analysis/KCPG/KCPG_woAR_TT-Neuroblastoma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Neuroblastoma\n",
    "----- \n",
    "- Date : 17th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse) \n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Muataional Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3016</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3016\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3016\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3016   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Var1     Freq\n",
       "------  -----\n",
       "CNS       613\n",
       "WLM       405\n",
       "NBL       343\n",
       "RHB       259\n",
       "OS        241\n",
       "HGG       229\n",
       "RB        221\n",
       "EWS       192\n",
       "STS       177\n",
       "GCT       139\n",
       "ACT        79\n",
       "LGG        52\n",
       "CA         35\n",
       "OST        18\n",
       "RCC         5\n",
       "GICT        2\n",
       "LMPRT       2\n",
       "PGL         2\n",
       "BCC         1\n",
       "NM          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( sort( table(df$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'known',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>290</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 290\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 290\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 290  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD    AR AR/AD   XLD \n",
       "  153    92    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       \n",
       "        Het Hom\n",
       "  AD    153   0\n",
       "  AR     92   0\n",
       "  AR/AD  43   0\n",
       "  XLD     1   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$Mode_of_Inheritance_abb , df$genotype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Mode_of_Inheritance_abb != 'AR',]\n",
    "df = droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD AR/AD   XLD \n",
       "  153    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "182"
      ],
      "text/latex": [
       "182"
      ],
      "text/markdown": [
       "182"
      ],
      "text/plain": [
       "[1] 182"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "54"
      ],
      "text/latex": [
       "54"
      ],
      "text/markdown": [
       "54"
      ],
      "text/plain": [
       "[1] 54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Tumor_Sample_Barcode'</li>\n",
       "\t<li>'Tumor_type'</li>\n",
       "\t<li>'Tumor_type2'</li>\n",
       "\t<li>'Age_of_Onset'</li>\n",
       "\t<li>'Sex'</li>\n",
       "\t<li>'Age_group'</li>\n",
       "\t<li>'sample_type'</li>\n",
       "\t<li>'sequencing_type'</li>\n",
       "\t<li>'Access_unit'</li>\n",
       "\t<li>'Status'</li>\n",
       "\t<li>'Ethnicity'</li>\n",
       "\t<li>'Race'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Tumor\\_Sample\\_Barcode'\n",
       "\\item 'Tumor\\_type'\n",
       "\\item 'Tumor\\_type2'\n",
       "\\item 'Age\\_of\\_Onset'\n",
       "\\item 'Sex'\n",
       "\\item 'Age\\_group'\n",
       "\\item 'sample\\_type'\n",
       "\\item 'sequencing\\_type'\n",
       "\\item 'Access\\_unit'\n",
       "\\item 'Status'\n",
       "\\item 'Ethnicity'\n",
       "\\item 'Race'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Tumor_Sample_Barcode'\n",
       "2. 'Tumor_type'\n",
       "3. 'Tumor_type2'\n",
       "4. 'Age_of_Onset'\n",
       "5. 'Sex'\n",
       "6. 'Age_group'\n",
       "7. 'sample_type'\n",
       "8. 'sequencing_type'\n",
       "9. 'Access_unit'\n",
       "10. 'Status'\n",
       "11. 'Ethnicity'\n",
       "12. 'Race'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Tumor_Sample_Barcode\" \"Tumor_type\"           \"Tumor_type2\"         \n",
       " [4] \"Age_of_Onset\"         \"Sex\"                  \"Age_group\"           \n",
       " [7] \"sample_type\"          \"sequencing_type\"      \"Access_unit\"         \n",
       "[10] \"Status\"               \"Ethnicity\"            \"Race\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       CCF PCGP SJLIFE\n",
       "  Adrenocortical Carcinoma               1   19      2\n",
       "  Basal Cell Carcinoma                   0    0      1\n",
       "  Carcinoma                              0    0     14\n",
       "  Central Nervous System                 1    0    322\n",
       "  Ewing's Sarcoma                        6    0     89\n",
       "  Germ Cell Tumor                        1    0     73\n",
       "  Giant Cell Tumor                       0    0      3\n",
       "  High Grade Glioma                      5   75      0\n",
       "  Low Grade Glioma                       2   15      7\n",
       "  Low Malignant Potential Renal Tumors   2    0      0\n",
       "  Neuroblastoma                          3   48    139\n",
       "  Non-Malignant Tumor                    1    0      3\n",
       "  Osteosarcoma                          14    2    113\n",
       "  Other Solid Tumor                      0    0     10\n",
       "  Paraganglioma                          0    0      1\n",
       "  Renal Cell Carcinoma                   1    0      2\n",
