analysis/KCPG/KCPG_woAR_TT-CentralNervousSystem_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for CNS\n",
    "----- \n",
    "- Date : 17th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse) \n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Muataional Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3016</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3016\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3016\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3016   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Var1     Freq\n",
       "------  -----\n",
       "CNS       613\n",
       "WLM       405\n",
       "NBL       343\n",
       "RHB       259\n",
       "OS        241\n",
       "HGG       229\n",
       "RB        221\n",
       "EWS       192\n",
       "STS       177\n",
       "GCT       139\n",
       "ACT        79\n",
       "LGG        52\n",
       "CA         35\n",
       "OST        18\n",
       "RCC         5\n",
       "GICT        2\n",
       "LMPRT       2\n",
       "PGL         2\n",
       "BCC         1\n",
       "NM          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( sort( table(df$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'known',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>290</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 290\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 290\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 290  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD    AR AR/AD   XLD \n",
       "  153    92    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       \n",
       "        Het Hom\n",
       "  AD    153   0\n",
       "  AR     92   0\n",
       "  AR/AD  43   0\n",
       "  XLD     1   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$Mode_of_Inheritance_abb , df$genotype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Mode_of_Inheritance_abb != 'AR',]\n",
    "df = droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD AR/AD   XLD \n",
       "  153    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "182"
      ],
      "text/latex": [
       "182"
      ],
      "text/markdown": [
       "182"
      ],
      "text/plain": [
       "[1] 182"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "54"
      ],
      "text/latex": [
       "54"
      ],
      "text/markdown": [
       "54"
      ],
      "text/plain": [
       "[1] 54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Tumor_Sample_Barcode'</li>\n",
       "\t<li>'Tumor_type'</li>\n",
       "\t<li>'Tumor_type2'</li>\n",
       "\t<li>'Age_of_Onset'</li>\n",
       "\t<li>'Sex'</li>\n",
       "\t<li>'Age_group'</li>\n",
       "\t<li>'sample_type'</li>\n",
       "\t<li>'sequencing_type'</li>\n",
       "\t<li>'Access_unit'</li>\n",
       "\t<li>'Status'</li>\n",
       "\t<li>'Ethnicity'</li>\n",
       "\t<li>'Race'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Tumor\\_Sample\\_Barcode'\n",
       "\\item 'Tumor\\_type'\n",
       "\\item 'Tumor\\_type2'\n",
       "\\item 'Age\\_of\\_Onset'\n",
       "\\item 'Sex'\n",
       "\\item 'Age\\_group'\n",
       "\\item 'sample\\_type'\n",
       "\\item 'sequencing\\_type'\n",
       "\\item 'Access\\_unit'\n",
       "\\item 'Status'\n",
       "\\item 'Ethnicity'\n",
       "\\item 'Race'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Tumor_Sample_Barcode'\n",
       "2. 'Tumor_type'\n",
       "3. 'Tumor_type2'\n",
       "4. 'Age_of_Onset'\n",
       "5. 'Sex'\n",
       "6. 'Age_group'\n",
       "7. 'sample_type'\n",
       "8. 'sequencing_type'\n",
       "9. 'Access_unit'\n",
       "10. 'Status'\n",
       "11. 'Ethnicity'\n",
       "12. 'Race'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Tumor_Sample_Barcode\" \"Tumor_type\"           \"Tumor_type2\"         \n",
       " [4] \"Age_of_Onset\"         \"Sex\"                  \"Age_group\"           \n",
       " [7] \"sample_type\"          \"sequencing_type\"      \"Access_unit\"         \n",
       "[10] \"Status\"               \"Ethnicity\"            \"Race\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       CCF PCGP SJLIFE\n",
       "  Adrenocortical Carcinoma               1   19      2\n",
       "  Basal Cell Carcinoma                   0    0      1\n",
       "  Carcinoma                              0    0     14\n",
       "  Central Nervous System                 1    0    322\n",
       "  Ewing's Sarcoma                        6    0     89\n",
       "  Germ Cell Tumor                        1    0     73\n",
       "  Giant Cell Tumor                       0    0      3\n",
       "  High Grade Glioma                      5   75      0\n",
       "  Low Grade Glioma                       2   15      7\n",
       "  Low Malignant Potential Renal Tumors   2    0      0\n",
       "  Neuroblastoma                          3   48    139\n",
       "  Non-Malignant Tumor                    1    0      3\n",
       "  Osteosarcoma                          14    2    113\n",
       "  Other Solid Tumor                      0    0     10\n",
       "  Paraganglioma                          0    0      1\n",
       "  Renal Cell Carcinoma                   1    0      2\n",
       "  Retinoblastoma                         0    0     98\n",
       "  Rhabdomyosarcoma                       3   29    102\n",
       "  Soft Tissue Sarcoma                    8    0     85\n",
       "  Wilms' tumor                           2    0    205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2$Tumor_type , df2$Access_unit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>f</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Central Nervous System              </td><td>323                                 </td></tr>\n",
