6IEBAAAAyKCzKJIWLVrUym1K65uGUiZMmDBs\n2LCamppJkyalBouLi++6664QQvv27e+4447vf//7S5YsWbJkSXK2vLx81KhR0QMDAAAAkEGZ\nPy1oxowZUR4rKCioqqqaOnVqUVFRQUFBcXHxzJkzq6urCwoKkg/88z//87x58/r379+uXbu+\nffvecccdv/zlL5NHbgMAAADw53cWO5KeffbZ5MWUKVMOHTqUuk2Jx+OXX375ZZddFnHBrl27\nLly4cOHChaedjcfjM2bMiFhLAQAAAHC+nUWR9NWvfjV5sXz58oMHD6ZuAQAAAGgL0vnVtsrK\nyoznAAAAACDHpVMkHTx4sJXZeDzepUuXdPMAAAAAkKPSKZK6d+/eymxxcXFtbW26eQAAAADI\nUZn/1TYAAAAAPpXS2ZF05ZVXfmTk+PHjdXV1J06c+NznPldcXJyJYAAAAADklnSKpN///ven\nDm7fvn3EiBGxWGzBggXnnAoAAACAnJOxV9uKioomT568e/fu73znO5laEwAAAIDckckzkjp2\n7BhCqK6uzuCaAAAAAOSIdF5ta2xs/MhIc3PzG2+8sXTp0hDC4cOHM5ALAAAAgByTTpFUWFjY\nyuywYcPSDQMAAABA7srkq20hhHg8/uCDD2Z2TQAAAAByQTo7kk6756hTp04DBgyYNm3akCFD\nzjkVAAAAADknnSLpD3/4Q8ZzAAAAAJDjMvxqGwAAAACfVlF3JPXu3Tv6ort3704rDAAAAAC5\nK2qRtGfPnvOaAwAAAIAc59U2AAAAACI568O2O3fufPXVV5eWlo4YMeKs3ncDAAAA4BMtapF0\n2WWXbdu2LYTQ2Ni4evXq1atXhxA+//nPjxw5srS0tLS0dOjQofn56fwGHAAAAACfCFGrn7q6\nukOHDq1fv766uvqVV15Zv379u++++9Zbb7311ltPPPFECKFTp05XXXVVslQaO3bs+cwMAAAA\nQBacxR6iiy66aOzYscmSKJFIbNmy5ZUPbd68+ejRo2vWrFmzZk0IoaWl5XzlBQAAACBL0nwZ\nLRaLFRcXFxcXT5kyZd++fb/+9a8XLly4cePGzIYDAAAAIHekUyQ1Nzdv3rz55Q/V19efPPuZ\nz3wmQ9kAAAAAyCFRi6SGhob169cnm6NXXnnlyJEjJ89edNFFpaWlI0eOHDly5PDhw89DTgAA\nAACyLGqR1KVLl4+cfNS/f/+RHxowYEBeXt55iAcAAABArohaJKVapM6dO1911VUjR47s3r17\nCOH48eNVVVVVVVUnP3z77bdnNiUAAAAAWXfWZyQ1Njae2hx9hCIJAAAA4NMnlu0AAAAAAHwy\nRN2RdMstt5zXHAAAAADkuKhF0rJly85rDgAAAABynFfbAAAAAIhEkQQAAABAJIokAAAAACJR\nJAEAAAAQiSIJAAAAgEgUSQAAAABEkn+On//ggw8qKyvXr18fi8VGjBhx66235uef65oAAAAA\n5KBzKn0aGhpGjx69cePG5O2SJUsWL168evXqwsLCTGQDAAAAIIec06tt99xzz8aNG3v27Dl3\n7twf/OAHvXv3fvXVV++9995MhQMAAAAgd0TakfTBBx/E4/FY7KOt0/PPPx9CePbZZ6+66qoQ\nQkVFxRVXXPHcc8/98Ic/zHhQAAAAALIr0o6kffv2XXHFFf/1X//V0tJy8vjx48dDCF27dk3e\nduvWLYRw9OjRTIcEAAAAIPsiFUkXXXRRTU3NV77ylYqKiurq6tR4RUVFCOHWW2994YUXVq9e\nfcstt4QQRo8efX6iAgAAAJBNkYqkjh07/vrXvy4pKXnppZdKS0tvuOGGN954I4Tw8MMPd+vW\n7fe///2Xv/zlv/zLv6yuru7Zs+eCBQvOc2YAAAAAsiDqYdt/9Vd/9dprr1VWVn7hC19YsWLF\nkCFDvvGNb+Tl5dXW1t5xxx1f/OIXy8vL77nnns2bN/fq1eu8JgYAAAAgKyIdtp0Ui8UmTpx4\n4403Llmy5MEHH1y6dOl//ud/3n777ffff3/qmCQAAAAAPq2i7khKad++/bRp0+rr6++9995Y\nLDZ//vy+ffvOmTPHGdsAAAAAn25nUSTV1dXdeOONl1xyyec+97kpU6ZMmjRp+/bt3/zmN48e\nPTp79ux+/fo9+uijTU1N5y8rAAAAAFkUtUjatGlTSUnJ008/vXv37r179z799NMlJSXvvPPO\nj3/849ra2htuuGHfvn3/+I//OGjQoGXLlp3XxAAAAABkRdQiacaMGcePHx87duyWLVu2bds2\nbty499577zvf+U4IYcCAAc8888zLL79cVlZWX19/6623ns/AAAAAAGRH1CJp/fr1IYSf/OQn\nAwYM6N+//09/+tMQwssvv5x6YMSIES+99NJzzz03aNCg8xEUAAAAgOyKWiR17949hPDGG28k\nb5MXycGUvLy8cePG/fGPf8xoQgAAAAByQtQi6ZZbbgkhfO1rX7vzzjunTp06fvz41OBHxOPx\niGueOHFiwYIFJSUlnTp1Kioq+sY3vrF79+7U7IYNG/JO4QAmAAAAgGzJj/jcfffdt3nz5hUr\nVvzbv/1bcmT8+PGzZ88+l++ePn364sWLQwh5eXn19fX19fW/+MUvNm3a9NnPfjaEsGXLlnNZ\nHAAAAIDMiloktW/f/plnnlm3bl11dXUI4eqrr66oqMjLy0v7i//0pz898sgjIYTZs2d/97vf\n3bRp0+jRo/fv3z9v3rx58+aFD4ukxx57bNSoUalPXXzxxWl/IwAAAADnImqRFELIy8urqKio\nqKjIyBfX1ta2tLSEEO65557PfOYzV1xxxXXXXffEE0+8/vrryQeSRdKoUaOKiooirplIJOrq\n6o4dO3bq1I4dOzISGwAAAKDNOosiKbP69++/dOnSeDzeqVOn5Mjhw4dDCAUFBcnbZJG0aNGi\np556qqWlpbS09OGHH26lVEokEmPGjFm3bt3HfnUikcjAHwAAAADQxmStSLr00ksvvfTS1O3y\n5ct/9atfhRAmTZoUQmhqaqqvrw8hLF68uGPHjseOHVu5cuXatWu3bNnSs2fP0y64a9euKC1S\nCGHv3r1DhgzJwN8AAAAA0JZE/dW286e5uXnWrFk33XRTCOHuu+9OXuzfv7+srKy8vLyqqqqx\nsXHjxo2FhYUNDQ1z58490zp9+vQpLy+P8o1nqqIAAAAAaEXWdiQl7d+//2/+5m+qqqo6duz4\nox/96LbbbkuO9+7de+3atanHSkpKxo8fX1lZ+dprr51pqVgstmbNmlbOSLr55ptTT2bybwAA\nAABoG7JZJNXV1Y0ePXrv3r2DBw9+8sknBw0alJqqrq5ev379hRdeOHny5ORIU1NTCCF1oNJp\nxWKxgQMHnnaqQ4cOGcsNAAAA0CZlrUhKJBI33HDD3r17L7744srKyo4dO+7cuTOE0KFDhx49\neuzYsWP69OnxeDwej19zzTXV1dUrVqwIIZSVlWUrMAAAAEAbl7UiKXlydghh3759JSUlqfHy\n8vK1a9dOmDBh2LBhNTU1ybO3k4qLi++6664sZAUAAAAgi4dtb9q0qZXZgoKCqqqqqVOnFhUV\nFRQUFBcXz5w5s7q6uqCg4M+WEAAAAICTZW1H0tSpU6dOndrKA127dl24cOHChQv/bJEAAAAA\naIXfLwMAAAAgEkUSAAAAAJEokgAAAACIRJEEAAAAQCSKJAAAAAAiUSQBAAAAEIkiCQAAAIBI\nFEkAAAAARKJIAgAAACASRRIAAAAAkSiSAAAAAIhEkQQAAABAJIokAAAAACJRJAEAAAAQiSIJ\nAAAAgEgUSQAAAABEokgCAAAAIBJFEgAAAACRKJIAAAAAiESRBAAAAEAkiiQAAAAAIlEkAQAA\nABCJIgkAAACASBRJAAAAAESiSAIAAAAgEkUSAAAAAJEokgAAAACIRJEEAAAAQCSKJAAAAAAi\nUSQBAAAAEIkiCQAAAIBIFEkAAAAARKJIAgAAACASRRIAAAAAkSiSAAAAAIhEkQQAAABAJIok\nAAAAACJRJAEAAAAQiSIJAAAAgEgUSQAAAABEokgCAAAAIBJFEgAAAACRKJIAAAAAiESRBAAA\nAEAkiiQAAAAAIlEkAQAAABCJIgkAAACASBRJAAAAAESiSAIAAAAgEkUSAAAAAJEokgAAAACI\nJHeLpEQiMX/+/Msvv7x9+/Y9evSYMmXKwYMHsx0KAAAAoO3K3SLpvvvumzFjxrZt2xKJxL59\n+x5//PFrr722qakp27kAAAAA2qgcLZLef//9xYsXhxBmzZr13nvv/eY3v4nFYjU1NVVVVdmO\nBgAAANBG5Wc7wOm9+OKL7777bgjhzjvvzM/PLysru/LKKzds2PDzn/987NixZ/pUIpGoq6s7\nduzYqVM7duw4j3EBAAAA2oAcLZKSvU9hYWGPHj2SI0VFRRs2bKivrz/TRxKJxJgxY9atW/ex\niycSiUzlBAAAAGg7crRIOnToUAihc+fOqZELLrgghHD48OEzfWTXrl1RWqQQwoEDB8454Pmy\ne/+fsh0BAML/qa/LdgRI04BsB0ipf/uM/wMKAJ9cOVoknSovLy+EEIud8VCnPn36lJeXf2yX\ndOGFF44aNSrD4TJn1spIXRjQipW3rcx2BPjEq/ifR7IdAT7xfjDtgWxHgE82/6iD3JSjRVKX\nLl1CCMljkpKOHDkSQujdu/eZPhKLxdasWXOmM5JCCIlEoqGhoaKiIj8/R/9qAAAAgFyWo5VK\n3759QwhHjx7ds2dPr169Qghbt24NISSvzyQWiw0cOPDPkxAAAACgrTnjm2LZNWbMmMLCwhDC\nnDlz9u7d++STT77++ushhAkTJmQ7GgAAAEAbldfS0pLtDKd37733fv/73z95pLy8/MUXX0we\nlgQAAADAn1n8gQceyHaG06uoqCgsLNy5c2djY2OfPn3+9m//dunSpe3atct2LgAAAIA2Knd3\nJAEAAACQU3L0jCQAAAAAco0iCQAAAIBIFEkAAAAARKJIAgAAACASRRIAAAAAkSiSAAAAAIhE\nkQQAAABAJIokAAAAACJRJAEAAAAQiSIJAAAAgEgUSQAAAABEokgCAAAAIBJFEgAAAACRKJIA\nAAAAiESRBAAAAEAkiiQAAAAAIlEkAQAAABCJIgkAAACASBRJAAAAAESiSAIAAAAgEkUSAAAA\nAJEokgAAAACIRJEEAAAAQCSKJAAAAAAiUSQBAAAAEIkiCQAAAIBIFEkAAAAARKJIAgAAACAS\nRRIAAAAAkSiSAAAAAIhEkQQAAABAJIokAAAAACJRJAEAAAAQiSIJAAAAgEgUSQAAAABE8n8B\n6PfW45IXoAsAAAAASUVORK5CYII=",
      "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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WQ9B2TcW8gnI04WO755kn+zrC528ERZ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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=8)\n",
    "set.seed(42)\n",
    "geneCloud(input = dfx, min=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=8, width=8)\n",
    "set.seed(42)\n",
    "geneCloud(input = dfx, min=0)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7aT6ajkvatGh8hNwtL33u20nQxAgKKpVCrXcwAAACAH3CEEAAAIlCAEAAAIlCAE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\nEEoQAgAAhBKEAAAAof4P7TBQrSLxHu8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "           min.freq =1,\n",
    "           max.words=50, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.35, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_genecloud_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Drug Interactions\n",
    "- plot shows potential druggable gene categories along with upto top 5 genes involved in them. \n",