       "  Retinoblastoma                         0    0     98\n",
       "  Rhabdomyosarcoma                       3   29    102\n",
       "  Soft Tissue Sarcoma                    8    0     85\n",
       "  Wilms' tumor                           2    0    205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2$Tumor_type , df2$Access_unit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>f</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Central Nervous System              </td><td>323                                 </td></tr>\n",
       "\t<tr><td>Wilms' tumor                        </td><td>207                                 </td></tr>\n",
       "\t<tr><td>Neuroblastoma                       </td><td>190                                 </td></tr>\n",
       "\t<tr><td>Rhabdomyosarcoma                    </td><td>134                                 </td></tr>\n",
       "\t<tr><td>Osteosarcoma                        </td><td>129                                 </td></tr>\n",
       "\t<tr><td>Retinoblastoma                      </td><td> 98                                 </td></tr>\n",
       "\t<tr><td>Ewing's Sarcoma                     </td><td> 95                                 </td></tr>\n",
       "\t<tr><td>Soft Tissue Sarcoma                 </td><td> 93                                 </td></tr>\n",
       "\t<tr><td>High Grade Glioma                   </td><td> 80                                 </td></tr>\n",
       "\t<tr><td>Germ Cell Tumor                     </td><td> 74                                 </td></tr>\n",
       "\t<tr><td>Low Grade Glioma                    </td><td> 24                                 </td></tr>\n",
       "\t<tr><td>Adrenocortical Carcinoma            </td><td> 22                                 </td></tr>\n",
       "\t<tr><td>Carcinoma                           </td><td> 14                                 </td></tr>\n",
       "\t<tr><td>Other Solid Tumor                   </td><td> 10                                 </td></tr>\n",
       "\t<tr><td>Non-Malignant Tumor                 </td><td>  4                                 </td></tr>\n",
       "\t<tr><td>Giant Cell Tumor                    </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Renal Cell Carcinoma                </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Low Malignant Potential Renal Tumors</td><td>  2                                 </td></tr>\n",
       "\t<tr><td>Basal Cell Carcinoma                </td><td>  1                                 </td></tr>\n",
       "\t<tr><td>Paraganglioma                       </td><td>  1                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " f & n\\\\\n",
       "\\hline\n",
       "\t Central Nervous System               & 323                                 \\\\\n",
       "\t Wilms' tumor                         & 207                                 \\\\\n",
       "\t Neuroblastoma                        & 190                                 \\\\\n",
       "\t Rhabdomyosarcoma                     & 134                                 \\\\\n",
       "\t Osteosarcoma                         & 129                                 \\\\\n",
       "\t Retinoblastoma                       &  98                                 \\\\\n",
       "\t Ewing's Sarcoma                      &  95                                 \\\\\n",
       "\t Soft Tissue Sarcoma                  &  93                                 \\\\\n",
       "\t High Grade Glioma                    &  80                                 \\\\\n",
       "\t Germ Cell Tumor                      &  74                                 \\\\\n",
       "\t Low Grade Glioma                     &  24                                 \\\\\n",
       "\t Adrenocortical Carcinoma             &  22                                 \\\\\n",
       "\t Carcinoma                            &  14                                 \\\\\n",
       "\t Other Solid Tumor                    &  10                                 \\\\\n",
       "\t Non-Malignant Tumor                  &   4                                 \\\\\n",
       "\t Giant Cell Tumor                     &   3                                 \\\\\n",
       "\t Renal Cell Carcinoma                 &   3                                 \\\\\n",
       "\t Low Malignant Potential Renal Tumors &   2                                 \\\\\n",
       "\t Basal Cell Carcinoma                 &   1                                 \\\\\n",
       "\t Paraganglioma                        &   1                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| f | n |\n",
       "|---|---|\n",
       "| Central Nervous System               | 323                                  |\n",
       "| Wilms' tumor                         | 207                                  |\n",
       "| Neuroblastoma                        | 190                                  |\n",
       "| Rhabdomyosarcoma                     | 134                                  |\n",
       "| Osteosarcoma                         | 129                                  |\n",
       "| Retinoblastoma                       |  98                                  |\n",
