notebooks/00_Div_validation_plots.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=\"#Dependencies\" data-toc-modified-id=\"Dependencies-1\">Dependencies</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-2\">Functions</a></span></li><li><span><a href=\"#Paths\" data-toc-modified-id=\"Paths-3\">Paths</a></span></li><li><span><a href=\"#Main\" data-toc-modified-id=\"Main-4\">Main</a></span></li><li><span><a href=\"#PROFYLE\" data-toc-modified-id=\"PROFYLE-5\">PROFYLE</a></span><ul class=\"toc-item\"><li><span><a href=\"#RNA\" data-toc-modified-id=\"RNA-5.1\">RNA</a></span></li><li><span><a href=\"#CapTCR-Seq\" data-toc-modified-id=\"CapTCR-Seq-5.2\">CapTCR-Seq</a></span></li><li><span><a href=\"#Bind-RNA-and-Cap\" data-toc-modified-id=\"Bind-RNA-and-Cap-5.3\">Bind RNA and Cap</a></span></li></ul></li><li><span><a href=\"#ICGC\" data-toc-modified-id=\"ICGC-6\">ICGC</a></span><ul class=\"toc-item\"><li><span><a href=\"#CapTCR-Seq\" data-toc-modified-id=\"CapTCR-Seq-6.1\">CapTCR-Seq</a></span></li><li><span><a href=\"#Bind-RNA-and-Cap\" data-toc-modified-id=\"Bind-RNA-and-Cap-6.2\">Bind RNA and Cap</a></span></li></ul></li><li><span><a href=\"#NPC\" data-toc-modified-id=\"NPC-7\">NPC</a></span><ul class=\"toc-item\"><li><span><a href=\"#RNA\" data-toc-modified-id=\"RNA-7.1\">RNA</a></span></li><li><span><a href=\"#CapTCR-Seq\" data-toc-modified-id=\"CapTCR-Seq-7.2\">CapTCR-Seq</a></span></li><li><span><a href=\"#Bind-RNA-and-Cap\" data-toc-modified-id=\"Bind-RNA-and-Cap-7.3\">Bind RNA and Cap</a></span></li></ul></li><li><span><a href=\"#Analysis-of-diversity-measures\" data-toc-modified-id=\"Analysis-of-diversity-measures-8\">Analysis of diversity measures</a></span><ul class=\"toc-item\"><li><span><a href=\"#linear-regression\" data-toc-modified-id=\"linear-regression-8.1\">linear regression</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: lattice\n",
      "\n",
      "Loading required package: survival\n",
      "\n",
      "Loading required package: Formula\n",
      "\n",
      "Loading required package: ggplot2\n",
      "\n",
      "\n",
      "Attaching package: ‘Hmisc’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    format.pval, units\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(Hmisc)\n",
    "library(broom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/OneDrive - UHN/R_src/Immune_diversity.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/ggplot2_theme.R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mordpath <- \"/Users/anabbi/Desktop/Sam/immpedcan/mixcr/PROFYLE/RNAseq/\"\n",
    "h4hpath <- \"/Users/anabbi/Desktop/H4H/immpedcan/PROFYLE/mixcr/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROFYLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "profyle_rna <- list.files(mordpath, pattern = \"CLONES_TRB\", recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_profyle_rna <- immunelistfx(profyle_rna, mordpath, \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6"
      ],
      "text/latex": [
       "6"
      ],
      "text/markdown": [
       "6"
      ],
      "text/plain": [
       "[1] 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trb_list_profyle_rna <- trb_list_profyle_rna[sapply(trb_list_profyle_rna, function(x) length(unlist(x))) > 1] # remove files with one clonotype\n",
    "                            \n",
    "length(trb_list_profyle_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_profyle_rna <- trb_list_profyle_rna[sapply(trb_list_profyle_rna, function(x) var(unlist(x))) > 0] # remove files no variance                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "4"
      ],
      "text/latex": [
       "4"
      ],
      "text/markdown": [
       "4"
      ],
      "text/plain": [
       "[1] 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(trb_list_profyle_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_profyle_rna <- Divstats.fx(trb_list_profyle_rna, \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 4 × 19 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TRB</th><th scope=col>Reads</th><th scope=col>CPKR</th><th scope=col>Average_reads</th><th scope=col>VMR</th><th scope=col>Max_reads</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>Sample_Coverage</th><th scope=col>observed_Richness</th><th scope=col>estimated_Richness</th><th scope=col>SE_Richeness</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Shannon</th><th scope=col>SE_Shannon</th><th scope=col>observed_Simpson</th><th scope=col>estimated_Simpson</th><th scope=col>SE_Simpson</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt</th><td>  47</td><td>  72</td><td>652.7778</td><td>1.531915</td><td>1.5390531</td><td>17</td><td>  39</td><td>  5</td><td>  81.288</td><td>0.584</td><td>  47</td><td> 196.988</td><td> 83.406</td><td>  30.566</td><td>  89.916</td><td> 28.789</td><td>  14.164</td><td>  17.388</td><td>  6.927</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt</th><td>   3</td><td>  13</td><td>230.7692</td><td>4.333333</td><td>1.1383576</td><td>10</td><td>   1</td><td>  1</td><td>   3.399</td><td>0.991</td><td>   3</td><td>   3.462</td><td>  1.236</td><td>   1.988</td><td>   2.225</td><td>  0.646</td><td>   1.610</td><td>   1.696</td><td>  0.504</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt</th><td>  15</td><td>  17</td><td>882.3529</td><td>1.133333</td><td>0.4556451</td><td> 3</td><td>  14</td><td>  0</td><td>  27.859</td><td>0.307</td><td>  15</td><td> 100.647</td><td> 47.553</td><td>  14.004</td><td>  88.167</td><td> 38.124</td><td>  12.565</td><td>  12.565</td><td> 36.911</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt</th><td>1215</td><td>1421</td><td>855.0317</td><td>1.169547</td><td>0.3615564</td><td> 4</td><td>1030</td><td>166</td><td>2095.307</td><td>0.475</td><td>1215</td><td>4408.233</td><td>322.829</td><td>1152.785</td><td>4409.446</td><td>208.632</td><td>1074.636</td><td>4405.721</td><td>195.507</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 4 × 19 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllll}\n",
       "  & TRB & Reads & CPKR & Average\\_reads & VMR & Max\\_reads & Singletons & Doubletons & qD & Sample\\_Coverage & observed\\_Richness & estimated\\_Richness & SE\\_Richeness & observed\\_Shannon & estimated\\_Shannon & SE\\_Shannon & observed\\_Simpson & estimated\\_Simpson & SE\\_Simpson\\\\\n",
       "\\hline\n",
       "\tclones/CLONES\\_TRB\\_PRO-00012N\\_R\\_totalRNA\\_chondrosarcoma\\_40055.txt &   47 &   72 & 652.7778 & 1.531915 & 1.5390531 & 17 &   39 &   5 &   81.288 & 0.584 &   47 &  196.988 &  83.406 &   30.566 &   89.916 &  28.789 &   14.164 &   17.388 &   6.927\\\\\n",
       "\tclones/CLONES\\_TRB\\_PRO-00013P\\_R\\_totalRNA\\_osteosarcoma\\_40063.txt &    3 &   13 & 230.7692 & 4.333333 & 1.1383576 & 10 &    1 &   1 &    3.399 & 0.991 &    3 &    3.462 &   1.236 &    1.988 &    2.225 &   0.646 &    1.610 &    1.696 &   0.504\\\\\n",
       "\tclones/CLONES\\_TRB\\_PRO-00015S\\_R\\_totalRNA\\_sarcoma\\_40065.txt &   15 &   17 & 882.3529 & 1.133333 & 0.4556451 &  3 &   14 &   0 &   27.859 & 0.307 &   15 &  100.647 &  47.553 &   14.004 &   88.167 &  38.124 &   12.565 &   12.565 &  36.911\\\\\n",
       "\tclones/CLONES\\_TRB\\_PRO-00019W\\_R\\_totalRNA\\_Thymus\\_40073.txt & 1215 & 1421 & 855.0317 & 1.169547 & 0.3615564 &  4 & 1030 & 166 & 2095.307 & 0.475 & 1215 & 4408.233 & 322.829 & 1152.785 & 4409.446 & 208.632 & 1074.636 & 4405.721 & 195.507\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 4 × 19 of type dbl\n",
       "\n",
       "| <!--/--> | TRB | Reads | CPKR | Average_reads | VMR | Max_reads | Singletons | Doubletons | qD | Sample_Coverage | observed_Richness | estimated_Richness | SE_Richeness | observed_Shannon | estimated_Shannon | SE_Shannon | observed_Simpson | estimated_Simpson | SE_Simpson |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt |   47 |   72 | 652.7778 | 1.531915 | 1.5390531 | 17 |   39 |   5 |   81.288 | 0.584 |   47 |  196.988 |  83.406 |   30.566 |   89.916 |  28.789 |   14.164 |   17.388 |   6.927 |\n",
       "| clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt |    3 |   13 | 230.7692 | 4.333333 | 1.1383576 | 10 |    1 |   1 |    3.399 | 0.991 |    3 |    3.462 |   1.236 |    1.988 |    2.225 |   0.646 |    1.610 |    1.696 |   0.504 |\n",
       "| clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt |   15 |   17 | 882.3529 | 1.133333 | 0.4556451 |  3 |   14 |   0 |   27.859 | 0.307 |   15 |  100.647 |  47.553 |   14.004 |   88.167 |  38.124 |   12.565 |   12.565 |  36.911 |\n",
       "| clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt | 1215 | 1421 | 855.0317 | 1.169547 | 0.3615564 |  4 | 1030 | 166 | 2095.307 | 0.475 | 1215 | 4408.233 | 322.829 | 1152.785 | 4409.446 | 208.632 | 1074.636 | 4405.721 | 195.507 |\n",
       "\n"
      ],
      "text/plain": [
       "                                                                 TRB  Reads\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   47   72 \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      3   13 \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          15   17 \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         1215 1421 \n",
       "                                                                 CPKR    \n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt 652.7778\n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt   230.7692\n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt        882.3529\n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         855.0317\n",
       "                                                                 Average_reads\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt 1.531915     \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt   4.333333     \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt        1.133333     \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         1.169547     \n",
       "                                                                 VMR      \n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt 1.5390531\n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt   1.1383576\n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt        0.4556451\n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         0.3615564\n",
       "                                                                 Max_reads\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt 17       \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt   10       \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt         3       \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt          4       \n",
       "                                                                 Singletons\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   39      \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      1      \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          14      \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         1030      \n",
       "                                                                 Doubletons\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   5       \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt     1       \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          0       \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         166       \n",
       "                                                                 qD      \n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   81.288\n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      3.399\n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          27.859\n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         2095.307\n",
       "                                                                 Sample_Coverage\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt 0.584          \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt   0.991          \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt        0.307          \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         0.475          \n",
       "                                                                 observed_Richness\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   47             \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      3             \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          15             \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         1215             \n",
       "                                                                 estimated_Richness\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt  196.988          \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      3.462          \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt         100.647          \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         4408.233          \n",
       "                                                                 SE_Richeness\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt  83.406     \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt     1.236     \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt         47.553     \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         322.829     \n",
       "                                                                 observed_Shannon\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   30.566        \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      1.988        \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          14.004        \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         1152.785        \n",
       "                                                                 estimated_Shannon\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   89.916         \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      2.225         \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          88.167         \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         4409.446         \n",
       "                                                                 SE_Shannon\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt  28.789   \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt     0.646   \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt         38.124   \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         208.632   \n",
       "                                                                 observed_Simpson\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   14.164        \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      1.610        \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          12.565        \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         1074.636        \n",
       "                                                                 estimated_Simpson\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   17.388         \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt      1.696         \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt          12.565         \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         4405.721         \n",
       "                                                                 SE_Simpson\n",
       "clones/CLONES_TRB_PRO-00012N_R_totalRNA_chondrosarcoma_40055.txt   6.927   \n",
       "clones/CLONES_TRB_PRO-00013P_R_totalRNA_osteosarcoma_40063.txt     0.504   \n",
       "clones/CLONES_TRB_PRO-00015S_R_totalRNA_sarcoma_40065.txt         36.911   \n",
       "clones/CLONES_TRB_PRO-00019W_R_totalRNA_Thymus_40073.txt         195.507   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "div_trb_profyle_rna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(div_trb_profyle_rna, file = paste0(datapath, \"diversity/div_trb_profyle_rna.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_profyle_rna <- as.data.frame(div_trb_profyle_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_profyle_rna$sample_id <- gsub(\".*TRB_\", \"\", rownames(div_trb_profyle_rna))\n",
    "\n",
    "div_trb_profyle_rna$sample_id <- gsub(\"_R_.*\", \"\", div_trb_profyle_rna$sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'PRO-00012N'</li><li>'PRO-00013P'</li><li>'PRO-00015S'</li><li>'PRO-00019W'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'PRO-00012N'\n",
       "\\item 'PRO-00013P'\n",
       "\\item 'PRO-00015S'\n",
       "\\item 'PRO-00019W'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'PRO-00012N'\n",
       "2. 'PRO-00013P'\n",
       "3. 'PRO-00015S'\n",
       "4. 'PRO-00019W'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"PRO-00012N\" \"PRO-00013P\" \"PRO-00015S\" \"PRO-00019W\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "div_trb_profyle_rna$sample_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CapTCR-Seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "profyle_cap <- list.files(h4hpath, pattern = \"CLONES_TRB\", recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'clones/CLONES_TRBPRO-00012N.txt'</li><li>'clones/CLONES_TRBPRO-00013P.txt'</li><li>'clones/CLONES_TRBPRO-00015S.txt'</li><li>'clones/CLONES_TRBPRO-00023A.txt'</li><li>'clones/CLONES_TRBPRO-00027E.txt'</li><li>'clones/CLONES_TRBPRO-00032L.txt'</li><li>'clones/CLONES_TRBPRO-00033M.txt'</li><li>'clones/CLONES_TRBPRO-00035O.txt'</li><li>'clones/CLONES_TRBPRO-00039T.txt'</li><li>'clones/CLONES_TRBPRO-00042X.txt'</li><li>'clones/CLONES_TRBPRO-00043Z.txt'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'clones/CLONES\\_TRBPRO-00012N.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00013P.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00015S.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00023A.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00027E.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00032L.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00033M.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00035O.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00039T.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00042X.txt'\n",
       "\\item 'clones/CLONES\\_TRBPRO-00043Z.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'clones/CLONES_TRBPRO-00012N.txt'\n",
       "2. 'clones/CLONES_TRBPRO-00013P.txt'\n",
       "3. 'clones/CLONES_TRBPRO-00015S.txt'\n",
       "4. 'clones/CLONES_TRBPRO-00023A.txt'\n",
       "5. 'clones/CLONES_TRBPRO-00027E.txt'\n",
       "6. 'clones/CLONES_TRBPRO-00032L.txt'\n",
       "7. 'clones/CLONES_TRBPRO-00033M.txt'\n",
       "8. 'clones/CLONES_TRBPRO-00035O.txt'\n",
       "9. 'clones/CLONES_TRBPRO-00039T.txt'\n",
       "10. 'clones/CLONES_TRBPRO-00042X.txt'\n",
       "11. 'clones/CLONES_TRBPRO-00043Z.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"clones/CLONES_TRBPRO-00012N.txt\" \"clones/CLONES_TRBPRO-00013P.txt\"\n",
       " [3] \"clones/CLONES_TRBPRO-00015S.txt\" \"clones/CLONES_TRBPRO-00023A.txt\"\n",
       " [5] \"clones/CLONES_TRBPRO-00027E.txt\" \"clones/CLONES_TRBPRO-00032L.txt\"\n",
       " [7] \"clones/CLONES_TRBPRO-00033M.txt\" \"clones/CLONES_TRBPRO-00035O.txt\"\n",
       " [9] \"clones/CLONES_TRBPRO-00039T.txt\" \"clones/CLONES_TRBPRO-00042X.txt\"\n",
       "[11] \"clones/CLONES_TRBPRO-00043Z.txt\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profyle_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_profyle_cap <- immunelistfx(profyle_cap, h4hpath, \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "11"
      ],
      "text/latex": [
       "11"
      ],
      "text/markdown": [
       "11"
      ],
      "text/plain": [
       "[1] 11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(trb_list_profyle_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "9"
      ],
      "text/latex": [
       "9"
      ],
      "text/markdown": [
       "9"
      ],
      "text/plain": [
       "[1] 9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trb_list_profyle_cap <- trb_list_profyle_cap[sapply(trb_list_profyle_cap, function(x) length(unlist(x))) > 1] # remove files with one clonotype\n",
    "                            \n",
    "length(trb_list_profyle_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_profyle_cap <- trb_list_profyle_cap[sapply(trb_list_profyle_cap, function(x) var(unlist(x))) > 0] # remove files no variance                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "9"
      ],
      "text/latex": [
       "9"
      ],
      "text/markdown": [
       "9"
      ],
      "text/plain": [
       "[1] 9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(trb_list_profyle_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_profyle_cap <- Divstats.fx(trb_list_profyle_cap, \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 9 × 19 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TRB</th><th scope=col>Reads</th><th scope=col>CPKR</th><th scope=col>Average_reads</th><th scope=col>VMR</th><th scope=col>Max_reads</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>Sample_Coverage</th><th scope=col>observed_Richness</th><th scope=col>estimated_Richness</th><th scope=col>SE_Richeness</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Shannon</th><th scope=col>SE_Shannon</th><th scope=col>observed_Simpson</th><th scope=col>estimated_Simpson</th><th scope=col>SE_Simpson</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00012N.txt</th><td>1152</td><td>7575348</td><td>0.15207222</td><td> 6575.823</td><td>8.276624</td><td>1510148</td><td>358</td><td>114</td><td>1416.791</td><td>1</td><td>1152</td><td>1714.123</td><td>82.852</td><td>53.468</td><td>53.475</td><td>0.037</td><td>16.589</td><td>16.589</td><td>0.015</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00013P.txt</th><td>   5</td><td>  19533</td><td>0.25597706</td><td> 3906.600</td><td>2.085137</td><td>  18468</td><td>  0</td><td>  0</td><td>   5.000</td><td>1</td><td>   5</td><td>   5.000</td><td> 0.218</td><td> 1.285</td><td> 1.285</td><td>0.007</td><td> 1.117</td><td> 1.117</td><td>0.003</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00015S.txt</th><td> 536</td><td>1023792</td><td>0.52354384</td><td> 1910.060</td><td>4.539396</td><td> 116823</td><td> 82</td><td> 19</td><td> 601.624</td><td>1</td><td> 536</td><td> 712.947</td><td>57.898</td><td>63.787</td><td>63.809</td><td>0.102</td><td>24.852</td><td>24.853</td><td>0.052</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00023A.txt</th><td> 102</td><td> 483655</td><td>0.21089413</td><td> 4741.716</td><td>2.318769</td><td>  92653</td><td> 29</td><td>  8</td><td> 124.289</td><td>1</td><td> 102</td><td> 154.562</td><td>27.910</td><td>28.426</td><td>28.430</td><td>0.043</td><td>16.129</td><td>16.130</td><td>0.051</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00033M.txt</th><td>  24</td><td> 118734</td><td>0.20213250</td><td> 4947.250</td><td>1.925753</td><td>  31359</td><td>  7</td><td>  4</td><td>  28.172</td><td>1</td><td>  24</td><td>  30.125</td><td> 6.078</td><td> 5.986</td><td> 5.987</td><td>0.012</td><td> 5.270</td><td> 5.270</td><td>0.010</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00035O.txt</th><td>   6</td><td>  66679</td><td>0.08998335</td><td>11113.167</td><td>2.445568</td><td>  66590</td><td>  3</td><td>  1</td><td>   8.190</td><td>1</td><td>   6</td><td>  10.500</td><td> 7.194</td><td> 1.011</td><td> 1.011</td><td>0.001</td><td> 1.003</td><td> 1.003</td><td>0.000</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00039T.txt</th><td>  24</td><td> 386262</td><td>0.06213399</td><td>16094.250</td><td>2.764918</td><td> 214789</td><td>  3</td><td>  3</td><td>  25.297</td><td>1</td><td>  24</td><td>  25.500</td><td> 2.291</td><td> 4.517</td><td> 4.517</td><td>0.009</td><td> 2.882</td><td> 2.882</td><td>0.006</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00042X.txt</th><td>   2</td><td>   4590</td><td>0.43572985</td><td> 2295.000</td><td>1.412981</td><td>   4588</td><td>  0</td><td>  1</td><td>   2.000</td><td>1</td><td>   2</td><td>   2.000</td><td> 0.342</td><td> 1.004</td><td> 1.004</td><td>0.002</td><td> 1.001</td><td> 1.001</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00043Z.txt</th><td>  98</td><td> 267465</td><td>0.36640308</td><td> 2729.235</td><td>6.324232</td><td> 162838</td><td> 25</td><td> 10</td><td> 115.208</td><td>1</td><td>  98</td><td> 129.250</td><td>16.887</td><td> 3.324</td><td> 3.325</td><td>0.007</td><td> 2.415</td><td> 2.415</td><td>0.006</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 9 × 19 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllll}\n",