       "\t<tr><td>Wilms' tumor                        </td><td>207                                 </td></tr>\n",
       "\t<tr><td>Neuroblastoma                       </td><td>190                                 </td></tr>\n",
       "\t<tr><td>Rhabdomyosarcoma                    </td><td>134                                 </td></tr>\n",
       "\t<tr><td>Osteosarcoma                        </td><td>129                                 </td></tr>\n",
       "\t<tr><td>Retinoblastoma                      </td><td> 98                                 </td></tr>\n",
       "\t<tr><td>Ewing's Sarcoma                     </td><td> 95                                 </td></tr>\n",
       "\t<tr><td>Soft Tissue Sarcoma                 </td><td> 93                                 </td></tr>\n",
       "\t<tr><td>High Grade Glioma                   </td><td> 80                                 </td></tr>\n",
       "\t<tr><td>Germ Cell Tumor                     </td><td> 74                                 </td></tr>\n",
       "\t<tr><td>Low Grade Glioma                    </td><td> 24                                 </td></tr>\n",
       "\t<tr><td>Adrenocortical Carcinoma            </td><td> 22                                 </td></tr>\n",
       "\t<tr><td>Carcinoma                           </td><td> 14                                 </td></tr>\n",
       "\t<tr><td>Other Solid Tumor                   </td><td> 10                                 </td></tr>\n",
       "\t<tr><td>Non-Malignant Tumor                 </td><td>  4                                 </td></tr>\n",
       "\t<tr><td>Giant Cell Tumor                    </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Renal Cell Carcinoma                </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Low Malignant Potential Renal Tumors</td><td>  2                                 </td></tr>\n",
       "\t<tr><td>Basal Cell Carcinoma                </td><td>  1                                 </td></tr>\n",
       "\t<tr><td>Paraganglioma                       </td><td>  1                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " f & n\\\\\n",
       "\\hline\n",
       "\t Central Nervous System               & 323                                 \\\\\n",
       "\t Wilms' tumor                         & 207                                 \\\\\n",
       "\t Neuroblastoma                        & 190                                 \\\\\n",
       "\t Rhabdomyosarcoma                     & 134                                 \\\\\n",
       "\t Osteosarcoma                         & 129                                 \\\\\n",
       "\t Retinoblastoma                       &  98                                 \\\\\n",
       "\t Ewing's Sarcoma                      &  95                                 \\\\\n",
       "\t Soft Tissue Sarcoma                  &  93                                 \\\\\n",
       "\t High Grade Glioma                    &  80                                 \\\\\n",
       "\t Germ Cell Tumor                      &  74                                 \\\\\n",
       "\t Low Grade Glioma                     &  24                                 \\\\\n",
       "\t Adrenocortical Carcinoma             &  22                                 \\\\\n",
       "\t Carcinoma                            &  14                                 \\\\\n",
       "\t Other Solid Tumor                    &  10                                 \\\\\n",
       "\t Non-Malignant Tumor                  &   4                                 \\\\\n",
       "\t Giant Cell Tumor                     &   3                                 \\\\\n",
       "\t Renal Cell Carcinoma                 &   3                                 \\\\\n",
       "\t Low Malignant Potential Renal Tumors &   2                                 \\\\\n",
       "\t Basal Cell Carcinoma                 &   1                                 \\\\\n",
       "\t Paraganglioma                        &   1                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| f | n |\n",
       "|---|---|\n",
       "| Central Nervous System               | 323                                  |\n",
       "| Wilms' tumor                         | 207                                  |\n",
       "| Neuroblastoma                        | 190                                  |\n",
       "| Rhabdomyosarcoma                     | 134                                  |\n",
       "| Osteosarcoma                         | 129                                  |\n",
       "| Retinoblastoma                       |  98                                  |\n",
       "| Ewing's Sarcoma                      |  95                                  |\n",
       "| Soft Tissue Sarcoma                  |  93                                  |\n",
       "| High Grade Glioma                    |  80                                  |\n",
       "| Germ Cell Tumor                      |  74                                  |\n",
       "| Low Grade Glioma                     |  24                                  |\n",
       "| Adrenocortical Carcinoma             |  22                                  |\n",
       "| Carcinoma                            |  14                                  |\n",
       "| Other Solid Tumor                    |  10                                  |\n",
       "| Non-Malignant Tumor                  |   4                                  |\n",
