analysis/KCPG/KCPG_woAR_TT-AdrenocorticalCarcinoma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Adrenocortical Carcinoma\n",
    "----- \n",
    "- Date : 17th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse) \n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutationa Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3016</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3016\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3016\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3016   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Var1     Freq\n",
       "------  -----\n",
       "CNS       613\n",
       "WLM       405\n",
       "NBL       343\n",
       "RHB       259\n",
       "OS        241\n",
       "HGG       229\n",
       "RB        221\n",
       "EWS       192\n",
       "STS       177\n",
       "GCT       139\n",
       "ACT        79\n",
       "LGG        52\n",
       "CA         35\n",
       "OST        18\n",
       "RCC         5\n",
       "GICT        2\n",
       "LMPRT       2\n",
       "PGL         2\n",
       "BCC         1\n",
       "NM          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( sort( table(df$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'known',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>290</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 290\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 290\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 290  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD    AR AR/AD   XLD \n",
       "  153    92    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       \n",
       "        Het Hom\n",
       "  AD    153   0\n",
       "  AR     92   0\n",
       "  AR/AD  43   0\n",
       "  XLD     1   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$Mode_of_Inheritance_abb , df$genotype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Mode_of_Inheritance_abb != 'AR',]\n",
    "df = droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD AR/AD   XLD \n",
       "  153    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "182"
      ],
      "text/latex": [
       "182"
      ],
      "text/markdown": [
       "182"
      ],
      "text/plain": [
       "[1] 182"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "54"
      ],
      "text/latex": [
       "54"
      ],
      "text/markdown": [
       "54"
      ],
      "text/plain": [
       "[1] 54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Tumor_Sample_Barcode'</li>\n",
       "\t<li>'Tumor_type'</li>\n",
       "\t<li>'Tumor_type2'</li>\n",
       "\t<li>'Age_of_Onset'</li>\n",
       "\t<li>'Sex'</li>\n",
       "\t<li>'Age_group'</li>\n",
       "\t<li>'sample_type'</li>\n",
       "\t<li>'sequencing_type'</li>\n",
       "\t<li>'Access_unit'</li>\n",
       "\t<li>'Status'</li>\n",
       "\t<li>'Ethnicity'</li>\n",
       "\t<li>'Race'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Tumor\\_Sample\\_Barcode'\n",
       "\\item 'Tumor\\_type'\n",
       "\\item 'Tumor\\_type2'\n",
       "\\item 'Age\\_of\\_Onset'\n",
       "\\item 'Sex'\n",
       "\\item 'Age\\_group'\n",
       "\\item 'sample\\_type'\n",
       "\\item 'sequencing\\_type'\n",
       "\\item 'Access\\_unit'\n",
       "\\item 'Status'\n",
       "\\item 'Ethnicity'\n",
       "\\item 'Race'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Tumor_Sample_Barcode'\n",
       "2. 'Tumor_type'\n",
       "3. 'Tumor_type2'\n",
       "4. 'Age_of_Onset'\n",
       "5. 'Sex'\n",
       "6. 'Age_group'\n",
       "7. 'sample_type'\n",
       "8. 'sequencing_type'\n",
       "9. 'Access_unit'\n",
       "10. 'Status'\n",
       "11. 'Ethnicity'\n",
       "12. 'Race'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Tumor_Sample_Barcode\" \"Tumor_type\"           \"Tumor_type2\"         \n",
       " [4] \"Age_of_Onset\"         \"Sex\"                  \"Age_group\"           \n",
       " [7] \"sample_type\"          \"sequencing_type\"      \"Access_unit\"         \n",
       "[10] \"Status\"               \"Ethnicity\"            \"Race\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       CCF PCGP SJLIFE\n",
       "  Adrenocortical Carcinoma               1   19      2\n",
       "  Basal Cell Carcinoma                   0    0      1\n",
       "  Carcinoma                              0    0     14\n",
       "  Central Nervous System                 1    0    322\n",
       "  Ewing's Sarcoma                        6    0     89\n",
       "  Germ Cell Tumor                        1    0     73\n",
       "  Giant Cell Tumor                       0    0      3\n",
       "  High Grade Glioma                      5   75      0\n",
       "  Low Grade Glioma                       2   15      7\n",
       "  Low Malignant Potential Renal Tumors   2    0      0\n",
       "  Neuroblastoma                          3   48    139\n",
       "  Non-Malignant Tumor                    1    0      3\n",
       "  Osteosarcoma                          14    2    113\n",
       "  Other Solid Tumor                      0    0     10\n",
       "  Paraganglioma                          0    0      1\n",
       "  Renal Cell Carcinoma                   1    0      2\n",
       "  Retinoblastoma                         0    0     98\n",
       "  Rhabdomyosarcoma                       3   29    102\n",
       "  Soft Tissue Sarcoma                    8    0     85\n",
       "  Wilms' tumor                           2    0    205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2$Tumor_type , df2$Access_unit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>f</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Central Nervous System              </td><td>323                                 </td></tr>\n",
       "\t<tr><td>Wilms' tumor                        </td><td>207                                 </td></tr>\n",
       "\t<tr><td>Neuroblastoma                       </td><td>190                                 </td></tr>\n",
       "\t<tr><td>Rhabdomyosarcoma                    </td><td>134                                 </td></tr>\n",
       "\t<tr><td>Osteosarcoma                        </td><td>129                                 </td></tr>\n",
       "\t<tr><td>Retinoblastoma                      </td><td> 98                                 </td></tr>\n",
       "\t<tr><td>Ewing's Sarcoma                     </td><td> 95                                 </td></tr>\n",
       "\t<tr><td>Soft Tissue Sarcoma                 </td><td> 93                                 </td></tr>\n",
       "\t<tr><td>High Grade Glioma                   </td><td> 80                                 </td></tr>\n",
       "\t<tr><td>Germ Cell Tumor                     </td><td> 74                                 </td></tr>\n",
       "\t<tr><td>Low Grade Glioma                    </td><td> 24                                 </td></tr>\n",
       "\t<tr><td>Adrenocortical Carcinoma            </td><td> 22                                 </td></tr>\n",
       "\t<tr><td>Carcinoma                           </td><td> 14                                 </td></tr>\n",
       "\t<tr><td>Other Solid Tumor                   </td><td> 10                                 </td></tr>\n",
       "\t<tr><td>Non-Malignant Tumor                 </td><td>  4                                 </td></tr>\n",
