notebooks/genexp_NBL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Immune-gene-expression-analysis-in-NBL\" data-toc-modified-id=\"Immune-gene-expression-analysis-in-NBL-1\">Immune gene expression analysis in NBL</a></span></li><li><span><a href=\"#Dependencies\" data-toc-modified-id=\"Dependencies-2\">Dependencies</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-3\">Functions</a></span></li><li><span><a href=\"#Paths\" data-toc-modified-id=\"Paths-4\">Paths</a></span></li><li><span><a href=\"#Main\" data-toc-modified-id=\"Main-5\">Main</a></span><ul class=\"toc-item\"><li><span><a href=\"#Palettes\" data-toc-modified-id=\"Palettes-5.1\">Palettes</a></span></li><li><span><a href=\"#Heatmaps-for-TARGET-NBL\" data-toc-modified-id=\"Heatmaps-for-TARGET-NBL-5.2\">Heatmaps for TARGET NBL</a></span><ul class=\"toc-item\"><li><span><a href=\"#Groups\" data-toc-modified-id=\"Groups-5.2.1\">Groups</a></span></li><li><span><a href=\"#MYCN\" data-toc-modified-id=\"MYCN-5.2.2\">MYCN</a></span></li><li><span><a href=\"#COG-risk\" data-toc-modified-id=\"COG-risk-5.2.3\">COG risk</a></span></li><li><span><a href=\"#clinical-data\" data-toc-modified-id=\"clinical-data-5.2.4\">clinical data</a></span></li><li><span><a href=\"#Immunescore\" data-toc-modified-id=\"Immunescore-5.2.5\">Immunescore</a></span></li><li><span><a href=\"#gene-expression\" data-toc-modified-id=\"gene-expression-5.2.6\">gene expression</a></span></li></ul></li><li><span><a href=\"#KF_NBL-dataset\" data-toc-modified-id=\"KF_NBL-dataset-5.3\">KF_NBL dataset</a></span><ul class=\"toc-item\"><li><span><a href=\"#MYC\" data-toc-modified-id=\"MYC-5.3.1\">MYC</a></span></li><li><span><a href=\"#Gene-expression\" data-toc-modified-id=\"Gene-expression-5.3.2\">Gene expression</a></span></li><li><span><a href=\"#Immunescore\" data-toc-modified-id=\"Immunescore-5.3.3\">Immunescore</a></span></li><li><span><a href=\"#clinical\" data-toc-modified-id=\"clinical-5.3.4\">clinical</a></span></li></ul></li></ul></li><li><span><a href=\"#Related-to-numbers-in-text\" data-toc-modified-id=\"Related-to-numbers-in-text-6\">Related to numbers in text</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Immune gene expression analysis in NBL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "========================================\n",
      "circlize version 0.4.9\n",
      "CRAN page: https://cran.r-project.org/package=circlize\n",
      "Github page: https://github.com/jokergoo/circlize\n",
      "Documentation: https://jokergoo.github.io/circlize_book/book/\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. circlize implements and enhances circular visualization\n",
      "  in R. Bioinformatics 2014.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(circlize))\n",
      "========================================\n",
      "\n",
      "\n",
      "Attaching package: ‘gridExtra’\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    combine\n",
      "\n",
      "Loading required package: grid\n",
      "========================================\n",
      "ComplexHeatmap version 1.99.5\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "  genomic data. Bioinformatics 2016.\n",
      "========================================\n",
      "\n",
      "This version is a major update of the package. The major new features are:\n",
      "\n",
      "1. Support splitting heatmaps by columns.\n",
      "2. Support concatenating heatmaps/annotations vertically.\n",
      "3. Provide more types of heatmap annotations.\n",
      "4. Support UpSet plot.\n",
      "\n",
      "Note this version is not 100% compatible with the older versions (< 1.99.0).\n",
      "Please check by `vignette('difference_to_old_versions', package = 'ComplexHeatmap')`.\n",
      "\n",
      "Above messages will be removed in the future.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "library(dplyr)\n",
    "library(circlize)\n",
    "library(ggbeeswarm)\n",
    "library(gridExtra)\n",
    "library(reshape2)\n",
    "library(ComplexHeatmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "clinical_hm <- function(cohort_vector, estimate_manifest){\n",
    "    \n",
    "    clin_matrix <- estimate_manifest[,c(\"sample_id\", \"gender\",\n",
    "                                        \"race\", \"age_class\")]\n",
    "    rownames(clin_matrix) <- clin_matrix$sample_id\n",
    "    clin_matrix$sample_id <- NULL\n",
    "    clin_matrix_cases <- clin_matrix[rownames(clin_matrix) %in% names(cohort_vector),]\n",
    "    # This is to match the order in matrix to the cohorts\n",
    "    clin_matrix_cases <- clin_matrix_cases[names(cohort_vector),]    \n",
    "\n",
    "    clin_matrix_cases <- as.matrix(clin_matrix_cases)\n",
    "    ht_clinical = Heatmap(clin_matrix_cases,\n",
    "                         #titles and names\n",
    "                         name = \"Clinical\",\n",
    "                         show_row_names = FALSE,\n",
    "                         show_column_names = TRUE,    \n",
    "                         #clusters\n",
    "                         cluster_columns = FALSE,\n",
    "                         cluster_rows = FALSE,\n",
    "                         #aesthestics\n",
    "                         col = c(agepal, genderpal, racepal),\n",
    "                         column_names_gp = gpar(fontsize = 22),\n",
    "                         width = unit(3, \"cm\"),\n",
    "                         column_title_gp = gpar(fontsize = 42),\n",
    "                         row_title = NULL)\n",
    "    return(ht_clinical)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_hm <- function(cohort_vector, estimate_manifest){\n",
    "\n",
    "    immune <- estimate_manifest$percread\n",
    "    names(immune) <- estimate_manifest$sample_id\n",
    "    immune_cases <- immune[names(immune) %in% names(cohort_vector)]\n",
    "    # This is to match the order in matrix to the cohorts\n",
    "    immune_cases <- immune_cases[names(cohort_vector)]    \n",
    " \n",
    "    col_fun_immun = colorRamp2(c(4,30), \n",
    "                               c(\"#ccebc5\", \"#084081\"))   \n",
    "    \n",
    "    ht_immune = Heatmap(immune_cases,\n",
    "                         #titles and names\n",
    "                         name = \"% immune reads\",\n",
    "                         show_row_names = TRUE,\n",
    "                         show_column_names = TRUE,    \n",
    "                         #clusters\n",
    "                         cluster_columns = FALSE,\n",
    "                         cluster_rows = FALSE,\n",
    "                         #aesthestics\n",
    "                         col = col_fun_immun,\n",
    "                         column_names_gp = gpar(fontsize = 28),\n",
    "                         width = unit(1, \"cm\"),\n",
    "                         column_title_gp = gpar(fontsize = 42),\n",
    "                         row_title = NULL)\n",
    "    \n",
    "    \n",
    "    return(ht_immune)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "geneset_hm_z <- function(gene_mat, cohort_vector, estimate_manifest, geneset){\n",
    "  \n",
    "    message(\"number of genes in geneset found in matrix:\")\n",
    "    print(length(rownames(gene_mat)[rownames(gene_mat) %in% geneset]) )\n",
    "    message(\"genes not found in matrix:\")\n",
    "    print(geneset[!geneset %in% rownames(gene_mat)])    \n",
    "    #keep only genes found in matrix\n",
    "    geneset <- geneset[geneset %in% rownames(gene_mat)]\n",
    "    \n",
    "    subst_exp <- gene_mat[geneset,]\n",
    "\n",
    " \n",
    "    ## To transpose and order exp data based on geneset\n",
    "    subst_exp <- t(subst_exp)\n",
    "    ## z score\n",
    "    subst_exp_z <- scale(subst_exp)\n",
    "\n",
    "    subst_exp_cases <- subst_exp_z[rownames(subst_exp_z) %in% names(cohort_vector),]\n",
    "    # This is to match the order in matrix to the cohorts\n",
    "    subst_exp_cases <- subst_exp_cases[names(cohort_vector),]\n",
    "    subst_exp_cases <- as.matrix(subst_exp_cases)\n",
    "    gene_hm = Heatmap(subst_exp_cases,\n",
    "                         #titles and names   \n",
    "                      name = \"Gene (z-score)\",   \n",
    "                      show_row_names = FALSE,\n",
    "                      show_column_names = TRUE,     \n",
    "                      #clusters and orders  \n",
    "                      cluster_columns = FALSE,\n",
    "                      cluster_rows = TRUE,\n",
    "                      show_column_dend = TRUE,\n",
    "                      row_dend_width = unit(8, \"cm\"),\n",
    "                      column_order = geneset, \n",
    "                      #aesthestics\n",
    "                      # col = col_fun_genes,\n",
    "                      column_names_gp = gpar(fontsize = 28),\n",
    "                      row_names_gp = gpar(fontsize = 2),\n",
    "                      width = unit(length(geneset), \"cm\"),\n",
    "                      column_title_gp = gpar(fontsize = 42),\n",
    "                      column_title = NULL,\n",
    "                      row_title = NULL\n",
    ")\n",
    "\n",
    "    return(gene_hm)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "geneset_hm <- function(gene_mat, cohort_vector, estimate_manifest, geneset){\n",
    "    subst_exp <- gene_mat[which(rownames(gene_mat) %in% geneset),]\n",
    "   \n",
    "    ## To order exp data based on geneset\n",
    "    subst_exp <- subst_exp[geneset,]\n",
    "    subst_exp <- log2(subst_exp + 1)   \n",
    "\n",
    "    subst_exp_cases <- subst_exp[, colnames(subst_exp) %in% names(cohort_vector)]\n",
    "    # This is to match the order in matrix to the cohorts\n",
    "    subst_exp_cases <- subst_exp_cases[,names(cohort_vector)]\n",
    "    subst_exp_cases <- as.matrix(subst_exp_cases)\n",
    "    gene_hm = Heatmap(t(subst_exp_cases),\n",
    "                         #titles and names   \n",
    "                      name = \"Gene expression (log2)\",   \n",
    "                      show_row_names = FALSE,\n",
    "                      show_column_names = TRUE,     \n",
    "                      #clusters and orders  \n",
    "                      cluster_columns = FALSE,\n",
    "                      cluster_rows = TRUE,\n",
    "                      show_column_dend = FALSE,\n",
    "                      row_dend_width = unit(8, \"cm\"),\n",
    "                      column_order = geneset, \n",
    "                      #aesthestics\n",
    "                      # col = col_fun_genes,\n",
    "                      column_names_gp = gpar(fontsize = 28),\n",
    "                      row_names_gp = gpar(fontsize = 2),\n",
    "                      width = unit(length(geneset), \"cm\"),\n",
    "                      column_title_gp = gpar(fontsize = 42),\n",
    "                      column_title = NULL,\n",
    "                      row_title = NULL\n",
    ")\n",
    "\n",
    "    return(gene_hm)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "manifestpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Manifests/\"\n",
    "datapath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Data/\"\n",
    "plotpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Plots/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(paste0(datapath,\"exp_mat/tpm_matrix_ped_final.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>30594</li>\n",
       "\t<li>1177</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 30594\n",
       "\\item 1177\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 30594\n",
       "2. 1177\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 30594  1177"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(tpm_matrix_ped_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"ESTIMATE/estimate_manifest_primary_clean_final.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_manifest_primary_clean$percread <- 8.0947988*exp(estimate_manifest_primary_clean$ImmuneScore*0.0006267)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " CBTTC   DKFZ KF_NBL   PNOC TARGET   TCGA \n",
       "   591    196    209     32    149   2452 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(estimate_manifest_primary_clean$group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cut at -1000 Immunescore subset to NBL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_manifest_primary_cutoff <- estimate_manifest_primary_clean[estimate_manifest_primary_clean$ImmuneScore > -1000,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL <- estimate_manifest_primary_cutoff[estimate_manifest_primary_cutoff$cohort == \"NBL\",]\n",
    "NBL <- NBL[NBL$group == \"TARGET\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "TARGET \n",
       "   122 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(NBL$group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 22</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>aliquot_id</th><th scope=col>StromalScore</th><th scope=col>ImmuneScore</th><th scope=col>ESTIMATEScore</th><th scope=col>name</th><th scope=col>case_id</th><th scope=col>sample_id</th><th scope=col>gender</th><th scope=col>ethnicity</th><th scope=col>race</th><th scope=col>⋯</th><th scope=col>days_to_death</th><th scope=col>days_to_progress</th><th scope=col>recurrence</th><th scope=col>disease_type</th><th scope=col>primary_site</th><th scope=col>group</th><th scope=col>cohort</th><th scope=col>sample_type</th><th scope=col>age_class</th><th scope=col>percread</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>TARGET-30-PAIFXV-01A-01R</td><td>-537.97880</td><td> 483.8556</td><td>  -54.1232</td><td>af82838c-9071-4dd7-8766-b818cf61cf82.bam</td><td>TARGET-30-PAIFXV</td><td>TARGET-30-PAIFXV-01A</td><td>Female</td><td>Unknown               </td><td>White  </td><td>⋯</td><td>2075</td><td>1675</td><td>Yes</td><td>Neuroblastoma                </td><td>Abdomen        </td><td>TARGET</td><td>NBL</td><td>Primary Tumor</td><td>Children</td><td>10.962211</td></tr>\n",
       "\t<tr><td>TARGET-30-PAIPGU-01A-01R</td><td>-319.22630</td><td>1240.6492</td><td>  921.4229</td><td>b1d954e7-21a0-441e-a9c7-47e6a6454625.bam</td><td>TARGET-30-PAIPGU</td><td>TARGET-30-PAIPGU-01A</td><td>Female</td><td>Unknown               </td><td>White  </td><td>⋯</td><td>1976</td><td>1976</td><td>No </td><td>Neuroblastoma                </td><td>Adrenal gland  </td><td>TARGET</td><td>NBL</td><td>Primary Tumor</td><td>NA      </td><td>17.614749</td></tr>\n",
       "\t<tr><td>TARGET-30-PAISNS-01A-01R</td><td>-899.30090</td><td>-128.2337</td><td>-1027.5347</td><td>7e145f1d-1807-4615-95b5-2684ccbcb348.bam</td><td>TARGET-30-PAISNS</td><td>TARGET-30-PAISNS-01A</td><td>Male  </td><td>Unknown               </td><td>White  </td><td>⋯</td><td>2829</td><td>2829</td><td>No </td><td>Neuroblastoma                </td><td>Abdomen        </td><td>TARGET</td><td>NBL</td><td>Primary Tumor</td><td>NA      </td><td> 7.469721</td></tr>\n",
       "\t<tr><td>TARGET-30-PAITCI-01A-01R</td><td>-327.26820</td><td> 200.6735</td><td> -126.5947</td><td>9704403e-aaf9-4a97-99bc-b2a9a9861767.bam</td><td>TARGET-30-PAITCI</td><td>TARGET-30-PAITCI-01A</td><td>Male  </td><td>Unknown               </td><td>White  </td><td>⋯</td><td> 333</td><td> 201</td><td>Yes</td><td>Neuroblastoma                </td><td>Adrenal gland  </td><td>TARGET</td><td>NBL</td><td>Primary Tumor</td><td>Infants </td><td> 9.179602</td></tr>\n",
       "\t<tr><td>TARGET-30-PAIVHE-01A-01R</td><td> -15.87499</td><td>1033.6469</td><td> 1017.7719</td><td>d8d30bd7-608d-4cbd-ba31-3f1fa6fefacf.bam</td><td>TARGET-30-PAIVHE</td><td>TARGET-30-PAIVHE-01A</td><td>Male  </td><td>not hispanic or latino</td><td>White  </td><td>⋯</td><td> 768</td><td> 709</td><td>Yes</td><td>Ganglioneuroblastoma, nodular</td><td>Adrenal gland  </td><td>TARGET</td><td>NBL</td><td>Primary Tumor</td><td>Children</td><td>15.471633</td></tr>\n",
       "\t<tr><td>TARGET-30-PAIXIF-01A-01R</td><td> 460.15050</td><td> 978.1672</td><td> 1438.3177</td><td>7c2b0108-1231-4543-a8bf-eb6644e76f41.bam</td><td>TARGET-30-PAIXIF</td><td>TARGET-30-PAIXIF-01A</td><td>Male  </td><td>Unknown               </td><td>Unknown</td><td>⋯</td><td>3122</td><td> 302</td><td>Yes</td><td>Neuroblastoma                </td><td>Retroperitoneum</td><td>TARGET</td><td>NBL</td><td>Primary Tumor</td><td>Children</td><td>14.942943</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 22\n",
       "\\begin{tabular}{r|llllllllllllllllllllll}\n",
       " aliquot\\_id & StromalScore & ImmuneScore & ESTIMATEScore & name & case\\_id & sample\\_id & gender & ethnicity & race & age\\_at\\_diagnosis & vital\\_status & days\\_to\\_death & days\\_to\\_progress & recurrence & disease\\_type & primary\\_site & group & cohort & sample\\_type & age\\_class & percread\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t TARGET-30-PAIFXV-01A-01R & -537.97880 &  483.8556 &   -54.1232 & af82838c-9071-4dd7-8766-b818cf61cf82.bam & TARGET-30-PAIFXV & TARGET-30-PAIFXV-01A & Female & Unknown                & White   & 5 & Deceased & 2075 & 1675 & Yes & Neuroblastoma                 & Abdomen         & TARGET & NBL & Primary Tumor & Children & 10.962211\\\\\n",
       "\t TARGET-30-PAIPGU-01A-01R & -319.22630 & 1240.6492 &   921.4229 & b1d954e7-21a0-441e-a9c7-47e6a6454625.bam & TARGET-30-PAIPGU & TARGET-30-PAIPGU-01A & Female & Unknown                & White   & 2 & Deceased & 1976 & 1976 & No  & Neuroblastoma                 & Adrenal gland   & TARGET & NBL & Primary Tumor & NA       & 17.614749\\\\\n",
       "\t TARGET-30-PAISNS-01A-01R & -899.30090 & -128.2337 & -1027.5347 & 7e145f1d-1807-4615-95b5-2684ccbcb348.bam & TARGET-30-PAISNS & TARGET-30-PAISNS-01A & Male   & Unknown                & White   & 2 & Deceased & 2829 & 2829 & No  & Neuroblastoma                 & Abdomen         & TARGET & NBL & Primary Tumor & NA       &  7.469721\\\\\n",
       "\t TARGET-30-PAITCI-01A-01R & -327.26820 &  200.6735 &  -126.5947 & 9704403e-aaf9-4a97-99bc-b2a9a9861767.bam & TARGET-30-PAITCI & TARGET-30-PAITCI-01A & Male   & Unknown                & White   & 1 & Deceased &  333 &  201 & Yes & Neuroblastoma                 & Adrenal gland   & TARGET & NBL & Primary Tumor & Infants  &  9.179602\\\\\n",
       "\t TARGET-30-PAIVHE-01A-01R &  -15.87499 & 1033.6469 &  1017.7719 & d8d30bd7-608d-4cbd-ba31-3f1fa6fefacf.bam & TARGET-30-PAIVHE & TARGET-30-PAIVHE-01A & Male   & not hispanic or latino & White   & 3 & Deceased &  768 &  709 & Yes & Ganglioneuroblastoma, nodular & Adrenal gland   & TARGET & NBL & Primary Tumor & Children & 15.471633\\\\\n",