       "| Ewing's Sarcoma                      |  95                                  |\n",
       "| Soft Tissue Sarcoma                  |  93                                  |\n",
       "| High Grade Glioma                    |  80                                  |\n",
       "| Germ Cell Tumor                      |  74                                  |\n",
       "| Low Grade Glioma                     |  24                                  |\n",
       "| Adrenocortical Carcinoma             |  22                                  |\n",
       "| Carcinoma                            |  14                                  |\n",
       "| Other Solid Tumor                    |  10                                  |\n",
       "| Non-Malignant Tumor                  |   4                                  |\n",
       "| Giant Cell Tumor                     |   3                                  |\n",
       "| Renal Cell Carcinoma                 |   3                                  |\n",
       "| Low Malignant Potential Renal Tumors |   2                                  |\n",
       "| Basal Cell Carcinoma                 |   1                                  |\n",
       "| Paraganglioma                        |   1                                  |\n",
       "\n"
      ],
      "text/plain": [
       "   f                                    n  \n",
       "1  Central Nervous System               323\n",
       "2  Wilms' tumor                         207\n",
       "3  Neuroblastoma                        190\n",
       "4  Rhabdomyosarcoma                     134\n",
       "5  Osteosarcoma                         129\n",
       "6  Retinoblastoma                        98\n",
       "7  Ewing's Sarcoma                       95\n",
       "8  Soft Tissue Sarcoma                   93\n",
       "9  High Grade Glioma                     80\n",
       "10 Germ Cell Tumor                       74\n",
       "11 Low Grade Glioma                      24\n",
       "12 Adrenocortical Carcinoma              22\n",
       "13 Carcinoma                             14\n",
       "14 Other Solid Tumor                     10\n",
       "15 Non-Malignant Tumor                    4\n",
       "16 Giant Cell Tumor                       3\n",
       "17 Renal Cell Carcinoma                   3\n",
       "18 Low Malignant Potential Renal Tumors   2\n",
       "19 Basal Cell Carcinoma                   1\n",
       "20 Paraganglioma                          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_count( df2$Tumor_type , sort = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       ACT BCC  CA CNS EWS GCT GICT HGG LGG\n",
       "  Adrenocortical Carcinoma              22   0   0   0   0   0    0   0   0\n",
       "  Basal Cell Carcinoma                   0   1   0   0   0   0    0   0   0\n",
       "  Carcinoma                              0   0  14   0   0   0    0   0   0\n",
       "  Central Nervous System                 0   0   0 323   0   0    0   0   0\n",
       "  Ewing's Sarcoma                        0   0   0   0  95   0    0   0   0\n",
       "  Germ Cell Tumor                        0   0   0   0   0  74    0   0   0\n",
       "  Giant Cell Tumor                       0   0   0   0   0   0    3   0   0\n",
       "  High Grade Glioma                      0   0   0   0   0   0    0  80   0\n",
       "  Low Grade Glioma                       0   0   0   0   0   0    0   0  24\n",
       "  Low Malignant Potential Renal Tumors   0   0   0   0   0   0    0   0   0\n",
       "  Neuroblastoma                          0   0   0   0   0   0    0   0   0\n",
       "  Non-Malignant Tumor                    0   0   0   0   0   0    0   0   0\n",
       "  Osteosarcoma                           0   0   0   0   0   0    0   0   0\n",
       "  Other Solid Tumor                      0   0   0   0   0   0    0   0   0\n",
       "  Paraganglioma                          0   0   0   0   0   0    0   0   0\n",
       "  Renal Cell Carcinoma                   0   0   0   0   0   0    0   0   0\n",
       "  Retinoblastoma                         0   0   0   0   0   0    0   0   0\n",
       "  Rhabdomyosarcoma                       0   0   0   0   0   0    0   0   0\n",
       "  Soft Tissue Sarcoma                    0   0   0   0   0   0    0   0   0\n",
       "  Wilms' tumor                           0   0   0   0   0   0    0   0   0\n",
       "                                      \n",
       "                                       LMPRT NBL  NM  OS OST PGL  RB RCC RHB\n",
       "  Adrenocortical Carcinoma                 0   0   0   0   0   0   0   0   0\n",
       "  Basal Cell Carcinoma                     0   0   0   0   0   0   0   0   0\n",
       "  Carcinoma                                0   0   0   0   0   0   0   0   0\n",
       "  Central Nervous System                   0   0   0   0   0   0   0   0   0\n",
       "  Ewing's Sarcoma                          0   0   0   0   0   0   0   0   0\n",
       "  Germ Cell Tumor                          0   0   0   0   0   0   0   0   0\n",
       "  Giant Cell Tumor                         0   0   0   0   0   0   0   0   0\n",
