analysis/KCPG/KCPG_woAR_TT-Osteosarcoma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Osteosarcoma\n",
    "----- \n",
    "- Date : 17th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse) \n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Muataional Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3016</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3016\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3016\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3016   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Var1     Freq\n",
       "------  -----\n",
       "CNS       613\n",
       "WLM       405\n",
       "NBL       343\n",
       "RHB       259\n",
       "OS        241\n",
       "HGG       229\n",
       "RB        221\n",
       "EWS       192\n",
       "STS       177\n",
       "GCT       139\n",
       "ACT        79\n",
       "LGG        52\n",
       "CA         35\n",
       "OST        18\n",
       "RCC         5\n",
       "GICT        2\n",
       "LMPRT       2\n",
       "PGL         2\n",
       "BCC         1\n",
       "NM          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( sort( table(df$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'known',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>290</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 290\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 290\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 290  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD    AR AR/AD   XLD \n",
       "  153    92    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       \n",
       "        Het Hom\n",
       "  AD    153   0\n",
       "  AR     92   0\n",
       "  AR/AD  43   0\n",
       "  XLD     1   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$Mode_of_Inheritance_abb , df$genotype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Mode_of_Inheritance_abb != 'AR',]\n",
    "df = droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD AR/AD   XLD \n",
       "  153    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "182"
      ],
      "text/latex": [
       "182"
      ],
      "text/markdown": [
       "182"
      ],
      "text/plain": [
       "[1] 182"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "54"
      ],
      "text/latex": [
       "54"
      ],
      "text/markdown": [
       "54"
      ],
      "text/plain": [
       "[1] 54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Tumor_Sample_Barcode'</li>\n",
       "\t<li>'Tumor_type'</li>\n",
       "\t<li>'Tumor_type2'</li>\n",
       "\t<li>'Age_of_Onset'</li>\n",
       "\t<li>'Sex'</li>\n",
       "\t<li>'Age_group'</li>\n",
       "\t<li>'sample_type'</li>\n",
       "\t<li>'sequencing_type'</li>\n",
       "\t<li>'Access_unit'</li>\n",
       "\t<li>'Status'</li>\n",
       "\t<li>'Ethnicity'</li>\n",
       "\t<li>'Race'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Tumor\\_Sample\\_Barcode'\n",
       "\\item 'Tumor\\_type'\n",
       "\\item 'Tumor\\_type2'\n",
       "\\item 'Age\\_of\\_Onset'\n",
       "\\item 'Sex'\n",
       "\\item 'Age\\_group'\n",
       "\\item 'sample\\_type'\n",
       "\\item 'sequencing\\_type'\n",
       "\\item 'Access\\_unit'\n",
       "\\item 'Status'\n",
       "\\item 'Ethnicity'\n",
       "\\item 'Race'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Tumor_Sample_Barcode'\n",
       "2. 'Tumor_type'\n",
       "3. 'Tumor_type2'\n",
       "4. 'Age_of_Onset'\n",
       "5. 'Sex'\n",
       "6. 'Age_group'\n",
       "7. 'sample_type'\n",
       "8. 'sequencing_type'\n",
       "9. 'Access_unit'\n",
       "10. 'Status'\n",
       "11. 'Ethnicity'\n",
       "12. 'Race'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Tumor_Sample_Barcode\" \"Tumor_type\"           \"Tumor_type2\"         \n",
       " [4] \"Age_of_Onset\"         \"Sex\"                  \"Age_group\"           \n",
       " [7] \"sample_type\"          \"sequencing_type\"      \"Access_unit\"         \n",
       "[10] \"Status\"               \"Ethnicity\"            \"Race\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       CCF PCGP SJLIFE\n",
       "  Adrenocortical Carcinoma               1   19      2\n",
       "  Basal Cell Carcinoma                   0    0      1\n",
       "  Carcinoma                              0    0     14\n",
       "  Central Nervous System                 1    0    322\n",
       "  Ewing's Sarcoma                        6    0     89\n",
       "  Germ Cell Tumor                        1    0     73\n",
       "  Giant Cell Tumor                       0    0      3\n",
       "  High Grade Glioma                      5   75      0\n",
       "  Low Grade Glioma                       2   15      7\n",
       "  Low Malignant Potential Renal Tumors   2    0      0\n",
       "  Neuroblastoma                          3   48    139\n",
       "  Non-Malignant Tumor                    1    0      3\n",
       "  Osteosarcoma                          14    2    113\n",
       "  Other Solid Tumor                      0    0     10\n",
       "  Paraganglioma                          0    0      1\n",
       "  Renal Cell Carcinoma                   1    0      2\n",
       "  Retinoblastoma                         0    0     98\n",
       "  Rhabdomyosarcoma                       3   29    102\n",
       "  Soft Tissue Sarcoma                    8    0     85\n",
       "  Wilms' tumor                           2    0    205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2$Tumor_type , df2$Access_unit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>f</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Central Nervous System              </td><td>323                                 </td></tr>\n",
