analysis/Candidate/CandidateGenes_TT-Carcinoma_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Suggesting for Carcinoma\n",
    "----- \n",
    "- Date : 19th 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(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$Known_Cancer_Predisposition_Genes == 'suggesting',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2726</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2726\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2726\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2726   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    Het Het-Alt     Hom \n",
       "   2710       8       8 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$genotype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "< table of extent 0 >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1273"
      ],
      "text/latex": [
       "1273"
      ],
      "text/markdown": [
       "1273"
      ],
      "text/plain": [
       "[1] 1273"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1175"
      ],
      "text/latex": [
       "1175"
      ],
      "text/markdown": [
       "1175"
      ],
      "text/plain": [
       "[1] 1175"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2726</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2726\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2726\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2726   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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Carcinoma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ss$Hugo_Symbol=as.character(df_ss$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           \n",
       "            CA\n",
       "  Carcinoma 14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$Tumor_type , df2_ss$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>35</td><td>14</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 35 & 14\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 35 | 14 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 35   14  \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": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Summarizing..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   ID summary  Mean Median\n",
      " 1:        NCBI_Build      38    NA     NA\n",
      " 2:            Center      NA    NA     NA\n",
      " 3:           Samples      13    NA     NA\n",
      " 4:            nGenes      29    NA     NA\n",
      " 5:   Frame_Shift_Del       3 0.231      0\n",
      " 6:   Frame_Shift_Ins       1 0.077      0\n",
      " 7: Missense_Mutation      23 1.769      1\n",
      " 8: Nonsense_Mutation       3 0.231      0\n",
      " 9:       Splice_Site       5 0.385      0\n",
      "10:             total      35 2.692      2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Gene Summary..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
      " 1:         PAH               0               0                 2\n",
      " 2:       CAPN3               0               0                 2\n",
      " 3:          F7               0               0                 2\n",
      " 4:        FBN2               0               0                 0\n",
      " 5:     SPATA16               0               0                 2\n",
      " 6:       ACSF3               0               0                 1\n",
      " 7:        ALG6               0               0                 0\n",
      " 8:        ASMT               0               0                 0\n",
      " 9:    ATP6V0A2               0               1                 0\n",
      "10:     CNTNAP2               0               0                 1\n",
      "11:        DAW1               0               0                 0\n",
      "12:        DSC2               1               0                 0\n",
      "13:        HPS4               0               0                 0\n",
      "14:     IGF2BP2               0               0                 1\n",
      "15:      IL17RD               0               0                 1\n",
      "16:       MECP2               1               0                 0\n",
      "17:      MMACHC               0               0                 1\n",
      "18:       MRE11               0               0                 1\n",
      "19:        NEU1               0               0                 1\n",
      "20:      NIPAL4               0               0                 1\n",
      "21:       NPHS2               0               0                 1\n",
      "22:        PMM2               0               0                 1\n",
      "23:        PRKN               0               0                 1\n",
      "24:        SIAE               0               0                 1\n",
      "25:        TGM5               0               0                 1\n",
      "26:    TMEM126B               0               0                 1\n",
      "27:      UBE2D3               1               0                 0\n",
      "28:        WHRN               0               0                 1\n",
      "29:      ZNHIT3               0               0                 0\n",
      "    Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
      "    Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
      " 1:                 0           1     3              2              2\n",
      " 2:                 0           0     2              1              1\n",
      " 3:                 0           0     2              1              1\n",
      " 4:                 0           2     2              1              1\n",
      " 5:                 0           0     2              1              1\n",
      " 6:                 0           0     1              1              1\n",
      " 7:                 0           1     1              1              1\n",
      " 8:                 0           1     1              1              1\n",
      " 9:                 0           0     1              1              1\n",
      "10:                 0           0     1              1              1\n",
      "11:                 1           0     1              1              1\n",
      "12:                 0           0     1              1              1\n",
      "13:                 1           0     1              1              1\n",
      "14:                 0           0     1              1              1\n",
      "15:                 0           0     1              1              1\n",
      "16:                 0           0     1              1              1\n",
      "17:                 0           0     1              1              1\n",
      "18:                 0           0     1              1              1\n",
      "19:                 0           0     1              1              1\n",
      "20:                 0           0     1              1              1\n",
      "21:                 0           0     1              1              1\n",
      "22:                 0           0     1              1              1\n",
      "23:                 0           0     1              1              1\n",
      "24:                 0           0     1              1              1\n",
      "25:                 0           0     1              1              1\n",
      "26:                 0           0     1              1              1\n",
      "27:                 0           0     1              1              1\n",
      "28:                 0           0     1              1              1\n",
      "29:                 1           0     1              1              1\n",
      "    Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Checking clinical data..\n",
      "Done !\n"
     ]
    }
   ],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss , isTCGA = FALSE, verbose = T, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>13               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td>29               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 3               </td><td>0.231            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 1               </td><td>0.077            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td>23               </td><td>1.769            </td><td> 1               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 3               </td><td>0.231            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 5               </td><td>0.385            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>35               </td><td>2.692            </td><td> 2               </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           & 13                &    NA             & NA               \\\\\n",