       "  High Grade Glioma                        0   0   0   0   0   0   0   0   0\n",
       "  Low Grade Glioma                         0   0   0   0   0   0   0   0   0\n",
       "  Low Malignant Potential Renal Tumors     2   0   0   0   0   0   0   0   0\n",
       "  Neuroblastoma                            0 190   0   0   0   0   0   0   0\n",
       "  Non-Malignant Tumor                      0   0   4   0   0   0   0   0   0\n",
       "  Osteosarcoma                             0   0   0 129   0   0   0   0   0\n",
       "  Other Solid Tumor                        0   0   0   0  10   0   0   0   0\n",
       "  Paraganglioma                            0   0   0   0   0   1   0   0   0\n",
       "  Renal Cell Carcinoma                     0   0   0   0   0   0   0   3   0\n",
       "  Retinoblastoma                           0   0   0   0   0   0  98   0   0\n",
       "  Rhabdomyosarcoma                         0   0   0   0   0   0   0   0 134\n",
       "  Soft Tissue Sarcoma                      0   0   0   0   0   0   0   0   0\n",
       "  Wilms' tumor                             0   0   0   0   0   0   0   0   0\n",
       "                                      \n",
       "                                       STS WLM\n",
       "  Adrenocortical Carcinoma               0   0\n",
       "  Basal Cell Carcinoma                   0   0\n",
       "  Carcinoma                              0   0\n",
       "  Central Nervous System                 0   0\n",
       "  Ewing's Sarcoma                        0   0\n",
       "  Germ Cell Tumor                        0   0\n",
       "  Giant Cell Tumor                       0   0\n",
       "  High Grade Glioma                      0   0\n",
       "  Low Grade Glioma                       0   0\n",
       "  Low Malignant Potential Renal Tumors   0   0\n",
       "  Neuroblastoma                          0   0\n",
       "  Non-Malignant Tumor                    0   0\n",
       "  Osteosarcoma                           0   0\n",
       "  Other Solid Tumor                      0   0\n",
       "  Paraganglioma                          0   0\n",
       "  Renal Cell Carcinoma                   0   0\n",
       "  Retinoblastoma                         0   0\n",
       "  Rhabdomyosarcoma                       0   0\n",
       "  Soft Tissue Sarcoma                   93   0\n",
       "  Wilms' tumor                           0 207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "table(df2$Tumor_type , df2$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Neuroblastoma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               \n",
       "                NBL\n",
       "  Neuroblastoma 190"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$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>19 </td><td>190</td></tr>\n",
       "\t<tr><td>84 </td><td> 12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 19  & 190\\\\\n",
       "\t 84  &  12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 19  | 190 |\n",
       "| 84  |  12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 19   190 \n",
       "[2,] 84    12 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(dim(df_ss), dim(df2_ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MAF object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss , isTCGA = FALSE, verbose = F, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>38               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Center           </td><td>NA               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Samples          </td><td>18               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td>15               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 2               </td><td>0.111            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 1               </td><td>0.056            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 8               </td><td>0.444            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 4               </td><td>0.222            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 4               </td><td>0.222            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>19               </td><td>1.056            </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           & 18                &    NA             & NA               \\\\\n",
       "\t nGenes            & 15                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   &  2                    & 0.111                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  1                    & 0.056                 &  0                   \\\\\n",
       "\t Missense\\_Mutation &  8                  & 0.444               &  0                 \\\\\n",
       "\t Nonsense\\_Mutation &  4                  & 0.222               &  0                 \\\\\n",
       "\t Splice\\_Site       &  4                  & 0.222               &  0                 \\\\\n",
       "\t total             & 19                & 1.056             &  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           | 18                |    NA             | NA                |\n",
