analysis/KCPG/KCPG_woAR_TT-Retinoblastoma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Known Cancer Predisposition Genes, without autosomal recessive genes for Retinoblastoma\n",
    "----- \n",
    "- Date : 17th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse) \n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Muataional Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3016</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3016\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3016\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3016   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Var1     Freq\n",
       "------  -----\n",
       "CNS       613\n",
       "WLM       405\n",
       "NBL       343\n",
       "RHB       259\n",
       "OS        241\n",
       "HGG       229\n",
       "RB        221\n",
       "EWS       192\n",
       "STS       177\n",
       "GCT       139\n",
       "ACT        79\n",
       "LGG        52\n",
       "CA         35\n",
       "OST        18\n",
       "RCC         5\n",
       "GICT        2\n",
       "LMPRT       2\n",
       "PGL         2\n",
       "BCC         1\n",
       "NM          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( sort( table(df$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'known',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>290</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 290\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 290\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 290  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD    AR AR/AD   XLD \n",
       "  153    92    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       \n",
       "        Het Hom\n",
       "  AD    153   0\n",
       "  AR     92   0\n",
       "  AR/AD  43   0\n",
       "  XLD     1   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$Mode_of_Inheritance_abb , df$genotype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Mode_of_Inheritance_abb != 'AR',]\n",
    "df = droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "   AD AR/AD   XLD \n",
       "  153    43     2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "182"
      ],
      "text/latex": [
       "182"
      ],
      "text/markdown": [
       "182"
      ],
      "text/plain": [
       "[1] 182"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "54"
      ],
      "text/latex": [
       "54"
      ],
      "text/markdown": [
       "54"
      ],
      "text/plain": [
       "[1] 54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Tumor_Sample_Barcode'</li>\n",
       "\t<li>'Tumor_type'</li>\n",
       "\t<li>'Tumor_type2'</li>\n",
       "\t<li>'Age_of_Onset'</li>\n",
       "\t<li>'Sex'</li>\n",
       "\t<li>'Age_group'</li>\n",
       "\t<li>'sample_type'</li>\n",
       "\t<li>'sequencing_type'</li>\n",
       "\t<li>'Access_unit'</li>\n",
       "\t<li>'Status'</li>\n",
       "\t<li>'Ethnicity'</li>\n",
       "\t<li>'Race'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Tumor\\_Sample\\_Barcode'\n",
       "\\item 'Tumor\\_type'\n",
       "\\item 'Tumor\\_type2'\n",
       "\\item 'Age\\_of\\_Onset'\n",
       "\\item 'Sex'\n",
       "\\item 'Age\\_group'\n",
       "\\item 'sample\\_type'\n",
       "\\item 'sequencing\\_type'\n",
       "\\item 'Access\\_unit'\n",
       "\\item 'Status'\n",
       "\\item 'Ethnicity'\n",
       "\\item 'Race'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Tumor_Sample_Barcode'\n",
       "2. 'Tumor_type'\n",
       "3. 'Tumor_type2'\n",
       "4. 'Age_of_Onset'\n",
       "5. 'Sex'\n",
       "6. 'Age_group'\n",
       "7. 'sample_type'\n",
       "8. 'sequencing_type'\n",
       "9. 'Access_unit'\n",
       "10. 'Status'\n",
       "11. 'Ethnicity'\n",
       "12. 'Race'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Tumor_Sample_Barcode\" \"Tumor_type\"           \"Tumor_type2\"         \n",
       " [4] \"Age_of_Onset\"         \"Sex\"                  \"Age_group\"           \n",
       " [7] \"sample_type\"          \"sequencing_type\"      \"Access_unit\"         \n",
       "[10] \"Status\"               \"Ethnicity\"            \"Race\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       CCF PCGP SJLIFE\n",
       "  Adrenocortical Carcinoma               1   19      2\n",
       "  Basal Cell Carcinoma                   0    0      1\n",
       "  Carcinoma                              0    0     14\n",
       "  Central Nervous System                 1    0    322\n",
       "  Ewing's Sarcoma                        6    0     89\n",
       "  Germ Cell Tumor                        1    0     73\n",
       "  Giant Cell Tumor                       0    0      3\n",
       "  High Grade Glioma                      5   75      0\n",
       "  Low Grade Glioma                       2   15      7\n",
       "  Low Malignant Potential Renal Tumors   2    0      0\n",
       "  Neuroblastoma                          3   48    139\n",
       "  Non-Malignant Tumor                    1    0      3\n",
       "  Osteosarcoma                          14    2    113\n",
       "  Other Solid Tumor                      0    0     10\n",
