Cleaned_Alignment_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(tidyverse))\n",
    "library(magrittr)\n",
    "\n",
    "setwd(\"~/../Dropbox//Computational Genomics/Term Project/TCR_annotation/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Smith Waterman Annotation with unfiltered output of MiTCR\n",
    "setwd(\"vdjdb_alignment_raw/\")\n",
    "sw_annotation <- suppressMessages(lapply(list.files(), read_tsv))\n",
    "names(sw_annotation) <- tools::file_path_sans_ext(list.files())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Smith Waterman Annotation with underscores in query sequences removed\n",
    "setwd(\"../vdjdb_alignment_remove_ambiguous/\")\n",
    "sw_annotation_RU <- suppressMessages(lapply(list.files(pattern = \"*underscores.txt\"), read_tsv))\n",
    "\n",
    "names(sw_annotation_RU) <- tools::file_path_sans_ext(list.files(pattern = \"*underscores.txt\"))\n",
    "## Smith Waterman Annotation with removed ambiguous bases\n",
    "sw_annotation_RA  <- suppressMessages(lapply(list.files(pattern = \"*ambiguous.txt\"), read_tsv))\n",
    "\n",
    "names(sw_annotation_RA) <- tools::file_path_sans_ext(list.files(pattern = \"*ambiguous.txt\"))\n",
    "# See how scores are distributed across all samples, using the different scoring protocols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"MiTCR output alignment with all ambiguous samples removed\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(3,1), bg = rgb(7, 44, 98, max = 255), \n",
    "    col.main = \"white\", col.lab = \"white\", col.axis = \"white\", \n",
    "    cex.axis = 2, cex.main = 1.5)\n",
    "par(mar = c(5,3,2,2))\n",
    "sw_annotation %>% map(\"score\") %>% unlist %>% hist(main = \"Score Distribution in Raw MiTCR output alignment\", col = \"firebrick\")\n",
    "sw_annotation_RU %>% map(\"score\") %>% unlist %>% hist(main = \"MiTCR output alignment with ambiguous characters removed\", col = \"blue2\")\n",
    "sw_annotation_RA %>% map(\"score\") %>% unlist %>% hist(main = \"MiTCR output alignment with all ambiguous samples removed\", col = \"green2\")# What was the coverage across all samples?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t58lxnOBCDSJGSZERmuuanLDGcCEGkSsqorMlxzU5cZzgQg0iRkVVdkuOamLjOc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\nwP8BWmMifKtsKjAAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"MiTCR output alignment with all ambiguous samples removed\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YNIiJSStEktEGmGfrJyhUiyIJI4iIRIKUmb1AKRZugnK1eIJAsiiSNXa82fCstb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\n",
      "text/plain": [
       "Plot with title \"TCR coverage RA\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(3,1), bg = rgb(7, 44, 98, max = 255), \n",
    "    col.main = \"white\", col.lab = \"white\", col.axis = \"white\", \n",
    "    cex.axis = 2, cex.main = 1.5)\n",
    "par(mar = c(5,3,2,2))\n",
    "sw_annotation %>% map(\"score\") %>% unlist %>% hist(main = \"Score Distribution in Raw MiTCR output alignment\", col = \"firebrick\")\n",
    "sw_annotation_RU %>% map(\"score\") %>% unlist %>% hist(main = \"MiTCR output alignment with ambiguous characters removed\", col = \"blue2\")\n",
    "sw_annotation_RA %>% map(\"score\") %>% unlist %>% hist(main = \"MiTCR output alignment with all ambiguous samples removed\", col = \"green2\")# What was the coverage across all samples?\n",
    "\n",
    "par(mfrow=c(1,2), bg = rgb(7, 44, 98, max = 255), \n",
    "    col.main = \"white\", col.lab = \"white\", col.axis = \"white\", \n",
    "    cex.axis = 1.2, cex.main = 1)\n",
    "sw_annotation_RU %>% map(\"freq\") %>% map_dbl(sum) %>% hist(main = \"TCR coverage - raw\", col = \"white\")\n",
    "\n",
    "sw_annotation_RA %>% map(\"freq\") %>% map_dbl(sum) %>% hist(main = \"TCR coverage RA\", col = \"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'bindrcpp' was built under R version 3.4.4\""
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error in vdjdb_second_search(x, vdjdb_human_beta): could not find function \"vdjdb_second_search\"\n",
     "output_type": "error",
     "traceback": [
      "Error in vdjdb_second_search(x, vdjdb_human_beta): could not find function \"vdjdb_second_search\"\nTraceback:\n",
      "1. sw_annotation_RA_HQ[[s]] %>% select(\"query\") %>% unlist %>% lapply(function(x) vdjdb_second_search(x, \n .     vdjdb_human_beta))",
      "2. withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))",
      "3. eval(quote(`_fseq`(`_lhs`)), env, env)",
      "4. eval(quote(`_fseq`(`_lhs`)), env, env)",
      "5. `_fseq`(`_lhs`)",
      "6. freduce(value, `_function_list`)",
      "7. withVisible(function_list[[k]](value))",
      "8. function_list[[k]](value)",
      "9. lapply(., function(x) vdjdb_second_search(x, vdjdb_human_beta))",
      "10. FUN(X[[i]], ...)"