       "  & TRB & Reads & CPKR & Average\\_reads & VMR & Max\\_reads & Singletons & Doubletons & qD & Sample\\_Coverage & observed\\_Richness & estimated\\_Richness & SE\\_Richeness & observed\\_Shannon & estimated\\_Shannon & SE\\_Shannon & observed\\_Simpson & estimated\\_Simpson & SE\\_Simpson\\\\\n",
       "\\hline\n",
       "\tclones/CLONES\\_TRBPRO-00012N.txt & 1152 & 7575348 & 0.15207222 &  6575.823 & 8.276624 & 1510148 & 358 & 114 & 1416.791 & 1 & 1152 & 1714.123 & 82.852 & 53.468 & 53.475 & 0.037 & 16.589 & 16.589 & 0.015\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00013P.txt &    5 &   19533 & 0.25597706 &  3906.600 & 2.085137 &   18468 &   0 &   0 &    5.000 & 1 &    5 &    5.000 &  0.218 &  1.285 &  1.285 & 0.007 &  1.117 &  1.117 & 0.003\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00015S.txt &  536 & 1023792 & 0.52354384 &  1910.060 & 4.539396 &  116823 &  82 &  19 &  601.624 & 1 &  536 &  712.947 & 57.898 & 63.787 & 63.809 & 0.102 & 24.852 & 24.853 & 0.052\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00023A.txt &  102 &  483655 & 0.21089413 &  4741.716 & 2.318769 &   92653 &  29 &   8 &  124.289 & 1 &  102 &  154.562 & 27.910 & 28.426 & 28.430 & 0.043 & 16.129 & 16.130 & 0.051\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00033M.txt &   24 &  118734 & 0.20213250 &  4947.250 & 1.925753 &   31359 &   7 &   4 &   28.172 & 1 &   24 &   30.125 &  6.078 &  5.986 &  5.987 & 0.012 &  5.270 &  5.270 & 0.010\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00035O.txt &    6 &   66679 & 0.08998335 & 11113.167 & 2.445568 &   66590 &   3 &   1 &    8.190 & 1 &    6 &   10.500 &  7.194 &  1.011 &  1.011 & 0.001 &  1.003 &  1.003 & 0.000\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00039T.txt &   24 &  386262 & 0.06213399 & 16094.250 & 2.764918 &  214789 &   3 &   3 &   25.297 & 1 &   24 &   25.500 &  2.291 &  4.517 &  4.517 & 0.009 &  2.882 &  2.882 & 0.006\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00042X.txt &    2 &    4590 & 0.43572985 &  2295.000 & 1.412981 &    4588 &   0 &   1 &    2.000 & 1 &    2 &    2.000 &  0.342 &  1.004 &  1.004 & 0.002 &  1.001 &  1.001 & 0.001\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00043Z.txt &   98 &  267465 & 0.36640308 &  2729.235 & 6.324232 &  162838 &  25 &  10 &  115.208 & 1 &   98 &  129.250 & 16.887 &  3.324 &  3.325 & 0.007 &  2.415 &  2.415 & 0.006\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 9 × 19 of type dbl\n",
       "\n",
       "| <!--/--> | TRB | Reads | CPKR | Average_reads | VMR | Max_reads | Singletons | Doubletons | qD | Sample_Coverage | observed_Richness | estimated_Richness | SE_Richeness | observed_Shannon | estimated_Shannon | SE_Shannon | observed_Simpson | estimated_Simpson | SE_Simpson |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| clones/CLONES_TRBPRO-00012N.txt | 1152 | 7575348 | 0.15207222 |  6575.823 | 8.276624 | 1510148 | 358 | 114 | 1416.791 | 1 | 1152 | 1714.123 | 82.852 | 53.468 | 53.475 | 0.037 | 16.589 | 16.589 | 0.015 |\n",
       "| clones/CLONES_TRBPRO-00013P.txt |    5 |   19533 | 0.25597706 |  3906.600 | 2.085137 |   18468 |   0 |   0 |    5.000 | 1 |    5 |    5.000 |  0.218 |  1.285 |  1.285 | 0.007 |  1.117 |  1.117 | 0.003 |\n",
       "| clones/CLONES_TRBPRO-00015S.txt |  536 | 1023792 | 0.52354384 |  1910.060 | 4.539396 |  116823 |  82 |  19 |  601.624 | 1 |  536 |  712.947 | 57.898 | 63.787 | 63.809 | 0.102 | 24.852 | 24.853 | 0.052 |\n",
       "| clones/CLONES_TRBPRO-00023A.txt |  102 |  483655 | 0.21089413 |  4741.716 | 2.318769 |   92653 |  29 |   8 |  124.289 | 1 |  102 |  154.562 | 27.910 | 28.426 | 28.430 | 0.043 | 16.129 | 16.130 | 0.051 |\n",
       "| clones/CLONES_TRBPRO-00033M.txt |   24 |  118734 | 0.20213250 |  4947.250 | 1.925753 |   31359 |   7 |   4 |   28.172 | 1 |   24 |   30.125 |  6.078 |  5.986 |  5.987 | 0.012 |  5.270 |  5.270 | 0.010 |\n",
       "| clones/CLONES_TRBPRO-00035O.txt |    6 |   66679 | 0.08998335 | 11113.167 | 2.445568 |   66590 |   3 |   1 |    8.190 | 1 |    6 |   10.500 |  7.194 |  1.011 |  1.011 | 0.001 |  1.003 |  1.003 | 0.000 |\n",
       "| clones/CLONES_TRBPRO-00039T.txt |   24 |  386262 | 0.06213399 | 16094.250 | 2.764918 |  214789 |   3 |   3 |   25.297 | 1 |   24 |   25.500 |  2.291 |  4.517 |  4.517 | 0.009 |  2.882 |  2.882 | 0.006 |\n",
       "| clones/CLONES_TRBPRO-00042X.txt |    2 |    4590 | 0.43572985 |  2295.000 | 1.412981 |    4588 |   0 |   1 |    2.000 | 1 |    2 |    2.000 |  0.342 |  1.004 |  1.004 | 0.002 |  1.001 |  1.001 | 0.001 |\n",
       "| clones/CLONES_TRBPRO-00043Z.txt |   98 |  267465 | 0.36640308 |  2729.235 | 6.324232 |  162838 |  25 |  10 |  115.208 | 1 |   98 |  129.250 | 16.887 |  3.324 |  3.325 | 0.007 |  2.415 |  2.415 | 0.006 |\n",
       "\n"
      ],
      "text/plain": [
       "                                TRB  Reads   CPKR       Average_reads VMR     \n",
       "clones/CLONES_TRBPRO-00012N.txt 1152 7575348 0.15207222  6575.823     8.276624\n",
       "clones/CLONES_TRBPRO-00013P.txt    5   19533 0.25597706  3906.600     2.085137\n",
       "clones/CLONES_TRBPRO-00015S.txt  536 1023792 0.52354384  1910.060     4.539396\n",
       "clones/CLONES_TRBPRO-00023A.txt  102  483655 0.21089413  4741.716     2.318769\n",
       "clones/CLONES_TRBPRO-00033M.txt   24  118734 0.20213250  4947.250     1.925753\n",
       "clones/CLONES_TRBPRO-00035O.txt    6   66679 0.08998335 11113.167     2.445568\n",
       "clones/CLONES_TRBPRO-00039T.txt   24  386262 0.06213399 16094.250     2.764918\n",
       "clones/CLONES_TRBPRO-00042X.txt    2    4590 0.43572985  2295.000     1.412981\n",
       "clones/CLONES_TRBPRO-00043Z.txt   98  267465 0.36640308  2729.235     6.324232\n",
       "                                Max_reads Singletons Doubletons qD      \n",
       "clones/CLONES_TRBPRO-00012N.txt 1510148   358        114        1416.791\n",
       "clones/CLONES_TRBPRO-00013P.txt   18468     0          0           5.000\n",
       "clones/CLONES_TRBPRO-00015S.txt  116823    82         19         601.624\n",
       "clones/CLONES_TRBPRO-00023A.txt   92653    29          8         124.289\n",
       "clones/CLONES_TRBPRO-00033M.txt   31359     7          4          28.172\n",
       "clones/CLONES_TRBPRO-00035O.txt   66590     3          1           8.190\n",
       "clones/CLONES_TRBPRO-00039T.txt  214789     3          3          25.297\n",
       "clones/CLONES_TRBPRO-00042X.txt    4588     0          1           2.000\n",
       "clones/CLONES_TRBPRO-00043Z.txt  162838    25         10         115.208\n",
       "                                Sample_Coverage observed_Richness\n",
       "clones/CLONES_TRBPRO-00012N.txt 1               1152             \n",
       "clones/CLONES_TRBPRO-00013P.txt 1                  5             \n",
       "clones/CLONES_TRBPRO-00015S.txt 1                536             \n",
       "clones/CLONES_TRBPRO-00023A.txt 1                102             \n",
       "clones/CLONES_TRBPRO-00033M.txt 1                 24             \n",
       "clones/CLONES_TRBPRO-00035O.txt 1                  6             \n",
       "clones/CLONES_TRBPRO-00039T.txt 1                 24             \n",
       "clones/CLONES_TRBPRO-00042X.txt 1                  2             \n",
       "clones/CLONES_TRBPRO-00043Z.txt 1                 98             \n",
       "                                estimated_Richness SE_Richeness\n",
       "clones/CLONES_TRBPRO-00012N.txt 1714.123           82.852      \n",
       "clones/CLONES_TRBPRO-00013P.txt    5.000            0.218      \n",
       "clones/CLONES_TRBPRO-00015S.txt  712.947           57.898      \n",
       "clones/CLONES_TRBPRO-00023A.txt  154.562           27.910      \n",
       "clones/CLONES_TRBPRO-00033M.txt   30.125            6.078      \n",
       "clones/CLONES_TRBPRO-00035O.txt   10.500            7.194      \n",
       "clones/CLONES_TRBPRO-00039T.txt   25.500            2.291      \n",
       "clones/CLONES_TRBPRO-00042X.txt    2.000            0.342      \n",
       "clones/CLONES_TRBPRO-00043Z.txt  129.250           16.887      \n",
       "                                observed_Shannon estimated_Shannon SE_Shannon\n",
       "clones/CLONES_TRBPRO-00012N.txt 53.468           53.475            0.037     \n",
       "clones/CLONES_TRBPRO-00013P.txt  1.285            1.285            0.007     \n",
       "clones/CLONES_TRBPRO-00015S.txt 63.787           63.809            0.102     \n",
       "clones/CLONES_TRBPRO-00023A.txt 28.426           28.430            0.043     \n",
       "clones/CLONES_TRBPRO-00033M.txt  5.986            5.987            0.012     \n",
       "clones/CLONES_TRBPRO-00035O.txt  1.011            1.011            0.001     \n",
       "clones/CLONES_TRBPRO-00039T.txt  4.517            4.517            0.009     \n",
       "clones/CLONES_TRBPRO-00042X.txt  1.004            1.004            0.002     \n",
       "clones/CLONES_TRBPRO-00043Z.txt  3.324            3.325            0.007     \n",
       "                                observed_Simpson estimated_Simpson SE_Simpson\n",
       "clones/CLONES_TRBPRO-00012N.txt 16.589           16.589            0.015     \n",
       "clones/CLONES_TRBPRO-00013P.txt  1.117            1.117            0.003     \n",
       "clones/CLONES_TRBPRO-00015S.txt 24.852           24.853            0.052     \n",
       "clones/CLONES_TRBPRO-00023A.txt 16.129           16.130            0.051     \n",
       "clones/CLONES_TRBPRO-00033M.txt  5.270            5.270            0.010     \n",
       "clones/CLONES_TRBPRO-00035O.txt  1.003            1.003            0.000     \n",
       "clones/CLONES_TRBPRO-00039T.txt  2.882            2.882            0.006     \n",
       "clones/CLONES_TRBPRO-00042X.txt  1.001            1.001            0.001     \n",
       "clones/CLONES_TRBPRO-00043Z.txt  2.415            2.415            0.006     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "div_trb_profyle_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(div_trb_profyle_cap, file = paste0(datapath, \"diversity/div_trb_profyle_cap.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_profyle_cap <- as.data.frame(div_trb_profyle_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_profyle_cap$sample_id <- gsub(\".*TRB\", \"\", rownames(div_trb_profyle_cap))\n",
    "\n",
    "div_trb_profyle_cap$sample_id <- gsub(\".txt\", \"\", div_trb_profyle_cap$sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 20</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TRB</th><th scope=col>Reads</th><th scope=col>CPKR</th><th scope=col>Average_reads</th><th scope=col>VMR</th><th scope=col>Max_reads</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>Sample_Coverage</th><th scope=col>observed_Richness</th><th scope=col>estimated_Richness</th><th scope=col>SE_Richeness</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Shannon</th><th scope=col>SE_Shannon</th><th scope=col>observed_Simpson</th><th scope=col>estimated_Simpson</th><th scope=col>SE_Simpson</th><th scope=col>sample_id</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>&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;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00012N.txt</th><td>1152</td><td>7575348</td><td>0.15207222</td><td> 6575.823</td><td>8.276624</td><td>1510148</td><td>358</td><td>114</td><td>1416.791</td><td>1</td><td>1152</td><td>1714.123</td><td>82.852</td><td>53.468</td><td>53.475</td><td>0.037</td><td>16.589</td><td>16.589</td><td>0.015</td><td>PRO-00012N</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00013P.txt</th><td>   5</td><td>  19533</td><td>0.25597706</td><td> 3906.600</td><td>2.085137</td><td>  18468</td><td>  0</td><td>  0</td><td>   5.000</td><td>1</td><td>   5</td><td>   5.000</td><td> 0.218</td><td> 1.285</td><td> 1.285</td><td>0.007</td><td> 1.117</td><td> 1.117</td><td>0.003</td><td>PRO-00013P</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00015S.txt</th><td> 536</td><td>1023792</td><td>0.52354384</td><td> 1910.060</td><td>4.539396</td><td> 116823</td><td> 82</td><td> 19</td><td> 601.624</td><td>1</td><td> 536</td><td> 712.947</td><td>57.898</td><td>63.787</td><td>63.809</td><td>0.102</td><td>24.852</td><td>24.853</td><td>0.052</td><td>PRO-00015S</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00023A.txt</th><td> 102</td><td> 483655</td><td>0.21089413</td><td> 4741.716</td><td>2.318769</td><td>  92653</td><td> 29</td><td>  8</td><td> 124.289</td><td>1</td><td> 102</td><td> 154.562</td><td>27.910</td><td>28.426</td><td>28.430</td><td>0.043</td><td>16.129</td><td>16.130</td><td>0.051</td><td>PRO-00023A</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00033M.txt</th><td>  24</td><td> 118734</td><td>0.20213250</td><td> 4947.250</td><td>1.925753</td><td>  31359</td><td>  7</td><td>  4</td><td>  28.172</td><td>1</td><td>  24</td><td>  30.125</td><td> 6.078</td><td> 5.986</td><td> 5.987</td><td>0.012</td><td> 5.270</td><td> 5.270</td><td>0.010</td><td>PRO-00033M</td></tr>\n",
       "\t<tr><th scope=row>clones/CLONES_TRBPRO-00035O.txt</th><td>   6</td><td>  66679</td><td>0.08998335</td><td>11113.167</td><td>2.445568</td><td>  66590</td><td>  3</td><td>  1</td><td>   8.190</td><td>1</td><td>   6</td><td>  10.500</td><td> 7.194</td><td> 1.011</td><td> 1.011</td><td>0.001</td><td> 1.003</td><td> 1.003</td><td>0.000</td><td>PRO-00035O</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 20\n",
       "\\begin{tabular}{r|llllllllllllllllllll}\n",
       "  & TRB & Reads & CPKR & Average\\_reads & VMR & Max\\_reads & Singletons & Doubletons & qD & Sample\\_Coverage & observed\\_Richness & estimated\\_Richness & SE\\_Richeness & observed\\_Shannon & estimated\\_Shannon & SE\\_Shannon & observed\\_Simpson & estimated\\_Simpson & SE\\_Simpson & sample\\_id\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\tclones/CLONES\\_TRBPRO-00012N.txt & 1152 & 7575348 & 0.15207222 &  6575.823 & 8.276624 & 1510148 & 358 & 114 & 1416.791 & 1 & 1152 & 1714.123 & 82.852 & 53.468 & 53.475 & 0.037 & 16.589 & 16.589 & 0.015 & PRO-00012N\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00013P.txt &    5 &   19533 & 0.25597706 &  3906.600 & 2.085137 &   18468 &   0 &   0 &    5.000 & 1 &    5 &    5.000 &  0.218 &  1.285 &  1.285 & 0.007 &  1.117 &  1.117 & 0.003 & PRO-00013P\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00015S.txt &  536 & 1023792 & 0.52354384 &  1910.060 & 4.539396 &  116823 &  82 &  19 &  601.624 & 1 &  536 &  712.947 & 57.898 & 63.787 & 63.809 & 0.102 & 24.852 & 24.853 & 0.052 & PRO-00015S\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00023A.txt &  102 &  483655 & 0.21089413 &  4741.716 & 2.318769 &   92653 &  29 &   8 &  124.289 & 1 &  102 &  154.562 & 27.910 & 28.426 & 28.430 & 0.043 & 16.129 & 16.130 & 0.051 & PRO-00023A\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00033M.txt &   24 &  118734 & 0.20213250 &  4947.250 & 1.925753 &   31359 &   7 &   4 &   28.172 & 1 &   24 &   30.125 &  6.078 &  5.986 &  5.987 & 0.012 &  5.270 &  5.270 & 0.010 & PRO-00033M\\\\\n",
       "\tclones/CLONES\\_TRBPRO-00035O.txt &    6 &   66679 & 0.08998335 & 11113.167 & 2.445568 &   66590 &   3 &   1 &    8.190 & 1 &    6 &   10.500 &  7.194 &  1.011 &  1.011 & 0.001 &  1.003 &  1.003 & 0.000 & PRO-00035O\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 20\n",
       "\n",
       "| <!--/--> | TRB &lt;dbl&gt; | Reads &lt;dbl&gt; | CPKR &lt;dbl&gt; | Average_reads &lt;dbl&gt; | VMR &lt;dbl&gt; | Max_reads &lt;dbl&gt; | Singletons &lt;dbl&gt; | Doubletons &lt;dbl&gt; | qD &lt;dbl&gt; | Sample_Coverage &lt;dbl&gt; | observed_Richness &lt;dbl&gt; | estimated_Richness &lt;dbl&gt; | SE_Richeness &lt;dbl&gt; | observed_Shannon &lt;dbl&gt; | estimated_Shannon &lt;dbl&gt; | SE_Shannon &lt;dbl&gt; | observed_Simpson &lt;dbl&gt; | estimated_Simpson &lt;dbl&gt; | SE_Simpson &lt;dbl&gt; | sample_id &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| clones/CLONES_TRBPRO-00012N.txt | 1152 | 7575348 | 0.15207222 |  6575.823 | 8.276624 | 1510148 | 358 | 114 | 1416.791 | 1 | 1152 | 1714.123 | 82.852 | 53.468 | 53.475 | 0.037 | 16.589 | 16.589 | 0.015 | PRO-00012N |\n",
       "| clones/CLONES_TRBPRO-00013P.txt |    5 |   19533 | 0.25597706 |  3906.600 | 2.085137 |   18468 |   0 |   0 |    5.000 | 1 |    5 |    5.000 |  0.218 |  1.285 |  1.285 | 0.007 |  1.117 |  1.117 | 0.003 | PRO-00013P |\n",
       "| clones/CLONES_TRBPRO-00015S.txt |  536 | 1023792 | 0.52354384 |  1910.060 | 4.539396 |  116823 |  82 |  19 |  601.624 | 1 |  536 |  712.947 | 57.898 | 63.787 | 63.809 | 0.102 | 24.852 | 24.853 | 0.052 | PRO-00015S |\n",
       "| clones/CLONES_TRBPRO-00023A.txt |  102 |  483655 | 0.21089413 |  4741.716 | 2.318769 |   92653 |  29 |   8 |  124.289 | 1 |  102 |  154.562 | 27.910 | 28.426 | 28.430 | 0.043 | 16.129 | 16.130 | 0.051 | PRO-00023A |\n",
       "| clones/CLONES_TRBPRO-00033M.txt |   24 |  118734 | 0.20213250 |  4947.250 | 1.925753 |   31359 |   7 |   4 |   28.172 | 1 |   24 |   30.125 |  6.078 |  5.986 |  5.987 | 0.012 |  5.270 |  5.270 | 0.010 | PRO-00033M |\n",
       "| clones/CLONES_TRBPRO-00035O.txt |    6 |   66679 | 0.08998335 | 11113.167 | 2.445568 |   66590 |   3 |   1 |    8.190 | 1 |    6 |   10.500 |  7.194 |  1.011 |  1.011 | 0.001 |  1.003 |  1.003 | 0.000 | PRO-00035O |\n",
       "\n"
      ],
      "text/plain": [
       "                                TRB  Reads   CPKR       Average_reads VMR     \n",
       "clones/CLONES_TRBPRO-00012N.txt 1152 7575348 0.15207222  6575.823     8.276624\n",
       "clones/CLONES_TRBPRO-00013P.txt    5   19533 0.25597706  3906.600     2.085137\n",
       "clones/CLONES_TRBPRO-00015S.txt  536 1023792 0.52354384  1910.060     4.539396\n",
       "clones/CLONES_TRBPRO-00023A.txt  102  483655 0.21089413  4741.716     2.318769\n",
       "clones/CLONES_TRBPRO-00033M.txt   24  118734 0.20213250  4947.250     1.925753\n",
       "clones/CLONES_TRBPRO-00035O.txt    6   66679 0.08998335 11113.167     2.445568\n",
       "                                Max_reads Singletons Doubletons qD      \n",
       "clones/CLONES_TRBPRO-00012N.txt 1510148   358        114        1416.791\n",
       "clones/CLONES_TRBPRO-00013P.txt   18468     0          0           5.000\n",
       "clones/CLONES_TRBPRO-00015S.txt  116823    82         19         601.624\n",
       "clones/CLONES_TRBPRO-00023A.txt   92653    29          8         124.289\n",
       "clones/CLONES_TRBPRO-00033M.txt   31359     7          4          28.172\n",
       "clones/CLONES_TRBPRO-00035O.txt   66590     3          1           8.190\n",
       "                                Sample_Coverage observed_Richness\n",
       "clones/CLONES_TRBPRO-00012N.txt 1               1152             \n",
       "clones/CLONES_TRBPRO-00013P.txt 1                  5             \n",
       "clones/CLONES_TRBPRO-00015S.txt 1                536             \n",
       "clones/CLONES_TRBPRO-00023A.txt 1                102             \n",
       "clones/CLONES_TRBPRO-00033M.txt 1                 24             \n",
       "clones/CLONES_TRBPRO-00035O.txt 1                  6             \n",
       "                                estimated_Richness SE_Richeness\n",
       "clones/CLONES_TRBPRO-00012N.txt 1714.123           82.852      \n",
       "clones/CLONES_TRBPRO-00013P.txt    5.000            0.218      \n",
       "clones/CLONES_TRBPRO-00015S.txt  712.947           57.898      \n",
       "clones/CLONES_TRBPRO-00023A.txt  154.562           27.910      \n",
       "clones/CLONES_TRBPRO-00033M.txt   30.125            6.078      \n",
       "clones/CLONES_TRBPRO-00035O.txt   10.500            7.194      \n",
       "                                observed_Shannon estimated_Shannon SE_Shannon\n",
       "clones/CLONES_TRBPRO-00012N.txt 53.468           53.475            0.037     \n",
       "clones/CLONES_TRBPRO-00013P.txt  1.285            1.285            0.007     \n",
       "clones/CLONES_TRBPRO-00015S.txt 63.787           63.809            0.102     \n",
       "clones/CLONES_TRBPRO-00023A.txt 28.426           28.430            0.043     \n",
       "clones/CLONES_TRBPRO-00033M.txt  5.986            5.987            0.012     \n",
       "clones/CLONES_TRBPRO-00035O.txt  1.011            1.011            0.001     \n",
       "                                observed_Simpson estimated_Simpson SE_Simpson\n",
       "clones/CLONES_TRBPRO-00012N.txt 16.589           16.589            0.015     \n",
       "clones/CLONES_TRBPRO-00013P.txt  1.117            1.117            0.003     \n",
       "clones/CLONES_TRBPRO-00015S.txt 24.852           24.853            0.052     \n",
       "clones/CLONES_TRBPRO-00023A.txt 16.129           16.130            0.051     \n",
       "clones/CLONES_TRBPRO-00033M.txt  5.270            5.270            0.010     \n",
       "clones/CLONES_TRBPRO-00035O.txt  1.003            1.003            0.000     \n",
       "                                sample_id \n",
       "clones/CLONES_TRBPRO-00012N.txt PRO-00012N\n",
       "clones/CLONES_TRBPRO-00013P.txt PRO-00013P\n",