       "  Paraganglioma                          0    0      1\n",
       "  Renal Cell Carcinoma                   1    0      2\n",
       "  Retinoblastoma                         0    0     98\n",
       "  Rhabdomyosarcoma                       3   29    102\n",
       "  Soft Tissue Sarcoma                    8    0     85\n",
       "  Wilms' tumor                           2    0    205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2$Tumor_type , df2$Access_unit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>f</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Central Nervous System              </td><td>323                                 </td></tr>\n",
       "\t<tr><td>Wilms' tumor                        </td><td>207                                 </td></tr>\n",
       "\t<tr><td>Neuroblastoma                       </td><td>190                                 </td></tr>\n",
       "\t<tr><td>Rhabdomyosarcoma                    </td><td>134                                 </td></tr>\n",
       "\t<tr><td>Osteosarcoma                        </td><td>129                                 </td></tr>\n",
       "\t<tr><td>Retinoblastoma                      </td><td> 98                                 </td></tr>\n",
       "\t<tr><td>Ewing's Sarcoma                     </td><td> 95                                 </td></tr>\n",
       "\t<tr><td>Soft Tissue Sarcoma                 </td><td> 93                                 </td></tr>\n",
       "\t<tr><td>High Grade Glioma                   </td><td> 80                                 </td></tr>\n",
       "\t<tr><td>Germ Cell Tumor                     </td><td> 74                                 </td></tr>\n",
       "\t<tr><td>Low Grade Glioma                    </td><td> 24                                 </td></tr>\n",
       "\t<tr><td>Adrenocortical Carcinoma            </td><td> 22                                 </td></tr>\n",
       "\t<tr><td>Carcinoma                           </td><td> 14                                 </td></tr>\n",
       "\t<tr><td>Other Solid Tumor                   </td><td> 10                                 </td></tr>\n",
       "\t<tr><td>Non-Malignant Tumor                 </td><td>  4                                 </td></tr>\n",
       "\t<tr><td>Giant Cell Tumor                    </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Renal Cell Carcinoma                </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Low Malignant Potential Renal Tumors</td><td>  2                                 </td></tr>\n",
       "\t<tr><td>Basal Cell Carcinoma                </td><td>  1                                 </td></tr>\n",
       "\t<tr><td>Paraganglioma                       </td><td>  1                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " f & n\\\\\n",
       "\\hline\n",
       "\t Central Nervous System               & 323                                 \\\\\n",
       "\t Wilms' tumor                         & 207                                 \\\\\n",
       "\t Neuroblastoma                        & 190                                 \\\\\n",
       "\t Rhabdomyosarcoma                     & 134                                 \\\\\n",
       "\t Osteosarcoma                         & 129                                 \\\\\n",
       "\t Retinoblastoma                       &  98                                 \\\\\n",
       "\t Ewing's Sarcoma                      &  95                                 \\\\\n",
       "\t Soft Tissue Sarcoma                  &  93                                 \\\\\n",
       "\t High Grade Glioma                    &  80                                 \\\\\n",
       "\t Germ Cell Tumor                      &  74                                 \\\\\n",
       "\t Low Grade Glioma                     &  24                                 \\\\\n",
       "\t Adrenocortical Carcinoma             &  22                                 \\\\\n",
       "\t Carcinoma                            &  14                                 \\\\\n",
       "\t Other Solid Tumor                    &  10                                 \\\\\n",
       "\t Non-Malignant Tumor                  &   4                                 \\\\\n",
       "\t Giant Cell Tumor                     &   3                                 \\\\\n",
       "\t Renal Cell Carcinoma                 &   3                                 \\\\\n",
       "\t Low Malignant Potential Renal Tumors &   2                                 \\\\\n",
       "\t Basal Cell Carcinoma                 &   1                                 \\\\\n",
       "\t Paraganglioma                        &   1                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| f | n |\n",
       "|---|---|\n",
       "| Central Nervous System               | 323                                  |\n",
       "| Wilms' tumor                         | 207                                  |\n",
       "| Neuroblastoma                        | 190                                  |\n",
       "| Rhabdomyosarcoma                     | 134                                  |\n",