       "| Giant Cell Tumor                     |   3                                  |\n",
       "| Renal Cell Carcinoma                 |   3                                  |\n",
       "| Low Malignant Potential Renal Tumors |   2                                  |\n",
       "| Basal Cell Carcinoma                 |   1                                  |\n",
       "| Paraganglioma                        |   1                                  |\n",
       "\n"
      ],
      "text/plain": [
       "   f                                    n  \n",
       "1  Central Nervous System               323\n",
       "2  Wilms' tumor                         207\n",
       "3  Neuroblastoma                        190\n",
       "4  Rhabdomyosarcoma                     134\n",
       "5  Osteosarcoma                         129\n",
       "6  Retinoblastoma                        98\n",
       "7  Ewing's Sarcoma                       95\n",
       "8  Soft Tissue Sarcoma                   93\n",
       "9  High Grade Glioma                     80\n",
       "10 Germ Cell Tumor                       74\n",
       "11 Low Grade Glioma                      24\n",
       "12 Adrenocortical Carcinoma              22\n",
       "13 Carcinoma                             14\n",
       "14 Other Solid Tumor                     10\n",
       "15 Non-Malignant Tumor                    4\n",
       "16 Giant Cell Tumor                       3\n",
       "17 Renal Cell Carcinoma                   3\n",
       "18 Low Malignant Potential Renal Tumors   2\n",
       "19 Basal Cell Carcinoma                   1\n",
       "20 Paraganglioma                          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_count( df2$Tumor_type , sort = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "|                                     | ACT| BCC| CA| CNS| EWS| GCT| GICT| HGG| LGG| LMPRT| NBL| NM|  OS| OST| PGL| RB| RCC| RHB| STS| WLM|\n",
       "|:------------------------------------|---:|---:|--:|---:|---:|---:|----:|---:|---:|-----:|---:|--:|---:|---:|---:|--:|---:|---:|---:|---:|\n",
       "|Adrenocortical Carcinoma             |  22|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Basal Cell Carcinoma                 |   0|   1|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Carcinoma                            |   0|   0| 14|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Central Nervous System               |   0|   0|  0| 323|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Ewing's Sarcoma                      |   0|   0|  0|   0|  95|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Germ Cell Tumor                      |   0|   0|  0|   0|   0|  74|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Giant Cell Tumor                     |   0|   0|  0|   0|   0|   0|    3|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|High Grade Glioma                    |   0|   0|  0|   0|   0|   0|    0|  80|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Low Grade Glioma                     |   0|   0|  0|   0|   0|   0|    0|   0|  24|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Low Malignant Potential Renal Tumors |   0|   0|  0|   0|   0|   0|    0|   0|   0|     2|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Neuroblastoma                        |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0| 190|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Non-Malignant Tumor                  |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  4|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Osteosarcoma                         |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0| 129|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Other Solid Tumor                    |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|  10|   0|  0|   0|   0|   0|   0|\n",
       "|Paraganglioma                        |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   1|  0|   0|   0|   0|   0|\n",
       "|Renal Cell Carcinoma                 |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   3|   0|   0|   0|\n",
       "|Retinoblastoma                       |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0| 98|   0|   0|   0|   0|\n",
       "|Rhabdomyosarcoma                     |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0| 134|   0|   0|\n",
       "|Soft Tissue Sarcoma                  |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|  93|   0|\n",
       "|Wilms' tumor                         |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0| 207|"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "knitr::kable( table(df2$Tumor_type , df2$Tumor_type2 ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype ='Central Nervous System'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                        \n",
       "                         CNS\n",
       "  Central Nervous System 323"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$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>47 </td><td>323</td></tr>\n",
       "\t<tr><td>84 </td><td> 12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 47  & 323\\\\\n",
       "\t 84  &  12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 47  | 323 |\n",
       "| 84  |  12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 47   323 \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": "markdown",
   "metadata": {},
   "source": [