    "- One can also extract information on drug-gene interactions. \n",
    "- [check this documentation](https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html#98_drug-gene_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9LhcF0jAABA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\nDgAAAID9+f98PmfaaSXgwgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Druggable categories”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=6, repr.plot.width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_drugInteractions_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oncogenic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pathway alteration fractions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pathway  N n_affected_genes fraction_affected\n",
      " 1:    RTK-RAS 85                2        0.02352941\n",
      " 2:       TP53  6                1        0.16666667\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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cHBwS3rEgAAADQX94EjKirK398/MTGx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\nAwAASEfgAAAA0hE4AACAdAQOAAAg3f8ATll5mHOQEKIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_OncogenicPathway_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_OncogenicPathway_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "\n",
    "svg(out_svg, height=9, width=12)\n",
    "op\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAADwCAIAAACfVArnAAAABmJLR0QA/wD/AP+gvaeTAAAb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\nOz7bAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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29uW7y4uFcBAAD8VLV94IHiXsIRCMIi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\nRU5ErkJggg==",
      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=2)\n",
    "for( i in 1:length( op$data$Pathway ) ){\n",
    "    pway = op$data$Pathway[i]\n",
    "    PlotOncogenicPathways(dfx,pway, fullPathway = F, removeNonMutated = T)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=8)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[10], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[9], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=9)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = F, removeNonMutated = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Soft_Tissue_Sarcoma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Soft_Tissue_Sarcoma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Soft_Tissue_Sarcoma\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)) )\n",
    "paste0(\"Saving MAF file to : \", out_file)\n",
    "maftools::write.mafSummary(dfx, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_drugInteractions.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions.tsv')\n",
    "paste0(\"Saving drug Interactions data to : \", out_file)\n",
    "write.table(dfx_drug, out_file, sep=\"\\t\", row.names = F, quote = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_mutation_data.tsv\")\n",
    "paste0(\"Saving mutation data to : \", out_file)\n",
    "write.table(df_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Soft\\_Tissue\\_Sarcoma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Soft_Tissue_Sarcoma_clinical_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_clinical_data.tsv\")\n",