       "| nGenes            | 15                |    NA             | NA                |\n",
       "| Frame_Shift_Del   |  2                | 0.111             |  0                |\n",
       "| Frame_Shift_Ins   |  1                | 0.056             |  0                |\n",
       "| Missense_Mutation |  8                | 0.444             |  0                |\n",
       "| Nonsense_Mutation |  4                | 0.222             |  0                |\n",
       "| Splice_Site       |  4                | 0.222             |  0                |\n",
       "| total             | 19                | 1.056             |  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           18         NA NA    \n",
       "4  nGenes            15         NA NA    \n",
       "5  Frame_Shift_Del    2      0.111  0    \n",
       "6  Frame_Shift_Ins    1      0.056  0    \n",
       "7  Missense_Mutation  8      0.444  0    \n",
       "8  Nonsense_Mutation  4      0.222  0    \n",
       "9  Splice_Site        4      0.222  0    \n",
       "10 total             19      1.056  1    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>CHEK2 </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>4     </td><td>4     </td><td>4     </td></tr>\n",
       "\t<tr><td>NF1   </td><td>0     </td><td>0     </td><td>2     </td><td>0     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>ALK   </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>BRCA2 </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>COL7A1</td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>ERBB4 </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t CHEK2  & 1      & 0      & 1      & 1      & 1      & 4      & 4      & 4     \\\\\n",
       "\t NF1    & 0      & 0      & 2      & 0      & 0      & 2      & 2      & 2     \\\\\n",
       "\t ALK    & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t BRCA2  & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t COL7A1 & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t ERBB4  & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| CHEK2  | 1      | 0      | 1      | 1      | 1      | 4      | 4      | 4      |\n",
       "| NF1    | 0      | 0      | 2      | 0      | 0      | 2      | 2      | 2      |\n",
       "| ALK    | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| BRCA2  | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| COL7A1 | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| ERBB4  | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 CHEK2       1               0               1                \n",
       "2 NF1         0               0               2                \n",
       "3 ALK         0               0               1                \n",
       "4 BRCA2       0               0               0                \n",
       "5 COL7A1      0               0               0                \n",
       "6 ERBB4       0               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 1                 1           4     4              4             \n",
       "2 0                 0           2     2              2             \n",
       "3 0                 0           1     1              1             \n",
       "4 0                 1           1     1              1             \n",
       "5 0                 1           1     1              1             \n",
       "6 1                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>15</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 15\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 15\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 15  9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples with top variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   INS   SNP   total\n",
       "---------------------  ----  ----  ----  ------\n",
       "SJNBL135                  0     0     2       2\n",
       "SJNBL006050               0     0     1       1\n",
       "SJNBL018745               0     0     1       1\n",
       "SJNBL019397               0     0     1       1\n",
       "SJNBL019416               0     0     1       1\n",
       "SJNBL019418               0     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",
       "SJNBL135                       2\n",
       "SJNBL006050                    1\n",
       "SJNBL018745                    1\n",
       "SJNBL019397                    1\n",
       "SJNBL019416                    1\n",
       "SJNBL019418                    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": [
       "2"
      ],
      "text/latex": [
       "2"
      ],
      "text/markdown": [
       "2"
      ],
      "text/plain": [
       "[1] 2"
      ]
     },
     "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": [
       "15"
      ],
      "text/latex": [
       "15"
      ],
      "text/markdown": [
       "15"
      ],
      "text/plain": [
       "[1] 15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>CHEK2 </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>4     </td><td>4     </td><td>4     </td></tr>\n",
       "\t<tr><td>NF1   </td><td>0     </td><td>0     </td><td>2     </td><td>0     </td><td>0     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