    "- dfx contain   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss , isTCGA = FALSE, verbose = F, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>38               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Center           </td><td>NA               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Samples          </td><td>44               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td>22               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td>10               </td><td>0.227            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 3               </td><td>0.068            </td><td> 0               </td></tr>\n",
       "\t<tr><td>In_Frame_Del     </td><td> 1               </td><td>0.023            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td>14               </td><td>0.318            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td>11               </td><td>0.250            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 8               </td><td>0.182            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>47               </td><td>1.068            </td><td> 1               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " ID & summary & Mean & Median\\\\\n",
       "\\hline\n",
       "\t NCBI\\_Build        & 38                  &    NA               & NA                 \\\\\n",
       "\t Center            & NA                &    NA             & NA               \\\\\n",
       "\t Samples           & 44                &    NA             & NA               \\\\\n",
       "\t nGenes            & 22                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   & 10                    & 0.227                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  3                    & 0.068                 &  0                   \\\\\n",
       "\t In\\_Frame\\_Del      &  1                    & 0.023                 &  0                   \\\\\n",
       "\t Missense\\_Mutation & 14                  & 0.318               &  0                 \\\\\n",
       "\t Nonsense\\_Mutation & 11                  & 0.250               &  0                 \\\\\n",
       "\t Splice\\_Site       &  8                  & 0.182               &  0                 \\\\\n",
       "\t total             & 47                & 1.068             &  1               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ID | summary | Mean | Median |\n",
       "|---|---|---|---|\n",
       "| NCBI_Build        | 38                |    NA             | NA                |\n",
       "| Center            | NA                |    NA             | NA                |\n",
       "| Samples           | 44                |    NA             | NA                |\n",
       "| nGenes            | 22                |    NA             | NA                |\n",
       "| Frame_Shift_Del   | 10                | 0.227             |  0                |\n",
       "| Frame_Shift_Ins   |  3                | 0.068             |  0                |\n",
       "| In_Frame_Del      |  1                | 0.023             |  0                |\n",
       "| Missense_Mutation | 14                | 0.318             |  0                |\n",
       "| Nonsense_Mutation | 11                | 0.250             |  0                |\n",
       "| Splice_Site       |  8                | 0.182             |  0                |\n",
       "| total             | 47                | 1.068             |  1                |\n",
       "\n"
      ],
      "text/plain": [
       "   ID                summary Mean  Median\n",
       "1  NCBI_Build        38         NA NA    \n",
       "2  Center            NA         NA NA    \n",
       "3  Samples           44         NA NA    \n",
       "4  nGenes            22         NA NA    \n",
       "5  Frame_Shift_Del   10      0.227  0    \n",
       "6  Frame_Shift_Ins    3      0.068  0    \n",
       "7  In_Frame_Del       1      0.023  0    \n",
       "8  Missense_Mutation 14      0.318  0    \n",
       "9  Nonsense_Mutation 11      0.250  0    \n",
       "10 Splice_Site        8      0.182  0    \n",
       "11 total             47      1.068  1    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>In_Frame_Del</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NF1  </td><td>1    </td><td>1    </td><td>0    </td><td>3    </td><td>7    </td><td>3    </td><td>15   </td><td>15   </td><td>15   </td></tr>\n",
       "\t<tr><td>CHEK2</td><td>3    </td><td>0    </td><td>1    </td><td>0    </td><td>0    </td><td>2    </td><td> 6   </td><td> 6   </td><td> 6   </td></tr>\n",
       "\t<tr><td>ATM  </td><td>0    </td><td>0    </td><td>0    </td><td>2    </td><td>0    </td><td>1    </td><td> 3   </td><td> 3   </td><td> 3   </td></tr>\n",
       "\t<tr><td>GJB2 </td><td>1    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>0    </td><td> 3   </td><td> 3   </td><td> 3   </td></tr>\n",
       "\t<tr><td>BRCA1</td><td>2    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td> 2   </td><td> 2   </td><td> 2   </td></tr>\n",
       "\t<tr><td>FANCM</td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td> 2   </td><td> 2   </td><td> 2   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & In\\_Frame\\_Del & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1   & 1     & 1     & 0     & 3     & 7     & 3     & 15    & 15    & 15   \\\\\n",
       "\t CHEK2 & 3     & 0     & 1     & 0     & 0     & 2     &  6    &  6    &  6   \\\\\n",
       "\t ATM   & 0     & 0     & 0     & 2     & 0     & 1     &  3    &  3    &  3   \\\\\n",
       "\t GJB2  & 1     & 0     & 0     & 1     & 1     & 0     &  3    &  3    &  3   \\\\\n",
       "\t BRCA1 & 2     & 0     & 0     & 0     & 0     & 0     &  2    &  2    &  2   \\\\\n",