       "\t<tr><td>Wilms' tumor                        </td><td>207                                 </td></tr>\n",
       "\t<tr><td>Neuroblastoma                       </td><td>190                                 </td></tr>\n",
       "\t<tr><td>Rhabdomyosarcoma                    </td><td>134                                 </td></tr>\n",
       "\t<tr><td>Osteosarcoma                        </td><td>129                                 </td></tr>\n",
       "\t<tr><td>Retinoblastoma                      </td><td> 98                                 </td></tr>\n",
       "\t<tr><td>Ewing's Sarcoma                     </td><td> 95                                 </td></tr>\n",
       "\t<tr><td>Soft Tissue Sarcoma                 </td><td> 93                                 </td></tr>\n",
       "\t<tr><td>High Grade Glioma                   </td><td> 80                                 </td></tr>\n",
       "\t<tr><td>Germ Cell Tumor                     </td><td> 74                                 </td></tr>\n",
       "\t<tr><td>Low Grade Glioma                    </td><td> 24                                 </td></tr>\n",
       "\t<tr><td>Adrenocortical Carcinoma            </td><td> 22                                 </td></tr>\n",
       "\t<tr><td>Carcinoma                           </td><td> 14                                 </td></tr>\n",
       "\t<tr><td>Other Solid Tumor                   </td><td> 10                                 </td></tr>\n",
       "\t<tr><td>Non-Malignant Tumor                 </td><td>  4                                 </td></tr>\n",
       "\t<tr><td>Giant Cell Tumor                    </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Renal Cell Carcinoma                </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Low Malignant Potential Renal Tumors</td><td>  2                                 </td></tr>\n",
       "\t<tr><td>Basal Cell Carcinoma                </td><td>  1                                 </td></tr>\n",
       "\t<tr><td>Paraganglioma                       </td><td>  1                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " f & n\\\\\n",
       "\\hline\n",
       "\t Central Nervous System               & 323                                 \\\\\n",
       "\t Wilms' tumor                         & 207                                 \\\\\n",
       "\t Neuroblastoma                        & 190                                 \\\\\n",
       "\t Rhabdomyosarcoma                     & 134                                 \\\\\n",
       "\t Osteosarcoma                         & 129                                 \\\\\n",
       "\t Retinoblastoma                       &  98                                 \\\\\n",
       "\t Ewing's Sarcoma                      &  95                                 \\\\\n",
       "\t Soft Tissue Sarcoma                  &  93                                 \\\\\n",
       "\t High Grade Glioma                    &  80                                 \\\\\n",
       "\t Germ Cell Tumor                      &  74                                 \\\\\n",
       "\t Low Grade Glioma                     &  24                                 \\\\\n",
       "\t Adrenocortical Carcinoma             &  22                                 \\\\\n",
       "\t Carcinoma                            &  14                                 \\\\\n",
       "\t Other Solid Tumor                    &  10                                 \\\\\n",
       "\t Non-Malignant Tumor                  &   4                                 \\\\\n",
       "\t Giant Cell Tumor                     &   3                                 \\\\\n",
       "\t Renal Cell Carcinoma                 &   3                                 \\\\\n",
       "\t Low Malignant Potential Renal Tumors &   2                                 \\\\\n",
       "\t Basal Cell Carcinoma                 &   1                                 \\\\\n",
       "\t Paraganglioma                        &   1                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| f | n |\n",
       "|---|---|\n",
       "| Central Nervous System               | 323                                  |\n",
       "| Wilms' tumor                         | 207                                  |\n",
       "| Neuroblastoma                        | 190                                  |\n",
       "| Rhabdomyosarcoma                     | 134                                  |\n",
       "| Osteosarcoma                         | 129                                  |\n",
       "| Retinoblastoma                       |  98                                  |\n",
       "| Ewing's Sarcoma                      |  95                                  |\n",
       "| Soft Tissue Sarcoma                  |  93                                  |\n",
       "| High Grade Glioma                    |  80                                  |\n",
       "| Germ Cell Tumor                      |  74                                  |\n",
       "| Low Grade Glioma                     |  24                                  |\n",
       "| Adrenocortical Carcinoma             |  22                                  |\n",
       "| Carcinoma                            |  14                                  |\n",
       "| Other Solid Tumor                    |  10                                  |\n",
       "| Non-Malignant Tumor                  |   4                                  |\n",
       "| Giant Cell Tumor                     |   3                                  |\n",
       "| Renal Cell Carcinoma                 |   3                                  |\n",
       "| Low Malignant Potential Renal Tumors |   2                                  |\n",