     ]
    }
   ],
   "source": [
    "# From here on, we will only be working with the sw_annotation_RA dataset\n",
    "sw_annotation_RA_HQ <- sw_annotation_RA %>% map(~ filter(., score > 20))\n",
    "\n",
    "\n",
    "# Maybe try to look at alignments to the query sequence beyond just the best matching one\n",
    "# I doubt this will be meaningful, however because TCR - epitope matches are very specific\n",
    "# A single aa difference can totally change the affinity. \n",
    "sw_second_annotation_RA_HQ <- list()\n",
    "for (s in names(sw_annotation_RA_HQ[1:3]) ) {\n",
    "  sw_second_annotation_RA_HQ[[s]] <- \n",
    "    sw_annotation_RA_HQ[[s]] %>% \n",
    "    select(\"query\") %>% \n",
    "    unlist %>% \n",
    "    lapply(function(x) vdjdb_second_search(x, vdjdb_human_beta))\n",
    "  names(sw_second_annotation_RA_HQ[[s]]) <- s\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>antigen.species</th><th scope=col>bcn1_remove_ambiguous</th><th scope=col>bcn10_remove_ambiguous</th><th scope=col>bcn11_remove_ambiguous</th><th scope=col>bcn12_remove_ambiguous</th><th scope=col>bcn13_remove_ambiguous</th><th scope=col>bcn14_remove_ambiguous</th><th scope=col>bcn15_remove_ambiguous</th><th scope=col>bcn16_remove_ambiguous</th><th scope=col>bcn17_remove_ambiguous</th><th scope=col>...</th><th scope=col>tp50_remove_ambiguous</th><th scope=col>tp51_remove_ambiguous</th><th scope=col>tp52_remove_ambiguous</th><th scope=col>tp53_remove_ambiguous</th><th scope=col>tp54_remove_ambiguous</th><th scope=col>tp55_remove_ambiguous</th><th scope=col>tp56_remove_ambiguous</th><th scope=col>tp57_remove_ambiguous</th><th scope=col>tp58_remove_ambiguous</th><th scope=col>tp59_remove_ambiguous</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>CMV             </td><td>0.06092843      </td><td>0.528804348     </td><td>0.188979964     </td><td>0.0000000       </td><td>0.032584270     </td><td>0.018881368     </td><td>0.045908184     </td><td>0.388201713     </td><td>0.19579106      </td><td>...             </td><td>0.038900415     </td><td>0.317476732     </td><td>0.037037037     </td><td>0.01572150      </td><td>0.8170674       </td><td>0.521523179     </td><td>0.018939394     </td><td>0.008865248     </td><td>0.014909478     </td><td>0.33632287      </td></tr>\n",
       "\t<tr><td>EBV             </td><td>0.01934236      </td><td>0.000000000     </td><td>0.015027322     </td><td>0.0000000       </td><td>0.000000000     </td><td>0.021731386     </td><td>0.005988024     </td><td>0.006184586     </td><td>0.00000000      </td><td>...             </td><td>0.000000000     </td><td>0.000000000     </td><td>0.039094650     </td><td>0.01909040      </td><td>0.0000000       </td><td>0.004139073     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.013312034     </td><td>0.05082212      </td></tr>\n",
       "\t<tr><td>HCV             </td><td>0.02901354      </td><td>0.000000000     </td><td>0.000000000     </td><td>0.0000000       </td><td>0.011235955     </td><td>0.002850018     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>...             </td><td>0.000000000     </td><td>0.007238883     </td><td>0.000000000     </td><td>0.41661988      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td></tr>\n",
       "\t<tr><td>HIV-1           </td><td>0.05125725      </td><td>0.005434783     </td><td>0.035519126     </td><td>0.0000000       </td><td>0.051685393     </td><td>0.000000000     </td><td>0.043912176     </td><td>0.007611798     </td><td>0.06426156      </td><td>...             </td><td>0.000000000     </td><td>0.000000000     </td><td>0.022633745     </td><td>0.00000000      </td><td>0.0000000       </td><td>0.013245033     </td><td>0.000000000     </td><td>0.007092199     </td><td>0.004259851     </td><td>0.00000000      </td></tr>\n",
       "\t<tr><td>HomoSapiens     </td><td>0.01450677      </td><td>0.003260870     </td><td>0.007285974     </td><td>0.0000000       </td><td>0.012359551     </td><td>0.010331315     </td><td>0.028942116     </td><td>0.000000000     </td><td>0.05712138      </td><td>...             </td><td>0.002593361     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.01066816      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.126262626     </td><td>0.015957447     </td><td>0.000000000     </td><td>0.01793722      </td></tr>\n",
       "\t<tr><td>InfluenzaA      </td><td>0.10638298      </td><td>0.017391304     </td><td>0.023679417     </td><td>0.9049826       </td><td>0.006741573     </td><td>0.019237620     </td><td>0.024950100     </td><td>0.005233111     </td><td>0.08944006      </td><td>...             </td><td>0.579875519     </td><td>0.027921406     </td><td>0.008230453     </td><td>0.03481190      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.033687943     </td><td>0.054313099     </td><td>0.01644245      </td></tr>\n",