       "\t<tr><td>Giant Cell Tumor                    </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Renal Cell Carcinoma                </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Low Malignant Potential Renal Tumors</td><td>  2                                 </td></tr>\n",
       "\t<tr><td>Basal Cell Carcinoma                </td><td>  1                                 </td></tr>\n",
       "\t<tr><td>Paraganglioma                       </td><td>  1                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " f & n\\\\\n",
       "\\hline\n",
       "\t Central Nervous System               & 323                                 \\\\\n",
       "\t Wilms' tumor                         & 207                                 \\\\\n",
       "\t Neuroblastoma                        & 190                                 \\\\\n",
       "\t Rhabdomyosarcoma                     & 134                                 \\\\\n",
       "\t Osteosarcoma                         & 129                                 \\\\\n",
       "\t Retinoblastoma                       &  98                                 \\\\\n",
       "\t Ewing's Sarcoma                      &  95                                 \\\\\n",
       "\t Soft Tissue Sarcoma                  &  93                                 \\\\\n",
       "\t High Grade Glioma                    &  80                                 \\\\\n",
       "\t Germ Cell Tumor                      &  74                                 \\\\\n",
       "\t Low Grade Glioma                     &  24                                 \\\\\n",
       "\t Adrenocortical Carcinoma             &  22                                 \\\\\n",
       "\t Carcinoma                            &  14                                 \\\\\n",
       "\t Other Solid Tumor                    &  10                                 \\\\\n",
       "\t Non-Malignant Tumor                  &   4                                 \\\\\n",
       "\t Giant Cell Tumor                     &   3                                 \\\\\n",
       "\t Renal Cell Carcinoma                 &   3                                 \\\\\n",
       "\t Low Malignant Potential Renal Tumors &   2                                 \\\\\n",
       "\t Basal Cell Carcinoma                 &   1                                 \\\\\n",
       "\t Paraganglioma                        &   1                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| f | n |\n",
       "|---|---|\n",
       "| Central Nervous System               | 323                                  |\n",
       "| Wilms' tumor                         | 207                                  |\n",
       "| Neuroblastoma                        | 190                                  |\n",
       "| Rhabdomyosarcoma                     | 134                                  |\n",
       "| Osteosarcoma                         | 129                                  |\n",
       "| Retinoblastoma                       |  98                                  |\n",
       "| Ewing's Sarcoma                      |  95                                  |\n",
       "| Soft Tissue Sarcoma                  |  93                                  |\n",
       "| High Grade Glioma                    |  80                                  |\n",
       "| Germ Cell Tumor                      |  74                                  |\n",
       "| Low Grade Glioma                     |  24                                  |\n",
       "| Adrenocortical Carcinoma             |  22                                  |\n",
       "| Carcinoma                            |  14                                  |\n",
       "| Other Solid Tumor                    |  10                                  |\n",
       "| Non-Malignant Tumor                  |   4                                  |\n",
       "| Giant Cell Tumor                     |   3                                  |\n",
       "| Renal Cell Carcinoma                 |   3                                  |\n",
       "| Low Malignant Potential Renal Tumors |   2                                  |\n",
       "| Basal Cell Carcinoma                 |   1                                  |\n",
       "| Paraganglioma                        |   1                                  |\n",
       "\n"
      ],
      "text/plain": [
       "   f                                    n  \n",
       "1  Central Nervous System               323\n",
       "2  Wilms' tumor                         207\n",
       "3  Neuroblastoma                        190\n",
       "4  Rhabdomyosarcoma                     134\n",
       "5  Osteosarcoma                         129\n",
       "6  Retinoblastoma                        98\n",
       "7  Ewing's Sarcoma                       95\n",
       "8  Soft Tissue Sarcoma                   93\n",
       "9  High Grade Glioma                     80\n",
       "10 Germ Cell Tumor                       74\n",
       "11 Low Grade Glioma                      24\n",
       "12 Adrenocortical Carcinoma              22\n",
       "13 Carcinoma                             14\n",
       "14 Other Solid Tumor                     10\n",
       "15 Non-Malignant Tumor                    4\n",
       "16 Giant Cell Tumor                       3\n",
       "17 Renal Cell Carcinoma                   3\n",
       "18 Low Malignant Potential Renal Tumors   2\n",
       "19 Basal Cell Carcinoma                   1\n",
       "20 Paraganglioma                          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_count( df2$Tumor_type , sort = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       ACT BCC  CA CNS EWS GCT GICT HGG LGG\n",
       "  Adrenocortical Carcinoma              22   0   0   0   0   0    0   0   0\n",
       "  Basal Cell Carcinoma                   0   1   0   0   0   0    0   0   0\n",
       "  Carcinoma                              0   0  14   0   0   0    0   0   0\n",
       "  Central Nervous System                 0   0   0 323   0   0    0   0   0\n",
       "  Ewing's Sarcoma                        0   0   0   0  95   0    0   0   0\n",
       "  Germ Cell Tumor                        0   0   0   0   0  74    0   0   0\n",
       "  Giant Cell Tumor                       0   0   0   0   0   0    3   0   0\n",
       "  High Grade Glioma                      0   0   0   0   0   0    0  80   0\n",
       "  Low Grade Glioma                       0   0   0   0   0   0    0   0  24\n",
       "  Low Malignant Potential Renal Tumors   0   0   0   0   0   0    0   0   0\n",
       "  Neuroblastoma                          0   0   0   0   0   0    0   0   0\n",
       "  Non-Malignant Tumor                    0   0   0   0   0   0    0   0   0\n",
       "  Osteosarcoma                           0   0   0   0   0   0    0   0   0\n",
       "  Other Solid Tumor                      0   0   0   0   0   0    0   0   0\n",
       "  Paraganglioma                          0   0   0   0   0   0    0   0   0\n",
       "  Renal Cell Carcinoma                   0   0   0   0   0   0    0   0   0\n",
       "  Retinoblastoma                         0   0   0   0   0   0    0   0   0\n",
       "  Rhabdomyosarcoma                       0   0   0   0   0   0    0   0   0\n",