       "\t TARGET-30-PAIXIF-01A-01R &  460.15050 &  978.1672 &  1438.3177 & 7c2b0108-1231-4543-a8bf-eb6644e76f41.bam & TARGET-30-PAIXIF & TARGET-30-PAIXIF-01A & Male   & Unknown                & Unknown & 3 & Alive    & 3122 &  302 & Yes & Neuroblastoma                 & Retroperitoneum & TARGET & NBL & Primary Tumor & Children & 14.942943\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 22\n",
       "\n",
       "| aliquot_id &lt;chr&gt; | StromalScore &lt;dbl&gt; | ImmuneScore &lt;dbl&gt; | ESTIMATEScore &lt;dbl&gt; | name &lt;chr&gt; | case_id &lt;chr&gt; | sample_id &lt;chr&gt; | gender &lt;chr&gt; | ethnicity &lt;chr&gt; | race &lt;chr&gt; | ⋯ ⋯ | days_to_death &lt;dbl&gt; | days_to_progress &lt;dbl&gt; | recurrence &lt;chr&gt; | disease_type &lt;chr&gt; | primary_site &lt;chr&gt; | group &lt;chr&gt; | cohort &lt;chr&gt; | sample_type &lt;chr&gt; | age_class &lt;chr&gt; | percread &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TARGET-30-PAIFXV-01A-01R | -537.97880 |  483.8556 |   -54.1232 | af82838c-9071-4dd7-8766-b818cf61cf82.bam | TARGET-30-PAIFXV | TARGET-30-PAIFXV-01A | Female | Unknown                | White   | ⋯ | 2075 | 1675 | Yes | Neuroblastoma                 | Abdomen         | TARGET | NBL | Primary Tumor | Children | 10.962211 |\n",
       "| TARGET-30-PAIPGU-01A-01R | -319.22630 | 1240.6492 |   921.4229 | b1d954e7-21a0-441e-a9c7-47e6a6454625.bam | TARGET-30-PAIPGU | TARGET-30-PAIPGU-01A | Female | Unknown                | White   | ⋯ | 1976 | 1976 | No  | Neuroblastoma                 | Adrenal gland   | TARGET | NBL | Primary Tumor | NA       | 17.614749 |\n",
       "| TARGET-30-PAISNS-01A-01R | -899.30090 | -128.2337 | -1027.5347 | 7e145f1d-1807-4615-95b5-2684ccbcb348.bam | TARGET-30-PAISNS | TARGET-30-PAISNS-01A | Male   | Unknown                | White   | ⋯ | 2829 | 2829 | No  | Neuroblastoma                 | Abdomen         | TARGET | NBL | Primary Tumor | NA       |  7.469721 |\n",
       "| TARGET-30-PAITCI-01A-01R | -327.26820 |  200.6735 |  -126.5947 | 9704403e-aaf9-4a97-99bc-b2a9a9861767.bam | TARGET-30-PAITCI | TARGET-30-PAITCI-01A | Male   | Unknown                | White   | ⋯ |  333 |  201 | Yes | Neuroblastoma                 | Adrenal gland   | TARGET | NBL | Primary Tumor | Infants  |  9.179602 |\n",
       "| TARGET-30-PAIVHE-01A-01R |  -15.87499 | 1033.6469 |  1017.7719 | d8d30bd7-608d-4cbd-ba31-3f1fa6fefacf.bam | TARGET-30-PAIVHE | TARGET-30-PAIVHE-01A | Male   | not hispanic or latino | White   | ⋯ |  768 |  709 | Yes | Ganglioneuroblastoma, nodular | Adrenal gland   | TARGET | NBL | Primary Tumor | Children | 15.471633 |\n",
       "| TARGET-30-PAIXIF-01A-01R |  460.15050 |  978.1672 |  1438.3177 | 7c2b0108-1231-4543-a8bf-eb6644e76f41.bam | TARGET-30-PAIXIF | TARGET-30-PAIXIF-01A | Male   | Unknown                | Unknown | ⋯ | 3122 |  302 | Yes | Neuroblastoma                 | Retroperitoneum | TARGET | NBL | Primary Tumor | Children | 14.942943 |\n",
       "\n"
      ],
      "text/plain": [
       "  aliquot_id               StromalScore ImmuneScore ESTIMATEScore\n",
       "1 TARGET-30-PAIFXV-01A-01R -537.97880    483.8556     -54.1232   \n",
       "2 TARGET-30-PAIPGU-01A-01R -319.22630   1240.6492     921.4229   \n",
       "3 TARGET-30-PAISNS-01A-01R -899.30090   -128.2337   -1027.5347   \n",
       "4 TARGET-30-PAITCI-01A-01R -327.26820    200.6735    -126.5947   \n",
       "5 TARGET-30-PAIVHE-01A-01R  -15.87499   1033.6469    1017.7719   \n",
       "6 TARGET-30-PAIXIF-01A-01R  460.15050    978.1672    1438.3177   \n",
       "  name                                     case_id         \n",
       "1 af82838c-9071-4dd7-8766-b818cf61cf82.bam TARGET-30-PAIFXV\n",
       "2 b1d954e7-21a0-441e-a9c7-47e6a6454625.bam TARGET-30-PAIPGU\n",
       "3 7e145f1d-1807-4615-95b5-2684ccbcb348.bam TARGET-30-PAISNS\n",
       "4 9704403e-aaf9-4a97-99bc-b2a9a9861767.bam TARGET-30-PAITCI\n",
       "5 d8d30bd7-608d-4cbd-ba31-3f1fa6fefacf.bam TARGET-30-PAIVHE\n",
       "6 7c2b0108-1231-4543-a8bf-eb6644e76f41.bam TARGET-30-PAIXIF\n",
       "  sample_id            gender ethnicity              race    ⋯ days_to_death\n",
       "1 TARGET-30-PAIFXV-01A Female Unknown                White   ⋯ 2075         \n",
       "2 TARGET-30-PAIPGU-01A Female Unknown                White   ⋯ 1976         \n",
       "3 TARGET-30-PAISNS-01A Male   Unknown                White   ⋯ 2829         \n",
       "4 TARGET-30-PAITCI-01A Male   Unknown                White   ⋯  333         \n",
       "5 TARGET-30-PAIVHE-01A Male   not hispanic or latino White   ⋯  768         \n",
       "6 TARGET-30-PAIXIF-01A Male   Unknown                Unknown ⋯ 3122         \n",
       "  days_to_progress recurrence disease_type                  primary_site   \n",
       "1 1675             Yes        Neuroblastoma                 Abdomen        \n",
       "2 1976             No         Neuroblastoma                 Adrenal gland  \n",
       "3 2829             No         Neuroblastoma                 Abdomen        \n",
       "4  201             Yes        Neuroblastoma                 Adrenal gland  \n",
       "5  709             Yes        Ganglioneuroblastoma, nodular Adrenal gland  \n",
       "6  302             Yes        Neuroblastoma                 Retroperitoneum\n",
       "  group  cohort sample_type   age_class percread \n",
       "1 TARGET NBL    Primary Tumor Children  10.962211\n",
       "2 TARGET NBL    Primary Tumor NA        17.614749\n",
       "3 TARGET NBL    Primary Tumor NA         7.469721\n",
       "4 TARGET NBL    Primary Tumor Infants    9.179602\n",
       "5 TARGET NBL    Primary Tumor Children  15.471633\n",
       "6 TARGET NBL    Primary Tumor Children  14.942943"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(NBL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get MYCN status and COG risk groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_manifest <- read.table(paste0(manifestpath, \"TARGET_NBL_Discovery_ClinicalData_20170525.txt\"),\n",
    "                          sep = \"\\t\", na.strings = \"\", header = T, stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(NBL_manifest)[colnames(NBL_manifest) == \"TARGET.USI\"] <- \"case_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_manifest_subset <- NBL_manifest[NBL_manifest$case_id %in% NBL$case_id,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>122</li>\n",
       "\t<li>32</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 122\n",
       "\\item 32\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 122\n",
       "2. 32\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 122  32"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(NBL_manifest_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_MYCstatus <- merge(NBL, NBL_manifest_subset[, c(\"case_id\", \"MYCN.status\", \"COG.Risk.Group\")], by = \"case_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>122</li>\n",
       "\t<li>24</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 122\n",
       "\\item 24\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 122\n",
       "2. 24\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 122  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(NBL_MYCstatus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Palettes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cogpal <- c(\"High Risk\" = \"#6a3d9a\",\n",
    "            \"Intermediate Risk\" = \"#a6cee3\",\n",
    "            \"Low Risk\" = \"#fb9a99\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycnpal <- c(\"Amplified\" = \"#e41a1c\",\n",
    "            \"Not Amplified\" = \"#377eb8\",\n",
    "            \"Unknown\" = \"light grey\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouppal <- c(\"TARGET\" = \"#678ba5\",\n",
    "             \"CBTTC\" = \"#e31a1c\",\n",
    "             \"KF_NBL\" = \"#8763ae\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "colpal <- c(\"MB_PDX\" = \"#878787\", #PDX\n",
    "            \"ETMR\" = \"#76afa9\", #ped\n",
    "            \"MNG\" = \"#a6cee3\", \n",
    "            \"MB\" =  \"#1f78b4\",\n",
    "            \"SCHW\" =  \"#678ba5\",\n",
    "            \"ATRT\" =  \"#33a02c\",\n",
    "            \"EPN\" = \"#e31a1c\",\n",
    "            \"pedHGG\" = \"#fdbf6f\",\n",
    "            \"pedLGG\" = \"#8763ae\",\n",
    "            \"NFB\" = \"#cab2d6\",\n",
    "            \"CPH\" = \"#cccc7a\",\n",
    "            \"TT/GN\" = \"#b15928\",\n",
    "            \"CP\" = \"#a9a9a9\",\n",
    "            \"NBL\" = \"#b2df8a\",\n",
    "            \"EWS\" = \"#C65370\",\n",
    "            \n",
    "            \"SKCM\" = \"#7c9c60\", #Adult\n",
    "            \"LGG\" = \"#753a00\",\n",
    "            \"GBM\" = \"#6a3d9a\",\n",
    "            \"OV\" = \"#afaf78\",\n",
    "            \"PRAD\" = \"#a85426\",\n",
    "            \"LUAD\" = \"#ad3837\",\n",
    "            \"COAD\" = \"#726579\",\n",
    "            \"EMPTY\" = \"#FFFFFF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clinical df for clinical heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "clin4hm <- NBL[,c(\"name\", \"sample_id\",\"gender\",\n",
    "                  \"race\", \"age_at_diagnosis\", \"age_class\",\n",
    "                  \"ImmuneScore\",\"cohort\", \"group\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a order_vector to order heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_groups <- factor(clin4hm$group, levels =  'TARGET')\n",
    "names(NBL_groups) <- clin4hm$sample_id\n",
    "NBL_groups <- NBL_groups[order(NBL_groups)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmaps for TARGET NBL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ht_groups = Heatmap(NBL_groups,\n",
    "                         #titles and names\n",
    "                         name = \"Group\",\n",
    "                         show_row_names = FALSE,\n",
    "                         show_column_names = TRUE,    \n",
    "                         #clusters\n",
    "                         cluster_columns = FALSE,\n",
    "                         cluster_rows = FALSE,\n",
    "                         #aesthestics\n",
    "                         col = grouppal,\n",
    "                         na_col = \"#ffffff\",\n",
    "                         column_names_gp = gpar(fontsize = 22),\n",
    "                         width = unit(length(levels(NBL_groups)), \"cm\"),\n",
    "                         column_title_gp = gpar(fontsize = 42),\n",
    "                         row_title = NULL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MYCN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    Amplified Not Amplified       Unknown \n",
       "           17           104             1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(NBL_MYCstatus$MYCN.status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycnstatus <- factor(NBL_MYCstatus$MYCN.status, levels =  c(\"Amplified\", \"Not Amplified\", \"Unknown\"))\n",
    "names(mycnstatus) <- NBL_MYCstatus$sample_id\n",
    "mycnstatus <- mycnstatus[names(NBL_groups)]\n",
    "    \n",
    "ht_mycn = Heatmap(mycnstatus,\n",
    "                         #titles and names\n",
    "                         name = \"MYCN status\",\n",
    "                         show_row_names = FALSE,\n",
    "                         show_column_names = TRUE,    \n",
    "                         #clusters\n",
    "                         cluster_columns = FALSE,\n",
    "                         cluster_rows = FALSE,\n",
    "                         #aesthestics\n",
    "                         col = mycnpal,\n",
    "                         na_col = \"#ffffff\",\n",
    "                         column_names_gp = gpar(fontsize = 22),\n",
    "                         width = unit(length(levels(NBL_groups)), \"cm\"),\n",
    "                         column_title_gp = gpar(fontsize = 42),\n",
    "                         row_title = NULL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COG risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        High Risk Intermediate Risk          Low Risk \n",
       "              102                10                10 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(NBL_MYCstatus$COG.Risk.Group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cogrisk <- factor(NBL_MYCstatus$COG.Risk.Group, levels =  c(\"High Risk\", \"Intermediate Risk\", \"Low Risk\"))\n",
    "names(cogrisk) <- NBL_MYCstatus$sample_id\n",
    "cogrisk <- cogrisk[names(NBL_groups)]\n",
    "    \n",
    "ht_cog = Heatmap(cogrisk,\n",
    "                         #titles and names\n",
    "                         name = \"COG risk group\",\n",
    "                         show_row_names = FALSE,\n",
    "                         show_column_names = TRUE,    \n",
    "                         #clusters\n",
    "                         cluster_columns = FALSE,\n",
    "                         cluster_rows = FALSE,\n",
    "                         #aesthestics\n",
    "                         col = cogpal,\n",
    "                         na_col = \"#ffffff\",\n",
    "                         column_names_gp = gpar(fontsize = 22),\n",
    "                         width = unit(length(levels(NBL_groups)), \"cm\"),\n",
    "                         column_title_gp = gpar(fontsize = 42),\n",
    "                         row_title = NULL)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### clinical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "clinical_NBL <- clinical_hm(NBL_groups, NBL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Immunescore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_NBL <- immune_hm(NBL_groups, NBL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gene expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygensig <- c(\"HAVCR2\", \"LAG3\",\"PDCD1\", \"CTLA4\",\"TIGIT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "geneset_NBL <- geneset_hm(tpm_matrix_ped_final,  NBL_groups, NBL, mygensig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "hm <- geneset_NBL + immune_NBL + ht_mycn + ht_cog + clinical_NBL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K233krWWRWDSNJUxqpVq6hTp0708ccf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\nFE93k7EggAACCCCAAAIIIICAI4H/AlSxPX4GQkK+AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file = paste0(plotpath,\"checkpoint_heatmap_TARGET_NBL.pdf\"),\n",
    "        width = 18, \n",
    "        height = 15,\n",
    "        useDingbats = FALSE)\n",
    "    draw(hm,\n",
    "         ht_gap = unit(1, \"cm\"),\n",
    "         padding = unit(c(10, 10, 2, 10), \"mm\"))\n",
    "    dev.off()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KF_NBL dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MYC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "46 samples mycn amp according to pedcbio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycn_kf <- c(\n",
    "\"GMKF-30-PASCDX03-01B-99R\",\n",
    "\"GMKF-30-PASCWD03-01B-99R\",\n",
    "\"GMKF-30-PASFKX03-01B-99R\",\n",
    "\"GMKF-30-PASFNY03-01B-99R\",\n",
    "\"GMKF-30-PASHEL03-01B-99R\",\n",
    "\"GMKF-30-PASIIU03-01B-99R\",\n",
    "\"GMKF-30-PASIUJ03-01B-99R\",\n",
    "\"GMKF-30-PASJRT03-01B-99R\",\n",
    "\"GMKF-30-PASKIS03-01B-99R\",\n",
    "\"GMKF-30-PASMET03-01B-99R\",\n",
    "\"GMKF-30-PASNUI03-01B-99R\",\n",
    "\"GMKF-30-PASPGB03-01B-99R\",\n",
    "\"GMKF-30-PASREV03-01B-99R\",\n",
    "\"GMKF-30-PASUMG03-01B-99R\",\n",
    "\"GMKF-30-PASUTC03-01B-99R\",\n",
    "\"GMKF-30-PASWXG03-01B-99R\",\n",
    "\"GMKF-30-PASWYR03-01B-99R\",\n",
    "\"GMKF-30-PASXIE03-01B-99R\",\n",
    "\"GMKF-30-PASXRG03-01B-99R\",\n",
    "\"GMKF-30-PASXSJ03-01B-99R\",\n",
    "\"GMKF-30-PASYIP03-01B-99R\",\n",
    "\"GMKF-30-PASZYY03-01B-99R\",\n",
    "\"GMKF-30-PATIYD03-01B-99R\",\n",
    "\"GMKF-30-PATKPD03-01B-99R\",\n",
    "\"GMKF-30-PATMZJ03-01B-99R\",\n",
    "\"GMKF-30-PATNZJ03-01B-99R\",\n",
    "\"GMKF-30-PATRVB03-01B-99R\",\n",
    "\"GMKF-30-PATSVA03-01B-99R\",\n",
    "\"GMKF-30-PATTPC03-01B-99R\",\n",
    "\"GMKF-30-PATWED03-01B-99R\",\n",
    "\"GMKF-30-PATWGR03-01B-99R\",\n",
    "\"GMKF-30-PATXKG03-01B-99R\",\n",
    "\"GMKF-30-PATYAU03-01B-99R\",\n",
    "\"GMKF-30-PATZRF03-01B-99R\",\n",
    "\"GMKF-30-PAUGJI03-01B-99R\",\n",
    "\"GMKF-30-PAUHSJ03-01B-99R\",\n",
    "\"GMKF-30-PAUIHH03-01B-99R\",\n",
    "\"GMKF-30-PAUJRW03-01B-99R\",\n",
    "\"GMKF-30-PAUKLK03-01B-99R\",\n",
    "\"GMKF-30-PAUMJN03-01B-99R\",\n",
    "\"GMKF-30-PAUNTY03-01B-99R\",\n",
    "\"GMKF-30-PAUTFT03-01B-99R\",\n",
    "\"GMKF-30-PAUTLP03-01B-99R\",\n",
    "\"GMKF-30-PAUUHH03-01B-99R\",\n",
    "\"GMKF-30-PAUUHY03-01B-99R\",\n",
    "\"GMKF-30-PAVFZW03-01B-99R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL <- estimate_manifest_primary_cutoff[estimate_manifest_primary_cutoff$cohort == \"NBL\",]\n",
    "NBL_KF <- NBL[NBL$group == \"KF_NBL\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "KF_NBL \n",
       "   163 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(NBL_KF$group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_KF_groups <- factor(NBL_KF$group, levels =  'KF_NBL')\n",
    "names(NBL_KF_groups) <- NBL_KF$sample_id\n",
    "NBL_KF_groups <- NBL_KF_groups[order(NBL_KF_groups)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_KF$MYCN.status <- NA\n",
    "NBL_KF$MYCN.status[ NBL_KF$sample_id %in% mycn_kf] <- \"Amplified\"\n",
    "NBL_KF$MYCN.status[!NBL_KF$sample_id %in% mycn_kf] <- \"Not Amplified\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    Amplified Not Amplified \n",
       "           11           152 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(NBL_KF$MYCN.status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycnstatus_kf <- factor(NBL_KF$MYCN.status, levels =  c(\"Amplified\", \"Not Amplified\"))\n",
    "names(mycnstatus_kf) <- NBL_KF$sample_id\n",
    "mycnstatus_kf <- mycnstatus_kf[names(NBL_KF_groups)]\n",
    "    \n",
    "ht_mycn_kf = Heatmap(mycnstatus_kf,\n",
    "                         #titles and names\n",
    "                         name = \"MYCN status\",\n",
    "                         show_row_names = FALSE,\n",
    "                         show_column_names = TRUE,    \n",