    "paste0(\"Saving clinical data to : \", out_file)\n",
    "write.table(df2_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               _                           \n",
       "platform       x86_64-redhat-linux-gnu     \n",
       "arch           x86_64                      \n",
       "os             linux-gnu                   \n",
       "system         x86_64, linux-gnu           \n",
       "status                                     \n",
       "major          3                           \n",
       "minor          5.0                         \n",
       "year           2018                        \n",
       "month          04                          \n",
       "day            23                          \n",
       "svn rev        74626                       \n",
       "language       R                           \n",
       "version.string R version 3.5.0 (2018-04-23)\n",
       "nickname       Joy in Playing              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.0 (2018-04-23)\n",
       "Platform: x86_64-redhat-linux-gnu (64-bit)\n",
       "Running under: Fedora 24 (Workstation Edition)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  grid      stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] wordcloud_2.6       RColorBrewer_1.1-2  knitr_1.22         \n",
       " [4] corrplot_0.84       maftools_1.8.0      Biobase_2.42.0     \n",
       " [7] BiocGenerics_0.28.0 vcd_1.4-4           ggpubr_0.2         \n",
       "[10] magrittr_1.5        xtable_1.8-4        mixOmics_6.6.2     \n",
       "[13] MASS_7.3-51.1       factoextra_1.0.5    FactoMineR_1.41    \n",
       "[16] DescTools_0.99.27   gtools_3.8.1        broom_0.5.1        \n",
       "[19] d3heatmap_0.6.1.2   pheatmap_1.0.12     reshape2_1.4.3     \n",
       "[22] mosaic_1.4.0        Matrix_1.2-15       mosaicData_0.17.0  \n",
       "[25] ggformula_0.9.0     ggstance_0.3.1      lattice_0.20-38    \n",
       "[28] xda_0.2             forcats_0.3.0       stringr_1.4.0      \n",
       "[31] dplyr_0.8.1         purrr_0.3.2         readr_1.3.1        \n",
       "[34] tidyr_0.8.3         tibble_2.1.1        ggplot2_3.1.1.9000 \n",
       "[37] tidyverse_1.2.1    \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] readxl_1.2.0                uuid_0.1-2                 \n",
       "  [3] backports_1.1.4             circlize_0.4.5             \n",
       "  [5] NMF_0.21.0                  plyr_1.8.4                 \n",
       "  [7] igraph_1.2.4.1              repr_0.19.2                \n",
       "  [9] lazyeval_0.2.2              splines_3.5.0              \n",
       " [11] BiocParallel_1.16.6         GenomeInfoDb_1.18.2        \n",
       " [13] gridBase_0.4-7              digest_0.6.18              \n",
       " [15] foreach_1.4.4               htmltools_0.3.6            \n",
       " [17] BSgenome_1.50.0             cluster_2.0.7-1            \n",
       " [19] doParallel_1.0.14           mosaicCore_0.6.0           \n",
       " [21] ComplexHeatmap_1.20.0       Biostrings_2.50.2          \n",
       " [23] modelr_0.1.2                matrixStats_0.54.0         \n",