       "\t<tr><td>YellowFeverVirus</td><td>0.00000000      </td><td>0.000000000     </td><td>0.069216758     </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.015699334     </td><td>0.00000000      </td><td>...             </td><td>0.000000000     </td><td>0.000000000     </td><td>0.008230453     </td><td>0.01516002      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.005050505     </td><td>0.000000000     </td><td>0.006389776     </td><td>0.00000000      </td></tr>\n",
       "\t<tr><td>DENV1           </td><td>0.00000000      </td><td>0.000000000     </td><td>0.000000000     </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>...             </td><td>0.000000000     </td><td>0.000000000     </td><td>0.020576132     </td><td>0.00000000      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.02017937      </td></tr>\n",
       "\t<tr><td>DENV3/4         </td><td>0.00000000      </td><td>0.000000000     </td><td>0.000000000     </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>...             </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td></tr>\n",
       "\t<tr><td>DENV2           </td><td>0.00000000      </td><td>0.000000000     </td><td>0.000000000     </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>...             </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td></tr>\n",
       "\t<tr><td>HSV-2           </td><td>0.00000000      </td><td>0.000000000     </td><td>0.000000000     </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>...             </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td></tr>\n",
       "\t<tr><td>HTLV-1          </td><td>0.00000000      </td><td>0.000000000     </td><td>0.000000000     </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>...             </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td><td>0.0000000       </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.000000000     </td><td>0.00000000      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       " antigen.species & bcn1\\_remove\\_ambiguous & bcn10\\_remove\\_ambiguous & bcn11\\_remove\\_ambiguous & bcn12\\_remove\\_ambiguous & bcn13\\_remove\\_ambiguous & bcn14\\_remove\\_ambiguous & bcn15\\_remove\\_ambiguous & bcn16\\_remove\\_ambiguous & bcn17\\_remove\\_ambiguous & ... & tp50\\_remove\\_ambiguous & tp51\\_remove\\_ambiguous & tp52\\_remove\\_ambiguous & tp53\\_remove\\_ambiguous & tp54\\_remove\\_ambiguous & tp55\\_remove\\_ambiguous & tp56\\_remove\\_ambiguous & tp57\\_remove\\_ambiguous & tp58\\_remove\\_ambiguous & tp59\\_remove\\_ambiguous\\\\\n",
       "\\hline\n",
       "\t CMV              & 0.06092843       & 0.528804348      & 0.188979964      & 0.0000000        & 0.032584270      & 0.018881368      & 0.045908184      & 0.388201713      & 0.19579106       & ...              & 0.038900415      & 0.317476732      & 0.037037037      & 0.01572150       & 0.8170674        & 0.521523179      & 0.018939394      & 0.008865248      & 0.014909478      & 0.33632287      \\\\\n",
       "\t EBV              & 0.01934236       & 0.000000000      & 0.015027322      & 0.0000000        & 0.000000000      & 0.021731386      & 0.005988024      & 0.006184586      & 0.00000000       & ...              & 0.000000000      & 0.000000000      & 0.039094650      & 0.01909040       & 0.0000000        & 0.004139073      & 0.000000000      & 0.000000000      & 0.013312034      & 0.05082212      \\\\\n",
       "\t HCV              & 0.02901354       & 0.000000000      & 0.000000000      & 0.0000000        & 0.011235955      & 0.002850018      & 0.000000000      & 0.000000000      & 0.00000000       & ...              & 0.000000000      & 0.007238883      & 0.000000000      & 0.41661988       & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000      \\\\\n",
       "\t HIV-1            & 0.05125725       & 0.005434783      & 0.035519126      & 0.0000000        & 0.051685393      & 0.000000000      & 0.043912176      & 0.007611798      & 0.06426156       & ...              & 0.000000000      & 0.000000000      & 0.022633745      & 0.00000000       & 0.0000000        & 0.013245033      & 0.000000000      & 0.007092199      & 0.004259851      & 0.00000000      \\\\\n",
       "\t HomoSapiens      & 0.01450677       & 0.003260870      & 0.007285974      & 0.0000000        & 0.012359551      & 0.010331315      & 0.028942116      & 0.000000000      & 0.05712138       & ...              & 0.002593361      & 0.000000000      & 0.000000000      & 0.01066816       & 0.0000000        & 0.000000000      & 0.126262626      & 0.015957447      & 0.000000000      & 0.01793722      \\\\\n",