       "| Osteosarcoma                         | 129                                  |\n",
       "| Retinoblastoma                       |  98                                  |\n",
       "| Ewing's Sarcoma                      |  95                                  |\n",
       "| Soft Tissue Sarcoma                  |  93                                  |\n",
       "| High Grade Glioma                    |  80                                  |\n",
       "| Germ Cell Tumor                      |  74                                  |\n",
       "| Low Grade Glioma                     |  24                                  |\n",
       "| Adrenocortical Carcinoma             |  22                                  |\n",
       "| Carcinoma                            |  14                                  |\n",
       "| Other Solid Tumor                    |  10                                  |\n",
       "| Non-Malignant Tumor                  |   4                                  |\n",
       "| Giant Cell Tumor                     |   3                                  |\n",
       "| Renal Cell Carcinoma                 |   3                                  |\n",
       "| Low Malignant Potential Renal Tumors |   2                                  |\n",
       "| Basal Cell Carcinoma                 |   1                                  |\n",
       "| Paraganglioma                        |   1                                  |\n",
       "\n"
      ],
      "text/plain": [
       "   f                                    n  \n",
       "1  Central Nervous System               323\n",
       "2  Wilms' tumor                         207\n",
       "3  Neuroblastoma                        190\n",
       "4  Rhabdomyosarcoma                     134\n",
       "5  Osteosarcoma                         129\n",
       "6  Retinoblastoma                        98\n",
       "7  Ewing's Sarcoma                       95\n",
       "8  Soft Tissue Sarcoma                   93\n",
       "9  High Grade Glioma                     80\n",
       "10 Germ Cell Tumor                       74\n",
       "11 Low Grade Glioma                      24\n",
       "12 Adrenocortical Carcinoma              22\n",
       "13 Carcinoma                             14\n",
       "14 Other Solid Tumor                     10\n",
       "15 Non-Malignant Tumor                    4\n",
       "16 Giant Cell Tumor                       3\n",
       "17 Renal Cell Carcinoma                   3\n",
       "18 Low Malignant Potential Renal Tumors   2\n",
       "19 Basal Cell Carcinoma                   1\n",
       "20 Paraganglioma                          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_count( df2$Tumor_type , sort = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "|                                     | ACT| BCC| CA| CNS| EWS| GCT| GICT| HGG| LGG| LMPRT| NBL| NM|  OS| OST| PGL| RB| RCC| RHB| STS| WLM|\n",
       "|:------------------------------------|---:|---:|--:|---:|---:|---:|----:|---:|---:|-----:|---:|--:|---:|---:|---:|--:|---:|---:|---:|---:|\n",
       "|Adrenocortical Carcinoma             |  22|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Basal Cell Carcinoma                 |   0|   1|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Carcinoma                            |   0|   0| 14|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Central Nervous System               |   0|   0|  0| 323|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Ewing's Sarcoma                      |   0|   0|  0|   0|  95|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Germ Cell Tumor                      |   0|   0|  0|   0|   0|  74|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Giant Cell Tumor                     |   0|   0|  0|   0|   0|   0|    3|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|High Grade Glioma                    |   0|   0|  0|   0|   0|   0|    0|  80|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Low Grade Glioma                     |   0|   0|  0|   0|   0|   0|    0|   0|  24|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Low Malignant Potential Renal Tumors |   0|   0|  0|   0|   0|   0|    0|   0|   0|     2|   0|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Neuroblastoma                        |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0| 190|  0|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Non-Malignant Tumor                  |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  4|   0|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Osteosarcoma                         |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0| 129|   0|   0|  0|   0|   0|   0|   0|\n",
       "|Other Solid Tumor                    |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|  10|   0|  0|   0|   0|   0|   0|\n",
       "|Paraganglioma                        |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   1|  0|   0|   0|   0|   0|\n",
       "|Renal Cell Carcinoma                 |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   3|   0|   0|   0|\n",