       "\t FANCM & 0     & 0     & 0     & 0     & 1     & 1     &  2    &  2    &  2   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | In_Frame_Del | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| NF1   | 1     | 1     | 0     | 3     | 7     | 3     | 15    | 15    | 15    |\n",
       "| CHEK2 | 3     | 0     | 1     | 0     | 0     | 2     |  6    |  6    |  6    |\n",
       "| ATM   | 0     | 0     | 0     | 2     | 0     | 1     |  3    |  3    |  3    |\n",
       "| GJB2  | 1     | 0     | 0     | 1     | 1     | 0     |  3    |  3    |  3    |\n",
       "| BRCA1 | 2     | 0     | 0     | 0     | 0     | 0     |  2    |  2    |  2    |\n",
       "| FANCM | 0     | 0     | 0     | 0     | 1     | 1     |  2    |  2    |  2    |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins In_Frame_Del Missense_Mutation\n",
       "1 NF1         1               1               0            3                \n",
       "2 CHEK2       3               0               1            0                \n",
       "3 ATM         0               0               0            2                \n",
       "4 GJB2        1               0               0            1                \n",
       "5 BRCA1       2               0               0            0                \n",
       "6 FANCM       0               0               0            0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 7                 3           15    15             15            \n",
       "2 0                 2            6     6              6            \n",
       "3 0                 1            3     3              3            \n",
       "4 1                 0            3     3              3            \n",
       "5 0                 0            2     2              2            \n",
       "6 1                 1            2     2              2            "
      ]
     },
     "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>22</li>\n",
       "\t<li>10</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 22\n",
       "\\item 10\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 22\n",
       "2. 10\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 22 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples with top variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   INS   SNP   total\n",
       "---------------------  ----  ----  ----  ------\n",
       "SJCNS042493               0     0     3       3\n",
       "SJCNS018601               1     0     1       2\n",
       "SJCNS001219               0     0     1       1\n",
       "SJCNS018567               0     0     1       1\n",
       "SJCNS018569               0     0     1       1\n",
       "SJCNS018579               1     0     0       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJCNS042493                    3\n",
       "SJCNS018601                    2\n",
       "SJCNS001219                    1\n",
       "SJCNS018567                    1\n",
       "SJCNS018569                    1\n",
       "SJCNS018579                    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": [
       "6"
      ],
      "text/latex": [
       "6"
      ],
      "text/markdown": [
       "6"
      ],
      "text/plain": [
       "[1] 6"
      ]
     },
     "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": [
       "22"
      ],
      "text/latex": [
       "22"
      ],
      "text/markdown": [
       "22"
      ],
      "text/plain": [
       "[1] 22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>In_Frame_Del</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NF1  </td><td>1    </td><td>1    </td><td>0    </td><td>3    </td><td>7    </td><td>3    </td><td>15   </td><td>15   </td><td>15   </td></tr>\n",
       "\t<tr><td>CHEK2</td><td>3    </td><td>0    </td><td>1    </td><td>0    </td><td>0    </td><td>2    </td><td> 6   </td><td> 6   </td><td> 6   </td></tr>\n",
       "\t<tr><td>ATM  </td><td>0    </td><td>0    </td><td>0    </td><td>2    </td><td>0    </td><td>1    </td><td> 3   </td><td> 3   </td><td> 3   </td></tr>\n",
       "\t<tr><td>GJB2 </td><td>1    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>0    </td><td> 3   </td><td> 3   </td><td> 3   </td></tr>\n",
       "\t<tr><td>BRCA1</td><td>2    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td> 2   </td><td> 2   </td><td> 2   </td></tr>\n",
       "\t<tr><td>FANCM</td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td> 2   </td><td> 2   </td><td> 2   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & In\\_Frame\\_Del & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1   & 1     & 1     & 0     & 3     & 7     & 3     & 15    & 15    & 15   \\\\\n",
       "\t CHEK2 & 3     & 0     & 1     & 0     & 0     & 2     &  6    &  6    &  6   \\\\\n",
       "\t ATM   & 0     & 0     & 0     & 2     & 0     & 1     &  3    &  3    &  3   \\\\\n",
       "\t GJB2  & 1     & 0     & 0     & 1     & 1     & 0     &  3    &  3    &  3   \\\\\n",
       "\t BRCA1 & 2     & 0     & 0     & 0     & 0     & 0     &  2    &  2    &  2   \\\\\n",
       "\t FANCM & 0     & 0     & 0     & 0     & 1     & 1     &  2    &  2    &  2   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | In_Frame_Del | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| NF1   | 1     | 1     | 0     | 3     | 7     | 3     | 15    | 15    | 15    |\n",
       "| CHEK2 | 3     | 0     | 1     | 0     | 0     | 2     |  6    |  6    |  6    |\n",
       "| ATM   | 0     | 0     | 0     | 2     | 0     | 1     |  3    |  3    |  3    |\n",