       "| Basal Cell Carcinoma                 |   1                                  |\n",
       "| Paraganglioma                        |   1                                  |\n",
       "\n"
      ],
      "text/plain": [
       "   f                                    n  \n",
       "1  Central Nervous System               323\n",
       "2  Wilms' tumor                         207\n",
       "3  Neuroblastoma                        190\n",
       "4  Rhabdomyosarcoma                     134\n",
       "5  Osteosarcoma                         129\n",
       "6  Retinoblastoma                        98\n",
       "7  Ewing's Sarcoma                       95\n",
       "8  Soft Tissue Sarcoma                   93\n",
       "9  High Grade Glioma                     80\n",
       "10 Germ Cell Tumor                       74\n",
       "11 Low Grade Glioma                      24\n",
       "12 Adrenocortical Carcinoma              22\n",
       "13 Carcinoma                             14\n",
       "14 Other Solid Tumor                     10\n",
       "15 Non-Malignant Tumor                    4\n",
       "16 Giant Cell Tumor                       3\n",
       "17 Renal Cell Carcinoma                   3\n",
       "18 Low Malignant Potential Renal Tumors   2\n",
       "19 Basal Cell Carcinoma                   1\n",
       "20 Paraganglioma                          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_count( df2$Tumor_type , sort = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       ACT BCC  CA CNS EWS GCT GICT HGG LGG\n",
       "  Adrenocortical Carcinoma              22   0   0   0   0   0    0   0   0\n",
       "  Basal Cell Carcinoma                   0   1   0   0   0   0    0   0   0\n",
       "  Carcinoma                              0   0  14   0   0   0    0   0   0\n",
       "  Central Nervous System                 0   0   0 323   0   0    0   0   0\n",
       "  Ewing's Sarcoma                        0   0   0   0  95   0    0   0   0\n",
       "  Germ Cell Tumor                        0   0   0   0   0  74    0   0   0\n",
       "  Giant Cell Tumor                       0   0   0   0   0   0    3   0   0\n",
       "  High Grade Glioma                      0   0   0   0   0   0    0  80   0\n",
       "  Low Grade Glioma                       0   0   0   0   0   0    0   0  24\n",
       "  Low Malignant Potential Renal Tumors   0   0   0   0   0   0    0   0   0\n",
       "  Neuroblastoma                          0   0   0   0   0   0    0   0   0\n",
       "  Non-Malignant Tumor                    0   0   0   0   0   0    0   0   0\n",
       "  Osteosarcoma                           0   0   0   0   0   0    0   0   0\n",
       "  Other Solid Tumor                      0   0   0   0   0   0    0   0   0\n",
       "  Paraganglioma                          0   0   0   0   0   0    0   0   0\n",
       "  Renal Cell Carcinoma                   0   0   0   0   0   0    0   0   0\n",
       "  Retinoblastoma                         0   0   0   0   0   0    0   0   0\n",
       "  Rhabdomyosarcoma                       0   0   0   0   0   0    0   0   0\n",
       "  Soft Tissue Sarcoma                    0   0   0   0   0   0    0   0   0\n",
       "  Wilms' tumor                           0   0   0   0   0   0    0   0   0\n",
       "                                      \n",
       "                                       LMPRT NBL  NM  OS OST PGL  RB RCC RHB\n",
       "  Adrenocortical Carcinoma                 0   0   0   0   0   0   0   0   0\n",
       "  Basal Cell Carcinoma                     0   0   0   0   0   0   0   0   0\n",
       "  Carcinoma                                0   0   0   0   0   0   0   0   0\n",
       "  Central Nervous System                   0   0   0   0   0   0   0   0   0\n",
       "  Ewing's Sarcoma                          0   0   0   0   0   0   0   0   0\n",
       "  Germ Cell Tumor                          0   0   0   0   0   0   0   0   0\n",
       "  Giant Cell Tumor                         0   0   0   0   0   0   0   0   0\n",
       "  High Grade Glioma                        0   0   0   0   0   0   0   0   0\n",
       "  Low Grade Glioma                         0   0   0   0   0   0   0   0   0\n",
       "  Low Malignant Potential Renal Tumors     2   0   0   0   0   0   0   0   0\n",
       "  Neuroblastoma                            0 190   0   0   0   0   0   0   0\n",
       "  Non-Malignant Tumor                      0   0   4   0   0   0   0   0   0\n",
       "  Osteosarcoma                             0   0   0 129   0   0   0   0   0\n",
       "  Other Solid Tumor                        0   0   0   0  10   0   0   0   0\n",
       "  Paraganglioma                            0   0   0   0   0   1   0   0   0\n",
       "  Renal Cell Carcinoma                     0   0   0   0   0   0   0   3   0\n",
       "  Retinoblastoma                           0   0   0   0   0   0  98   0   0\n",
       "  Rhabdomyosarcoma                         0   0   0   0   0   0   0   0 134\n",
       "  Soft Tissue Sarcoma                      0   0   0   0   0   0   0   0   0\n",
       "  Wilms' tumor                             0   0   0   0   0   0   0   0   0\n",
       "                                      \n",
       "                                       STS WLM\n",
       "  Adrenocortical Carcinoma               0   0\n",
       "  Basal Cell Carcinoma                   0   0\n",
       "  Carcinoma                              0   0\n",
       "  Central Nervous System                 0   0\n",
       "  Ewing's Sarcoma                        0   0\n",
       "  Germ Cell Tumor                        0   0\n",
       "  Giant Cell Tumor                       0   0\n",
       "  High Grade Glioma                      0   0\n",
       "  Low Grade Glioma                       0   0\n",
       "  Low Malignant Potential Renal Tumors   0   0\n",
       "  Neuroblastoma                          0   0\n",
       "  Non-Malignant Tumor                    0   0\n",
       "  Osteosarcoma                           0   0\n",
       "  Other Solid Tumor                      0   0\n",
       "  Paraganglioma                          0   0\n",
       "  Renal Cell Carcinoma                   0   0\n",
       "  Retinoblastoma                         0   0\n",
       "  Rhabdomyosarcoma                       0   0\n",
       "  Soft Tissue Sarcoma                   93   0\n",
       "  Wilms' tumor                           0 207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "table(df2$Tumor_type , df2$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Osteosarcoma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              \n",
       "                OS\n",
       "  Osteosarcoma 129"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$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>13 </td><td>129</td></tr>\n",
       "\t<tr><td>84 </td><td> 12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 13  & 129\\\\\n",
       "\t 84  &  12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 13  | 129 |\n",
       "| 84  |  12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 13   129 \n",
       "[2,] 84    12 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(dim(df_ss), dim(df2_ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MAF object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Summarizing..\n",
      "NOTE: Mutiple reference builds found!\n",
      "37;38\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   ID summary  Mean Median\n",
      " 1:        NCBI_Build   37;38    NA     NA\n",
      " 2:            Center    <NA>    NA     NA\n",
      " 3:           Samples      12    NA     NA\n",
      " 4:            nGenes      10    NA     NA\n",
      " 5:   Frame_Shift_Del       3 0.250      0\n",
      " 6:   Frame_Shift_Ins       1 0.083      0\n",
      " 7:      In_Frame_Del       1 0.083      0\n",
      " 8: Missense_Mutation       3 0.250      0\n",
      " 9: Nonsense_Mutation       4 0.333      0\n",
      "10:       Splice_Site       1 0.083      0\n",
      "11:             total      13 1.083      1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Gene Summary..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins In_Frame_Del Missense_Mutation\n",
      " 1:         ATM               0               0            1                 1\n",
      " 2:        GJB2               2               0            0                 0\n",
      " 3:       RECQL               0               0            0                 0\n",
      " 4:       BRCA2               0               1            0                 0\n",
      " 5:       BRIP1               0               0            0                 0\n",
      " 6:        CDH1               0               0            0                 1\n",
      " 7:       CHEK2               1               0            0                 0\n",
      " 8:      CREBBP               0               0            0                 1\n",
      " 9:         RB1               0               0            0                 0\n",
      "10:        TP53               0               0            0                 0\n",
      "    Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
      " 1:                 0           0     2              2              2\n",
      " 2:                 0           0     2              2              2\n",
      " 3:                 2           0     2              2              2\n",
      " 4:                 0           0     1              1              1\n",
      " 5:                 0           1     1              1              1\n",
      " 6:                 0           0     1              1              1\n",
      " 7:                 0           0     1              1              1\n",
      " 8:                 0           0     1              1              1\n",
      " 9:                 1           0     1              1              1\n",
      "10:                 1           0     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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>37;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>12               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td>10               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td>3                </td><td>0.250            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td>1                </td><td>0.083            </td><td> 0               </td></tr>\n",
       "\t<tr><td>In_Frame_Del     </td><td>1                </td><td>0.083            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td>3                </td><td>0.250            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td>4                </td><td>0.333            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td>1                </td><td>0.083            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>13               </td><td>1.083            </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        & 37;38               &    NA               & NA                 \\\\\n",
       "\t Center            & NA                &    NA             & NA               \\\\\n",
       "\t Samples           & 12                &    NA             & NA               \\\\\n",
       "\t nGenes            & 10                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   & 3                     & 0.250                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   & 1                     & 0.083                 &  0                   \\\\\n",