       "\t InfluenzaA       & 0.10638298       & 0.017391304      & 0.023679417      & 0.9049826        & 0.006741573      & 0.019237620      & 0.024950100      & 0.005233111      & 0.08944006       & ...              & 0.579875519      & 0.027921406      & 0.008230453      & 0.03481190       & 0.0000000        & 0.000000000      & 0.000000000      & 0.033687943      & 0.054313099      & 0.01644245      \\\\\n",
       "\t YellowFeverVirus & 0.00000000       & 0.000000000      & 0.069216758      & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.015699334      & 0.00000000       & ...              & 0.000000000      & 0.000000000      & 0.008230453      & 0.01516002       & 0.0000000        & 0.000000000      & 0.005050505      & 0.000000000      & 0.006389776      & 0.00000000      \\\\\n",
       "\t DENV1            & 0.00000000       & 0.000000000      & 0.000000000      & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & ...              & 0.000000000      & 0.000000000      & 0.020576132      & 0.00000000       & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.02017937      \\\\\n",
       "\t DENV3/4          & 0.00000000       & 0.000000000      & 0.000000000      & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & ...              & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000      \\\\\n",
       "\t DENV2            & 0.00000000       & 0.000000000      & 0.000000000      & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & ...              & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000      \\\\\n",
       "\t HSV-2            & 0.00000000       & 0.000000000      & 0.000000000      & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & ...              & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000      \\\\\n",
       "\t HTLV-1           & 0.00000000       & 0.000000000      & 0.000000000      & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & ...              & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000       & 0.0000000        & 0.000000000      & 0.000000000      & 0.000000000      & 0.000000000      & 0.00000000      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "antigen.species | bcn1_remove_ambiguous | bcn10_remove_ambiguous | bcn11_remove_ambiguous | bcn12_remove_ambiguous | bcn13_remove_ambiguous | bcn14_remove_ambiguous | bcn15_remove_ambiguous | bcn16_remove_ambiguous | bcn17_remove_ambiguous | ... | tp50_remove_ambiguous | tp51_remove_ambiguous | tp52_remove_ambiguous | tp53_remove_ambiguous | tp54_remove_ambiguous | tp55_remove_ambiguous | tp56_remove_ambiguous | tp57_remove_ambiguous | tp58_remove_ambiguous | tp59_remove_ambiguous | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CMV              | 0.06092843       | 0.528804348      | 0.188979964      | 0.0000000        | 0.032584270      | 0.018881368      | 0.045908184      | 0.388201713      | 0.19579106       | ...              | 0.038900415      | 0.317476732      | 0.037037037      | 0.01572150       | 0.8170674        | 0.521523179      | 0.018939394      | 0.008865248      | 0.014909478      | 0.33632287       | \n",
       "| EBV              | 0.01934236       | 0.000000000      | 0.015027322      | 0.0000000        | 0.000000000      | 0.021731386      | 0.005988024      | 0.006184586      | 0.00000000       | ...              | 0.000000000      | 0.000000000      | 0.039094650      | 0.01909040       | 0.0000000        | 0.004139073      | 0.000000000      | 0.000000000      | 0.013312034      | 0.05082212       | \n",
       "| HCV              | 0.02901354       | 0.000000000      | 0.000000000      | 0.0000000        | 0.011235955      | 0.002850018      | 0.000000000      | 0.000000000      | 0.00000000       | ...              | 0.000000000      | 0.007238883      | 0.000000000      | 0.41661988       | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | \n",
       "| HIV-1            | 0.05125725       | 0.005434783      | 0.035519126      | 0.0000000        | 0.051685393      | 0.000000000      | 0.043912176      | 0.007611798      | 0.06426156       | ...              | 0.000000000      | 0.000000000      | 0.022633745      | 0.00000000       | 0.0000000        | 0.013245033      | 0.000000000      | 0.007092199      | 0.004259851      | 0.00000000       | \n",
       "| HomoSapiens      | 0.01450677       | 0.003260870      | 0.007285974      | 0.0000000        | 0.012359551      | 0.010331315      | 0.028942116      | 0.000000000      | 0.05712138       | ...              | 0.002593361      | 0.000000000      | 0.000000000      | 0.01066816       | 0.0000000        | 0.000000000      | 0.126262626      | 0.015957447      | 0.000000000      | 0.01793722       | \n",