       "| GJB2  | 1     | 0     | 0     | 1     | 1     | 0     |  3    |  3    |  3    |\n",
       "| BRCA1 | 2     | 0     | 0     | 0     | 0     | 0     |  2    |  2    |  2    |\n",
       "| FANCM | 0     | 0     | 0     | 0     | 1     | 1     |  2    |  2    |  2    |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins In_Frame_Del Missense_Mutation\n",
       "1 NF1         1               1               0            3                \n",
       "2 CHEK2       3               0               1            0                \n",
       "3 ATM         0               0               0            2                \n",
       "4 GJB2        1               0               0            1                \n",
       "5 BRCA1       2               0               0            0                \n",
       "6 FANCM       0               0               0            0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 7                 3           15    15             15            \n",
       "2 0                 2            6     6              6            \n",
       "3 0                 1            3     3              3            \n",
       "4 1                 0            3     3              3            \n",
       "5 0                 0            2     2              2            \n",
       "6 1                 1            2     2              2            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U3TkmQaPp8vxfNN+vTpM2nSpMWLF2/d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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=6)\n",
    "gs %>% ggdotchart(\"Hugo_Symbol\",\"MutatedSamples\", \n",
    "                  rotate = TRUE,\n",
    "                  sorting = \"descending\",\n",
    "                  show.label = F,size = \"MutatedSamples\", \n",
    "                  ggtheme = theme_pubr() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotmafSummary\n",
    "to plot the summary of the maf file, which displays number of variants in each sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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esWPHv/71L0mSitUscUp1uTwzM7NPnz5F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\netmWDjDoAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Top 22\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_Systemplotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Central\\_Nervous\\_Systemplotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_Systemplotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_Systemplotmafsummary_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(' ','_',ttype),'plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5qmpaSkCCHi4+O13z+Sms3m9PR07X8/39ufxdPTMzMzU9O0xx57TAjx7rvvapoW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\n+Q8qSA1ZSREDBQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Variant Classification summary”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=5)\n",
    "plotmafSummary(maf = dfx, rmOutlier = FALSE ,dashboard = F,addStat = 'median', textSize = 2, top = gf_sx, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oncoplots or waterfall plots\n",
    "- Adding Clinical Data on oncoplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Oncoplots with the total genes from the gene summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ONY+/j4WFlZseX58+ebmZlpaGhoaGhERka2+SXd4mN/JVnffQAArYEABwC00NOnT9n/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\nitP+76m9e/eyU+3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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Central\\_Nervous\\_System\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_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(' ','_',ttype),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kJMiZUjNDfwAABPlJREFUFDp06NCsWTMDAxw5OTmpqakyPWPGjJkzZ964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\nMC9SZgCwAR4AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=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",
    "        writeMatrix = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Central\\_Nervous\\_System\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_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(' ','_',ttype),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Transition and Transversions\n",
    "returns a list of summarized tables in various ways. Summarized data can also be visualized as a boxplot showing overall distribution of six different conversions and as a stacked barplot showing fraction of conversions in each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class  MAF \n",
       "                   ID summary  Mean Median\n",
       " 1:        NCBI_Build      38    NA     NA\n",
       " 2:            Center      NA    NA     NA\n",
       " 3:           Samples      44    NA     NA\n",
       " 4:            nGenes      22    NA     NA\n",
       " 5:   Frame_Shift_Del      10 0.227      0\n",
       " 6:   Frame_Shift_Ins       3 0.068      0\n",
       " 7:      In_Frame_Del       1 0.023      0\n",
       " 8: Missense_Mutation      14 0.318      0\n",
       " 9: Nonsense_Mutation      11 0.250      0\n",
       "10:       Splice_Site       8 0.182      0\n",
       "11:             total      47 1.068      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c6rbQAAAABE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\nC93HQcrAXIwnAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=13,repr.plot.height=6)\n",
    "dfx.titv = titv(maf = dfx, plot = F, useSyn = T  )\n",
    "plotTiTv(res = dfx.titv ,showBarcodes = F, textSize=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "22"