       "\t In\\_Frame\\_Del      & 1                     & 0.083                 &  0                   \\\\\n",
       "\t Missense\\_Mutation & 3                   & 0.250               &  0                 \\\\\n",
       "\t Nonsense\\_Mutation & 4                   & 0.333               &  0                 \\\\\n",
       "\t Splice\\_Site       & 1                   & 0.083               &  0                 \\\\\n",
       "\t total             & 13                & 1.083             &  1               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ID | summary | Mean | Median |\n",
       "|---|---|---|---|\n",
       "| NCBI_Build        | 37;38             |    NA             | NA                |\n",
       "| Center            | NA                |    NA             | NA                |\n",
       "| Samples           | 12                |    NA             | NA                |\n",
       "| nGenes            | 10                |    NA             | NA                |\n",
       "| Frame_Shift_Del   | 3                 | 0.250             |  0                |\n",
       "| Frame_Shift_Ins   | 1                 | 0.083             |  0                |\n",
       "| In_Frame_Del      | 1                 | 0.083             |  0                |\n",
       "| Missense_Mutation | 3                 | 0.250             |  0                |\n",
       "| Nonsense_Mutation | 4                 | 0.333             |  0                |\n",
       "| Splice_Site       | 1                 | 0.083             |  0                |\n",
       "| total             | 13                | 1.083             |  1                |\n",
       "\n"
      ],
      "text/plain": [
       "   ID                summary Mean  Median\n",
       "1  NCBI_Build        37;38      NA NA    \n",
       "2  Center            NA         NA NA    \n",
       "3  Samples           12         NA NA    \n",
       "4  nGenes            10         NA NA    \n",
       "5  Frame_Shift_Del   3       0.250  0    \n",
       "6  Frame_Shift_Ins   1       0.083  0    \n",
       "7  In_Frame_Del      1       0.083  0    \n",
       "8  Missense_Mutation 3       0.250  0    \n",
       "9  Nonsense_Mutation 4       0.333  0    \n",
       "10 Splice_Site       1       0.083  0    \n",
       "11 total             13      1.083  1    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NF1   </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>ATM   </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>ATR   </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>BRCA2 </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CHEK2 </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>COL7A1</td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t NF1    & 0      & 0      & 1      & 1      & 0      & 2      & 2      & 2     \\\\\n",
       "\t ATM    & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t ATR    & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t BRCA2  & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t CHEK2  & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t COL7A1 & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| NF1    | 0      | 0      | 1      | 1      | 0      | 2      | 2      | 2      |\n",
       "| ATM    | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| ATR    | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| BRCA2  | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| CHEK2  | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| COL7A1 | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 NF1         0               0               1                \n",
       "2 ATM         0               0               1                \n",
       "3 ATR         0               0               0                \n",
       "4 BRCA2       0               0               0                \n",
       "5 CHEK2       1               0               0                \n",
       "6 COL7A1      1               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 1                 0           2     2              2             \n",
       "2 0                 0           1     1              1             \n",
       "3 1                 0           1     1              1             \n",
       "4 0                 1           1     1              1             \n",
       "5 0                 0           1     1              1             \n",
       "6 0                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>10</li>\n",
       "\t<li>10</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 10\n",
       "\\item 10\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 10\n",
       "2. 10\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 10 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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   INS   SNP   total\n",
       "---------------------  ----  ----  ----  ------\n",
       "CCF12237                  0     1     1       2\n",
       "CCF11829                  1     0     0       1\n",
       "SJOS017932                0     0     1       1\n",
       "SJOS018800                0     0     1       1\n",
       "SJOS018803                1     0     0       1\n",
       "SJOS018816                0     0     1       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "CCF12237                       2\n",
       "CCF11829                       1\n",
       "SJOS017932                     1\n",
       "SJOS018800                     1\n",
       "SJOS018803                     1\n",
       "SJOS018816                     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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3"
      ],
      "text/latex": [
       "3"