       "\t nGenes            & 29                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   &  3                    & 0.231                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  1                    & 0.077                 &  0                   \\\\\n",
       "\t Missense\\_Mutation & 23                  & 1.769               &  1                 \\\\\n",
       "\t Nonsense\\_Mutation &  3                  & 0.231               &  0                 \\\\\n",
       "\t Splice\\_Site       &  5                  & 0.385               &  0                 \\\\\n",
       "\t total             & 35                & 2.692             &  2               \\\\\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           | 13                |    NA             | NA                |\n",
       "| nGenes            | 29                |    NA             | NA                |\n",
       "| Frame_Shift_Del   |  3                | 0.231             |  0                |\n",
       "| Frame_Shift_Ins   |  1                | 0.077             |  0                |\n",
       "| Missense_Mutation | 23                | 1.769             |  1                |\n",
       "| Nonsense_Mutation |  3                | 0.231             |  0                |\n",
       "| Splice_Site       |  5                | 0.385             |  0                |\n",
       "| total             | 35                | 2.692             |  2                |\n",
       "\n"
      ],
      "text/plain": [
       "   ID                summary Mean  Median\n",
       "1  NCBI_Build        38         NA NA    \n",
       "2  Center            NA         NA NA    \n",
       "3  Samples           13         NA NA    \n",
       "4  nGenes            29         NA NA    \n",
       "5  Frame_Shift_Del    3      0.231  0    \n",
       "6  Frame_Shift_Ins    1      0.077  0    \n",
       "7  Missense_Mutation 23      1.769  1    \n",
       "8  Nonsense_Mutation  3      0.231  0    \n",
       "9  Splice_Site        5      0.385  0    \n",
       "10 total             35      2.692  2    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>PAH    </td><td>0      </td><td>0      </td><td>2      </td><td>0      </td><td>1      </td><td>3      </td><td>2      </td><td>2      </td></tr>\n",
       "\t<tr><td>CAPN3  </td><td>0      </td><td>0      </td><td>2      </td><td>0      </td><td>0      </td><td>2      </td><td>1      </td><td>1      </td></tr>\n",
       "\t<tr><td>F7     </td><td>0      </td><td>0      </td><td>2      </td><td>0      </td><td>0      </td><td>2      </td><td>1      </td><td>1      </td></tr>\n",
       "\t<tr><td>FBN2   </td><td>0      </td><td>0      </td><td>0      </td><td>0      </td><td>2      </td><td>2      </td><td>1      </td><td>1      </td></tr>\n",
       "\t<tr><td>SPATA16</td><td>0      </td><td>0      </td><td>2      </td><td>0      </td><td>0      </td><td>2      </td><td>1      </td><td>1      </td></tr>\n",
       "\t<tr><td>ACSF3  </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 PAH     & 0       & 0       & 2       & 0       & 1       & 3       & 2       & 2      \\\\\n",
       "\t CAPN3   & 0       & 0       & 2       & 0       & 0       & 2       & 1       & 1      \\\\\n",
       "\t F7      & 0       & 0       & 2       & 0       & 0       & 2       & 1       & 1      \\\\\n",
       "\t FBN2    & 0       & 0       & 0       & 0       & 2       & 2       & 1       & 1      \\\\\n",
       "\t SPATA16 & 0       & 0       & 2       & 0       & 0       & 2       & 1       & 1      \\\\\n",
       "\t ACSF3   & 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",
       "| PAH     | 0       | 0       | 2       | 0       | 1       | 3       | 2       | 2       |\n",
       "| CAPN3   | 0       | 0       | 2       | 0       | 0       | 2       | 1       | 1       |\n",
       "| F7      | 0       | 0       | 2       | 0       | 0       | 2       | 1       | 1       |\n",
       "| FBN2    | 0       | 0       | 0       | 0       | 2       | 2       | 1       | 1       |\n",
       "| SPATA16 | 0       | 0       | 2       | 0       | 0       | 2       | 1       | 1       |\n",
       "| ACSF3   | 0       | 0       | 1       | 0       | 0       | 1       | 1       | 1       |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 PAH         0               0               2                \n",
       "2 CAPN3       0               0               2                \n",
       "3 F7          0               0               2                \n",
       "4 FBN2        0               0               0                \n",
       "5 SPATA16     0               0               2                \n",
       "6 ACSF3       0               0               1                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 0                 1           3     2              2             \n",
       "2 0                 0           2     1              1             \n",
       "3 0                 0           2     1              1             \n",
       "4 0                 2           2     1              1             \n",
       "5 0                 0           2     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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>29</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 29\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 29\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 29  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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   INS   SNP   total\n",
       "---------------------  ----  ----  ----  ------\n",
       "SJCA042494                0     0    10      10\n",
       "SJCA041981                0     0     4       4\n",
       "SJCA019193                0     0     3       3\n",
       "SJCA019767                1     0     2       3\n",
       "SJCA042488                1     0     2       3\n",
       "SJCA018563                0     0     2       2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJCA042494                    10\n",
       "SJCA041981                     4\n",
       "SJCA019193                     3\n",
       "SJCA019767                     3\n",
       "SJCA042488                     3\n",
       "SJCA018563                     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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1"
      ],
      "text/latex": [
       "1"
      ],
      "text/markdown": [
       "1"
      ],
      "text/plain": [
       "[1] 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = as.data.frame( dfx@gene.summary )  \n",
    "gf_sx = nrow( gs[ gs$MutatedSamples >= 2,] )\n",
    "gf_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_genes=length(gs$Hugo_Symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "29"
      ],
      "text/latex": [
       "29"
      ],
      "text/markdown": [
       "29"
      ],
      "text/plain": [
       "[1] 29"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>PAH    </td><td>0      </td><td>0      </td><td>2      </td><td>0      </td><td>1      </td><td>3      </td><td>2      </td><td>2      </td></tr>\n",
       "\t<tr><td>CAPN3  </td><td>0      </td><td>0      </td><td>2      </td><td>0      </td><td>0      </td><td>2      </td><td>1      </td><td>1      </td></tr>\n",
       "\t<tr><td>F7     </td><td>0      </td><td>0      </td><td>2      </td><td>0      </td><td>0      </td><td>2      </td><td>1      </td><td>1      </td></tr>\n",
       "\t<tr><td>FBN2   </td><td>0      </td><td>0      </td><td>0      </td><td>0      </td><td>2      </td><td>2      </td><td>1      </td><td>1      </td></tr>\n",
       "\t<tr><td>SPATA16</td><td>0      </td><td>0      </td><td>2      </td><td>0      </td><td>0      </td><td>2      </td><td>1      </td><td>1      </td></tr>\n",
       "\t<tr><td>ACSF3  </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 PAH     & 0       & 0       & 2       & 0       & 1       & 3       & 2       & 2      \\\\\n",
       "\t CAPN3   & 0       & 0       & 2       & 0       & 0       & 2       & 1       & 1      \\\\\n",
       "\t F7      & 0       & 0       & 2       & 0       & 0       & 2       & 1       & 1      \\\\\n",