    "                         #clusters\n",
    "                         cluster_columns = FALSE,\n",
    "                         cluster_rows = FALSE,\n",
    "                         #aesthestics\n",
    "                         col = mycnpal,\n",
    "                         na_col = \"#ffffff\",\n",
    "                         column_names_gp = gpar(fontsize = 22),\n",
    "                         width = unit(length(levels(NBL_groups)), \"cm\"),\n",
    "                         column_title_gp = gpar(fontsize = 42),\n",
    "                         row_title = NULL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ECAAAECBAgQIFCAgIBUQCcogQABAgQI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\nyitxqo1geAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ht_mycn_kf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gene expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "geneset_hm_KF <- function(gene_mat, cohort_vector, estimate_manifest, geneset){\n",
    "    subst_exp <- gene_mat[which(rownames(gene_mat) %in% geneset),]\n",
    "   \n",
    "    ## To order exp data based on geneset\n",
    "    subst_exp <- subst_exp[geneset,]\n",
    "    subst_exp <- log2(subst_exp + 1)   \n",
    "\n",
    "    subst_exp_cases <- subst_exp[, colnames(subst_exp) %in% names(cohort_vector)]\n",
    "    # This is to match the order in matrix to the cohorts\n",
    "    subst_exp_cases <- subst_exp_cases[,names(cohort_vector)]\n",
    "    subst_exp_cases <- as.matrix(subst_exp_cases)\n",
    "    gene_hm = Heatmap(t(subst_exp_cases),\n",
    "                         #titles and names   \n",
    "                      name = \"Gene expression (log2)\",   \n",
    "                      show_row_names = TRUE,\n",
    "                      show_column_names = TRUE,     \n",
    "                      #clusters and orders  \n",
    "                      cluster_columns = FALSE,\n",
    "                      cluster_rows = TRUE,\n",
    "                      show_column_dend = FALSE,\n",
    "                      row_dend_width = unit(8, \"cm\"),\n",
    "                      column_order = geneset, \n",
    "                      #aesthestics\n",
    "                      # col = col_fun_genes,\n",
    "                      column_names_gp = gpar(fontsize = 28),\n",
    "                      row_names_gp = gpar(fontsize = 2),\n",
    "                      width = unit(length(geneset), \"cm\"),\n",
    "                      column_title_gp = gpar(fontsize = 42),\n",
    "                      column_title = NULL,\n",
    "                      row_title = NULL\n",
    ")\n",
    "\n",
    "    return(gene_hm)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygensig <- c(\"HAVCR2\", \"LAG3\",\"PDCD1\", \"CTLA4\",\"TIGIT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "geneset_KF_NBL <- geneset_hm(tpm_matrix_ped_final,  NBL_KF_groups, NBL_KF, mygensig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AABBGwKkCDZhKIYAgjEJqBnIs4//3wZ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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geneset_KF_NBL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Immunescore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_KF <- NBL_KF$percread\n",
    "names(immune_KF) <- NBL_KF$sample_id\n",
    "immune_cases_KF <- immune_KF[names(immune_KF) %in% names(NBL_KF_groups)]\n",
    "    # This is to match the order in matrix to the cohorts\n",
    "immune_cases_KF <- immune_cases_KF[names(NBL_KF_groups)]    \n",
    " \n",
    "col_fun_immun = colorRamp2(c(4,30), \n",
    "                           c(\"#ccebc5\", \"#084081\"))   \n",
    "    \n",
    "ht_immune_KF = Heatmap(immune_cases_KF,\n",
    "                         #titles and names\n",
    "                         name = \"% immune reads\",\n",
    "                         show_row_names = TRUE,\n",
    "                         show_column_names = TRUE,    \n",
    "                         #clusters\n",
    "                         cluster_columns = FALSE,\n",
    "                         cluster_rows = FALSE,\n",
    "                         #aesthestics\n",
    "                         col = col_fun_immun,\n",
    "                         column_names_gp = gpar(fontsize = 28),\n",
    "                         width = unit(1, \"cm\"),\n",
    "                         column_title_gp = gpar(fontsize = 42),\n",
    "                         row_title = NULL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### clinical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "manifest file is from kids first drc as of sep 12 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "KFNBL_manifest <- read.table(paste0(manifestpath, \"clinicalDataFileRepo_KF_NBL.txt\"),\n",
    "                          sep = \"\\t\", na.strings = \"\", header = T, stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 14</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Participant.ID</th><th scope=col>External.Id</th><th scope=col>Family.Id</th><th scope=col>Proband</th><th scope=col>Study</th><th scope=col>Family.Composition</th><th scope=col>Diagnosis.Category</th><th scope=col>Gender</th><th scope=col>Race</th><th scope=col>Ethnicity</th><th scope=col>Vital.Status</th><th scope=col>Age.at.the.Last.Vital.Status..Days.</th><th scope=col>Is.Outcome.Disease.Related</th><th scope=col>Affected.Status</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>PT_1X9YQF9W</td><td>GMKF-30-PASMUB03</td><td>FM_04BZFE4G</td><td>Yes</td><td>Kids First: Neuroblastoma</td><td>trio</td><td>Cancer</td><td>Male</td><td>White                    </td><td>Reported Unknown      </td><td>Alive   </td><td>2925</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><td>PT_MK375DCF</td><td>GMKF-30-PARYAG03</td><td>FM_0FKGXBZB</td><td>Yes</td><td>Kids First: Neuroblastoma</td><td>trio</td><td>Cancer</td><td>Male</td><td>White                    </td><td>Not Hispanic or Latino</td><td>Alive   </td><td>2366</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><td>PT_XDPN4357</td><td>GMKF-30-PASFXC03</td><td>FM_0NTB2D12</td><td>Yes</td><td>Kids First: Neuroblastoma</td><td>trio</td><td>Cancer</td><td>Male</td><td>Black or African American</td><td>Not Hispanic or Latino</td><td>Alive   </td><td>3005</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><td>PT_K5709E5B</td><td>GMKF-30-PAUNTY03</td><td>FM_0S71PRSG</td><td>Yes</td><td>Kids First: Neuroblastoma</td><td>trio</td><td>Cancer</td><td>Male</td><td>Asian                    </td><td>Not Hispanic or Latino</td><td>Deceased</td><td>1423</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><td>PT_4FTZAAC4</td><td>GMKF-30-PATLSY03</td><td>FM_10V2K6AD</td><td>Yes</td><td>Kids First: Neuroblastoma</td><td>trio</td><td>Cancer</td><td>Male</td><td>White                    </td><td>Not Hispanic or Latino</td><td>Alive   </td><td>4235</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><td>PT_BZZY1BM4</td><td>GMKF-30-PASEWZ03</td><td>FM_11H8JVMW</td><td>Yes</td><td>Kids First: Neuroblastoma</td><td>trio</td><td>Cancer</td><td>Male</td><td>White                    </td><td>Not Hispanic or Latino</td><td>Alive   </td><td>3320</td><td>NA</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 14\n",
       "\\begin{tabular}{r|llllllllllllll}\n",
       " Participant.ID & External.Id & Family.Id & Proband & Study & Family.Composition & Diagnosis.Category & Gender & Race & Ethnicity & Vital.Status & Age.at.the.Last.Vital.Status..Days. & Is.Outcome.Disease.Related & Affected.Status\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <lgl> & <lgl>\\\\\n",
       "\\hline\n",
       "\t PT\\_1X9YQF9W & GMKF-30-PASMUB03 & FM\\_04BZFE4G & Yes & Kids First: Neuroblastoma & trio & Cancer & Male & White                     & Reported Unknown       & Alive    & 2925 & NA & NA\\\\\n",
       "\t PT\\_MK375DCF & GMKF-30-PARYAG03 & FM\\_0FKGXBZB & Yes & Kids First: Neuroblastoma & trio & Cancer & Male & White                     & Not Hispanic or Latino & Alive    & 2366 & NA & NA\\\\\n",
       "\t PT\\_XDPN4357 & GMKF-30-PASFXC03 & FM\\_0NTB2D12 & Yes & Kids First: Neuroblastoma & trio & Cancer & Male & Black or African American & Not Hispanic or Latino & Alive    & 3005 & NA & NA\\\\\n",
       "\t PT\\_K5709E5B & GMKF-30-PAUNTY03 & FM\\_0S71PRSG & Yes & Kids First: Neuroblastoma & trio & Cancer & Male & Asian                     & Not Hispanic or Latino & Deceased & 1423 & NA & NA\\\\\n",
       "\t PT\\_4FTZAAC4 & GMKF-30-PATLSY03 & FM\\_10V2K6AD & Yes & Kids First: Neuroblastoma & trio & Cancer & Male & White                     & Not Hispanic or Latino & Alive    & 4235 & NA & NA\\\\\n",
       "\t PT\\_BZZY1BM4 & GMKF-30-PASEWZ03 & FM\\_11H8JVMW & Yes & Kids First: Neuroblastoma & trio & Cancer & Male & White                     & Not Hispanic or Latino & Alive    & 3320 & NA & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 14\n",
       "\n",
       "| Participant.ID &lt;chr&gt; | External.Id &lt;chr&gt; | Family.Id &lt;chr&gt; | Proband &lt;chr&gt; | Study &lt;chr&gt; | Family.Composition &lt;chr&gt; | Diagnosis.Category &lt;chr&gt; | Gender &lt;chr&gt; | Race &lt;chr&gt; | Ethnicity &lt;chr&gt; | Vital.Status &lt;chr&gt; | Age.at.the.Last.Vital.Status..Days. &lt;int&gt; | Is.Outcome.Disease.Related &lt;lgl&gt; | Affected.Status &lt;lgl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| PT_1X9YQF9W | GMKF-30-PASMUB03 | FM_04BZFE4G | Yes | Kids First: Neuroblastoma | trio | Cancer | Male | White                     | Reported Unknown       | Alive    | 2925 | NA | NA |\n",
       "| PT_MK375DCF | GMKF-30-PARYAG03 | FM_0FKGXBZB | Yes | Kids First: Neuroblastoma | trio | Cancer | Male | White                     | Not Hispanic or Latino | Alive    | 2366 | NA | NA |\n",
       "| PT_XDPN4357 | GMKF-30-PASFXC03 | FM_0NTB2D12 | Yes | Kids First: Neuroblastoma | trio | Cancer | Male | Black or African American | Not Hispanic or Latino | Alive    | 3005 | NA | NA |\n",
       "| PT_K5709E5B | GMKF-30-PAUNTY03 | FM_0S71PRSG | Yes | Kids First: Neuroblastoma | trio | Cancer | Male | Asian                     | Not Hispanic or Latino | Deceased | 1423 | NA | NA |\n",
       "| PT_4FTZAAC4 | GMKF-30-PATLSY03 | FM_10V2K6AD | Yes | Kids First: Neuroblastoma | trio | Cancer | Male | White                     | Not Hispanic or Latino | Alive    | 4235 | NA | NA |\n",
       "| PT_BZZY1BM4 | GMKF-30-PASEWZ03 | FM_11H8JVMW | Yes | Kids First: Neuroblastoma | trio | Cancer | Male | White                     | Not Hispanic or Latino | Alive    | 3320 | NA | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  Participant.ID External.Id      Family.Id   Proband Study                    \n",
       "1 PT_1X9YQF9W    GMKF-30-PASMUB03 FM_04BZFE4G Yes     Kids First: Neuroblastoma\n",
       "2 PT_MK375DCF    GMKF-30-PARYAG03 FM_0FKGXBZB Yes     Kids First: Neuroblastoma\n",
       "3 PT_XDPN4357    GMKF-30-PASFXC03 FM_0NTB2D12 Yes     Kids First: Neuroblastoma\n",
       "4 PT_K5709E5B    GMKF-30-PAUNTY03 FM_0S71PRSG Yes     Kids First: Neuroblastoma\n",
       "5 PT_4FTZAAC4    GMKF-30-PATLSY03 FM_10V2K6AD Yes     Kids First: Neuroblastoma\n",
       "6 PT_BZZY1BM4    GMKF-30-PASEWZ03 FM_11H8JVMW Yes     Kids First: Neuroblastoma\n",
       "  Family.Composition Diagnosis.Category Gender Race                     \n",
       "1 trio               Cancer             Male   White                    \n",
       "2 trio               Cancer             Male   White                    \n",
       "3 trio               Cancer             Male   Black or African American\n",
       "4 trio               Cancer             Male   Asian                    \n",
       "5 trio               Cancer             Male   White                    \n",
       "6 trio               Cancer             Male   White                    \n",
       "  Ethnicity              Vital.Status Age.at.the.Last.Vital.Status..Days.\n",
       "1 Reported Unknown       Alive        2925                               \n",
       "2 Not Hispanic or Latino Alive        2366                               \n",
       "3 Not Hispanic or Latino Alive        3005                               \n",
       "4 Not Hispanic or Latino Deceased     1423                               \n",
       "5 Not Hispanic or Latino Alive        4235                               \n",
       "6 Not Hispanic or Latino Alive        3320                               \n",
       "  Is.Outcome.Disease.Related Affected.Status\n",
       "1 NA                         NA             \n",
       "2 NA                         NA             \n",
       "3 NA                         NA             \n",
       "4 NA                         NA             \n",
       "5 NA                         NA             \n",
       "6 NA                         NA             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(KFNBL_manifest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clean up external id to match case id in NBL_KF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "KFNBL_manifest$case_id <- gsub(\"GMKF-30-\", \"\",KFNBL_manifest$External.Id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "delete gender and race from NBL_KF, merge with the KDNBL manifest to fill them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_KF$race <- NULL\n",
    "NBL_KF$gender <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_KF_clinical <- merge(NBL_KF, KFNBL_manifest[,c(\"case_id\", \"Gender\", \"Race\")], by = \"case_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(NBL_KF_clinical)[colnames(NBL_KF_clinical) == \"Race\"] <- \"race\"\n",
    "colnames(NBL_KF_clinical)[colnames(NBL_KF_clinical) == \"Gender\"] <- \"gender\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBL_KF_clinical$race[NBL_KF_clinical$race == \"\"] <- NA\n",
    "NBL_KF_clinical$race[grepl(\"African\",NBL_KF_clinical$race, ignore.case = T)] <- \"African\"\n",
    "NBL_KF_clinical$race[grepl(\"Asian\",NBL_KF_clinical$race, ignore.case = T)] <- \"Asian\"\n",
    "NBL_KF_clinical$race[grepl(\"reported\",NBL_KF_clinical$race, ignore.case = T)] <- \"Unknown\"\n",
    "NBL_KF_clinical$race[grepl(\"Alaska\",NBL_KF_clinical$race, ignore.case = T)] <- \"Other\"\n",
    "NBL_KF_clinical$race[grepl(\"Other\",NBL_KF_clinical$race, ignore.case = T)] <- \"Other\"\n",
    "NBL_KF_clinical$race[grepl(\"Pacific\",NBL_KF_clinical$race, ignore.case = T)] <- \"Other\"\n",
    "NBL_KF_clinical$race[grepl(\"white\",NBL_KF_clinical$race)] <- \"White\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "African   Asian   Other Unknown   White \n",
       "      9       4       2       9     139 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(NBL_KF_clinical$race)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "clin_matrix_KF <- NBL_KF_clinical[,c(\"sample_id\", \"gender\", \"race\", \"age_class\")]\n",
    "    rownames(clin_matrix_KF) <- clin_matrix_KF$sample_id\n",
    "    clin_matrix_KF$sample_id <- NULL\n",
    "    clin_matrix_KF_cases <- clin_matrix_KF[rownames(clin_matrix_KF) %in% names(NBL_KF_groups),]\n",
    "    # This is to match the order in matrix to the cohorts\n",
    "    clin_matrix_KF_cases <- clin_matrix_KF_cases[names(NBL_KF_groups),]    \n",
    "\n",
    "    clin_matrix_KF_cases <- as.matrix(clin_matrix_KF_cases)\n",
    "    ht_clinical_KF = Heatmap(clin_matrix_KF_cases,\n",
    "                         #titles and names\n",
    "                         name = \"Clinical\",\n",
    "                         show_row_names = FALSE,\n",
    "                         show_column_names = TRUE,    \n",
    "                         #clusters\n",
    "                         cluster_columns = FALSE,\n",
    "                         cluster_rows = FALSE,\n",
    "                         #aesthestics\n",
    "                         col = c(agepal, genderpal, racepal),\n",
    "                         column_names_gp = gpar(fontsize = 22),\n",
    "                         width = unit(3, \"cm\"),\n",
    "                         column_title_gp = gpar(fontsize = 42),\n",
    "                         row_title = NULL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Heatmap/annotation names are duplicated: MYCN status”Warning message:\n",
      "“Heatmap/annotation names are duplicated: MYCN status”"
     ]
    }
   ],
   "source": [
    "hm_KF <- geneset_KF_NBL + ht_immune_KF + ht_mycn_kf + ht_mycn_kf + ht_clinical_KF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tN8VwZIWgwGOkEABEAABEAABEAABOwI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FTcoLqQvWp9dff91WY9bhnEiBgyn/cFk1XUtL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\nCSiQqrSbciFAgAABAgQIECBAIJfA/wCDJHUIqiWkkAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm_KF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file = paste0(plotpath,\"checkpoint_heatmap_KF_NBL.pdf\"),\n",
    "        width = 18, \n",
    "        height = 15,\n",
    "        useDingbats = FALSE)\n",
    "    draw(hm_KF,\n",
    "         ht_gap = unit(1, \"cm\"),\n",
    "         padding = unit(c(10, 10, 2, 10), \"mm\"))\n",
    "    dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Related to numbers in text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "TARGETids_highTIM3 <- c(\n",
    "\"TARGET-30-PANGXK-01A\",\n",
    "\"TARGET-30-PAUDDK-01A\",\n",
    "\"TARGET-30-PALCBW-01A\",\n",
    "\"TARGET-30-PAPTCR-01A\",\n",
    "\"TARGET-30-PANSBN-01A\",\n",
    "\"TARGET-30-PATEKG-01A\",\n",
    "\"TARGET-30-PASGAP-01A\",\n",
    "\"TARGET-30-PANXJL-01A\",\n",
    "\"TARGET-30-PANKFE-01A\",\n",
    "\"TARGET-30-PAIXIF-01A\",\n",
    "\"TARGET-30-PASMJG-01A\",\n",
    "\"TARGET-30-PARSBI-01A\",\n",
    "\"TARGET-30-PALVKK-01A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 1177</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>7316-1926</th><th scope=col>7316-489</th><th scope=col>7316-13</th><th scope=col>7316-3936</th><th scope=col>7316-873</th><th scope=col>7316-2669</th><th scope=col>7316-1928</th><th scope=col>7316-1655</th><th scope=col>7316-2622</th><th scope=col>7316-2893</th><th scope=col>⋯</th><th scope=col>ICGC_PA89</th><th scope=col>ICGC_PA9</th><th scope=col>ICGC_PA91</th><th scope=col>ICGC_PA92</th><th scope=col>ICGC_PA93</th><th scope=col>ICGC_PA94</th><th scope=col>ICGC_PA95</th><th scope=col>ICGC_PA96</th><th scope=col>ICGC_PA97</th><th scope=col>ICGC_PA99</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>7SK</th><td>101.08</td><td>113.29</td><td>143.37</td><td>167.31</td><td>222.65</td><td>133.89</td><td>114.69</td><td>95.22</td><td> 8.11</td><td>96.12</td><td>⋯</td><td> 0.287477450</td><td>1.510045e-01</td><td> 1.316372799</td><td> 0.623302011</td><td>5.390079e-01</td><td> 0.2323849</td><td> 0.00000000</td><td> 0.62421705</td><td> 0.204103009</td><td>  0.27949627</td></tr>\n",