       "|Retinoblastoma                       |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0| 98|   0|   0|   0|   0|\n",
       "|Rhabdomyosarcoma                     |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0| 134|   0|   0|\n",
       "|Soft Tissue Sarcoma                  |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|  93|   0|\n",
       "|Wilms' tumor                         |   0|   0|  0|   0|   0|   0|    0|   0|   0|     0|   0|  0|   0|   0|   0|  0|   0|   0|   0| 207|"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "knitr::kable( table(df2$Tumor_type , df2$Tumor_type2 ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>198</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 198\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 198\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 198  84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype ='Retinoblastoma'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$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$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                \n",
       "                 RB\n",
       "  Retinoblastoma 98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$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>44</td><td>98</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 44 & 98\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 44 | 98 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 44   98  \n",
       "[2,] 84   12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(dim(df_ss), dim(df2_ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MAF object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- dfx contain   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss , isTCGA = FALSE, verbose = F, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>38               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Center           </td><td>NA               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Samples          </td><td>38               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td>13               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 8               </td><td>0.211            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 4               </td><td>0.105            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 9               </td><td>0.237            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td>18               </td><td>0.474            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 5               </td><td>0.132            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>44               </td><td>1.158            </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           & 38                &    NA             & NA               \\\\\n",
       "\t nGenes            & 13                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   &  8                    & 0.211                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  4                    & 0.105                 &  0                   \\\\\n",
       "\t Missense\\_Mutation &  9                  & 0.237               &  0                 \\\\\n",
       "\t Nonsense\\_Mutation & 18                  & 0.474               &  0                 \\\\\n",
       "\t Splice\\_Site       &  5                  & 0.132               &  0                 \\\\\n",
       "\t total             & 44                & 1.158             &  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           | 38                |    NA             | NA                |\n",
       "| nGenes            | 13                |    NA             | NA                |\n",
       "| Frame_Shift_Del   |  8                | 0.211             |  0                |\n",
       "| Frame_Shift_Ins   |  4                | 0.105             |  0                |\n",
       "| Missense_Mutation |  9                | 0.237             |  0                |\n",
       "| Nonsense_Mutation | 18                | 0.474             |  0                |\n",
       "| Splice_Site       |  5                | 0.132             |  0                |\n",
       "| total             | 44                | 1.158             |  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           38         NA NA    \n",
       "4  nGenes            13         NA NA    \n",
       "5  Frame_Shift_Del    8      0.211  0    \n",
       "6  Frame_Shift_Ins    4      0.105  0    \n",
       "7  Missense_Mutation  9      0.237  0    \n",
       "8  Nonsense_Mutation 18      0.474  0    \n",
       "9  Splice_Site        5      0.132  0    \n",
       "10 total             44      1.158  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>RB1  </td><td>6    </td><td>3    </td><td>2    </td><td>16   </td><td>4    </td><td>31   </td><td>31   </td><td>31   </td></tr>\n",
       "\t<tr><td>BRCA1</td><td>1    </td><td>0    </td><td>0    </td><td> 1   </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>ATM  </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>BAP1 </td><td>0    </td><td>0    </td><td>0    </td><td> 1   </td><td>0    </td><td> 1   </td><td> 1   </td><td> 1   </td></tr>\n",