       "\t<tr><td>ALK   </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>BRCA2 </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>COL7A1</td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>ERBB4 </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t CHEK2  & 1      & 0      & 1      & 1      & 1      & 4      & 4      & 4     \\\\\n",
       "\t NF1    & 0      & 0      & 2      & 0      & 0      & 2      & 2      & 2     \\\\\n",
       "\t ALK    & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t BRCA2  & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t COL7A1 & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t ERBB4  & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| CHEK2  | 1      | 0      | 1      | 1      | 1      | 4      | 4      | 4      |\n",
       "| NF1    | 0      | 0      | 2      | 0      | 0      | 2      | 2      | 2      |\n",
       "| ALK    | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| BRCA2  | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| COL7A1 | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| ERBB4  | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 CHEK2       1               0               1                \n",
       "2 NF1         0               0               2                \n",
       "3 ALK         0               0               1                \n",
       "4 BRCA2       0               0               0                \n",
       "5 COL7A1      0               0               0                \n",
       "6 ERBB4       0               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 1                 1           4     4              4             \n",
       "2 0                 0           2     2              2             \n",
       "3 0                 0           1     1              1             \n",
       "4 0                 1           1     1              1             \n",
       "5 0                 1           1     1              1             \n",
       "6 1                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fyKderUicfjyVekmErPPgkhGzZsEP1n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0lVldXb19+/azZ8/K8BAAAK1aW5ssc/78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\ncs9Pj6K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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=6)\n",
    "gs %>% ggdotchart(\"Hugo_Symbol\",\"MutatedSamples\", \n",
    "                  rotate = TRUE,\n",
    "                  sorting = \"descending\",\n",
    "                  show.label = F,size = \"MutatedSamples\", \n",
    "                  ggtheme = theme_pubr() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotmafSummary\n",
    "to plot the summary of the maf file, which displays number of variants in each sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r6b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\nAIi9/wHlbpr2uy2R+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Top 15\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-Neuroblastoma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_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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AVoPKjRs3AgMDjUZjs2bN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\nH6tkEUrHXdDTAAAAAElFTkSuQmCC",
      "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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ysjEzcfj6+sbE\nxHh6elpaWioqKnbs2NHR0dHf3z8uLk5dXb3h67B582Y2tmXJkiWKioq+vr5s/P8333zzuvFB\n0Cq4Z9HY2Fg2bIGa+OD966+/jh49mgtkiESiixcv/vDDD7169erevTs32URjinFdLRpQ68GP\nm86GqbdDhJSqbpJ3v85NlZmZ+fHHH5ubm0+fPv33338/f/7806dPJZeraKq8vDxuRICBgUGt\nrVxO3Xf1jfmO3hsCgaBHjx5TpkzZsmVLWlpaTEyMvr4+2xQdHc3NqcRwmzjcZeQW3GkkyVVp\nWGeZekn1d96kc38jad+SAAAtAD04AEDqpk+fPn369Ho3cV2Cq6urX716NXHixK+++ooN0+CG\nELP+EXw+f968eVyn2carrq4uKytr4MgFBQX17ujo6MjGhtSlrq6+fft2yZkvsrKyWIKbhVRR\nUXHnzp1NbS29aYmHBq7DnDlz5syZw31UUVF58uTJWzQApM3Ozk5FReXly5exsbFs4UYej+fo\n6Nj4SWG1tbWPHDnyzz//hIaGhoeHX716lXszn5ycPHTo0KysLB0dncYUMzQ0ZDkaGhpnzpyp\ntzpuhdTGa8WqOW93nWs9Eza+y0NlZaW7u3taWhoRaWtre3p6Dho0yMnJaf369dykDE1lYGDA\n5/NZjOPRo0cODg6SW7nZf4yNjd/u+DIqPT2dG2m4YsUKyd4TRCQUCufPn89G5xUVFZWWlrKl\nTJi60xJxl7Gp68tw67DS6/8dIaLm/Z2/y7m/kbRvSQCAFoAABwC0CQUFBeyPRU9Pz4CAAG4A\ncPv27bt169Y2jwzwdgQCQZ8+faKiomJjY1kvG1tbW3V19UYGOJ4/f3737l2Wnjdv3vz58589\ne3b69OmgoKBLly4RUVFRUXR09ODBgxtTjJs4oKioyNra+u1Wyny7FkqjaklNus5cl4daT6ps\noE1jJCUlsegGEcXHx3PTG3OjBt6CoqJip06d2FrXf/75p+TItbi4OK6p3ByiHwgVFZXz58+z\ntJWVVa2HfCJ68eIFSygoKKiqqkpuysrKysjI6NKlC/v44MEDbgYTyRGUjaGtrd2+fXs29OnK\nlSsDBw5k+SKRaOHChWzm16lTpzbv7/xdzv2NpH1LAgC0AAxRAYA2QVdXNyAgQCAQHDlyxNjY\n2MDA4ODBgzo6OocPH+YWRKxXcHAw7/V0dHTe+sgA0sNGT2RnZ7PB/00aN1FQUNDvX+wtq7a2\n9vjx47mh/kSkrKzcyGI2Njbci+gNGzZwm5KTk62tra2srN5itsJWrLqWxl9nNp8OEd28eZMt\nASMWiw8ePLhv376Gq+CeJyU7THExlFOnTnGPow3v/jps3h8iCgoK4tbHycvL+/rrr1laU1Oz\ne/fuDR/kPWNiYmJvb8/SmzdvXrx4Mbd0VHl5+Z49e7if05AhQ2oNz6mpqfHx8WGxoefPn0+e\nPJktv62srDx48OCmtoSNsiSi3377LSUlhaU3btz466+/btu2bdu2bQYGBs37O3+Xc38d7kfY\nArckAIC0oQcHALQV3377rbe3d0RExIMHDxQUFMzNzT/77DPJefLq5eHh0cBMHOyt7NsdGUB6\nuOkh2JimJgU4LCwsLC0t2Uj4UaNGde/e3cLCoqCggFsIuV27dn379m3fvn1jiikpKa1evdrH\nx4eIVq1adeXKFSsrq6ysrCtXrpSVlRGRh4dHU9dVbWQLpVF1LY2/zpKbhg0bZmRkVFpa+rro\nQ7t27RQUFNjUNoGBgRUVFV5eXpLdKHr27Nm7d++ysjLJJZ+5wS/17q6trV1vXT/88MPhw4dL\nSkqqqqrc3d1tbGzat29/8+ZNLoby448/foD/N/vll1+GDx/OLunatWvXrl2rpaWlpKT05MkT\nbpygQCBYsWJF3X2vXr1qYGBgYWFx7949Ft0gogULFtSdnuON1q1b9+eff1ZWVhYUFPTu3btX\nr15VVVXc+uKff/45G/TRvL/zdzl3zut+hNK+JQEApE4MAADwAUsvSOu1066p/13850Ljq/jt\nt9+4f3YzMzPFYnGtlTVSUlLEYjE3FwARrVq1iu27detWlqOtrf1/bU5Pf93zMBHt3bu3ScWq\nq6vHjh1bb5khQ4YUFxezYsuWLWOZtra2kmcn2S+jqS1sZNUtcJ1ramrqLuSsrKzs5eXF0oaG\nhpJ1sTWbOcnJyTU1NV9++WWtI1haWo4ZM4alTU1N2YK19e7+uu9aLBafO3fudYMF5s6dW1NT\nw5Vs0nckRQ8TxMuoyf/dOdGkSg4fPtxASMLIyOjUqVNc4UmTJrH8Xr161Z2u1dPT8+XLl1zh\ner+I1307W7durbejRL9+/UpLS1mZ5v2dN/XcX9fyen+Ezd5UAIAWhh4cAADwQTNSM/71k41N\n3ctOx/5dKtXR0enSpQtblFFNTa2pg//Za9WQkJCjR4/m5uY+evRIRUXFyMhIKBTOmTOH60rQ\nyGJ8Pn///v0eHh6hoaEpKSk5OTl6eno2NjZTp04dNWoUm5q3qVqxakmNv848Hi8sLMzf3//w\n4cMZGRnKysqDBg1atWpVfHw8W/KzY8eOkuV37Njh5+d35cqVFy9eqKioyMnJ8Xi8oKAga2vr\n3bt35+bmWltbDx48eOnSpQkJCWwSDSIKDw/39PSsd/cGzmLw4MHp6embN2+Oi4tLS0srLi62\ntra2s7Pz8fGpOwVDm6BlTl+EN3kvwz5NKu7t7e3m5hYSEhIREZGTk3P//n0+n29qampiYuLh\n4TF+/Ph6VwTX0dEJDQ1dsGBBVFRUdXV1nz59PDw8Zs6c+dYLzcyYMaNPnz5btmxJTk5OT09X\nVVW1traeOHHihAkTuJWzmv13/nbnXku9P0Jp35IAANLGEzdxBSkAAAAAABkyefLkXbt2EZGr\nq+vr1gcBAID3AAKxANBy/jx7ODM7rUm7WHa2GeE6RkrtaWuSgzc9SYxv0i4dHRztp82SUnsA\nAAAAAGQIAhwA0HIys9Ni46NbuxVt15PE+Ow/j7V2KwAAAAAAZBKWiQUAAIA2atGiRQ2sAy3J\n2dm5tRsL8JbwOwcAaC7owQEAMqmmpmbTpk3Hjx9PSkpSUFCws7NbuHDhkCFD2FZFRcWKior4\n+Pi6K8hqamoWFRVdvnzZ2dm5gWJDhgw5f/78b7/95ufn1xLn06wEAkF1dTURzZgxg5s/f8aM\nGdu2bWPpqqqqhueKa+DKALSkbt26vW5Nh1q6du0q7cYASAl+5wAAzQUBDgCQPRUVFZ999tm5\nc+e4nPz8/HPnzsloPEJ6rl+/zqXj45s2uwdAWzBu3Lhx48a1ditA5u3cuXPnzp2t3YrXwu8c\nAKC5YIgKAMiezZs3nzt3js/nr1+//vHjxwUFBTNnziSiBQsW5OTkNLzvtWvXkpOTHRwcWqKh\nrYrP59++ffvVq1dEVF5enpyczK1ZCAAAAADw/sEfuwAgY2pqan7++Wci8vX1/e677zp27Kit\nrb1x40ZTU9Pq6urIyEiu5OPHj93c3DQ0NKysrE6ePMkye/bsaW9vn56eXuuwFRUVc+fONTIy\nMjU1Xb16NbeEdnl5ORv5fPPmzbFjx44cOZKIiouLv/nmm86dO2tpaQ0ePDguLo47iIWFhYWF\nxeXLl4cOHaqhodGlS5f9+/dL+5rUy9bWtrq6+ubNm0R069atqqoqW1tbyQIpKSlubm66urrK\nyso2Njb//e9/6104/HUnCwAAAADQpmCICgDImNzc3MLCQiKaPHkylyknJ5eQkFBWVqaqqspl\nfvnll0RUUlJSXFz8+eefZ2dn6+rqvu6wkyZNOnToEBEZGBgsWbKkXbt2tQrMmjUrJiZm8ODB\nNTU1Li4ucXFx5ubm3bt3v3DhgrOz85UrV/r06SMWi+/du0dEbm5uPB6vtLS0uLh44sSJjo6O\nlpaWzXkVGkEoFCYnJ1+/ft3Z2ZmNT+nbt29ycjLbWlJSMmzYsLy8PE1NTRMTk/T0dD8/P319\n/TFj/mdR3gZOtoVPBwAAAACgYejBAQAyJisriyU6d+4sma+trW1kZKSpqcnleHh4FBQUpKam\nEtGrV68a6Hpw69YtFt2IiorKy8vbs2dPZWVlrTJqamr3798PDw8/ceJEXFycrq5uSkpKdHT0\n/Pnzq6qqli1bJll40qRJz549+/vvvxUVFaurqy9fvvxuJ/02HB0deTwem4aDnbtkVOLatWv5\n+fnGxsZPnjy5c+fOlClTiCg6OrrWQRpzsgAAAAAAbQF6cACAjOGGUcjJyTVccurUqQKBwNra\n2srK6s6dO8+ePXtdyatXrxLRgAEDPv74YyL68ssvf/jhhwcPHkiW+fHHH01MTOjfmTuNjIwC\nAwOJiIVCLl26JDm+Y9q0aXw+38TEpE+fPpcuXWJdTlqYmpqara0ta218fLyJiYm+vj631dXV\nVSQSlZSUXL16NSkpKSoqiojYhB2SGjhZHo/XYucCAAAAAPBGCHAAgIyxsLBgifv379vZ2XH5\nT548efXqlZqamra2NstRVFRkCRUVlYaP+c8//xARi18QEY/HMzExqRXg6NixI0vk5uYSUUJC\nQkJCArf11atXT58+VVdXZx+VlJRqJVqFUCgMDg7OyMi4e/eul5eX5CaRSOTr6xsSElJVVcXn\n87mLVksDJ8tdEAAAAACAtgBDVABAxhgZGWlpaRGR5OSdYrF48ODBnTp1+uOPP97imKxrQ15e\nHne0hw8f1irDdVjQ09Mjojlz5oj/Vxt84BcKhUS0detWsVhca9aMffv2bd++vX379mFhYYWF\nhRMmTKj3CDJ0sgAAAADwgUOAAwBkjJyc3OLFi4low4YNO3bsKC0tLS0tXbFiRUpKChENGTLk\nLY7JAgEXL168du0aEYWGhv7999+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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=6)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_oncoplot_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=6, width=12)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpAsXLig5ubm5ubm5P/3005o1a1555ZWwsLDmzZvXY42f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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-Neuroblastoma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_oncoplot_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=6, width=12)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Transition and Transversions\n",
    "returns a list of summarized tables in various ways. Summarized data can also be visualized as a boxplot showing overall distribution of six different conversions and as a stacked barplot showing fraction of conversions in each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class  MAF \n",
       "                   ID summary  Mean Median\n",
       " 1:        NCBI_Build      38    NA     NA\n",
       " 2:            Center      NA    NA     NA\n",
       " 3:           Samples      18    NA     NA\n",
       " 4:            nGenes      15    NA     NA\n",
       " 5:   Frame_Shift_Del       2 0.111      0\n",
       " 6:   Frame_Shift_Ins       1 0.056      0\n",
       " 7: Missense_Mutation       8 0.444      0\n",
       " 8: Nonsense_Mutation       4 0.222      0\n",
       " 9:       Splice_Site       4 0.222      0\n",
       "10:             total      19 1.056      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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CDHebUNAAAA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",
      "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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BDQAAAQENAAABAQ0AAAEBDQAAAQENAAABAQ0AAAEBDQAAAQENAAABAQ0AAAEBDQ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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": {