       "\t<tr><th scope=row>A1BG</th><td>  0.82</td><td>  3.00</td><td>  2.33</td><td>  2.07</td><td>  3.87</td><td>  0.60</td><td>  1.64</td><td> 1.01</td><td> 3.29</td><td> 0.92</td><td>⋯</td><td> 0.730796870</td><td>5.787472e-01</td><td> 1.432226226</td><td> 1.305533013</td><td>1.757023e+00</td><td> 0.6259475</td><td> 0.66270817</td><td> 0.95084569</td><td> 0.856170521</td><td>  0.90799219</td></tr>\n",
       "\t<tr><th scope=row>A1BG-AS1</th><td>  0.34</td><td>  1.54</td><td>  0.66</td><td>  1.74</td><td>  2.21</td><td>  0.10</td><td>  0.49</td><td> 0.33</td><td> 0.24</td><td> 0.34</td><td>⋯</td><td> 0.146521078</td><td>1.951477e-01</td><td> 0.359481416</td><td> 0.459126757</td><td>2.827870e-01</td><td> 0.4634999</td><td> 0.18452624</td><td> 0.49039428</td><td> 0.170731858</td><td>  0.18425727</td></tr>\n",
       "\t<tr><th scope=row>A1CF</th><td>  0.00</td><td>  0.00</td><td>  0.00</td><td>  0.00</td><td>  0.00</td><td>  0.00</td><td>  0.00</td><td> 0.00</td><td> 0.00</td><td> 0.00</td><td>⋯</td><td> 0.003605546</td><td>5.714179e-03</td><td> 0.003864498</td><td> 0.008657391</td><td>7.485289e-03</td><td> 0.0099566</td><td> 0.01394094</td><td> 0.01546211</td><td> 0.007234758</td><td>  0.01149084</td></tr>\n",
       "\t<tr><th scope=row>A2M</th><td>270.92</td><td> 22.46</td><td> 58.90</td><td> 40.02</td><td> 39.74</td><td> 52.95</td><td> 34.72</td><td> 4.87</td><td>11.78</td><td>34.41</td><td>⋯</td><td>35.557882596</td><td>1.358033e+02</td><td>63.220854602</td><td>63.728948340</td><td>1.480167e+02</td><td>79.4678037</td><td>97.16209388</td><td>64.46924760</td><td>71.294566832</td><td>120.79589095</td></tr>\n",
       "\t<tr><th scope=row>A2M-AS1</th><td>  0.38</td><td>  0.21</td><td>  0.23</td><td>  0.43</td><td>  0.35</td><td>  0.38</td><td>  0.98</td><td> 0.14</td><td> 0.05</td><td> 0.33</td><td>⋯</td><td> 0.744254574</td><td>4.421134e-01</td><td> 0.197240583</td><td> 0.916434869</td><td>1.896832e-01</td><td> 0.7646672</td><td> 0.21666408</td><td> 0.50357142</td><td> 0.318341732</td><td>  0.40298729</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 1177\n",
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       "  & 7316-1926 & 7316-489 & 7316-13 & 7316-3936 & 7316-873 & 7316-2669 & 7316-1928 & 7316-1655 & 7316-2622 & 7316-2893 & 7316-2860 & 7316-918 & 7316-136 & 7316-2780 & 7316-258 & 7316-692 & 7316-120 & 7316-331 & 7316-2150 & 7316-345 & 7316-2244 & 7316-1955 & 7316-1641 & 7316-523 & 7316-3062 & 7316-2324 & 7316-386 & 7316-676 & 7316-6 & 7316-3075 & 7316-3071 & 7316-1942 & 7316-334 & 7316-878 & 7316-2310 & 7316-2177 & 7316-741 & 7316-1808 & 7316-2757 & 7316-249 & 7316-3572 & 7316-306 & 7316-3485 & 7316-121 & 7316-324 & 7316-2689 & 7316-1653 & 7316-726 & 7316-1658 & 7316-315 & 7316-1060 & 7316-332 & 7316-853 & 7316-2897 & 7316-3132 & 7316-1700 & 7316-2989 & 7316-3809 & 7316-471 & 7316-1087 & 7316-389 & 7316-3204 & 7316-1660 & 7316-1081 & 7316-1652 & 7316-1977 & 7316-1958 & 7316-519 & 7316-1987 & 7316-330 & 7316-515 & 7316-2195 & 7316-659 & 7316-733 & 7316-3622 & 7316-2662 & 7316-2567 & 7316-2204 & 7316-2167 & 7316-2169 & 7316-1643 & 7316-1455 & 7316-2186 & 7316-553 & 7316-1095 & 7316-556 & 7316-2782 & 7316-3627 & 7316-2155 & 7316-3054 & 7316-1104 & 7316-77 & 7316-3065 & 7316-2099 & 7316-3064 & 7316-197 & 7316-3890 & 7316-362 & 7316-532 & 7316-739 & 7316-84 & 7316-925 & 7316-88 & 7316-440 & 7316-3623 & 7316-30 & 7316-2133 & 7316-2221 & 7316-175 & 7316-1083 & 7316-459 & 7316-39 & 7316-358 & 7316-3511 & 7316-2610 & 7316-1763 & 7316-1844 & 7316-764 & 7316-2640 & 7316-3773 & 7316-1802 & 7316-153 & 7316-1664 & 7316-681 & 7316-321 & 7316-1103 & 7316-2862 & 7316-1845 & 7316-2809 & 7316-1671 & 7316-3576 & 7316-168 & 7316-1113 & 7316-1710 & 7316-359 & 7316-1778 & 7316-116 & 7316-2986 & 7316-1059 & 7316-3566 & 7316-1983 & 7316-1089 & 7316-38 & 7316-133 & 7316-472 & 7316-1650 & 7316-3768 & 7316-2227 & 7316-1115 & 7316-762 & 7316-3555 & 7316-394 & 7316-2187 & 7316-2558 & 7316-391 & 7316-3504 & 7316-1460 & 7316-3765 & 7316-1793 & 7316-936 & 7316-2009 & 7316-307 & 7316-1954 & 7316-1949 & 7316-384 & 7316-2557 & 7316-441 & 7316-642 & 7316-499 & 7316-313 & 7316-3028 & 7316-622 & 7316-2648 & 7316-205 & 7316-387 & 7316-2582 & 7316-2241 & 7316-409 & 7316-22 & 7316-1094 & 7316-460 & 7316-198 & 7316-3140 & 7316-2562 & 7316-2275 & 7316-2274 & 7316-3029 & 7316-5 & 7316-2152 & 7316-146 & 7316-41 & 7316-2658 & 7316-152 & 7316-1108 & 7316-429 & 7316-322 & 7316-3323 & 7316-435 & 7316-1950 & 7316-2590 & 7316-1944 & 7316-2084 & 7316-1963 & 7316-1805 & 7316-663 & 7316-1093 & 7316-1077 & 7316-2172 & 7316-550 & 7316-2170 & 7316-122 & 7316-2857 & 7316-876 & 7316-643 & 7316-521 & 7316-1783 & 7316-2184 & 7316-80 & 7316-2107 & 7316-3846 & 7316-2217 & 7316-1806 & 7316-1052 & 7316-2313 & 7316-309 & 7316-3068 & 7316-1114 & 7316-966 & 7316-235 & 7316-2663 & 7316-1137 & 7316-380 & 7316-736 & 7316-1975 & 7316-1635 & 7316-3055 & 7316-2138 & 7316-2672 & 7316-487 & 7316-2307 & 7316-2171 & 7316-2571 & 7316-397 & 7316-1099 & 7316-3299 & 7316-2588 & 7316-255 & 7316-350 & 7316-639 & 7316-2699 & 7316-3345 & 7316-644 & 7316-3020 & 7316-1038 & 7316-216 & 7316-2110 & 7316-1715 & 7316-3817 & 7316-3326 & 7316-70 & 7316-267 & 7316-346 & 7316-286 & 7316-2156 & 7316-2578 & 7316-2267 & 7316-3025 & 7316-178 & 7316-1711 & 7316-91 & 7316-747 & 7316-466 & 7316-207 & 7316-2174 & 7316-291 & 7316-173 & 7316-3626 & 7316-1952 & 7316-2688 & 7316-938 & 7316-1088 & 7316-2329 & 7316-952 & 7316-1252 & 7316-29 & 7316-476 & 7316-341 & 7316-2181 & 7316-119 & 7316-364 & 7316-184 & 7316-2858 & 7316-240 & 7316-2250 & 7316-4033 & 7316-9 & 7316-2287 & 7316-923 & 7316-1886 & 7316-1085 & 7316-729 & 7316-1118 & 7316-392 & 7316-3756 & 7316-897 & 7316-946 & 7316-3771 & 7316-875 & 7316-4341 & 7316-1693 & 7316-2149 & 7316-620 & 7316-4028 & 7316-2291 & 7316-288 & 7316-50 & 7316-1678 & 7316-2230 & 7316-1654 & 7316-2708 & 7316-535 & 7316-716 & 7316-2308 & 7316-1646 & 7316-3308 & 7316-193 & 7316-3625 & 7316-97 & 7316-2729 & 7316-3631 & 7316-3060 & 7316-219 & 7316-1970 & 7316-1702 & 7316-2756 & 7316-247 & 7316-2300 & 7316-1312 & 7316-641 & 7316-3203 & 7316-438 & 7316-932 & 7316-455 & 7316-869 & 7316-619 & 7316-310 & 7316-195 & 7316-1771 & 7316-2006 & 7316-554 & 7316-367 & 7316-302 & 7316-2591 & 7316-1676 & 7316-437 & 7316-1786 & 7316-1760 & 7316-124 & 7316-109 & 7316-329 & 7316-2182 & 7316-2075 & 7316-2865 & 7316-160 & 7316-1936 & 7316-456 & 7316-127 & 7316-957 & 7316-1945 & 7316-3169 & 7316-1986 & 7316-1943 & 7316-480 & 7316-495 & 7316-3 & 7316-3624 & 7316-423 & 7316-147 & 7316-874 & 7316-47 & 7316-2141 & 7316-3139 & 7316-3554 & 7316-922 & 7316-1972 & 7316-1086 & 7316-290 & 7316-1659 & 7316-1866 & 7316-506 & 7316-405 & 7316-889 & 7316-100 & 7316-368 & 7316-448 & 7316-2972 & 7316-872 & 7316-2966 & 7316-1965 & 7316-162 & 7316-292 & 7316-304 & 7316-1803 & 7316-1079 & 7316-1851 & 7316-1679 & 7316-2305 & 7316-1801 & 7316-393 & 7316-470 & 7316-3515 & 7316-496 & 7316-2671 & 7316-2934 & 7316-2666 & 7316-32 & 7316-436 & 7316-183 & 7316-196 & 7316-186 & 7316-1078 & 7316-2594 & 7316-161 & 7316-3769 & 7316-2178 & 7316-934 & 7316-347 & 7316-1637 & 7316-261 & 7316-2581 & 7316-1090 & 7316-245 & 7316-914 & 7316-749 & 7316-1134 & 7316-612 & 7316-2269 & 7316-1957 & 7316-3857 & 7316-3150 & 7316-3122 & 7316-2005 & 7316-2215 & 7316-458 & 7316-2971 & 7316-3621 & 7316-2130 & 7316-905 & 7316-1978 & 7316-2125 & 7316-677 & 7316-1651 & 7316-1214 & 7316-1854 & 7316-3919 & 7316-3520 & 7316-1648 & 7316-2243 & 7316-735 & 7316-1680 & 7316-1636 & 7316-1084 & 7316-2224 & 7316-915 & 7316-2135 & 7316-2245 & 7316-3154 & 7316-3192 & 7316-10 & 7316-2322 & 7316-3074 & 7316-1107 & 7316-2719 & 7316-85 & 7316-381 & 7316-1775 & 7316-1767 & 7316-1668 & 7316-906 & 7316-475 & 7316-3774 & 7316-297 & 7316-14 & 7316-3488 & 7316-2191 & 7316-1779 & 7316-2665 & 7316-257 & 7316-516 & 7316-3030 & 7316-1940 & 7316-3510 & 7316-737 & 7316-230 & 7316-3491 & 7316-931 & 7316-2252 & 7316-820 & 7316-3138 & 7316-445 & 7316-924 & 7316-1748 & 7316-223 & 7316-20 & 7316-361 & 7316-1946 & 7316-443 & 7316-3067 & 7316-2309 & 7316-2608 & 7316-1683 & 7316-1744 & 7316-1681 & 7316-1461 & 7316-370 & 7316-1953 & 7316-2071 & 7316-2197 & 7316-2240 & 7316-1776 & 7316-287 & 7316-343 & 7316-71 & 7316-3516 & 7316-117 & 7316-2572 & 7316-715 & 7316-1226 & 7316-2575 & 7316-561 & 7316-4034 & 7316-375 & 7316-2811 & 7316-206 & 7316-3070 & 7316-1956 & 7316-3316 & 7316-666 & 7316-275 & 7316-222 & 7316-390 & 7316-3048 & 7316-1985 & 7316-388 & 7316-314 & 7316-111 & 7316-1670 & 7316-3630 & 7316-1948 & 7316-954 & 7316-1075 & 7316-1937 & 7316-1082 & 7316-2755 & 7316-1781 & 7316-670 & 7316-2609 & 7316-937 & 7316-2306 & 7316-2266 & 7316-101 & 7316-2659 & 7316-1669 & 7316-1064 & 7316-3574 & 7316-2959 & 7316-2980 & 7316-908 & 7316-562 & 7316-189 & 7316-1794 & 7316-894 & 7316-2723 & 7316-948 & 7316-2732 & 7316-746 & 7316-400 & 7316-398 & 7316-134 & 7316-262 & 7316-891 & 7316-2988 & 7316-2144 & 7316-525 & 7316-884 & 7316-3052 & 7316-974 & 7316-2589 & 7316-1785 & 7316-775 & 7316-1750 & TARGET-30-PAPZYP-01A & TARGET-30-PASWFB-01A & TARGET-30-PATINJ-01A & TARGET-30-PARZCJ-01A & TARGET-30-PASNZU-01A & TARGET-30-PASHFA-01A & TARGET-30-PATAYJ-01A & TARGET-30-PAPBGH-01A & TARGET-30-PARFWB-01A & TARGET-30-PASNEF-01A & TARGET-30-PARYNK-01A & TARGET-30-PASSRN-01A & TARGET-30-PAISNS-01A & TARGET-30-PALVKK-01A & TARGET-30-PATESI-01A & TARGET-30-PASNML-01A & TARGET-30-PARACS-01A & TARGET-30-PATYIL-01A & TARGET-30-PARDIW-01A & TARGET-30-PANBSP-01A & TARGET-30-PAUDDK-01A & TARGET-30-PAPUAR-01A & TARGET-30-PAIVHE-01A & TARGET-30-PAPEAV-01A & TARGET-30-PANKFE-01A & TARGET-30-PARKGJ-01A & TARGET-30-PAMEZH-01A & TARGET-30-PAITCI-01A & TARGET-30-PALXTB-01A & TARGET-30-PARFRE-01A & TARGET-30-PANRVJ-01A & TARGET-30-PARZIP-01A & TARGET-30-PAKYZS-01A & TARGET-30-PARZHA-01A & TARGET-30-PASSRS-01A & TARGET-30-PANZVU-01A & TARGET-30-PARAMT-01A & TARGET-30-PALCBW-01A & TARGET-30-PALBFW-01A & TARGET-30-PATGJU-01A & TARGET-30-PARGKK-01A & TARGET-30-PAPHPE-01A & TARGET-30-PAPTFZ-01A & TARGET-30-PAPTLV-01A & TARGET-30-PALWVJ-01A & TARGET-30-PANUIF-01A & TARGET-30-PASRFS-01A & TARGET-30-PAPEFE-01A & TARGET-30-PASWVY-01A & TARGET-30-PALEVG-01A & TARGET-30-PATDXG-01A & TARGET-30-PAPLSD-01A & TARGET-30-PASTCN-01A & TARGET-30-PAPTCR-01A & TARGET-30-PANYGR-01A & TARGET-30-PANBJH-01A & TARGET-30-PALTEG-01A & TARGET-30-PASYPX-01A & TARGET-30-PAPTDH-01A & TARGET-30-PAPVXS-01A & TARGET-30-PASPBZ-01A & TARGET-30-PASUCB-01A & TARGET-30-PAIXIF-01A & TARGET-30-PAMZMG-01A & TARGET-30-PASZKE-01A & TARGET-30-PARBGP-01A & TARGET-30-PANWRR-01A & TARGET-30-PATGLU-01A & TARGET-30-PANJLH-01A & TARGET-30-PASTKC-01A & TARGET-30-PANGXK-01A & TARGET-30-PARSBI-01A & TARGET-30-PALIIN-01A & TARGET-30-PAKZRH-01A & TARGET-30-PALETP-01A & TARGET-30-PAPKXS-01A & TARGET-30-PANRRW-01A & TARGET-30-PALNVP-01A & TARGET-30-PAPVRN-01A & TARGET-30-PATDXC-01A & TARGET-30-PASWIJ-01A & TARGET-30-PAMNLH-01A & TARGET-30-PARHAM-01A & TARGET-30-PASNPG-01A & TARGET-30-PASZPI-01A & TARGET-30-PASMDM-01A & TARGET-30-PATFXV-01A & TARGET-30-PASMNT-01A & TARGET-30-PAPKWN-01A & TARGET-30-PASUYG-01A & TARGET-30-PASFIC-01A & TARGET-30-PASVRU-01A & TARGET-30-PASXRG-01A & TARGET-30-PAMYCE-01A & TARGET-30-PAPICY-01A & TARGET-30-PANXJL-01A & TARGET-30-PARBAJ-01A & TARGET-30-PAPBZI-01A & TARGET-30-PASGAP-01A & TARGET-30-PAPBJE-01A & TARGET-30-PARJVP-01A & TARGET-30-PAPVFD-01A & TARGET-30-PASXRJ-01A & TARGET-30-PANSBN-01A & TARGET-30-PASCTR-01A & TARGET-30-PAIFXV-01A & TARGET-30-PAPUWY-01A & TARGET-30-PASSWW-01A & TARGET-30-PASMJG-01A & TARGET-30-PATHKB-01A & TARGET-30-PANZPV-01A & TARGET-30-PASCFC-01A & TARGET-30-PALKUC-01A & TARGET-30-PARACM-01A & TARGET-30-PATDWN-01A & TARGET-30-PASFGG-01A & TARGET-30-PATBMM-01A & TARGET-30-PARVLK-01A & TARGET-30-PASUML-01A & TARGET-30-PASWYR-01A & TARGET-30-PATFCY-01A & TARGET-30-PATEPF-01A & TARGET-30-PAMVRA-01A & TARGET-30-PAPZYZ-01A & TARGET-30-PARNNC-01A & TARGET-30-PAMMXF-01A & TARGET-30-PARDCK-01A & TARGET-30-PASEGA-01A & TARGET-30-PAPTAN-01A & TARGET-30-PALUYS-01A & TARGET-30-PALZZV-01A & TARGET-30-PAPCTS-01A & TARGET-30-PATHVK-01A & TARGET-30-PANNMS-01A & TARGET-30-PANBMJ-01A & TARGET-30-PATCFL-01A & TARGET-30-PASPER-01A & TARGET-30-PAMZGT-01A & TARGET-30-PASXGP-01A & TARGET-30-PANUKV-01A & TARGET-30-PASJZC-01A & TARGET-30-PASGUT-01A & TARGET-30-PATEKG-01A & TARGET-30-PAPUEB-01A & TARGET-30-PANLET-01A & TARGET-30-PANZRV-01A & TARGET-30-PASXHE-01A & TARGET-30-PAIPGU-01A & TARGET-30-PASXIE-01A & GMKF-30-PATSIJ03-01B-99R & GMKF-30-PASZJB03-01B-99R & GMKF-30-PASKRS03-01B-99R & GMKF-30-PAVNLK03-01A-01R & GMKF-30-PAVJEV03-01B-99R & GMKF-30-PASWYR03-01B-99R & GMKF-30-PASPGB03-01B-99R & GMKF-30-PAUCRL03-01B-99R & GMKF-30-PATWPL03-01B-99R & GMKF-30-PATEEE03-01B-99R & GMKF-30-PARYEH03-01B-99R & GMKF-30-PARUZA03-01B-99R & GMKF-30-PATTHB03-01B-99R & GMKF-30-PAUUWG03-01B-99R & GMKF-30-PASXRG03-01B-99R & GMKF-30-PASLPG03-01B-99R & GMKF-30-PASUFD03-01B-99R & GMKF-30-PATCVK03-01B-99R & GMKF-30-PATSCT03-01B-99R & GMKF-30-PASFEU03-01B-99R & GMKF-30-PATUAI03-01B-99R & GMKF-30-PASCGM03-01B-99R & GMKF-30-PASREY03-01B-99R & GMKF-30-PAVMHB03-01A-01R & GMKF-30-PAVHEU03-01B-99R & GMKF-30-PAVHWG03-01B-99R & GMKF-30-PASFIC03-01B-99R & GMKF-30-PAUIJR03-01B-99R & GMKF-30-PASKFM03-01B-99R & GMKF-30-PAVINT03-01B-99R & GMKF-30-PATLDR03-01B-99R & GMKF-30-PAULDT03-01A-01R & GMKF-30-PASGBE03-01B-99R & GMKF-30-PASMUB03-01B-99R & GMKF-30-PASGGD03-01B-99R & GMKF-30-PASIIB03-01B-99R & GMKF-30-PATLKI03-01B-99R & GMKF-30-PASLTC03-01B-99R & GMKF-30-PATWPC03-01B-99R & GMKF-30-PATUBI03-01B-99R & GMKF-30-PATBGF03-01B-99R & GMKF-30-PATMDE03-01B-99R & GMKF-30-PASWEE03-01B-99R & GMKF-30-PARWEV03-01B-99R & GMKF-30-PATWTJ03-01B-99R & GMKF-30-PATCMD03-01B-99R & GMKF-30-PASRRY03-01B-99R & GMKF-30-PARTRP03-01B-99R & GMKF-30-PASTZR03-01B-99R & GMKF-30-PATWIL03-01B-99R & GMKF-30-PATNUK03-01B-99R & GMKF-30-PAUJRW03-01B-99R & GMKF-30-PATJDG03-01B-99R & GMKF-30-PATETW03-01B-99R & GMKF-30-PASEWZ03-01B-99R & GMKF-30-PAUHXD03-01B-99R & GMKF-30-PAUYFR03-01B-99R & GMKF-30-PASSMV03-01B-99R & GMKF-30-PASAVJ03-01B-99R & GMKF-30-PAUGVZ03-01B-99R & GMKF-30-PASNII03-01B-99R & GMKF-30-PAUFHY03-01B-99R & GMKF-30-PAULMT03-01B-99R & GMKF-30-PATWDV03-01B-99R & GMKF-30-PAUGJI03-01B-99R & GMKF-30-PATSTU03-01B-99R & GMKF-30-PAUUHY03-01B-99R & GMKF-30-PASMET03-01B-99R & GMKF-30-PAUAEE03-01B-99R & GMKF-30-PATSNC03-01B-99R & GMKF-30-PATTPC03-01B-99R & GMKF-30-PASDDP03-01B-99R & GMKF-30-PATFIK03-01B-99R & GMKF-30-PASAHC03-01B-99R & GMKF-30-PAVCLX03-01B-99R & GMKF-30-PAUPMU03-01B-99R & GMKF-30-PASFXC03-01B-99R & GMKF-30-PAUMJN03-01B-99R & GMKF-30-PAUUTW03-01B-99R & GMKF-30-PASAGA03-01B-99R & GMKF-30-PAVGYW03-01B-99R & GMKF-30-PAVINE03-01B-99R & GMKF-30-PAUPWN03-01B-99R & GMKF-30-PASVYV03-01B-99R & GMKF-30-PAUJTY03-01B-99R & GMKF-30-PATYWG03-01B-99R & GMKF-30-PATNVA03-01B-99R & GMKF-30-PASLIH03-01B-99R & GMKF-30-PATLFF03-01B-99R & GMKF-30-PAVKZC03-01A-01R & GMKF-30-PASWXG03-01B-99R & GMKF-30-PAUYJS03-01B-99R & GMKF-30-PATBCM03-01B-99R & GMKF-30-PASBDN03-01B-99R & GMKF-30-PATBFN03-01B-99R & GMKF-30-PASXWF03-01B-99R & GMKF-30-PAVIAZ03-01B-99R & GMKF-30-PATKGB03-01B-99R & GMKF-30-PATIRK03-01B-99R & GMKF-30-PASTRD03-01B-99R & GMKF-30-PASUMK03-01B-99R & GMKF-30-PAVAEE03-01B-99R & GMKF-30-PAUPKS03-01B-99R & GMKF-30-PATPPY03-01B-99R & GMKF-30-PAUSJB03-01B-99R & GMKF-30-PATVHJ03-01B-99R & GMKF-30-PASIUJ03-01B-99R & GMKF-30-PASKIS03-01B-99R & GMKF-30-PASBHS03-01B-99R & GMKF-30-PATNXJ03-01B-99R & GMKF-30-PATTNX03-01B-99R & GMKF-30-PATVKI03-01B-99R & GMKF-30-PAUNST03-01B-99R & GMKF-30-PAVBLZ03-01B-99R & GMKF-30-PARXPD03-01B-99R & GMKF-30-PATSRY03-01B-99R & GMKF-30-PAVFNX03-01B-99R & GMKF-30-PASUCU03-01B-99R & GMKF-30-PAVIZV03-01B-99R & GMKF-30-PAVBLT03-01B-99R & GMKF-30-PAUTFT03-01B-99R & GMKF-30-PASMGS03-01B-99R & GMKF-30-PAULZB03-01B-99R & GMKF-30-PAVHGP03-01B-99R & GMKF-30-PAUPNR03-01B-99R & GMKF-30-PASXIE03-01B-99R & GMKF-30-PATPFG03-01B-99R & GMKF-30-PATVRG03-01B-99R & GMKF-30-PATDKL03-01B-99R & GMKF-30-PASWMY03-01B-99R & GMKF-30-PATAAV03-01B-99R & GMKF-30-PASZKE03-01B-99R & GMKF-30-PASSWW03-01B-99R & GMKF-30-PAUVPT03-01B-99R & GMKF-30-PATUWG03-01B-99R & GMKF-30-PATLFX03-01B-99R & GMKF-30-PATSUL03-01B-99R & GMKF-30-PASTWY03-01B-99R & GMKF-30-PAUKLK03-01A-01R & GMKF-30-PATLSY03-01B-99R & GMKF-30-PAVFAP03-01B-99R & GMKF-30-PASRTR03-01B-99R & GMKF-30-PATLBP03-01B-99R & GMKF-30-PATDCJ03-01B-99R & GMKF-30-PASKZT03-01B-99R & GMKF-30-PASZRF03-01B-99R & GMKF-30-PATBMM03-01B-99R & GMKF-30-PATDWR03-01B-99R & GMKF-30-PASCDX03-01B-99R & GMKF-30-PATCFL03-01B-99R & GMKF-30-PATTCU03-01B-99R & GMKF-30-PATBHY03-01B-99R & GMKF-30-PAUVML03-01B-99R & GMKF-30-PASFCG03-01B-99R & GMKF-30-PASCWD03-01B-99R & GMKF-30-PAUXGR03-01B-99R & GMKF-30-PASDYT03-01B-99R & GMKF-30-PAUNTY03-01B-99R & GMKF-30-PATGIW03-01B-99R & GMKF-30-PASNHY03-01B-99R & GMKF-30-PASMIU03-01B-99R & GMKF-30-PASJTA03-01B-99R & GMKF-30-PASUEZ03-01B-99R & GMKF-30-PAURJG03-01B-99R & GMKF-30-PAUEXM03-01B-99R & GMKF-30-PASNUI03-01B-99R & GMKF-30-PAVJEG03-01B-99R & GMKF-30-PASKWI03-01B-99R & GMKF-30-PARYRJ03-01B-99R & GMKF-30-PATNWM03-01B-99R & GMKF-30-PATUPN03-01B-99R & GMKF-30-PAUKYI03-01B-99R & GMKF-30-PARZBH03-01B-99R & GMKF-30-PASUVU03-01B-99R & GMKF-30-PATXTF03-01B-99R & GMKF-30-PAUIRD03-01B-99R & GMKF-30-PASFEV03-01B-99R & GMKF-30-PAUWBU03-01B-99R & GMKF-30-PAUNZM03-01B-99R & GMKF-30-PAVDFW03-01B-99R & GMKF-30-PATXKN03-01B-99R & GMKF-30-PASYYZ03-01B-99R & GMKF-30-PATVKN03-01B-99R & GMKF-30-PAUCIK03-01B-99R & GMKF-30-PARYAG03-01B-99R & GMKF-30-PARVPD03-01B-99R & GMKF-30-PATIIT03-01B-99R & GMKF-30-PASVXS03-01B-99R & GMKF-30-PAVLFB03-01A-01R & GMKF-30-PATSUB03-01B-99R & GMKF-30-PAVALS03-01B-99R & GMKF-30-PAVKRF03-01A-01R & GMKF-30-PATNXK03-01B-99R & GMKF-30-PATWED03-01B-99R & GMKF-30-PASXSJ03-01B-99R & GMKF-30-PAUWHB03-01B-99R & GMKF-30-PAUDFR03-01B-99R & GMKF-30-PASXHE03-01B-99R & GMKF-30-PAUJEJ03-01A-01R & GMKF-30-PAUCNL03-01B-99R & GMKF-30-PASZTC03-01B-99R & GMKF-30-PASXRJ03-01B-99R & GMKF-30-PARZCJ03-01B-99R & GMKF-30-PARUTJ03-01B-99R & GMKF-30-PASFWL03-01B-99R & GMKF-30-PASFKX03-01B-99R & GMKF-30-PATFTR03-01B-99R & GMKF-30-PATZRF03-01B-99R & GMKF-30-PAUMFN03-01B-99R & A15170-T.RNA-Seq & A18552-T.RNA-Seq & A12748-T.RNA-Seq & A14565-T.RNA-Seq & A18777-T.RNA-Seq & A08958-T.RNA-Seq & A14449-T.RNA-Seq & A16538-T.RNA-Seq & A03942-T.RNA-Seq & A20091-T.RNA-Seq & A19015-T.RNA-Seq & A09985-T.RNA-Seq & A14709-T.RNA-Seq & 7316-3214-T-A07083.RNA-Seq & A15726-T.RNA-Seq & A08732-T.RNA-Seq & A17096-T.RNA-Seq & A09689-T.RNA-Seq & A19649-T.RNA-Seq & A12398-T.RNA-Seq & A12911-T.RNA-Seq & A09446-T.RNA-Seq & A08692-T.RNA-Seq & A09576-T.RNA-Seq & A08768-T.RNA-Seq & A16915-T.RNA-Seq & A16405-T.RNA-Seq & A09410-T.RNA-Seq & A14567-T.RNA-Seq & A07150-T.RNA-Seq & A05262-T.RNA-Seq & A17997-T.RNA-Seq & H049-0CWK & H049-7SD3 & H049-FG38 & H049-GP41 & H049-GWNW & H049-JVCT & H049-PF1J & H049-SPP6 & H049-UV6K & H049-WB9V & ICGC\\_ET1 & ICGC\\_ET14 & ICGC\\_ET16 & ICGC\\_ET22 & ICGC\\_ET7 & ICGC\\_ET8 & ICGC\\_ET9 & ICGC\\_GBM1 & ICGC\\_GBM15 & ICGC\\_GBM2 & ICGC\\_GBM24 & ICGC\\_GBM25 & ICGC\\_GBM26 & ICGC\\_GBM27 & ICGC\\_GBM28 & ICGC\\_GBM32 & ICGC\\_GBM38 & ICGC\\_GBM4 & ICGC\\_GBM5 & ICGC\\_GBM52 & ICGC\\_GBM53 & ICGC\\_GBM56 & ICGC\\_GBM57 & ICGC\\_GBM58 & ICGC\\_GBM59 & ICGC\\_GBM6 & ICGC\\_GBM63 & ICGC\\_GBM65 & ICGC\\_GBM67 & ICGC\\_MB102 & ICGC\\_MB106 & ICGC\\_MB107 & ICGC\\_MB108 & ICGC\\_MB110 & ICGC\\_MB111 & ICGC\\_MB112 & ICGC\\_MB113 & ICGC\\_MB114 & ICGC\\_MB117 & ICGC\\_MB118 & ICGC\\_MB119 & ICGC\\_MB121 & ICGC\\_MB122 & ICGC\\_MB124 & ICGC\\_MB129 & ICGC\\_MB130 & ICGC\\_MB131 & ICGC\\_MB132 & ICGC\\_MB134 & ICGC\\_MB137 & ICGC\\_MB139 & ICGC\\_MB141 & ICGC\\_MB146 & ICGC\\_MB165 & ICGC\\_MB166 & ICGC\\_MB170 & ICGC\\_MB174 & ICGC\\_MB175 & ICGC\\_MB176 & ICGC\\_MB177 & ICGC\\_MB179 & ICGC\\_MB181 & ICGC\\_MB183 & ICGC\\_MB184 & ICGC\\_MB185 & ICGC\\_MB188 & ICGC\\_MB19 & ICGC\\_MB193 & ICGC\\_MB198 & ICGC\\_MB199 & ICGC\\_MB2 & ICGC\\_MB204 & ICGC\\_MB205 & ICGC\\_MB206 & ICGC\\_MB224 & ICGC\\_MB226 & ICGC\\_MB227 & ICGC\\_MB228 & ICGC\\_MB230 & ICGC\\_MB232 & ICGC\\_MB233 & ICGC\\_MB235 & ICGC\\_MB236 & ICGC\\_MB237 & ICGC\\_MB241 & ICGC\\_MB243 & ICGC\\_MB244 & ICGC\\_MB246 & ICGC\\_MB247 & ICGC\\_MB248 & ICGC\\_MB250 & ICGC\\_MB26 & ICGC\\_MB260 & ICGC\\_MB264 & ICGC\\_MB265 & ICGC\\_MB269 & ICGC\\_MB270 & ICGC\\_MB271 & ICGC\\_MB277 & ICGC\\_MB278 & ICGC\\_MB28 & ICGC\\_MB280 & ICGC\\_MB281 & ICGC\\_MB286 & ICGC\\_MB287 & ICGC\\_MB289 & ICGC\\_MB292 & ICGC\\_MB294 & ICGC\\_MB295 & ICGC\\_MB297 & ICGC\\_MB299 & ICGC\\_MB302 & ICGC\\_MB303 & ICGC\\_MB304 & ICGC\\_MB31 & ICGC\\_MB34 & ICGC\\_MB39 & ICGC\\_MB40 & ICGC\\_MB53 & ICGC\\_MB57 & ICGC\\_MB58 & ICGC\\_MB60 & ICGC\\_MB64 & ICGC\\_MB65 & ICGC\\_MB68 & ICGC\\_MB71 & ICGC\\_MB83 & ICGC\\_MB84 & ICGC\\_MB85 & ICGC\\_MB86 & ICGC\\_MB88 & ICGC\\_MB89 & ICGC\\_MB90 & ICGC\\_MB91 & ICGC\\_MB92 & ICGC\\_MB94 & ICGC\\_MB95 & ICGC\\_MB96 & ICGC\\_MB98 & ICGC\\_MB99 & ICGC\\_PA100 & ICGC\\_PA102 & ICGC\\_PA103 & ICGC\\_PA107 & ICGC\\_PA108 & ICGC\\_PA109 & ICGC\\_PA11 & ICGC\\_PA110 & ICGC\\_PA112 & ICGC\\_PA113 & ICGC\\_PA134 & ICGC\\_PA135 & ICGC\\_PA136 & ICGC\\_PA138 & ICGC\\_PA14 & ICGC\\_PA140 & ICGC\\_PA143 & ICGC\\_PA144 & ICGC\\_PA145 & ICGC\\_PA147 & ICGC\\_PA148 & ICGC\\_PA150 & ICGC\\_PA157 & ICGC\\_PA158 & ICGC\\_PA159 & ICGC\\_PA162 & ICGC\\_PA165 & ICGC\\_PA4 & ICGC\\_PA8 & ICGC\\_PA82 & ICGC\\_PA83 & ICGC\\_PA84 & ICGC\\_PA85 & ICGC\\_PA86 & ICGC\\_PA87 & ICGC\\_PA88 & ICGC\\_PA89 & ICGC\\_PA9 & ICGC\\_PA91 & ICGC\\_PA92 & ICGC\\_PA93 & ICGC\\_PA94 & ICGC\\_PA95 & ICGC\\_PA96 & ICGC\\_PA97 & ICGC\\_PA99\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t7SK & 101.08 & 113.29 & 143.37 & 167.31 & 222.65 & 133.89 & 114.69 & 95.22 &  8.11 & 96.12 & 144.13 & 109.09 & 80.59 & 133.72 & 93.42 &  63.03 &  98.77 & 89.94 & 381.32 & 128.59 & 129.03 & 188.10 & 100.69 & 152.34 &  87.07 &  76.44 & 168.25 & 74.29 & 167.37 & 116.62 & 139.47 &  58.85 &   4.30 & 133.48 & 127.47 & 112.14 & 55.48 & 147.58 & 134.07 & 215.71 & 46.44 & 309.90 & 111.82 & 117.24 & 112.98 &  79.69 &  79.08 &  53.63 &  50.51 & 156.19 & 142.29 & 98.20 & 184.99 & 214.25 & 62.89 & 197.44 &  60.77 & 181.62 & 205.03 & 63.73 & 116.22 & 125.40 &  84.02 & 155.83 &  73.02 & 96.37 & 135.99 & 84.31 & 147.03 & 92.85 & 115.52 & 110.87 & 36.22 &  65.90 &  70.83 & 190.92 & 322.48 & 119.23 & 67.00 & 284.60 & 115.28 &   0.00 & 151.22 & 359.99 &  80.66 & 163.95 & 227.70 & 144.47 & 143.06 & 119.03 & 292.27 & 89.90 &  80.54 & 81.38 &  56.66 & 236.32 & 114.42 & 156.51 & 34.09 & 102.20 & 69.24 &  55.49 & 148.61 & 92.45 & 53.15 & 122.38 & 267.14 & 147.81 & 140.82 & 120.40 & 96.03 & 55.74 & 73.72 & 120.23 & 96.18 & 90.41 & 19.81 & 37.40 & 102.50 & 129.20 & 309.61 & 89.11 & 115.40 & 72.52 & 58.18 & 152.45 & 198.99 & 142.77 & 77.94 & 214.44 & 143.71 & 142.13 & 92.45 &  98.32 & 23.67 & 76.66 & 117.13 & 178.54 & 112.08 & 74.52 & 127.96 & 28.66 & 160.32 & 144.24 &  79.84 & 205.21 & 48.58 & 180.99 & 247.55 & 18.80 & 81.23 & 127.04 & 34.13 &  95.12 & 118.70 & 78.78 & 45.36 & 90.65 & 109.12 &  53.15 & 50.87 & 181.04 & 228.35 & 107.24 & 143.75 & 53.56 & 59.87 & 80.63 & 114.45 & 155.88 & 3576.80 &  0.00 & 310.80 & 251.70 & 161.44 & 158.77 & 62.02 &  42.80 & 238.51 & 84.26 & 144.82 & 85.76 &  51.22 & 70.40 & 201.99 & 112.79 & 100.34 & 111.77 & 313.37 & 84.47 & 78.08 & 103.90 & 58.00 &  45.36 & 86.32 & 215.72 & 180.02 & 135.41 &  83.89 & 238.86 & 176.44 &  50.87 & 91.99 &  67.42 & 70.80 & 82.04 & 48.86 & 121.72 & 85.54 & 150.01 & 86.30 & 159.08 & 234.13 & 47.82 & 77.14 & 42.93 & 136.70 & 100.50 & 184.38 & 52.37 & 106.97 & 325.01 & 202.44 & 213.87 & 94.70 & 141.90 & 94.03 & 120.23 &  98.68 & 111.65 & 149.16 & 203.11 & 13.03 & 80.99 &  94.24 & 104.73 & 166.50 & 148.90 &  78.36 &  76.68 & 94.08 &  46.47 & 78.49 & 29.85 & 219.16 & 100.24 &    0.00 & 173.91 & 122.22 & 123.97 & 35.53 &  50.65 & 186.29 & 120.91 & 103.54 & 2.87 & 175.22 & 113.69 & 98.89 & 99.30 &  75.60 & 76.61 & 236.84 & 86.94 & 55.26 & 129.55 & 221.47 & 142.00 & 94.89 & 126.23 & 296.72 &   7.27 & 141.00 & 127.14 & 290.09 &  66.73 & 93.64 & 156.02 & 67.23 &  73.04 & 48.80 & 91.48 & 65.16 & 26.79 & 155.71 &  94.93 & 236.65 & 118.43 & 137.71 & 127.20 & 119.88 & 225.15 & 354.75 & 325.82 & 439.68 & 191.05 & 101.92 & 46.76 & 166.42 & 151.01 & 120.81 & 250.61 &   0.00 & 121.36 & 102.72 &  71.08 & 137.25 & 154.77 &  83.87 & 99.09 & 15.32 &   0.00 & 174.51 & 52.38 &   0.00 & 58.89 &  91.30 & 192.91 & 238.98 & 134.46 & 147.25 &   0.00 & 113.05 & 90.49 & 210.73 & 61.55 &  92.53 & 196.67 &  63.16 & 158.43 & 164.40 &  8.61 & 62.52 & 142.50 & 71.22 & 142.67 & 136.15 & 85.58 &  66.73 & 72.39 & 127.09 & 131.74 & 244.40 & 150.94 &   0.00 & 199.51 &  76.05 & 56.37 &  52.37 & 161.05 & 275.32 & 41.21 & 144.12 & 78.65 & 170.59 & 161.32 & 152.76 & 266.68 & 60.53 & 93.88 & 172.10 & 127.52 & 180.59 & 229.51 & 119.24 & 271.44 & 66.74 & 153.59 & 176.31 &  67.86 & 107.31 &  83.95 & 54.66 & 121.72 & 19.71 & 145.80 & 17.45 & 107.65 &  77.85 & 191.95 & 203.00 &  93.24 &  61.94 & 134.30 & 85.70 & 111.26 & 62.77 & 130.91 & 72.03 & 245.71 &  78.51 & 209.95 & 111.35 & 79.45 & 152.74 & 114.50 &  31.80 & 46.17 &  80.93 & 69.55 & 49.37 & 149.51 & 102.68 & 109.70 & 159.62 & 86.44 & 48.53 & 211.47 & 82.54 & 115.82 & 171.56 & 94.40 & 100.46 & 119.37 & 240.66 & 51.01 &  59.77 & 87.45 & 99.28 &  75.87 & 158.05 & 162.23 &  13.05 & 77.63 & 213.83 &  96.28 & 121.82 & 61.04 & 118.33 & 143.49 & 64.25 & 90.39 & 126.23 & 125.43 & 42.60 &  61.12 &  9.88 &  43.74 & 131.05 & 103.92 & 111.13 & 66.56 & 320.77 & 147.69 & 61.62 & 46.33 & 114.90 & 55.93 & 104.86 & 143.84 & 51.15 & 183.15 & 20.23 & 54.75 &  66.80 & 60.01 &  70.72 & 137.90 & 158.26 & 142.31 & 49.25 &  67.03 & 154.13 & 95.10 &  74.00 & 109.34 &  97.24 & 208.96 & 210.15 & 229.66 &  57.93 & 129.22 & 128.94 &   0.00 &  42.25 & 178.42 & 116.94 & 103.16 & 175.62 & 58.25 & 286.70 & 167.35 & 183.35 & 239.69 & 114.27 & 73.23 & 202.37 & 169.07 & 70.25 & 101.76 & 96.41 & 193.23 & 120.98 & 147.13 & 66.67 & 168.92 & 278.22 & 167.69 & 123.09 &   8.62 & 160.82 & 88.34 & 114.57 & 84.03 &  64.78 & 127.59 & 132.77 & 186.09 & 221.11 & 210.36 & 66.65 & 32.01 &  60.08 & 147.11 & 35.66 & 405.38 & 207.86 &  51.99 & 118.77 & 163.34 &   0.00 &   0.00 & 25.74 & 148.67 & 108.96 &  59.67 & 86.96 &  60.96 & 95.01 & 131.76 & 79.37 & 61.86 & 106.84 & 148.60 & 122.74 &  51.02 & 120.79 & 101.10 &  89.96 & 178.23 & 100.77 & 75.91 & 149.09 &   0.00 & 87.31 & 34.65 & 108.98 & 546.14 & 135.37 & 77.15 & 78.82 &  71.29 & 83.28 & 127.85 & 102.00 & 156.68 & 196.15 & 107.34 & 84.82 & 128.36 & 36.12 & 207.03 & 128.64 & 129.10 & 63.22 & 100.45 & 40.28 & 116.83 &  84.64 & 26.84 & 150.73 & 191.82 & 53.87 & 53.07 & 238.46 & 52.76 & 72.63 & 278.47 & 106.73 & 132.25 & 103.75 & 273.72 & 356.59 & 99.48 & 150.08 &  89.58 & 85.42 & 116.48 & 70.86 & 19.09 & 46.64 &   0.00 &  0.25 &   0.44 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.26 &  0.00 &  0.26 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.27 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.21 &   0.00 &  0.00 &   0.00 &  0.22 &   0.00 &  0.00 &   0.00 &  0.23 &   0.00 &   0.41 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.43 &  0.00 &  0.47 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.51 &   0.00 &   0.73 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.27 &  0.00 &   0.00 &   0.26 &   0.00 &   0.19 &  0.00 &   0.00 &  0.00 &  0.00 &  0.27 &   0.00 &   0.00 &  0.00 &  0.20 &   0.64 &   0.00 &   0.00 &  0.00 &  0.00 &  0.19 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.22 &  0.20 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 & 59.87 & 121.17 & 71.05 & 100.78 & 67.35 & 67.48 & 57.46 & 50.35 & 117.65 & 110.58 & 45.02 & 68.02 & 94.11 &  67.95 & 29.16 &  49.41 & 12.10 & 11.18 & 140.19 &  64.21 &  0.00 & 131.69 &  59.69 & 16.65 & 73.75 & 31.74 & 119.53 & 89.90 & 18.62 & 43.69 & 269.74 & 95.24 &  58.14 & 74.01 & 37.65 & 57.93 & 177.57 & 58.78 & 83.51 & 76.44 & 43.69 &  36.14 & 17.47 & 88.41 & 436.63 & 52.59 & 24.19 &  58.14 & 59.95 &  0.00 & 85.99 & 33.01 &  43.16 &  0.00 & 67.62 & 109.56 & 47.02 & 47.79 & 43.51 & 36.69 & 49.92 &  78.01 & 71.99 & 84.04 &  36.33 &  64.26 & 82.15 & 82.79 & 49.01 & 63.92 & 46.08 & 91.77 & 66.80 & 122.62 & 29.23 &  24.58 & 40.59 &  35.32 & 38.23 & 39.27 & 88.17 & 21.62 & 115.13 & 29.38 & 89.55 & 209.57 &  0.00 & 19.66 & 138.45 &  48.86 & 46.05 & 52.76 &  23.09 & 54.70 & 58.46 &  37.51 & 143.05 & 60.53 &  73.81 & 27.14 & 113.52 & 43.07 & 366.33 & 121.90 & 72.80 &  0.00 & 67.71 & 67.46 & 75.11 & 36.73 & 46.33 & 90.75 & 61.68 & 79.33 & 41.53 & 58.98 & 51.81 & 64.78 & 51.79 & 33.53 & 55.39 & 65.37 & 86.09 & 68.97 &  0.00 & 63.06 &  49.39 & 80.93 &  16.79 & 90.05 & 75.01 & 73.75 & 56.12 & 85.31 &  0.00 & 35.54 &  10.12 & 18.06 & 75.82 &  75.26 & 58.00 & 20.88 & 39.69 & 58.75 & 89.47 & 11.48 & 136.65 & 60.49 & 47.73 & 31.13 &   0.00 & 45.88 & 81.50 & 138.97 & 51.40 & 65.31 & 56.94 & 37.75 &  52.99 & 57.83 & 12.45 & 29.11 & 88.17 & 27.08 & 97.38 & 85.51 &  32.39 &  43.43 & 35.48 & 13.81 & 117.97 & 19.37 & 56.39 & 82.74 & 27.42 & 28.09 & 94.79 & 83.83 & 101.42 & 26.89 & 91.79 &  79.41 & 68.61 & 64.81 &  87.18 & 80.06 & 76.95 & 71.96 & 62.15 & 60.33 & 58.50 &  71.16 & 99.06 & 30.40 & 82.50 & 21.60 & 45.45 & 44.02 & 78.97 & 277.99 & 60.99 & 67.52 & 121.20 & 62.52 & 40.49 & 51.20 & 109.02 &  0.00 & 201.79 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   6.16 &   0.00 &   0.00 &   0.00 &   7.00 &   0.00 &   0.00 &   0.00 &   0.00 &   3.35 &   0.00 &  0.00 &   0.00 &   0.00 &  16.25 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   2.74380650 &   0.21039338 &  0.47589589 &  0.00000000 &  0.109703793 &   0.26519850 &  0.07071421 &   0.11878204 & 2.326997e-01 &  2.356724 &  1.20041589 &  1.20712467 &   0.66190845 &  1.79801371 &  1.69688518 &  1.44055915 &  0.8252733 &  2.120041553 &  2.49201582 &   2.12895423 &   2.00345953 &   1.08477412 &  2.35515563 &   4.03536331 &  3.1850206 &   1.83234283 &  1.55780843 &   1.94355412 &   1.92337749 &   0.76906191 &  1.44619745 & 4.078696e-01 &   1.66149189 &   0.90669509 &  0.796343498 &   0.98038999 &   1.91975645 &  1.29127495 &   1.13723501 &  1.123400773 &  0.823355553 &  1.18041572 &  1.76264590 &  1.26252010 &  1.786301214 &  0.72409304 & 1.443101910 &  0.941425732 &  2.166772215 &  1.52052462 &  1.22144252 &  1.292699851 &  6.956325859 &  3.73876317 &  1.051650235 &  0.900297231 &  0.545720096 &  1.343651312 &  1.273388319 &  3.93645645 &  2.96192930 & 0.671491256 &  0.36011130 &  1.01868080 &  1.244313104 &  0.790280344 &  1.00401654 &  0.89431543 &  0.42747236 &  1.87333275 &  0.487638741 &  1.0500147 &  0.93250552 &  1.24521300 &  0.67934414 &  0.901450598 &  0.50423557 &  0.853452042 &  0.90883813 &  0.465109700 &  0.67077210 &  2.761508570 &  0.95364636 &  0.813271533 & 1.04771990 & 0.819712266 &  1.102347264 &  0.87635508 &  1.06627640 &  0.51930097 &  0.354981588 &  5.875975473 &  0.72762588 &  2.36255382 &  0.66445274 &  1.3016152 &  0.376618893 &  0.24056202 &  3.11620605 &  0.919127190 &  0.98110671 &  0.99532755 &  0.36580311 &  2.68977329 &  2.77607257 &  0.912693169 &  3.6642982 &  1.34998183 &  1.6295004 &  1.54446181 &  0.8822126 &  0.400517229 &  0.633550834 &  0.94475582 &  0.878376491 &  0.71382280 &  0.63463238 &  1.44600280 &  0.65969816 &  0.71782208 &  0.51587232 &  0.85150826 &  0.79960430 & 0.90751906 &  0.860984976 &  1.8311168 &  0.79154674 &  0.45286138 &  1.06345490 &  2.0303942 &  1.75719435 & 0.68827258 &  2.097935621 &  1.07271630 &  1.516693564 &  1.08194051 &  1.575662031 &  1.8600521 &  2.44738134 &  1.94657369 &  2.90401798 & 12.96726576 &  1.216199539 &  1.30997564 &  2.96042471 &  1.23628363 &  2.6906781 &  2.23877420 &  1.188685955 &  5.41803442 &  0.42213962 &  0.5025693 & 3.224137e-01 &   0.35871577 &  0.50314833 &  0.426085880 &   0.1694091 & 1.187211e+00 &  1.142302391 & 7.978633e-01 & 2.386181e-01 &   0.45865045 &  0.121538761 &  0.000000000 & 4.559680e-01 & 4.453855e-01 &  0.687379603 &   0.53810794 &  0.213453993 &  0.388851084 &   0.59547571 &   1.73155107 &   0.53980342 &  1.582928430 & 7.949002e-01 & 8.444790e-01 &   0.38741330 & 3.598497e-01 &  0.000000000 &   0.46397491 &  1.10971184 &  0.86589211 &   0.00000000 &   0.90980114 & 0.000000e+00 &  0.060454143 &  0.287477450 & 1.510045e-01 &  1.316372799 &  0.623302011 & 5.390079e-01 &  0.2323849 &  0.00000000 &  0.62421705 &  0.204103009 &   0.27949627\\\\\n",
       "\tA1BG &   0.82 &   3.00 &   2.33 &   2.07 &   3.87 &   0.60 &   1.64 &  1.01 &  3.29 &  0.92 &   5.49 &   1.91 &  5.54 &   3.94 & 15.53 &   2.27 &   2.34 &  2.97 &   9.10 &   0.60 &   2.43 &   1.51 &   9.14 &   8.19 &   1.37 &   0.89 &   5.51 &  0.35 &  10.88 &  12.74 &  10.35 &   2.21 &   1.87 &  12.10 &   5.57 &   2.14 &  0.97 &   1.73 &   5.08 &   2.22 &  5.02 &   4.04 &  10.91 &   0.62 &   6.05 &   0.84 &   4.19 &   2.13 &   6.81 &   2.05 &   8.33 &  0.23 &   4.25 &   2.54 &  1.53 &   1.72 &   0.78 &   3.12 &   8.97 &  8.63 &   0.81 &   5.06 &   5.40 &   2.98 &   6.41 &  7.35 &   5.14 &  6.96 &  15.93 &  4.33 &   4.22 &   2.97 &  1.53 &   1.15 &   1.49 &   4.10 &   2.89 &   2.10 &  1.39 &   0.95 &   1.31 &   9.14 &   6.70 &   0.16 &   3.39 &   0.21 &   6.24 &   3.33 &   1.51 &   0.56 &  11.76 &  0.58 &   6.82 &  1.63 &   5.05 &   8.19 &  10.19 &   3.67 &  1.11 &   6.77 &  2.60 &   2.30 &   4.22 &  2.92 &  2.35 &   9.63 &   7.18 &   0.93 &   4.21 &   0.36 &  1.24 &  1.01 &  5.89 &   0.57 &  6.05 &  5.60 &  1.35 &  1.73 &   2.12 &   6.17 &   1.46 & 10.72 &   1.99 &  5.78 &  6.58 &   8.35 &   6.26 &   0.24 &  5.10 &   8.84 &   4.79 &   4.88 &  0.50 &   0.26 &  5.68 &  2.65 &  19.87 &   2.08 &   8.42 &  1.07 &  15.18 &  2.33 &   3.48 &   9.43 &   0.97 &   1.43 &  3.46 &   0.18 &  10.30 &  2.14 &  1.62 &   4.15 &  5.60 &   6.72 &   1.21 &  2.36 &  0.86 &  2.41 &   3.14 &   2.91 &  0.09 &   0.28 &   8.92 &   6.11 &   7.78 &  6.08 &  2.41 &  2.42 &   4.63 &   0.91 &    4.27 & 28.09 &   0.58 &   4.66 &  10.20 &   5.03 &  4.81 &  33.38 &   0.29 & 12.43 &   6.78 &  0.64 &   3.36 &  0.58 &   1.71 &  12.53 &   7.71 &   8.97 &   4.47 &  3.08 &  5.49 &  13.09 &  3.65 &   3.41 &  3.60 &   8.78 &   1.76 &   0.20 &  11.19 &   8.09 &   9.91 &  18.05 &  2.34 &   4.25 &  4.43 &  3.64 &  3.25 &   0.29 &  8.62 &  10.60 & 10.57 &   3.21 &  11.12 &  1.10 &  6.10 &  3.11 &   1.14 &   1.44 &   4.52 &  0.80 &   1.94 &   6.62 &   6.02 &   8.90 &  0.47 &   1.47 &  6.75 &   1.97 &   8.57 &   0.96 &   6.03 &   8.24 &  3.58 &  1.06 &   1.75 &   1.11 &   2.96 &   0.75 &   1.35 &   2.02 &  4.97 &   3.99 &  5.47 & 11.90 &   6.50 &   0.95 &   43.49 &   8.02 &   3.82 &   5.69 &  6.40 &  10.80 &   8.19 &   5.37 &   2.73 & 2.33 &   0.09 &  10.24 &  8.58 &  4.05 &   1.36 &  2.38 &   3.31 &  3.17 &  4.65 &   7.49 &  16.35 &   6.15 &  2.24 &   2.08 &   1.27 &  15.94 &   1.68 &   4.53 &   8.71 &   5.60 &  1.01 &   4.72 &  5.61 &   3.12 &  1.12 &  3.68 &  3.00 &  1.56 &  13.71 &   2.54 &   5.08 &   5.61 &   1.65 &   9.22 &   5.37 &   6.19 &   6.16 &   3.35 &   1.18 &   2.54 &   7.17 &  4.11 &   5.86 &   0.45 &   9.54 &   2.15 &   1.89 &   1.42 &   0.16 &   1.95 &  11.71 &   3.76 &  11.33 &  7.09 &  2.25 &  39.07 &   1.34 &  1.39 &   3.25 &  0.91 &   6.14 &  13.19 &   6.06 &   5.52 &   5.79 &  35.45 &   8.97 &  1.56 &   2.06 &  3.26 &  10.30 &   3.63 &   1.29 &   3.02 &  11.05 &  7.37 &  0.88 &   1.08 &  9.96 &   9.95 &   0.60 &  8.86 &  19.85 &  1.47 &   6.93 &   3.92 &   8.02 &   1.26 &  34.31 &   5.09 &   6.17 & 11.91 &   2.74 &   0.41 &   0.31 &  1.97 &   9.66 &  2.37 &   1.00 &   3.74 &   8.65 &   7.20 &  0.83 &  7.98 &   6.75 &   7.50 &   6.29 &   5.13 &   7.45 &  14.35 &  1.21 &   1.20 &   7.07 &   0.65 &   1.55 &   6.03 &  4.62 &   1.04 &  4.51 &   6.36 &  2.81 &  11.45 &   2.81 &   7.75 &   4.22 &   6.99 &   7.78 &   4.79 &  0.78 &   5.39 &  7.16 &   2.31 &  3.79 &  11.00 &   6.35 &   5.55 &   5.90 &  0.64 &  11.83 &   3.23 &   1.25 &  8.86 &   4.91 &  1.97 &  3.16 &   4.12 &   1.35 &   4.26 &   0.45 &  5.10 &  0.51 &   7.13 &  1.36 &   2.41 &   2.69 &  8.67 &   4.80 &  10.62 &   7.76 &  3.25 &   0.71 &  3.61 &  4.67 &   3.41 &   6.97 &  12.12 &  42.12 & 10.47 &  10.26 &   1.89 &   2.49 &  2.03 &   3.15 &   6.13 &  0.79 &  7.23 &   7.79 &   6.33 &  2.14 &   1.80 &  2.48 &   4.54 &   6.14 &   2.67 &   8.89 &  3.73 &   1.10 &   4.26 &  2.85 &  0.75 &   5.98 &  5.10 &   4.64 &   8.23 &  7.02 &   9.55 &  3.27 &  3.51 &   0.92 &  8.33 &   9.22 &   4.67 &   3.91 &   6.82 &  3.59 &   5.05 &  11.03 &  4.41 &   7.96 &   0.41 &   1.30 &   4.23 &   3.53 &   8.93 &   3.94 &   3.38 &   5.03 &  36.71 &   3.80 &   8.45 &   1.72 &   0.51 &  13.18 &  0.83 &   0.34 &   5.76 &   0.44 &   3.92 &   0.68 &  0.51 &   8.60 &   7.57 &  0.89 &   5.64 &  2.80 &   3.40 &   9.04 &   6.14 &  1.82 &   6.93 &   9.48 &  10.32 &   2.71 &   9.07 &   3.96 &  5.86 &   3.45 &  0.69 &   4.17 &   0.73 &   4.53 &   2.70 &   4.96 &   2.75 &  0.43 &  2.16 &   5.87 &   0.50 &  3.08 &   5.34 &   3.24 &   1.33 &   0.75 &   3.13 &  16.96 &  33.17 &  1.33 &  18.58 &   1.24 &   5.80 &  3.91 &   3.94 &  4.94 &   6.95 &  1.58 &  4.73 &   1.31 &   3.93 &  11.69 &   4.11 &  10.12 &   2.18 &   1.47 &   6.69 &   3.39 &  1.48 &   3.75 &  15.27 &  0.33 &  4.82 &   6.91 &   0.38 &   0.67 &  1.22 &  8.86 &   9.28 &  6.65 &   0.92 &   8.11 &   4.68 &   3.82 &   0.34 &  1.21 &   5.21 &  2.95 &  13.29 &   4.75 &   9.12 &  3.71 &  12.58 & 12.39 &   0.46 &   1.46 &  5.56 &   2.39 &   7.58 &  4.41 & 15.76 &   0.28 & 10.38 &  0.98 &   4.88 &   2.20 &   0.38 &   2.26 &   7.64 &   1.68 &  4.48 &  16.45 &   0.81 &  2.13 &   4.15 &  2.54 &  0.76 &  3.03 &  26.08 & 29.22 &  31.25 &  19.33 & 30.75 & 35.36 & 13.54 & 25.77 & 26.55 & 21.08 & 21.66 &  23.84 & 17.67 &  40.00 & 23.86 & 13.63 &  19.20 & 27.55 & 24.02 & 39.48 &  37.83 & 20.30 &  59.51 & 36.07 &  28.37 &  28.13 &  23.08 & 15.71 &  27.24 & 11.85 &  29.26 & 33.07 &  24.23 & 22.61 &  52.52 &  15.61 & 75.05 &  27.99 &  25.56 &  20.32 &  21.48 & 16.02 & 23.45 &  28.80 &  18.56 &  49.82 & 16.64 & 26.28 & 21.46 & 37.99 & 33.43 &  30.99 & 15.59 &  36.57 & 20.53 & 32.01 &  27.60 & 21.52 &  46.40 &  13.64 & 14.96 & 31.14 &  32.85 & 24.40 & 29.18 &  15.88 & 21.32 & 34.97 &  24.71 &  23.73 &  52.01 &  35.46 & 40.83 &  29.57 &  33.16 & 35.27 &  26.39 & 22.37 & 16.15 &  30.80 &  19.58 & 41.75 & 25.95 & 13.64 &  36.38 & 743.69 & 35.76 & 39.55 &  24.21 & 15.25 &  19.94 & 50.08 & 15.22 &  39.04 &  35.57 &  70.34 &  13.26 & 21.93 &  29.89 &  63.14 & 16.50 & 22.17 & 23.51 &  31.77 & 556.57 &  18.86 &  26.43 & 15.39 &  49.26 & 27.00 & 23.99 & 20.36 &  37.42 &  21.74 & 41.54 & 24.22 &  41.90 &  37.70 &  22.99 & 31.13 & 39.65 & 48.61 & 54.51 &  36.70 &  14.53 & 42.08 & 24.87 &  43.97 &  28.91 &  19.74 & 11.47 & 14.46 & 18.26 & 25.86 & 13.19 & 14.04 & 19.38 &  31.20 & 29.75 & 20.84 &  74.55 & 21.15 &  33.25 & 15.47 & 32.38 &  21.76 & 47.10 & 14.77 & 19.05 &  7.26 &   6.28 &  6.18 &   5.38 &  6.89 &  9.58 &  7.30 &  7.72 &   6.36 &   4.85 &  8.53 &  0.39 &  8.51 &   7.32 &  3.23 &   8.31 &  8.96 &  6.86 &   4.95 &  10.05 &  6.16 &   7.76 & 315.27 &  5.22 &  8.47 &  3.49 &   5.41 &  2.74 & 10.10 &  4.05 &   1.72 &  6.96 &   8.63 &  3.56 &  8.03 &  4.32 &   5.75 &  9.12 &  4.53 &  6.46 &  6.67 &   8.14 &  7.61 &  7.47 &   2.98 & 14.44 &  7.77 &   4.45 &  7.29 &  4.85 &  8.25 &  8.15 &   8.00 &  7.59 &  9.67 &   5.08 &  6.88 &  6.40 &  5.11 &  4.85 &  4.50 &   7.66 &  6.98 &  1.82 & 136.04 &   4.24 &  4.63 &  2.02 &  7.62 &  4.94 &  5.73 &  6.31 &  5.63 &   5.70 &  7.09 &   8.84 &  5.87 &   5.47 &  4.51 &  7.90 &  5.22 &  4.94 &   6.89 &  5.88 &  4.65 &   2.64 &  3.76 &  5.64 &   4.27 &   4.52 & 12.97 &  6.88 &   9.77 &  2.45 &  7.42 &   5.51 &   6.13 &  5.94 &   7.95 &  4.96 &  10.96 &  8.30 &   4.45 &   5.32 &  6.04 &  9.62 &  7.29 &  5.29 &  4.40 &  4.76 &  4.71 &  3.50 &  9.03 &  6.38 &  4.77 &  5.50 &  6.68 &  4.68 &  7.78 &  4.01 &  5.45 &  6.45 &  4.23 &  8.66 &  5.77 &  5.36 &   9.41 &  6.38 &   7.62 &  6.62 &  2.07 &  7.38 &  4.98 &  7.26 & 27.56 &  7.77 &   4.04 &  9.46 & 12.23 &   4.15 &  5.63 &  7.47 &  5.20 &  3.94 &  5.32 &  7.04 &   9.26 &  6.29 &  1.60 &  4.98 &   0.00 &  4.48 &  4.02 &   5.45 &  9.16 &  6.09 &  6.49 &  4.86 &  10.14 & 11.57 &  4.91 &  6.54 &  4.35 & 11.43 &  7.07 &  8.82 &   7.10 &   7.65 &  4.60 &  5.10 &   3.57 &  3.78 &  5.42 &  5.03 &  6.85 &  5.13 &  8.12 &  9.17 &   6.71 &  7.61 &  6.39 &   8.05 &  5.90 &  7.96 &   7.18 &  6.67 &  7.16 &  4.98 &  4.80 &  7.18 &  5.91 &   5.23 &  5.06 &  4.01 &  6.69 &  4.92 &  3.67 &  9.18 &  8.10 &   6.88 &  8.85 &  4.72 &   5.28 &  8.46 &  7.40 &  1.84 &   6.28 & 27.07 &   7.94 &  15.06 &  20.29 &  21.58 &  16.97 &  31.91 &  25.37 &  11.52 &  30.65 &  23.21 &  17.56 &  21.26 &  23.52 &  31.32 &  27.13 &  14.83 &  25.44 &  15.63 &  13.94 &  27.79 &  18.66 & 23.29 &   3.71 &  19.18 &  63.98 &  1.64 &  24.30 &  41.47 &   4.81 &  37.04 &  10.50 &  28.63 &  26.53 &  72.33861117 &  17.91226239 & 38.64479645 & 19.30396026 & 49.834991793 &  56.87796601 & 26.47577410 &  35.01896359 & 2.971430e+01 & 11.362088 &  2.81275871 &  4.26439359 &   1.43672032 &  1.24712151 &  3.10628277 &  3.60535934 &  2.0486414 & 29.255806027 &  9.12610646 &   1.81047723 &   4.12966974 &   3.32886367 &  3.66569260 &  12.71880041 &  3.3648328 &  13.16458967 & 10.93379834 &  12.45646222 &   4.55305551 &   9.48932587 &  5.44838543 & 4.692293e+00 &   7.82145192 &   5.74794720 &  8.406784337 &   6.72789163 &   7.79876247 &  2.75756746 &   7.35068962 & 11.706102580 & 10.935721719 &  9.35241130 &  1.02111681 &  6.49224606 & 15.068008681 &  9.26390858 & 8.900242936 &  1.107540322 &  6.884469612 &  0.73456520 &  0.83347709 &  0.795801059 &  9.173129416 &  9.53019536 &  6.970604262 & 13.761252777 & 22.120565966 &  6.690180465 &  3.813925540 &  2.02223788 &  5.42149014 & 1.935535810 &  2.56411849 &  4.68758629 &  0.649283191 & 19.567269877 &  1.73970110 &  0.98150801 &  6.22683473 &  2.79826468 & 11.237671327 &  9.5068264 &  2.99786236 &  1.32181255 &  0.54714720 & 10.832731953 &  0.69500313 &  8.277549509 &  4.51735864 &  0.771209552 &  0.85405161 & 14.030903741 & 13.13798980 &  9.065081417 & 0.94454907 & 9.291560136 &  0.682001078 &  1.09259209 &  0.82924703 &  8.84446591 &  0.495756691 &  3.979065472 &  0.80723421 &  0.42539247 &  0.84806840 &  5.3883247 &  0.437699047 &  5.63642777 &  9.61287727 & 12.344332266 &  4.03301706 &  0.63421375 &  8.23740386 &  1.49785272 &  0.96023736 &  0.377667484 &  1.6430213 &  2.15984244 &  3.0292599 &  1.05123813 &  1.5585990 &  0.579876846 &  2.351825273 &  1.14459312 &  1.205358811 &  6.73039308 &  2.03245068 &  2.45596605 &  5.75728659 &  0.75501491 &  1.22072126 &  1.08137064 &  0.86400803 & 4.52173054 & 11.644771464 &  8.2945861 &  7.38633103 &  1.32460242 &  2.06146219 &  1.5835837 &  0.58170537 & 9.22550733 &  0.436803544 &  0.73193082 &  8.008447488 &  0.68044389 &  1.242340103 &  2.5156514 &  0.91273609 &  0.83044283 &  6.92350738 & 13.03714625 &  5.089587592 &  1.95648496 &  4.33254959 &  4.03320028 & 13.6891434 &  8.86194817 &  1.246473020 &  0.99115398 &  1.05409339 &  2.5851461 & 3.989903e+00 &   1.54298413 &  1.43880036 &  0.692697090 &   2.4624239 & 3.581134e+00 &  2.505170887 & 5.209036e-01 & 1.920467e+00 &   3.54289602 &  1.796860807 &  1.568215901 & 5.764483e+00 & 4.790005e+00 &  1.694518799 &   1.93001825 &  1.642540558 &  1.336269692 &   1.55749232 &   1.16568349 &   3.05683159 &  2.978240016 & 2.194789e+00 & 2.273487e+00 &   0.87994081 & 4.635965e+00 &  0.553107605 &   1.06383441 &  0.98066208 &  2.03521986 &   0.64644416 &   1.00426262 & 1.304706e+00 &  2.399184924 &  0.730796870 & 5.787472e-01 &  1.432226226 &  1.305533013 & 1.757023e+00 &  0.6259475 &  0.66270817 &  0.95084569 &  0.856170521 &   0.90799219\\\\\n",
       "\tA1BG-AS1 &   0.34 &   1.54 &   0.66 &   1.74 &   2.21 &   0.10 &   0.49 &  0.33 &  0.24 &  0.34 &   2.26 &   0.12 &  4.46 &   2.32 &  2.48 &   0.31 &   1.03 &  0.67 &   1.75 &   0.19 &   1.39 &   0.36 &   3.20 &   2.27 &   0.31 &   0.30 &   1.85 &  0.16 &   4.69 &   4.28 &   2.97 &   0.77 &   0.70 &   2.74 &   2.34 &   0.60 &  0.39 &   0.28 &   2.19 &   1.19 &  1.19 &   0.57 &   4.27 &   0.33 &   2.83 &   0.24 &   1.23 &   0.60 &   2.23 &   1.00 &   3.18 &  0.15 &   0.92 &   1.10 &  0.30 &   1.04 &   0.18 &   1.35 &   4.93 &  3.65 &   0.25 &   1.86 &   1.81 &   1.34 &   2.41 &  2.29 &   1.89 &  1.95 &   4.69 &  1.74 &   1.21 &   1.20 &  0.36 &   0.36 &   0.53 &   2.79 &   1.22 &   0.35 &  0.40 &   0.50 &   0.72 &   1.57 &   2.21 &   0.07 &   1.53 &   0.10 &   1.72 &   1.12 &   2.17 &   0.10 &   2.75 &  0.10 &   2.57 &  1.50 &   2.27 &   2.82 &   4.90 &   1.76 &  0.46 &   2.20 &  1.93 &   1.21 &   0.84 &  2.14 &  0.95 &   3.29 &   4.42 &   0.16 &   1.30 &   0.10 &  0.30 &  0.43 &  1.06 &   0.19 &  4.09 &  2.31 &  0.51 &  0.94 &   2.48 &   1.77 &   0.35 &  1.99 &   0.65 &  2.88 &  3.52 &   1.33 &   2.28 &   0.14 &  1.45 &   2.62 &   0.58 &   1.77 &  0.18 &   0.10 &  2.64 &  2.00 &   2.89 &   1.09 &   3.78 &  0.31 &   3.69 &  1.03 &   1.42 &   2.09 &   0.32 &   0.57 &  1.74 &   0.04 &   2.30 &  0.97 &  0.49 &   1.88 &  1.37 &   3.50 &   0.27 &  0.64 &  0.57 &  0.97 &   1.68 &   1.36 &  0.03 &   0.17 &   3.31 &   4.20 &   2.21 &  2.22 &  0.93 &  1.49 &   2.82 &   0.19 &    0.95 &  2.86 &   0.22 &   0.43 &   3.66 &   1.87 &  2.49 &   4.60 &   0.19 &  3.75 &   1.82 &  0.24 &   0.91 &  0.16 &   1.06 &   1.64 &   3.21 &   3.66 &   1.70 &  1.37 &  1.21 &   2.12 &  2.26 &   2.01 &  2.06 &   4.82 &   0.67 &   0.02 &   3.40 &   2.05 &   3.26 &   2.21 &  0.70 &   1.66 &  2.32 &  1.21 &  1.53 &   0.08 &  1.93 &   2.11 &  3.84 &   0.57 &   1.88 &  0.31 &  2.14 &  1.86 &   0.32 &   0.46 &   1.32 &  0.60 &   0.77 &   2.33 &   3.05 &   6.50 &  0.31 &   0.63 &  3.66 &   0.36 &   2.77 &   0.35 &   3.17 &   2.87 &  1.50 &  0.37 &   0.40 &   0.22 &   0.18 &   0.22 &   0.41 &   0.80 &  1.43 &   1.95 &  1.73 &  4.47 &   2.83 &   0.23 &    5.51 &   1.71 &   1.08 &   1.21 &  1.79 &   3.22 &   2.18 &   2.84 &   1.37 & 3.24 &   0.15 &   2.21 &  3.43 &  2.28 &   0.38 &  0.50 &   0.93 &  0.97 &  1.97 &   2.80 &   2.72 &   1.58 &  0.75 &   1.29 &   0.63 &   4.71 &   0.77 &   3.94 &   3.19 &   2.98 &  0.84 &   1.72 &  2.91 &   1.52 &  0.98 &  0.78 &  1.21 &  0.34 &   4.76 &   0.89 &   2.48 &   2.72 &   0.77 &   2.37 &   4.15 &   3.00 &   1.52 &   1.06 &   0.37 &   1.15 &   2.38 &  1.41 &   2.10 &   0.11 &   2.36 &   1.32 &   0.45 &   0.29 &   0.06 &   0.77 &   2.10 &   1.51 &   3.87 &  1.52 &  0.77 &   2.82 &   1.05 &  0.56 &   0.44 &  0.31 &   1.44 &   1.78 &   2.03 &   3.40 &   3.02 &   4.76 &   2.34 &  1.22 &   0.55 &  1.76 &   2.89 &   1.23 &   0.35 &   1.59 &   4.37 &  2.58 &  0.46 &   0.26 &  2.61 &   4.90 &   0.08 &  2.88 &   3.48 &  1.33 &   1.87 &   1.75 &   2.25 &   0.69 &   4.81 &   2.19 &   2.23 &  2.38 &   0.93 &   0.08 &   0.05 &  1.96 &   7.92 &  0.72 &   0.61 &   1.01 &   3.10 &   4.19 &  0.14 &  2.63 &   2.29 &   2.39 &   1.59 &   1.85 &   4.43 &   4.55 &  0.39 &   0.29 &   2.42 &   0.18 &   0.35 &   2.06 &  1.14 &   0.47 &  3.50 &   2.12 &  1.13 &   4.15 &   0.95 &   3.16 &   1.63 &   3.33 &   3.17 &   1.51 &  0.46 &   2.77 &  3.30 &   0.35 &  1.08 &   3.09 &   2.21 &   2.21 &   2.36 &  0.28 &   4.36 &   0.24 &   0.44 &  3.11 &   2.10 &  1.11 &  0.36 &   1.72 &   0.36 &   1.10 &   0.13 &  3.92 &  0.24 &   2.54 &  0.40 &   2.05 &   1.81 &  2.35 &   1.74 &   3.11 &   1.65 &  0.83 &   0.29 &  0.88 &  2.40 &   1.37 &   1.78 &   3.55 &  16.32 &  2.78 &   1.70 &   0.98 &   0.82 &  1.29 &   0.99 &   1.95 &  0.31 &  2.79 &   1.31 &   2.03 &  1.20 &   0.58 &  1.87 &   1.78 &   1.55 &   0.68 &   2.96 &  2.50 &   0.27 &   1.36 &  0.54 &  0.11 &   4.97 &  1.55 &   2.89 &   2.11 &  2.35 &   2.49 &  1.82 &  1.43 &   0.30 &  2.05 &   4.61 &   2.76 &   1.75 &   1.75 &  2.11 &   1.34 &   2.01 &  4.19 &   3.00 &   0.19 &   0.42 &   0.96 &   0.63 &   1.53 &   1.64 &   1.71 &   0.97 &   8.64 &   1.20 &   2.96 &   0.41 &   0.21 &   2.92 &  0.16 &   0.12 &   2.81 &   0.29 &   0.85 &   0.22 &  0.28 &   1.48 &   3.00 &  0.12 &   2.42 &  1.10 &   1.09 &   4.67 &   2.10 &  1.22 &   1.49 &   2.67 &   5.42 &   1.98 &   1.41 &   1.11 &  2.56 &   1.68 &  0.14 &   1.11 &   0.20 &   2.01 &   1.07 &   1.06 &   2.01 &  0.20 &  1.09 &   2.80 &   0.25 &  1.00 &   1.90 &   0.91 &   0.42 &   0.24 &   1.66 &   3.94 &   9.55 &  1.19 &   3.22 &   0.28 &   2.85 &  1.72 &   2.27 &  1.57 &   2.32 &  0.44 &  1.67 &   0.26 &   1.65 &   1.94 &   1.55 &   2.84 &   0.63 &   0.52 &   3.08 &   1.54 &  0.55 &   2.50 &   3.11 &  0.09 &  1.68 &   7.53 &   0.11 &   0.32 &  0.61 &  2.48 &   3.71 &  1.10 &   0.15 &   3.94 &   1.28 &   2.52 &   0.05 &  0.79 &   5.11 &  1.85 &   4.34 &   1.65 &   4.05 &  2.26 &   2.07 &  5.85 &   0.19 &   0.24 &  1.15 &   0.74 &   2.78 &  1.61 &  3.63 &   0.13 &  4.01 &  0.45 &   1.65 &   1.08 &   0.21 &   0.37 &   1.70 &   0.06 &  2.10 &   3.10 &   0.26 &  1.50 &   0.55 &  0.92 &  0.51 &  1.98 &   4.23 &  5.65 &   9.56 &   6.01 &  4.76 &  5.98 &  6.86 &  5.61 &  6.79 &  5.25 &  9.64 &   3.53 &  1.99 &  13.59 &  2.35 &  9.06 &   6.12 & 11.63 &  5.09 &  5.69 &   5.11 &  8.01 &   4.48 &  4.76 &   6.52 &   2.27 &  12.31 &  3.05 &   6.18 &  2.06 &   7.59 &  7.45 &   4.83 &  6.78 &   8.66 &   7.88 & 10.86 &   9.95 &   8.57 &  11.58 &   8.43 & 10.50 &  5.09 &   5.86 &   3.48 &   5.03 &  4.87 &  4.97 &  7.77 &  7.33 &  5.88 &   5.98 &  3.35 &   8.92 &  6.28 &  7.30 &   6.25 &  4.39 &   3.86 &   4.96 &  5.87 &  6.50 &   7.12 &  1.49 &  8.58 &   8.33 &  5.36 &  6.11 &   5.61 &   5.77 &   5.64 &   6.48 &  4.78 &   3.60 &   6.02 & 11.73 &  16.96 &  5.57 &  6.61 &   9.04 &   6.76 &  3.33 &  9.11 &  4.60 &   7.71 &   4.15 &  5.26 &  9.40 &   3.25 &  3.96 &   5.43 &  4.71 &  1.98 &   7.56 &   6.72 &   8.49 &   8.10 &  6.85 &   8.19 &   2.79 &  9.23 & 10.62 &  6.85 &   9.28 &   4.67 &   3.18 &   5.82 &  4.89 &  12.72 &  5.50 &  5.96 &  5.94 &   4.43 &  12.77 &  4.74 &  6.47 &   6.05 &   8.40 &   6.06 &  7.21 &  7.36 &  5.98 &  5.52 &   4.31 &   7.77 &  1.77 &  8.25 &   8.34 &   3.31 &  10.02 &  7.08 &  2.78 &  5.77 &  5.45 &  3.41 &  2.66 &  6.21 &   4.22 &  8.32 &  3.96 &   5.44 &  6.01 &  13.05 &  6.94 &  8.13 &   6.41 &  7.11 &  3.41 &  6.45 &  6.93 &   7.88 &  4.83 &   1.76 &  6.80 &  5.22 &  4.07 &  6.31 &   8.05 &   7.39 &  9.57 &  0.18 &  4.47 &   5.95 &  1.73 &   6.94 &  7.28 &  7.15 &  11.52 &  10.94 &  7.70 &   4.65 &   3.80 &  2.61 &  4.84 &  3.42 &   5.13 &  7.08 &  5.25 &  2.96 &   3.27 &  6.51 &   5.26 &  4.27 &  9.11 &  4.41 &   5.15 & 10.50 &  8.82 &  6.28 &  6.23 &   6.29 &  8.34 &  7.79 &   5.61 & 10.26 &  9.68 &   4.00 &  6.71 &  5.08 &  7.12 &  4.44 &   7.15 &  6.11 & 11.83 &   5.36 &  7.43 &  6.97 & 12.81 &  6.77 &  4.61 &   6.53 &  4.14 &  6.73 &   3.58 &   2.06 &  6.17 &  2.19 &  7.69 &  4.59 &  4.17 &  7.08 &  8.06 &   7.56 &  7.75 &   5.99 & 12.28 &   3.02 &  5.89 &  4.87 &  7.01 &  6.06 &   6.26 & 10.12 &  3.99 &   8.38 &  4.53 &  7.65 &   4.76 &   1.42 &  6.97 &  9.02 &   5.13 &  3.24 &  8.97 &   3.83 &   4.31 &  9.59 &   4.99 &  5.76 &   7.84 &  9.57 &   9.12 &   6.13 &  9.44 &  4.95 &  2.93 &  2.15 &  7.87 &  6.38 &  5.43 &  4.57 &  5.28 &  7.33 &  6.93 &  4.79 &  6.59 &  6.81 &  7.00 &  5.47 &  3.51 &  8.75 &  5.97 &  7.19 &  8.16 &  3.60 &   5.99 &  3.60 &   6.14 &  5.11 &  2.75 & 10.38 &  4.82 &  5.03 &  0.00 &  7.34 &   5.39 &  5.44 &  1.66 &   4.60 &  4.23 & 10.29 &  3.82 &  3.95 &  1.99 &  7.24 &   6.29 &  5.79 &  0.58 &  2.72 &   0.00 &  5.69 &  6.81 &   6.19 &  4.97 &  7.78 &  6.10 &  4.15 &   9.68 & 10.55 &  7.82 &  8.04 & 10.97 &  8.54 &  6.28 &  3.84 &   5.60 &   7.27 &  5.42 &  5.82 &   4.73 &  4.13 &  7.89 &  9.14 &  8.29 &  8.32 &  6.81 & 10.66 &   7.17 &  3.92 &  8.43 &   6.44 &  5.30 &  6.82 &   5.27 &  5.56 & 10.13 &  5.47 &  5.99 &  4.81 &  6.16 &   2.42 &  3.83 &  2.75 &  5.53 &  5.20 &  1.67 &  7.55 &  4.90 &   9.02 & 10.51 &  7.15 &   5.79 &  4.51 & 11.22 &  0.57 &   8.32 &  4.57 &  10.05 &   1.54 &   3.08 &   5.10 &   4.33 &   8.79 &   3.09 &   5.45 &   3.13 &   1.25 &   3.83 &   3.90 &   3.43 &   3.66 &   3.67 &   5.70 &   6.64 &   4.76 &   1.65 &   5.66 &   3.88 &  0.72 &   1.05 &   4.09 &   7.52 &  0.11 &   5.10 &  10.22 &   1.23 &   4.31 &   2.44 &   7.81 &   4.45 &   3.25521736 &   4.55358903 &  2.46533388 &  3.82168586 &  8.191458809 &   6.37464846 &  4.30455360 &   5.28082535 & 7.631907e+00 &  1.974974 &  0.88589662 &  2.83550933 &   0.57047154 &  0.56939257 &  1.86453144 &  2.41678196 &  0.9775521 &  3.659290946 &  2.56917023 &   0.45967078 &   0.67549654 &   1.57337224 &  0.47043797 &   5.26674967 &  1.9493669 &   5.72866133 &  4.06152257 &   4.28587187 &   2.22756367 &   4.33524087 &  2.81325174 & 1.826101e+00 &   2.09950735 &   2.65268984 &  5.540953152 &   2.25927013 &   3.59709251 &  3.42135347 &   4.64770493 &  2.187240235 &  1.084621721 &  2.29113152 &  0.11643411 &  1.08136356 &  1.953996252 &  1.86746645 & 2.372120584 &  0.622983348 &  1.931329038 &  0.24063062 &  0.24508135 &  0.197775734 &  2.230842226 &  2.50269813 &  1.031471132 &  2.236068585 &  1.116135959 &  0.824251407 &  0.531802782 &  0.37935600 &  2.71896601 & 0.482651456 &  1.23990870 &  1.54751009 &  0.222207747 &  0.859422344 &  0.54485531 &  0.15433703 &  1.61466556 &  0.42731682 &  1.979637589 &  1.5855133 &  0.91299174 &  0.24843964 &  0.11280020 &  2.158267601 &  0.19547223 &  1.114266627 &  0.68548388 &  0.178424552 &  0.35071095 &  1.893534358 &  1.25777188 &  2.076957546 & 0.11670293 & 1.195303406 &  0.270433179 &  0.17536839 &  0.19022152 &  0.91500472 &  0.105399280 &  0.853778846 &  0.21709385 &  0.18755940 &  0.35256922 &  2.7311010 &  0.187679834 &  1.35103808 &  1.83901160 &  1.044091492 &  1.56214563 &  0.11498509 &  1.65927895 &  0.20839642 &  0.41178197 &  0.139706614 &  0.6560754 &  0.85281804 &  2.0054140 &  0.44173991 &  0.6755840 &  0.158339942 &  0.884752092 &  0.20503148 &  0.619990685 &  1.55869218 &  1.24921194 &  1.44118327 &  3.06025410 &  0.32036052 &  1.58762140 &  0.11583184 &  0.80650632 & 1.33797461 &  1.322338847 &  1.3209025 &  1.48808610 &  0.22530672 &  0.28196735 &  0.5276532 &  0.27802085 & 1.46973669 &  0.123481704 &  0.11960521 &  1.782468251 &  0.17309468 &  0.278322769 &  0.7035458 &  0.18562384 &  0.65318627 &  1.15690716 &  0.99745660 &  1.437240711 &  0.30290387 &  2.21475849 &  1.01912875 &  2.5120535 &  1.50206738 &  0.349980747 &  0.15867135 &  0.36818558 &  0.8990744 & 7.419083e-01 &   0.65276523 &  0.37647642 &  0.138671997 &   1.6043452 & 6.782774e-01 &  0.922962108 & 2.585307e-01 & 5.526091e-01 &   0.91381857 &  0.167708381 &  0.214637221 & 2.248114e+00 & 1.446811e+00 &  0.302153789 &   1.18601107 &  0.287430594 &  0.288245209 &   0.93412485 &   0.27215219 &   1.57089202 &  1.310801802 & 7.572497e-01 & 9.241359e-01 &   0.21832713 & 1.489705e+00 &  0.147669359 &   0.78622930 &  0.17031230 &  0.97599579 &   0.27677436 &   0.43949406 & 4.089445e-01 &  0.993171188 &  0.146521078 & 1.951477e-01 &  0.359481416 &  0.459126757 & 2.827870e-01 &  0.4634999 &  0.18452624 &  0.49039428 &  0.170731858 &   0.18425727\\\\\n",