       "  Soft Tissue Sarcoma                    0   0   0   0   0   0    0   0   0\n",
       "  Wilms' tumor                           0   0   0   0   0   0    0   0   0\n",
       "                                      \n",
       "                                       LMPRT NBL  NM  OS OST PGL  RB RCC RHB\n",
       "  Adrenocortical Carcinoma                 0   0   0   0   0   0   0   0   0\n",
       "  Basal Cell Carcinoma                     0   0   0   0   0   0   0   0   0\n",
       "  Carcinoma                                0   0   0   0   0   0   0   0   0\n",
       "  Central Nervous System                   0   0   0   0   0   0   0   0   0\n",
       "  Ewing's Sarcoma                          0   0   0   0   0   0   0   0   0\n",
       "  Germ Cell Tumor                          0   0   0   0   0   0   0   0   0\n",
       "  Giant Cell Tumor                         0   0   0   0   0   0   0   0   0\n",
       "  High Grade Glioma                        0   0   0   0   0   0   0   0   0\n",
       "  Low Grade Glioma                         0   0   0   0   0   0   0   0   0\n",
       "  Low Malignant Potential Renal Tumors     2   0   0   0   0   0   0   0   0\n",
       "  Neuroblastoma                            0 190   0   0   0   0   0   0   0\n",
       "  Non-Malignant Tumor                      0   0   4   0   0   0   0   0   0\n",
       "  Osteosarcoma                             0   0   0 129   0   0   0   0   0\n",
       "  Other Solid Tumor                        0   0   0   0  10   0   0   0   0\n",
       "  Paraganglioma                            0   0   0   0   0   1   0   0   0\n",
       "  Renal Cell Carcinoma                     0   0   0   0   0   0   0   3   0\n",
       "  Retinoblastoma                           0   0   0   0   0   0  98   0   0\n",
       "  Rhabdomyosarcoma                         0   0   0   0   0   0   0   0 134\n",
       "  Soft Tissue Sarcoma                      0   0   0   0   0   0   0   0   0\n",
       "  Wilms' tumor                             0   0   0   0   0   0   0   0   0\n",
       "                                      \n",
       "                                       STS WLM\n",
       "  Adrenocortical Carcinoma               0   0\n",
       "  Basal Cell Carcinoma                   0   0\n",
       "  Carcinoma                              0   0\n",
       "  Central Nervous System                 0   0\n",
       "  Ewing's Sarcoma                        0   0\n",
       "  Germ Cell Tumor                        0   0\n",
       "  Giant Cell Tumor                       0   0\n",
       "  High Grade Glioma                      0   0\n",
       "  Low Grade Glioma                       0   0\n",
       "  Low Malignant Potential Renal Tumors   0   0\n",
       "  Neuroblastoma                          0   0\n",
       "  Non-Malignant Tumor                    0   0\n",
       "  Osteosarcoma                           0   0\n",
       "  Other Solid Tumor                      0   0\n",
       "  Paraganglioma                          0   0\n",
       "  Renal Cell Carcinoma                   0   0\n",
       "  Retinoblastoma                         0   0\n",
       "  Rhabdomyosarcoma                       0   0\n",
       "  Soft Tissue Sarcoma                   93   0\n",
       "  Wilms' tumor                           0 207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "table(df2$Tumor_type , df2$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Adrenocortical Carcinoma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                          \n",
       "                           ACT\n",
       "  Adrenocortical Carcinoma  22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$Tumor_type , df2_ss$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setdiff( df_ss$Tumor_Sample_Barcode, df2_ss$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>11</td><td>22</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 11 & 22\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 11 | 22 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 11   22  \n",
       "[2,] 84   12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(dim(df_ss), dim(df2_ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MAF object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Summarizing..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  ID summary  Mean Median\n",
      "1:        NCBI_Build      38    NA     NA\n",
      "2:            Center      NA    NA     NA\n",
      "3:           Samples       9    NA     NA\n",
      "4:            nGenes       5    NA     NA\n",
      "5:   Frame_Shift_Ins       2 0.222      0\n",
      "6: Missense_Mutation       3 0.333      0\n",
      "7: Nonsense_Mutation       3 0.333      0\n",
      "8:       Splice_Site       3 0.333      0\n",
      "9:             total      11 1.222      1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Gene Summary..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hugo_Symbol Frame_Shift_Ins Missense_Mutation Nonsense_Mutation Splice_Site\n",
      "1:        TP53               1                 3                 1           2\n",
      "2:       EP300               0                 0                 1           0\n",
      "3:        HMBS               0                 0                 0           1\n",
      "4:        MEN1               1                 0                 0           0\n",
      "5:       NTRK1               0                 0                 1           0\n",
      "   total MutatedSamples AlteredSamples\n",
      "1:     7              7              7\n",
      "2:     1              1              1\n",
      "3:     1              1              1\n",
      "4:     1              1              1\n",
      "5:     1              1              1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Checking clinical data..\n",
      "Done !\n"
     ]
    }
   ],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss, isTCGA = FALSE, verbose = T, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>38               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Center           </td><td>NA               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Samples          </td><td> 9               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td> 5               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 2               </td><td>0.222            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 3               </td><td>0.333            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 3               </td><td>0.333            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 3               </td><td>0.333            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>11               </td><td>1.222            </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           &  9                &    NA             & NA               \\\\\n",
       "\t nGenes            &  5                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  2                    & 0.222                 &  0                   \\\\\n",
       "\t Missense\\_Mutation &  3                  & 0.333               &  0                 \\\\\n",
       "\t Nonsense\\_Mutation &  3                  & 0.333               &  0                 \\\\\n",