      ],
      "text/markdown": [
       "3"
      ],
      "text/plain": [
       "[1] 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = as.data.frame( dfx@gene.summary )  \n",
    "gf_sx = nrow( gs[ gs$MutatedSamples >= 2,] )\n",
    "gf_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_genes=length(gs$Hugo_Symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "10"
      ],
      "text/latex": [
       "10"
      ],
      "text/markdown": [
       "10"
      ],
      "text/plain": [
       "[1] 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>ATM   </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>0     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>GJB2  </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>RECQL </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>2     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>BRCA2 </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>BRIP1 </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CDH1  </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CHEK2 </td><td>1     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CREBBP</td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>RB1   </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>TP53  </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</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 ATM    & 0      & 0      & 1      & 1      & 0      & 0      & 2      & 2      & 2     \\\\\n",
       "\t GJB2   & 2      & 0      & 0      & 0      & 0      & 0      & 2      & 2      & 2     \\\\\n",
       "\t RECQL  & 0      & 0      & 0      & 0      & 2      & 0      & 2      & 2      & 2     \\\\\n",
       "\t BRCA2  & 0      & 1      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t BRIP1  & 0      & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t CDH1   & 0      & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t CHEK2  & 1      & 0      & 0      & 0      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t CREBBP & 0      & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t RB1    & 0      & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t TP53   & 0      & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\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",
       "| ATM    | 0      | 0      | 1      | 1      | 0      | 0      | 2      | 2      | 2      |\n",
       "| GJB2   | 2      | 0      | 0      | 0      | 0      | 0      | 2      | 2      | 2      |\n",
       "| RECQL  | 0      | 0      | 0      | 0      | 2      | 0      | 2      | 2      | 2      |\n",
       "| BRCA2  | 0      | 1      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| BRIP1  | 0      | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| CDH1   | 0      | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| CHEK2  | 1      | 0      | 0      | 0      | 0      | 0      | 1      | 1      | 1      |\n",
       "| CREBBP | 0      | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| RB1    | 0      | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| TP53   | 0      | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "   Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins In_Frame_Del Missense_Mutation\n",
       "1  ATM         0               0               1            1                \n",
       "2  GJB2        2               0               0            0                \n",
       "3  RECQL       0               0               0            0                \n",
       "4  BRCA2       0               1               0            0                \n",
       "5  BRIP1       0               0               0            0                \n",
       "6  CDH1        0               0               0            1                \n",
       "7  CHEK2       1               0               0            0                \n",
       "8  CREBBP      0               0               0            1                \n",
       "9  RB1         0               0               0            0                \n",
       "10 TP53        0               0               0            0                \n",
       "   Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1  0                 0           2     2              2             \n",
       "2  0                 0           2     2              2             \n",
       "3  2                 0           2     2              2             \n",
       "4  0                 0           1     1              1             \n",
       "5  0                 1           1     1              1             \n",
       "6  0                 0           1     1              1             \n",
       "7  0                 0           1     1              1             \n",
       "8  0                 0           1     1              1             \n",
       "9  1                 0           1     1              1             \n",
       "10 1                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sr279/Pz8+fmZnJnqNREARB30K8vLxs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\nCyEEQRA0ocFCCEEQBE1o/wd+6KuD9hZmOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs_mat = gs %>% dplyr::select(Frame_Shift_Del, Frame_Shift_Ins, In_Frame_Del, Missense_Mutation, Nonsense_Mutation, Splice_Site )\n",
    "rownames(gs_mat)=gs$Hugo_Symbol\n",
    "options(repr.plot.height=4)\n",
    "ggballoonplot(gs_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tl527ZtPj4+9vb2vJldXFwEjxGzWKx27dp9+vSJzWbLyzfQdnV1dUJIbm6u4GzM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\nAABJRU5ErkJggg==",
      "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvvuam5sXL16c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",
      "text/plain": [