      "image/png": 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TJvEDpw4MDTTz/96HYrKiqaMmVKjx49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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=2]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "           min.freq =1,\n",
    "           max.words=50, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.35, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=2]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Drug Interactions\n",
    "- plot shows potential druggable gene categories along with upto top 5 genes involved in them. \n",
    "- One can also extract information on drug-gene interactions. \n",
    "- [check this documentation](https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html#98_drug-gene_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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=4, 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-Neuroblastoma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_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=4, width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oncogenic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pathway alteration fractions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pathway  N n_affected_genes fraction_affected\n",
      " 1:    RTK-RAS 85                4        0.04705882\n",
      " 2:       TP53  6                2        0.33333333\n",
      " 3: Cell_Cycle 15                0        0.00000000\n",
      " 4:      Hippo 38                0        0.00000000\n",
      " 5:        MYC 13                0        0.00000000\n",
      " 6:      NOTCH 71                0        0.00000000\n",
      " 7:       NRF2  3                0        0.00000000\n",
      " 8:       PI3K 29                0        0.00000000\n",
      " 9:   TGF-Beta  7                0        0.00000000\n",
      "10:        WNT 68                0        0.00000000\n"
     ]
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "op = OncogenicPathways(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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muHzQaOzp0779+//86dO/r6+s7OzsOG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\nAgcAAJCOwAEAAKT7P829LhbAmnLrAAAAAElFTkSuQmCC",
      "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-Neuroblastoma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Neuroblastoma_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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Pzmm2822qmpqSdPnjTaN9544969ez/9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\nAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAADwCAIAAACfVArnAAAABmJLR0QA/wD/AP+gvaeTAAAZ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",
      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=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-Neuroblastoma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Neuroblastoma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Neuroblastoma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Neuroblastoma\""
      ]
     },
     "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-Neuroblastoma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Neuroblastoma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TT-Neuroblastoma_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-Neuroblastoma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Neuroblastoma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Neuroblastoma_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-Neuroblastoma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TT-Neuroblastoma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Neuroblastoma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TT-Neuroblastoma_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",
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       " [77] RSpectra_0.13-1             IRdisplay_0.7.0            \n",
       " [79] pillar_1.4.0                lmtest_0.9-36              \n",
       " [81] GlobalOptions_0.1.0         cowplot_0.9.4              \n",
       " [83] data.table_1.12.2           bitops_1.0-6               \n",
       " [85] corpcor_1.6.9               rtracklayer_1.42.1         \n",
       " [87] GenomicRanges_1.34.0        R6_2.4.0                   \n",
       " [89] gridExtra_2.3               IRanges_2.16.0             \n",
       " [91] codetools_0.2-16            boot_1.3-20                \n",
       " [93] assertthat_0.2.1            SummarizedExperiment_1.12.0\n",
       " [95] pkgmaker_0.27               rjson_0.2.20               \n",
       " [97] withr_2.1.2                 GenomicAlignments_1.18.1   \n",
       " [99] Rsamtools_1.34.1            S4Vectors_0.20.1           \n",
       "[101] GenomeInfoDbData_1.2.0      expm_0.999-3               \n",
       "[103] hms_0.4.2                   IRkernel_0.8.15            \n",
       "[105] Cairo_1.5-9                 pbdZMQ_0.3-3               \n",
       "[107] scatterplot3d_0.3-41        lubridate_1.7.4            \n",
       "[109] base64enc_0.1-3             ellipse_0.4.1              "
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