       "clones/CLONES_TRBPRO-00015S.txt PRO-00015S\n",
       "clones/CLONES_TRBPRO-00023A.txt PRO-00023A\n",
       "clones/CLONES_TRBPRO-00033M.txt PRO-00033M\n",
       "clones/CLONES_TRBPRO-00035O.txt PRO-00035O"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(div_trb_profyle_cap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bind RNA and Cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(div_trb_profyle_cap)[1:ncol(div_trb_profyle_cap)-1] <-\n",
    "paste0(colnames(div_trb_profyle_cap)[1:ncol(div_trb_profyle_cap)-1], \"_TCRCap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(div_trb_profyle_rna)[1:ncol(div_trb_profyle_rna)-1] <-\n",
    "paste0(colnames(div_trb_profyle_rna)[1:ncol(div_trb_profyle_rna)-1], \"_RNAseq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'TRB_RNAseq'</li><li>'Reads_RNAseq'</li><li>'CPKR_RNAseq'</li><li>'Average_reads_RNAseq'</li><li>'VMR_RNAseq'</li><li>'Max_reads_RNAseq'</li><li>'Singletons_RNAseq'</li><li>'Doubletons_RNAseq'</li><li>'qD_RNAseq'</li><li>'Sample_Coverage_RNAseq'</li><li>'observed_Richness_RNAseq'</li><li>'estimated_Richness_RNAseq'</li><li>'SE_Richeness_RNAseq'</li><li>'observed_Shannon_RNAseq'</li><li>'estimated_Shannon_RNAseq'</li><li>'SE_Shannon_RNAseq'</li><li>'observed_Simpson_RNAseq'</li><li>'estimated_Simpson_RNAseq'</li><li>'SE_Simpson_RNAseq'</li><li>'sample_id'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'TRB\\_RNAseq'\n",
       "\\item 'Reads\\_RNAseq'\n",
       "\\item 'CPKR\\_RNAseq'\n",
       "\\item 'Average\\_reads\\_RNAseq'\n",
       "\\item 'VMR\\_RNAseq'\n",
       "\\item 'Max\\_reads\\_RNAseq'\n",
       "\\item 'Singletons\\_RNAseq'\n",
       "\\item 'Doubletons\\_RNAseq'\n",
       "\\item 'qD\\_RNAseq'\n",
       "\\item 'Sample\\_Coverage\\_RNAseq'\n",
       "\\item 'observed\\_Richness\\_RNAseq'\n",
       "\\item 'estimated\\_Richness\\_RNAseq'\n",
       "\\item 'SE\\_Richeness\\_RNAseq'\n",
       "\\item 'observed\\_Shannon\\_RNAseq'\n",
       "\\item 'estimated\\_Shannon\\_RNAseq'\n",
       "\\item 'SE\\_Shannon\\_RNAseq'\n",
       "\\item 'observed\\_Simpson\\_RNAseq'\n",
       "\\item 'estimated\\_Simpson\\_RNAseq'\n",
       "\\item 'SE\\_Simpson\\_RNAseq'\n",
       "\\item 'sample\\_id'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'TRB_RNAseq'\n",
       "2. 'Reads_RNAseq'\n",
       "3. 'CPKR_RNAseq'\n",
       "4. 'Average_reads_RNAseq'\n",
       "5. 'VMR_RNAseq'\n",
       "6. 'Max_reads_RNAseq'\n",
       "7. 'Singletons_RNAseq'\n",
       "8. 'Doubletons_RNAseq'\n",
       "9. 'qD_RNAseq'\n",
       "10. 'Sample_Coverage_RNAseq'\n",
       "11. 'observed_Richness_RNAseq'\n",
       "12. 'estimated_Richness_RNAseq'\n",
       "13. 'SE_Richeness_RNAseq'\n",
       "14. 'observed_Shannon_RNAseq'\n",
       "15. 'estimated_Shannon_RNAseq'\n",
       "16. 'SE_Shannon_RNAseq'\n",
       "17. 'observed_Simpson_RNAseq'\n",
       "18. 'estimated_Simpson_RNAseq'\n",
       "19. 'SE_Simpson_RNAseq'\n",
       "20. 'sample_id'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"TRB_RNAseq\"                \"Reads_RNAseq\"             \n",
       " [3] \"CPKR_RNAseq\"               \"Average_reads_RNAseq\"     \n",
       " [5] \"VMR_RNAseq\"                \"Max_reads_RNAseq\"         \n",
       " [7] \"Singletons_RNAseq\"         \"Doubletons_RNAseq\"        \n",
       " [9] \"qD_RNAseq\"                 \"Sample_Coverage_RNAseq\"   \n",
       "[11] \"observed_Richness_RNAseq\"  \"estimated_Richness_RNAseq\"\n",
       "[13] \"SE_Richeness_RNAseq\"       \"observed_Shannon_RNAseq\"  \n",
       "[15] \"estimated_Shannon_RNAseq\"  \"SE_Shannon_RNAseq\"        \n",
       "[17] \"observed_Simpson_RNAseq\"   \"estimated_Simpson_RNAseq\" \n",
       "[19] \"SE_Simpson_RNAseq\"         \"sample_id\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(div_trb_profyle_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_profyle_cap_rna <- merge(div_trb_profyle_cap, div_trb_profyle_rna, by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ICGC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CapTCR-Seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "icgcpath <- \"/Users/anabbi/Desktop/H4H/immpedcan/ICGC/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "icgc_cap <- list.files(icgcpath, pattern = \"CLONES_TRB\", recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "icgc_cap <- icgc_cap[!grepl(\"MiSeq\", icgc_cap)]\n",
    "\n",
    "icgc_cap <- icgc_cap[!grepl(\"ds_batch\", icgc_cap)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt'</li><li>'batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt'</li><li>'batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt'</li><li>'batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt'</li><li>'batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt'</li><li>'batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt'</li><li>'batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt'</li><li>'batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt'</li><li>'batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt'</li><li>'batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'batch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM1.txt'\n",
       "\\item 'batch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM2.txt'\n",
       "\\item 'batch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM28.txt'\n",
       "\\item 'batch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM32.txt'\n",
       "\\item 'batch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM38.txt'\n",
       "\\item 'batch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM53.txt'\n",
       "\\item 'batch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM58.txt'\n",
       "\\item 'batch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM6.txt'\n",
       "\\item 'batch2/mixcr/clones/CLONES\\_TRBICGC\\_GBM4.txt'\n",
       "\\item 'batch2/mixcr/clones/CLONES\\_TRBICGC\\_GBM56.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt'\n",
       "2. 'batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt'\n",
       "3. 'batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt'\n",
       "4. 'batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt'\n",
       "5. 'batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt'\n",
       "6. 'batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt'\n",
       "7. 'batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt'\n",
       "8. 'batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt'\n",
       "9. 'batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt'\n",
       "10. 'batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt\" \n",
       " [2] \"batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt\" \n",
       " [3] \"batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt\"\n",
       " [4] \"batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt\"\n",
       " [5] \"batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt\"\n",
       " [6] \"batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt\"\n",
       " [7] \"batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt\"\n",
       " [8] \"batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt\" \n",
       " [9] \"batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt\" \n",
       "[10] \"batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "icgc_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_icgc_cap <- immunelistfx(icgc_cap, icgcpath, \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "10"
      ],
      "text/latex": [
       "10"
      ],
      "text/markdown": [
       "10"
      ],
      "text/plain": [
       "[1] 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(trb_list_icgc_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "10"
      ],
      "text/latex": [
       "10"
      ],
      "text/markdown": [
       "10"
      ],
      "text/plain": [
       "[1] 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trb_list_icgc_cap <- trb_list_icgc_cap[sapply(trb_list_icgc_cap, function(x) length(unlist(x))) > 1] # remove files with one clonotype\n",
    "                            \n",
    "length(trb_list_icgc_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_icgc_cap <- trb_list_icgc_cap[sapply(trb_list_icgc_cap, function(x) var(unlist(x))) > 0] # remove files no variance                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "10"
      ],
      "text/latex": [
       "10"
      ],
      "text/markdown": [
       "10"
      ],
      "text/plain": [
       "[1] 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(trb_list_icgc_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_icgc_cap <- Divstats.fx(trb_list_icgc_cap, \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 10 × 19 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TRB</th><th scope=col>Reads</th><th scope=col>CPKR</th><th scope=col>Average_reads</th><th scope=col>VMR</th><th scope=col>Max_reads</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>Sample_Coverage</th><th scope=col>observed_Richness</th><th scope=col>estimated_Richness</th><th scope=col>SE_Richeness</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Shannon</th><th scope=col>SE_Shannon</th><th scope=col>observed_Simpson</th><th scope=col>estimated_Simpson</th><th scope=col>SE_Simpson</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt</th><td> 30</td><td> 649</td><td> 46.224961</td><td> 21.63333</td><td>2.7320230</td><td> 313</td><td> 5</td><td> 7</td><td> 31.674</td><td>1.000</td><td> 30</td><td> 31.783</td><td> 2.186</td><td> 6.946</td><td> 7.128</td><td>0.392</td><td> 3.652</td><td> 3.667</td><td>0.210</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt</th><td> 37</td><td>1434</td><td> 25.801953</td><td> 38.75676</td><td>3.3840113</td><td> 763</td><td> 8</td><td> 5</td><td> 41.564</td><td>0.998</td><td> 37</td><td> 43.396</td><td> 5.918</td><td> 5.526</td><td> 5.617</td><td>0.218</td><td> 3.047</td><td> 3.052</td><td>0.110</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt</th><td> 19</td><td> 869</td><td> 21.864212</td><td> 45.73684</td><td>2.3790594</td><td> 460</td><td> 5</td><td> 1</td><td> 23.119</td><td>0.996</td><td> 19</td><td> 31.486</td><td>17.120</td><td> 4.721</td><td> 4.802</td><td>0.214</td><td> 2.986</td><td> 2.993</td><td>0.123</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt</th><td>177</td><td>1938</td><td> 91.331269</td><td> 10.94915</td><td>3.7159476</td><td> 519</td><td>24</td><td>19</td><td>189.040</td><td>0.997</td><td>177</td><td>192.150</td><td> 8.092</td><td>50.295</td><td>53.035</td><td>2.356</td><td>12.016</td><td>12.085</td><td>0.773</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt</th><td> 11</td><td> 615</td><td> 17.886179</td><td> 55.90909</td><td>2.0584413</td><td> 389</td><td> 2</td><td> 1</td><td> 12.263</td><td>0.999</td><td> 11</td><td> 12.997</td><td> 3.736</td><td> 3.309</td><td> 3.344</td><td>0.148</td><td> 2.267</td><td> 2.272</td><td>0.111</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt</th><td> 17</td><td>2012</td><td>  8.449304</td><td>118.35294</td><td>2.6796651</td><td>1304</td><td> 3</td><td> 1</td><td> 19.189</td><td>0.999</td><td> 17</td><td> 21.498</td><td> 7.190</td><td> 3.334</td><td> 3.351</td><td>0.092</td><td> 2.191</td><td> 2.193</td><td>0.056</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt</th><td>134</td><td>2076</td><td> 64.547206</td><td> 15.49254</td><td>3.2178316</td><td> 549</td><td>13</td><td> 5</td><td>143.066</td><td>0.997</td><td>134</td><td>150.892</td><td>12.718</td><td>43.980</td><td>45.666</td><td>1.666</td><td>11.882</td><td>11.945</td><td>0.683</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt</th><td> 32</td><td>1547</td><td> 20.685197</td><td> 48.34375</td><td>3.0431747</td><td> 790</td><td> 4</td><td> 2</td><td> 34.527</td><td>0.999</td><td> 32</td><td> 35.997</td><td> 5.288</td><td> 6.090</td><td> 6.163</td><td>0.264</td><td> 3.209</td><td> 3.214</td><td>0.115</td></tr>\n",
       "\t<tr><th scope=row>batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt</th><td>  8</td><td>  80</td><td>100.000000</td><td> 10.00000</td><td>0.6391959</td><td>  18</td><td> 2</td><td> 0</td><td>  8.854</td><td>0.997</td><td>  8</td><td>  8.988</td><td> 2.223</td><td> 6.348</td><td> 6.708</td><td>0.322</td><td> 5.893</td><td> 6.282</td><td>0.433</td></tr>\n",
       "\t<tr><th scope=row>batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt</th><td>  4</td><td> 112</td><td> 35.714286</td><td> 28.00000</td><td>0.4870779</td><td>  40</td><td> 0</td><td> 0</td><td>  4.000</td><td>1.000</td><td>  4</td><td>  4.000</td><td> 0.007</td><td> 3.611</td><td> 3.660</td><td>0.131</td><td> 3.396</td><td> 3.471</td><td>0.187</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 10 × 19 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllll}\n",
       "  & TRB & Reads & CPKR & Average\\_reads & VMR & Max\\_reads & Singletons & Doubletons & qD & Sample\\_Coverage & observed\\_Richness & estimated\\_Richness & SE\\_Richeness & observed\\_Shannon & estimated\\_Shannon & SE\\_Shannon & observed\\_Simpson & estimated\\_Simpson & SE\\_Simpson\\\\\n",
       "\\hline\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM1.txt &  30 &  649 &  46.224961 &  21.63333 & 2.7320230 &  313 &  5 &  7 &  31.674 & 1.000 &  30 &  31.783 &  2.186 &  6.946 &  7.128 & 0.392 &  3.652 &  3.667 & 0.210\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM2.txt &  37 & 1434 &  25.801953 &  38.75676 & 3.3840113 &  763 &  8 &  5 &  41.564 & 0.998 &  37 &  43.396 &  5.918 &  5.526 &  5.617 & 0.218 &  3.047 &  3.052 & 0.110\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM28.txt &  19 &  869 &  21.864212 &  45.73684 & 2.3790594 &  460 &  5 &  1 &  23.119 & 0.996 &  19 &  31.486 & 17.120 &  4.721 &  4.802 & 0.214 &  2.986 &  2.993 & 0.123\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM32.txt & 177 & 1938 &  91.331269 &  10.94915 & 3.7159476 &  519 & 24 & 19 & 189.040 & 0.997 & 177 & 192.150 &  8.092 & 50.295 & 53.035 & 2.356 & 12.016 & 12.085 & 0.773\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM38.txt &  11 &  615 &  17.886179 &  55.90909 & 2.0584413 &  389 &  2 &  1 &  12.263 & 0.999 &  11 &  12.997 &  3.736 &  3.309 &  3.344 & 0.148 &  2.267 &  2.272 & 0.111\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM53.txt &  17 & 2012 &   8.449304 & 118.35294 & 2.6796651 & 1304 &  3 &  1 &  19.189 & 0.999 &  17 &  21.498 &  7.190 &  3.334 &  3.351 & 0.092 &  2.191 &  2.193 & 0.056\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM58.txt & 134 & 2076 &  64.547206 &  15.49254 & 3.2178316 &  549 & 13 &  5 & 143.066 & 0.997 & 134 & 150.892 & 12.718 & 43.980 & 45.666 & 1.666 & 11.882 & 11.945 & 0.683\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM6.txt &  32 & 1547 &  20.685197 &  48.34375 & 3.0431747 &  790 &  4 &  2 &  34.527 & 0.999 &  32 &  35.997 &  5.288 &  6.090 &  6.163 & 0.264 &  3.209 &  3.214 & 0.115\\\\\n",
       "\tbatch2/mixcr/clones/CLONES\\_TRBICGC\\_GBM4.txt &   8 &   80 & 100.000000 &  10.00000 & 0.6391959 &   18 &  2 &  0 &   8.854 & 0.997 &   8 &   8.988 &  2.223 &  6.348 &  6.708 & 0.322 &  5.893 &  6.282 & 0.433\\\\\n",
       "\tbatch2/mixcr/clones/CLONES\\_TRBICGC\\_GBM56.txt &   4 &  112 &  35.714286 &  28.00000 & 0.4870779 &   40 &  0 &  0 &   4.000 & 1.000 &   4 &   4.000 &  0.007 &  3.611 &  3.660 & 0.131 &  3.396 &  3.471 & 0.187\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 10 × 19 of type dbl\n",
       "\n",
       "| <!--/--> | TRB | Reads | CPKR | Average_reads | VMR | Max_reads | Singletons | Doubletons | qD | Sample_Coverage | observed_Richness | estimated_Richness | SE_Richeness | observed_Shannon | estimated_Shannon | SE_Shannon | observed_Simpson | estimated_Simpson | SE_Simpson |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt |  30 |  649 |  46.224961 |  21.63333 | 2.7320230 |  313 |  5 |  7 |  31.674 | 1.000 |  30 |  31.783 |  2.186 |  6.946 |  7.128 | 0.392 |  3.652 |  3.667 | 0.210 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt |  37 | 1434 |  25.801953 |  38.75676 | 3.3840113 |  763 |  8 |  5 |  41.564 | 0.998 |  37 |  43.396 |  5.918 |  5.526 |  5.617 | 0.218 |  3.047 |  3.052 | 0.110 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt |  19 |  869 |  21.864212 |  45.73684 | 2.3790594 |  460 |  5 |  1 |  23.119 | 0.996 |  19 |  31.486 | 17.120 |  4.721 |  4.802 | 0.214 |  2.986 |  2.993 | 0.123 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt | 177 | 1938 |  91.331269 |  10.94915 | 3.7159476 |  519 | 24 | 19 | 189.040 | 0.997 | 177 | 192.150 |  8.092 | 50.295 | 53.035 | 2.356 | 12.016 | 12.085 | 0.773 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt |  11 |  615 |  17.886179 |  55.90909 | 2.0584413 |  389 |  2 |  1 |  12.263 | 0.999 |  11 |  12.997 |  3.736 |  3.309 |  3.344 | 0.148 |  2.267 |  2.272 | 0.111 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt |  17 | 2012 |   8.449304 | 118.35294 | 2.6796651 | 1304 |  3 |  1 |  19.189 | 0.999 |  17 |  21.498 |  7.190 |  3.334 |  3.351 | 0.092 |  2.191 |  2.193 | 0.056 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt | 134 | 2076 |  64.547206 |  15.49254 | 3.2178316 |  549 | 13 |  5 | 143.066 | 0.997 | 134 | 150.892 | 12.718 | 43.980 | 45.666 | 1.666 | 11.882 | 11.945 | 0.683 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt |  32 | 1547 |  20.685197 |  48.34375 | 3.0431747 |  790 |  4 |  2 |  34.527 | 0.999 |  32 |  35.997 |  5.288 |  6.090 |  6.163 | 0.264 |  3.209 |  3.214 | 0.115 |\n",
       "| batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt |   8 |   80 | 100.000000 |  10.00000 | 0.6391959 |   18 |  2 |  0 |   8.854 | 0.997 |   8 |   8.988 |  2.223 |  6.348 |  6.708 | 0.322 |  5.893 |  6.282 | 0.433 |\n",
       "| batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt |   4 |  112 |  35.714286 |  28.00000 | 0.4870779 |   40 |  0 |  0 |   4.000 | 1.000 |   4 |   4.000 |  0.007 |  3.611 |  3.660 | 0.131 |  3.396 |  3.471 | 0.187 |\n",
       "\n"
      ],
      "text/plain": [
       "                                             TRB Reads CPKR       Average_reads\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   30  649   46.224961  21.63333    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   37 1434   25.801953  38.75676    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  19  869   21.864212  45.73684    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 177 1938   91.331269  10.94915    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  11  615   17.886179  55.90909    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  17 2012    8.449304 118.35294    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 134 2076   64.547206  15.49254    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   32 1547   20.685197  48.34375    \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt    8   80  100.000000  10.00000    \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt   4  112   35.714286  28.00000    \n",
       "                                             VMR       Max_reads Singletons\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt  2.7320230  313       5        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt  3.3840113  763       8        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt 2.3790594  460       5        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 3.7159476  519      24        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt 2.0584413  389       2        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt 2.6796651 1304       3        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 3.2178316  549      13        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt  3.0431747  790       4        \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt  0.6391959   18       2        \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt 0.4870779   40       0        \n",
       "                                             Doubletons qD      Sample_Coverage\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   7          31.674 1.000          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   5          41.564 0.998          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  1          23.119 0.996          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 19         189.040 0.997          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  1          12.263 0.999          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  1          19.189 0.999          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt  5         143.066 0.997          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   2          34.527 0.999          \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt   0           8.854 0.997          \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt  0           4.000 1.000          \n",
       "                                             observed_Richness\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   30              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   37              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  19              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 177              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  11              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  17              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 134              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   32              \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt    8              \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt   4              \n",
       "                                             estimated_Richness SE_Richeness\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   31.783             2.186      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   43.396             5.918      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  31.486            17.120      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 192.150             8.092      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  12.997             3.736      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  21.498             7.190      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 150.892            12.718      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   35.997             5.288      \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt    8.988             2.223      \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt   4.000             0.007      \n",