       "\t FBN2    & 0       & 0       & 0       & 0       & 2       & 2       & 1       & 1      \\\\\n",
       "\t SPATA16 & 0       & 0       & 2       & 0       & 0       & 2       & 1       & 1      \\\\\n",
       "\t ACSF3   & 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",
       "| PAH     | 0       | 0       | 2       | 0       | 1       | 3       | 2       | 2       |\n",
       "| CAPN3   | 0       | 0       | 2       | 0       | 0       | 2       | 1       | 1       |\n",
       "| F7      | 0       | 0       | 2       | 0       | 0       | 2       | 1       | 1       |\n",
       "| FBN2    | 0       | 0       | 0       | 0       | 2       | 2       | 1       | 1       |\n",
       "| SPATA16 | 0       | 0       | 2       | 0       | 0       | 2       | 1       | 1       |\n",
       "| ACSF3   | 0       | 0       | 1       | 0       | 0       | 1       | 1       | 1       |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 PAH         0               0               2                \n",
       "2 CAPN3       0               0               2                \n",
       "3 F7          0               0               2                \n",
       "4 FBN2        0               0               0                \n",
       "5 SPATA16     0               0               2                \n",
       "6 ACSF3       0               0               1                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 0                 1           3     2              2             \n",
       "2 0                 0           2     1              1             \n",
       "3 0                 0           2     1              1             \n",
       "4 0                 2           2     1              1             \n",
       "5 0                 0           2     1              1             \n",
       "6 0                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KqqlpaWqqqqHWv19fV1cnJKS0ur77oq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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=4)\n",
    "gs[ gs$MutatedSamples >= 2,] %>% 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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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viUClAkqawLAAAATiyXXnppenp6CCE3N3fr1q2tW7dOS0sLIVSpUqXkA//2t78l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\nx+QWlYkTJ04t5p///GcsFmvTps1///d/n3nmmVu2bJkwYUKLFi1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      "text/plain": [
       "Plot with title “Top 29\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=10)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Carcinoma\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_plotmafsummary_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=15)\n",
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=10, 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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6XwAAAEDLzJ9ROXTokOXL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\nRU5ErkJggg==",
      "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": {
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u/evT/44IM7d+501sFlOesXAAAAALzWsrOzPTw8rK2tN27cWFhYGBYW9vjx4/37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\nAAAArYNABQAAAAAAtM7/A7kNJZZC2TQMAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, 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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Carcinoma\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_oncoplot_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qcf3Lt3r60OzuesXwAAAADQoWVkZLi7u1tbW2/cuDEvLy8kJOTp06f79+/n90Rs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Vkqlaampmpra7/00kvPflPW1tZz585999132cvjx4+fOnWKiObMmUN/tmQhIi8vrzNn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\nTHULow9cdplOpyssLFyzZs2SJ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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, 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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Carcinoma\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_oncoplot_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=6, repr.plot.height=4)\n",
    "# oncoplot(maf = dfx,bgCol = \"white\",\n",
    "#         top = gf_sx,\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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_03.svg')\n",
    "# paste0(\"Saving : \", out_svg)\n",
    "# svg(out_svg, height=4, width=6)\n",
    "\n",
    "# oncoplot(maf = dfx,bgCol = \"white\",\n",
    "#         top = gf_sx,\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": 53,
   "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      13    NA     NA\n",
       " 4:            nGenes      29    NA     NA\n",
       " 5:   Frame_Shift_Del       3 0.231      0\n",
       " 6:   Frame_Shift_Ins       1 0.077      0\n",
       " 7: Missense_Mutation      23 1.769      1\n",
       " 8: Nonsense_Mutation       3 0.231      0\n",
       " 9:       Splice_Site       5 0.385      0\n",
       "10:             total      35 2.692      2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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CB3ZXJq2znnnPPaa68988wzo0aNGjJk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\noEHcuQAAoH4xMwegUu4ekQQAAABATsnRayQBAAAAkGsUSQAAAAB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AAIhEkQQAAABAJIokAAAAACJRJAEAAAAQiSIJAAAAgEgUSQAAAABE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\nuUoVKAAAAABJRU5ErkJggg==",
      "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "29"
      ],
      "text/latex": [
       "29"
      ],
      "text/markdown": [
       "29"
      ],
      "text/plain": [
       "[1] 29"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5l6nahtSZzFmV/ImzELHli/4YkeL53aLy/wFQQhvmfrN1zC0iYbTK27vBXu/rx9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\nmmj8EUyEMSABbTv8ZoK9i+o0nngAOq8D3RF6Vf/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+0B4s32W+gO4og31boOWiUcbsI1B/HBa8AumS1L0AACAASURBVPNfAmOen6/vhgNb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\nNRU9s4S+gRM3bHL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3O+MwigDfZPudEu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\naqLdNFe0ntx8ju16+rsvfG7mb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N68Iv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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, top=total_genes, minMut = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vfTKsPKR8caV1BP1MS2lyUrd3iXgU54Yr3lZK4ZJ+xN38Kz8LzkQ8sCypYTTVYz\nLy+sx5tOsD1AdrwFAGQMQ0YBCPVoVfWPlyXLdqng8UW2tky28X1tqrxsdyyxW1pcq7lnRfFz\nX27uh1GLM68NYx9Y0HJlyeNMFuw2qbX9P5A6z/zS4C/fSxxHlu1uvMuIeeyleV/ybvtELSBH\n3ztoFL3Nb7jnmjHE5vcYYynlN3a5JjUxhNWW7zGO9aDcuEIm9N15Z1svKZ/2G6NftLEueQsA\n6EEIhIDV6ZFY1e1LzD9O5f1wtmtK0xOcmuWw+R+cX/HFfyTGnsW2lYWf35xz86T0NjU7eK46\nz9gJzVna9NwtQ/xwpXFs7+8nY6eRvb/feWH/xI5/0bf26vdcITnrzX1Njt4cXuwY1dLSSt3k\nifKKPcaKUJLHEVnyaQuVJb9bGIFwxR7/3Zc3HhEg+ZNRSqvpqVEqO94CADKHQAhYmy4C/3el\neRCjZ94Y3y1T2nEn57g+vq9NDT2zMVEMPr7GPWekvb8/Pe3MIm364zVPOHRNZPnWNPMuKE3E\nM61GVtYfcc9K7segHq+J7TiVODaGenbzJ8qm9UV1OR7660cpXqhVR6LrDrsvHd7gvHlrzfiB\ninY0qTvIjrcAgMxh9BFgaeF/bjJvROYYXpT/s/ntniuV+98z7IPqxkDqkVjgvpWipe3W0Arl\ng8OhP28wir7FU7uwMVnJM2+M8d0uL99t/qj+6M0OrS/aOU9UTwaUT463Xq8ZkaVNbEjoHN/X\nOI7tOCXUdv59rvr2y6fG/zLxX9W3X25nE9srO94CADKHHkLAumJbTgZ+sdoo1u1B72t9D/rm\nSB5n/v1zK7/+70QxuvZQZOlO79XNTmRKXdq3JuuG9GhcD0S1mohWLccPVkRX7Yu+n1x/P++O\nWY4xrYwvRVvZeue6Jg9MdMPKb+/z3xc31suVV9SlNcfoEseI4mZv0W2eKLe4xXyrom/v1yMx\nyes0n3Rd0N841kNKbM9p59i2b+we15QNR40Y1vK+ppmQHW8BAJlDIAQsSqsIVX1/iTCtuZf/\n0ALH8I7+4OuaPsR73fjIK9sTxeDD77hnDLUV5XTwtmnfmqwbOj3jd3ok1vi8lOvK+/6snJsm\ndn6TrMCz8LxEPNNDSnTtIc/lo4QQ6rHq2KfldRXS1z2YwSfq9fajz/n8BP/981q9SPngcOU3\n/1N3AzkWXbXfc/V55gr2gQW2Ep92JpQoRl7Z0Y4opWw7af7Gdjaz52HmZA6Z5jQAACAASURB\nVMdbAEDmMGQUsCRVq/7h69rpWuOE72tT0vWDb95ds40EqFVHgo+sbrk+WmAr9hW/8BXSYOZ4\n5o4WtrpBnPKyuj468+7wnnnpDoQZeGJs92nz7DjPVSl1y7umDTb/sqaJUaOS8F57vlGSX/u0\nyd9ZtExZf9RcdE7u9Kmw2fEWAJAxBELAioKPrVE2JrdBc00ZmHfHpem6uS3f47/nCqMYeWOn\neegj2kSrCFV87m/BR97Rml9VHx1hK8pxTRucOI6uPpCICsboTWdpb0dre0V2hyeax4smRqWm\ndJnd5rlytFGKrj2sVUUaVMn5wgXGpEctGK393bq2tUzV5eW7jJJzQj97vy5YaCo73gIAMoQh\no4DlyCv3mpcftPVqwx70KfLMHeO+bER09YFEMXDfyl6vLe7I7MS0b03WDfnvmyvimi7H1LKg\nerIm9slxtbxWJJaL/PsnysZjhU9/vuODb9GYd0Gpsv6ISAybfPeAc1yf2K7TiY/SPl40I09U\ndfPSUN6rzkt9exLPgtLwC1vO3UeT39yTc+OF5gr2gfnuy0ZG39mfKIb+9rH78lGuSan2j0WW\n7owfTG6dkpbtHNshO94CADKEHkLAWuIHK2vuXp4s220FTywyL8ueHpLw33ulkQDVskDw1++n\n+RFZx3vNWO915+d8aWLeHbMKfnlNrxW35v3oMtu5LdRiu09X3fYiq7ZmgvvK0cJe96+hvHx3\nvdGbmQmE6X2isvGoMUFOCOG56rwWKjfgmjzQVpL86y+/satxHf/dl0s5536bo+nV33rJ2B6j\nZeqx6uBDq4yirSgn5/MTUm9bemXHWwBAJhAIAQvRQ0r17a+al9/03zXbNamNe9Cnxt4nL++H\nyWGo4ec2JbZfQ4okt8N3y5TCpz9vdN7GdpZH1xzs2lZlJVu+x33x0MRxdM1BYz9357g+9sGF\n3f+J5uVkHEMLnee1ZcUUu2SesqhsOq6eDDSs0t+fd9dso6gFo5VffyHy8nahtfT7ifie05WL\nXzAPdc799oyODBPooOx4CwDIBAIhYBm6qPnpivhB08oTC0pzbp6cuQfmfOGC5KAsXdTcs0KP\nqZl7XFZynt835/rxRjH8761d2JgsZmwErytq/FDd4MCMjgxM1xN1OSa/tTd526vHtnUfUe+C\neg+VlzbRSZjz+QtyvpgcSqrXKjU/XVFx03ORl7dr1fWnHap6bNfpwANvVXzhWXO2dF863HyH\nLpEdbwEAacccQsAqQn/72LzztWNEcf6D89q9B31KbJL/gfkV1/2/RA6MH6gIPbUh9zszMvnI\nLOSaOsjIgeqRqq5tTLbyzBkZcNob/MIi7euLZuKJ0dUH9FCyz9+zsA3jRROcF/S3981TTwUT\nxcjSXb5bpzWsJAn/vVcKlz38j0+Mc7FtZTXbyoRNsvfOtZXkSl6HHoiqJ2oaL4DkLO1d8Mtr\nUp/ZmCnZ8RYAkG70EAKWoHx8PPird42ilOMq+M1nkzNqMsYxvMj3rc8Yxdo/rTcvjm9BsW1l\n4Re2JP4z9p1rmXk/evVkgGmEmSDluV2XDDOfcY7vZx+Y3/2faB4v6jy/r2No28e42iSPqZMw\nvvdMfO+ZJqpJwv/jOfkPzLMVeOud13T1VDC2vUzZeCy2+3TjHOW9ZmzRs1/qLsMss+MtACCt\nCIRA9tNO11b/4DWhJpNE/sML0r6YfnN837jIMapXXSGu1dyzouVJO9kttvVk4P63Ev+F/7m5\nzdfr1v2jyzRv/b61DC0nk94nalUR854ubVpOpt6j648ajTQ1alQIISThvWFCr2XfyLlpopTb\nejRyXti/8HfX5T9yVSf87qkNsuMtACB9GDIKZLu4Vn3Ha1pFchFC39cv8swd3cIV6SU57fkP\nzq/40rOJrq3YlpPh59sehLKFfWCBcRzfezqVS+L7k32qtl6+zI7yzUb+++b675vbajXPwtK+\nC1Oawue9YYL3hpbWmey0J9oKvX23/zCVO7TMeX7f1Hd2sRV4/T+9Iu+uy5QNR6Or9sUPVWpn\nQ+qZWl2O2/K9tny3fWiRa/JA1/QhzvN6p3JD16SB7dhXxqzPhtvbekna3wIAei4CIZDlAv+7\nWtl0wii6LhqU94NLOrkNzgn9cm6ebMzbCT7+vnN8305uQzfhGJv84TK+76wuxyVPK/8fju9J\n5kbHiOJMtQxoC8lld18yzF1/1GuPkx1vAQAdxJBRIJvJS3eFn91kFG29cwt+dY2xAVpnyvve\nTHt/f+JYDyvKhqOd34buwN4nzxhAqytq5NUdLdfX5Vj45e1G0X3p8Aw2DgAAWA+BEMha8X1n\na+55M1m22wofX2QrTvce9KmRclypDKKzAq9polfwsffiByubrapqNT9dYew5LvlcGd0IAQAA\nWBBDRoGsFfzVe7ocM4o2v7v2D+vbdyvPwlLvZ8/vYHvcM4d5rxlrXhTRmnJumhR65iMtIAsh\n9Fql8qbn8u6e4114nrEBfULs0/Lg/65WPjpmnMm9bbqt0NvwdgAAAB1AIASylq7U2+JMq4pE\n1x5qrnLLnOP6pKNFIu/Hc6JrD2lVkdarZi8p1+W/b271Ha8lilpArvnxssDP3nYMLbIPLrDl\nuuNHquIHK4yOwQT3ZSN8X7+oK9oLAACyGYEQQOexFXrzfjKn5q6lXd2QLuaZPyavbHbwl+8a\nmwrqtUpsx6nYjlNN1583Jv/hBeyIDQAA0o45hAA6lfeqsazpJ4TwLZ5a9OfPt7obpH1QQf5D\nCwoeWyR5nZ3TMAAAYCmSzjbHANBVND267nD0/YOxLSfVU0E9GBWaLhV47L1znRMHuD8z1D1r\nWJesCgsAACyCQAgAAAAAFsUvngEAAADAogiEAAAAAGBRBEIAAAAAsCgCIQAAAABYFIEQAAAA\nACyKQAgAAAAAFkUgBAAAAACLIhACAAAAgEURCAEAAADAogiEAAAAAGBRBEIAAAAAsCgCIQAA\nAABYFIEQAAAAACyKQAgAAAAAFkUgBAAAAACLIhACAAAAgEU5uroBAACgIa06El29X9l0Iv5p\nuVYV1qplIYTkd9sKvI4xJa4J/d2zhtkHF3Z1MwEAPZ6k63pXtwEAgI5SNh2vvPl5o9h3551t\nvUP5tN/owWjiuPD317tnj2hc58y8P6vHqlO5m5TjsuV77IPyXZMGuueMdJ7fN8VmqKeCtb9b\nJy/dpUfjLdd0zRia++3PuCYNTPHOAAA0Rg8hAADpp4cVNayoZQFl47HaP653TRno/+kVjtEl\nLV8lL99dc99KI5e2TPngcOX6wzk3T/bfOVs42j8HJPS3j4O/WC2EsPf3l7z9/7X7PgCAnohA\nCABAxikfH6/44rP5P1/omT+muTqR13fW/Ghpg5O2Yp9jaKF9cKGuxNXjNeqJGu1sKPmxLsL/\n+ESvjuT/fKGwSe1oWGz36drH1rTjQgBAdiAQAgDQHo4RxbaS3CY/0kOKeqRKC8j1TkbjNT9a\nauud65o0oPElsZ3lNXcvM5/xXD4q93szHSN7NaipVYQir+wI/XmDdq4jMfL6TseYEt/XL2rr\nK+iRWM3/vK7H1LZeCADIGgRCAADaw3fbdO81Y5v9WBdadSTy4rbapz7UQ0rduZgauPfNXksW\nC7vUoHLw4VVCrZvVL+W4Cp+8zjVtcJM3thX7fN+c5v3s+VW3vRjbfTpxsvYP673XjrMV+9r0\nCsFfrI4frGzTJQCALMO2EwAAZIAkbIVe363TSt681TGmt3E6frBCXrWvQd3YrnJl0wmjmP/I\nwubSoMHWy1f49OdtBd5EUQ8pkdd2tqmB8lt7w//e2qZLAADZh0AIAEAG2YpyCn9zrXnRl+i7\nBxrUiZoiovP8vp4rRqV4Z9+t05I3Wb0/9Vap5cHAPW+mXh8AkK0IhAAAZJZ9UIF7+hCjqHxy\nvEGF+N6zxrF75rDU7+yZl1yiJrbrdKqXqXrNj5Y2mOIIALAmAiEAABnnKE2OGtXKg6L+HsDq\nmVrjWPJ7Ur+tvZ9fctctB6CHFF2OpXJV6C8blI3HEse+xVNTfxwAIPuwqAwAABnnGFxgHOuK\nqkdjksdpnJEcduNYPVrVhvtKovDJ63Tl3DKhUus7T8S2lQV/uzZx7L50eM6XJob++lEbnggA\nyC4EQgAAMi5+tDpZsEuSq96/v/YhhWJT3ThS+Z39ud+7xJafaj+ha8bQ1Juh1yrVd76RWM7U\nVuzLf2iBHkmpUxEAkK0YMgoAQMbFdycn+Nl75TbYRN4xvMg41k7XVv/wda0ynIlmBH72tnqs\nLprm/3yBrSgnE08BAPQgBEIAADJLPV6jbDhqFJ1TBzao4L3ufMnnMorKB4fPLng69PQG9WQg\njc2Q39gVee3TxLHvq5PbtHoNACBbEQgBAMggrSpS/YMlekw1zjTeVcJWlJNr2kBCCKEFo8HH\n1py54k8VNz5b+4f1sa0nhap1pBnq8ZqaB1Ymjh1jSnJ/MKsjdwMAZA3mEAIAslDVbS+29RI9\nrKS3DXowGn5pe+0fPtCDUeOkY2Qvz+WjG1f2feMirTIc+vsnDc7HtpXFtpXV/natlOtyTxvi\nmjHEPWOofXChaH35GBNVq77rDb1WEUJIbkfBo1cba5MCACyOfw8AAFkouvZQph8R+stG+fWd\nTX6kh5X4kWqtItTgvOS05z84T9ibCnN2W96P5zjO61P7xBq1vLbx53qtIq/aJ6/aJ4Sw9/O7\nZgzxXD7Kfcnwpu9WX+2TH8S2nEwc59012zGyV6uXAAAsgkAIAEB7xPeeie89k3p9yWnPf2Sh\n84L+LdTxXjvOe/XY6LpDkZe2R9cdbq7TUi0LRF7aHnlpu72/P+cLF3hvvNDW/O6FysfHa5/6\nMHHsnj0i58aJqbcZAJD1CIQAAGScY0xJ/v3znBP6tV7VLrlnDXfPGi7imrLtpLL+iLL+iLK1\nrMk5hOrJQPCJ98MvbS/8w/WO4cWNK2gBueauN4SmCyFsvXz5P5vftrGmAIBsRyAEAGShvjvv\nbOsl5dN+Y57s13FSrsvey+ecONBzxUj3pSMabDXROofNNWmga9JA8Z2L9ZCifHQsuv6Isv5I\nfP/ZBhXVY9UVNz5b+LvrXRcNqveBLgL3rlRPBROl/J8vZJ8JAEADBEIAANoj/xdXea8Z2znP\nknwu9+wR7tkjhBDxAxWRl7ZFXttp3qtQr1Vqfrq81+tfN68WE3l5u7xyT+LYd8sU98VDO6e1\nAIAehG0nAADoSRwjivPuuqzk7dt8X5ti7nVUj9eETYuUxg9VBh5aVXfJmN7sMwEAaBKBEACA\nnkfyOPPuuqzw99eb5wSGX9puHAd/9Z4ux0Rin4lfXi257J3fSABA98eQUQAAulLtnz7UygKJ\nY983p9kH5qd+rXvWcN/ii0LPbEwU1ePVuhyXPA4hhB6OJU7q0fjZa55J5W7qycCpsY8aRc+C\n0oJfXZN6YwAAPRGBEACArqRsOKp8eCRx7L5iVJsCoRDCs7DUCIRC0+OHK52lvdPbQgBAFmPI\nKAAAXclWnFz5M36goq2XN9xlPqZ2vEkAAOughxAAgK5k3j8wunq/75YpbbpcD8jmoq13XuLA\nfckw+wB/65eHFHlF3UqkktfpWVhqfOQcn8KuiQCAHo5ACABAV3LPHlH727WJY2XjMWXTcdek\ngalfHl1/xDi25Xvsver6G32Lp6ZyuXqixgiEtkJv/oPzU380ACALMGQUAICu5CztbR9UYBRr\nfrxMOxtK8Vpdjof+uN4oui8fJez8yw4AaAP+2QAAoEtJIu+O5CaB6vGaiq88H9txqtXrtOpI\n1bdfjh+qrCvbpJybJmaojQCAbMWQUQAAuphn7hj3zGHRtYcSRfVIVcWNz3quGOW9frxr4gAp\nz22urCuqeqhCfnNv+LlNWjBqnPfdPMk5tk+nthsA0PMRCAHAunQ5ph6p1irDejRuK86x9/Xb\nSnxd3ShLkkTBE4sqv/Gf2NaTdWc0XV65V165V0jCPrDAVuCVcpxadUQ7XatVRRrfwDN3dN7/\nzO7MJgMAsgOBEOg8gftWhv+91Xwm787ZKS780ED4uU2Bh1aZz3g/e37+wwsa16y48dnYtrJU\n7ysJW65b8nscI4udE/p7FpQ6hha2etHpS36vVaQ65ak5vTfcbqvfDYL200Vsywnl4+Oui4c2\n3WWk6fLKveH/bI19clxX6u1S4Bhe5L50RM5XJ9v75HVSayGEEELKcRX9+fOBn78TeWV7vQ90\noR6rVo9VN3ulXfItvijv9pnCwTQQAECbEQiBriQv392+QCi/uSftjRFCCF1owagIRtUTNdH3\nDtb+dq370uH+e66092998frOpAXk0zOfFHHNOaFf8b9u7ia36hY0PfLqjto/b1CPVAkhCoYX\nNQ6E8f1nq//njfjeM03eIH6wMn6wMvzsppyvTcm7fSYrlHQmKdeV/9B871Wltc98pHxwuPX6\nTrv7ilG5t01zjGEnegBAOxEIga4U23FKPVZtXmAwFdrpWuWT4xlqUgPR9w6e/eSvhX/8XJvW\nwc80+c29Iq51t1t1OS0gV39vibLhaAt1lA+PVH3nFT0Sa/lWekwN/XlDbOvJwt9dL+W60trM\nTHFNGth3550duUOfDbe3WqfkzVs78ohUuGYMLZoxVD1WHV17KLb9VGzXab06rAWiuhK3+dyS\n323vnesY28c5vp971nBbvqeDj7MPyO/gnxsAoEcjEAJdTF6+23fb9LZd8tZeobfnWfbBhS31\n9em6diaknqzR5Xi907VK9bdfKX7pq/YB+a0+wjG6xFaU0462SXYp9cryGzvb8YhM36pr6WGl\n8pZ/xfc03e+XoB6pqv7eksZp0Fbotff3a7WKerxGqMl4rGw8Vn3XG4W/vU605auDtLAPKsj5\n0kTxpa5uBwAg2xEIgS4WWdb2QLi8neNFc754QesjVHWhnQ1Flu8OPbnOWMBQC8jBJ94vePTq\nVh+R+92LPZePal/zUqR8dEz56Fh3u1WXCzz8jjkNSh6He85I5+iSenUeWmVelNI9e4Tvq5Md\nY/vY/HW9THpIkVfuCT72vjEpNPrugfDzm3NunpT5NwAAAF2AySFA1zB+BI/vPRM/UJH6hWp5\nrbL5eIObpJMkbCU+31cnF790i7mvT162S6sMp/9xbRTbcar6jte72626XPxgpXklEvdlI0re\n/VbBL68xj0aObSszdjUQQvjvm1v45PWu6UPM30WSz+W9bnyvZd/wXjvOOFn7x/V6SMnwGwAA\ngK5BIAS6hvvKZDeavHx36hdGV+4xxouab5J29oH5eT+8NFnWhbL+SOYe1xJdaKdr5ZV7Kxe/\nUPGFf3RoRdM03qo7ibyy3fiu8MwdU/jb6xr/skBeudc49i2emvOFC0Qz40Btee78ny1wzxyW\nKGqVYXnVvrS3GQAAdAcMGQW6hnNcX2XjscRS8vLy3bnfubi5n84bMNYXtffzOyf0j7y0veX6\nHeGePcJcjB+uytyzmqR8cjz42Jr4/rO6aaBjl9+qGzIWkpHcjryfXCZsTXwzKR+ey/N2yfdf\nn2nljnYp7+7LowufTpSi7x/yLhrX8hUAAKAnoocQ6CKS8CwoTRzGD1XG9pxO5SK1PKhsOpE4\n9swfI2V4pQ9bgdfc0dT5Q0bVk4HY5hNpiXBpvFU3pB6vSRw4x/Vpbv9A9VQwceAYUpTKlo+O\noYXO8/smjuM7y9PRTAAA0O0QCIEu4z0XCEXKo0blN5Oj/jymyzNFErZeyWmEPWX7AQsyloqx\nDy1qtk6NnDhwjO6V4m0do+rWpNHOZsnYWgAA0ABDRoEu4xhd4hheFD9YKYSQl+3O+/6sVkeN\nyivqcqN9QL5zXN/47pT6FdtP09UTAaNk79t011PmuC8eWvz8lxucjO87W3Pvm114q27IVpyj\nna4VQujReLN1Crx1cyYdqf4qUHLbEwct3BYAAPRoBEKg60jCs6C09skPhBDqiZrYjjLn+H4t\nVFdPBWNbTiaOPQtKU5xz2BFqWcCcBBxjemf8kfXZinLat6thRm/VDdn75CUCYXzvGaGLJr83\n7H1yE4Ewvu9sirdVT9b9OsDWy5eehgIAgG6GIaNAVzIP+5SXtTJqVF6R3H7QM39MptpkElny\nqXHsLO3tmjigEx6KdkhO9tt3NrbtZJN1XJ8ZkjhQD1bqitrqPfVITNlYt0mjrXduOpoJAAC6\nHQIh0JUcw4sd57YOl1fsFpreQuXk+qKDCpzn9clsy3Qhr9xT+8f1dUVJ5H53Zif0SaJ9zNsG\n1vyfFU1uG+i56rzEgR5TzZsWNif01490OZY4dk8fko5mAgCAbocho0AX8y4sDe49I+p2nD/h\nmjywyWpqWSC2NcPjRTVdqwyrp4Lq4arQs5/EtpUZn/jvvdJ92YgWLjWEnvwg8sLWtj4556uT\njV3v0A7O8f2c4/rEPi0XQsQPVlQufqHgsUX2gfn16pT29l47LtHrG/zVe66LBjuGNbsCjbL+\nSO0f1htFz7zO6JEGAACdj0AIdDHPgtLgE+8njuXlu5sLhObxot4F7fzpPPjou8FH323TJbYS\nn/8nl6c+QjXWrnVuPHNHt+MqJEki/xdXVXzu74k5n7Edp85+9v/l3HhhzlcmmXehyPvxHGXz\nSfVolV6rVN78z/xfXOW+eFiDXy7oihp+blPw8TVC1RJn3JeNNPqxAQBAliEQAl3MPqjA6NuR\nV+zx/2SOsDcxltsIhI6hhY7RnbS4i/PC/kV/ukFKYc86dDnH8GL/g/NqfrJMqLoQQg8roWc2\nhp7Z6Dyvt2v6EMeIYvvQIntxTsETi2r+5/X4wUqtKlJ124vO8f3cc0Y6hhVKbqdWFY7tOCW/\nvS+xPk2Cze/Jv28uo4UBAMhWBEKg63kWlCYCoVYZVj465mo0X0s9URPbXmZU7rSfzmNbTlb9\n4DX/jy5zjEp15zp0Ie/VY+0ludXfW6IFZONkbNfp2K5mu21j28uMb63G7H1yC//4OVsJS4wC\nAJC1CIRA1/PMLw3+8r3EcWTZ7saB0FhOJlG53Q+yDy609/e3VEOJx49Wm3chVz44XPHl54qf\nv9kxojiVRxT89rOey0e1u4XoINe0wcVLFof+8EH4pe3GmM/2cV820n/PFZ2/+SQAAOhMBEKg\n69n7+50X9k/sMRh9a69+zxWS026ukBwvOry4I511OV+8wLd4aqvV9Fql9ukNob9sTCQKvVap\n+vbLJcu+0eRYVnQ39j65/vvm+r4+Nfzi9uiqffFDlW26XPI63TOH+RZPdV7YP0MtRFsF7n8r\n/MIW85mif3ypufnGBj2slE/5dbraILnsfbbcYT5zZt6f1WPVKV2b47Lle+yD8l2TBrrnjDR2\nScmc0NMbgo+tqSvYpN7vfqsde2nKK/dWf3+JUez12mLHyE4dK9G+rzsAtBWBEOgWvAtKE4FQ\nq5GV9Ufcs4YbH6nHa2I7TiWOzfsWZo6U68r7/iXO8X2rv/tqXRuOVSubT7qm8INIj2EfXJh3\nx6y8O2bFD1XGNp2IHzgbP1ARP1Kt10b1sKLL8UQ1yeOUcpy2klzH8CLH8CLn+f1c0wdLbv5p\n6Eb0mCqvaLhJqfzGrh4UDPSwooYVtSygbDxW+8f1rikD/T+9ItWVinQR+/RUZMmnsR2ntFNB\ntSJk83vsA/Jd0wZ7rx/vGFrY5EWea8YGH18jEvv4aLq8Yk/OzZPa2mx5ZXJohmN0SQtpMH6g\nIvLap8qGo1p5rVYZthV5bb3zXFMHeReNbfeCTGn+usc1+Z39yodHYltPqmdDerUsnHZbkdcx\ntMj1mSGeeWNaGTwCIKvxrz7QLXjmjQk88k7ixxd5+W5zIKw/XrTzVv/3zBnlmjpI+ahua/Lo\n6v0Ewp7IMayoie0lVF2PxSWXQ9hYLqa7U9Ye0mrkBiflFbvz7p7TYChBT6F8fLzii8/m/3xh\nq/9Dix+srPnJsgbTXLXKsFYZjm0vCz2zMeeGCXk/ukzyOhtcaO+T57posLLhaKIYWbqrrYFQ\nl+PRdw8aRe+5bTwbVgtGax58S35jl/mkWl6rltcmWuiZO8b/wFyb39Omp4u0ft3lpbuCj69R\nTwbqnY2palhRj9dE1x4KPrbGu2hc3g9n2Ypy2tpOAFmAQAh0C7beua7JA5WPjwsh5Lf3+e+L\nG700xi+JHaNLUpzIlx6S8MwbYwTC+OGqzns0Ms0uSfaGP0Oje4q8trPxSa1GVtYdds9ucXdQ\nm63xhGQzPRqPbT5hFF2TBgpXs0lDcrQ0YtwxothWktv0U0KKeqTKvNBR4tE1P1pq653rmjSg\nuXvKS3fV3POmLseafaqmh/+9VdlaVvT3G22NFkP2LhprBMLY1pPq8ZoGO3O2LLrukB5WjKJn\nYROjM9TyYOVXnleP17RwH3nlnti2k0X/+JJ9QBueLjrydTfTReDnq8LPbmqlmqpFXtkeXXeo\n8NfXOi9grDhgOQRCoLvwLDwvEQj1kBJdeyixNIt6rDqxAKno3O7BBPMgIr3R76oBZJoejEZX\n7zeKtkKvVhVJHEeW7mo5GEgeR9EzX2ihgnqi5syVTxnFgicWtWOiXYLvtunea8Y2+7EutOpI\n5MVttU99qIfqUpYeUwP3vtlryWJhb6KbWtlwtPrHy8wLIzlGFDtGl0geZ/xQRXxnua6oifPx\nPaerv7ek6C9faLD8sufKMYEH3k7szCmEkJft8t02PfU3ir651zh2Xti/cZzT5Xj1d181p0Fb\nnts5ZaC9JFc9Uxv7+LgWjCbOq6eCVf/9avE/b2rck9mcjnzdzULPbGyQBm0lPsfIXo7hxVp1\nJL7nTPxQRWKjGiGEdrq26rYXi/755U79zSOAboBACHQXnrmjAz97W2i6EEJetjsRCM370Xvm\ndXog7JdcYVKriXTy0wHIb+81ko+tKCf39pmB+1YmitFV+/WwIuW4uq51KZOErdDru3Wa93Pj\nK7/xn/ieun1Q4gcr5FX7PHNHN6iultdWf3+JkQYdw4v8D8x3TRxgIoSf4QAAIABJREFURD61\nLBB8ZLX8Vl1mUz48Enl1h/e68+s9M9flnjNSXl43wiKybHfqgVBXVNmUx5ocL1r75DpjdrcQ\nwvfNabn/Nd34cughpfZPH4ae3lD3pntO1/76/bwfz0mxAWn5uscPVgYfX2MUbb1z8x+Y575k\nuDk5q8eqAw++HV17KFHUgtHqO17r9fItLCEGWAp/4YHuwlaU45o2OHEcXX1Aj8SEabyos7R3\nEzPBMt6m5P8iGoz4Qg8V33NG2Xisrf+Zx86hM5nHDXqvGeuZN0acG7qpyzH5nf3NXNdN2Ypy\nCn9zrTCNPo2+e6BxtdAfPjCmzznH9Sn+11dckwaYY4y9n7/g8UXmjrLQnz+sW0LGxNxpGd97\nJr7vbIrtVNYdNnoyhU1qPDpDqwyHn9tsFHO/OzPvjlnmkCb5XHl3zMr97kzjTPiFrVpFSKQm\nLV/30F82Jn7DKISw5XuKn7vJPWt4g35U+6CCwj/dkPtfnzHOxPedjbz6aYrtBJAd6CEEuhHv\nglJl/REhhC7Hou8ecI7rY2wp3vnjRYUQumm8lrEuJXqE+J7T0bWHtepI3g8vNZ8P/uo9ozcg\ndcUv3+Is7Z2+1iElanlQ2XjUKHoWjbPle9wXD42+V7fYifzGLu/VzQ/U7Jbsgwrc04cY34TK\nJ8cbVFCPVYdf2p44lnJcBY9fK+U21Rtmk/IfWXhm9h8S/2uKH66KbTnhnFhvRqJ75rAGgy3z\nvn9JKo00ry/qmj7EVtxwJG34H58Ykxudpb1zb5vW5H1yb5sWXbUvtrNcCKFH46F/bEqlAWn5\nuuvRuLw0mSrz7pzd7CRGSeR+Z0b0wyOJla6FEJElO7yfG99qOwFkDXoIgW7EfeVoY6COvHx3\nvfGiXREIjd8uCyEEgbCHiK49VPnV589e97fgr94zLxmCHkdeusvo9XKM7JXI5Oa9Z6LrDmmV\n4S5pW0c4TL9c0MqDDXr2wv/ZZgwWzbnxwhZWgrH5Pe45I41iYg52/SfZPAuToz3lZbsa9yI2\npsdUcxdck+NF5bf2Gcc5i6c2O8DSbvN9Lbn1a/TtfU1Xa3DzdHzdY1tOJged5ns8LQdIuy33\nOxcbJWXTiWQHKQALoIcQ6Ebqfg285qAQIrrmYPxAReK8c1wf++Cmt9vKbHtMC/fpMVWritgK\nvZ3fDKRID0YDD62KvJbO4V6O4UWuyQPtJe1cawQdEXk9uZmB99pxicF+njmjAv8/e/cdH0WZ\nPw78mZmdsj2bSgmBACGhCVIE6T105GzH6YnY7hTLeZ769U78Kd7Z79RTPLHenRUPC0KUotJb\nkCIthJJQE9Kzffr8/phldpJsdjfJZhPC5/3ij5nJs88+y6bsZ57n+XwoIvBZX1LYdYWmBVe3\n0QCbyZCRoB0rvKRwAsYEs61wP+lirUi1IqwPjdUiJaJziEp6xrn9fJ8EsqpI553CoVLyqs7h\n++R3nlEu5YPBSIKZklWvgXSuViwK/HLGjCQztf4eSD16ahZmotRF12JRVTTJTmPyvvOXpvsQ\nQuTAzljj+WMDbQZ0Cp7IilTmMfSM+yYFAEAbgYAQgPaFmZGjBoQKL4nF1YGL0+NRj74h3FGn\nJpV4opK6plv4h3iX7fCv+KU5z5VgtL80qxkPBCq52ld922diUXX0D7E8MEbx8nKVVzxVJRy5\nqJ88wWiD/a/TqWu7Q12ytiKeqNSSryAMMbMD81SYhaLH9WQvzTWxeQWXXUAonq0NnhAYRgU/\niojF1dr3MJmTSnSyorCIbglEt4QwDcgBnQ09HFrVHH9eQcSAkNXlF6XH98QaFLTgdgXXc5KD\numBMuI9SGG0gB3fhd5wOPHbnGdONV4VpH6v3XS5za8eGrOQwLVW4ncGTzNouR8XDRXwIAKDD\ngIAQgPaFmdTbRRKKINW5GPf8oirMTGFGUk1vgxDyrzocMSAUjpWHb9AYIi10ETMQDcXDV9/5\nv3rRIJ5ooq4NV4bOcm8wk4Rc7fOvOuJ9d7dc60cIKZzoX3OUbpD+EcSNf3VwAxg1sjuRFgyN\nmBk5WmDA77sgXXA2tcZd2xJ1vyWIZAvCg3lOhH26uogjM2LwZBhi5vT3vLFNPWO/P2Z7bGLI\nQheXBidzPwanKLV4TE+6ND2IEKIGRQgvEUKULiCUiqvCto3Z+y5XBhPYRBMQKpwo1wSXoYYP\nswEAHQzsIQSgfcGsNDU2U3+FHNi5SfWUYzqaOp/J/KuO+C8lewDtivuVTcFZBYTwRJPtmdzU\nTffq9wWFhyeazIuGp6y7W6sBwG0ucv7fd1qNMhBXssKu0eWZnNtf/0V6Qi/9Gkv2u2PxG1iL\nSeedWr14hBA5PF3/VVEXaxl6Rw5jomHUBXVypZffczZMY27XGS2jMmai6HEhKv5pazcQQniX\nyL+c9WtZxTM14ZrG7n23PTU1ZcM96r9o1pgIv5TqU5LC7gAArigQEILLgYJkJyscKWM3HOd3\nnBZPVnbsD6nGmXXuSbdNOplL6PG6z0Oy4nlzW9uNBYTG77vg+yK4TJcc0Cnpy4WmG6/SJ/eP\nEmalE/4x1/Tboeop+/0x7yf7wj8EtAZ+73npYmDJH8aQzNQ629gwI0lP6Kmd+nUhRDsn1/hr\nH16lXwFRb4eeeDJYGYLoEdg4LRaWu1/dUnXTR+Xj37p41d/Lx71VddNH7r9vFg6WRvOkRLcE\nakgw+6g/ryBMY3Z9cL0oMyUr5HJQfVCH2+svKG0IszPasRQ2IIzh+44nm4mudvVf+EWtKs/y\nndqxVgAJAHCFgCWjoH1TELf5lGfZduFImf4y7jDSk3pb7hl5eS1rsT09zfb0tIjNmJk5nWZG\ntWnQeMNVxhvCbUdBCCV9fmtUg2uE6aZBppsGhW+TuvW+ljxFU5GDunQ6+mh766oNed/P146J\n7g7H8htadHcfx2yPTRRPVamL3DxvbDNOz8ZTYUFvXOnXDTJTsxpWIWdm9tWyEIsnKsXjFYY+\nKfEbX9Mpbs735SHPv3Zo+VoQQobeyczkOsuSxTPB7YVEkll2se4XN/q/PqxvI1d65UqvcPii\n9/18elxP258nRcy5xczpx19ajMqtP64smRo6yYok6xOBhlwvihBSnMGirLg98s8anhBso9XA\nCKmt3nfvf/eqFY9U+sXkAIArAQSEoA0orMCuLeQ2F0lna2QPT3S2Et0SjHP6U8O71amZKym1\nj61hvw+xKkau8fu/POT/5oj1wTHmu0agxveDANCxSRfd3OZgaW/732bEYK0XgdmfmVYx4z0k\nyoqXd7+yGfL9xJPCiey6YMkZ47z+DdvQYzMxM6XVBvDnFVjbNCD0vp/Prg49YaX4ePFMbcOa\n7BhJ2J/NrbedT5/LRGGF6pu/DD+lxm0pqjpQkvD6vPCTWsz0HNfffkSijBCS3Ry/vZie2Lth\nMz7/nLqHFqm3HUeG3oKrbatGdWf/GoPr2ugfW7/bNnnfFeT7dJ/7hZ+0C8y0bEM2FB0F4MoC\nASGIN27TKdfSDdqqGISQdK4W5Z/zf3mIHNAp4bV5RBcbQggpyPXshpDRYJAku1/dIpW5bU9O\naeVRA9BOcVuKtJ0/9MTe+qVxLUF0tZt+NVBdicquK7Q9NTV0cXDQCrjNRdo0Gp5ipkaECEsw\n2sBM6q1NKLFrCqwPjdVnZ4kz8XiFeLwi+vYYSdhfmEkO6lLvulqeQVX7yOpgNEjghp6Jhj4p\nSJTF4xXimRrt2152sTWLv05acauhV1JjT4fbGWZ8L/ZSthj/moKQAaE+HmNys0MvupYUhQsW\nZcWbGhCyIpKUkFlt4v++yxVe51NrtXr3CCEyJ9X2bG7zegMAXL5gDyGIK//KgzWLv9JHg3rC\n4YtVCz5Wi+9xG0/qt0WpcBvTcDLQ9+l+/zeH618F4Mog/BKsNma6YWAMe9bKuymCxG0tCt8Y\nxFCdtCJz+jeWElNfcl0qdfEHLoRs1g4ZslMSP1qgr7QeICkKG4y11Ky5mIWyPTkl7eeHklct\nSnh5dsKrc5Pz7kzdfr954TCtpeLja/+0WgsRQ2LmBiuzcxtP6iNP7dnZaNaLCqL+FDNHvlFS\nr43Chp4kjOf7rrCC5+2dFdPf1UeDhh4Ox3s34g3KbAAAOjyYIQTxIxwsdT6zAYVNByNXeJ2P\nrUla8VvPm9u1i+TgLtYHxxhyUvEEo+IXxFNV3g/36CcP3X/fwkzPiWbfPAAdjHD00vZaHKOG\nxzIVBHl1V4wxqB/Q2Y0nQ3x8B61AdrH6z+hGXRhTDzWqO25jtJSY7JoCakh6Y43bHGahiGQz\neXU6M6U3Pb5XyEmterEWQgh3GBM/+k3DCum4nbE+PpHo4XA9s0G9IhZWsOuPh0nBRY/vhVtp\n2c0hhBRW5H46VS/k438+J1cH6i4QnazU4NCT7RhZ5w+N4heQLcIkoeypG3ySIbYvxu99lxX/\n6qOe17ZIZR79ZWZaH9tTU6HuKABXJvgADeJFQa5nNyBJ1i4Q3RLokRmG3skIw8QTFdyus9K5\nWoSQUFBes/grrZyd5aGxlrtHaJ8eMCNJDuiU8Pc57NQ+tX/8Vr0oV3nZvALj9bGcHgHgsqDl\nqCDSE2K7qhOjCKKTVa3oLZ1zxrBnEAa7tlBLwokxBv+qI2EaYzYaaYHB2kLbnyc3I7VsTNhf\nnGWc02gMEyV9hfpAty/MahgNakw3DeY2ndLiKN//fgkTEGIUwczI0Rae+L8rqBcQ1skvOrNv\no+swCQyjDdqqUaWWRbpSgSEpTl0iGQLDQgWE8XnfxZOVzr+sFQ7Vyc6KO4y2JVPbNp01AKBt\nQUAI4oTfe16fKdS8aLj14XF1/oaJsvvVLd4P9yCEuK3F6jV6TKblnpEhc8Yw07ONm/trfzW5\n7cUQEIIrkJbwMMpcMvSEXkRGtLl58SQzOl2DEJIrPBEbg5jQp2ZRWFH9lRgNudbPbT9Nj+8Z\nuWm7hWMYQ2orKsmBnekxmeHaY8h82zAtIBT2nldYMcxSEWZOPy0g5LYWy7X+YP5PSWE36ALC\nRtaLBp7WRGoBoTZTF4bsCmbKwU1UyL9orf6+i7Ln/XzPsu1qZh0VRhtMtwwx3z0imp2QAIAO\nDAJCECfcxpPaMdkvzfqnCfX/KBpw658m8LvPBpfAIWS8YWCYDKKWxaO1gFA4dDGm4wXgMoE1\nOAjL9Juro+9bm7KQK+uniAStQSpx8XvPN/vh/ryjl3dAqMZalwJCelLviN/Vhv5p2rHCS9KZ\nGkN2o1k3qau7El3t0gUnQghJMrv+uFZTh99/QfsmN2QmkmHTbGI2Bl2amZedkQNCRRc0YpYQ\nO/Ra+30XC8udT3yvrbtBCCECM84faFk8iog0vQkAuBJAUhkQJ/q/dpbFo0L/mceQ5b5R+guG\n7uGqSxFd7dpm/Whu0wLQ8eApgQqBckXsYzatT4whY945aIhtWYl57oeTYaoaXBa072eEkCEz\nQnVBhBBuY/Rz49omwEZaY0bd1B/7XbBCfZ38orP6hg9E9X+YlCgCQn3QSIT6o9aq7zu3pahq\nwSf6aJAem5n87R32pbkQDQIAVDBDCOJEKnUFjjBENb4KiBqbiTCkJZ4h0sOubcOQoVeScLAU\nIaQvdgzAlYNIMaubb+UKD5JkRMTsNp8iSNKllaJQmD4elDp1yU03XmV7JnIBAH7H6eq7/hfo\ngBW4H0+GX+7Yzhl6JoqFgdAFo6O6DUF0sWk7afUFIUJi5vT3LN+lHvN7zkllHiLNguS660Vn\nRvgPNGQmclsC61Sl8sirqfUrrkPUxmjN953bVlyz+Gtt9z6eYLQ+Mck4ux8U7wUA6MEMIYgT\npfbSTqdkc8gt9SqMJPR3iCNm9MYjpXcDoGPDL93jV3iJ33Muhj3zu89q243wFHMMewYhCcfK\n1aI7KmZWVDlaqBEZ+syQ/rwWzTW1OX0KGbkm7HSf1kyXwxNPipAk09AzkRzQKXCiIHbtMYQQ\nf6BEvhTXkQM6GXpEmJkkegaDOuFwaZiWgTa6HQ2GzPo5clrvfZcuOJ1/WqNFg2ROavKqRcY5\nEA0CAOqDgBDEibYZKeIaFaKzrfWHA0AHQV8brF7Nri0M07KpON2cCdm/U5iWICb06wbxVAs1\nNLpaAgTOTO2jnXHbTmvTZZcjckBn7Vg4HHlnuOLlpfPBFLjRrIE0zu2vHbN5BQghbr1uvWik\n6UGEED0yWN9F2F+CpPDFlBT+QLBYKDWyfrn51nvf3S9t0jZTUEPSE/+7AO7sAABCgoAQxFvI\nLfV6UBUXgOjRk3pr+fH93x6RSlzh20dJKnHpl7Ex0/qEaQxiQFL8+rrks/o2Vpe8oTolIiVZ\nvx3uskONyNA2rLI/nFB4KXx7btMpbQbMkJkYzdpmZmaO9n8rHL4onakJFpzAUDT1NoluCdpM\nplzr53aeDtOYzz8rVwX24hLdHfWraLTa+y4cK9fWwWJmyv7yrNiWpQEAdCQQEAIAwGUMTzRp\nUwoKK7pf3IjCTldEyf3CT2pJeoQQkWaBGcLWxuef1acFYmY1YR8gNTRdP/PDrikI07idwxgD\nPTawyVwu9/g+3R+mscJLnnd2aaf0hF7RPAWeaKJHB/exu174SbroVo+p4d2ItKi2y9JTgrdI\nfB/vC9NS/xIa3lhpvfddX8fCvHAYLL0BAIQBASEAAFzeLPdeqx2zG47rPyI3j/edXewPJ7RT\n850jYNNRa9PPxxp6OMi+aWEa10dgTG6wqji/73ysJorbhPn2Ydqx+x+b+Z1nQreTFddffxBP\nVKpnGEmYfjs0yqcwzgnu09PKGKLo1osGBvnbIVrBQ25LEfvjiZDNuE2ntB8ljCHNt9UfYeu9\n7/rXFWWoDAC4YkGWUQAAuLxRI7sz0/poy948r2/FMGS+a4S2lLQJFOR9f7f7ta3aBUPPJNOv\nB8dqqCAkhRXqVkVvctoP44wc/TwVm1dgvntErIYXZ+TVXemJvQOla0W5+ncrLYtHmxcO1dc+\nkc7WuP72I7e1WLtiWjiM6BRtEQV6cm/MRCk+vs5VAo9+aTSeZDbdMsT7fr566nwsD3t9Hl03\ngTa3rbj2T2uCI7x1CJ5UZwtfK77vkiKerdGuV9/2eVN7Tst/MIYpiwEA7RwEhAAAcNmzPjaR\n33lGvlR8xf3aVm7XWftzM6L/iIzUnIRL1vG7dBMyBGZbMgUZ4HNh6+I2nlK8weAk+nkqDTmo\nC9HJqi199F/OASFCyLZkStWRi4HMn6LseX2r951d1IgMorNN4UXxRKVabUhDXdPN+uCY6PvH\nGJKZ1sf/zWH9RXpsJp5gbOwhDVkWj+Z3nRGOlCGEFL9Qc89KekwmPTYTTzRJFV5uS5H+R4kc\n2LlelV3Umu+7VObWUgQjhBT28q5OCQBobfBnHgAALntEF5vjnRswY3AKhd91pnL6u84n1woF\n5RF2FSpIOFDi/L/vKme9XycaRMj+7HRqREZjjwOxol83GE3ZgxBwTJ9iRDxeIR6viMnY2gTR\nyep4+3p9gjHFL3CbTvk+2+//8lC9aJAe19Pxxvym3rYwzq1f3cHYxHgMYwwJb84nutq1K9y2\nYtfzP9U+usb90kb9jxKR4XC8OV9bYqppvfddrU0KAABRghlCAADoCMhBXRxv/arm919qtbkV\nXvJ/dcj/1SEizUoOSycHdCKSzFgCgxlJxc3JLlYu8wgHS/kDJVoWRD3r/00yXjcgvi/iSiTX\n+PVLH5uUVkSPmZHj/XCPdurPK7D2SWnp4NoOmZOavPoO59PruU2nGmuDO4zme0aabx0afWZO\nDTU8A0+1aOUHMcZAT2ryRjsizZr81ULn0g1q+YqQjNcNsP1lcsOauq36vutLcQAAQESYosQi\nIR0AkVzs97J6QI3snvjBTWFa1tyzktsW+DPZ6eij4bttUmMAOjyxqMr5WJ5wtKwlneAp5oTn\nZ1KjesRoUAA0l4KEwnJ2bSG/64xc4ZUrvYgxEEkmQ980emwmM7VPw0CrTYgnK/3fHOHzz0pl\nbrnGjyeaiFQLdW1347z+Bl0VewAAaJ8gIARxogWEmJEkMsItjJHO1Wp7/Q3ZqeG71TeGgBAA\nhJAiSN7lu7z/+Vm/PSlaBG6c08/66ATc0YTNVAAAAAC4fEFACOJECwhbDwSEAGgUD+//+pD3\nk/2SLtlgGHiC0XjDVaZbhkRZhw0AAAAAHQPsIQSgdVXkvhvl/n7MROF2huhmp4ak05N6kwMi\nlAKv+vXH9ZIrhO0d4RYaszGG3knkVV2YGTkRExj4VhxwPbNBPTZkpyZ/vTDa50JIOFBSfdf/\nglndCTzh1bnMlCz1zP3yJv2mF/vzM43z+kffOULI+efvtQyB9qW5xhuuatLDrwSYhTL9dqjp\n1qHSuRr+5/P83vNiUbVc61dq/bKHw00U5jDhCQzR2UYNTaeGpxuyUppTpgIAAAAAlzkICAFo\nLxQfL/l4qdTF55/zvL2TGpZue3KKIVZpIRQkuznk5qQLTm5zkeeNbfT4nrYlU4kuttj0ryMc\nLK2+u240+NpcZnJWY+3dL/yk5mqP+UgAwhCR4TBmOIy/GtjWQwEAAABAewQBIYgT2/+b2tZD\nuMzwP5+vuvlj+/MzmenZrdE/t7mocu+Hjrevp4akx7Bb4fDF6ru+CO5eM+CO1+bRk3qHeYjs\nZF3P/ZjwypwYDgMAAAAAAEQDAkIQJ6abB7f1ENqeoVcSnhJ6g5bi5aUzNbKLrXORE52P5+Gp\nFmpI1/A9ExmOcHN9iiJXeKUSp8KKdS57+Nr7vk768jZ9Ka2WEI6UVd/5heJpQjSoYr87xs3u\nR09octp30HrEwgrZGfiGpK7p1raDAQAAAEArgYAQgPgx3zPSOKd+NeQgBcm1fv/Kg553dmkz\nbIoguZ5al7xqUfhCW6abB5kXDY/w9AqSK73+7495l22X3Zx6TXax7te2Jrw8u0kvJCThaFnN\nHSuUSz0jA+54fR49MXI0qHI9syF5eLd2kkQeIITcf98MNV0AAACADg9v6wEAAC7BEO4wmu8e\nkbLubn29DbGoiv3xRGz6TzGbbxua9OVC/YY99rsCudrXwr6FY+U1d34h14kGr4s+GkQISWVu\n96tbWjgMAAAAAADQJBAQAtDu4Ikmxz/nIUPwx5PbdCqG/RPpdusj44PnCuJ3nmlJh2JhRc0d\nX2jLCzGScPzzOnpiVOs/9VlzfJ/t5/ddaMlIAAAAAABAk0BACC4DCiuIhRX8zjPcplPCoVK5\nwtvWI2p1RLcEemR37ZTfez62/dfbrSeejqpUXUjiicrqO1bItX71FCOJhH9eF/1uQOPcfsH9\naQpyPbVO4aVmDwYAAAAAADQJ7CEEbUdBwoEL/M/nqdE9yH5pIRrICrv+uO9/vwh7z9cLEgw9\nE+nxvUy3DSXSrHEabdwZclK1HVxymRspCMWuShyeYMRtjJbDptlLRsWTldWLVsg1umjwjevo\ncT2b0AWG2Z/Jrbzu3wonIoTEoirvO7ss949u3ngAAAAAAECTQEAI2oKs+L857Hl3t3SmBiGU\n0DOxYUAonqys/dMa8XhFyA7EomqxqNr38T7T7cOsD45BRAec6zZkJGjHCi8pnIAxZMx6xxCe\nbNICQszSnFQuYlFV9e0rtGASI4mEN+fTYzOb2g/R3WFZPMr9j8AGQs87u5jcbENWcjOGBAAA\nAAAAmqQDfowG7ZzsYqvv/ML55Fo1GgyJ33Wm6uaPG4sGNYoged/dXafOQQcinq0NnhAYRsX0\n9o2sSBdcwe47NXmiVSyurhMNUkTCsuZEgyrz7cPJnEt5dETZuWQtkpTmdQUAAAAAAKIHASGI\nK8XHVy/8nN99Nkwb6UxN7UOrFL9Q7zruMJL904jujnrzgXz+udrH1nS8+EE8Vq4dE8kWhMdu\nwShCUqlLXaKp0ic1jYZ4uqb69s/lysBmTowiEt6cT49pZjSIEEIG3PbsdO01CgdLfZ/tb35v\nAAAAAAAgOrBkFMSV67mfxMLgvB/GGOhJvUldnkmEkOtvPwarFyBET+hlvm2ooV8abmPUK4qX\nZ9cXuv+xVa4KBCTcplO+z/abbh3S+q8gTqTzTn3YTA5Pj23//lVHgp3npFJXRyh8ryedram+\n/XMttQ9GEQnLfkWP7tHCIZH908y3D/d+kK+eul/dQk/qTXSxtbBbAAAAAAAQBswQgvgRi6r9\nXx/STumJvVI23ZvwyhyiW3CznHCwVMukghCyPT3NsexX1MjuWjSIEMLMlHH+wOTv7jTO669d\n9Ly9UyvmfrmTa/y1D69ShGAeHWZKVsx6VxC7vtDz9s7AKYYsD4yJPl2NdK62euEKudyjXTH/\n7tqWR4Mqy/2jtG8GxS+4nl6POtq8LwAAAABA+wIBIYgf/9eHtM/3zLRsxxvz9WGeil1/XDs2\nLxpuumlQY7EKbqXtf52hLVOUq32xqd7ephQ35/33zxXT3xWOlGkXDb2Tmcl9WtSvrMiVXuHw\nRXZNQdWCj2v/8C0SZfUrtqemRlkwECEknXdWL/xcKnPrL/o+3qutHW0hjCHtz0zTTrltxf68\nozHpGQAAAAAAhARLRkH8aGsgMdpgfWJiyE1x/K5LFdIJzPz7ayP0SGDWP0/mZr6nnnFbi41z\n+4d/RNvyvp/Prg4d4Sg+XjxTqy2C1WAkYX82FxERpvDcL29yv7ypSYPBU8y2JyYz07OjbC9d\ncFYv/Fy66K53Xa7xO//yvePtG2JSFYMa2d04f6A2k+x+7id6VA880RSDrgEAAAAAQAMQEIL4\nkc471QOyf1pj9QO1eMPQPRG30hH7NPRwkAM6CYcvIoTEo2UR27ct8XhFxNSpehhJ2F+YSQ7q\nEvORkIO7JC6/AYvif1il1Pqrb18hlQYSk1JD0slh6d53dqmn3NZi34oDpl8PjsnYrI9N4Daf\nUvOXyrV+9wsb7S/NiknPAAAAAACgHlgyCuJHSxVD9EhstI0zUBnP0CfaMnSGrEBOmlgtXGwn\nDNkpiR8tYGbktEbnwoGSmoe/FU9URtleKnNLFwLxPDUk3bFIrSgeAAAgAElEQVT8eusDo8nB\nwUjV/dJGsbg6JmPD7YxtyRTt1L/mKLe1OEx7AAAAAADQbDBDCOIHTzKpyUj0BQ/qt0kwBpZN\nGqK9W4HRhHoQptvLBWahiGQzeXU6M6U3Pb5X9KUmiAxHhIScvCierdXHzPyO01W3fJL02a2G\nXknRj5Aalu54+3rMRCGEEl6YWXndfxRWQAgprOh8PC/p01uif+PCYKZl0xN7cRtPqaeup9cn\nf7sIM1Mt7xkAAAAAAOhBQAjih0izqgGheLwCKSjkljMizaIGhE2YvCoJrGPEk82xGWirsb84\nyzinX2v0bLp5kHnR8IjNFA/veW+39/18JMnqac19X6V8d2e90o6N0UeDCCEiw2F9fILrmQ3q\nqXD4ouftnZb7Rzf3RehgyPbU1Mr8c2rmWKnU5X59q+3Pk2PQMwAAAAAA0IGAEMQPOaCTcKgU\nISSeqBQOloTcGkdd2104WoYQkoqqFV7CKCJ8n4pf4PPPqcd4qiXWQ+5oMAtl/cNYcmCn2ge+\nUa9I52r5/SXUsMh1Dqlrujn+dT1mJPUXTTcN5n46qS3p9CzfSY/NjMmmRyLNan1kvGtpINr0\nfbLPOLOvfpEq0FTkvtsa3coVnsiNAAAAAHCZgz2EIH70ZQOdf1kbsmwgM6uveqAIkr5oYWO8\nH+5RlywihOiR3WMxzI6PmZRFDe+mnXIbT0Z8CJFmbRgNIoQQhux/nYHbL5UPkZTax/IUX2wK\nQppuGkQN6Ro4UZBzyVp9bUagkc7VtsY/hb3s12ADAAAAICKYIQTxQw7sTPZPUyvsiUVV1YtW\nJPxjLpFur9MmJ9U4r79/1RGEkPvvm6lrMgyZjWag4Xee8fxrp3bK5EZbQeFKhyEmN5vfE5hZ\nFU/XRH5EgjFENIgQUstXPD2t9uFv1VPpXK37xY22Z3JjME4csy2dXjX/32ocKJ6q8r6z27J4\nVAx6Bpeb8rFvNSzK0iQJr8xhZgZTNPlWHNBWO4eHUQTuMOKdbdTQdHpCL2pIevgKK+6XN3k/\n3KOd2p+fqb8XFg3nn7/3f3M48PClucYbrmrSwwEAAIAmgRlCEEcYsr84C6MDtyGEwxcrr/u3\n+5XN9QqdW/9vEpHhQAgpHr761k+5bcVaOXuNwkveD/dU/26luhcOIURP7G3ok9LqL6Gj0Geg\nUS5ldm02Jjebmd1XO/X972A0s47RMPRMNN8bLEfpWb5TPFUVk54BiJLCS1KZRzhQ4n0/v/q3\nn1Xd/JFa5yZK7hd+UmuotBZRZtcfdy3dUHX9f8rHv1U26B9lw16vmPZOzT0rvR/u0bZYAwAA\nAI2BGUIQV4aeSbZnc51PfIckBSGk+HjvB/neD/LJvqnUyO6GXklEj0QiyZTw2lznn1aLRdVy\njb/mnpXkwM70pN6GTAdGk3KNTzh8kf3hhJqfRoXbGPvT02JSGP0KQXQO1oGUnf6Wd2h7coqw\n55xUFnhTnEvWJa/qjCfFIM2P+c5r2O+PBZIMibJzydqkj3/T8m47EvvfZrT1EK4gwuGLVQs+\nsf+/qVFO3MlO1vXcjwmvzGmNwbB5Be5Xt9SP+gRJ8vHSeSe3rdj9jy3Guf2tj4zDE02tMQAA\nAAAdAASEIN6Ms/sRKZbah1bJruDElFBQLhSUN/YQ4VCpmo0mJCLN4nj7ejylvacYbV/w4OoA\n/RvR/P5sjP25mdV3fhHos9rnXLLOsexXLY/SMZKwPzu9asHH6kSxcKDE99n+lnbasRjnD2jr\nIcSVoU9KM8IbPKnRh2BGsrFMSIqHk0rd9VerSrLz6fV4ioUe3zOap2a/O8bN7kdP6NWU8Uai\nINfzP/o+3hehmST7vz7EbS92vD4vJtmeAAAAdDwQEII2QI3ISFq1yPuvHb4vD2lrPpuHntjb\ntmQK0ckauSnQUXT/7bHKHUJd2910yxDfJ4FPqNymU76VB003xmD7E3lVZ9OtQ30f7VVP3a9u\nJQd2anm34DJleWA0Mzkrhh0SGY7ED24K00DhJW5rkfetHcH7VrLiWrohOe9OjInqz6jrmQ3J\nw7vFsJam94P8etEgnmI29E429EySa/1iYYVYXKUuxEAIyeWemntWJn56S5OKjgIAALhCQEAI\n2gaRZrE9Pc18x3DfykPcjyfE4uomPRwzkvSYTPOi4VCHoJlk3b7M2CWTtD4yjt9xWns33S/8\nRI/opu4IbWnPD43hfjyhLo1TfDy/+2zL+wQgShhFMJOzmElZ7leCCWOkUhe7rjDKhDFSmdv9\n6hbbk1NiMh6xqNr96hbtFE+12Jfm0mN76ifkpXO1rmd/4LYFSsLIbq72j98mf7UwyqKjAAAA\nrhzwhwG0JSLDYf3juOS8O5Pz7rQ/O918+zB6bCaR4cATTfr77hhD4okmQ3YqMyPHsniU41/X\np+64P+H1eRANNhtupbVjRZDkmhhsI0QIYQxpf3EWIgIfSxW/UPtYXgsngQM9myjb09Na3g9o\nKvF4hfu1rW09ivYBQ9aHxxHdErQL3KZT4R+hz3Tl+2w/v+9CTAbifT9fu6eD25mkT35Dj+tZ\nb3k20S3BsfwGy++DOZnEE5X+b47EZAAAAAA6EpghBO2CITMxRHkJSVEEEaMMCId0MTGGO+rs\npxJPVFLXdGuscZOQAzpZfn+tZ9kO9VQ4WOpZvstyXwxqRdBjMo1z+vlXH215VyAiqcTF5hX4\n8wrE4xUIIesfxrb1iNoHA06PzPCdq1XPpLMRSrYY5/bjthTx+ecQQkhBrqfWJX21EKOIlgxB\n4UQ2L/hTYH10AtHVHrophiyLR3G7zggHStQL/lWHjdcPbMmzAwAA6HhghhC0YwSGMSREg60B\nM1P6uoL+VYdj2Lnld9eSA4J7/Dz/2hEmJ1CTWP9vEu4wxqQrEJJc4/d9fqD61s8qpix3v7pF\njQaBniErOOkne/gIrTHM/kyuVmtHLKryvrOrhQMQDpQovKQe43aGmd0vXGsCtywerZ3x+y4o\n3khjBgAAcIWBgBCAKxKGqJEZ2pl/1RH/l4di1rkBt78QLDiJJKX2sTzFL7S8Y9xhtD4xqeX9\ngHoUv8CuKai598vycW+5lm7g951v6xG1Y0pw/62+fEtjiO4Oy+LgDLnnnV2BGirNxV+a7kMI\nkQM7R5xv1N+dQbKi1YYBAAAAVBAQgjameHmxqEo4WNrMbWySHPwHmoIer0uCLyueN7fFsHND\nz0Trn8Zrp9KZGvdLm2LSs3FWP3psZky6AoogcZuLah9bUz56We1ja7jNRfBzFJF4MhjO6fcT\nhmG+fTiZk3rp8bJzyVot/2czyGVu7diQlRyxPW5n9BVBFQ/X7KcGAADQIcEeQtBGJNn/9WH/\nqiP8vvPo0kcj3M5QI7pb7r3WkJ0S9sFBZdf8U5t66nT00dYYaQulrLu7lXpO+vzWljzcdNMg\n002DwjW4ebDp5sHN7/+WIaZbhoT8kvXRCdZHJzSzXww5lt/Q7FEBhBCSFf7ABXZNAbu2UK4N\ndyOG6GRlZuQwM3LiNrT2TpS5XcEMt8Y5UaUYRQbc9uz0qps/UjPBCAdLfZ/tN90a+qcjIrky\nWBQxmoBQ4US5xqedRhnEAgAAuHJAQAjagHiysvaPq/U32lWyk2XXF7LrC5lp2bZnc/WZMAEA\nLScer/DnFbBrCqRSV5hmeIqZyc02zsghB3WBTbxBCnK/ukW6lFGGHNCJGh5tKiayf5r59uHe\nD/LVU/erW+hJvYkutmaMwvbUVOvjE9Vj/dRfY4RfSvUpSWEXLgAAgHogIATxJvxSUnPPStkd\nbtkSu75QOlvjeO9GPNEUphkAIBrSBSf73TH/mqPhd6/hiSZmah9mRg41NF2rHQIQQkiUua3F\nnre2C0fK1At4ijnhtXmoKf9JlvtHsRuOq/Gk4hdcT693LL+hST0Enjo5chCo51m+UzumRmSE\naQkAAODKBAEhiCu52lfz0KqG0SCeZJZrfPpq6cKx8urffpb40QKICQFoHrnax64rZPMKoil/\nl/jejdSIjMuiarl32Q7/il+ib29bmkt0Cpf9RTpXW3PPypBfkt2cfNElVXj1v53InFT7S7Oa\nOr+HMaT9mWnVd3yhnnLbiv15R43hc4S2mPe/e/mdZ7RTy73XhmkMAADgygQBIYgr9yub5fJg\njjvy6q6W+0aRAzrhdkbhROGXUs/yndrHF7G42rX0h4TX5rbRYAG4LCk+nv3pJLumgNteHCZ5\nCW5jMBstnXeqp9SoHnEaX4sJx8qb1F7xRchwq/h4bltxlL3RYzMdy36FDM2JnKmR3Y3zB/q/\nDmT0dT/3Ez2qR2vd81KQ79N97hd+0i4w07IN2alhHgEAAODKBAEhiB/pgtO/+oh2arn3Wst9\no7QZCYw2UNd0S7ymm/c/P7tf3KheZNcXsusKmdzsNhguAJcVRZD47af9eQXcjycVttEQCDNT\nzKTezMy+1Kjunte2ej/cE89BdgDc1uKqmz+y3D+GntCrGQs+rY9N4Dafkqt9CCG51u9+YaP9\npVkxH6Rc4XU+tZbbXKRdIXNSbc/mxvyJQAvJNX5u00luxxn5oluq8Ko3THE7gycaDf3SqMFd\nqTE9iLTI1U0AAKAlICAE8cOuK9TmK+ixmZb7x4T8OGVeOAwz4K6//aieup79gRrZHbczcRsn\nAJcXfu95dk0Bu/aY7GQba4MxJD2hJzOzLz02M1giEjSLUFBes/gr082DbU9OaepmS9zO2JZM\nqX34W/XUv+YoMyeWlVQUVvD++2fvu7v1lT8NPRyO926ENF2twfvhHvfLm9Rj8qrO0Sd/5vPP\nef61g//5XMNpfIkVpDK3UFDu//IQIjBmYm/zHdeQg7tE02352LfkKm/kdo1LeGUOM7NVEgv7\nVhxwPbMhmpYYReAOI97ZRg1Npyf0ooakR7z5UpH7rpbwKULnJgq3M0Q3OzUknZ7Uu06hTgCu\nVPCxAMQPr6VrJzDbkqlhfr+bfjOE21rMbSlCCMnVPu97u62PjG+0NQBXturfftbYlzCKoMf1\nZGbk0BN6YUYynqNqPQlvXMdMzophh4bs1OSvF4ZpoAiS4mL5AyWeN3eIhYEFq74VB2QXm/DK\nnKbOEzLTsumJvbiNp9RT19Prk79dhJmpZo1dR1b8q496XttSr/Q8M62P7ampsBm7/ZAuut0v\nbWTXFkbXWmF/OMH+cML068HWP47HLC3+PmkJUWZ/OsnvOiP8UiJVepVaFpEEnmg09Eikru3O\n5GY3L3FuQwovSWUeqcwjHCjxvp9PDuhke2pqrCI3xcdLPl4qdfH55zxv76SGpduenGLoE0Wx\nq3i9fADiDwJCED9iUZV6YOiVTKTbwzXFkH1pbsWM99Sb3L6P95luHQLLZgCIlgGnR/dgZuQw\nk7La+BNkh4CRBJZkZiZnMRN6ed7c7lm+S73Ofn+Mm9OPntCrid0h21NTK/PPKV4eISSVutyv\nb7X9eXJLRiierHT+Za1wqFR/EXcYbUumMtNhyX07IhwsrblnpexqMJlvwIkUC55qQbIi1/ql\n87Wo7sSh7/MDwoGSNky+zeYVuF/dIpXUrVgjSJKPl847uW3F7n9sMc7tb31kXMxHKBy+WLXg\nE/v/m2q84arY9owQ4n8+X3Xzx/bnZ4b/SWnDlw9AHEBACOJHrg6UwDb0iVxMGU+1mO8a4Xlj\nG0JI4UTvWztsz8AGGAAiwB1Gy0NjmdxsWGXdKgjc8sAYbvdZ4UCJesGzbHuTA0KEiDSr9ZHx\nrqWB5XO+T/YZZ/aNck1gfaLseT/fs2w7EmXtGkYbTLcMMd89Ar4N2hV+99maxV8rPl67gtEG\n49x+9LRsekSGPlOR7Ob4/LP+lQf1G0GFY+XVCz9P+uzWKO/yGPqkNCM4wZMaPERBrud/9H28\nL8IjJdn/9SFue7Hj9XnkoAjfzJiRbKyN4uGkUnf9ha+S7Hx6PZ5iocf3jDAMhAy9kvAUS+jO\nvbx0pqZeQK5wovPxPDzVQg3pGuoxsX/5ALQ3EBCC+FFEST3A7VFVRjYvGub/4oC6/Mn31SHT\n7cMNmYmtOD4ALn9yjd/zz21ScTUztz+Zk9qMrCcgAhyzPjCm+s5A9QjhSJnsYnFbk+Mu002D\n2DVHAxVBFORcsjbpq4UYSTSpE7Gw3PnE93XSrhKYcf5Ay+JRsKSivRGLqmp+/6XCidoVelJv\n2xOTiK4h1svgVpqZnMVMzuJ/Pu98cq10tibQyakq13M/2p+bEc0zWh4YHZPF1d4P8uuFQ3iK\n2dA72dAzSa71i4UVYnGVthNSLvfU3LMy8dNbDL2SwvRJZDgSP7gpTAOFl7itRd63dggFl769\nZcW1dENy3p0YE+Gzq/mekcY5jRd0UZBc6/evPOh5Z5c6S48QUgTJ9dS65FWLGu4Kbo2XD0B7\ncxmUnAIdhvaZSTpdHU17jCEtD40NnEiK+4WfUKMp9AEAAXK1z/ufn6uu/0/lvA+97+dLZe62\nHlFHY+hbp3iDdM7ZnF5wzLZ0uhYBiqeqvO/sblIH3JaiqgWf6KNBemxm8rd32JfmQjTY7oiy\n8/E8fTRouX+04435IaNBPWpYetKnvyH7pWlX/N8cjr5KSsuJRdXuV7dop3iqxfH29amb7kt8\n/ybbXyYnvDw7+dtFKd/dRY8JJkaS3VztH79Fkhyqv2hhFMFMzkpaudC8aLh2USp1seui23sZ\nrmuEO4zmu0ekrLtbX4hFLKpifzxRr21bvXwA4gwCQhA/eIpZPRBOVEYZ2hnn9icHdlaPua3F\nbF5BK40NgMuX6deDQ64MFE9Wuv++uWLS29V3fuFfdUS/UA20BJ5g1GfslEqaFRAiZOiZaNZV\nivcs3ymeqorysdy24prFXyusqA3J/uIsx9s3wDKK9smzfJdwpEw7tf5hrOW+UVFO4OOJpoRl\n8/WLP73vNu3eQUt4389HcuAPNm5nkj75DT2uZ72RE90SHMtvsPw++M0snqj0f3MEtRyGrA+P\nI7olaBe4Tadi0C1CCCE80eT45zz9St2GnbfxywcgXiAgBPFDXVpVL5d7uO3R3eDEMftfp2u/\nr13P/yhXtiihNgAdj+2pqSlb7nMsm8/kZmNUgzWHCuJ3nnE+8V35mLecj+fxO06HqVYPoqH4\nBdnDaad4CxKEmu+8xpB1aU+1KDuXrNU+fYYhXXA6/7RGm4Igc1KTVy0yzukHK4TbJ8XLe/8d\nLPhJjcgw3zWiST0QaVbLg2O0U37PObGwImbja5zCiWzeUe3U+uiERqc0MWRZPEq/D9a/6nBs\nBmHA6ZEZ2pm2ejYmiG4J9Mju2im/97z+q+3i5QMQFxAQgvihRgV/7bqWrK2frasRhqxky32j\n1GO5xl/zu5Xaon8AgAojCXpi74RX56ZuXWz/23RqREbD2EBhBf/qo9V3/a980r/cr2wWj8fj\nA2WHJJ6q0q9xaCx9RTQwkrA/O117s4QDJb7P9kd8lPulTVpWDGpIeuJ/F2jrL0A75P/6sPZn\nCyMJ+3MzEN7k2N30q4G4I7j9Ptqbqi0jHChReG3zP8PMbnxjHkKIwC2LR2tn/L4LsfpjbcgK\n1oSQPTH+AGDICa4alcvc+h/tdvLyAYgDCAhB/DCTsojOgSo9Upmn6ob/+r8+rP22DcNy1wht\nob9QUF7zuy+li7AtCoAQMCttnD8w8cObU376vfVP4/U7ZDRyhdf7QX7ldf+OJvYADelne/AE\no6Fni7JHkFd1Nt06VDt1v7pVKg13s0w4Vs5uOK4eY2bK/vIsqCzSrinI+0kwJQkzu6/2d7Bp\nDLjpN1dTQ7qq/+TaBoUrWgF/KZsuQogc2DnEAoS66pQKlJV6JTGbTwlGaUTnGO+PNWQE16Mq\nvKRwgnbaXl4+AK0PsoyCODLglofHOh/LU8/kWr/zL9+7X95EDuhkyEo2ZCUz03NCZw8z4Pbn\nZ1Qv+ETdkc/vO1855wPzwmH0hF6QZgaAkIg0q/mOa8x3XCOeqPSvPsquOdrwNoq2Aw0h5Hxq\nnXF2P2poesMke0CPXVfIfndMO6Un9W75/5j1oTHcjyfURROKj+d3nw03gNXBNWzmhcOaGV2A\neJHO1Uhngqsc9cF/U1kWj0a6Oag4kHUpqYJrmxuH2xk8yaxVjFB0K6tbQjxZqR3r9xPGpvOz\ntcETAsOo4IeQdvLyAYgDCAhBXBln9xN+KfXpbpfKtX5uW7GaM40a3q2xBfpkTmrCa/Nq7v9a\n3TajeHnPWzs8b+2Iz7ABuHwZspKtfxxn/cNYfu95/+qj7LpCxR3iY4p/5UH/yoN4qsU4qy8z\nqy/ZNw02pNWhIKnU5Vm23f+1bmsQgVnuadpmsJAwE2V7elrNPSujaawvTNeMEoggzvTb0og0\nC5kTYtK+3dJv2o8mIlI4Ua7xaaexCd5EmdsVvEVinNM/Bn3qu9fl6SWSLfrVvO3i5QMQFxAQ\ngniz/XkykWZxv7Y1mtwJevT4no63fuV8dE29krIAgMhwjBrejRrezfaXydzmInbNUW5zkSLU\nX7Atl3u8H+7xfrjH0MPBzO5nnNWX6O5ok/HGk3SuNnwwJrtYsahKabB5yfrAGCIjNv8/9JhM\n45x+ft3sX2iSIuqSalTf9nlT4/a0/AcRAbtF4of/ORgQkkPTL6/7LLanplofn6ge40mR96kK\nv5Tqc3LqNz02k4Lcr26RzgUm8cgBnajh3Vrap4503qmfkCeHp+u/2vYvH4B4gYAQxB2GzHeN\noCf19r6/h807Gs0eQg09NjPpy9vcL23SttAAAJoEow3MtD7MtD6yi2XXFrKrj9ZLrKcST9d4\n3tzueXM7OaATM7uf+bbmr3Nr/xQf34zCbqYFV5vvHhnDYVj/bxK3rViu8YdpI5W5kRisb6aw\nQpjGoD0Qi4J1d7UqSpcLPLlpyYo8y3dqx9SIjDAtIxNlbmux563tWrkOPMWc8Nq8GEbUco2/\n9uFV+vtizJQsfYO2fPkAxBcEhKBtGHom2f823bZkivBLCX+gRC5zy5VejI78DUl0tSe8Pk8o\nKGfXHOU2nxLP1uo/HgEAooTbGNNNg0w3DZJKXOyao/7VR0MWwRMOXxQOX+zYAWFT4Ykm6yPj\njPMHxrhbh9H6xCRtl3VI2lQJuFzItcEIn4hilimGvMt2+Ff8En1729JcolPzU7Z4/7uX33lG\nO7Xoamw2FGZaXnZz8kWXVOHVLyMic1LtL80iusRmx6zi5nxfHvL8a4d+/byhdzIzuU+z+2zS\nywegvYGAELQljDFQIzKacSON7JtK9k21PjoBSYpU5pbO1cLnJACah+hiM98z0nz3SOFYObvm\nqH/NUbkCqn3Wh5kp3M6QAzpTo3sY5/TFGLI1nsU4qx+7+ii3tdHpSum8szWeF7QeRRcQYglM\nxPa+T/dHWXvd+sh4Q3ZKmAaCbndcNBRfcyecFeT7dJ/7hZ+0C8y07JApjnXP1YRpeXpspmPZ\nr/QV5MPzvp/PNrL6WvHx4plaLe+LBiMJ+7O5zUwQ1fSXD0B7AwEhuMwRGNHFRnSxIVieAUBL\nYJfus/xxPJ9/1r/6KLvhePupo5W69b7Ydmi6ebDp5sGx7VNlfXSC9dEJzXwwhhzLbwjzdeP1\nA43Xx3hmErQqWZdqEjNGvo8gFlVFGSk1tbp9K5ErvM6n1upzHZE5qbZnc2P4FNzW4qqbP7Lc\nP4ae0CuaJaPi8Yom1VnFSML+wkxyUJfITRuIw8sHIA4gIAQAAKBDYNS13alru9uemsJtPOUP\nO2EFAAgPN1HypXWJIRP8Xr4UVvD++2fvu7sVf3Bq0dDD4XjvRtxKx/a5hILymsVfmW4ebHty\nSmxL4xiyU+zP5JJXNXl7ZzxfPgCtDQJCAAAAIWAMyczIYWbkyNW+yK0BAKFgCUZ0KQ6Mc4rs\nhDeuYyZnRW7XDLLiX33U89qWerXXmWl9bE9NxRNNETswZKcmf70wTANFkBQXyx8o8by5QywM\nrH31rTggu9iEV+a0MLUMZqGIZDN5dTozpTc9vpe+1ERUWvzyAWhvICAEAAAQDny+AaDZcIdR\n2+Iu140fQrI9OcX25JTGvur5107PG9tiNrhmEU9WOv+yVjhUqr+IO4y2JVOZ6dmxehaMJLAk\nMzM5i5nQy/Pmds/yXep19vtj3Jx+4Stw2l+cZZzTL1YjqSc+Lx+AOIOAEICOpmzoa/oVLI3B\nrTSeYsFTLdSwdGZqH0Pv5Jan83Y9s8G34oD+SuJHC6ih6Y2111RMWS6VuNRjQ6+k5NV3RPN0\nlfP/o905xkgidef9mImKPMilG3yfBweZsu5uKB8MAGglRCebcDAQPGgHzdbG23pF2fN+vmfZ\ndn1yb4w2mG4ZYr57BG6PnDKnOQjc8sAYbvdZ4UCJesGzbHv4gLC1tMnLByAuICAE4AoluznZ\nzaGiKn7XGc+b2w19UmxPTGpJ6SRFkNi1x+pdZNcURBMQUiO6+78+pB6Lp6rkWj+eEKGkr1zl\n1aJB9dn5/HPRfErg95dox0QnK5EeVTQou9jyMcuQKJNXdU76/NZoHgIAANSwdHZ9oXrMHyhB\nCmrJrbeG6THjRiwsdz7xfZ3MpQRmnD/QsngUkdb8YhVRwTHrA2Oq7/xCPROOlMkuFrfFNQBr\ny5cPQOuLNocvAKBjE49XVC9aUfvommhmF0PitxXLzvo7ZNi1x/RlfxtDjawTiAq6mK0x3PbT\n9a9EkZpP8fLiiWD2OWpk9yg/nLHrjkPFSwBAU5HDgnfE5CqvcDDyL7cwQhYLjQNuS1HVgk/0\n4RA9NjP52zvsS3PjEw4Z+tap4iCdi2v9lTZ/+QC0NpghBKAjM2Sn4o4QU20KK8iVXrnco/B1\nojU2r0C+6Ha8fT1mjrz2sh7/tyHqPslOlt9+OuLEHXVNnYCQ33+BnhjhIXzDgDCKZJjCwVJ9\nsWPqmm4RH6Ji14SuagUAAGGQfVJwO6PdLPN9fsDerPIGCCG52iccuRi7oUWL21Zcs/hrJAXu\niOEJRusTk4yz+7V8l0H08AQjbqW1fK1SiZPsnxafp/BzrHAAACAASURBVG4PLx+A1gYBIQAd\nmfWRcfSYzMa+qvASn3+WzSvwf3sEXYqS+L3na37/ZeK/b0ZEE1YQKG6O23hSO8UdRrkmUI7Z\nn1cQMSAk0iyGzESxuFo9Ffadj/B8ssLtOFPvmnSuVjpTQ3R3hHkcv/+C/jTKJbL8nnP8nnPR\ntAQAgDpwzHjDVd7389Uz9vtjlgfGEF1szejJvyr4izpupAtO55/WaOEQmZPqWH4DnmKO8zAU\nv6Cv6Ig3/ZZl87STlw9Aa4MlowBcuTCKoMdk2p+fmbTit4bMRO06v/e89797m9QV+8NxbbIR\nTzRZHhqrfYn78aTii5wIQR+bCYcuKpwYprFwvCLkXpqIq0YFXUBIdEsgOkf+WCYcvlj7x9UR\nmwEAQEimW4do99cUXnIuWduMuE7x8N73dsd4ZFFwv7RJq5ZBDUlP/O+CNgmHxFNV+v80PMUS\nn+dtJy8fgNYGM4Qgfqp+/XGr9g+pPpqNHNAp8T+/rr59hVgU2KDi+ec2ZkpW9Ok39etFjXP6\nMbnZrr/+oG66U1iB/emkcXaEJODUyAwt+aciSMKRMmpI18Ya69eLMrnZ7LpAzgZua7HpliGN\nPoek8L8EN/CEmx5UkFzh4Q+U+D7bz+8+G37kAAAQBpFmNc7u6191RD3ld57xfpBvvvOaJnSh\nIOeStdqyi7gRjpWzG46rx5iZsr88C7PEaWquHu+/92jHeILR0DMpDk/afl4+AK0NAkIQPy3P\nuA1aD55sTnjjusp5HwaiOE70/nev7S+To3msVObm84NREzO3P25n6NE9uM1F6hV2TUHkgHB4\nvbwyF8IEhPqZQMuDY7jNRQorIIT4/LMKJ2J06N9s4qlKxROcq6SvCREQ8nvPu/+xRTxZqbi5\nhl8FAIBmsD42kdt+Wq4MrGtw/30zUhTzXSOierCkuF/eqN32iid2dfBOn3nhsGiWVLTKMNYV\nst8FU1jTk3ojIh4b+NrJywcgDmDJKIgfjCTaegggHENmovmO4E1r/7dH1CgrIjavQFvMY+id