       " [25] rARPACK_0.11-0              colorspace_1.4-1           \n",
       " [27] rvest_0.3.2                 ggrepel_0.8.1              \n",
       " [29] xfun_0.5                    haven_2.1.0                \n",
       " [31] crayon_1.3.4                RCurl_1.95-4.11            \n",
       " [33] jsonlite_1.6                survival_2.43-3            \n",
       " [35] zoo_1.8-4                   iterators_1.0.10           \n",
       " [37] glue_1.3.1                  registry_0.5               \n",
       " [39] gtable_0.3.0                zlibbioc_1.28.0            \n",
       " [41] XVector_0.22.0              DelayedArray_0.8.0         \n",
       " [43] GetoptLong_0.1.7            shape_1.4.4                \n",
       " [45] scales_1.0.0                mvtnorm_1.0-8              \n",
       " [47] rngtools_1.3.1              bibtex_0.4.2               \n",
       " [49] Rcpp_1.0.1                  mclust_5.4.2               \n",
       " [51] flashClust_1.01-2           foreign_0.8-71             \n",
       " [53] stats4_3.5.0                htmlwidgets_1.3            \n",
       " [55] httr_1.4.0                  XML_3.98-1.16              \n",
       " [57] pkgconfig_2.0.2             manipulate_1.0.1           \n",
       " [59] labeling_0.3                tidyselect_0.2.5           \n",
       " [61] rlang_0.3.4                 munsell_0.5.0              \n",
       " [63] cellranger_1.1.0            tools_3.5.0                \n",
       " [65] cli_1.1.0                   generics_0.0.2             \n",
       " [67] evaluate_0.13               ggdendro_0.1-20            \n",
       " [69] nlme_3.1-137                leaps_3.0                  \n",
       " [71] xml2_1.2.0                  compiler_3.5.0             \n",
       " [73] rstudioapi_0.10             png_0.1-7                  \n",
       " [75] stringi_1.4.3               highr_0.8                  \n",
       " [77] RSpectra_0.13-1             IRdisplay_0.7.0            \n",
       " [79] pillar_1.4.0                lmtest_0.9-36              \n",
       " [81] GlobalOptions_0.1.0         cowplot_0.9.4              \n",
       " [83] data.table_1.12.2           bitops_1.0-6               \n",
       " [85] corpcor_1.6.9               rtracklayer_1.42.1         \n",
       " [87] GenomicRanges_1.34.0        R6_2.4.0                   \n",
       " [89] gridExtra_2.3               IRanges_2.16.0             \n",
       " [91] codetools_0.2-16            boot_1.3-20                \n",
       " [93] assertthat_0.2.1            SummarizedExperiment_1.12.0\n",
       " [95] pkgmaker_0.27               rjson_0.2.20               \n",
       " [97] withr_2.1.2                 GenomicAlignments_1.18.1   \n",
       " [99] Rsamtools_1.34.1            S4Vectors_0.20.1           \n",
       "[101] GenomeInfoDbData_1.2.0      expm_0.999-3               \n",
       "[103] hms_0.4.2                   IRkernel_0.8.15            \n",
       "[105] Cairo_1.5-9                 pbdZMQ_0.3-3               \n",
       "[107] scatterplot3d_0.3-41        lubridate_1.7.4            \n",
       "[109] base64enc_0.1-3             ellipse_0.4.1              "
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