       "\t<tr><td>CHEK2</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",
       "</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 RB1   & 6     & 3     & 2     & 16    & 4     & 31    & 31    & 31   \\\\\n",
       "\t BRCA1 & 1     & 0     & 0     &  1    & 0     &  2    &  2    &  2   \\\\\n",
       "\t ALK   & 0     & 0     & 1     &  0    & 0     &  1    &  1    &  1   \\\\\n",
       "\t ATM   & 0     & 0     & 0     &  0    & 1     &  1    &  1    &  1   \\\\\n",
       "\t BAP1  & 0     & 0     & 0     &  1    & 0     &  1    &  1    &  1   \\\\\n",
       "\t CHEK2 & 0     & 0     & 1     &  0    & 0     &  1    &  1    &  1   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| RB1   | 6     | 3     | 2     | 16    | 4     | 31    | 31    | 31    |\n",
       "| BRCA1 | 1     | 0     | 0     |  1    | 0     |  2    |  2    |  2    |\n",
       "| ALK   | 0     | 0     | 1     |  0    | 0     |  1    |  1    |  1    |\n",
       "| ATM   | 0     | 0     | 0     |  0    | 1     |  1    |  1    |  1    |\n",
       "| BAP1  | 0     | 0     | 0     |  1    | 0     |  1    |  1    |  1    |\n",
       "| CHEK2 | 0     | 0     | 1     |  0    | 0     |  1    |  1    |  1    |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 RB1         6               3               2                \n",
       "2 BRCA1       1               0               0                \n",
       "3 ALK         0               0               1                \n",
       "4 ATM         0               0               0                \n",
       "5 BAP1        0               0               0                \n",
       "6 CHEK2       0               0               1                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 16                4           31    31             31            \n",
       "2  1                0            2     2              2            \n",
       "3  0                0            1     1              1            \n",
       "4  0                1            1     1              1            \n",
       "5  1                0            1     1              1            \n",
       "6  0                0            1     1              1            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>13</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 13\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 13\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 13  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",
       "SJRB018827                0     0     2       2\n",
       "SJRB018831                0     2     0       2\n",
       "SJRB019571                0     0     2       2\n",
       "SJRB019819                0     0     2       2\n",
       "SJRB041658                0     0     2       2\n",
       "SJRB041662                0     0     2       2"
      ]
     },
     "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",
       "SJRB018827                     2\n",
       "SJRB018831                     2\n",
       "SJRB019571                     2\n",
       "SJRB019819                     2\n",
       "SJRB041658                     2\n",
       "SJRB041662                     2"
      ]
     },
     "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": [
       "13"
      ],
      "text/latex": [
       "13"
      ],
      "text/markdown": [
       "13"
      ],
      "text/plain": [
       "[1] 13"
      ]
     },
     "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>RB1  </td><td>6    </td><td>3    </td><td>2    </td><td>16   </td><td>4    </td><td>31   </td><td>31   </td><td>31   </td></tr>\n",
       "\t<tr><td>BRCA1</td><td>1    </td><td>0    </td><td>0    </td><td> 1   </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>ATM  </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>BAP1 </td><td>0    </td><td>0    </td><td>0    </td><td> 1   </td><td>0    </td><td> 1   </td><td> 1   </td><td> 1   </td></tr>\n",
       "\t<tr><td>CHEK2</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",
       "</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 RB1   & 6     & 3     & 2     & 16    & 4     & 31    & 31    & 31   \\\\\n",
       "\t BRCA1 & 1     & 0     & 0     &  1    & 0     &  2    &  2    &  2   \\\\\n",
       "\t ALK   & 0     & 0     & 1     &  0    & 0     &  1    &  1    &  1   \\\\\n",
       "\t ATM   & 0     & 0     & 0     &  0    & 1     &  1    &  1    &  1   \\\\\n",
       "\t BAP1  & 0     & 0     & 0     &  1    & 0     &  1    &  1    &  1   \\\\\n",
       "\t CHEK2 & 0     & 0     & 1     &  0    & 0     &  1    &  1    &  1   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| RB1   | 6     | 3     | 2     | 16    | 4     | 31    | 31    | 31    |\n",
       "| BRCA1 | 1     | 0     | 0     |  1    | 0     |  2    |  2    |  2    |\n",
       "| ALK   | 0     | 0     | 1     |  0    | 0     |  1    |  1    |  1    |\n",
       "| ATM   | 0     | 0     | 0     |  0    | 1     |  1    |  1    |  1    |\n",
       "| BAP1  | 0     | 0     | 0     |  1    | 0     |  1    |  1    |  1    |\n",