       "                                             observed_Shannon estimated_Shannon\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   6.946            7.128           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   5.526            5.617           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  4.721            4.802           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 50.295           53.035           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  3.309            3.344           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  3.334            3.351           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 43.980           45.666           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   6.090            6.163           \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt   6.348            6.708           \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt  3.611            3.660           \n",
       "                                             SE_Shannon observed_Simpson\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt  0.392       3.652          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt  0.218       3.047          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt 0.214       2.986          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 2.356      12.016          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt 0.148       2.267          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt 0.092       2.191          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 1.666      11.882          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt  0.264       3.209          \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt  0.322       5.893          \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt 0.131       3.396          \n",
       "                                             estimated_Simpson SE_Simpson\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   3.667            0.210     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   3.052            0.110     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  2.993            0.123     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 12.085            0.773     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  2.272            0.111     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  2.193            0.056     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 11.945            0.683     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   3.214            0.115     \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt   6.282            0.433     \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt  3.471            0.187     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "div_trb_icgc_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(div_trb_icgc_cap, file = paste0(datapath, \"diversity/div_trb_icgc_cap.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_icgc_cap <- as.data.frame(div_trb_icgc_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_icgc_cap$sample_id <- gsub(\".*TRB\", \"\", rownames(div_trb_icgc_cap))\n",
    "\n",
    "div_trb_icgc_cap$sample_id <- gsub(\".txt\", \"\", div_trb_icgc_cap$sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 20</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TRB</th><th scope=col>Reads</th><th scope=col>CPKR</th><th scope=col>Average_reads</th><th scope=col>VMR</th><th scope=col>Max_reads</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>Sample_Coverage</th><th scope=col>observed_Richness</th><th scope=col>estimated_Richness</th><th scope=col>SE_Richeness</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Shannon</th><th scope=col>SE_Shannon</th><th scope=col>observed_Simpson</th><th scope=col>estimated_Simpson</th><th scope=col>SE_Simpson</th><th scope=col>sample_id</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>&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;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt</th><td> 30</td><td> 649</td><td> 46.224961</td><td> 21.63333</td><td>2.7320230</td><td> 313</td><td> 5</td><td> 7</td><td> 31.674</td><td>1.000</td><td> 30</td><td> 31.783</td><td> 2.186</td><td> 6.946</td><td> 7.128</td><td>0.392</td><td> 3.652</td><td> 3.667</td><td>0.210</td><td>ICGC_GBM1 </td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt</th><td> 37</td><td>1434</td><td> 25.801953</td><td> 38.75676</td><td>3.3840113</td><td> 763</td><td> 8</td><td> 5</td><td> 41.564</td><td>0.998</td><td> 37</td><td> 43.396</td><td> 5.918</td><td> 5.526</td><td> 5.617</td><td>0.218</td><td> 3.047</td><td> 3.052</td><td>0.110</td><td>ICGC_GBM2 </td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt</th><td> 19</td><td> 869</td><td> 21.864212</td><td> 45.73684</td><td>2.3790594</td><td> 460</td><td> 5</td><td> 1</td><td> 23.119</td><td>0.996</td><td> 19</td><td> 31.486</td><td>17.120</td><td> 4.721</td><td> 4.802</td><td>0.214</td><td> 2.986</td><td> 2.993</td><td>0.123</td><td>ICGC_GBM28</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt</th><td>177</td><td>1938</td><td> 91.331269</td><td> 10.94915</td><td>3.7159476</td><td> 519</td><td>24</td><td>19</td><td>189.040</td><td>0.997</td><td>177</td><td>192.150</td><td> 8.092</td><td>50.295</td><td>53.035</td><td>2.356</td><td>12.016</td><td>12.085</td><td>0.773</td><td>ICGC_GBM32</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt</th><td> 11</td><td> 615</td><td> 17.886179</td><td> 55.90909</td><td>2.0584413</td><td> 389</td><td> 2</td><td> 1</td><td> 12.263</td><td>0.999</td><td> 11</td><td> 12.997</td><td> 3.736</td><td> 3.309</td><td> 3.344</td><td>0.148</td><td> 2.267</td><td> 2.272</td><td>0.111</td><td>ICGC_GBM38</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt</th><td> 17</td><td>2012</td><td>  8.449304</td><td>118.35294</td><td>2.6796651</td><td>1304</td><td> 3</td><td> 1</td><td> 19.189</td><td>0.999</td><td> 17</td><td> 21.498</td><td> 7.190</td><td> 3.334</td><td> 3.351</td><td>0.092</td><td> 2.191</td><td> 2.193</td><td>0.056</td><td>ICGC_GBM53</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt</th><td>134</td><td>2076</td><td> 64.547206</td><td> 15.49254</td><td>3.2178316</td><td> 549</td><td>13</td><td> 5</td><td>143.066</td><td>0.997</td><td>134</td><td>150.892</td><td>12.718</td><td>43.980</td><td>45.666</td><td>1.666</td><td>11.882</td><td>11.945</td><td>0.683</td><td>ICGC_GBM58</td></tr>\n",
       "\t<tr><th scope=row>batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt</th><td> 32</td><td>1547</td><td> 20.685197</td><td> 48.34375</td><td>3.0431747</td><td> 790</td><td> 4</td><td> 2</td><td> 34.527</td><td>0.999</td><td> 32</td><td> 35.997</td><td> 5.288</td><td> 6.090</td><td> 6.163</td><td>0.264</td><td> 3.209</td><td> 3.214</td><td>0.115</td><td>ICGC_GBM6 </td></tr>\n",
       "\t<tr><th scope=row>batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt</th><td>  8</td><td>  80</td><td>100.000000</td><td> 10.00000</td><td>0.6391959</td><td>  18</td><td> 2</td><td> 0</td><td>  8.854</td><td>0.997</td><td>  8</td><td>  8.988</td><td> 2.223</td><td> 6.348</td><td> 6.708</td><td>0.322</td><td> 5.893</td><td> 6.282</td><td>0.433</td><td>ICGC_GBM4 </td></tr>\n",
       "\t<tr><th scope=row>batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt</th><td>  4</td><td> 112</td><td> 35.714286</td><td> 28.00000</td><td>0.4870779</td><td>  40</td><td> 0</td><td> 0</td><td>  4.000</td><td>1.000</td><td>  4</td><td>  4.000</td><td> 0.007</td><td> 3.611</td><td> 3.660</td><td>0.131</td><td> 3.396</td><td> 3.471</td><td>0.187</td><td>ICGC_GBM56</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 20\n",
       "\\begin{tabular}{r|llllllllllllllllllll}\n",
       "  & TRB & Reads & CPKR & Average\\_reads & VMR & Max\\_reads & Singletons & Doubletons & qD & Sample\\_Coverage & observed\\_Richness & estimated\\_Richness & SE\\_Richeness & observed\\_Shannon & estimated\\_Shannon & SE\\_Shannon & observed\\_Simpson & estimated\\_Simpson & SE\\_Simpson & sample\\_id\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM1.txt &  30 &  649 &  46.224961 &  21.63333 & 2.7320230 &  313 &  5 &  7 &  31.674 & 1.000 &  30 &  31.783 &  2.186 &  6.946 &  7.128 & 0.392 &  3.652 &  3.667 & 0.210 & ICGC\\_GBM1 \\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM2.txt &  37 & 1434 &  25.801953 &  38.75676 & 3.3840113 &  763 &  8 &  5 &  41.564 & 0.998 &  37 &  43.396 &  5.918 &  5.526 &  5.617 & 0.218 &  3.047 &  3.052 & 0.110 & ICGC\\_GBM2 \\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM28.txt &  19 &  869 &  21.864212 &  45.73684 & 2.3790594 &  460 &  5 &  1 &  23.119 & 0.996 &  19 &  31.486 & 17.120 &  4.721 &  4.802 & 0.214 &  2.986 &  2.993 & 0.123 & ICGC\\_GBM28\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM32.txt & 177 & 1938 &  91.331269 &  10.94915 & 3.7159476 &  519 & 24 & 19 & 189.040 & 0.997 & 177 & 192.150 &  8.092 & 50.295 & 53.035 & 2.356 & 12.016 & 12.085 & 0.773 & ICGC\\_GBM32\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM38.txt &  11 &  615 &  17.886179 &  55.90909 & 2.0584413 &  389 &  2 &  1 &  12.263 & 0.999 &  11 &  12.997 &  3.736 &  3.309 &  3.344 & 0.148 &  2.267 &  2.272 & 0.111 & ICGC\\_GBM38\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM53.txt &  17 & 2012 &   8.449304 & 118.35294 & 2.6796651 & 1304 &  3 &  1 &  19.189 & 0.999 &  17 &  21.498 &  7.190 &  3.334 &  3.351 & 0.092 &  2.191 &  2.193 & 0.056 & ICGC\\_GBM53\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM58.txt & 134 & 2076 &  64.547206 &  15.49254 & 3.2178316 &  549 & 13 &  5 & 143.066 & 0.997 & 134 & 150.892 & 12.718 & 43.980 & 45.666 & 1.666 & 11.882 & 11.945 & 0.683 & ICGC\\_GBM58\\\\\n",
       "\tbatch1/mixcr/clones/CLONES\\_TRBICGC\\_GBM6.txt &  32 & 1547 &  20.685197 &  48.34375 & 3.0431747 &  790 &  4 &  2 &  34.527 & 0.999 &  32 &  35.997 &  5.288 &  6.090 &  6.163 & 0.264 &  3.209 &  3.214 & 0.115 & ICGC\\_GBM6 \\\\\n",
       "\tbatch2/mixcr/clones/CLONES\\_TRBICGC\\_GBM4.txt &   8 &   80 & 100.000000 &  10.00000 & 0.6391959 &   18 &  2 &  0 &   8.854 & 0.997 &   8 &   8.988 &  2.223 &  6.348 &  6.708 & 0.322 &  5.893 &  6.282 & 0.433 & ICGC\\_GBM4 \\\\\n",
       "\tbatch2/mixcr/clones/CLONES\\_TRBICGC\\_GBM56.txt &   4 &  112 &  35.714286 &  28.00000 & 0.4870779 &   40 &  0 &  0 &   4.000 & 1.000 &   4 &   4.000 &  0.007 &  3.611 &  3.660 & 0.131 &  3.396 &  3.471 & 0.187 & ICGC\\_GBM56\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 20\n",
       "\n",
       "| <!--/--> | TRB &lt;dbl&gt; | Reads &lt;dbl&gt; | CPKR &lt;dbl&gt; | Average_reads &lt;dbl&gt; | VMR &lt;dbl&gt; | Max_reads &lt;dbl&gt; | Singletons &lt;dbl&gt; | Doubletons &lt;dbl&gt; | qD &lt;dbl&gt; | Sample_Coverage &lt;dbl&gt; | observed_Richness &lt;dbl&gt; | estimated_Richness &lt;dbl&gt; | SE_Richeness &lt;dbl&gt; | observed_Shannon &lt;dbl&gt; | estimated_Shannon &lt;dbl&gt; | SE_Shannon &lt;dbl&gt; | observed_Simpson &lt;dbl&gt; | estimated_Simpson &lt;dbl&gt; | SE_Simpson &lt;dbl&gt; | sample_id &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt |  30 |  649 |  46.224961 |  21.63333 | 2.7320230 |  313 |  5 |  7 |  31.674 | 1.000 |  30 |  31.783 |  2.186 |  6.946 |  7.128 | 0.392 |  3.652 |  3.667 | 0.210 | ICGC_GBM1  |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt |  37 | 1434 |  25.801953 |  38.75676 | 3.3840113 |  763 |  8 |  5 |  41.564 | 0.998 |  37 |  43.396 |  5.918 |  5.526 |  5.617 | 0.218 |  3.047 |  3.052 | 0.110 | ICGC_GBM2  |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt |  19 |  869 |  21.864212 |  45.73684 | 2.3790594 |  460 |  5 |  1 |  23.119 | 0.996 |  19 |  31.486 | 17.120 |  4.721 |  4.802 | 0.214 |  2.986 |  2.993 | 0.123 | ICGC_GBM28 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt | 177 | 1938 |  91.331269 |  10.94915 | 3.7159476 |  519 | 24 | 19 | 189.040 | 0.997 | 177 | 192.150 |  8.092 | 50.295 | 53.035 | 2.356 | 12.016 | 12.085 | 0.773 | ICGC_GBM32 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt |  11 |  615 |  17.886179 |  55.90909 | 2.0584413 |  389 |  2 |  1 |  12.263 | 0.999 |  11 |  12.997 |  3.736 |  3.309 |  3.344 | 0.148 |  2.267 |  2.272 | 0.111 | ICGC_GBM38 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt |  17 | 2012 |   8.449304 | 118.35294 | 2.6796651 | 1304 |  3 |  1 |  19.189 | 0.999 |  17 |  21.498 |  7.190 |  3.334 |  3.351 | 0.092 |  2.191 |  2.193 | 0.056 | ICGC_GBM53 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt | 134 | 2076 |  64.547206 |  15.49254 | 3.2178316 |  549 | 13 |  5 | 143.066 | 0.997 | 134 | 150.892 | 12.718 | 43.980 | 45.666 | 1.666 | 11.882 | 11.945 | 0.683 | ICGC_GBM58 |\n",
       "| batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt |  32 | 1547 |  20.685197 |  48.34375 | 3.0431747 |  790 |  4 |  2 |  34.527 | 0.999 |  32 |  35.997 |  5.288 |  6.090 |  6.163 | 0.264 |  3.209 |  3.214 | 0.115 | ICGC_GBM6  |\n",
       "| batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt |   8 |   80 | 100.000000 |  10.00000 | 0.6391959 |   18 |  2 |  0 |   8.854 | 0.997 |   8 |   8.988 |  2.223 |  6.348 |  6.708 | 0.322 |  5.893 |  6.282 | 0.433 | ICGC_GBM4  |\n",
       "| batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt |   4 |  112 |  35.714286 |  28.00000 | 0.4870779 |   40 |  0 |  0 |   4.000 | 1.000 |   4 |   4.000 |  0.007 |  3.611 |  3.660 | 0.131 |  3.396 |  3.471 | 0.187 | ICGC_GBM56 |\n",
       "\n"
      ],
      "text/plain": [
       "                                             TRB Reads CPKR       Average_reads\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   30  649   46.224961  21.63333    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   37 1434   25.801953  38.75676    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  19  869   21.864212  45.73684    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 177 1938   91.331269  10.94915    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  11  615   17.886179  55.90909    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  17 2012    8.449304 118.35294    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 134 2076   64.547206  15.49254    \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   32 1547   20.685197  48.34375    \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt    8   80  100.000000  10.00000    \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt   4  112   35.714286  28.00000    \n",
       "                                             VMR       Max_reads Singletons\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt  2.7320230  313       5        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt  3.3840113  763       8        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt 2.3790594  460       5        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 3.7159476  519      24        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt 2.0584413  389       2        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt 2.6796651 1304       3        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 3.2178316  549      13        \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt  3.0431747  790       4        \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt  0.6391959   18       2        \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt 0.4870779   40       0        \n",
       "                                             Doubletons qD      Sample_Coverage\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   7          31.674 1.000          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   5          41.564 0.998          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  1          23.119 0.996          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 19         189.040 0.997          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  1          12.263 0.999          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  1          19.189 0.999          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt  5         143.066 0.997          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   2          34.527 0.999          \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt   0           8.854 0.997          \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt  0           4.000 1.000          \n",
       "                                             observed_Richness\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   30              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   37              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  19              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 177              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  11              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  17              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 134              \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   32              \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt    8              \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt   4              \n",
       "                                             estimated_Richness SE_Richeness\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   31.783             2.186      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   43.396             5.918      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  31.486            17.120      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 192.150             8.092      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  12.997             3.736      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  21.498             7.190      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 150.892            12.718      \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   35.997             5.288      \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt    8.988             2.223      \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt   4.000             0.007      \n",
       "                                             observed_Shannon estimated_Shannon\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   6.946            7.128           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   5.526            5.617           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  4.721            4.802           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 50.295           53.035           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  3.309            3.344           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  3.334            3.351           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 43.980           45.666           \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   6.090            6.163           \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt   6.348            6.708           \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt  3.611            3.660           \n",
       "                                             SE_Shannon observed_Simpson\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt  0.392       3.652          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt  0.218       3.047          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt 0.214       2.986          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 2.356      12.016          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt 0.148       2.267          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt 0.092       2.191          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 1.666      11.882          \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt  0.264       3.209          \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt  0.322       5.893          \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt 0.131       3.396          \n",
       "                                             estimated_Simpson SE_Simpson\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt   3.667            0.210     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt   3.052            0.110     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt  2.993            0.123     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt 12.085            0.773     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt  2.272            0.111     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt  2.193            0.056     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt 11.945            0.683     \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt   3.214            0.115     \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt   6.282            0.433     \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt  3.471            0.187     \n",
       "                                             sample_id \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM1.txt  ICGC_GBM1 \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM2.txt  ICGC_GBM2 \n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM28.txt ICGC_GBM28\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM32.txt ICGC_GBM32\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM38.txt ICGC_GBM38\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM53.txt ICGC_GBM53\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM58.txt ICGC_GBM58\n",
       "batch1/mixcr/clones/CLONES_TRBICGC_GBM6.txt  ICGC_GBM6 \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM4.txt  ICGC_GBM4 \n",
       "batch2/mixcr/clones/CLONES_TRBICGC_GBM56.txt ICGC_GBM56"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "div_trb_icgc_cap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bind RNA and Cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"diversity/metadata_IC_TRB.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(div_trb_icgc_cap)[1:ncol(div_trb_icgc_cap)-1] <-\n",
    "paste0(colnames(div_trb_icgc_cap)[1:ncol(div_trb_icgc_cap)-1], \"_TCRCap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "icgc_trb_rna <- metadata_IC_TRB[ metadata_IC_TRB$group == \"ICGC\",c(25:43,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(icgc_trb_rna)[1:ncol(icgc_trb_rna)-1] <-\n",
    "paste0(colnames(icgc_trb_rna)[1:ncol(icgc_trb_rna)-1], \"_RNAseq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_icgc_cap_rna <- merge(div_trb_icgc_cap,\n",
    "                                icgc_trb_rna, by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>4</li><li>39</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 4\n",
       "\\item 39\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 4\n",