      ],
      "text/latex": [
       "22"
      ],
      "text/markdown": [
       "22"
      ],
      "text/plain": [
       "[1] 22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2fT57f0HSn1lAsN+ZaZzUNPzhdyKaXLzPdNn0oppbmr0oLbUkppzVtTSmnbZ1O5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\natHtAAAAAElFTkSuQmCC",
      "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, top=total_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5cnl3bHZAHoBX5DCEDf0fz46x2fS85837bvWHL6+JJTD9iwkOrxp8rsGElT67rP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\nShACAABE/RutoZ7fBnjEFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=2]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Central\\_Nervous\\_System\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(' ','_',ttype),'_genecloud_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=2]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Drug Interactions\n",
    "- plot shows potential druggable gene categories along with upto top 5 genes involved in them. \n",
    "- One can also extract information on drug-gene interactions. \n",
    "- [check this documentation](https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html#98_drug-gene_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “Druggable categories”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=6, repr.plot.width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Central\\_Nervous\\_System\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_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(' ','_',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": 57,
   "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                3        0.03529412\n",
      " 2:       TP53  6                2        0.33333333\n",
      " 3:        WNT 68                1        0.01470588\n",
      " 4: Cell_Cycle 15                0        0.00000000\n",
      " 5:      Hippo 38                0        0.00000000\n",
      " 6:        MYC 13                0        0.00000000\n",
      " 7:      NOTCH 71                0        0.00000000\n",
      " 8:       NRF2  3                0        0.00000000\n",
      " 9:       PI3K 29                0        0.00000000\n",
      "10:   TGF-Beta  7                0        0.00000000\n"
     ]
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "op = OncogenicPathways(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2VlNXjw4MuXL9f/EScnJ6YWLy+vxn4t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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Central\\_Nervous\\_System\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Central_Nervous_System_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(' ','_',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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\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": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7pCRERERETkEtwmEJbqDN3D5EqFYP5hoaAoIiRAdm+q998/qDldbmgziOcuGZeu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AQUpPewgj7t7atv+gL76yenHBpdOzFq9r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\nby6YyLQMQ49/2vbbtstnL1wuNLNEpGl08Ejk7z6b3lsYH0uVF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0WF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\n7osAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “WNT pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=8)\n",
    "PlotOncogenicPathways(dfx,pathway_lst[10], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BIIAQAAEkogBAAASCiBEAAAIKEEQgAA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",
      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=3)\n",
    "PlotOncogenicPathways(dfx,pathway_lst[9], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=9)\n",
    "PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KwsfaR7COHDDz+cM2fOLrvskj7KL4SQ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\nAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Central_Nervous_System'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TumorType\\_Central\\_Nervous\\_System'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Central_Nervous_System'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Central_Nervous_System\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TumorType_',gsub(' ','_',ttype))\n",
    "paste0(\"Saving MAF file to : \", out_file)\n",
    "maftools::write.mafSummary(dfx, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Central_Nervous_System_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TumorType\\_Central\\_Nervous\\_System\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Central_Nervous_System_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Central_Nervous_System_drugInteractions.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TumorType_',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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Central_Nervous_System_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TumorType\\_Central\\_Nervous\\_System\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Central_Nervous_System_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Central_Nervous_System_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TumorType_',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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Central_Nervous_System_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TumorType\\_Central\\_Nervous\\_System\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Central_Nervous_System_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Central_Nervous_System_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_fileout_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TumorType_',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": 70,