       "| CHEK2 | 0     | 0     | 1     |  0    | 0     |  1    |  1    |  1    |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 RB1         6               3               2                \n",
       "2 BRCA1       1               0               0                \n",
       "3 ALK         0               0               1                \n",
       "4 ATM         0               0               0                \n",
       "5 BAP1        0               0               0                \n",
       "6 CHEK2       0               0               1                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 16                4           31    31             31            \n",
       "2  1                0            2     2              2            \n",
       "3  0                0            1     1              1            \n",
       "4  0                1            1     1              1            \n",
       "5  1                0            1     1              1            \n",
       "6  0                0            1     1              1            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAALQCAIAAADD55k+AAAABmJLR0QA/wD/AP+gvaeTAAAg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\nghAAAHjt/wCGZjvf16j+6AAAAABJRU5ErkJggg==",
      "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": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAIAAAC+Bd9KAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Top 13\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Retinoblastoma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_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(' ','_',ttype),'_plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDgAAAJYCAIAAAAIVsG4AAAABmJLR0QA/wD/AP+gvaeTAAAg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\nIyoAAMBObyOuF37a1akAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Variant Classification summary”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=5)\n",
    "plotmafSummary(maf = dfx, rmOutlier = FALSE ,dashboard = F,addStat = 'median', textSize = 2, top = gf_sx, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oncoplots or waterfall plots\n",
    "- Adding Clinical Data on oncoplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Oncoplots with the total genes from the gene summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAIAAAC+Bd9KAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nHAAAAAAAwOAR4AAAAAAAAAbv/wCyjzzgmPPFYAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Retinoblastoma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_oncoplot_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(' ','_',ttype),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAIAAAC+Bd9KAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=7)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Retinoblastoma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_oncoplot_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(' ','_',ttype),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=12)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Transition and Transversions\n",
    "returns a list of summarized tables in various ways. Summarized data can also be visualized as a boxplot showing overall distribution of six different conversions and as a stacked barplot showing fraction of conversions in each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class  MAF \n",
       "                   ID summary  Mean Median\n",
       " 1:        NCBI_Build      38    NA     NA\n",
       " 2:            Center      NA    NA     NA\n",
       " 3:           Samples      38    NA     NA\n",
       " 4:            nGenes      13    NA     NA\n",
       " 5:   Frame_Shift_Del       8 0.211      0\n",
       " 6:   Frame_Shift_Ins       4 0.105      0\n",
       " 7: Missense_Mutation       9 0.237      0\n",
       " 8: Nonsense_Mutation      18 0.474      0\n",
       " 9:       Splice_Site       5 0.132      0\n",
       "10:             total      44 1.158      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABhgAAALQCAIAAADthO0cAAAABmJLR0QA/wD/AP+gvaeTAAAg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\ncoksAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=13,repr.plot.height=6)\n",
    "dfx.titv = titv(maf = dfx, plot = F, useSyn = T  )\n",
    "plotTiTv(res = dfx.titv ,showBarcodes = F, textSize=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8AAAAPACAIAAAB1tIfMAAAABmJLR0QA/wD/AP+gvaeTAAAd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\ngIAGAIDA/wBGz8FgTjJBDAAAAABJRU5ErkJggg==",
      "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAASwCAIAAABkQySYAAAABmJLR0QA/wD/AP+gvaeTAAAg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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Retinoblastoma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(' ','_',ttype),'_genecloud_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=2]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Drug Interactions\n",
    "- plot shows potential druggable gene categories along with upto top 5 genes involved in them. \n",