\nTOakIoSYGTlaA257sVztC98J7jAasoN55PjGtxEqfkHYF1j5aejhMGQmUiMCuWEUVhT2NvpA\n/kCd/H4hM8pIJS5h/wWIBgEAMYQ7jPbnZ+qvuP+xpfYP30qlrvAPlC66a+79UlvD39jdrlai\n3dRDCLVB6T8FSSUu51++r3342+BFArPcE10g3WJt/PIBiCOYIQTxk7L1PnZtIbvmKP9zpJQh\noI2Ybx3ifW+3modTcXPshhPGORFm9hBC/tUF2rFxXn819xozKctFEYGNhZLCris0Lbg6fD/0\nyAyttKCw7wKSFYSHuA3M7zmnlbKgRmcihOgxmdpfbm5rMTWqR8j+9RsIDT0T8dQ47UIBAAB6\ndA/r4xPdL27UrrDrC7ktp4xz+9NT+9AjMpAheI9eESThQAn7/TH/l4e0X3f0uJ7GOf1qH10T\npxFLini2Rjurvu3zpubVTMt/sLHkZNK52pp7VoZ5rOxixaIq/ZoOlfWBMURGuMxhMdOaLx+A\n9gYCQhA/uI0x3TTIdNMgfvfZ2kdW66eM7EtzDVnJbTg2oMKTzfSoHtqCTH7PuYgBoXiiMlgg\nHkPM7L6BQwtFj+vJ/nBCPWXzCiIGhNSIDO9/flaPZScrnq4OuVFEX4GQHt0DIUSPDWZS5bYW\nWx+fGLJ/fl8wIKRCrRdVO0z67JZ6F8UTlc6n1oUfPAAAhGdeOAy30c4l67TiNwor+r74xffF\nLxhF4CkWPMWMGXC52i+VuOot0GBys+0vzYrn4gWpzK0vvhrlgpEoKT4+msqx9ZgWXG2+e2QM\nhxFGq758ANobCAhBG6BGZDjevr7qpo+0K4asZLK5pZlAbFGjgwGhcCjytk+/bpcFNbK7vkov\nMyNHCwj5fRekC06iqz3cUw/rhggMSYGPSsK+CyEDwmBGGQKnhndDCBEZDqJbgnSuFiEkFlVJ\nJa6GWd3lap/a4NJQQweEeKIJTzSFGSQAADSbcf5AorPN9ewPWpUdlcJL0gWndCFEvXXMRFkW\njzLfNgwRGJZoMvRMFIuqGzaLOf0vzDaHJ5qsj4wzzh8Yt2dsVy8fgNYGc9mgbZADOkVZAg7E\nmboDUCWeqIxQMUJW9BXbjXP7679IT+iFMaR2qs8KEBJmocj+nbRT/YSeRrro1lKhUkO6Ypeq\nUdWZJAx147l+BcJh0ZakBwCAGKJGdk9etcj6+MSIq9YxkjDO7pecd6d50XAtjQo1NL31x4gQ\nQtL5ENFpPGFmiuhiY6Zl257JTfnhnnhGg6gdvHwA4glmCEGbMS0YDAn926E6a3dlRSr3Gno0\nmmib33teuuhWjzGGZKZm6b+KGUl6Qk92bSA5nn/NUfPdEZIBUCO7a9loQwaEfIP1ooHjMZm+\nT/cH2mwrNt00qN4Dhf3BjDKGPikwDQgAaDnzouHmRcOb/DADbl44zPzbocKhUnbDCeHIRbnC\nI1V4ESfiSWY82Ux0szMTe9PjezWsc2B5cIxxwdUIIUNGiLJAqVvva9brCMF4/UDj9bGMwUw3\nDzbdPDiGHeqlrLs7th3G/OUD0J5BQAjaDKwRbZ9wu1F/qlyqyRuSfr0oMzULM9X/7MLM7KsF\nhOKJSvF4haFPSpgO6REZ3nd2qcfS2Rq50osn16kCrJ/90yePoUZkYCShZl/gdp5RBKleVlvh\nwAV94zBjAACAeMAxclCXpv4pxJPMeBLURgcAxBIsGQVthki1Yla6rUcBGiAwfVwnNx4QKpyo\nL41lnNe/YRt6bKa2qhMh5M8raNhGj7y6iz6Qq7fOE0kKv/OMeojbGbJvmvYVzEiSwwIrqRQv\nL9StMKHwknD4YnBUEBACAAAAACCEYIYQtCUMGXol1fvgDtoDzEhqWwfD7CHkNhdpKe/wFDM1\nonuIrmgDM6m3NpHIrimwPjQ2ZDGJQHuGJAd11gqTCPsuMFP7aF8VjlzUAlRqVI96tYnpMZla\nuMhtK1bzzajEo2WBAhgIIQxpoSPoMMrHviVXeVvSQ8Irc5iZOZHbAQAAAB0LBISgLVkfGS+V\nuBBCRLcQeyFAW1H8wfzaDVeBauqkk5nTv154pmFm9tUCQqnUxR+4QA0JF49RI7prAWG9bYQN\nC07o0WMz3S9vCjxwazF6eJz2Jf1MI9kvDbcxYQYAQPmoN+Vaf7MfzszISfj7nBiOBwAAAGg9\nsGQUtCVqaLpxTj/jnH6Q4aMdkRT9rCBuDx07yS5WqwWPEDLObbRcITWquz4AY9dEWDWqLwgh\nHC3TV3/SZ5RpWH3e0CtZK3ohHCuXK4LzRfqJ6MYqEAIAAAAAXIFghhAAUEe9TYONTaaxawvV\nDC4IIYwx+FcdCdMnZqPRpW7ZtYW2P09GhkbvRpFXdcYYMhAHSrJw8CJ1TTeEkOLm+F8CE32G\nnklEJ2v9R2KIGpvpX3lQPeO2FRvnD0AIIaXODCFklOnwmpdFFk+C21IAAACuRBAQAgDqEE9U\nBk9wrLFKWawuv6jCit4P90TZv1zr57afpsf3bKwBRhLU0K7a6lB+33k1IOTyz2o166kG60VV\ndKiAULrglCsvzRYSWNyqeIG2YnlgNDM5K3K7MCgCo4jIzXQUSda+Pw2ZiS16dgAAACCOICAE\nANQhHCvXjg1ZKZiRbNhGKnHxe883+yn8eUfDBIQIIWpEhhYQCpe2EfLbTmsNGm4gDFwf2R0R\nmPq5nN9+GkkKIrA6GwgHdNZnPQUgpNRN9zapvcKJVTd+JJ6sRAhRQ7pafn9t64wLAAAAiD3Y\nQwgAqIPfriv0N6hzyDb6dDLNwP1wUp+3piFqZDBhKX/gghrgcTtOq1cwktBnENXDrDQ1uKt6\nLLtY4XApQkiA9aKglXle36pGg3iiKeEfc8OsiAYAAADaG5ghBAAEyVVeLe5CCFHDQsVdSp16\n9KYbr7I9kxuxZ37H6eq7/hfogBW4H08ys/s21pjsm4ZZabWmheLhxZMVmImSztUGvjqka8h5\nSxU9NlObveS2FZODuvD7gxlloAIhiDl+91nvv39GCCEMJfx9TmOrrAEAAID2Ce5iAgCCfB/v\n0/ZB4VaanhJiI5ZwrFw8VaWdMrMazS+qR43I0Of58OeFnWMkMH0syu+7wG0Lzls2tl408ERj\nMrVjbkux4uXFExWBcwNOXt0lmtECECXZzTmf+E49Nt82DKagAQAAXHZghhDEifPP37f2U9if\nm9HaT9GxiadrvB8Ec8Mw1w3AmBC/IvTrRfFUS7Q5WgicmdrHt+KAesZtOy3X+HGHsbHm9MgM\nbuNJ9Zjfd0G/xJQandnIgxBCiMxJxRNNcrUPISQcLuU2FyH5UiqaQV0wptGpRQCawf3sD9JF\nN0LI0MNheWhsWw8HAAAAaDIICEGc+L853NpPAQFhS8jVvtoHv9FXkjD/dmiIdpLi19ejn9W3\nsXr0DTEzcrSAEEkyu67Q9OvBjTXWz7QIP5+TPYHSiHiiicxOCfc0OEaPyfR/ewQhhBTk+deO\nYJ8jYfYGxBK7tjDw44Bj9udnhryBAgAAALRzsGQUAICEgvLq2z5Xs2KoLH8YR6TbG7bk88/q\nC74zsxrdB9gQNTQdTzFrp+Er1Bt6J2tLTKUyj+INBITUqO4IjxCC0mODU4j61a1Qkh7EkFzr\ndz29Xj023TiIHASrkQEAAFyWICAE4Aomyvzus84la6tu/K9YpA+cuplvGRLyEfp0MoYeDrJv\nWhOejsCY3GztjN93XipxNdoYx9Tyg/XQo3pEfB5qVA/UIGbEaAN5VeikqQA0g/ftnbKLRQjh\nVtry4Ji2Hg4AAADQTLC+BbQN3MbQU7PIq7pg0a43BM3heW2r7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Cv60at+PEH3TLHPNzF7z3bc95XrBjTr2HqKWpLr/S0AAD9ByigIE8Hs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\nhFuYW5lb287KL8qm8O4UdKw/m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EX7A6Ly/N+/XHo8+lBACcXQiEAAYL7wMLxazT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\nAAAALIpACAAAAAAWRSAEAAAAAIv6/yqDkge693BZAAAAAElFTkSuQmCC",
      "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 >=1]\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=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Carcinoma\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','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 >=1]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Carcinoma\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_genecloud_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=28, repr.plot.height=28)\n",
    "set.seed(42)\n",
    "\n",
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "geneCloud(input = dfx, top=total_genes)\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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XV1dZMmTTr99NM/85nPFB7OUIx/+qd/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\n8qZVAAAAAAAA7dLMExwAAAAAAABX8h+oQU82lJxwlQAAAABJRU5ErkJggg==",
      "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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Carcinoma\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_drugInteractions_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oncogenic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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: Cell_Cycle 15                0                 0\n",
      " 2:      Hippo 38                0                 0\n",
      " 3:        MYC 13                0                 0\n",
      " 4:      NOTCH 71                0                 0\n",
      " 5:       NRF2  3                0                 0\n",
      " 6:       PI3K 29                0                 0\n",
      " 7:    RTK-RAS 85                0                 0\n",
      " 8:   TGF-Beta  7                0                 0\n",
      " 9:       TP53  6                0                 0\n",
      "10:        WNT 68                0                 0\n"
     ]
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "op = OncogenicPathways(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Carcinoma\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Carcinoma_OncogenicPathway_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "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('../Suggesting_Variants/plots/Suggesting_','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": 66,
   "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": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=8)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[1], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[2], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=9)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[4], fullPathway = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../Suggesting\\_Variants/maf\\_res/Suggesting\\_TT-Carcinoma'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../Suggesting_Variants/maf_res/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)) )\n",
    "paste0(\"Saving MAF file to : \", out_file)\n",
    "maftools::write.mafSummary(dfx, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../Suggesting\\_Variants/maf\\_res/Suggesting\\_TT-Carcinoma\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_drugInteractions.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../Suggesting_Variants/maf_res/Suggesting_','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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../Suggesting\\_Variants/maf\\_res/Suggesting\\_TT-Carcinoma\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../Suggesting_Variants/maf_res/Suggesting_','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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../Suggesting\\_Variants/maf\\_res/Suggesting\\_TT-Carcinoma\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Carcinoma_clinical_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../Suggesting_Variants/maf_res/Suggesting_','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": 75,
   "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": 76,
   "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"
    }
   ],
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    "sessionInfo()"
   ]
  }
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