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.0 (2018-04-23)\n",
       "Platform: x86_64-redhat-linux-gnu (64-bit)\n",
       "Running under: Fedora 24 (Workstation Edition)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  grid      stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] wordcloud_2.6       RColorBrewer_1.1-2  knitr_1.22         \n",
       " [4] corrplot_0.84       maftools_1.8.0      Biobase_2.42.0     \n",
       " [7] BiocGenerics_0.28.0 vcd_1.4-4           ggpubr_0.2         \n",
       "[10] magrittr_1.5        xtable_1.8-4        mixOmics_6.6.2     \n",
       "[13] MASS_7.3-51.1       factoextra_1.0.5    FactoMineR_1.41    \n",
       "[16] DescTools_0.99.27   gtools_3.8.1        broom_0.5.1        \n",
       "[19] d3heatmap_0.6.1.2   pheatmap_1.0.12     reshape2_1.4.3     \n",
       "[22] mosaic_1.4.0        Matrix_1.2-15       mosaicData_0.17.0  \n",
       "[25] ggformula_0.9.0     ggstance_0.3.1      lattice_0.20-38    \n",
       "[28] xda_0.2             forcats_0.3.0       stringr_1.4.0      \n",
       "[31] dplyr_0.8.1         purrr_0.3.2         readr_1.3.1        \n",
       "[34] tidyr_0.8.3         tibble_2.1.1        ggplot2_3.1.1.9000 \n",
       "[37] tidyverse_1.2.1    \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] readxl_1.2.0                uuid_0.1-2                 \n",
       "  [3] backports_1.1.4             circlize_0.4.5             \n",
       "  [5] NMF_0.21.0                  plyr_1.8.4                 \n",
       "  [7] igraph_1.2.4.1              repr_0.19.2                \n",
       "  [9] lazyeval_0.2.2              splines_3.5.0              \n",
       " [11] BiocParallel_1.16.6         GenomeInfoDb_1.18.2        \n",
       " [13] gridBase_0.4-7              digest_0.6.18              \n",
       " [15] foreach_1.4.4               htmltools_0.3.6            \n",
       " [17] BSgenome_1.50.0             cluster_2.0.7-1            \n",
       " [19] doParallel_1.0.14           mosaicCore_0.6.0           \n",
       " [21] ComplexHeatmap_1.20.0       Biostrings_2.50.2          \n",
       " [23] modelr_0.1.2                matrixStats_0.54.0         \n",
       " [25] rARPACK_0.11-0              colorspace_1.4-1           \n",
       " [27] rvest_0.3.2                 ggrepel_0.8.1              \n",
       " [29] xfun_0.5                    haven_2.1.0                \n",
       " [31] crayon_1.3.4                RCurl_1.95-4.11            \n",
       " [33] jsonlite_1.6                survival_2.43-3            \n",
       " [35] zoo_1.8-4                   iterators_1.0.10           \n",
       " [37] glue_1.3.1                  registry_0.5               \n",
       " [39] gtable_0.3.0                zlibbioc_1.28.0            \n",
       " [41] XVector_0.22.0              DelayedArray_0.8.0         \n",
       " [43] GetoptLong_0.1.7            shape_1.4.4                \n",
       " [45] scales_1.0.0                mvtnorm_1.0-8              \n",
       " [47] rngtools_1.3.1              bibtex_0.4.2               \n",
       " [49] Rcpp_1.0.1                  mclust_5.4.2               \n",
       " [51] flashClust_1.01-2           foreign_0.8-71             \n",
       " [53] stats4_3.5.0                htmlwidgets_1.3            \n",
       " [55] httr_1.4.0                  XML_3.98-1.16              \n",
       " [57] pkgconfig_2.0.2             manipulate_1.0.1           \n",
       " [59] labeling_0.3                tidyselect_0.2.5           \n",
       " [61] rlang_0.3.4                 munsell_0.5.0              \n",
       " [63] cellranger_1.1.0            tools_3.5.0                \n",
       " [65] cli_1.1.0                   generics_0.0.2             \n",
       " [67] evaluate_0.13               ggdendro_0.1-20            \n",
       " [69] nlme_3.1-137                leaps_3.0                  \n",
       " [71] xml2_1.2.0                  compiler_3.5.0             \n",
       " [73] rstudioapi_0.10             png_0.1-7                  \n",
       " [75] stringi_1.4.3               highr_0.8                  \n",
       " [77] RSpectra_0.13-1             IRdisplay_0.7.0            \n",
       " [79] pillar_1.4.0                lmtest_0.9-36              \n",
       " [81] GlobalOptions_0.1.0         cowplot_0.9.4              \n",
       " [83] data.table_1.12.2           bitops_1.0-6               \n",
       " [85] corpcor_1.6.9               rtracklayer_1.42.1         \n",
       " [87] GenomicRanges_1.34.0        R6_2.4.0                   \n",
       " [89] gridExtra_2.3               IRanges_2.16.0             \n",
       " [91] codetools_0.2-16            boot_1.3-20                \n",
       " [93] assertthat_0.2.1            SummarizedExperiment_1.12.0\n",
       " [95] pkgmaker_0.27               rjson_0.2.20               \n",
       " [97] withr_2.1.2                 GenomicAlignments_1.18.1   \n",
       " [99] Rsamtools_1.34.1            S4Vectors_0.20.1           \n",
       "[101] GenomeInfoDbData_1.2.0      expm_0.999-3               \n",
       "[103] hms_0.4.2                   IRkernel_0.8.15            \n",
       "[105] Cairo_1.5-9                 pbdZMQ_0.3-3               \n",
       "[107] scatterplot3d_0.3-41        lubridate_1.7.4            \n",
       "[109] base64enc_0.1-3             ellipse_0.4.1              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
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