       "\t Splice\\_Site       &  3                  & 0.333               &  0                 \\\\\n",
       "\t total             & 11                & 1.222             &  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           |  9                |    NA             | NA                |\n",
       "| nGenes            |  5                |    NA             | NA                |\n",
       "| Frame_Shift_Ins   |  2                | 0.222             |  0                |\n",
       "| Missense_Mutation |  3                | 0.333             |  0                |\n",
       "| Nonsense_Mutation |  3                | 0.333             |  0                |\n",
       "| Splice_Site       |  3                | 0.333             |  0                |\n",
       "| total             | 11                | 1.222             |  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            9         NA NA    \n",
       "4 nGenes             5         NA NA    \n",
       "5 Frame_Shift_Ins    2      0.222  0    \n",
       "6 Missense_Mutation  3      0.333  0    \n",
       "7 Nonsense_Mutation  3      0.333  0    \n",
       "8 Splice_Site        3      0.333  0    \n",
       "9 total             11      1.222  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_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>TP53 </td><td>1    </td><td>3    </td><td>1    </td><td>2    </td><td>7    </td><td>7    </td><td>7    </td></tr>\n",
       "\t<tr><td>EP300</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>HMBS </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>MEN1 </td><td>1    </td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>NTRK1</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|llllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t TP53  & 1     & 3     & 1     & 2     & 7     & 7     & 7    \\\\\n",
       "\t EP300 & 0     & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\t HMBS  & 0     & 0     & 0     & 1     & 1     & 1     & 1    \\\\\n",
       "\t MEN1  & 1     & 0     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t NTRK1 & 0     & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| TP53  | 1     | 3     | 1     | 2     | 7     | 7     | 7     |\n",
       "| EP300 | 0     | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "| HMBS  | 0     | 0     | 0     | 1     | 1     | 1     | 1     |\n",
       "| MEN1  | 1     | 0     | 0     | 0     | 1     | 1     | 1     |\n",
       "| NTRK1 | 0     | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Ins Missense_Mutation Nonsense_Mutation Splice_Site\n",
       "1 TP53        1               3                 1                 2          \n",
       "2 EP300       0               0                 1                 0          \n",
       "3 HMBS        0               0                 0                 1          \n",
       "4 MEN1        1               0                 0                 0          \n",
       "5 NTRK1       0               0                 1                 0          \n",
       "  total MutatedSamples AlteredSamples\n",
       "1 7     7              7             \n",
       "2 1     1              1             \n",
       "3 1     1              1             \n",
       "4 1     1              1             \n",
       "5 1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples with top variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    INS   SNP   total\n",
       "---------------------  ----  ----  ------\n",
       "SJACT028                  0     3       3\n",
       "SJACT010                  0     1       1\n",
       "SJACT013                  0     1       1\n",
       "SJACT014                  0     1       1\n",
       "SJACT015                  0     1       1\n",
       "SJACT018                  0     1       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJACT028                       3\n",
       "SJACT010                       1\n",
       "SJACT013                       1\n",
       "SJACT014                       1\n",
       "SJACT015                       1\n",
       "SJACT018                       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variants.per.sample %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Genes with 2 or more mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1"
      ],
      "text/latex": [
       "1"
      ],
      "text/markdown": [
       "1"
      ],
      "text/plain": [
       "[1] 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = as.data.frame( dfx@gene.summary )  \n",
    "gf_sx = nrow( gs[ gs$MutatedSamples >= 2,] )\n",
    "gf_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_genes=length(gs$Hugo_Symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>TP53 </td><td>1    </td><td>3    </td><td>1    </td><td>2    </td><td>7    </td><td>7    </td><td>7    </td></tr>\n",
       "\t<tr><td>EP300</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>HMBS </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>MEN1 </td><td>1    </td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td></tr>\n",
       "\t<tr><td>NTRK1</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|llllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t TP53  & 1     & 3     & 1     & 2     & 7     & 7     & 7    \\\\\n",
       "\t EP300 & 0     & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\t HMBS  & 0     & 0     & 0     & 1     & 1     & 1     & 1    \\\\\n",
       "\t MEN1  & 1     & 0     & 0     & 0     & 1     & 1     & 1    \\\\\n",
       "\t NTRK1 & 0     & 0     & 1     & 0     & 1     & 1     & 1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| TP53  | 1     | 3     | 1     | 2     | 7     | 7     | 7     |\n",
       "| EP300 | 0     | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "| HMBS  | 0     | 0     | 0     | 1     | 1     | 1     | 1     |\n",
       "| MEN1  | 1     | 0     | 0     | 0     | 1     | 1     | 1     |\n",
       "| NTRK1 | 0     | 0     | 1     | 0     | 1     | 1     | 1     |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Ins Missense_Mutation Nonsense_Mutation Splice_Site\n",
       "1 TP53        1               3                 1                 2          \n",
       "2 EP300       0               0                 1                 0          \n",
       "3 HMBS        0               0                 0                 1          \n",
       "4 MEN1        1               0                 0                 0          \n",
       "5 NTRK1       0               0                 1                 0          \n",
       "  total MutatedSamples AlteredSamples\n",
       "1 7     7              7             \n",
       "2 1     1              1             \n",
       "3 1     1              1             \n",
       "4 1     1              1             \n",
       "5 1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hLy/fv3WbNmmZubS0tL79u3LzMzMzs7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\n/D97FwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs_mat = gs %>% dplyr::select(-c('Hugo_Symbol','total', 'AlteredSamples', 'MutatedSamples') )\n",
    "rownames(gs_mat)=gs$Hugo_Symbol\n",
    "options(repr.plot.height=4,repr.plot.width=5)\n",
    "ggballoonplot(gs_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rOOaJs2bSwsLEaOHDl06FBEYBOCIAQAAFaT/7BpAAAAOUIQAgAAqyEIAQCA1RCE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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=4)\n",