    "- One can also extract information on drug-gene interactions. \n",
    "- [check this documentation](https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html#98_drug-gene_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAALQCAIAAADpR0DQAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nW5FmDw4AAAAAAIBb+T+qjJATTDNWmAAAAABJRU5ErkJggg==",
      "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Retinoblastoma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_drugInteractions_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(' ','_',ttype),'_drugInteractions_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oncogenic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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                2        0.33333333\n",
      " 2:    RTK-RAS 85                2        0.02352941\n",
      " 3: Cell_Cycle 15                1        0.06666667\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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAHgCAIAAAADp837AAAABmJLR0QA/wD/AP+gvaeTAAAg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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../KCPG\\_Variants/plots/KCP\\_wo\\_AR\\_TT-Retinoblastoma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../KCPG_Variants/plots/KCP_wo_AR_TT-Retinoblastoma_OncogenicPathway_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../KCPG_Variants/plots/KCP_wo_AR_','TT-',gsub(' ','_',ttype),'_OncogenicPathway_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "\n",
    "svg(out_svg, height=9, width=12)\n",
    "op\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# for( i in 1:length( op$data$Pathway ) ){\n",
    "#     pway = op$data$Pathway[i]\n",
    "#     PlotOncogenicPathways(dfx,pway, fullPathway = F, removeNonMutated = T)\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAFoCAIAAADIFpV9AAAABmJLR0QA/wD/AP+gvaeTAAAg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\n2lO8AAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “TP53 pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=3)\n",
    "PlotOncogenicPathways(dfx,pathway_lst[9], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAQ4CAIAAACWoLItAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nqmiERgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=9)\n",
    "PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAFoCAIAAADIFpV9AAAABmJLR0QA/wD/AP+gvaeTAAAd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\nABAoQQgAABAoQQgAABCo/w+sSRncRTk+nAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “RTK-RAS pathway”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=3)\n",
    "PlotOncogenicPathways(dfx,pathway_lst[7], fullPathway = F, removeNonMutated = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Retinoblastoma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TumorType\\_Retinoblastoma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Retinoblastoma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Retinoblastoma\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TumorType_',gsub(' ','_',ttype))\n",
    "paste0(\"Saving MAF file to : \", out_file)\n",
    "maftools::write.mafSummary(dfx, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Retinoblastoma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../KCPG\\_Variants/maf\\_res/KCP\\_wo\\_AR\\_TumorType\\_Retinoblastoma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Retinoblastoma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../KCPG_Variants/maf_res/KCP_wo_AR_TumorType_Retinoblastoma_drugInteractions.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/maf_res/KCP_wo_AR_','TumorType_',gsub(' ','_',ttype),'_drugInteractions.tsv')\n",
    "paste0(\"Saving drug Interactions data to : \", out_file)\n",
    "write.table(dfx_drug, out_file, sep=\"\\t\", row.names = F, quote = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Retinoblastoma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TumorType\\_Retinoblastoma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Retinoblastoma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Retinoblastoma_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TumorType_',gsub(' ','_',ttype),\"_mutation_data.tsv\")\n",
    "paste0(\"Saving mutation data to : \", out_file)\n",
    "write.table(df_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Retinoblastoma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../KCPG\\_Variants/data/KCP\\_wo\\_AR\\_TumorType\\_Retinoblastoma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Retinoblastoma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../KCPG_Variants/data/KCP_wo_AR_TumorType_Retinoblastoma_clinical_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../KCPG_Variants/data/KCP_wo_AR_','TumorType_',gsub(' ','_',ttype),\"_clinical_data.tsv\")\n",
    "paste0(\"Saving clinical data to : \", out_file)\n",
    "write.table(df2_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 70,
   "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "p3_R_3.5.0",
   "language": "R",
   "name": "ir3.5.0"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.0"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}