       "2. 39\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  4 39"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(div_trb_icgc_cap_rna)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NPC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "npc_rna <- list.files(paste0(datapath, \"mixcr/NPC/clones/\"), pattern = \"CLONES_TRB\", recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_npc_rna <- immunelistfx(npc_rna, paste0(datapath, \"mixcr/NPC/clones/\"), \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "39"
      ],
      "text/latex": [
       "39"
      ],
      "text/markdown": [
       "39"
      ],
      "text/plain": [
       "[1] 39"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trb_list_npc_rna <- trb_list_npc_rna[sapply(trb_list_npc_rna, function(x) length(unlist(x))) > 1] # remove files with one clonotype\n",
    "                            \n",
    "length(trb_list_npc_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_npc_rna <- trb_list_npc_rna[sapply(trb_list_npc_rna, function(x) var(unlist(x))) > 0] # remove files no variance                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "38"
      ],
      "text/latex": [
       "38"
      ],
      "text/markdown": [
       "38"
      ],
      "text/plain": [
       "[1] 38"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(trb_list_npc_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_rna <- Divstats.fx(trb_list_npc_rna, \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 19 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TRB</th><th scope=col>Reads</th><th scope=col>CPKR</th><th scope=col>Average_reads</th><th scope=col>VMR</th><th scope=col>Max_reads</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>Sample_Coverage</th><th scope=col>observed_Richness</th><th scope=col>estimated_Richness</th><th scope=col>SE_Richeness</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Shannon</th><th scope=col>SE_Shannon</th><th scope=col>observed_Simpson</th><th scope=col>estimated_Simpson</th><th scope=col>SE_Simpson</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CLONES_TRB104FF.txt</th><td> 3</td><td>24</td><td>125.0000</td><td>8.000000</td><td>0.3307189</td><td>11</td><td>0</td><td>0</td><td> 3.000</td><td>1.000</td><td> 3</td><td> 3.000</td><td>0.058</td><td> 2.897</td><td> 3.024</td><td>0.177</td><td> 2.796</td><td> 3.033</td><td>0.316</td></tr>\n",
       "\t<tr><th scope=row>CLONES_TRB107JP.txt</th><td>23</td><td>66</td><td>348.4848</td><td>2.869565</td><td>0.4734587</td><td> 5</td><td>4</td><td>6</td><td>24.246</td><td>0.997</td><td>23</td><td>24.313</td><td>1.823</td><td>20.578</td><td>25.014</td><td>1.881</td><td>18.939</td><td>26.159</td><td>2.443</td></tr>\n",
       "\t<tr><th scope=row>CLONES_TRB108W03.txt</th><td>19</td><td>52</td><td>365.3846</td><td>2.736842</td><td>0.5286810</td><td> 6</td><td>3</td><td>8</td><td>19.549</td><td>1.000</td><td>19</td><td>19.552</td><td>0.998</td><td>16.748</td><td>20.379</td><td>1.958</td><td>15.022</td><td>20.719</td><td>2.429</td></tr>\n",
       "\t<tr><th scope=row>CLONES_TRB10C02.txt</th><td> 7</td><td>17</td><td>411.7647</td><td>2.428571</td><td>0.7075145</td><td> 6</td><td>2</td><td>3</td><td> 7.594</td><td>0.995</td><td> 7</td><td> 7.627</td><td>1.243</td><td> 5.826</td><td> 7.292</td><td>1.657</td><td> 4.898</td><td> 6.476</td><td>1.926</td></tr>\n",
       "\t<tr><th scope=row>CLONES_TRB116JC.txt</th><td>16</td><td>57</td><td>280.7018</td><td>3.562500</td><td>0.5516700</td><td> 7</td><td>3</td><td>2</td><td>17.632</td><td>0.987</td><td>16</td><td>18.211</td><td>3.342</td><td>13.771</td><td>16.264</td><td>1.496</td><td>12.448</td><td>15.647</td><td>1.635</td></tr>\n",
       "\t<tr><th scope=row>CLONES_TRB119AY.txt</th><td>11</td><td>29</td><td>379.3103</td><td>2.636364</td><td>0.8183391</td><td> 8</td><td>4</td><td>3</td><td>13.008</td><td>0.971</td><td>11</td><td>13.575</td><td>3.378</td><td> 8.546</td><td>10.980</td><td>2.103</td><td> 6.837</td><td> 8.638</td><td>2.203</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 19 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllll}\n",
       "  & TRB & Reads & CPKR & Average\\_reads & VMR & Max\\_reads & Singletons & Doubletons & qD & Sample\\_Coverage & observed\\_Richness & estimated\\_Richness & SE\\_Richeness & observed\\_Shannon & estimated\\_Shannon & SE\\_Shannon & observed\\_Simpson & estimated\\_Simpson & SE\\_Simpson\\\\\n",
       "\\hline\n",
       "\tCLONES\\_TRB104FF.txt &  3 & 24 & 125.0000 & 8.000000 & 0.3307189 & 11 & 0 & 0 &  3.000 & 1.000 &  3 &  3.000 & 0.058 &  2.897 &  3.024 & 0.177 &  2.796 &  3.033 & 0.316\\\\\n",
       "\tCLONES\\_TRB107JP.txt & 23 & 66 & 348.4848 & 2.869565 & 0.4734587 &  5 & 4 & 6 & 24.246 & 0.997 & 23 & 24.313 & 1.823 & 20.578 & 25.014 & 1.881 & 18.939 & 26.159 & 2.443\\\\\n",
       "\tCLONES\\_TRB108W03.txt & 19 & 52 & 365.3846 & 2.736842 & 0.5286810 &  6 & 3 & 8 & 19.549 & 1.000 & 19 & 19.552 & 0.998 & 16.748 & 20.379 & 1.958 & 15.022 & 20.719 & 2.429\\\\\n",
       "\tCLONES\\_TRB10C02.txt &  7 & 17 & 411.7647 & 2.428571 & 0.7075145 &  6 & 2 & 3 &  7.594 & 0.995 &  7 &  7.627 & 1.243 &  5.826 &  7.292 & 1.657 &  4.898 &  6.476 & 1.926\\\\\n",
       "\tCLONES\\_TRB116JC.txt & 16 & 57 & 280.7018 & 3.562500 & 0.5516700 &  7 & 3 & 2 & 17.632 & 0.987 & 16 & 18.211 & 3.342 & 13.771 & 16.264 & 1.496 & 12.448 & 15.647 & 1.635\\\\\n",
       "\tCLONES\\_TRB119AY.txt & 11 & 29 & 379.3103 & 2.636364 & 0.8183391 &  8 & 4 & 3 & 13.008 & 0.971 & 11 & 13.575 & 3.378 &  8.546 & 10.980 & 2.103 &  6.837 &  8.638 & 2.203\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 19 of type dbl\n",
       "\n",
       "| <!--/--> | TRB | Reads | CPKR | Average_reads | VMR | Max_reads | Singletons | Doubletons | qD | Sample_Coverage | observed_Richness | estimated_Richness | SE_Richeness | observed_Shannon | estimated_Shannon | SE_Shannon | observed_Simpson | estimated_Simpson | SE_Simpson |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CLONES_TRB104FF.txt |  3 | 24 | 125.0000 | 8.000000 | 0.3307189 | 11 | 0 | 0 |  3.000 | 1.000 |  3 |  3.000 | 0.058 |  2.897 |  3.024 | 0.177 |  2.796 |  3.033 | 0.316 |\n",
       "| CLONES_TRB107JP.txt | 23 | 66 | 348.4848 | 2.869565 | 0.4734587 |  5 | 4 | 6 | 24.246 | 0.997 | 23 | 24.313 | 1.823 | 20.578 | 25.014 | 1.881 | 18.939 | 26.159 | 2.443 |\n",
       "| CLONES_TRB108W03.txt | 19 | 52 | 365.3846 | 2.736842 | 0.5286810 |  6 | 3 | 8 | 19.549 | 1.000 | 19 | 19.552 | 0.998 | 16.748 | 20.379 | 1.958 | 15.022 | 20.719 | 2.429 |\n",
       "| CLONES_TRB10C02.txt |  7 | 17 | 411.7647 | 2.428571 | 0.7075145 |  6 | 2 | 3 |  7.594 | 0.995 |  7 |  7.627 | 1.243 |  5.826 |  7.292 | 1.657 |  4.898 |  6.476 | 1.926 |\n",
       "| CLONES_TRB116JC.txt | 16 | 57 | 280.7018 | 3.562500 | 0.5516700 |  7 | 3 | 2 | 17.632 | 0.987 | 16 | 18.211 | 3.342 | 13.771 | 16.264 | 1.496 | 12.448 | 15.647 | 1.635 |\n",
       "| CLONES_TRB119AY.txt | 11 | 29 | 379.3103 | 2.636364 | 0.8183391 |  8 | 4 | 3 | 13.008 | 0.971 | 11 | 13.575 | 3.378 |  8.546 | 10.980 | 2.103 |  6.837 |  8.638 | 2.203 |\n",
       "\n"
      ],
      "text/plain": [
       "                     TRB Reads CPKR     Average_reads VMR       Max_reads\n",
       "CLONES_TRB104FF.txt   3  24    125.0000 8.000000      0.3307189 11       \n",
       "CLONES_TRB107JP.txt  23  66    348.4848 2.869565      0.4734587  5       \n",
       "CLONES_TRB108W03.txt 19  52    365.3846 2.736842      0.5286810  6       \n",
       "CLONES_TRB10C02.txt   7  17    411.7647 2.428571      0.7075145  6       \n",
       "CLONES_TRB116JC.txt  16  57    280.7018 3.562500      0.5516700  7       \n",
       "CLONES_TRB119AY.txt  11  29    379.3103 2.636364      0.8183391  8       \n",
       "                     Singletons Doubletons qD     Sample_Coverage\n",
       "CLONES_TRB104FF.txt  0          0           3.000 1.000          \n",
       "CLONES_TRB107JP.txt  4          6          24.246 0.997          \n",
       "CLONES_TRB108W03.txt 3          8          19.549 1.000          \n",
       "CLONES_TRB10C02.txt  2          3           7.594 0.995          \n",
       "CLONES_TRB116JC.txt  3          2          17.632 0.987          \n",
       "CLONES_TRB119AY.txt  4          3          13.008 0.971          \n",
       "                     observed_Richness estimated_Richness SE_Richeness\n",
       "CLONES_TRB104FF.txt   3                 3.000             0.058       \n",
       "CLONES_TRB107JP.txt  23                24.313             1.823       \n",
       "CLONES_TRB108W03.txt 19                19.552             0.998       \n",
       "CLONES_TRB10C02.txt   7                 7.627             1.243       \n",
       "CLONES_TRB116JC.txt  16                18.211             3.342       \n",
       "CLONES_TRB119AY.txt  11                13.575             3.378       \n",
       "                     observed_Shannon estimated_Shannon SE_Shannon\n",
       "CLONES_TRB104FF.txt   2.897            3.024            0.177     \n",
       "CLONES_TRB107JP.txt  20.578           25.014            1.881     \n",
       "CLONES_TRB108W03.txt 16.748           20.379            1.958     \n",
       "CLONES_TRB10C02.txt   5.826            7.292            1.657     \n",
       "CLONES_TRB116JC.txt  13.771           16.264            1.496     \n",
       "CLONES_TRB119AY.txt   8.546           10.980            2.103     \n",
       "                     observed_Simpson estimated_Simpson SE_Simpson\n",
       "CLONES_TRB104FF.txt   2.796            3.033            0.316     \n",
       "CLONES_TRB107JP.txt  18.939           26.159            2.443     \n",
       "CLONES_TRB108W03.txt 15.022           20.719            2.429     \n",
       "CLONES_TRB10C02.txt   4.898            6.476            1.926     \n",
       "CLONES_TRB116JC.txt  12.448           15.647            1.635     \n",
       "CLONES_TRB119AY.txt   6.837            8.638            2.203     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(div_trb_npc_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(div_trb_npc_rna, file = paste0(datapath, \"diversity/div_trb_npc_rna.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_rna <- as.data.frame(div_trb_npc_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_rna$sample_id <- gsub(\".*TRB\", \"\", rownames(div_trb_npc_rna))\n",
    "\n",
    "div_trb_npc_rna$sample_id <- gsub(\".txt\", \"\", div_trb_npc_rna$sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'104FF'</li><li>'107JP'</li><li>'108W03'</li><li>'10C02'</li><li>'116JC'</li><li>'119AY'</li><li>'119S01'</li><li>'12109'</li><li>'124M02'</li><li>'126Q03'</li><li>'130YL'</li><li>'133B01'</li><li>'14Y05'</li><li>'18W06'</li><li>'24VP'</li><li>'32S04'</li><li>'43K03'</li><li>'48S07'</li><li>'51M07'</li><li>'58C06'</li><li>'5Z05'</li><li>'60S03'</li><li>'68W02'</li><li>'70WS'</li><li>'72C06'</li><li>'73M07'</li><li>'79HZ'</li><li>'79L01'</li><li>'83CG'</li><li>'8C07'</li><li>'91MO'</li><li>'98KK'</li><li>'9AW'</li><li>'N1'</li><li>'N4'</li><li>'N5'</li><li>'N6'</li><li>'N7'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '104FF'\n",
       "\\item '107JP'\n",
       "\\item '108W03'\n",
       "\\item '10C02'\n",
       "\\item '116JC'\n",
       "\\item '119AY'\n",
       "\\item '119S01'\n",
       "\\item '12109'\n",
       "\\item '124M02'\n",
       "\\item '126Q03'\n",
       "\\item '130YL'\n",
       "\\item '133B01'\n",
       "\\item '14Y05'\n",
       "\\item '18W06'\n",
       "\\item '24VP'\n",
       "\\item '32S04'\n",
       "\\item '43K03'\n",
       "\\item '48S07'\n",
       "\\item '51M07'\n",
       "\\item '58C06'\n",
       "\\item '5Z05'\n",
       "\\item '60S03'\n",
       "\\item '68W02'\n",
       "\\item '70WS'\n",
       "\\item '72C06'\n",
       "\\item '73M07'\n",
       "\\item '79HZ'\n",
       "\\item '79L01'\n",
       "\\item '83CG'\n",
       "\\item '8C07'\n",
       "\\item '91MO'\n",
       "\\item '98KK'\n",
       "\\item '9AW'\n",
       "\\item 'N1'\n",
       "\\item 'N4'\n",
       "\\item 'N5'\n",
       "\\item 'N6'\n",
       "\\item 'N7'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '104FF'\n",
       "2. '107JP'\n",
       "3. '108W03'\n",
       "4. '10C02'\n",
       "5. '116JC'\n",
       "6. '119AY'\n",
       "7. '119S01'\n",
       "8. '12109'\n",
       "9. '124M02'\n",
       "10. '126Q03'\n",
       "11. '130YL'\n",
       "12. '133B01'\n",
       "13. '14Y05'\n",
       "14. '18W06'\n",
       "15. '24VP'\n",
       "16. '32S04'\n",
       "17. '43K03'\n",
       "18. '48S07'\n",
       "19. '51M07'\n",
       "20. '58C06'\n",
       "21. '5Z05'\n",
       "22. '60S03'\n",
       "23. '68W02'\n",
       "24. '70WS'\n",
       "25. '72C06'\n",
       "26. '73M07'\n",
       "27. '79HZ'\n",
       "28. '79L01'\n",
       "29. '83CG'\n",
       "30. '8C07'\n",
       "31. '91MO'\n",
       "32. '98KK'\n",
       "33. '9AW'\n",
       "34. 'N1'\n",
       "35. 'N4'\n",
       "36. 'N5'\n",
       "37. 'N6'\n",
       "38. 'N7'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"104FF\"  \"107JP\"  \"108W03\" \"10C02\"  \"116JC\"  \"119AY\"  \"119S01\" \"12109\" \n",
       " [9] \"124M02\" \"126Q03\" \"130YL\"  \"133B01\" \"14Y05\"  \"18W06\"  \"24VP\"   \"32S04\" \n",
       "[17] \"43K03\"  \"48S07\"  \"51M07\"  \"58C06\"  \"5Z05\"   \"60S03\"  \"68W02\"  \"70WS\"  \n",
       "[25] \"72C06\"  \"73M07\"  \"79HZ\"   \"79L01\"  \"83CG\"   \"8C07\"   \"91MO\"   \"98KK\"  \n",
       "[33] \"9AW\"    \"N1\"     \"N4\"     \"N5\"     \"N6\"     \"N7\"    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "div_trb_npc_rna$sample_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove normal tissues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_rna <- div_trb_npc_rna[!div_trb_npc_rna$sample_id %in% c(\"N1\", \"N4\", \"N5\", \"N6\", \"N7\"),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>33</li><li>20</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 33\n",
       "\\item 20\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 33\n",
       "2. 20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 33 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(div_trb_npc_rna)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CapTCR-Seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "npc_cap <- list.files(paste0(datapath, \"mixcr/NPC/TCRcap/\"), pattern = \"CLONES_TRB\", recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# downsampled to two million\n",
    "\n",
    "npc_cap <- npc_cap[ grepl(\"2000000\", npc_cap)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_npc_cap <- immunelistfx(npc_cap, paste0(datapath, \"mixcr/NPC/TCRcap/\"), \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "28"
      ],
      "text/latex": [
       "28"
      ],
      "text/markdown": [
       "28"
      ],
      "text/plain": [
       "[1] 28"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(trb_list_npc_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "28"
      ],
      "text/latex": [
       "28"
      ],
      "text/markdown": [
       "28"
      ],
      "text/plain": [
       "[1] 28"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trb_list_npc_cap <- trb_list_npc_cap[sapply(trb_list_npc_cap, function(x) length(unlist(x))) > 1] # remove files with one clonotype\n",
    "                            \n",
    "length(trb_list_npc_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "trb_list_npc_cap <- trb_list_npc_cap[sapply(trb_list_npc_cap, function(x) var(unlist(x))) > 0] # remove files no variance                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_cap <- Divstats.fx(trb_list_npc_cap, \"TRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(div_trb_npc_cap, file = paste0(datapath, \"diversity/div_trb_npc_cap.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_cap <- as.data.frame(div_trb_npc_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_cap$sample_id <- gsub(\".*NPC_\", \"\", rownames(div_trb_npc_cap))\n",
    "\n",
    "div_trb_npc_cap$sample_id <- gsub(\"_2000000.txt\", \"\", div_trb_npc_cap$sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'107JP'</li><li>'108W03'</li><li>'111S03'</li><li>'116JC'</li><li>'119AY'</li><li>'119S01'</li><li>'12109'</li><li>'14Y05'</li><li>'18W06'</li><li>'29MT'</li><li>'32S04'</li><li>'43K03'</li><li>'47B08'</li><li>'51M07'</li><li>'58C06'</li><li>'59GA'</li><li>'70WS'</li><li>'72C06'</li><li>'79HZ'</li><li>'79L01'</li><li>'83CG'</li><li>'8C07'</li><li>'91MO'</li><li>'9AW'</li><li>'N1'</li><li>'N4'</li><li>'N5'</li><li>'N6'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '107JP'\n",
       "\\item '108W03'\n",
       "\\item '111S03'\n",
       "\\item '116JC'\n",
       "\\item '119AY'\n",
       "\\item '119S01'\n",
       "\\item '12109'\n",
       "\\item '14Y05'\n",
       "\\item '18W06'\n",
       "\\item '29MT'\n",
       "\\item '32S04'\n",
       "\\item '43K03'\n",
       "\\item '47B08'\n",
       "\\item '51M07'\n",
       "\\item '58C06'\n",
       "\\item '59GA'\n",
       "\\item '70WS'\n",
       "\\item '72C06'\n",
       "\\item '79HZ'\n",
       "\\item '79L01'\n",
       "\\item '83CG'\n",
       "\\item '8C07'\n",
       "\\item '91MO'\n",
       "\\item '9AW'\n",
       "\\item 'N1'\n",
       "\\item 'N4'\n",
       "\\item 'N5'\n",
       "\\item 'N6'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '107JP'\n",
       "2. '108W03'\n",
       "3. '111S03'\n",
       "4. '116JC'\n",
       "5. '119AY'\n",
       "6. '119S01'\n",
       "7. '12109'\n",
       "8. '14Y05'\n",
       "9. '18W06'\n",
       "10. '29MT'\n",
       "11. '32S04'\n",
       "12. '43K03'\n",
       "13. '47B08'\n",
       "14. '51M07'\n",
       "15. '58C06'\n",
       "16. '59GA'\n",
       "17. '70WS'\n",
       "18. '72C06'\n",
       "19. '79HZ'\n",
       "20. '79L01'\n",
       "21. '83CG'\n",
       "22. '8C07'\n",
       "23. '91MO'\n",
       "24. '9AW'\n",
       "25. 'N1'\n",
       "26. 'N4'\n",
       "27. 'N5'\n",
       "28. 'N6'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"107JP\"  \"108W03\" \"111S03\" \"116JC\"  \"119AY\"  \"119S01\" \"12109\"  \"14Y05\" \n",
       " [9] \"18W06\"  \"29MT\"   \"32S04\"  \"43K03\"  \"47B08\"  \"51M07\"  \"58C06\"  \"59GA\"  \n",
       "[17] \"70WS\"   \"72C06\"  \"79HZ\"   \"79L01\"  \"83CG\"   \"8C07\"   \"91MO\"   \"9AW\"   \n",
       "[25] \"N1\"     \"N4\"     \"N5\"     \"N6\"    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "div_trb_npc_cap$sample_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "remove Ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_cap <- div_trb_npc_cap[ !div_trb_npc_cap$sample_id %in% c(\"N1\", \"N4\", \"N5\", \"N6\"),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bind RNA and Cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'TRB'</li><li>'Reads'</li><li>'CPKR'</li><li>'Average_reads'</li><li>'VMR'</li><li>'Max_reads'</li><li>'Singletons'</li><li>'Doubletons'</li><li>'qD'</li><li>'Sample_Coverage'</li><li>'observed_Richness'</li><li>'estimated_Richness'</li><li>'SE_Richeness'</li><li>'observed_Shannon'</li><li>'estimated_Shannon'</li><li>'SE_Shannon'</li><li>'observed_Simpson'</li><li>'estimated_Simpson'</li><li>'SE_Simpson'</li><li>'sample_id'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'TRB'\n",
       "\\item 'Reads'\n",
       "\\item 'CPKR'\n",
       "\\item 'Average\\_reads'\n",
       "\\item 'VMR'\n",
       "\\item 'Max\\_reads'\n",
       "\\item 'Singletons'\n",
       "\\item 'Doubletons'\n",
       "\\item 'qD'\n",
       "\\item 'Sample\\_Coverage'\n",
       "\\item 'observed\\_Richness'\n",
       "\\item 'estimated\\_Richness'\n",
       "\\item 'SE\\_Richeness'\n",
       "\\item 'observed\\_Shannon'\n",
       "\\item 'estimated\\_Shannon'\n",
       "\\item 'SE\\_Shannon'\n",
       "\\item 'observed\\_Simpson'\n",
       "\\item 'estimated\\_Simpson'\n",
       "\\item 'SE\\_Simpson'\n",
       "\\item 'sample\\_id'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'TRB'\n",
       "2. 'Reads'\n",
       "3. 'CPKR'\n",
       "4. 'Average_reads'\n",
       "5. 'VMR'\n",
       "6. 'Max_reads'\n",
       "7. 'Singletons'\n",
       "8. 'Doubletons'\n",
       "9. 'qD'\n",
       "10. 'Sample_Coverage'\n",
       "11. 'observed_Richness'\n",
       "12. 'estimated_Richness'\n",
       "13. 'SE_Richeness'\n",
       "14. 'observed_Shannon'\n",
       "15. 'estimated_Shannon'\n",
       "16. 'SE_Shannon'\n",
       "17. 'observed_Simpson'\n",
       "18. 'estimated_Simpson'\n",
       "19. 'SE_Simpson'\n",
       "20. 'sample_id'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"TRB\"                \"Reads\"              \"CPKR\"              \n",
       " [4] \"Average_reads\"      \"VMR\"                \"Max_reads\"         \n",
       " [7] \"Singletons\"         \"Doubletons\"         \"qD\"                \n",
       "[10] \"Sample_Coverage\"    \"observed_Richness\"  \"estimated_Richness\"\n",
       "[13] \"SE_Richeness\"       \"observed_Shannon\"   \"estimated_Shannon\" \n",
       "[16] \"SE_Shannon\"         \"observed_Simpson\"   \"estimated_Simpson\" \n",
       "[19] \"SE_Simpson\"         \"sample_id\"         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(div_trb_npc_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(div_trb_npc_cap)[1:ncol(div_trb_npc_cap)-1] <-\n",
    "paste0(colnames(div_trb_npc_cap)[1:ncol(div_trb_npc_cap)-1], \"_TCRCap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(div_trb_npc_rna)[1:ncol(div_trb_npc_rna)-1] <-\n",
    "paste0(colnames(div_trb_npc_rna)[1:ncol(div_trb_npc_rna)-1], \"_RNAseq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_trb_npc_cap_rna <- merge(div_trb_npc_cap, div_trb_npc_rna, by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap_rna <- rbind(div_trb_profyle_cap_rna,div_trb_icgc_cap_rna, div_trb_npc_cap_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap_rna$group <- NA\n",
    "\n",
    "cap_rna$group[ grepl(\"PRO\", cap_rna$sample_id)] <- \"Pediatric\"\n",
    "cap_rna$group[ grepl(\"ICGC\", cap_rna$sample_id)] <- \"Pediatric\"\n",
    "cap_rna$group[ is.na(cap_rna$group)] <- \"Adult\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap_rna_plot <- ggplot(data = cap_rna, aes(y = estimated_Shannon_RNAseq, x = observed_Shannon_TCRCap, \n",
    "                                           label = sample_id)) + \n",
    "geom_point(size = 5, aes(color = group)) +\n",
    "geom_smooth(method = \"lm\", se = FALSE) + myplot +\n",
    "scale_y_continuous(trans = \"log10\") + \n",
    "scale_x_continuous(trans = \"log10\") +\n",
    "annotation_logticks(sides = \"bl\") +\n",
    "#geom_text()+\n",
    "theme(legend.position = \"bottom\", legend.title = element_blank()) +\n",
    "theme(axis.title = element_text(size = 35),\n",
    "      axis.line = element_line(color = \"black\"),\n",
    "      axis.text.x = element_text(size = 35, color = \"black\"),\n",
    "      axis.text.y = element_text(size = 35, color = \"black\")) +\n",