    "gs %>% ggdotchart(\"Hugo_Symbol\",\"MutatedSamples\", \n",
    "                  rotate = TRUE,\n",
    "                  sorting = \"descending\",\n",
    "                  show.label = F,size = \"MutatedSamples\", \n",
    "                  ggtheme = theme_pubr() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotmafSummary\n",
    "to plot the summary of the maf file, which displays number of variants in each sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p5eXV29fv36AwcOVFRUaLXa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\nAAAgexQcAAAAAABA9ig4AAAAAACA7P0fJuDc7/EP8LgAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Top 5\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_plotmafsummary_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UAAAAAEiHoAIAAABAOgQV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\nmscAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Variant Classification summary”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=5)\n",
    "plotmafSummary(maf = dfx, rmOutlier = FALSE ,dashboard = F,addStat = 'median', textSize = 2, top = gf_sx, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oncoplots or waterfall plots\n",
    "- Adding Clinical Data on oncoplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Oncoplots with the total genes from the gene summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tp16tSpk3zfxtu3b2/evFkI8eKLL5b5\nO1i++nC11kN7e3tLYWD//v1SZtBqtXLMe/HFFyvtUGLAQ4cO/fDDD02bNpUyiTS3IM8gSWd8\nSQvo9at1cHAos7yWLVtKt87cs2ePfLdKrVb7+eefS49LXEerYjV4V48ePTphwoR33303Jydn\nzpw5hw4d+vPPP+XdU1JSfv3116KiIiHEyJEjw8PDX3nllfv37586darqVVUqNjZWeqDT6eTH\n5R14pR/vSo/IgJUDAOoSa1SAGpLP9pk3b15ERIT+psmTJ69atUoIsWXLFj8/vzJ3l3/pnjx5\n8ubNmzY2Nq+++qqfn19qaurXX3/9wgsvdO3a9cKFC8uWLZPSzsGDB8urpNIdfXx83n777W++\n+ebGjRsdO3YcM2bMnTt3pArrjLOz88WLF/Py8pKTk3Nzc729vePj43NycoQQ48aNK/OiXtJq\nkPT09AruB1KaSqUaPHjwypUrb9682bNnzzfeeOPw4cPSW9GrVy/pRKNKO8h0Ot2MGTOEEOHh\n4dIi+M6dO//yyy/h4eGenp7Xr1+Xbp4j31bl/v37Uvzw9/cvszxTU9OZM2fOmzfv0aNH3bp1\nmzp1qp2d3aZNm6TLMPj4+AwYMKDqB1uDd9XW1vZf//qXECIuLm7WrFkvvPDCzZs35cjt5eUl\nn2j33XffSfdRWb16tbSuxlBiY2MLCgo6d+6clJQkRcTnnntuxIgRZXau9ONtb29f8REJIYKC\nghISEoQQX3zxxciRIw14LACAx8iQK/OBp0ZRUZG8wrv0XTX2798vbWrevLlWq9WVde2sErMZ\n0nW3kpOTpVublxARESHtJV9xq8R1uirdMScnR75risTc3Lxp06Zljqavgqt+6e81cOBAqTEt\nLa28oRITE0svZenevfuDBw/K20W6Tm617qOi0+muXr1aIm8IIWxtbeW/VKUdZPHx8UIId3d3\nucgbN240a9ZMf8eXX3754cOH0lb5T3/kyJHyDkqj0fTs2bP0H6tp06b6e1Xlql81e1enTp1a\n+tWlw7x69WpBQYH8wZA4ODjIHx7pSmXyVb/y8vKkMeVrqS1YsEB+IamwFi1aSE/lq3698MIL\n+uNbWFhs375d3qv0R67Sj3fFR6TT6eQLP3z11VcVvJkAAEXh1C+gJhITE6VzqHx8fErfVaNr\n167ST71Lly6VNxPi4eERExPTvHlzU1PThg0bSucOdejQIT09ffTo0d7e3paWls2bNx80aFBS\nUpJ8e8fyVLqjq6trSkrKhAkTvLy8bG1te/To8fPPPw8ZMsTb29vb27sqC6Brr0+fPmlpaUOG\nDHFxcbGwsPD09Fy0aNFPP/1UwasPHTpUCJGbmysHgKpwcHA4cuTIhAkTmjdvbmFh4eHhMWzY\nsMOHD8t/qUo7SLRa7UcffSSEWLhwoVxk48aNjxw5EhISYm9v37x584kTJyYlJcl3G/z++++F\nEC1btnzppZfKK8/CwiIpKenLL7/s06ePs7Nzo0aNOnToMGXKlIyMjPLmYSpQg3d16dKl//73\nv994442WLVtaWlo2bty4bdu2c+bM+f333x0cHOzs7BITE/v27WtjY9OiRYuRI0eeOHEiNDRU\n+qh888031a2wtH//+9/Dhg1r0aKFg4NDUFDQ77//Pnjw4Ar6V/rxrviIal8wAMAoVLrqLPYF\ngLqk0+k6dOhw9OjRgQMH7tixw9jlVKKoqMjJyen69esrVqz44IMPjF2O4vj6+p44cUIIcefO\nHfnieAAAlIcZFQDKpVKppNuix8fHV/e6vXVv27Zt169ft7OzGz16tLFrAQCg3iOoAFC0kJCQ\n7t2763S6Ol79X13FxcXSDSKjoqKkW6kAAIDaIKgAUDSVSrVq1SozM7MvvviivNveK8GOHTvO\nnDnj7+8/duxYY9cCAMCTgDUqAAAAABSHGRUAAAAAikNQAQAAAKA4BBUAAAAAikNQAQAAAKA4\nBBUAAAAAikNQAQAAAKA4BBUAAAAAikNQAQAAAKA4BBUAAAAAikNQAQAAAKA4RgsqDx8+jI6O\n9vHxsbKycnNzmzRpUkFBgbGKAQAAAKAoKp1OZ5QX/uijj5YuXTp+/PiOHTuq1epPPvmkffv2\n+/btMzU1NUo9AAAAAJTDOEHl4cOHjRo1GjVqVGxsrNSyYsWKsLCw9PR0b2/vuq8HAAAAgKIY\n59SvP//809fXNygoSG554YUXhBC3b982Sj0AAAAAFMVop35JdDrd/fv3s7Ozp06dmp2d/ccf\nf5iZmZXXWavVJiQkaDSa8joUFhYOGzasghEAAAAA1AtG/k2fnJzcuXNnIYS9vf2RI0cqzhj7\n9u3Tn4Qp06lTpz7++GNDlggAQJ2JUBm7AkOIMOa/gQJ4Yhg5qHh6eiYkJNy7d2/16tVdunQ5\nePBgixYtyuvco0eP+Pj48mZU1Gr1rFmzHBwcHluxAAAAAOqIkYPKs88+269fPyFEQECAnZ3d\n6tWrlyxZUl5nU1PTwMDA8rampKQIIUxMuDMMAAAAUO8Z52f91q1b7e3tc3Jy5JZGjRrZ2tre\nuHHDKPUAAAAAUBTjBBUfH5/r16//8MMPcktKSkpBQUG7du2MUg8AAAAARTHOqV9t2rQZMmTI\njBkzLl686Ovre+HChZiYGA8PjzFjxhilHgAAAACKYrQVHevXr//HP/6xc+fO8ePHb9y48e9/\n//vvv/9uZWVlrHoAAAAAKIfRFtM3atRoyZIlFSydBwAAAPDU4hpZAAAAABTHkEFFp9N9//33\nHTt2tLW1dXFxeeutt86dO1de59TUVFUpmZmZBqwHAAAAQD1lyFO/tmzZMmzYsL59+y5btqyg\noGDlypUvv/zyqVOnnJycSndWq9VCiBkzZlhaWsqN9vb2BqwHAAAAQD1lyKAyb9689u3b79q1\nS7rr4pAhQ7y9vZcvXx4dHV26s1qttra2joqKUqlUBqwBAAAAwBPAYKd+3b17NysrKzg4WL43\nvIeHh5ubW3p6epn91Wp169atSSkAAAAASjPYjIq5uXlaWpqrq6vckp+fn5WV1bNnzzL7q9Vq\nExOTwMDAAwcO2NjYdOvWbfHixc2bN6/gJbRabUJCgkajKW9AIURxcXEtDgIAAACAIhgsqFhY\nWPj6+spPc3NzBwwYYGVlFRYWVmZ/tVqdn58/ZsyY6OjoCxcurFq1Kikp6fTp0xUsU9m3b19Q\nUFDFZWRnZ9esfgAAAADKYfj7qGg0mhUrVkRFRdnY2OzatcvDw6N0H51ON3v2bC8vr169ekkt\nAwcO7NSp07Jly6KiosobuUePHvHx8RXMqMyaNcvd3d0gRwEAAADAiAwcVA4dOjR69OhLly5N\nmTJl+vTpNjY2ZXZTqVSTJk3Sb+nQoYOHh0dqamoFg5uamgYGBpa3NSUlRQghr5ABAAAAUH8Z\nMqgcOHAgICDAz88vMTFRf7FKaVeuXDl58mTXrl2tra3//1LMzPSfAgAAAHhqGWz+obi4ODQ0\n1M/Pb//+/RWnFCGERqPp16/fqlWr5JZTp05lZmZ26dLFUPUAAAAAqL8MNqNy9OjRrKwsLy+v\nxYsX67d7enqGhIQIIWJjY9evXx8TE9OhQwc3N7dRo0bNmTMnLy/P39///PnzMTExXl5eJc4H\nAwAAAPB0MlhQycrKEkLEx8fHx8frtwcHB0tBJTc3Nzk5+fbt21J7bGxsy5YtN2zY8Pnnn7u6\nuo4YMSIyMtLCwsJQ9QAAAACov1Q6nc7YNRhGSkpKx44dP/7442nTphm7FgAAaiTiibgPcsQT\n8tMCgHFxjSwAAAAAikNQAQAAAKA4RgsqOp3u+++/79ixo62trYuLy1tvvXXu3DljFQMAAABA\nUYwWVLZs2RISEvLss88uW7Zs0qRJ+/fvf/nll69cuWKsegAAAAAoh4HvTF918+bNa9++/a5d\nu6R7yQ8ZMsTb23v58uXR0dHGKgkAAACAQhhnRuXu3btZWVnBwcFSShFCeHh4uLm5paenG6Ue\nAAAAAIpinBkVc3PztLQ0/RvY5+fnZ2Vl9ezZs4K9tFptQkKCRqMpc6tarRZCFBcXG7ZUAAAA\nAHXPOEHFwsLC19dXfpqbmztgwAArK6uwsLAK9tq3b19QUFDFI2dnZxumRAAAAADGY7Q1KhKN\nRrNixYqoqCgbG5tdu3Z5eHhU0LlHjx7x8fEVzKjMmjXL3d398VQKAMBjl7vWxdglGIBLhLEr\nAPBEMGZQOXTo0OjRoy9dujRlypTp06fb2NhU3N/U1DQwMLC8rSkpKUIIedELAAAAgPrLaEHl\nwIEDAQEBfn5+iYmJ+otVAAAAAMA4QaW4uDg0NNTPz2///v1mZkY+/QwAAACA0hgnJBw9ejQr\nK8vLy2vx4sX67Z6eniEhIUYpCQAAAIByGCeoZGVlCSHi4+Pj4+P124ODgwkqAAAAAIyz9Pzt\nt9/WlSUuLs4o9QAAAABQFK6RBQAAAEBxHktQOXjwoLW1dcV9UlNTVaVkZmY+jnoAAAAA1C+G\nX6Ny9+7duXPnVtpNrVYLIWbMmGFpaSk32tvbG7weAAAAAPWOIYNKRkbG+++/n5KScvv27YYN\nG1bcWa1WW1tbR0VFqVQqA9YAAAAA4AlgyFO/GjZs2KtXr48++uiVV16ptLNarW7dujUpBQAA\nAEBphpxRcXV1nTlzphAiPz//xIkTFXdWq9UmJiaBgYEHDhywsbHp1q3b4sWLmzdvXsEuWq02\nISFBo9GUN6AQori4uKblAwAAAFAKo90VXq1W5+fnjxkzJjo6+sKFC6tWrUpKSjp9+nQFy1T2\n7dsXFBRU8bDZ2dmGrhQAAABAXTNOUNHpdLNnz/by8urVq5fUMnDgwE6dOi1btiwqKqq8vXr0\n6BEfH1/BjMqsWbPc3d0fS8UAAAAA6pBxgopKpZo0aZJ+S4cOHTw8PFJTUyvYy9TUNDAwsLyt\nKSkpQggTE+4MAwAAANR7xvlZf+XKld27d9+9e1e/0czMrNK7rwAAAAB4GhgnqGg0mn79+q1a\ntUpuOXXqVGZmZpcuXYxSDwAAAABFqbtTv2JjY9evXx8TE9OhQwc3N7dRo0bNmTMnLy/P39//\n/PnzMTExXl5eJc4HAwAAAPB0qrugkpubm5ycfPv2belpbGxsy5YtN2zY8Pnnn7u6uo4YMSIy\nMtLCwqLO6gEAAACgWCqdTmfsGgwjJSWlY8eOH3/88bRp04xdCwAANZHrXNH9xOoLl8uXjF0C\ngCcB18gCAAAAoDjGDyoHDx7kYl8AAAAA9Bk5qNy9e3fu3LnGrQEAAACA0hgtqGRkZPTp08fZ\n2Xnv3r3GqgEAAACAMhktqDRs2LBXr14fffTRK6+8YqwaAAAAACiT0YKKq6vrzJkzZ86c+fLL\nL1dxF61Wu3Pnzm3l2LNnjxCiuLj4cVYNAACA/5GSkhIQENC4ceO2bdtGRETU8sdY7Vcv63S6\n77//vmPHjra2ti4uLm+99da5c+dqMM7Dhw+jo6N9fHysrKzc3NwmTZpUUFBQm8Ik69evV6lU\nubm5Ndg3NTVVVUpmZmbNKrl58+a4ceNatWrVpEmTkJCQnJyc6o5w+PDh0vWoVCp7e/ualVRC\n3d1Hpfb27dsXFBRUcZ/s7OwyWiNUj6WgOhZRzQtJP4VH/RQesngqj/opPGTxVB71U3jITyf+\n0PVcWlpaz549vby85s+ff/bs2cjIyKtXr65evbpmoxlk9fKWLVuGDRvWt2/fZcuWFRQUrFy5\n8uWXXz516pSTk1O1xgkPD1+6dOn48eOnT5+uVqs/+eSTkydP7tu3z9TUtMa1nTlzZuLEiTXe\nXa1WCyFmzJhhaWkpN9YsFdy8ebN79+43b96cMGHCM888s3Tp0n79+qWmpjZo0KDqg7i4uMyb\nN69E45o1a9q2bVuDkkqrT0GlR48e8fHxGo2mzK1qtXrWrFnu7u6lN+WudXnMpdUFl4jq9fdv\n6fNY6qhbR41dgPI9hX/op/CQn04T935u7BIM4LMIY1egePyh67vo6GhbW9ukpCRbW1shhI2N\nzdKlS8PDw5s1a1atcTIyMt5///2UlJTbt283bNiwNiXNmzevffv2u3btMjExEUIMGTLE29t7\n+fLl0dHRVR/k4cOHn3zyybhx4+TQZWdnFxYWlpmZ6e3tXbPC7t+/P3ToUOV8mikAACAASURB\nVGtr6/v379dsBLVabW1tHRUVpVLVNuGvXbs2Kyvr5MmTrVq1EkJIU0//+c9/QkJCqj6Ii4tL\nRESEfsuWLVsePXq0fv36WpYnqU9BxdTUNDAwsLytKSkpQgjpEwkAAIDH7cGDB3FxcePHj5dS\nihBizJgxUVFR27Ztmzx5crWGklYv9+rVa+fOnSdOnKhxSXfv3s3Kylq0aJH8m9DDw8PNzS09\nPb1a4/z555++vr765/K88MILQojbt2/XuLbJkyfrdLrZs2d/8MEHNRtBrVa3bt269ilFp9Ot\nXbs2KChISilCiKFDhz7//PPOzs61GTY3N/fdd9/96quvXFwMM0lQn4IKAAAAlCM/P7+oqKhd\nu3ZyS6tWraysrGqw2kFavSyNWZugYm5unpaW5urqql9kVlZWz549qzWOi4vL4cOHhRA6ne7+\n/fvZ2dnLly9v2bJl1RdXl7Bp06bNmzcfOXJEGrZm1Gq1iYlJYGDggQMHbGxsunXrtnjx4ubN\nm1d3nDt37qjV6g8++KCoqOjkyZN3795t165dp06dalyYZPr06R07dhw0aFAtx5Ex/wAAAICa\nyMvLE0I0adJEv9HOzk5qNwoLCwtfX1+5pNzc3Ndff93KyiosLKxmAyYnJ1tbW7dr1+748eN7\n9+41M6vJv/KfPXt2/Pjxn376qY9Prc5eVqvVqampzz33XHR09P/93//9+OOP/v7+NVjif+XK\nFSHE5cuXW7Ro8fLLL/fo0cPJyalap8aVdvTo0a1bty5ZsqQ2g5RAUAEAAEBN6HRlX0Xg4cOH\ndVxJaRqNZvHixd7e3tevX9+1a5eHh0fNxvH09ExISNi2bVvbtm27dOlSg8miwsLCoUOHBgYG\njh07tmY1SKTTxpKSkr744otx48YtWrTo559/vnbt2rJly6o71NWrV4UQn3zySVRU1I0bN3Jz\nc4cPHz59+vSdO3fWuLxZs2YNGTJEf3qt9jj1CwAAADXh6OgohLh586Z+440bN2q51KH2Dh06\nNHr06EuXLk2ZMmX69Ok2NjY1HurZZ5/t16+fECIgIMDOzm716tXVnTRYuXJlZmbmxIkTv/vu\nO/HfZdX//ve/HR0dhwwZUvVxVCrVpEmT9Fs6dOjg4eGRmpparXqEEI0bNxZCTJ48edSoUdLT\n1atX79y5c/PmzRUsCK9AZmbmzz///NNPP9Vg3woYP6isWLFixYoVxq4CAAAA1ePo6Ghubn7m\nzBm55eLFi/fu3Xv++eeNV5Q4cOBAQECAn59fYmKi/mKVatm6det7772XmpraokULqaVRo0a2\ntrY3btyo7lC3bt0qKioaP368fuOkSZPs7OyqFVSuXLly8uTJrl276t9nxszMrAa3nZGSpP6f\nydzc3MnJ6dq1a9UdShIbG+vq6tqrV6+a7V4eTv0CAABATVhYWAQHB2/fvl2+e8TmzZtNTU2r\ndYlbwyouLg4NDfXz89u/f3+NU4oQwsfH5/r16z/88IPckpKSUlBQUINTmxYvXqzTs3btWiHE\npUuXqru2RKPR9OvXb9WqVXLLqVOnMjMzu3TpUt2SmjRp0qVLl+3bt8sn7507d+706dP+/v7V\nHUoIodVqv/nmm8GDB9fmDjNlMv6MCgAAAOqpadOmdevWbfDgwaGhoRkZGZGRkRMmTDDiqV9H\njx7Nysry8vJavHixfrunp2e14lObNm2GDBkyY8aMixcv+vr6XrhwISYmxsPDY8yYMYYuuarc\n3NxGjRo1Z86cvLw8f3//8+fPx8TEeHl5lTgfrIrCw8PfeOONvn37Dh069NatWytXrrSzs5s+\nfXoNhjpy5Mj169dfffXVGuxbMQMHlZSUlLlz56akpLi4uAQHB4eHh5d3Y5PU1NTSoS0jI8PT\n09OwJQEAAOAx8fPz27t37+zZs8eOHevi4jJ37tw5c+YYsZ6srCwhRHx8fHx8vH57cHBwded5\n1q9fv3Dhwu3bt69Zs8bFxeXvf/97RESElZWVIcutptjY2JYtW27YsOHzzz93dXUdMWJEZGSk\nhYVFDYbq27dvYmLiggULPvzww0aNGvXq1Wvx4sV2dnY1GGr37t1CiK5du9Zg34oZMqikpaX1\n7NnTy8tr/vz5Z8+ejYyMvHr1qnw7zxLUarUQYsaMGZaWlnKjvb29AesBAADA49axY8ekpCRD\njVbL1ctvv/3222+/bZBKGjVqtGTJEsNeb1cIERoaGhoaWrN9LSwswsPDw8PDDVJJz549q3t7\nmTJFRESUuD+9oRgyqERHR9va2iYlJUl3J7WxsVm6dGl4eHizZs1Kd1ar1dbW1lFRUbW/uSYA\nAACAJ4zBFtM/ePAgLi4uODhYSilCiDFjxmi12m3btpXZX61Wt27dmpQCAAAAoDSDzajk5+cX\nFRXpXwmhVatWVlZW5d0WR61Wm5iYBAYGHjhwwMbGplu3bosXL27evHkFL6HVahMSEuTLSpQe\nUAhRXFxci4MAAAAAoAgGCyp5eXlCiCZNmug32tnZSe2lqdXq/Pz8MWPGREdHX7hwYdWqVUlJ\nSadPn65gmcq+ffuCgoIqLiM7O7v6tQMAAABQFoMFFfkyzCU8fPiwzM6zZ8/28vKS7wszcODA\nTp06LVu2LCoqqryX6NGjR3x8fAUzKrNmzXJ3d69+7QAAAACUxWBBxdHRUQhx8+ZN/cYbN26U\neSFtlUpV4pLPHTp08PDwSE1NreAlTE1NAwMDy9uakpIihCjvasgAAAAA6hGD/ax3dHQ0Nzc/\nc+aM3HLx4sV79+49//zzpTtfuXJl9+7dd+/e1W80MzOztrY2VD0AAAAA6i+DBRULC4vg4ODt\n27fLp2Zt3rzZ1NS0zHvraDSafv36rVq1Sm45depUZmZmly5dDFUPAAAAgPrLkPdRmTZtWrdu\n3QYPHhwaGpqRkREZGTlhwgT51K/Y2Nj169fHxMR06NDBzc1t1KhRc+bMycvL8/f3P3/+fExM\njJeXV4nzwQAAAAA8nQy5osPPz2/v3r0PHz4cO3bsli1b5s6dGxMTI2/Nzc1NTk6+ffu29DQ2\nNjY8PPw///nPO++8s2nTphEjRvz+++8WFhYGrAcAAABAPWXIGRUhRMeOHZOSksrctHDhwoUL\nF8pPLSwswsPDw8PDDVsAAAAAgCcA18gCAABArWi12sTERK1Wq5yhKKmelqTPmEElJSUlICCg\ncePGbdu2jYiI4KbyAAAA9dGuXbsCAgJ27dqlnKEoqZ6WpM9oQSUtLa1nz543btyYP3/+a6+9\nFhkZyUp6AACA+qiwsFD+X4UMRUn1tCR9Bl6jUnXR0dG2trZJSUm2trZCCBsbm6VLl4aHhzdr\n1sxYJQEAAABQCOPMqDx48CAuLi44OFhKKUKIMWPGaLXabdu2GaUeAAAAAIpinBmV/Pz8oqKi\ndu3ayS2tWrWysrLKycmpYC+tVpuQkCDfULIEtVothGChCwAAAPAEME5QycvLE0I0adJEv9HO\nzk5qL8++ffuCgoIqHrniqAMAAACgXjBOUNHpdGW2P3z4sIK9evToER8fX96Mik6nO378+Icf\nflh6k8vlSzUosr47OvKUsUuocxFlf66ebE/hH/opPGQhnsaP92cHxhm7BCN4Cv8P6+n8QwOo\nCuMEFUdHRyHEzZs39Rtv3Ljh7OxcwV6mpqaBgYEVdBg6dKhBygMAAABgXMZZTO/o6Ghubn7m\nzBm55eLFi/fu3Xv++eeNUg8AAAAARTHOjIqFhUVwcPD27dsXLlxoaWkphNi8ebOpqWlISIhR\n6gEAAECNFRUVCSE2bdqUmppay6EyMzMNMpShxqGkag1l2DvTq8pbLvK4paamduvWrVu3bqGh\noRkZGZGRkePHj1+1apVRigEAAECNrVmz5r333jN2FTC+L7/8cvTo0YYazWhBRQiRnJw8e/bs\no0ePuri4DB06dM6cOSYmxjkVDQAAADWm1Wrj4uLat29vampa+6EOHjz4yiuv1HIoQ41DSVUf\nKj09PSgoqPZDyYwZVB6HXOfmxi6hTknXh/H/uq2xC6lTR0eeEhEqY1dRtyJ04qn8Qz+FhyyE\neAo/3hO7rTV2EXWK61wBQFUwgwEAAABAcQgqAAAAABSHoAIAAABAcQgqAAAAABSHoAIAAABA\ncQgqAAAAABSHoAIAAABAccyMXQAAAADqPQPeECl5bIyhhlKaHbNuGWool3G5hhrKkCIMeYdG\nZlQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5B\nBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQA\nAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAA\nAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAA\nKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiEFQAAAAAKA5BBQAAAIDiKDGoaDSagoICY1cBAADw\nuHTt2lVVvv379w8YMKBEo5OTU//+/Y8dO2bs2oE6UvOgMmfOnPK+Xbdu3RJCaLXazz///KWX\nXrK2tm7ZsuU777xz5coVefeDBw926dKlUaNG/v7+iYmJcvuBAwcaNGiwfPny2hwVAADAEyYv\nLy8hIaFz586ZmZlCiCNHjpT+DbZu3Tqp8/379yMjI728vKysrNzd3d999938/Hyjlg9Um9nj\nGFSlUgkhZsyYsWzZMqnl3r17a9eu3bNnT2pq6rPPPnv27NnevXs/ePDA19f3xIkT/fv3P3z4\nsJ+fX3FxcVhYmIODw8yZMx9HYQAAAEqwadOmwsJC6fGLL7748OHDoUOHRkZGSi2urq7SAycn\npz179gghCgsL4+PjIyIiioqKlixZ8tVXX2VkZJQ3uE6nGz169NatW4UQKpXq3LlzsbGxv/76\na0pKipWV1eM9MMBwaj6j8sEHH2ToOX36dPv27YUQoaGhtra2d+7ckWZFPv3001u3biUnJ7du\n3frcuXObN28WQmzcuLGwsPD7779PTU1NSEh49OjR119/LYTYsGHDsWPH5s+fb2NjY6ADBAAA\nUJwWLVp4/peJiYkQonHjxnKLHCfMzc2llpdeemnevHlt27YVQqSnpwshpKCyZs0a/d9jISEh\nQgi1Wi2llLi4OI1Gs2rVKiHE6dOnpcwD1Bc1n1FxcHBwcHCQny5evPjYsWM+Pj4rV64UQly5\nckWn0zVq1GjixIkqlapDhw4DBw6Mjo6+fPmyEKK4uFgIYW5uLoSwsLAQQjx69OjevXuzZ8/2\n8vIKDQ2t5VEBAAA8eaQAI/2CkoJK9+7dPT09S3T7448/bG1tmzRpEhwcLIQIDQ2dPHmyTqf7\n66+/6rxkoOYMs5j+zJkzc+fONTU13bJli/QVat26tbOz8507d/75z3/m5eXt3bt327ZtQoie\nPXsKIYYPH25paTlo0KCuXbv26dPHzMxs9OjRS5YsuXLlSnR0tJnZYzkhDQAAoJ4qLCzctm1b\nSkqKEMLHx0cIIa1U+eyzz5o1a2Zvbx8YGHj27Fmpc//+/W/dunXu3DmdTnfu3LmlS5fqdDon\nJ6eAgAAjHgJQXQYIKjqdbvLkyY8ePQoNDfX29v5/45qY7N27t1mzZnPmzHFycurVq1dOTk5M\nTEyvXr2EEG3atElMTPT39z9+/Hjbtm0TEhKaNWsWHR3du3fvfv361b4kAACAJ0BOTo60St7K\nymro0KE6nc7MzGzatGlFRUVqtVoIsWrVqjt37ly/fv3HH3/09/e/ePGi/u4//fSTu7t7eHi4\niYlJXFyc/rkwgPIZIKjs27dv7969DRo0mDdvntyo0WhiYmLy8/MtLS379u3brl07IcTq1asv\nXLggdXj11VcPHTp09+7do0eP9unT56OPPtJoNNHR0WfOnOnbt6+NjY2Pj8+3335b+/IAAACe\nDK1atfrpp588PDyuXr3aqVOnLl267N69++7duydOnHj22Wfv3r0bFRWl39/Z2XnSpEkBAQHF\nxcUBAQFc2hj1iwFOsvryyy+FEEFBQY6OjnLjmjVrPvvsM1tb27NnzzZt2lSn0w0dOjQuLu6d\nd97RvxixJCUlZdOmTWPHjnV0dPTx8bl27Zqvr29GRsawYcOsra0DAwNrXyQAAEC9I1/1Swhh\nb29vb28vPXZxcfntt9/kbi+++GJISMi6detSU1P1d2/btu2nn36q0+n69++/a9eu+fPn79ix\no86KB2qptjMqt2/f3r59uxBCWq0lk6YjXV1dmzZtKoRQqVT+/v5CiBLfHyGETqcLCwuzsrJa\nsGDB9u3br127tmLFirS0tOPHj+tfDhwAAOBpI1/1y9PTU04pQohDhw6tWLFC+sdiSVFRkRCi\nYcOGQojVq1dbW1u7u7vrdDohhEqlkva9ceNGXR8AUAu1nVE5evSodBXwEsuzfH19hRDp6enL\nly8fNGhQRkaGFDnatGlTYoStW7cePHgwMjLS0dFR+jpJ17IwNzc3MTF59OhRLSsEAAB4wpw/\nfz4sLMzExMTExKRv377JycnS9Yi7du0qhPDx8bl37560jH7kyJGHDx+Wrmn00ksvGbluoDpq\nO6Ny/PhxIUTTpk1L3PlkxIgR/v7+Op1u6tSp7u7uAwYMUKvV5ubmH3/8sX43jUYzY8YMJyen\nqVOnCiEGDx5sZ2c3adKkV155xdfXV6vVvvPOO7WsEAAA4AkzZMiQl156qbi4ePTo0c7OzkOG\nDNFoNB4eHjNmzBBCvPrqqwMHDhRCzJw509HRcfDgwRqNxsHBYdasWcYuHKiG2gaVEydOCCHc\n3d1LtFtaWu7duzc8PNzb27tBgwbNmzcfMmTI0aNHO3XqpN9txYoVOTk5ixYtkmYqmzVrtm/f\nvh49epw6dapFixbffvut9DUDAACAzNLScs+ePVOmTGndunWDBg3atGkzderUI0eOWFtbCyFU\nKtV33323YMEC6WeYm5vbqFGj0tLSpBPygfpCJZ1t9cTIdW5u7BLqlMvlS0II/6/bGruQOnV0\n5CkRoTJ2FXUrQieeyj/0U3jIQoin8OM9sdtaYxdRpz47MM7YJQCGZ8AvcvLYGEMNpTQ7Zt0y\n1FAu43INNZQhRRgyWRjmho8AAAAAYEAEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgE\nFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1AB\nAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAA\nAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAA\noDgEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAAAACKQ1ABAAAAoDgEFQAAAACK\nY2bsAgAAAFDvfXZgnOEGM+BQCjPS2AXUK8yoAAAAAFAcggoAAAAAxSGoAAAAAFAcggoAAAAA\nxSGoAAAAAFAcggoAAAAAxSGoAAAAAFAclU6nM3YNAAAAAPA/mFEBAAAAoDgEFQAAAACKQ1AB\nAAAAoDhmxi4AAICnUa5zc2OXUKdcLl8ydgkA6hlmVAAAAAAoDkEFAAAAgOIQVAAAAAAoDkEF\nAAAAgOIQVAAAAAAoDkEFAAAAgOIQVAAAAAAoDvdRAQAAQG0Z9tZA3HgHghkVAAAAAApEUAEA\nAACgOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOAQVAAAA\nAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACg\nOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpD\nUAEAAACgOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOAQV\nAAAAAIpDUAEAAACgOAQVAAAAAIpDUAEAAACgOEoMKhqNpqCgwNhVAAAAPEZ///vfVeVYt26d\nEGLAgAEl2p2cnPr373/s2DFj1w7UBcMEla+++kqlUi1cuLDqWw8ePNilS5dGjRr5+/snJibK\n7QcOHGjQoMHy5csNUhgAAMATIy8vLyEhoXPnzpmZmUKI+/fvR0ZGenl5WVlZubu7v/vuu/n5\n+XLn4uLiTz75xNPT09LSslmzZqNHj7569WoVtwJKYFabnTUaTW5u7rZt2yIjI6u19ezZs717\n937w4IGvr++JEyf69+9/+PBhPz+/4uLisLAwBweHmTNn1qYwAACAesHW1vbw4cMlGps1ayY/\ndnJy2rNnjxCisLAwPj4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zbdunVr6dKlCQkJiYmJKpUqLCzM1BUEAACoWCqVSho1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jIcJ4Xbp06devnzTetKHmzZtv3ry5X79+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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=7,repr.plot.width=9)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_oncoplot_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=6)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_oncoplot_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Transition and Transversions\n",
    "returns a list of summarized tables in various ways. Summarized data can also be visualized as a boxplot showing overall distribution of six different conversions and as a stacked barplot showing fraction of conversions in each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class  MAF \n",
       "                  ID summary  Mean Median\n",
       "1:        NCBI_Build      38    NA     NA\n",
       "2:            Center      NA    NA     NA\n",
       "3:           Samples       9    NA     NA\n",
       "4:            nGenes       5    NA     NA\n",
       "5:   Frame_Shift_Ins       2 0.222      0\n",
       "6: Missense_Mutation       3 0.333      0\n",
       "7: Nonsense_Mutation       3 0.333      0\n",
       "8:       Splice_Site       3 0.333      0\n",
       "9:             total      11 1.222      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DxixIgRI0YM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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=13,repr.plot.height=6)\n",
    "dfx.titv = titv(maf = dfx, plot = F, useSyn = T  )\n",
    "plotTiTv(res = dfx.titv ,showBarcodes = F, textSize=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=8)\n",
    "set.seed(42)\n",
    "geneCloud(input = dfx, min=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=8, width=8)\n",
    "set.seed(42)\n",
    "geneCloud(input = dfx, min=0)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VEUy+l9+lf7X/CT7ave3nTPt7avmN/S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\nAAAg6g+KgivXv5nOBwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "           min.freq =1,\n",
    "           max.words=50, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.35, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_genecloud_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=0]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Drug Interactions\n",
    "- plot shows potential druggable gene categories along with upto top 5 genes involved in them. \n",
    "- One can also extract information on drug-gene interactions. \n",
    "- [check this documentation](https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html#98_drug-gene_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “Druggable categories”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=6, repr.plot.width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_drugInteractions_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oncogenic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pathway alteration fractions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pathway  N n_affected_genes fraction_affected\n",
      " 1:       TP53  6                1        0.16666667\n",
      " 2:      NOTCH 71                1        0.01408451\n",
      " 3:    RTK-RAS 85                1        0.01176471\n",
      " 4: Cell_Cycle 15                0        0.00000000\n",
      " 5:      Hippo 38                0        0.00000000\n",
      " 6:        MYC 13                0        0.00000000\n",
      " 7:       NRF2  3                0        0.00000000\n",
      " 8:       PI3K 29                0        0.00000000\n",
      " 9:   TGF-Beta  7                0        0.00000000\n",
      "10:        WNT 68                0        0.00000000\n"
     ]
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "op = OncogenicPathways(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YEDB9LS0qqqqmqsYGpqGhwczG2nAAAA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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Adrenocortical_Carcinoma_OncogenicPathway_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_OncogenicPathway_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "\n",
    "svg(out_svg, height=9, width=12)\n",
    "op\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IDn1T/MmjXYQ3gKAuGANN640VpePtij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\nLeAAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title “NOTCH pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GSRMIqiI444Ij5+pmHDhuknqaaP2d46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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=2)\n",
    "for( i in 1:length( op$data$Pathway ) ){\n",
    "    pway = op$data$Pathway[i]\n",
    "    PlotOncogenicPathways(dfx,pway, fullPathway = F, removeNonMutated = T)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=8)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[10], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[9], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=9)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = F, removeNonMutated = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Adrenocortical_Carcinoma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Adrenocortical_Carcinoma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Adrenocortical_Carcinoma\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)) )\n",
    "paste0(\"Saving MAF file to : \", out_file)\n",
    "maftools::write.mafSummary(dfx, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Adrenocortical_Carcinoma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Adrenocortical_Carcinoma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Adrenocortical_Carcinoma_drugInteractions.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions.tsv')\n",