       "\tA1CF &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.01 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.01 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.01 &   0.00 &   0.01 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.01 &   0.01 &   0.02 &  0.00 &   0.00 &   0.01 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   9.85 &   0.00 &   0.00 &  0.00 &   0.00 &   0.01 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.01 &   0.00 &   0.01 &   0.00 &   0.00 &  0.00 &   0.00 &  0.01 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.00 &  0.03 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.01 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.01 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.01 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.02 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &   0.00 &    0.00 &  0.01 &   0.00 &   0.04 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.01 &   0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.01 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &    0.01 &   0.01 &   0.00 &   0.00 &  0.00 &   0.00 &   0.01 &   0.00 &   0.00 & 0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.01 &   0.00 &   0.02 &   0.00 &   0.00 &  0.03 &   0.00 &  0.02 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.01 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.01 &   0.00 &   0.03 &   0.00 &   0.00 &   0.01 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.01 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.01 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.01 &   0.02 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.02 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.18 &   0.00 &   0.00 &   0.00 &  0.00 &   0.01 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.01 &  0.00 &   0.00 &  0.00 &   0.03 &   0.01 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.01 &   0.00 &   0.01 &   0.00 &   0.01 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.05 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.01 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.01 &   0.00 &   0.03 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.01 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.01 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.01 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.05 &  0.00 &   0.00 &   0.00 &   0.00 &  0.01 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.01 &   0.00 &  0.00 &   0.01 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.01 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &   0.01 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &   0.00 &  0.03 &   0.00 &   0.01 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &   0.02 &  0.08 &   0.00 &   0.00 &  0.05 &  0.01 &  0.08 &  0.06 &  0.00 &  0.04 &  0.03 &   0.14 &  0.00 &   0.02 &  0.01 &  0.01 &   0.03 &  0.00 &  0.00 &  0.02 &   0.00 &  0.00 &   0.00 &  0.00 &   0.00 &   0.00 &   0.08 &  0.00 &   0.00 &  0.36 &   0.01 &  0.00 &   0.00 &  0.10 &   0.03 &   0.21 &  0.09 &   0.19 &   0.01 &   0.02 &   0.04 &  0.08 &  0.02 &   0.07 &   0.00 &   0.02 &  0.00 &  0.00 &  0.00 &  0.01 &  0.00 &   0.10 &  0.03 &   0.00 &  0.00 &  0.03 &   0.08 &  0.00 &   0.00 &   0.01 &  0.00 &  0.98 &   0.00 &  0.00 &  1.16 &   0.12 &  0.01 &  0.03 &   0.00 &   0.00 &   0.00 &   0.03 &  0.00 &   0.00 &   0.05 &  0.00 &   0.05 &  0.00 &  0.02 &   0.00 &   0.00 &  0.00 &  0.10 &  0.01 &   0.00 &  10.29 &  0.01 &  0.00 &   0.14 &  0.03 &   0.03 &  0.03 &  0.04 &   0.00 &   0.00 &   0.00 &   0.06 &  0.00 &   0.00 &   0.00 &  0.06 &  0.01 &  0.06 &   0.00 &  14.75 &   0.02 &   0.02 &  0.03 &   0.00 &  0.03 &  0.00 &  0.06 &   0.00 &   0.01 &  0.02 &  0.01 &   0.01 &   0.04 &   0.03 &  0.02 &  0.00 &  0.00 &  0.00 &   0.00 &   0.03 &  0.00 &  0.01 &   0.01 &   0.00 &   0.19 &  0.03 &  0.03 &  0.00 &  0.00 &  0.00 &  0.00 &  0.33 &   0.00 &  0.01 &  0.00 &   5.76 &  0.01 &   0.03 &  0.00 &  0.03 &   0.04 &  0.02 &  0.03 &  0.01 &  0.00 &   2.78 &  0.00 &   0.00 &  0.00 &  0.00 &  0.05 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  33.30 &  0.00 &  0.00 &  0.05 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.05 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  17.51 &   0.00 &  1.32 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.00 &  0.01 &   0.00 &  0.00 &  0.00 &  0.00 &  0.06 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &   0.03 &  0.00 &  0.00 &   0.00 &   0.00 &  0.00 &   0.00 &  0.00 &   0.04 &  0.00 &   0.00 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.01 &  0.00 &  0.00 &  0.01 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &   0.04 &  0.00 &  0.04 &  1.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.02 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.01 &  0.00 &   0.00 &  0.00 &  0.00 &   0.06 &  0.00 &  0.00 &  0.00 &  0.04 &   0.00 &  0.00 &  0.01 &  0.00 &  0.00 &  0.00 &  0.00 &  0.04 &   0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.01 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &   0.00 &  0.07 &  0.00 &   0.00 &  0.00 &  0.01 &  0.05 &   0.00 &  6.97 &   0.00 &   0.00 &   0.02 &   0.02 &   0.00 &   0.00 &   0.01 &   0.01 &   0.00 &   0.00 &   0.02 &   0.01 &   0.01 &   0.00 &   0.00 &   0.00 &   0.03 &   0.02 &   0.01 &   0.03 &   0.01 &  0.01 &   0.01 &   0.02 &   0.02 &  0.00 &   0.00 &   0.02 &   0.06 &   0.00 &   0.01 &   0.00 &   0.01 &   0.02935278 &   0.07728156 &  0.01566325 &  0.03911834 &  0.003779552 &   0.02485198 &  0.03571537 &   0.07707391 & 3.918489e-03 &  0.000000 &  0.03742963 &  0.02675004 &   0.01091011 &  0.02130605 &  0.03483906 &  0.01964607 &  0.0155075 &  0.008304596 &  0.02460925 &   0.02279064 &   0.06646166 &   0.01788035 &  0.07040259 &   0.03921144 &  0.1342367 &   0.03223938 &  0.02968814 &   0.01587898 &   0.02236296 &   0.03464762 &  0.02373841 & 8.463386e-03 &   0.01656273 &   0.01716322 &  0.008762159 &   0.02077186 &   0.07190365 &  0.02187946 &   0.02891768 &  0.005226231 &  0.004549274 &  0.02011863 &  0.02561121 &  0.01766339 &  0.005444255 &  0.01894951 & 0.009647415 &  0.007094948 &  0.009191256 &  0.01141811 &  0.01514696 &  0.009850675 &  0.008307141 &  0.01716282 &  0.008649064 &  0.006516893 &  0.008498707 &  0.004614465 &  0.001386939 &  0.05860169 &  0.02915508 & 0.004197411 &  0.01190598 &  0.01147319 &  0.004625102 &  0.009821252 &  0.01486632 &  0.01395564 &  0.01098286 &  0.03302921 &  0.002962082 &  0.0132839 &  0.03008383 &  0.02314931 &  0.02871412 &  0.005672046 &  0.01190287 &  0.008427672 &  0.00958158 &  0.007471885 &  0.01338302 &  0.007994527 &  0.01136538 &  0.003074704 & 0.02071311 & 0.008507942 &  0.008350058 &  0.01425451 &  0.02118206 &  0.01239532 &  0.004058173 &  0.008428447 &  0.01236686 &  0.07380204 &  0.01841725 &  0.0244244 &  0.006678309 &  0.01058522 &  0.03675781 &  0.003917981 &  0.01219193 &  0.01315344 &  0.01471557 &  0.03392453 &  0.06372715 &  0.004056967 &  0.1511407 &  0.01286446 &  0.1439761 &  0.05060124 &  0.0162301 &  0.008436457 &  0.008721119 &  0.01771128 &  0.005971006 &  0.03597375 &  0.01107842 &  0.02151567 &  0.03208887 &  0.03541401 &  0.04429334 &  0.01918459 &  0.01730762 & 0.02072468 &  0.007666258 &  0.0179314 &  0.01656471 &  0.02957933 &  0.00832385 &  0.0319169 &  0.03106525 & 0.01918611 &  0.004185136 &  0.01308713 &  0.007913394 &  0.01149366 &  0.006939461 &  0.0193869 &  0.01713017 &  0.02021023 &  0.01486614 &  0.01890339 &  0.009155806 &  0.02740355 &  0.01078368 &  0.02508726 &  0.0608514 &  0.01025134 &  0.004714803 &  0.01804492 &  0.01467839 &  0.0165651 & 6.006073e-03 &   0.01490767 &  0.01847272 &  0.003154763 &   0.0107550 & 7.680832e-03 &  0.005369185 & 4.079245e-03 & 7.318885e-03 &   0.01073936 &  0.007005618 &  0.002138034 & 3.315323e-03 & 2.325367e-03 &  0.008919463 &   0.01523514 &  0.009859341 &  0.007277171 &   0.02113593 &   0.01076589 &   0.01263312 &  0.009678645 & 9.682515e-03 & 3.498382e-03 &   0.01923126 & 7.373351e-03 &  0.001989187 &   0.02898765 &  0.01499254 &  0.01653283 &   0.01184973 &   0.00786159 & 3.860995e-03 &  0.005078435 &  0.003605546 & 5.714179e-03 &  0.003864498 &  0.008657391 & 7.485289e-03 &  0.0099566 &  0.01394094 &  0.01546211 &  0.007234758 &   0.01149084\\\\\n",
       "\tA2M & 270.92 &  22.46 &  58.90 &  40.02 &  39.74 &  52.95 &  34.72 &  4.87 & 11.78 & 34.41 &  22.58 &  18.76 & 95.90 &  23.22 & 66.89 & 281.30 & 115.79 & 35.24 &  19.22 &  84.39 &  19.63 &   6.83 &  12.61 &  19.23 & 126.88 & 132.30 &   7.93 & 13.97 &  14.05 & 198.84 &  24.25 & 114.99 & 101.45 &  10.34 & 130.44 &  54.52 & 62.10 &  25.25 &  26.44 &  37.63 & 44.56 &  20.82 &  28.38 &  78.58 & 137.18 & 104.06 & 120.91 & 194.51 & 112.28 &  30.79 & 419.90 & 53.31 &  11.48 &  92.01 & 50.77 &  19.52 & 168.84 &  20.19 &  38.49 & 29.40 &  34.55 &  17.56 & 129.37 & 129.39 & 151.61 & 26.80 & 151.86 & 48.60 &  72.47 & 26.38 & 106.94 &  49.18 & 19.89 & 136.57 & 126.69 &  43.44 &  17.11 &  42.42 & 35.12 &  24.10 &  34.35 & 145.94 &   7.99 &  12.49 & 149.26 &  12.99 &  69.39 & 190.88 &  19.13 &  55.53 &  29.04 & 44.59 & 148.80 & 43.97 & 124.07 &  19.31 &  29.94 & 241.46 & 27.34 &  11.63 & 34.16 & 181.88 &  97.14 & 40.40 & 26.80 &  89.61 &   7.72 &   5.44 &  46.68 &  16.74 & 94.28 & 58.30 & 20.22 &  10.90 & 81.37 & 88.38 & 39.30 & 19.43 & 107.89 &  31.60 &  52.69 & 12.24 &  46.20 & 71.97 & 31.10 &  47.67 &  38.03 & 549.40 & 82.18 &  28.56 &  15.45 & 179.93 & 51.55 & 175.19 & 36.00 & 18.05 & 105.72 &   9.87 & 645.00 & 59.28 &  81.25 & 18.85 &  30.61 &  51.58 & 115.61 &  26.89 & 31.34 &  14.97 &  56.25 & 57.87 & 86.97 &  34.64 & 83.71 & 128.77 & 117.46 & 68.43 & 18.84 & 40.97 & 110.18 & 102.21 &  9.58 &  11.47 &  26.81 &  86.41 & 175.43 & 36.74 & 83.60 & 25.50 & 195.98 &  20.42 &   55.50 & 39.94 &  14.86 &  27.65 &  19.29 & 207.30 & 26.03 & 387.54 &  14.00 & 84.66 &  93.33 & 23.67 & 119.52 & 17.23 & 627.82 &  46.63 & 167.94 &  12.52 & 233.82 & 83.71 & 81.86 &  21.21 & 15.80 & 107.72 & 39.16 &  10.62 &  57.61 &   9.13 & 121.06 & 122.29 & 249.42 & 135.45 & 71.56 & 167.25 & 81.45 & 55.75 & 48.16 & 114.12 & 23.69 &  18.37 & 58.78 &  15.42 &  33.81 & 55.15 & 56.27 & 44.58 & 114.44 &  52.63 &  13.12 & 14.28 &  27.10 &  47.82 &  74.19 &  44.83 & 22.70 &  36.40 & 31.32 &  18.83 & 171.15 &  72.85 &  63.95 &  62.82 & 49.10 & 89.80 & 106.42 &  84.98 &   3.75 &  12.26 & 133.60 & 102.68 & 66.20 & 109.50 & 74.54 & 27.90 &  14.71 &  41.54 & 1669.08 & 153.14 &  81.91 &  19.85 & 14.99 & 539.60 &  21.31 &  15.92 & 114.14 & 8.19 &  15.77 &  40.96 & 35.76 & 40.58 & 183.41 & 50.30 & 134.19 & 16.34 & 51.99 &  20.82 &   3.42 &  20.01 & 43.66 &  64.46 &  22.41 & 155.53 & 112.73 &  30.53 &  35.79 & 137.50 & 21.29 &  66.02 & 48.59 & 338.92 & 30.78 & 55.72 & 28.44 & 85.57 &  58.65 & 133.87 &  69.42 &  52.76 & 169.71 &  12.28 & 219.81 & 100.23 & 116.33 &  20.40 &  16.48 &  51.72 &   7.99 & 32.42 &  28.10 &  37.48 &  12.61 &  18.09 & 247.70 &  14.82 &  16.62 & 313.38 &  36.70 &  39.38 & 118.52 & 25.82 & 25.51 & 698.53 &  11.10 & 79.98 & 150.00 & 74.37 & 185.48 &  22.47 &  29.82 &  15.09 &  42.30 & 269.58 & 124.91 & 46.20 &  71.15 & 69.03 & 131.79 &  31.09 & 135.47 &  25.57 & 106.99 & 16.94 & 48.20 &  21.51 & 43.48 &  73.72 &  62.03 & 30.65 & 115.79 & 98.02 &  18.42 &  84.18 & 127.94 & 133.76 & 324.01 &   7.28 & 136.17 & 40.64 & 247.60 &  58.91 &  16.33 & 21.77 &  89.58 & 12.84 &  48.06 &  24.14 & 217.44 &  13.08 & 11.36 & 96.99 &  20.10 &  63.97 &  15.38 &  52.33 & 126.38 & 122.66 & 56.51 & 110.37 &   8.43 & 109.62 &  84.00 & 100.80 & 48.40 &  62.84 & 17.13 &  39.20 & 24.40 &  75.41 & 134.14 &  43.85 &   6.89 & 206.42 & 295.56 &  70.50 & 78.59 &  32.32 & 58.28 & 196.48 & 77.37 & 114.29 & 194.95 &  44.85 &  42.78 & 52.28 &  66.47 &  15.69 & 107.44 & 24.07 & 176.04 & 93.91 & 24.25 &  17.16 &  90.23 &   7.94 &  47.41 & 15.42 & 96.11 &  33.99 & 46.04 &  20.38 &  48.00 & 28.89 &  65.73 &  26.06 &  30.67 & 83.64 & 142.14 & 32.26 & 38.48 & 175.35 &  13.48 & 174.08 & 470.22 & 46.52 &  15.36 & 135.60 &  15.66 & 41.34 & 216.87 & 263.20 & 77.75 & 41.45 & 101.89 &  25.60 & 37.88 & 169.20 & 13.44 & 837.87 &   7.37 & 190.89 &  97.22 & 51.90 &  92.26 &   7.54 & 31.48 & 36.22 & 310.38 & 29.53 & 184.75 & 135.23 & 28.22 &  17.11 & 32.55 & 73.30 & 116.07 & 76.78 & 114.16 & 560.84 &  14.23 &  20.29 & 30.55 & 324.78 &  28.88 & 30.95 & 504.23 &  27.69 & 138.38 &   7.84 &  68.51 &  53.57 & 118.05 & 111.86 & 172.36 & 761.73 & 197.45 &  69.68 & 203.74 &  59.74 &   2.74 & 49.96 &  11.87 & 155.34 &  12.85 &  13.58 &  41.24 & 99.46 & 101.70 &  16.29 & 99.72 &  46.29 & 45.73 &  10.87 &  18.78 &  77.60 & 67.89 &  12.48 &  14.69 &  16.41 &   6.58 & 445.32 & 112.54 & 36.16 & 160.51 & 85.04 & 224.57 &  69.04 & 175.84 &  66.59 &  52.37 &  12.34 & 85.90 & 40.38 & 223.21 &  57.54 & 63.49 &  82.16 &  20.34 & 208.60 &  17.58 &  67.58 & 385.16 & 444.85 & 72.79 &  25.85 &  74.81 & 181.87 & 60.12 & 397.82 & 91.97 &  91.92 & 14.71 & 59.96 &  22.75 &  70.34 &  16.95 & 144.86 &  46.53 &  72.87 & 126.06 & 154.01 & 101.47 & 59.84 &  53.31 & 333.37 &  9.76 & 21.00 &  11.85 &  24.36 &  35.06 & 67.82 & 50.46 & 261.63 & 74.45 &  48.45 &  12.86 &  20.08 & 103.84 &  11.04 & 64.14 &  18.01 & 43.52 &   6.93 &  10.83 &  18.36 & 46.64 &  14.51 & 68.91 &  16.70 & 106.74 & 15.58 & 133.82 & 149.83 & 86.76 & 47.03 &  12.91 & 18.42 & 71.67 & 285.23 & 281.46 &  35.51 &  67.54 &  67.03 &   0.89 & 43.91 &  74.88 & 126.00 & 95.19 &  50.34 & 23.88 & 35.61 & 49.32 & 167.30 & 51.63 & 129.23 & 245.80 & 50.80 & 66.03 & 89.94 & 88.51 & 56.98 & 47.98 & 33.22 & 152.03 & 75.97 & 520.82 & 17.41 & 60.07 & 162.53 & 37.67 & 84.65 & 54.08 & 287.51 & 88.05 & 142.42 & 86.99 & 539.97 & 164.08 & 141.13 & 36.80 & 154.30 & 30.50 & 289.33 & 42.61 & 127.11 & 50.57 & 104.87 & 139.35 & 42.67 & 551.62 & 208.23 & 165.84 & 100.07 & 89.59 & 41.68 & 140.04 & 100.33 & 382.91 & 78.02 & 69.06 & 73.63 & 75.94 & 24.55 & 143.99 & 87.16 & 337.24 & 76.03 & 80.99 & 128.79 & 50.77 & 368.35 & 121.56 & 36.64 & 43.01 & 306.66 & 50.84 & 38.11 & 106.26 & 43.84 & 84.60 & 213.83 & 167.35 & 334.68 & 226.11 & 97.13 & 111.03 & 129.00 & 32.49 & 192.31 & 65.40 & 67.32 & 199.21 & 143.38 & 21.96 & 72.78 & 85.82 & 198.32 & 714.49 & 28.47 & 74.14 & 178.52 & 20.27 & 238.00 & 25.44 & 53.85 & 210.34 & 124.78 & 564.12 & 143.19 & 86.15 & 286.37 & 195.18 & 54.25 & 35.50 & 92.95 & 373.63 & 548.26 & 127.59 & 159.91 & 53.20 & 472.83 & 56.54 & 87.10 & 56.78 & 141.27 & 215.08 & 20.33 & 99.53 & 113.78 & 180.99 & 109.64 & 34.95 & 99.74 & 20.29 & 51.54 & 380.52 & 195.55 & 36.18 & 66.07 & 112.61 & 262.20 & 106.71 & 30.22 & 45.33 & 97.14 & 93.57 & 96.69 & 77.49 & 63.71 & 117.20 & 26.05 & 23.23 & 187.70 & 51.89 & 496.87 & 54.90 & 58.89 & 164.63 & 51.50 & 85.10 & 13.36 & 50.86 &  34.92 & 57.43 &  22.33 & 69.31 & 18.15 & 13.41 & 92.38 &  50.15 & 145.78 & 39.87 & 68.25 & 25.64 & 223.53 & 21.67 & 175.47 & 90.67 & 58.36 &  55.39 & 221.57 & 43.30 & 337.67 & 387.54 & 63.14 & 17.27 & 89.11 &  96.67 & 47.02 & 60.37 & 50.05 & 118.54 & 49.81 & 165.84 & 15.24 & 94.81 & 21.04 &  17.06 & 28.85 & 43.51 & 16.89 & 14.98 & 231.36 & 50.87 & 76.92 &  48.06 & 33.32 & 31.15 & 146.71 & 42.75 & 19.16 & 58.94 & 28.48 & 240.68 & 31.30 & 73.08 &  45.27 & 60.62 & 29.30 & 44.32 & 40.32 & 40.07 & 292.97 & 15.71 & 28.72 & 190.88 & 155.35 & 15.28 & 57.87 & 44.52 & 30.00 & 10.16 & 61.18 & 51.32 &  40.63 & 43.17 & 200.91 & 36.09 & 109.30 & 40.43 & 66.92 & 32.22 & 47.68 &  33.80 & 20.49 & 59.67 &  47.26 & 21.97 & 20.72 &  54.75 & 209.55 & 32.50 & 20.83 & 187.73 & 13.72 & 28.19 & 119.67 & 238.10 & 46.91 & 162.24 & 61.24 & 167.92 & 33.07 &  41.90 &  30.21 & 38.96 & 35.37 & 10.02 & 16.40 & 61.15 & 30.57 & 32.40 & 57.73 & 43.19 & 38.29 & 21.51 & 43.04 & 40.72 & 40.59 & 16.00 & 48.08 & 84.78 & 37.59 & 55.65 & 29.01 & 13.26 &  7.64 & 107.31 & 36.72 & 204.04 & 60.27 & 45.90 & 14.62 & 24.86 & 71.47 & 87.86 & 22.63 & 104.83 & 17.51 & 17.36 & 140.15 & 57.71 & 34.48 & 53.08 & 80.93 & 84.24 & 34.43 &  54.74 & 25.92 & 30.48 & 31.84 & 186.42 & 28.51 & 55.04 &  43.53 & 13.87 & 28.94 & 28.03 & 50.97 & 133.73 & 63.56 & 36.14 & 42.45 & 33.85 & 47.75 & 74.52 & 49.09 & 129.89 & 122.60 & 14.27 & 39.33 &  32.20 & 96.55 & 22.18 & 25.75 & 33.08 & 50.29 & 79.22 & 19.61 &  43.99 & 53.75 & 51.87 & 199.22 & 43.10 & 33.73 & 169.10 & 64.57 & 33.69 & 71.10 & 27.72 & 75.57 & 62.20 & 100.47 & 46.91 & 59.80 &  9.41 & 26.82 &  8.87 & 22.86 & 23.07 &  41.79 & 12.28 & 54.09 &  97.25 & 42.59 & 35.44 & 14.96 & 108.00 & 99.53 &  42.49 & 227.06 & 202.01 & 134.79 & 349.75 & 535.94 & 150.50 & 192.35 & 215.15 & 152.20 & 156.06 & 198.72 & 238.98 & 161.92 & 113.74 & 238.37 & 194.87 & 484.41 & 198.22 & 181.75 & 356.04 & 41.38 & 524.49 & 249.14 & 154.19 & 43.42 & 145.14 & 239.60 & 207.89 & 143.55 & 200.32 & 254.82 & 250.08 & 369.71611851 & 172.27997580 & 32.77416826 & 58.16029322 & 76.531985280 & 113.49042705 & 85.86828711 & 572.85877571 & 1.212859e+02 & 54.152234 & 31.26855427 & 13.99214176 & 220.52767784 & 16.33881415 & 64.42065224 & 29.05739879 & 26.6623710 & 26.930648681 & 57.48600996 & 145.94385785 & 307.46264932 & 186.51479987 & 46.19853511 & 164.29207285 & 24.0446285 & 781.27982171 & 65.32051088 & 182.60345578 & 120.80384283 & 108.83691339 & 43.29967934 & 3.910134e+02 & 210.65268233 & 271.58950023 & 82.552353026 & 119.00708274 & 574.03414309 & 78.90158091 & 132.42521700 & 22.772140615 & 19.627669980 & 16.04494932 & 12.26962257 & 24.94807669 & 38.890238613 & 15.72450209 & 7.003046759 & 20.699769543 & 22.245173514 & 22.01922192 & 17.63289318 & 39.811912501 & 10.890269228 & 20.90628810 & 21.047834636 & 24.432792685 &  7.116825953 & 16.650692626 & 21.882036899 & 22.80664063 & 10.62938992 & 6.204043700 & 19.73894898 & 20.20623876 & 28.820605851 & 54.287778760 & 24.75762614 & 18.02331978 & 30.11779895 & 25.58303961 & 30.155235099 & 15.9969494 & 36.41416122 & 19.83216528 & 27.24430932 & 14.784273921 & 49.09992781 & 33.061925747 & 21.11020157 & 16.549001265 & 33.98670390 & 15.414514853 & 37.64908821 & 13.901822484 & 9.79334476 & 9.943593309 & 14.824818577 & 10.69976470 & 11.08536984 & 23.30519341 & 13.416008634 & 20.546135025 & 19.93266772 & 19.38608704 & 13.25030564 & 42.3528973 & 32.526051046 & 37.80101083 & 12.40966338 & 16.095401146 & 13.68783129 & 10.84702721 & 22.79442586 & 23.18669694 & 24.04185539 & 20.172776476 & 26.0889401 & 11.43704602 & 34.6316371 & 27.16162911 & 21.9941668 & 22.309476183 & 22.860773767 & 36.09755054 & 20.590725440 & 33.67076782 & 17.77582811 & 26.26522714 & 11.57651291 & 17.62694971 & 35.47437858 & 21.26670282 & 25.55015791 & 9.05708124 &  5.149836058 & 15.3813074 & 39.18616890 & 23.20853465 & 44.30217876 & 42.5221260 & 55.17207026 & 7.04572100 & 13.853399858 & 28.12522068 & 22.233515911 & 58.55697259 & 31.928903591 & 51.6507343 & 14.45386400 & 16.41618228 & 21.17996077 &  8.99012153 & 14.016722554 & 20.61838818 & 31.37719322 & 11.67147048 & 17.3671346 & 10.10426951 & 23.780219480 & 12.41233662 & 92.14215680 & 78.1920925 & 1.227412e+02 & 115.91220657 & 93.44900950 & 88.843223854 & 161.4348545 & 1.509197e+02 & 60.254430001 & 1.010185e+02 & 1.140615e+02 & 137.00249552 & 99.500507599 & 85.235111568 & 3.041836e+02 & 2.239884e+02 & 87.844371871 & 123.05378046 & 45.828315800 & 64.859180548 & 138.87025042 & 117.90558951 & 107.41756761 & 54.353700028 & 1.796108e+02 & 1.444122e+02 & 599.32629020 & 1.454991e+02 & 61.603201725 & 111.59614357 & 70.83410410 & 97.10165088 & 149.13176845 & 165.58977232 & 1.236894e+02 & 76.828946449 & 35.557882596 & 1.358033e+02 & 63.220854602 & 63.728948340 & 1.480167e+02 & 79.4678037 & 97.16209388 & 64.46924760 & 71.294566832 & 120.79589095\\\\\n",