    "labs(y = \"Estimated Shannon diversity\\n(RNAseq)\", x = \"Shannon diversity\\n(CapTCR-seq)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cap_rna_plot + annotate(\"text\", x=40, y=3, label= \"R^2 = 0.66\", size = 10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n",
      "\n"
     ]
    },
    {
     "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,\"RNAseq_CapTCR_TRB.pdf\"),\n",
    "        width = 10, height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(cap_rna_plot + annotate(\"text\", x=40, y=3, label= \"R^2 = 0.66\", size = 10) )\n",
    "dev.off()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  2.225   4.923  11.253  22.208  26.591  89.916 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(cap_rna$estimated_Shannon_RNAseq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "lmreg_TRB <- lm(log10(estimated_Shannon_RNAseq) ~ log10(observed_Shannon_TCRCap), \n",
    "                data = cap_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th><th scope=col>nobs</th></tr>\n",
       "\t<tr><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>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.6626533</td><td>0.6491595</td><td>0.3031272</td><td>49.10774</td><td>2.404808e-07</td><td>1</td><td>-5.045091</td><td>16.09018</td><td>19.97769</td><td>2.297152</td><td>25</td><td>27</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 12\n",
       "\\begin{tabular}{llllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual & nobs\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 0.6626533 & 0.6491595 & 0.3031272 & 49.10774 & 2.404808e-07 & 1 & -5.045091 & 16.09018 & 19.97769 & 2.297152 & 25 & 27\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 12\n",
       "\n",
       "| r.squared &lt;dbl&gt; | adj.r.squared &lt;dbl&gt; | sigma &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | df &lt;dbl&gt; | logLik &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | deviance &lt;dbl&gt; | df.residual &lt;int&gt; | nobs &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.6626533 | 0.6491595 | 0.3031272 | 49.10774 | 2.404808e-07 | 1 | -5.045091 | 16.09018 | 19.97769 | 2.297152 | 25 | 27 |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared adj.r.squared sigma     statistic p.value      df logLik   \n",
       "1 0.6626533 0.6491595     0.3031272 49.10774  2.404808e-07 1  -5.045091\n",
       "  AIC      BIC      deviance df.residual nobs\n",
       "1 16.09018 19.97769 2.297152 25          27  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "broom::glance(lmreg_TRB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath,\"PROFYLE_Div_bulk_TRA.RData\"))\n",
    "load(file = paste0(datapath, \"PROFYLE_Div_bulk_TRB.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath,\"PROFYLE_Div_cap_TRA.RData\"))\n",
    "load(file = paste0(datapath,\"PROFYLE_Div_cap_TRB.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 16</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>filename</th><th scope=col>TRB</th><th scope=col>Reads</th><th scope=col>Average_reads</th><th scope=col>Max_reads</th><th scope=col>IDis</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>estimated_Richness</th><th scope=col>observed_Richness</th><th scope=col>estimated_Shannon</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Simpson</th><th scope=col>observed_Simpson</th><th scope=col>Divscore</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&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>&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><td>_PRO-00012N_R_totalRNA_chondrosarcoma_40055</td><td>  47</td><td>  72</td><td>1.531915</td><td>17</td><td>1.5390531</td><td>  39</td><td>  5</td><td>  81.288</td><td> 196.988</td><td>  47</td><td>  89.916</td><td>  30.566</td><td>  17.388</td><td>  14.164</td><td>   5.289176</td></tr>\n",
       "\t<tr><td>_PRO-00013P_R_totalRNA_osteosarcoma_40063  </td><td>   3</td><td>  13</td><td>4.333333</td><td>10</td><td>1.1383576</td><td>   1</td><td>  1</td><td>   3.399</td><td>   3.462</td><td>   3</td><td>   2.225</td><td>   1.988</td><td>   1.696</td><td>   1.610</td><td>   0.222500</td></tr>\n",
       "\t<tr><td>_PRO-00015S_R_totalRNA_sarcoma_40065       </td><td>  15</td><td>  17</td><td>1.133333</td><td> 3</td><td>0.4556451</td><td>  14</td><td>  0</td><td>  27.859</td><td> 100.647</td><td>  15</td><td>  88.167</td><td>  14.004</td><td>  12.565</td><td>  12.565</td><td>  29.389000</td></tr>\n",
       "\t<tr><td>_PRO-00019W_R_totalRNA_Thymus_40073        </td><td>1215</td><td>1421</td><td>1.169547</td><td> 4</td><td>0.3615564</td><td>1030</td><td>166</td><td>2095.307</td><td>4408.233</td><td>1215</td><td>4409.446</td><td>1152.785</td><td>4405.721</td><td>1074.636</td><td>1102.361500</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 16\n",
       "\\begin{tabular}{r|llllllllllllllll}\n",
       " filename & TRB & Reads & Average\\_reads & Max\\_reads & IDis & Singletons & Doubletons & qD & estimated\\_Richness & observed\\_Richness & estimated\\_Shannon & observed\\_Shannon & estimated\\_Simpson & observed\\_Simpson & Divscore\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t \\_PRO-00012N\\_R\\_totalRNA\\_chondrosarcoma\\_40055 &   47 &   72 & 1.531915 & 17 & 1.5390531 &   39 &   5 &   81.288 &  196.988 &   47 &   89.916 &   30.566 &   17.388 &   14.164 &    5.289176\\\\\n",
       "\t \\_PRO-00013P\\_R\\_totalRNA\\_osteosarcoma\\_40063   &    3 &   13 & 4.333333 & 10 & 1.1383576 &    1 &   1 &    3.399 &    3.462 &    3 &    2.225 &    1.988 &    1.696 &    1.610 &    0.222500\\\\\n",
       "\t \\_PRO-00015S\\_R\\_totalRNA\\_sarcoma\\_40065        &   15 &   17 & 1.133333 &  3 & 0.4556451 &   14 &   0 &   27.859 &  100.647 &   15 &   88.167 &   14.004 &   12.565 &   12.565 &   29.389000\\\\\n",
       "\t \\_PRO-00019W\\_R\\_totalRNA\\_Thymus\\_40073         & 1215 & 1421 & 1.169547 &  4 & 0.3615564 & 1030 & 166 & 2095.307 & 4408.233 & 1215 & 4409.446 & 1152.785 & 4405.721 & 1074.636 & 1102.361500\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 16\n",
       "\n",
       "| filename &lt;fct&gt; | TRB &lt;dbl&gt; | Reads &lt;dbl&gt; | Average_reads &lt;dbl&gt; | Max_reads &lt;dbl&gt; | IDis &lt;dbl&gt; | Singletons &lt;dbl&gt; | Doubletons &lt;dbl&gt; | qD &lt;dbl&gt; | estimated_Richness &lt;dbl&gt; | observed_Richness &lt;dbl&gt; | estimated_Shannon &lt;dbl&gt; | observed_Shannon &lt;dbl&gt; | estimated_Simpson &lt;dbl&gt; | observed_Simpson &lt;dbl&gt; | Divscore &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| _PRO-00012N_R_totalRNA_chondrosarcoma_40055 |   47 |   72 | 1.531915 | 17 | 1.5390531 |   39 |   5 |   81.288 |  196.988 |   47 |   89.916 |   30.566 |   17.388 |   14.164 |    5.289176 |\n",
       "| _PRO-00013P_R_totalRNA_osteosarcoma_40063   |    3 |   13 | 4.333333 | 10 | 1.1383576 |    1 |   1 |    3.399 |    3.462 |    3 |    2.225 |    1.988 |    1.696 |    1.610 |    0.222500 |\n",
       "| _PRO-00015S_R_totalRNA_sarcoma_40065        |   15 |   17 | 1.133333 |  3 | 0.4556451 |   14 |   0 |   27.859 |  100.647 |   15 |   88.167 |   14.004 |   12.565 |   12.565 |   29.389000 |\n",
       "| _PRO-00019W_R_totalRNA_Thymus_40073         | 1215 | 1421 | 1.169547 |  4 | 0.3615564 | 1030 | 166 | 2095.307 | 4408.233 | 1215 | 4409.446 | 1152.785 | 4405.721 | 1074.636 | 1102.361500 |\n",
       "\n"
      ],
      "text/plain": [
       "  filename                                    TRB  Reads Average_reads\n",
       "1 _PRO-00012N_R_totalRNA_chondrosarcoma_40055   47   72  1.531915     \n",
       "2 _PRO-00013P_R_totalRNA_osteosarcoma_40063      3   13  4.333333     \n",
       "3 _PRO-00015S_R_totalRNA_sarcoma_40065          15   17  1.133333     \n",
       "4 _PRO-00019W_R_totalRNA_Thymus_40073         1215 1421  1.169547     \n",
       "  Max_reads IDis      Singletons Doubletons qD       estimated_Richness\n",
       "1 17        1.5390531   39         5          81.288  196.988          \n",
       "2 10        1.1383576    1         1           3.399    3.462          \n",
       "3  3        0.4556451   14         0          27.859  100.647          \n",
       "4  4        0.3615564 1030       166        2095.307 4408.233          \n",
       "  observed_Richness estimated_Shannon observed_Shannon estimated_Simpson\n",
       "1   47                89.916            30.566           17.388         \n",
       "2    3                 2.225             1.988            1.696         \n",
       "3   15                88.167            14.004           12.565         \n",
       "4 1215              4409.446          1152.785         4405.721         \n",
       "  observed_Simpson Divscore   \n",
       "1   14.164            5.289176\n",
       "2    1.610            0.222500\n",
       "3   12.565           29.389000\n",
       "4 1074.636         1102.361500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PROFYLE_Div_bulk_TRB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 16</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>filename</th><th scope=col>TRB</th><th scope=col>Reads</th><th scope=col>Average_reads</th><th scope=col>Max_reads</th><th scope=col>IDis</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>estimated_Richness</th><th scope=col>observed_Richness</th><th scope=col>estimated_Shannon</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Simpson</th><th scope=col>observed_Simpson</th><th scope=col>Divscore</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&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>&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><td>PRO-00012N</td><td>1219</td><td>10222504</td><td> 8385.975</td><td>1885593</td><td>7.824990</td><td>409</td><td>119</td><td>1529.080</td><td>1921.861</td><td>1219</td><td>61.538</td><td>61.532</td><td>19.604</td><td>19.604</td><td>3.263589e-05</td></tr>\n",
       "\t<tr><td>PRO-00013P</td><td>  13</td><td>   57359</td><td> 4412.231</td><td>  35590</td><td>2.486470</td><td>  3</td><td>  1</td><td>  15.190</td><td>  17.500</td><td>  13</td><td> 2.125</td><td> 2.125</td><td> 1.938</td><td> 1.938</td><td>5.970778e-05</td></tr>\n",
       "\t<tr><td>PRO-00015S</td><td> 610</td><td> 2156721</td><td> 3535.608</td><td> 378633</td><td>6.155721</td><td>100</td><td> 20</td><td> 692.420</td><td> 860.000</td><td> 610</td><td>41.507</td><td>41.499</td><td>15.709</td><td>15.709</td><td>1.096233e-04</td></tr>\n",
       "\t<tr><td>PRO-00023A</td><td> 111</td><td>  828065</td><td> 7460.045</td><td> 373985</td><td>4.829176</td><td> 30</td><td>  8</td><td> 134.251</td><td> 167.250</td><td> 111</td><td>13.511</td><td>13.510</td><td> 4.604</td><td> 4.604</td><td>3.612712e-05</td></tr>\n",
       "\t<tr><td>PRO-00027E</td><td>   2</td><td>     242</td><td>  121.000</td><td>    229</td><td>1.262273</td><td>  0</td><td>  0</td><td>   2.000</td><td>   2.000</td><td>   2</td><td> 1.235</td><td> 1.233</td><td> 1.114</td><td> 1.113</td><td>5.393013e-03</td></tr>\n",
       "\t<tr><td>PRO-00033M</td><td>  28</td><td>  118840</td><td> 4244.286</td><td>  31335</td><td>2.114446</td><td>  6</td><td>  5</td><td>  30.920</td><td>  31.600</td><td>  28</td><td> 6.000</td><td> 5.999</td><td> 5.272</td><td> 5.272</td><td>1.914792e-04</td></tr>\n",
       "\t<tr><td>PRO-00035O</td><td>   6</td><td>   66697</td><td>11116.167</td><td>  66608</td><td>2.445569</td><td>  3</td><td>  1</td><td>   8.190</td><td>  10.500</td><td>   6</td><td> 1.011</td><td> 1.011</td><td> 1.003</td><td> 1.003</td><td>1.517836e-05</td></tr>\n",
       "\t<tr><td>PRO-00039T</td><td>  27</td><td>  389371</td><td>14421.148</td><td> 214671</td><td>2.921789</td><td>  3</td><td>  3</td><td>  28.297</td><td>  28.500</td><td>  27</td><td> 4.681</td><td> 4.681</td><td> 2.928</td><td> 2.928</td><td>2.180546e-05</td></tr>\n",
       "\t<tr><td>PRO-00042X</td><td>  25</td><td>  133471</td><td> 5338.840</td><td> 128456</td><td>4.805610</td><td>  3</td><td>  2</td><td>  26.657</td><td>  27.250</td><td>  25</td><td> 1.244</td><td> 1.243</td><td> 1.079</td><td> 1.079</td><td>9.684250e-06</td></tr>\n",
       "\t<tr><td>PRO-00043Z</td><td>  97</td><td>  268613</td><td> 2769.206</td><td> 163281</td><td>6.281666</td><td> 22</td><td>  9</td><td> 112.025</td><td> 123.889</td><td>  97</td><td> 3.349</td><td> 3.348</td><td> 2.422</td><td> 2.422</td><td>2.051065e-05</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 16\n",
       "\\begin{tabular}{r|llllllllllllllll}\n",
       " filename & TRB & Reads & Average\\_reads & Max\\_reads & IDis & Singletons & Doubletons & qD & estimated\\_Richness & observed\\_Richness & estimated\\_Shannon & observed\\_Shannon & estimated\\_Simpson & observed\\_Simpson & Divscore\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t PRO-00012N & 1219 & 10222504 &  8385.975 & 1885593 & 7.824990 & 409 & 119 & 1529.080 & 1921.861 & 1219 & 61.538 & 61.532 & 19.604 & 19.604 & 3.263589e-05\\\\\n",
       "\t PRO-00013P &   13 &    57359 &  4412.231 &   35590 & 2.486470 &   3 &   1 &   15.190 &   17.500 &   13 &  2.125 &  2.125 &  1.938 &  1.938 & 5.970778e-05\\\\\n",
       "\t PRO-00015S &  610 &  2156721 &  3535.608 &  378633 & 6.155721 & 100 &  20 &  692.420 &  860.000 &  610 & 41.507 & 41.499 & 15.709 & 15.709 & 1.096233e-04\\\\\n",
       "\t PRO-00023A &  111 &   828065 &  7460.045 &  373985 & 4.829176 &  30 &   8 &  134.251 &  167.250 &  111 & 13.511 & 13.510 &  4.604 &  4.604 & 3.612712e-05\\\\\n",
       "\t PRO-00027E &    2 &      242 &   121.000 &     229 & 1.262273 &   0 &   0 &    2.000 &    2.000 &    2 &  1.235 &  1.233 &  1.114 &  1.113 & 5.393013e-03\\\\\n",
       "\t PRO-00033M &   28 &   118840 &  4244.286 &   31335 & 2.114446 &   6 &   5 &   30.920 &   31.600 &   28 &  6.000 &  5.999 &  5.272 &  5.272 & 1.914792e-04\\\\\n",
       "\t PRO-00035O &    6 &    66697 & 11116.167 &   66608 & 2.445569 &   3 &   1 &    8.190 &   10.500 &    6 &  1.011 &  1.011 &  1.003 &  1.003 & 1.517836e-05\\\\\n",
       "\t PRO-00039T &   27 &   389371 & 14421.148 &  214671 & 2.921789 &   3 &   3 &   28.297 &   28.500 &   27 &  4.681 &  4.681 &  2.928 &  2.928 & 2.180546e-05\\\\\n",
       "\t PRO-00042X &   25 &   133471 &  5338.840 &  128456 & 4.805610 &   3 &   2 &   26.657 &   27.250 &   25 &  1.244 &  1.243 &  1.079 &  1.079 & 9.684250e-06\\\\\n",
       "\t PRO-00043Z &   97 &   268613 &  2769.206 &  163281 & 6.281666 &  22 &   9 &  112.025 &  123.889 &   97 &  3.349 &  3.348 &  2.422 &  2.422 & 2.051065e-05\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 16\n",
       "\n",
       "| filename &lt;fct&gt; | TRB &lt;dbl&gt; | Reads &lt;dbl&gt; | Average_reads &lt;dbl&gt; | Max_reads &lt;dbl&gt; | IDis &lt;dbl&gt; | Singletons &lt;dbl&gt; | Doubletons &lt;dbl&gt; | qD &lt;dbl&gt; | estimated_Richness &lt;dbl&gt; | observed_Richness &lt;dbl&gt; | estimated_Shannon &lt;dbl&gt; | observed_Shannon &lt;dbl&gt; | estimated_Simpson &lt;dbl&gt; | observed_Simpson &lt;dbl&gt; | Divscore &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| PRO-00012N | 1219 | 10222504 |  8385.975 | 1885593 | 7.824990 | 409 | 119 | 1529.080 | 1921.861 | 1219 | 61.538 | 61.532 | 19.604 | 19.604 | 3.263589e-05 |\n",
       "| PRO-00013P |   13 |    57359 |  4412.231 |   35590 | 2.486470 |   3 |   1 |   15.190 |   17.500 |   13 |  2.125 |  2.125 |  1.938 |  1.938 | 5.970778e-05 |\n",
       "| PRO-00015S |  610 |  2156721 |  3535.608 |  378633 | 6.155721 | 100 |  20 |  692.420 |  860.000 |  610 | 41.507 | 41.499 | 15.709 | 15.709 | 1.096233e-04 |\n",
       "| PRO-00023A |  111 |   828065 |  7460.045 |  373985 | 4.829176 |  30 |   8 |  134.251 |  167.250 |  111 | 13.511 | 13.510 |  4.604 |  4.604 | 3.612712e-05 |\n",
       "| PRO-00027E |    2 |      242 |   121.000 |     229 | 1.262273 |   0 |   0 |    2.000 |    2.000 |    2 |  1.235 |  1.233 |  1.114 |  1.113 | 5.393013e-03 |\n",
       "| PRO-00033M |   28 |   118840 |  4244.286 |   31335 | 2.114446 |   6 |   5 |   30.920 |   31.600 |   28 |  6.000 |  5.999 |  5.272 |  5.272 | 1.914792e-04 |\n",
       "| PRO-00035O |    6 |    66697 | 11116.167 |   66608 | 2.445569 |   3 |   1 |    8.190 |   10.500 |    6 |  1.011 |  1.011 |  1.003 |  1.003 | 1.517836e-05 |\n",
       "| PRO-00039T |   27 |   389371 | 14421.148 |  214671 | 2.921789 |   3 |   3 |   28.297 |   28.500 |   27 |  4.681 |  4.681 |  2.928 |  2.928 | 2.180546e-05 |\n",
       "| PRO-00042X |   25 |   133471 |  5338.840 |  128456 | 4.805610 |   3 |   2 |   26.657 |   27.250 |   25 |  1.244 |  1.243 |  1.079 |  1.079 | 9.684250e-06 |\n",
       "| PRO-00043Z |   97 |   268613 |  2769.206 |  163281 | 6.281666 |  22 |   9 |  112.025 |  123.889 |   97 |  3.349 |  3.348 |  2.422 |  2.422 | 2.051065e-05 |\n",
       "\n"
      ],
      "text/plain": [
       "   filename   TRB  Reads    Average_reads Max_reads IDis     Singletons\n",
       "1  PRO-00012N 1219 10222504  8385.975     1885593   7.824990 409       \n",
       "2  PRO-00013P   13    57359  4412.231       35590   2.486470   3       \n",
       "3  PRO-00015S  610  2156721  3535.608      378633   6.155721 100       \n",
       "4  PRO-00023A  111   828065  7460.045      373985   4.829176  30       \n",
       "5  PRO-00027E    2      242   121.000         229   1.262273   0       \n",
       "6  PRO-00033M   28   118840  4244.286       31335   2.114446   6       \n",
       "7  PRO-00035O    6    66697 11116.167       66608   2.445569   3       \n",
       "8  PRO-00039T   27   389371 14421.148      214671   2.921789   3       \n",
       "9  PRO-00042X   25   133471  5338.840      128456   4.805610   3       \n",
       "10 PRO-00043Z   97   268613  2769.206      163281   6.281666  22       \n",
       "   Doubletons qD       estimated_Richness observed_Richness estimated_Shannon\n",
       "1  119        1529.080 1921.861           1219              61.538           \n",
       "2    1          15.190   17.500             13               2.125           \n",
       "3   20         692.420  860.000            610              41.507           \n",
       "4    8         134.251  167.250            111              13.511           \n",
       "5    0           2.000    2.000              2               1.235           \n",
       "6    5          30.920   31.600             28               6.000           \n",
       "7    1           8.190   10.500              6               1.011           \n",
       "8    3          28.297   28.500             27               4.681           \n",
       "9    2          26.657   27.250             25               1.244           \n",
       "10   9         112.025  123.889             97               3.349           \n",
       "   observed_Shannon estimated_Simpson observed_Simpson Divscore    \n",
       "1  61.532           19.604            19.604           3.263589e-05\n",
       "2   2.125            1.938             1.938           5.970778e-05\n",
       "3  41.499           15.709            15.709           1.096233e-04\n",
       "4  13.510            4.604             4.604           3.612712e-05\n",
       "5   1.233            1.114             1.113           5.393013e-03\n",
       "6   5.999            5.272             5.272           1.914792e-04\n",
       "7   1.011            1.003             1.003           1.517836e-05\n",
       "8   4.681            2.928             2.928           2.180546e-05\n",
       "9   1.243            1.079             1.079           9.684250e-06\n",
       "10  3.348            2.422             2.422           2.051065e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PROFYLE_Div_cap_TRB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordershanTRB <- PROFYLE_Div_cap_TRB$filename[order(PROFYLE_Div_cap_TRB$observed_Shannon)]\n",
    "\n",
    "ordershanTRA <- PROFYLE_Div_cap_TRA$filename[order(PROFYLE_Div_cap_TRA$observed_Shannon)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>PRO-00012N</li>\n",
       "\t<li>PRO-00013P</li>\n",
       "\t<li>PRO-00015S</li>\n",
       "\t<li>PRO-00023A</li>\n",
       "\t<li>PRO-00032L</li>\n",
       "\t<li>PRO-00033M</li>\n",
       "\t<li>PRO-00035O</li>\n",
       "\t<li>PRO-00039T</li>\n",
       "\t<li>PRO-00042X</li>\n",
       "\t<li>PRO-00043Z</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'PRO-00012N'</li>\n",
       "\t\t<li>'PRO-00013P'</li>\n",
       "\t\t<li>'PRO-00015S'</li>\n",
       "\t\t<li>'PRO-00023A'</li>\n",
       "\t\t<li>'PRO-00032L'</li>\n",
       "\t\t<li>'PRO-00033M'</li>\n",
       "\t\t<li>'PRO-00035O'</li>\n",
       "\t\t<li>'PRO-00039T'</li>\n",
       "\t\t<li>'PRO-00042X'</li>\n",
       "\t\t<li>'PRO-00043Z'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item PRO-00012N\n",
       "\\item PRO-00013P\n",
       "\\item PRO-00015S\n",
       "\\item PRO-00023A\n",
       "\\item PRO-00032L\n",
       "\\item PRO-00033M\n",
       "\\item PRO-00035O\n",
       "\\item PRO-00039T\n",
       "\\item PRO-00042X\n",
       "\\item PRO-00043Z\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'PRO-00012N'\n",
       "\\item 'PRO-00013P'\n",
       "\\item 'PRO-00015S'\n",
       "\\item 'PRO-00023A'\n",
       "\\item 'PRO-00032L'\n",
       "\\item 'PRO-00033M'\n",
       "\\item 'PRO-00035O'\n",
       "\\item 'PRO-00039T'\n",
       "\\item 'PRO-00042X'\n",
       "\\item 'PRO-00043Z'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. PRO-00012N\n",
       "2. PRO-00013P\n",
       "3. PRO-00015S\n",
       "4. PRO-00023A\n",
       "5. PRO-00032L\n",
       "6. PRO-00033M\n",
       "7. PRO-00035O\n",
       "8. PRO-00039T\n",
       "9. PRO-00042X\n",
       "10. PRO-00043Z\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'PRO-00012N'\n",
       "2. 'PRO-00013P'\n",
       "3. 'PRO-00015S'\n",
       "4. 'PRO-00023A'\n",
       "5. 'PRO-00032L'\n",
       "6. 'PRO-00033M'\n",
       "7. 'PRO-00035O'\n",
       "8. 'PRO-00039T'\n",
       "9. 'PRO-00042X'\n",
       "10. 'PRO-00043Z'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] PRO-00012N PRO-00013P PRO-00015S PRO-00023A PRO-00032L PRO-00033M\n",
       " [7] PRO-00035O PRO-00039T PRO-00042X PRO-00043Z\n",
       "10 Levels: PRO-00012N PRO-00013P PRO-00015S PRO-00023A ... PRO-00043Z"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PROFYLE_Div_cap_TRA$filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>103.594</li>\n",
       "\t<li>5.213</li>\n",
       "\t<li>51.676</li>\n",
       "\t<li>45.806</li>\n",
       "\t<li>2.502</li>\n",
       "\t<li>9.029</li>\n",
       "\t<li>4.943</li>\n",
       "\t<li>5.669</li>\n",
       "\t<li>1.561</li>\n",
       "\t<li>8.801</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 103.594\n",
       "\\item 5.213\n",
       "\\item 51.676\n",
       "\\item 45.806\n",
       "\\item 2.502\n",
       "\\item 9.029\n",
       "\\item 4.943\n",
       "\\item 5.669\n",
       "\\item 1.561\n",
       "\\item 8.801\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 103.594\n",