    "paste0(\"Saving drug Interactions data to : \", out_file)\n",
    "write.table(dfx_drug, out_file, sep=\"\\t\", row.names = F, quote = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Adrenocortical_Carcinoma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Adrenocortical_Carcinoma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Adrenocortical_Carcinoma_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_mutation_data.tsv\")\n",
    "paste0(\"Saving mutation data to : \", out_file)\n",
    "write.table(df_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Adrenocortical_Carcinoma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Adrenocortical\\_Carcinoma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Adrenocortical_Carcinoma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Adrenocortical_Carcinoma_clinical_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_clinical_data.tsv\")\n",
    "paste0(\"Saving clinical data to : \", out_file)\n",
    "write.table(df2_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               _                           \n",
       "platform       x86_64-redhat-linux-gnu     \n",
       "arch           x86_64                      \n",
       "os             linux-gnu                   \n",
       "system         x86_64, linux-gnu           \n",
       "status                                     \n",
       "major          3                           \n",
       "minor          5.0                         \n",
       "year           2018                        \n",
       "month          04                          \n",
       "day            23                          \n",
       "svn rev        74626                       \n",
       "language       R                           \n",
       "version.string R version 3.5.0 (2018-04-23)\n",
       "nickname       Joy in Playing              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.0 (2018-04-23)\n",
       "Platform: x86_64-redhat-linux-gnu (64-bit)\n",
       "Running under: Fedora 24 (Workstation Edition)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  grid      stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] wordcloud_2.6       RColorBrewer_1.1-2  knitr_1.22         \n",
       " [4] corrplot_0.84       maftools_1.8.0      Biobase_2.42.0     \n",
       " [7] BiocGenerics_0.28.0 vcd_1.4-4           ggpubr_0.2         \n",
       "[10] magrittr_1.5        xtable_1.8-4        mixOmics_6.6.2     \n",
       "[13] MASS_7.3-51.1       factoextra_1.0.5    FactoMineR_1.41    \n",
       "[16] DescTools_0.99.27   gtools_3.8.1        broom_0.5.1        \n",
       "[19] d3heatmap_0.6.1.2   pheatmap_1.0.12     reshape2_1.4.3     \n",
       "[22] mosaic_1.4.0        Matrix_1.2-15       mosaicData_0.17.0  \n",
       "[25] ggformula_0.9.0     ggstance_0.3.1      lattice_0.20-38    \n",
       "[28] xda_0.2             forcats_0.3.0       stringr_1.4.0      \n",
       "[31] dplyr_0.8.1         purrr_0.3.2         readr_1.3.1        \n",
       "[34] tidyr_0.8.3         tibble_2.1.1        ggplot2_3.1.1.9000 \n",
       "[37] tidyverse_1.2.1    \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] readxl_1.2.0                uuid_0.1-2                 \n",
       "  [3] backports_1.1.4             circlize_0.4.5             \n",
       "  [5] NMF_0.21.0                  plyr_1.8.4                 \n",
       "  [7] igraph_1.2.4.1              repr_0.19.2                \n",
       "  [9] lazyeval_0.2.2              splines_3.5.0              \n",
       " [11] BiocParallel_1.16.6         GenomeInfoDb_1.18.2        \n",
       " [13] gridBase_0.4-7              digest_0.6.18              \n",
       " [15] foreach_1.4.4               htmltools_0.3.6            \n",
       " [17] BSgenome_1.50.0             cluster_2.0.7-1            \n",
       " [19] doParallel_1.0.14           mosaicCore_0.6.0           \n",
       " [21] ComplexHeatmap_1.20.0       Biostrings_2.50.2          \n",
       " [23] modelr_0.1.2                matrixStats_0.54.0         \n",
       " [25] rARPACK_0.11-0              colorspace_1.4-1           \n",
       " [27] rvest_0.3.2                 ggrepel_0.8.1              \n",
       " [29] xfun_0.5                    haven_2.1.0                \n",
       " [31] crayon_1.3.4                RCurl_1.95-4.11            \n",
       " [33] jsonlite_1.6                survival_2.43-3            \n",
       " [35] zoo_1.8-4                   iterators_1.0.10           \n",
       " [37] glue_1.3.1                  registry_0.5               \n",
       " [39] gtable_0.3.0                zlibbioc_1.28.0            \n",
       " [41] XVector_0.22.0              DelayedArray_0.8.0         \n",
       " [43] GetoptLong_0.1.7            shape_1.4.4                \n",
       " [45] scales_1.0.0                mvtnorm_1.0-8              \n",
       " [47] rngtools_1.3.1              bibtex_0.4.2               \n",
       " [49] Rcpp_1.0.1                  mclust_5.4.2               \n",
       " [51] flashClust_1.01-2           foreign_0.8-71             \n",
       " [53] stats4_3.5.0                htmlwidgets_1.3            \n",
       " [55] httr_1.4.0                  XML_3.98-1.16              \n",
       " [57] pkgconfig_2.0.2             manipulate_1.0.1           \n",
       " [59] labeling_0.3                tidyselect_0.2.5           \n",
       " [61] rlang_0.3.4                 munsell_0.5.0              \n",
       " [63] cellranger_1.1.0            tools_3.5.0                \n",
       " [65] cli_1.1.0                   generics_0.0.2             \n",
       " [67] evaluate_0.13               ggdendro_0.1-20            \n",
       " [69] nlme_3.1-137                leaps_3.0                  \n",
       " [71] xml2_1.2.0                  compiler_3.5.0             \n",
       " [73] rstudioapi_0.10             png_0.1-7                  \n",
       " [75] stringi_1.4.3               highr_0.8                  \n",
       " [77] RSpectra_0.13-1             IRdisplay_0.7.0            \n",
       " [79] pillar_1.4.0                lmtest_0.9-36              \n",
       " [81] GlobalOptions_0.1.0         cowplot_0.9.4              \n",
       " [83] data.table_1.12.2           bitops_1.0-6               \n",
       " [85] corpcor_1.6.9               rtracklayer_1.42.1         \n",
       " [87] GenomicRanges_1.34.0        R6_2.4.0                   \n",
       " [89] gridExtra_2.3               IRanges_2.16.0             \n",
       " [91] codetools_0.2-16            boot_1.3-20                \n",
       " [93] assertthat_0.2.1            SummarizedExperiment_1.12.0\n",
       " [95] pkgmaker_0.27               rjson_0.2.20               \n",
       " [97] withr_2.1.2                 GenomicAlignments_1.18.1   \n",
       " [99] Rsamtools_1.34.1            S4Vectors_0.20.1           \n",
       "[101] GenomeInfoDbData_1.2.0      expm_0.999-3               \n",
       "[103] hms_0.4.2                   IRkernel_0.8.15            \n",
       "[105] Cairo_1.5-9                 pbdZMQ_0.3-3               \n",
       "[107] scatterplot3d_0.3-41        lubridate_1.7.4            \n",
       "[109] base64enc_0.1-3             ellipse_0.4.1              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
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