       "Plot with title “Top 10\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n0VFxfXsGHDVatWFRYWXr9+PTEx8ZFHHvHw8LDTd9s+2lbVr1+/yMjIXbt2xcbG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\nkpMJCj4rKpYAAAAASUVORK5CYII=",
      "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CFEfFMBAQAAvDtdXV1FRcXCwkKm5MGDB42NjZ988okED1RVVZWXlzdmzBjhyd4U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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=4,repr.plot.width=7)\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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_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": 80,
   "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class  MAF \n",
       "                   ID summary  Mean Median\n",
       " 1:        NCBI_Build   37;38    NA     NA\n",
       " 2:            Center    <NA>    NA     NA\n",
       " 3:           Samples      12    NA     NA\n",
       " 4:            nGenes      10    NA     NA\n",
       " 5:   Frame_Shift_Del       3 0.250      0\n",
       " 6:   Frame_Shift_Ins       1 0.083      0\n",
       " 7:      In_Frame_Del       1 0.083      0\n",
       " 8: Missense_Mutation       3 0.250      0\n",
       " 9: Nonsense_Mutation       4 0.333      0\n",
       "10:       Splice_Site       1 0.083      0\n",
       "11:             total      13 1.083      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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FxcXFM2fOrK6uLigoSCMwAAAAABcunSJpzZo1M2fObG1tDSGcOHHi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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=13,repr.plot.height=6)\n",
    "dfx.titv = titv(maf = dfx, plot = F, useSyn = T  )\n",
    "plotTiTv(res = dfx.titv ,showBarcodes = F, textSize=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Am9H6eQN2/evKqqqkZOrmXy/y6xfPny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\nAAggQAMAAAAC/n/fti7IjFgMFAAAAABJRU5ErkJggg==",
      "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_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": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jQ15v/8SfmpUwe5JfP8msLvaJL1FRXn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VV101ZcqUQQwEAMBAEQgBIDI0Go1su8lk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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "           min.freq =1,\n",
    "           max.words=50, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.35, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 93,
   "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": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_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": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pathway alteration fractions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pathway  N n_affected_genes fraction_affected\n",
      " 1:       TP53  6                3        0.50000000\n",
      " 2: Cell_Cycle 15                1        0.06666667\n",
      " 3:      NOTCH 71                1        0.01408451\n",
      " 4:      Hippo 38                0        0.00000000\n",
      " 5:        MYC 13                0        0.00000000\n",
      " 6:       NRF2  3                0        0.00000000\n",
      " 7:       PI3K 29                0        0.00000000\n",
      " 8:    RTK-RAS 85                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": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v5JSUlisVhVVZUtSiSSuLi4MWPGrF27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\nqPsPp9N/dxy6YuYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Osteosarcoma_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": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “Cell_Cycle pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sW3a5ZC8diLbyCMtW7dOhnv9ESfEq0z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\nCJQgBAAACJQgBAAACNT/AXcB5wKZjgqoAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “NOTCH pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=2)\n",
    "for( i in 1:length( op$data$Pathway ) ){\n",
    "    pway = op$data$Pathway[i]\n",
    "    PlotOncogenicPathways(dfx,pway, fullPathway = F, removeNonMutated = T)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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": 101,
   "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": 102,
   "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": 103,
   "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": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Osteosarcoma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Osteosarcoma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Osteosarcoma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Osteosarcoma\""
      ]
     },
     "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": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Osteosarcoma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Osteosarcoma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Osteosarcoma_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": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Osteosarcoma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Osteosarcoma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Osteosarcoma_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": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Osteosarcoma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Osteosarcoma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Osteosarcoma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Osteosarcoma_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": 108,
   "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": 109,
   "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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