       "2. 5.213\n",
       "3. 51.676\n",
       "4. 45.806\n",
       "5. 2.502\n",
       "6. 9.029\n",
       "7. 4.943\n",
       "8. 5.669\n",
       "9. 1.561\n",
       "10. 8.801\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 103.594   5.213  51.676  45.806   2.502   9.029   4.943   5.669   1.561\n",
       "[10]   8.801"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PROFYLE_Div_cap_TRA$observed_Shannon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>40.346</li>\n",
       "\t<li>4.437</li>\n",
       "\t<li>18.651</li>\n",
       "\t<li>39.61</li>\n",
       "\t<li>2.135</li>\n",
       "\t<li>8.034</li>\n",
       "\t<li>4.301</li>\n",
       "\t<li>3.99</li>\n",
       "\t<li>1.32</li>\n",
       "\t<li>7.387</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 40.346\n",
       "\\item 4.437\n",
       "\\item 18.651\n",
       "\\item 39.61\n",
       "\\item 2.135\n",
       "\\item 8.034\n",
       "\\item 4.301\n",
       "\\item 3.99\n",
       "\\item 1.32\n",
       "\\item 7.387\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 40.346\n",
       "2. 4.437\n",
       "3. 18.651\n",
       "4. 39.61\n",
       "5. 2.135\n",
       "6. 8.034\n",
       "7. 4.301\n",
       "8. 3.99\n",
       "9. 1.32\n",
       "10. 7.387\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 40.346  4.437 18.651 39.610  2.135  8.034  4.301  3.990  1.320  7.387"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PROFYLE_Div_cap_TRA$observed_Simpson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 16</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>filename</th><th scope=col>TRA</th><th scope=col>Reads</th><th scope=col>Average_reads</th><th scope=col>Max_reads</th><th scope=col>IDis</th><th scope=col>Singletons</th><th scope=col>Doubletons</th><th scope=col>qD</th><th scope=col>estimated_Richness</th><th scope=col>observed_Richness</th><th scope=col>estimated_Shannon</th><th scope=col>observed_Shannon</th><th scope=col>estimated_Simpson</th><th scope=col>observed_Simpson</th><th scope=col>Divscore</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&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>&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>9</th><td>PRO-00042X</td><td>  30</td><td> 272841</td><td> 9094.700000</td><td>234731</td><td>4.7417903</td><td>  7</td><td> 2</td><td>  35.332</td><td>  42.250</td><td>  30</td><td>  1.561</td><td>  1.561</td><td> 1.320</td><td> 1.320</td><td>6.650166e-06</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>PRO-00032L</td><td>  17</td><td>  17602</td><td> 1035.411765</td><td>  9929</td><td>2.7200754</td><td>  6</td><td> 1</td><td>  22.102</td><td>  34.999</td><td>  17</td><td>  2.504</td><td>  2.502</td><td> 2.135</td><td> 2.135</td><td>2.521906e-04</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>PRO-00035O</td><td>   6</td><td>     23</td><td>    3.833333</td><td>     8</td><td>0.6885507</td><td>  1</td><td> 1</td><td>   6.414</td><td>   6.478</td><td>   6</td><td>  5.642</td><td>  4.943</td><td> 5.060</td><td> 4.301</td><td>7.052500e-01</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>PRO-00013P</td><td>  75</td><td>2057580</td><td>27434.400000</td><td>604080</td><td>4.0147359</td><td> 20</td><td> 7</td><td>  89.383</td><td> 103.571</td><td>  75</td><td>  5.213</td><td>  5.213</td><td> 4.437</td><td> 4.437</td><td>8.629652e-06</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>PRO-00039T</td><td>  80</td><td> 114288</td><td> 1428.600000</td><td> 39911</td><td>4.3923122</td><td> 11</td><td> 5</td><td>  87.225</td><td>  92.100</td><td>  80</td><td>  5.671</td><td>  5.669</td><td> 3.990</td><td> 3.990</td><td>1.420912e-04</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>PRO-00043Z</td><td>  43</td><td> 235159</td><td> 5468.813953</td><td> 51568</td><td>2.2217414</td><td>  5</td><td> 3</td><td>  45.912</td><td>  47.167</td><td>  43</td><td>  8.802</td><td>  8.801</td><td> 7.387</td><td> 7.387</td><td>1.706872e-04</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>PRO-00033M</td><td>  48</td><td> 261524</td><td> 5448.416667</td><td> 52874</td><td>2.2539316</td><td> 12</td><td> 4</td><td>  56.758</td><td>  66.000</td><td>  48</td><td>  9.031</td><td>  9.029</td><td> 8.035</td><td> 8.034</td><td>1.708023e-04</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>PRO-00023A</td><td> 144</td><td> 544578</td><td> 3781.791667</td><td> 25608</td><td>1.6290669</td><td> 36</td><td>10</td><td> 171.621</td><td> 208.800</td><td> 144</td><td> 45.815</td><td> 45.806</td><td>39.613</td><td>39.610</td><td>1.789089e-03</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>PRO-00015S</td><td> 651</td><td>2296620</td><td> 3527.834101</td><td>386112</td><td>5.8272455</td><td>115</td><td>25</td><td> 744.261</td><td> 915.500</td><td> 651</td><td> 51.686</td><td> 51.676</td><td>18.651</td><td>18.651</td><td>1.338627e-04</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>PRO-00012N</td><td>1070</td><td>6116941</td><td> 5716.767290</td><td>681738</td><td>5.0541566</td><td>309</td><td>83</td><td>1309.063</td><td>1645.187</td><td>1070</td><td>103.608</td><td>103.594</td><td>40.346</td><td>40.346</td><td>1.519763e-04</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 16\n",
       "\\begin{tabular}{r|llllllllllllllll}\n",
       "  & filename & TRA & Reads & Average\\_reads & Max\\_reads & IDis & Singletons & Doubletons & qD & estimated\\_Richness & observed\\_Richness & estimated\\_Shannon & observed\\_Shannon & estimated\\_Simpson & observed\\_Simpson & Divscore\\\\\n",
       "  & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t9 & PRO-00042X &   30 &  272841 &  9094.700000 & 234731 & 4.7417903 &   7 &  2 &   35.332 &   42.250 &   30 &   1.561 &   1.561 &  1.320 &  1.320 & 6.650166e-06\\\\\n",
       "\t5 & PRO-00032L &   17 &   17602 &  1035.411765 &   9929 & 2.7200754 &   6 &  1 &   22.102 &   34.999 &   17 &   2.504 &   2.502 &  2.135 &  2.135 & 2.521906e-04\\\\\n",
       "\t7 & PRO-00035O &    6 &      23 &     3.833333 &      8 & 0.6885507 &   1 &  1 &    6.414 &    6.478 &    6 &   5.642 &   4.943 &  5.060 &  4.301 & 7.052500e-01\\\\\n",
       "\t2 & PRO-00013P &   75 & 2057580 & 27434.400000 & 604080 & 4.0147359 &  20 &  7 &   89.383 &  103.571 &   75 &   5.213 &   5.213 &  4.437 &  4.437 & 8.629652e-06\\\\\n",
       "\t8 & PRO-00039T &   80 &  114288 &  1428.600000 &  39911 & 4.3923122 &  11 &  5 &   87.225 &   92.100 &   80 &   5.671 &   5.669 &  3.990 &  3.990 & 1.420912e-04\\\\\n",
       "\t10 & PRO-00043Z &   43 &  235159 &  5468.813953 &  51568 & 2.2217414 &   5 &  3 &   45.912 &   47.167 &   43 &   8.802 &   8.801 &  7.387 &  7.387 & 1.706872e-04\\\\\n",
       "\t6 & PRO-00033M &   48 &  261524 &  5448.416667 &  52874 & 2.2539316 &  12 &  4 &   56.758 &   66.000 &   48 &   9.031 &   9.029 &  8.035 &  8.034 & 1.708023e-04\\\\\n",
       "\t4 & PRO-00023A &  144 &  544578 &  3781.791667 &  25608 & 1.6290669 &  36 & 10 &  171.621 &  208.800 &  144 &  45.815 &  45.806 & 39.613 & 39.610 & 1.789089e-03\\\\\n",
       "\t3 & PRO-00015S &  651 & 2296620 &  3527.834101 & 386112 & 5.8272455 & 115 & 25 &  744.261 &  915.500 &  651 &  51.686 &  51.676 & 18.651 & 18.651 & 1.338627e-04\\\\\n",
       "\t1 & PRO-00012N & 1070 & 6116941 &  5716.767290 & 681738 & 5.0541566 & 309 & 83 & 1309.063 & 1645.187 & 1070 & 103.608 & 103.594 & 40.346 & 40.346 & 1.519763e-04\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 16\n",
       "\n",
       "| <!--/--> | filename &lt;fct&gt; | TRA &lt;dbl&gt; | Reads &lt;dbl&gt; | Average_reads &lt;dbl&gt; | Max_reads &lt;dbl&gt; | IDis &lt;dbl&gt; | Singletons &lt;dbl&gt; | Doubletons &lt;dbl&gt; | qD &lt;dbl&gt; | estimated_Richness &lt;dbl&gt; | observed_Richness &lt;dbl&gt; | estimated_Shannon &lt;dbl&gt; | observed_Shannon &lt;dbl&gt; | estimated_Simpson &lt;dbl&gt; | observed_Simpson &lt;dbl&gt; | Divscore &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | PRO-00042X |   30 |  272841 |  9094.700000 | 234731 | 4.7417903 |   7 |  2 |   35.332 |   42.250 |   30 |   1.561 |   1.561 |  1.320 |  1.320 | 6.650166e-06 |\n",
       "| 5 | PRO-00032L |   17 |   17602 |  1035.411765 |   9929 | 2.7200754 |   6 |  1 |   22.102 |   34.999 |   17 |   2.504 |   2.502 |  2.135 |  2.135 | 2.521906e-04 |\n",
       "| 7 | PRO-00035O |    6 |      23 |     3.833333 |      8 | 0.6885507 |   1 |  1 |    6.414 |    6.478 |    6 |   5.642 |   4.943 |  5.060 |  4.301 | 7.052500e-01 |\n",
       "| 2 | PRO-00013P |   75 | 2057580 | 27434.400000 | 604080 | 4.0147359 |  20 |  7 |   89.383 |  103.571 |   75 |   5.213 |   5.213 |  4.437 |  4.437 | 8.629652e-06 |\n",
       "| 8 | PRO-00039T |   80 |  114288 |  1428.600000 |  39911 | 4.3923122 |  11 |  5 |   87.225 |   92.100 |   80 |   5.671 |   5.669 |  3.990 |  3.990 | 1.420912e-04 |\n",
       "| 10 | PRO-00043Z |   43 |  235159 |  5468.813953 |  51568 | 2.2217414 |   5 |  3 |   45.912 |   47.167 |   43 |   8.802 |   8.801 |  7.387 |  7.387 | 1.706872e-04 |\n",
       "| 6 | PRO-00033M |   48 |  261524 |  5448.416667 |  52874 | 2.2539316 |  12 |  4 |   56.758 |   66.000 |   48 |   9.031 |   9.029 |  8.035 |  8.034 | 1.708023e-04 |\n",
       "| 4 | PRO-00023A |  144 |  544578 |  3781.791667 |  25608 | 1.6290669 |  36 | 10 |  171.621 |  208.800 |  144 |  45.815 |  45.806 | 39.613 | 39.610 | 1.789089e-03 |\n",
       "| 3 | PRO-00015S |  651 | 2296620 |  3527.834101 | 386112 | 5.8272455 | 115 | 25 |  744.261 |  915.500 |  651 |  51.686 |  51.676 | 18.651 | 18.651 | 1.338627e-04 |\n",
       "| 1 | PRO-00012N | 1070 | 6116941 |  5716.767290 | 681738 | 5.0541566 | 309 | 83 | 1309.063 | 1645.187 | 1070 | 103.608 | 103.594 | 40.346 | 40.346 | 1.519763e-04 |\n",
       "\n"
      ],
      "text/plain": [
       "   filename   TRA  Reads   Average_reads Max_reads IDis      Singletons\n",
       "9  PRO-00042X   30  272841  9094.700000  234731    4.7417903   7       \n",
       "5  PRO-00032L   17   17602  1035.411765    9929    2.7200754   6       \n",
       "7  PRO-00035O    6      23     3.833333       8    0.6885507   1       \n",
       "2  PRO-00013P   75 2057580 27434.400000  604080    4.0147359  20       \n",
       "8  PRO-00039T   80  114288  1428.600000   39911    4.3923122  11       \n",
       "10 PRO-00043Z   43  235159  5468.813953   51568    2.2217414   5       \n",
       "6  PRO-00033M   48  261524  5448.416667   52874    2.2539316  12       \n",
       "4  PRO-00023A  144  544578  3781.791667   25608    1.6290669  36       \n",
       "3  PRO-00015S  651 2296620  3527.834101  386112    5.8272455 115       \n",
       "1  PRO-00012N 1070 6116941  5716.767290  681738    5.0541566 309       \n",
       "   Doubletons qD       estimated_Richness observed_Richness estimated_Shannon\n",
       "9   2           35.332   42.250             30                1.561          \n",
       "5   1           22.102   34.999             17                2.504          \n",
       "7   1            6.414    6.478              6                5.642          \n",
       "2   7           89.383  103.571             75                5.213          \n",
       "8   5           87.225   92.100             80                5.671          \n",
       "10  3           45.912   47.167             43                8.802          \n",
       "6   4           56.758   66.000             48                9.031          \n",
       "4  10          171.621  208.800            144               45.815          \n",
       "3  25          744.261  915.500            651               51.686          \n",
       "1  83         1309.063 1645.187           1070              103.608          \n",
       "   observed_Shannon estimated_Simpson observed_Simpson Divscore    \n",
       "9    1.561           1.320             1.320           6.650166e-06\n",
       "5    2.502           2.135             2.135           2.521906e-04\n",
       "7    4.943           5.060             4.301           7.052500e-01\n",
       "2    5.213           4.437             4.437           8.629652e-06\n",
       "8    5.669           3.990             3.990           1.420912e-04\n",
       "10   8.801           7.387             7.387           1.706872e-04\n",
       "6    9.029           8.035             8.034           1.708023e-04\n",
       "4   45.806          39.613            39.610           1.789089e-03\n",
       "3   51.676          18.651            18.651           1.338627e-04\n",
       "1  103.594          40.346            40.346           1.519763e-04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PROFYLE_Div_cap_TRA[order(PROFYLE_Div_cap_TRA$observed_Shannon),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "clonesTRB <- clonplotfx(paste0(datapath,\"PROFYLE/TCRcap/\"),\"TRB\", ordershanTRB,\"PROFYLE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "clonesTRA <- clonplotfx(paste0(datapath,\"PROFYLE/TCRcap/\"),\"TRA\", ordershanTRB,\"PROFYLE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
      "replace the existing scale.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clonesTRA + scale_x_discrete(labels = ordershanTRA) + labs(title = \"clonesTRA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
      "replace the existing scale.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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\nD+SaMwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clonesTRB + scale_x_discrete(labels = ordershanTRB) + labs(title = \"clonesTRB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "if(file.exists(paste(plotpath,\"PROFYLE_clonesTRA.pdf\", sep = \"\"))){\n",
    "    message(\"file exists!\")\n",
    "}else{\n",
    "    pdf(file = paste(plotpath,\"PROFYLE_clonesTRA.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(clonesTRA + labs(title = \"clonesTRA\"))\n",
    "    dev.off()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "if(file.exists(paste(plotpath,\"PROFYLE_clonesTRB.pdf\", sep = \"\"))){\n",
    "    message(\"file exists!\")\n",
    "}else{\n",
    "    pdf(file = paste(plotpath,\"PROFYLE_clonesTRB.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(clonesTRB + labs(title = \"clonesTRB\"))\n",
    "    dev.off()\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of diversity measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in readChar(con, 5L, useBytes = TRUE):\n",
      "“cannot open compressed file '/Users/anabbi/OneDrive - UHN/Documents/IPD2/Data/NPC_PROFYLE_Div_bulk_cap_TRA.RData', probable reason 'No such file or directory'”\n"
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error in readChar(con, 5L, useBytes = TRUE): cannot open the connection\n",
     "output_type": "error",
     "traceback": [
      "Error in readChar(con, 5L, useBytes = TRUE): cannot open the connection\nTraceback:\n",
      "1. load(file = paste0(datapath, \"NPC_PROFYLE_Div_bulk_cap_TRA.RData\"))",
      "2. readChar(con, 5L, useBytes = TRUE)"
     ]
    }
   ],
   "source": [
    "load(file = paste0(datapath,\"NPC_PROFYLE_Div_bulk_cap_TRA.RData\"))\n",
    "\n",
    "load(file = paste0(datapath,\"NPC_PROFYLE_Div_bulk_cap_TRB.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 32</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>filename</th><th scope=col>cap_TRA</th><th scope=col>cap_Reads</th><th scope=col>cap_Average_reads</th><th scope=col>cap_Max_reads</th><th scope=col>cap_IDis</th><th scope=col>cap_Singletons</th><th scope=col>cap_Doubletons</th><th scope=col>cap_qD</th><th scope=col>cap_estimated_Richness</th><th scope=col>⋯</th><th scope=col>bulk_Doubletons</th><th scope=col>bulk_qD</th><th scope=col>bulk_estimated_Richness</th><th scope=col>bulk_observed_Richness</th><th scope=col>bulk_estimated_Shannon</th><th scope=col>bulk_observed_Shannon</th><th scope=col>bulk_estimated_Simpson</th><th scope=col>bulk_observed_Simpson</th><th scope=col>bulk_Divscore</th><th scope=col>group</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;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;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>107JP </td><td> 62</td><td> 99023</td><td>1597.1452</td><td> 42267</td><td>3.776326</td><td> 29</td><td>11</td><td> 82.325</td><td>100.227</td><td>⋯</td><td>6</td><td>24.247</td><td>24.314</td><td>23</td><td>24.102</td><td>20.055</td><td>23.676</td><td>17.883</td><td>3.0127500</td><td>NPC</td></tr>\n",
       "\t<tr><td>108W03</td><td>108</td><td>323717</td><td>2997.3796</td><td> 45448</td><td>2.536044</td><td> 58</td><td>10</td><td>157.057</td><td>276.199</td><td>⋯</td><td>3</td><td>43.243</td><td>76.307</td><td>29</td><td>44.419</td><td>23.948</td><td>31.429</td><td>20.364</td><td>8.8838000</td><td>NPC</td></tr>\n",
       "\t<tr><td>10C02 </td><td>322</td><td> 33596</td><td> 104.3354</td><td> 11517</td><td>7.798831</td><td>146</td><td>61</td><td>420.959</td><td>496.716</td><td>⋯</td><td>1</td><td>12.153</td><td>14.359</td><td>10</td><td>10.227</td><td> 8.169</td><td> 8.857</td><td> 7.111</td><td>1.4610000</td><td>NPC</td></tr>\n",
       "\t<tr><td>111S03</td><td> 71</td><td>150261</td><td>2116.3521</td><td> 25757</td><td>2.706038</td><td> 29</td><td> 7</td><td> 94.003</td><td>131.071</td><td>⋯</td><td>1</td><td> 4.025</td><td> 4.500</td><td> 3</td><td> 4.891</td><td> 2.828</td><td> 2.667</td><td> 2.667</td><td>2.4455000</td><td>NPC</td></tr>\n",
       "\t<tr><td>116JC </td><td> 62</td><td>438883</td><td>7078.7581</td><td> 51037</td><td>2.012853</td><td> 31</td><td> 6</td><td> 87.705</td><td>142.083</td><td>⋯</td><td>3</td><td>22.165</td><td>22.165</td><td>22</td><td>22.191</td><td>19.978</td><td>22.440</td><td>18.664</td><td>2.4656667</td><td>NPC</td></tr>\n",
       "\t<tr><td>119AY </td><td> 34</td><td>332858</td><td>9789.9412</td><td>210780</td><td>3.818541</td><td> 16</td><td> 6</td><td> 45.256</td><td> 55.333</td><td>⋯</td><td>1</td><td>16.121</td><td>20.840</td><td>13</td><td>12.000</td><td> 9.795</td><td> 9.074</td><td> 7.812</td><td>0.9230769</td><td>NPC</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 32\n",
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllll}\n",
       " filename & cap\\_TRA & cap\\_Reads & cap\\_Average\\_reads & cap\\_Max\\_reads & cap\\_IDis & cap\\_Singletons & cap\\_Doubletons & cap\\_qD & cap\\_estimated\\_Richness & cap\\_observed\\_Richness & cap\\_estimated\\_Shannon & cap\\_observed\\_Shannon & cap\\_estimated\\_Simpson & cap\\_observed\\_Simpson & cap\\_Divscore & bulk\\_TRA & bulk\\_Reads & bulk\\_Average\\_reads & bulk\\_Max\\_reads & bulk\\_IDis & bulk\\_Singletons & bulk\\_Doubletons & bulk\\_qD & bulk\\_estimated\\_Richness & bulk\\_observed\\_Richness & bulk\\_estimated\\_Shannon & bulk\\_observed\\_Shannon & bulk\\_estimated\\_Simpson & bulk\\_observed\\_Simpson & bulk\\_Divscore & group\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 107JP  &  62 &  99023 & 1597.1452 &  42267 & 3.776326 &  29 & 11 &  82.325 & 100.227 &  62 &  5.965 &  5.962 &  4.125 &  4.125 & 1.411266e-04 & 23 &  70 & 3.043478 &  8 & 0.5469260 &  4 & 6 & 24.247 & 24.314 & 23 & 24.102 & 20.055 & 23.676 & 17.883 & 3.0127500 & NPC\\\\\n",
       "\t 108W03 & 108 & 323717 & 2997.3796 &  45448 & 2.536044 &  58 & 10 & 157.057 & 276.199 & 108 & 17.939 & 17.932 & 14.651 & 14.650 & 3.947148e-04 & 29 &  56 & 1.931034 &  5 & 0.6627623 & 17 & 3 & 43.243 & 76.307 & 29 & 44.419 & 23.948 & 31.429 & 20.364 & 8.8838000 & NPC\\\\\n",
       "\t 10C02  & 322 &  33596 &  104.3354 &  11517 & 7.798831 & 146 & 61 & 420.959 & 496.716 & 322 &  8.513 &  8.443 &  5.225 &  5.224 & 7.391682e-04 & 10 &  32 & 3.200000 &  7 & 0.6718548 &  3 & 1 & 12.153 & 14.359 & 10 & 10.227 &  8.169 &  8.857 &  7.111 & 1.4610000 & NPC\\\\\n",
       "\t 111S03 &  71 & 150261 & 2116.3521 &  25757 & 2.706038 &  29 &  7 &  94.003 & 131.071 &  71 & 10.180 & 10.176 &  8.638 &  8.638 & 3.952324e-04 &  3 &   4 & 1.333333 &  2 & 0.4330127 &  2 & 1 &  4.025 &  4.500 &  3 &  4.891 &  2.828 &  2.667 &  2.667 & 2.4455000 & NPC\\\\\n",
       "\t 116JC  &  62 & 438883 & 7078.7581 &  51037 & 2.012853 &  31 &  6 &  87.705 & 142.083 &  62 & 13.894 & 13.892 & 12.435 & 12.434 & 2.722339e-04 & 22 & 106 & 4.818182 &  9 & 0.4326908 &  1 & 3 & 22.165 & 22.165 & 22 & 22.191 & 19.978 & 22.440 & 18.664 & 2.4656667 & NPC\\\\\n",
       "\t 119AY  &  34 & 332858 & 9789.9412 & 210780 & 3.818541 &  16 &  6 &  45.256 &  55.333 &  34 &  3.081 &  3.081 &  2.244 &  2.244 & 1.461714e-05 & 13 &  50 & 3.846154 & 13 & 0.8481352 &  4 & 1 & 16.121 & 20.840 & 13 & 12.000 &  9.795 &  9.074 &  7.812 & 0.9230769 & NPC\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 32\n",
       "\n",
       "| filename &lt;chr&gt; | cap_TRA &lt;dbl&gt; | cap_Reads &lt;dbl&gt; | cap_Average_reads &lt;dbl&gt; | cap_Max_reads &lt;dbl&gt; | cap_IDis &lt;dbl&gt; | cap_Singletons &lt;dbl&gt; | cap_Doubletons &lt;dbl&gt; | cap_qD &lt;dbl&gt; | cap_estimated_Richness &lt;dbl&gt; | ⋯ ⋯ | bulk_Doubletons &lt;dbl&gt; | bulk_qD &lt;dbl&gt; | bulk_estimated_Richness &lt;dbl&gt; | bulk_observed_Richness &lt;dbl&gt; | bulk_estimated_Shannon &lt;dbl&gt; | bulk_observed_Shannon &lt;dbl&gt; | bulk_estimated_Simpson &lt;dbl&gt; | bulk_observed_Simpson &lt;dbl&gt; | bulk_Divscore &lt;dbl&gt; | group &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 107JP  |  62 |  99023 | 1597.1452 |  42267 | 3.776326 |  29 | 11 |  82.325 | 100.227 | ⋯ | 6 | 24.247 | 24.314 | 23 | 24.102 | 20.055 | 23.676 | 17.883 | 3.0127500 | NPC |\n",
       "| 108W03 | 108 | 323717 | 2997.3796 |  45448 | 2.536044 |  58 | 10 | 157.057 | 276.199 | ⋯ | 3 | 43.243 | 76.307 | 29 | 44.419 | 23.948 | 31.429 | 20.364 | 8.8838000 | NPC |\n",
       "| 10C02  | 322 |  33596 |  104.3354 |  11517 | 7.798831 | 146 | 61 | 420.959 | 496.716 | ⋯ | 1 | 12.153 | 14.359 | 10 | 10.227 |  8.169 |  8.857 |  7.111 | 1.4610000 | NPC |\n",
       "| 111S03 |  71 | 150261 | 2116.3521 |  25757 | 2.706038 |  29 |  7 |  94.003 | 131.071 | ⋯ | 1 |  4.025 |  4.500 |  3 |  4.891 |  2.828 |  2.667 |  2.667 | 2.4455000 | NPC |\n",
       "| 116JC  |  62 | 438883 | 7078.7581 |  51037 | 2.012853 |  31 |  6 |  87.705 | 142.083 | ⋯ | 3 | 22.165 | 22.165 | 22 | 22.191 | 19.978 | 22.440 | 18.664 | 2.4656667 | NPC |\n",
       "| 119AY  |  34 | 332858 | 9789.9412 | 210780 | 3.818541 |  16 |  6 |  45.256 |  55.333 | ⋯ | 1 | 16.121 | 20.840 | 13 | 12.000 |  9.795 |  9.074 |  7.812 | 0.9230769 | NPC |\n",
       "\n"
      ],
      "text/plain": [
       "  filename cap_TRA cap_Reads cap_Average_reads cap_Max_reads cap_IDis\n",
       "1 107JP     62      99023    1597.1452          42267        3.776326\n",
       "2 108W03   108     323717    2997.3796          45448        2.536044\n",
       "3 10C02    322      33596     104.3354          11517        7.798831\n",
       "4 111S03    71     150261    2116.3521          25757        2.706038\n",
       "5 116JC     62     438883    7078.7581          51037        2.012853\n",