       "\tA2M-AS1 &   0.38 &   0.21 &   0.23 &   0.43 &   0.35 &   0.38 &   0.98 &  0.14 &  0.05 &  0.33 &   0.52 &   0.15 &  0.66 &   0.20 &  0.11 &   0.23 &   0.51 &  0.73 &   0.05 &   0.75 &   0.65 &   0.28 &   0.03 &   0.11 &   0.78 &   0.42 &   1.58 &  0.22 &   0.71 &   0.27 &   0.35 &   0.23 &   0.21 &   0.13 &   1.11 &   2.18 &  0.74 &   1.06 &   0.93 &   1.18 &  0.21 &   0.20 &   1.17 &   0.71 &   0.22 &   0.42 &   0.26 &   0.22 &   0.41 &   0.30 &   0.12 &  0.51 &   0.70 &   0.40 &  0.27 &   0.62 &   0.30 &   0.43 &   1.82 &  0.14 &   0.13 &   1.23 &   0.17 &   0.37 &   0.37 &  0.37 &   0.54 &  0.13 &   1.05 &  0.51 &   0.25 &   0.30 &  0.07 &   0.35 &   0.28 &   0.40 &   0.11 &   0.47 &  0.97 &   0.31 &   0.29 &   1.21 &   1.40 &   1.39 &   0.14 &   0.50 &   0.82 &   0.41 &   0.53 &   0.16 &   0.13 &  0.50 &   0.26 &  0.50 &   0.32 &   0.33 &   0.97 &   0.35 &  0.40 &   0.31 &  0.38 &   1.00 &   0.04 &  0.21 &  1.42 &   0.43 &   1.44 &   0.62 &   0.08 &   0.14 &  0.44 &  0.45 &  0.34 &   0.68 &  0.46 &  0.27 &  0.11 &  0.64 &   0.97 &   0.27 &   0.23 &  0.29 &   0.46 &  0.90 &  0.15 &   0.65 &   0.28 &   0.23 &  0.45 &   0.38 &   0.21 &   0.31 &  1.58 &   0.31 &  0.17 &  1.08 &   1.02 &   0.32 &   0.69 &  0.50 &   2.15 &  0.13 &   0.39 &   0.23 &   0.71 &   0.17 &  0.18 &   0.99 &   0.11 &  0.10 &  0.26 &   0.22 &  0.11 &   0.20 &   0.12 &  0.43 &  1.40 &  0.44 &   0.47 &   0.37 &  0.17 &   0.61 &   0.24 &   1.12 &   0.16 &  1.09 &  0.13 &  0.62 &   0.83 &   0.30 &    0.49 &  0.72 &   0.33 &   0.15 &   0.08 &   0.49 &  0.09 &   3.69 &   0.35 &  0.30 &   0.21 &  0.11 &   0.44 &  0.51 &   0.11 &   0.08 &   0.40 &   0.04 &   0.48 &  0.54 &  0.27 &   0.15 &  0.27 &   0.16 &  0.43 &   0.75 &   0.33 &   0.18 &   0.28 &   0.41 &   0.19 &   0.60 &  0.50 &   0.41 &  0.43 &  0.34 &  0.70 &   0.74 &  0.92 &   0.03 &  0.56 &   0.16 &   0.26 &  0.63 &  0.17 &  0.56 &   0.48 &   0.75 &   0.27 &  0.57 &   0.15 &   0.77 &   0.24 &   1.49 &  0.25 &   0.83 &  0.19 &   0.84 &   0.46 &   0.33 &   0.29 &   0.41 &  0.10 &  0.15 &   0.11 &   0.45 &   0.27 &   0.50 &   0.54 &   1.12 &  0.22 &   0.46 &  0.36 &  0.15 &   0.07 &   0.63 &    3.85 &   0.29 &   0.85 &   0.30 &  0.26 &   0.19 &   0.24 &   0.51 &   0.39 & 1.26 &   0.75 &   0.12 &  0.18 &  0.76 &   0.57 &  0.23 &   0.30 &  0.06 &  0.25 &   0.49 &   1.55 &   0.13 &  1.11 &   0.27 &   0.80 &   3.23 &   0.35 &   0.49 &   0.13 &   0.50 &  0.47 &   0.35 &  1.54 &   0.25 &  0.55 &  0.20 &  0.15 &  0.15 &   0.48 &   0.23 &   0.49 &   1.61 &   0.20 &   0.13 &   0.94 &   0.27 &   0.50 &   0.21 &   0.06 &   0.21 &   0.51 &  0.32 &   0.37 &   1.03 &   0.29 &   0.36 &   2.45 &   0.38 &   0.74 &   0.52 &   0.59 &   0.47 &   0.79 &  0.47 &  0.33 &  27.51 &   0.39 &  0.72 &   2.40 &  0.24 &   0.22 &   0.34 &   0.39 &   2.91 &   0.72 &   0.67 &   0.28 &  0.89 &   0.70 &  0.31 &   0.32 &   0.38 &   0.26 &   0.29 &   0.58 &  1.06 &  0.39 &   0.14 &  0.29 &   0.15 &   0.60 &  2.30 &   0.32 &  0.43 &   0.20 &   1.34 &   0.17 &   0.11 &   4.70 &   0.14 &   0.28 &  0.88 &   0.43 &   0.44 &   0.38 &  0.22 &   0.82 &  0.76 &   1.69 &   0.26 &   0.74 &   0.35 &  0.72 &  0.17 &   0.28 &   0.15 &   0.24 &   0.25 &   0.69 &   1.77 &  0.82 &   0.33 &   1.57 &   0.74 &   0.85 &   0.19 &  0.41 &   0.80 &  0.44 &   0.11 &  0.09 &   1.78 &   0.46 &   0.15 &   0.32 &   0.38 &   0.63 &   0.26 &  0.81 &   0.24 &  0.21 &   0.65 &  0.26 &   0.06 &   0.67 &   0.51 &   0.12 &  0.63 &   0.22 &   0.85 &   0.11 &  0.48 &   0.43 &  1.01 &  0.03 &   0.07 &   0.51 &   0.35 &   0.78 &  0.87 &  0.29 &   0.32 &  0.66 &   0.54 &   0.94 &  0.10 &   0.36 &   0.59 &   0.09 &  0.43 &   0.31 &  0.31 &  0.27 &   0.81 &   0.30 &   0.50 &   1.93 &  0.43 &   0.20 &   0.28 &   0.55 &  0.35 &   0.65 &   0.54 &  0.50 &  0.27 &   0.21 &   0.19 &  0.21 &   0.24 &  1.14 &   0.23 &   0.28 &   0.23 &   0.38 &  0.44 &   0.21 &   0.10 &  0.60 &  0.55 &   0.87 &  0.29 &   0.33 &   0.38 &  0.28 &   0.48 &  0.74 &  0.42 &   0.53 &  0.13 &   0.30 &   0.37 &   0.90 &   0.24 &  0.29 &   0.25 &   0.24 &  0.17 &   0.31 &   0.82 &   0.38 &   0.20 &   0.22 &   0.25 &   0.36 &   0.29 &   0.18 &   5.24 &   0.24 &   0.19 &   0.19 &   0.70 &   0.65 &  0.22 &   0.25 &   0.68 &   0.18 &   0.15 &   0.76 &  0.13 &   0.19 &   0.80 &  0.62 &   0.31 &  0.19 &   0.22 &   0.18 &   0.15 &  0.23 &   0.08 &   2.22 &   0.86 &   0.71 &   1.96 &   0.65 &  0.26 &   0.48 &  0.36 &   0.32 &   0.60 &   0.16 &   0.27 &   0.14 &   0.99 &  1.05 &  0.32 &   0.15 &   0.60 &  0.21 &   0.98 &   0.33 &   0.31 &   0.49 &   0.33 &   1.47 &   8.51 &  0.45 &   0.23 &   0.51 &   0.23 &  1.22 &   0.26 &  0.25 &   0.18 &  0.08 &  0.36 &   0.50 &   0.40 &   0.20 &   0.26 &   0.32 &   0.99 &   0.25 &   0.62 &   0.19 &  0.70 &   0.34 &   5.38 &  0.21 &  0.17 &   0.23 &   1.44 &   1.18 &  0.25 &  0.28 &   0.24 &  0.21 &   0.90 &   0.79 &   0.12 &   1.02 &   0.54 &  0.98 &   0.49 &  0.45 &   2.07 &   0.11 &   1.48 &  0.29 &   0.03 &  0.45 &   0.41 &   0.35 &  0.10 &   0.32 &   1.03 &  0.28 &  0.25 &   1.15 &  0.20 &  0.51 &   0.31 &   0.34 &   0.53 &   0.18 &   0.18 &   0.03 &  0.77 &   0.33 &   0.69 &  1.97 &   0.10 &  0.20 &  0.26 &  0.60 &   0.89 &  1.35 &   3.22 &   1.81 &  3.00 &  0.49 &  0.82 &  0.24 &  2.40 &  3.97 &  0.36 &   1.12 &  0.52 &   3.20 &  0.23 &  0.96 &   0.55 &  6.04 &  3.26 &  0.37 &   0.98 &  1.67 &   1.42 &  0.62 &   2.83 &   0.57 &   1.81 &  0.31 &   3.90 &  4.41 &   2.46 &  2.67 &   4.69 &  2.57 &   0.57 &   0.78 &  0.67 &   4.07 &   6.38 &   3.74 &   1.89 & 15.37 &  1.26 &   2.31 &   0.28 &   1.14 &  1.95 &  0.79 &  2.88 &  0.56 &  0.12 &   0.92 &  5.15 &   4.77 &  1.41 &  0.40 &   0.66 &  0.53 &   0.77 &   0.54 &  1.01 &  4.50 &   2.21 &  0.25 &  1.62 &   3.71 &  0.60 &  2.40 &   2.60 &   1.19 &   3.07 &   1.16 &  1.43 &   5.90 &   0.25 &  0.58 &   1.34 &  8.47 &  4.23 &   0.84 &   0.92 &  0.76 &  2.08 &  0.49 &   1.21 &   2.77 &  0.51 &  0.70 &   0.52 &  0.71 &   4.32 &  1.31 &  0.70 &   1.32 &   0.77 &   2.87 &   2.12 &  0.12 &   2.30 &   0.93 &  0.87 &  7.93 &  2.97 &   3.49 &   2.60 &   1.15 &   0.76 &  2.06 &   3.69 &  2.38 &  2.74 &  0.46 &   0.30 &   1.35 &  2.03 &  2.05 &   2.80 &   0.85 &   5.80 &  1.52 &  5.51 &  0.41 &  0.28 &   1.31 &   1.57 &  0.11 &  2.72 &   4.84 &   1.56 &   0.37 &  2.80 &  1.09 &  6.88 &  2.16 &  2.52 &  3.38 & 14.59 &   0.78 &  0.34 &  0.52 &   1.79 &  0.85 &   3.13 &  0.84 &  0.58 &   0.77 &  1.08 &  0.40 &  0.75 &  4.81 &   2.89 &  2.14 &   1.10 &  4.13 &  2.15 &  1.65 &  1.71 &   8.55 &   4.15 &  4.80 &  0.77 &  4.15 &   1.77 &  2.44 &   2.22 &  2.26 &  2.48 &   3.85 &   2.27 &  9.14 &   0.68 &   0.77 &  1.32 &  5.23 &  4.03 &   3.84 &  2.54 &  1.10 &  1.28 &   1.77 &  3.16 &   1.28 &  0.98 &  2.09 &  1.81 &   4.60 &  2.74 &  1.22 &  6.01 &  2.55 &   1.09 &  2.06 &  2.16 &   4.18 &  2.18 &  4.21 &   2.90 &  2.15 &  5.19 &  2.92 &  0.75 &   1.79 &  3.03 &  5.23 &   3.12 &  5.18 &  2.87 &  1.55 &  2.95 &  2.78 &   2.07 &  2.08 &  4.65 &   1.44 &   1.18 &  0.48 &  0.75 &  2.35 &  1.22 &  2.05 &  1.10 &  1.89 &   1.98 &  4.35 &   1.85 &  3.04 &   1.26 &  4.13 &  3.55 &  3.61 &  3.63 &   3.25 &  1.85 &  4.96 &   8.40 &  4.22 &  8.90 &   3.01 &   1.01 &  0.19 &  5.91 &   1.04 &  2.10 &  1.41 &   1.24 &   1.29 &  4.42 &   1.72 &  5.67 &   1.44 &  3.40 &   0.70 &   5.77 &  7.09 &  3.65 &  2.92 &  1.31 &  2.98 &  4.61 &  3.47 &  5.86 &  3.52 &  3.53 &  2.39 &  2.65 &  3.74 &  1.71 &  3.11 &  2.03 &  3.85 &  1.85 &  4.09 &  4.74 &  5.32 &  1.55 &   1.79 &  4.26 &   3.76 &  1.62 &  1.52 &  2.34 &  3.75 &  4.28 &  3.92 &  1.45 &   3.59 &  3.82 &  0.48 &   3.00 &  2.13 &  2.76 &  2.92 &  2.52 &  0.40 &  1.17 &   9.10 &  2.68 &  2.17 &  6.10 &  10.71 &  1.67 &  1.82 &   4.21 &  2.75 &  2.86 &  4.65 &  1.81 &   3.33 &  0.99 &  1.85 &  3.98 &  3.32 &  2.56 &  5.04 &  1.61 &   0.70 &   1.70 &  2.33 &  3.70 &   3.83 &  0.59 &  2.90 &  2.05 &  3.54 &  4.02 &  4.29 &  1.25 &   1.93 &  0.23 &  8.45 &   1.70 &  1.64 &  2.38 &   1.69 &  3.52 &  2.05 &  1.49 &  0.76 &  2.10 &  3.67 &   1.61 &  6.59 &  1.87 &  2.32 &  2.50 &  0.87 &  3.09 &  0.20 &   3.28 &  4.99 &  3.26 &   1.73 &  0.71 &  4.95 &  1.62 &   3.44 &  1.80 &   3.38 &   1.69 &   1.79 &   0.79 &   0.74 &   3.75 &   2.05 &   0.79 &   1.92 &   1.48 &   3.44 &   3.18 &   2.99 &  10.00 &   1.19 &   3.31 &   4.48 &   0.91 &   1.02 &   4.67 &   4.79 &  1.38 &   2.27 &   2.82 &   0.63 &  0.37 &   1.06 &   3.98 &   0.71 &   0.44 &   3.81 &   2.18 &   2.28 &   2.84165070 &   5.67298630 &  3.57880809 &  4.91822994 &  0.365401458 &   0.36827120 &  5.17265165 &   3.33363720 & 2.028670e+00 &  1.163256 &  1.83330625 &  1.73650208 &   0.49388607 &  3.95636605 &  1.67456841 &  0.51535282 &  0.2248095 &  0.177430250 &  0.79692356 &   1.74688805 &   1.30605523 &   0.80387035 &  1.78832302 &   2.80685320 &  0.4984438 &   0.94067064 &  1.10427914 &   1.97602942 &   0.50327268 &   0.37521493 &  0.46587199 & 4.665779e-01 &   4.57841604 &   0.55206244 &  0.561837149 &   0.72415574 &   1.06454206 &  0.27487346 &   1.84082628 &  0.170635958 &  0.104369558 &  0.29985715 &  0.85144390 &  0.21337633 &  0.229449071 &  0.75553360 & 2.890354698 &  0.267866711 &  0.937857304 &  0.49156221 &  1.09273440 &  0.322159261 &  0.758261085 &  1.28149807 &  0.401527555 &  0.054579515 &  0.079703483 &  0.124513406 &  0.102032543 &  0.04164825 &  0.52579009 & 0.148607658 &  1.02683555 &  0.25272444 &  0.450428946 &  0.586480230 &  0.40719657 &  1.50990471 &  1.33783537 &  0.20860107 &  0.062689508 &  0.5179628 &  0.52336887 &  1.39693271 &  1.33073921 &  0.102591495 &  0.52565689 &  0.485137528 &  0.25423576 &  1.268013774 &  0.71843994 &  0.623875808 &  0.32625616 &  0.049869633 & 0.56099414 & 0.303871219 &  0.880017812 &  0.79922904 &  0.16077211 &  0.22565832 &  0.825162746 &  0.178797719 &  0.55750980 &  0.17765496 &  1.23909338 &  0.5511577 &  0.304181563 &  0.43348949 &  0.22779892 &  0.053531929 &  0.18080593 &  0.37205192 &  0.24173964 &  1.27749583 &  0.28923330 &  0.620775364 &  0.2335651 &  0.94738179 &  0.7094551 &  0.16147824 &  1.1870275 &  0.539661997 &  0.388787530 &  2.56924851 &  0.318849936 &  0.21594938 &  1.42986165 &  1.02068881 &  0.42702488 &  0.64355634 &  0.69399297 &  2.57141022 &  0.30651264 & 0.27573353 &  0.655034881 &  0.6561404 &  1.10516992 &  0.68113118 &  0.35314824 &  0.9107047 &  0.84602306 & 0.08525128 &  0.228914086 &  0.32728989 &  0.414336003 &  1.05428710 &  1.595543819 &  0.3988146 &  1.17475428 &  0.54992232 &  0.12524934 &  0.23499301 &  0.270348132 &  0.54750508 &  0.34327149 &  1.11534073 &  2.2882998 &  0.04671910 &  0.105200080 &  0.23364680 &  0.45377366 &  0.4999724 & 7.912480e-01 &   0.96453833 &  0.35211471 &  0.301978059 &   0.2889535 & 3.607198e-01 &  0.705424053 & 3.648603e-01 & 3.630921e-01 &   0.68410888 &  0.417183545 &  0.590561197 & 2.358709e-01 & 5.671682e-01 &  0.605203371 &   0.44551761 &  0.440075407 &  0.558276416 &   0.99301062 &   0.41730695 &   0.37931098 &  0.702495503 & 9.990898e-01 & 9.884554e-01 &   0.12949450 & 3.860496e-01 &  0.416059564 &   1.46058064 &  0.28152156 &  0.53981485 &   0.30033441 &   0.34694641 & 8.413952e-01 &  0.444276182 &  0.744254574 & 4.421134e-01 &  0.197240583 &  0.916434869 & 1.896832e-01 &  0.7646672 &  0.21666408 &  0.50357142 &  0.318341732 &   0.40298729\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 1177\n",
       "\n",
       "| <!--/--> | 7316-1926 &lt;dbl&gt; | 7316-489 &lt;dbl&gt; | 7316-13 &lt;dbl&gt; | 7316-3936 &lt;dbl&gt; | 7316-873 &lt;dbl&gt; | 7316-2669 &lt;dbl&gt; | 7316-1928 &lt;dbl&gt; | 7316-1655 &lt;dbl&gt; | 7316-2622 &lt;dbl&gt; | 7316-2893 &lt;dbl&gt; | ⋯ ⋯ | ICGC_PA89 &lt;dbl&gt; | ICGC_PA9 &lt;dbl&gt; | ICGC_PA91 &lt;dbl&gt; | ICGC_PA92 &lt;dbl&gt; | ICGC_PA93 &lt;dbl&gt; | ICGC_PA94 &lt;dbl&gt; | ICGC_PA95 &lt;dbl&gt; | ICGC_PA96 &lt;dbl&gt; | ICGC_PA97 &lt;dbl&gt; | ICGC_PA99 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 7SK | 101.08 | 113.29 | 143.37 | 167.31 | 222.65 | 133.89 | 114.69 | 95.22 |  8.11 | 96.12 | ⋯ |  0.287477450 | 1.510045e-01 |  1.316372799 |  0.623302011 | 5.390079e-01 |  0.2323849 |  0.00000000 |  0.62421705 |  0.204103009 |   0.27949627 |\n",
       "| A1BG |   0.82 |   3.00 |   2.33 |   2.07 |   3.87 |   0.60 |   1.64 |  1.01 |  3.29 |  0.92 | ⋯ |  0.730796870 | 5.787472e-01 |  1.432226226 |  1.305533013 | 1.757023e+00 |  0.6259475 |  0.66270817 |  0.95084569 |  0.856170521 |   0.90799219 |\n",
       "| A1BG-AS1 |   0.34 |   1.54 |   0.66 |   1.74 |   2.21 |   0.10 |   0.49 |  0.33 |  0.24 |  0.34 | ⋯ |  0.146521078 | 1.951477e-01 |  0.359481416 |  0.459126757 | 2.827870e-01 |  0.4634999 |  0.18452624 |  0.49039428 |  0.170731858 |   0.18425727 |\n",
       "| A1CF |   0.00 |   0.00 |   0.00 |   0.00 |   0.00 |   0.00 |   0.00 |  0.00 |  0.00 |  0.00 | ⋯ |  0.003605546 | 5.714179e-03 |  0.003864498 |  0.008657391 | 7.485289e-03 |  0.0099566 |  0.01394094 |  0.01546211 |  0.007234758 |   0.01149084 |\n",
       "| A2M | 270.92 |  22.46 |  58.90 |  40.02 |  39.74 |  52.95 |  34.72 |  4.87 | 11.78 | 34.41 | ⋯ | 35.557882596 | 1.358033e+02 | 63.220854602 | 63.728948340 | 1.480167e+02 | 79.4678037 | 97.16209388 | 64.46924760 | 71.294566832 | 120.79589095 |\n",
       "| A2M-AS1 |   0.38 |   0.21 |   0.23 |   0.43 |   0.35 |   0.38 |   0.98 |  0.14 |  0.05 |  0.33 | ⋯ |  0.744254574 | 4.421134e-01 |  0.197240583 |  0.916434869 | 1.896832e-01 |  0.7646672 |  0.21666408 |  0.50357142 |  0.318341732 |   0.40298729 |\n",
       "\n"
      ],
      "text/plain": [
       "         7316-1926 7316-489 7316-13 7316-3936 7316-873 7316-2669 7316-1928\n",
       "7SK      101.08    113.29   143.37  167.31    222.65   133.89    114.69   \n",
       "A1BG       0.82      3.00     2.33    2.07      3.87     0.60      1.64   \n",
       "A1BG-AS1   0.34      1.54     0.66    1.74      2.21     0.10      0.49   \n",
       "A1CF       0.00      0.00     0.00    0.00      0.00     0.00      0.00   \n",
       "A2M      270.92     22.46    58.90   40.02     39.74    52.95     34.72   \n",
       "A2M-AS1    0.38      0.21     0.23    0.43      0.35     0.38      0.98   \n",
       "         7316-1655 7316-2622 7316-2893 ⋯ ICGC_PA89    ICGC_PA9     ICGC_PA91   \n",
       "7SK      95.22      8.11     96.12     ⋯  0.287477450 1.510045e-01  1.316372799\n",
       "A1BG      1.01      3.29      0.92     ⋯  0.730796870 5.787472e-01  1.432226226\n",
       "A1BG-AS1  0.33      0.24      0.34     ⋯  0.146521078 1.951477e-01  0.359481416\n",
       "A1CF      0.00      0.00      0.00     ⋯  0.003605546 5.714179e-03  0.003864498\n",
       "A2M       4.87     11.78     34.41     ⋯ 35.557882596 1.358033e+02 63.220854602\n",
       "A2M-AS1   0.14      0.05      0.33     ⋯  0.744254574 4.421134e-01  0.197240583\n",
       "         ICGC_PA92    ICGC_PA93    ICGC_PA94  ICGC_PA95   ICGC_PA96  \n",
       "7SK       0.623302011 5.390079e-01  0.2323849  0.00000000  0.62421705\n",
       "A1BG      1.305533013 1.757023e+00  0.6259475  0.66270817  0.95084569\n",
       "A1BG-AS1  0.459126757 2.827870e-01  0.4634999  0.18452624  0.49039428\n",
       "A1CF      0.008657391 7.485289e-03  0.0099566  0.01394094  0.01546211\n",
       "A2M      63.728948340 1.480167e+02 79.4678037 97.16209388 64.46924760\n",
       "A2M-AS1   0.916434869 1.896832e-01  0.7646672  0.21666408  0.50357142\n",
       "         ICGC_PA97    ICGC_PA99   \n",
       "7SK       0.204103009   0.27949627\n",
       "A1BG      0.856170521   0.90799219\n",
       "A1BG-AS1  0.170731858   0.18425727\n",
       "A1CF      0.007234758   0.01149084\n",
       "A2M      71.294566832 120.79589095\n",
       "A2M-AS1   0.318341732   0.40298729"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tpm_matrix_ped_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tim3 <- tpm_matrix_ped_final[\"HAVCR2\", TARGETids_highTIM3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "   9.50   12.04   14.54   15.06   17.41   26.17 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(tim3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "KFids_highTIM3 <- c(\n",
    "\"GMKF-30-PASKZT03-01B-99R\",\n",
    "\"GMKF-30-PAUIRD03-01B-99R\",\n",
    "\"GMKF-30-PAUTFT03-01B-99R\",\n",
    "\"GMKF-30-PATLBP03-01B-99R\",\n",
    "\"GMKF-30-PAUYFR03-01B-99R\",\n",
    "\"GMKF-30-PASKFM03-01B-99R\",\n",
    "\"GMKF-30-PATLDR03-01B-99R\",\n",
    "\"GMKF-30-PATJDG03-01B-99R\",\n",
    "\"GMKF-30-PAUPMU03-01B-99R\",\n",
    "\"GMKF-30-PATBCM03-01B-99R\",\n",
    "\"GMKF-30-PATSUL03-01B-99R\",\n",
    "\"GMKF-30-PAUKYI03-01B-99R\",\n",
    "\"GMKF-30-PASCGM03-01B-99R\",\n",
    "\"GMKF-30-PASUMK03-01B-99R\",\n",
    "\"GMKF-30-PARWEV03-01B-99R\",\n",
    "\"GMKF-30-PARZCJ03-01B-99R\",\n",
    "\"GMKF-30-PASXWF03-01B-99R\",\n",
    "\"GMKF-30-PASYYZ03-01B-99R\",\n",
    "\"GMKF-30-PARYAG03-01B-99R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tim3_KF <- tpm_matrix_ped_final[\"HAVCR2\", KFids_highTIM3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  6.750   8.835   9.740  10.228  10.580  16.810 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(tim3_KF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>30594</li>\n",
       "\t<li>1177</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 30594\n",
       "\\item 1177\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 30594\n",
       "2. 1177\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 30594  1177"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(tpm_matrix_ped_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_tpm <- tpm_matrix_ped_final[,colnames(tpm_matrix_ped_final) %in% NBL$sample_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.680   3.175   4.380   5.800   6.372  26.170 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(nbl_tpm[\"HAVCR2\",]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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