       "6 119AY     34     332858    9789.9412         210780        3.818541\n",
       "  cap_Singletons cap_Doubletons cap_qD  cap_estimated_Richness ⋯\n",
       "1  29            11              82.325 100.227                ⋯\n",
       "2  58            10             157.057 276.199                ⋯\n",
       "3 146            61             420.959 496.716                ⋯\n",
       "4  29             7              94.003 131.071                ⋯\n",
       "5  31             6              87.705 142.083                ⋯\n",
       "6  16             6              45.256  55.333                ⋯\n",
       "  bulk_Doubletons bulk_qD bulk_estimated_Richness bulk_observed_Richness\n",
       "1 6               24.247  24.314                  23                    \n",
       "2 3               43.243  76.307                  29                    \n",
       "3 1               12.153  14.359                  10                    \n",
       "4 1                4.025   4.500                   3                    \n",
       "5 3               22.165  22.165                  22                    \n",
       "6 1               16.121  20.840                  13                    \n",
       "  bulk_estimated_Shannon bulk_observed_Shannon bulk_estimated_Simpson\n",
       "1 24.102                 20.055                23.676                \n",
       "2 44.419                 23.948                31.429                \n",
       "3 10.227                  8.169                 8.857                \n",
       "4  4.891                  2.828                 2.667                \n",
       "5 22.191                 19.978                22.440                \n",
       "6 12.000                  9.795                 9.074                \n",
       "  bulk_observed_Simpson bulk_Divscore group\n",
       "1 17.883                3.0127500     NPC  \n",
       "2 20.364                8.8838000     NPC  \n",
       "3  7.111                1.4610000     NPC  \n",
       "4  2.667                2.4455000     NPC  \n",
       "5 18.664                2.4656667     NPC  \n",
       "6  7.812                0.9230769     NPC  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(Div_TRA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "bulkcap_TRAplot_simp <- ggplot(data = Div_TRA,\n",
    "                    aes(x = cap_observed_Simpson, \n",
    "                        y = bulk_estimated_Simpson)) +\n",
    "geom_point(aes(color = group),\n",
    "          size = 5) +\n",
    "#plot format\n",
    "theme(plot.title = element_text(size = 22),\n",
    "      panel.grid.major = element_blank(),\n",
    "      panel.grid.minor = element_blank(),\n",
    "      panel.background = element_rect(fill = \"transparent\", \n",
    "                                      colour = NA),\n",
    "      panel.border=element_blank(),\n",
    "      plot.margin = unit(c(0.5,0,0,0),\"cm\")) +\n",
    "#axis\n",
    "theme(axis.text = element_text(size = 24),\n",
    "      axis.text.x = element_text( hjust = 1),\n",
    "      axis.title = element_text(size = 28,\n",
    "                                 margin = margin(r = 4, unit = \"mm\")),\n",
    "      axis.line = element_line(color = \"black\")) +\n",
    "#legends\n",
    "theme(legend.position = \"none\",\n",
    "      legend.key = element_rect(fill = \"white\", \n",
    "                                colour = \"white\"),\n",
    "      legend.text = element_text(size = 20)) +\n",
    "guides(colour = guide_legend(override.aes = list(alpha = 1, \n",
    "                                                 size = 8))) + \n",
    "scale_x_continuous(trans = \"log10\") +\n",
    "scale_y_continuous(trans = \"log10\") +\n",
    "labs(y = \"Inferred Simpson diversity (RNAseq)\",\n",
    "    x = \"Simpson diversity (TCRcap)\",\n",
    "    title = \"TRA diversity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "bulkcap_TRAplot_rich <- ggplot(data = Div_TRA,\n",
    "                    aes(x = cap_observed_Richness, \n",
    "                        y = bulk_estimated_Richness)) +\n",
    "geom_point(aes(color = group),\n",
    "          size = 5) +\n",
    "#plot format\n",
    "theme(plot.title = element_text(size = 22),\n",
    "      panel.grid.major = element_blank(),\n",
    "      panel.grid.minor = element_blank(),\n",
    "      panel.background = element_rect(fill = \"transparent\", \n",
    "                                      colour = NA),\n",
    "      panel.border=element_blank(),\n",
    "      plot.margin = unit(c(0.5,0,0,0),\"cm\")) +\n",
    "#axis\n",
    "theme(axis.text = element_text(size = 24),\n",
    "      axis.text.x = element_text( hjust = 1),\n",
    "      axis.title = element_text(size = 28,\n",
    "                                 margin = margin(r = 4, unit = \"mm\")),\n",
    "      axis.line = element_line(color = \"black\")) +\n",
    "#legends\n",
    "theme(legend.position = \"none\",\n",
    "      legend.key = element_rect(fill = \"white\", \n",
    "                                colour = \"white\"),\n",
    "      legend.text = element_text(size = 20)) +\n",
    "guides(colour = guide_legend(override.aes = list(alpha = 1, \n",
    "                                                 size = 8))) + \n",
    "scale_x_continuous(trans = \"log10\") +\n",
    "scale_y_continuous(trans = \"log10\") +\n",
    "labs(y = \"Inferred richness (RNAseq)\",\n",
    "    x = \"Richness (TCRcap)\",\n",
    "    title = \"TRA diversity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bulkcap_TRAplot_rich"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "file exists!\n"
     ]
    }
   ],
   "source": [
    "if(file.exists(paste(plotpath,\"osimp_esimp_NPC_PROFYLE_TRA.pdf\", sep = \"\"))){\n",
    "    message(\"file exists!\")\n",
    "}else{\n",
    "    pdf(file = paste(plotpath,\"osimp_esimp_NPC_PROFYLE_TRA.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(bulkcap_TRAplot_simp + annotation_logticks(sides = \"lb\"))\n",
    "    dev.off()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "file exists!\n"
     ]
    }
   ],
   "source": [
    "if(file.exists(paste(plotpath,\"oshan_eshan_NPC_PROFYLE_TRA.pdf\", sep = \"\"))){\n",
    "    message(\"file exists!\")\n",
    "}else{\n",
    "    pdf(file = paste(plotpath,\"oshan_eshan_NPC_PROFYLE_TRA.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(bulkcap_TRAplot_shan + annotation_logticks(sides = \"lb\"))\n",
    "    dev.off()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": [
    "if(file.exists(paste(plotpath,\"orich_erich_NPC_PROFYLE_TRA.pdf\", sep = \"\"))){\n",
    "    message(\"file exists!\")\n",
    "}else{\n",
    "    pdf(file = paste(plotpath,\"orich_erich_NPC_PROFYLE_TRA.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(bulkcap_TRAplot_rich + annotation_logticks(sides = \"lb\"))\n",
    "    dev.off()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "bulkcap_TRBplot_rich <- ggplot(data = Div_TRB,\n",
    "                    aes(x = (cap_observed_Richness), \n",
    "                        y = (bulk_estimated_Richness))) +\n",
    "geom_point(aes(color = group),\n",
    "          size = 5) +\n",
    "#plot format\n",
    "theme(plot.title = element_text(size = 22),\n",
    "      panel.grid.major = element_blank(),\n",
    "      panel.grid.minor = element_blank(),\n",
    "      panel.background = element_rect(fill = \"transparent\", \n",
    "                                      colour = NA),\n",
    "      panel.border=element_blank(),\n",
    "      plot.margin = unit(c(0.5,0,0,0),\"cm\")) +\n",
    "#axis\n",
    "theme(axis.text = element_text(size = 24),\n",
    "      axis.ticks.x = element_blank(),\n",
    "      axis.text.x = element_text( hjust = 1),\n",
    "      axis.title = element_text(size = 28,\n",
    "                                 margin = margin(r = 4, unit = \"mm\")),\n",
    "      axis.line = element_line(color = \"black\")) +\n",
    "#legends\n",
    "theme(legend.position = \"none\",\n",
    "      legend.key = element_rect(fill = \"white\", \n",
    "                                colour = \"white\"), \n",
    "      legend.text = element_text(size = 20)) +\n",
    "guides(colour = guide_legend(override.aes = list(alpha = 1, \n",
    "                                                 size = 8))) + \n",
    "scale_x_continuous(trans = \"log10\") +\n",
    "scale_y_continuous(trans = \"log10\") +\n",
    "labs(y = \"Inferred richness (RNAseq)\",\n",
    "    x = \"Richness (TCRcap)\",\n",
    "    title = \"TRB diversity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "bulkcap_TRBplot_simp <- ggplot(data = Div_TRB,\n",
    "                    aes(x = (cap_observed_Simpson), \n",
    "                        y = (bulk_estimated_Simpson))) +\n",
    "geom_point(aes(color = group),\n",
    "          size = 5) +\n",
    "#plot format\n",
    "theme(plot.title = element_text(size = 22),\n",
    "      panel.grid.major = element_blank(),\n",
    "      panel.grid.minor = element_blank(),\n",
    "      panel.background = element_rect(fill = \"transparent\", \n",
    "                                      colour = NA),\n",
    "      panel.border=element_blank(),\n",
    "      plot.margin = unit(c(0.5,0,0,0),\"cm\")) +\n",
    "#axis\n",
    "theme(axis.text = element_text(size = 24),\n",
    "      axis.text.x = element_text( hjust = 1),\n",
    "      axis.title = element_text(size = 28,\n",
    "                                 margin = margin(r = 4, unit = \"mm\")),\n",
    "      axis.line = element_line(color = \"black\")) +\n",
    "#legends\n",
    "theme(legend.position = \"none\",\n",
    "      legend.key = element_rect(fill = \"white\", \n",
    "                                colour = \"white\"), \n",
    "      legend.text = element_text(size = 20)) +\n",
    "guides(colour = guide_legend(override.aes = list(alpha = 1, \n",
    "                                                 size = 8))) + \n",
    "scale_x_continuous(trans = \"log10\") +\n",
    "scale_y_continuous(trans = \"log10\") +\n",
    "labs(y = \"Inferred Simpson diversity (RNAseq)\",\n",
    "    x = \"Simpson diversity (TCRcap)\",\n",
    "    title = \"TRB diversity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "bulkcap_TRBplot_shan <- ggplot(data = Div_TRB,\n",
    "                    aes(x = (cap_observed_Shannon), \n",
    "                        y = (bulk_estimated_Shannon))) +\n",
    "geom_point(aes(color = group),\n",
    "          size = 5) +\n",
    "#plot format\n",
    "theme(plot.title = element_text(size = 22),\n",
    "      panel.grid.major = element_blank(),\n",
    "      panel.grid.minor = element_blank(),\n",
    "      panel.background = element_rect(fill = \"transparent\", \n",
    "                                      colour = NA),\n",
    "      panel.border=element_blank(),\n",
    "      plot.margin = unit(c(0.5,0,0,0),\"cm\")) +\n",
    "#axis\n",
    "theme(axis.text = element_text(size = 24),\n",
    "      axis.text.x = element_text( hjust = 1),\n",
    "      axis.title = element_text(size = 28,\n",
    "                                 margin = margin(r = 4, unit = \"mm\")),\n",
    "      axis.line = element_line(color = \"black\")) +\n",
    "#legends\n",
    "theme(legend.position = \"none\",\n",
    "      legend.key = element_rect(fill = \"white\", \n",
    "                                colour = \"white\"), \n",
    "      legend.text = element_text(size = 20)) +\n",
    "guides(colour = guide_legend(override.aes = list(alpha = 1, \n",
    "                                                 size = 8))) + \n",
    "scale_x_continuous(trans = \"log10\") +\n",
    "scale_y_continuous(trans = \"log10\") +\n",
    "labs(y = \"Inferred Shannon diversity (RNAseq)\",\n",
    "    x = \"Shannon diversity (TCRcap)\",\n",
    "    title = \"TRB diversity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "34.999"
      ],
      "text/latex": [
       "34.999"
      ],
      "text/markdown": [
       "34.999"
      ],
      "text/plain": [
       "[1] 34.999"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max(Div_TRB$cap_estimated_Simpson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bulkcap_TRBplot_shan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": [
    "if(file.exists(paste(plotpath,\"osimp_esimp_NPC_PROFYLE_TRB.pdf\", sep = \"\"))){\n",
    "    message(\"file exists!\")\n",
    "}else{\n",
    "    pdf(file = paste(plotpath,\"osimp_esimp_NPC_PROFYLE_TRB.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(bulkcap_TRBplot_simp + annotation_logticks(sides = \"lb\"))\n",
    "    dev.off()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": [
    "if(file.exists(paste(plotpath,\"orich_erich_NPC_PROFYLE_TRB.pdf\", sep = \"\"))){\n",
    "    message(\"file exists!\")\n",
    "}else{\n",
    "    pdf(file = paste(plotpath,\"orich_erich_NPC_PROFYLE_TRB.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(bulkcap_TRBplot_rich + annotation_logticks(sides = \"lb\"))\n",
    "    dev.off()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": [
    "if(file.exists(paste(plotpath,\"oshan_eshan_NPC_PROFYLE_TRB.pdf\", sep = \"\"))){\n",
    "    message(\"file exists!\")\n",
    "}else{\n",
    "    pdf(file = paste(plotpath,\"oshan_eshan_NPC_PROFYLE_TRB.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(bulkcap_TRBplot_shan + annotation_logticks(sides = \"lb\"))\n",
    "    dev.off()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bulkcap_TRBplot_shan + annotation_logticks(sides = \"lb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "lmreg_TRA_rich <- lm(bulk_estimated_Richness ~ cap_observed_Richness, data = Div_TRA)\n",
    "lmreg_TRA_shan <- lm(bulk_estimated_Shannon ~ cap_observed_Shannon, data = Div_TRA)\n",
    "lmreg_TRA_simp <- lm(bulk_estimated_Simpson ~ cap_observed_Simpson, data = Div_TRA)\n",
    "\n",
    "lmreg_TRB_rich <- lm(bulk_estimated_Richness ~ cap_observed_Richness, data = Div_TRB)\n",
    "lmreg_TRB_shan <- lm(bulk_estimated_Shannon ~ cap_observed_Shannon, data = Div_TRB)\n",
    "lmreg_TRB_simp <- lm(bulk_estimated_Simpson ~ cap_observed_Simpson, data = Div_TRB)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th></tr>\n",
       "\t<tr><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;int&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;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.2028585</td><td>0.1771443</td><td>25.87559</td><td>7.888955</td><td>0.008532656</td><td>2</td><td>-153.1523</td><td>312.3046</td><td>316.7941</td><td>20755.93</td><td>31</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t 0.2028585 & 0.1771443 & 25.87559 & 7.888955 & 0.008532656 & 2 & -153.1523 & 312.3046 & 316.7941 & 20755.93 & 31\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 11\n",
       "\n",
       "| r.squared &lt;dbl&gt; | adj.r.squared &lt;dbl&gt; | sigma &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | df &lt;int&gt; | logLik &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | deviance &lt;dbl&gt; | df.residual &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.2028585 | 0.1771443 | 25.87559 | 7.888955 | 0.008532656 | 2 | -153.1523 | 312.3046 | 316.7941 | 20755.93 | 31 |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared adj.r.squared sigma    statistic p.value     df logLik    AIC     \n",
       "1 0.2028585 0.1771443     25.87559 7.888955  0.008532656 2  -153.1523 312.3046\n",
       "  BIC      deviance df.residual\n",
       "1 316.7941 20755.93 31         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th></tr>\n",
       "\t<tr><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;int&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;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.6556894</td><td>0.6445826</td><td>15.86145</td><td>59.03498</td><td>1.149394e-08</td><td>2</td><td>-137.0018</td><td>280.0036</td><td>284.4932</td><td>7799.157</td><td>31</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t 0.6556894 & 0.6445826 & 15.86145 & 59.03498 & 1.149394e-08 & 2 & -137.0018 & 280.0036 & 284.4932 & 7799.157 & 31\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 11\n",
       "\n",
       "| r.squared &lt;dbl&gt; | adj.r.squared &lt;dbl&gt; | sigma &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | df &lt;int&gt; | logLik &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | deviance &lt;dbl&gt; | df.residual &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.6556894 | 0.6445826 | 15.86145 | 59.03498 | 1.149394e-08 | 2 | -137.0018 | 280.0036 | 284.4932 | 7799.157 | 31 |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared adj.r.squared sigma    statistic p.value      df logLik    AIC     \n",
       "1 0.6556894 0.6445826     15.86145 59.03498  1.149394e-08 2  -137.0018 280.0036\n",
       "  BIC      deviance df.residual\n",
       "1 284.4932 7799.157 31         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th></tr>\n",
       "\t<tr><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;int&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;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.5159319</td><td>0.5003168</td><td>18.92679</td><td>33.04058</td><td>2.517063e-06</td><td>2</td><td>-142.8325</td><td>291.665</td><td>296.1545</td><td>11104.93</td><td>31</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t 0.5159319 & 0.5003168 & 18.92679 & 33.04058 & 2.517063e-06 & 2 & -142.8325 & 291.665 & 296.1545 & 11104.93 & 31\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 11\n",
       "\n",
       "| r.squared &lt;dbl&gt; | adj.r.squared &lt;dbl&gt; | sigma &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | df &lt;int&gt; | logLik &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | deviance &lt;dbl&gt; | df.residual &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.5159319 | 0.5003168 | 18.92679 | 33.04058 | 2.517063e-06 | 2 | -142.8325 | 291.665 | 296.1545 | 11104.93 | 31 |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared adj.r.squared sigma    statistic p.value      df logLik    AIC    \n",
       "1 0.5159319 0.5003168     18.92679 33.04058  2.517063e-06 2  -142.8325 291.665\n",
       "  BIC      deviance df.residual\n",
       "1 296.1545 11104.93 31         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th></tr>\n",
       "\t<tr><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;int&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;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.8646988</td><td>0.8582559</td><td>16.63847</td><td>134.2093</td><td>1.389909e-10</td><td>2</td><td>-96.25892</td><td>198.5178</td><td>201.9243</td><td>5813.615</td><td>21</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t 0.8646988 & 0.8582559 & 16.63847 & 134.2093 & 1.389909e-10 & 2 & -96.25892 & 198.5178 & 201.9243 & 5813.615 & 21\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 11\n",
       "\n",
       "| r.squared &lt;dbl&gt; | adj.r.squared &lt;dbl&gt; | sigma &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | df &lt;int&gt; | logLik &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | deviance &lt;dbl&gt; | df.residual &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.8646988 | 0.8582559 | 16.63847 | 134.2093 | 1.389909e-10 | 2 | -96.25892 | 198.5178 | 201.9243 | 5813.615 | 21 |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared adj.r.squared sigma    statistic p.value      df logLik    AIC     \n",
       "1 0.8646988 0.8582559     16.63847 134.2093  1.389909e-10 2  -96.25892 198.5178\n",
       "  BIC      deviance df.residual\n",
       "1 201.9243 5813.615 21         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th></tr>\n",
       "\t<tr><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;int&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;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.7710513</td><td>0.7601489</td><td>13.69583</td><td>70.72359</td><td>3.663709e-08</td><td>2</td><td>-91.78251</td><td>189.565</td><td>192.9715</td><td>3939.09</td><td>21</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t 0.7710513 & 0.7601489 & 13.69583 & 70.72359 & 3.663709e-08 & 2 & -91.78251 & 189.565 & 192.9715 & 3939.09 & 21\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 11\n",
       "\n",
       "| r.squared &lt;dbl&gt; | adj.r.squared &lt;dbl&gt; | sigma &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | df &lt;int&gt; | logLik &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | deviance &lt;dbl&gt; | df.residual &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.7710513 | 0.7601489 | 13.69583 | 70.72359 | 3.663709e-08 | 2 | -91.78251 | 189.565 | 192.9715 | 3939.09 | 21 |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared adj.r.squared sigma    statistic p.value      df logLik    AIC    \n",
       "1 0.7710513 0.7601489     13.69583 70.72359  3.663709e-08 2  -91.78251 189.565\n",
       "  BIC      deviance df.residual\n",
       "1 192.9715 3939.09  21         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th></tr>\n",
       "\t<tr><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;int&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;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.5107369</td><td>0.4874386</td><td>16.08662</td><td>21.92169</td><td>0.0001273898</td><td>2</td><td>-95.48314</td><td>196.9663</td><td>200.3728</td><td>5434.368</td><td>21</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t 0.5107369 & 0.4874386 & 16.08662 & 21.92169 & 0.0001273898 & 2 & -95.48314 & 196.9663 & 200.3728 & 5434.368 & 21\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 11\n",
       "\n",
       "| r.squared &lt;dbl&gt; | adj.r.squared &lt;dbl&gt; | sigma &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | df &lt;int&gt; | logLik &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | deviance &lt;dbl&gt; | df.residual &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.5107369 | 0.4874386 | 16.08662 | 21.92169 | 0.0001273898 | 2 | -95.48314 | 196.9663 | 200.3728 | 5434.368 | 21 |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared adj.r.squared sigma    statistic p.value      df logLik    AIC     \n",
       "1 0.5107369 0.4874386     16.08662 21.92169  0.0001273898 2  -95.48314 196.9663\n",
       "  BIC      deviance df.residual\n",
       "1 200.3728 5434.368 21         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glance(lmreg_TRA_rich)\n",
    "glance(lmreg_TRA_shan)\n",
    "glance(lmreg_TRA_simp)\n",
    "\n",
    "glance(lmreg_TRB_rich)\n",
    "glance(lmreg_TRB_shan)\n",
    "glance(lmreg_TRB_simp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 4.0",
   "language": "R",
   "name": "ir40"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "227.969px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}