       "| InfluenzaA       | 0.10638298       | 0.017391304      | 0.023679417      | 0.9049826        | 0.006741573      | 0.019237620      | 0.024950100      | 0.005233111      | 0.08944006       | ...              | 0.579875519      | 0.027921406      | 0.008230453      | 0.03481190       | 0.0000000        | 0.000000000      | 0.000000000      | 0.033687943      | 0.054313099      | 0.01644245       | \n",
       "| YellowFeverVirus | 0.00000000       | 0.000000000      | 0.069216758      | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.015699334      | 0.00000000       | ...              | 0.000000000      | 0.000000000      | 0.008230453      | 0.01516002       | 0.0000000        | 0.000000000      | 0.005050505      | 0.000000000      | 0.006389776      | 0.00000000       | \n",
       "| DENV1            | 0.00000000       | 0.000000000      | 0.000000000      | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | ...              | 0.000000000      | 0.000000000      | 0.020576132      | 0.00000000       | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.02017937       | \n",
       "| DENV3/4          | 0.00000000       | 0.000000000      | 0.000000000      | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | ...              | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | \n",
       "| DENV2            | 0.00000000       | 0.000000000      | 0.000000000      | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | ...              | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | \n",
       "| HSV-2            | 0.00000000       | 0.000000000      | 0.000000000      | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | ...              | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | \n",
       "| HTLV-1           | 0.00000000       | 0.000000000      | 0.000000000      | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | ...              | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | 0.0000000        | 0.000000000      | 0.000000000      | 0.000000000      | 0.000000000      | 0.00000000       | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   antigen.species  bcn1_remove_ambiguous bcn10_remove_ambiguous\n",
       "1  CMV              0.06092843            0.528804348           \n",
       "2  EBV              0.01934236            0.000000000           \n",
       "3  HCV              0.02901354            0.000000000           \n",
       "4  HIV-1            0.05125725            0.005434783           \n",
       "5  HomoSapiens      0.01450677            0.003260870           \n",
       "6  InfluenzaA       0.10638298            0.017391304           \n",
       "7  YellowFeverVirus 0.00000000            0.000000000           \n",
       "8  DENV1            0.00000000            0.000000000           \n",
       "9  DENV3/4          0.00000000            0.000000000           \n",
       "10 DENV2            0.00000000            0.000000000           \n",
       "11 HSV-2            0.00000000            0.000000000           \n",
       "12 HTLV-1           0.00000000            0.000000000           \n",
       "   bcn11_remove_ambiguous bcn12_remove_ambiguous bcn13_remove_ambiguous\n",
       "1  0.188979964            0.0000000              0.032584270           \n",
       "2  0.015027322            0.0000000              0.000000000           \n",
       "3  0.000000000            0.0000000              0.011235955           \n",
       "4  0.035519126            0.0000000              0.051685393           \n",
       "5  0.007285974            0.0000000              0.012359551           \n",
       "6  0.023679417            0.9049826              0.006741573           \n",
       "7  0.069216758            0.0000000              0.000000000           \n",
       "8  0.000000000            0.0000000              0.000000000           \n",
       "9  0.000000000            0.0000000              0.000000000           \n",
       "10 0.000000000            0.0000000              0.000000000           \n",
       "11 0.000000000            0.0000000              0.000000000           \n",
       "12 0.000000000            0.0000000              0.000000000           \n",
       "   bcn14_remove_ambiguous bcn15_remove_ambiguous bcn16_remove_ambiguous\n",
       "1  0.018881368            0.045908184            0.388201713           \n",
       "2  0.021731386            0.005988024            0.006184586           \n",
       "3  0.002850018            0.000000000            0.000000000           \n",
       "4  0.000000000            0.043912176            0.007611798           \n",
       "5  0.010331315            0.028942116            0.000000000           \n",
       "6  0.019237620            0.024950100            0.005233111           \n",
       "7  0.000000000            0.000000000            0.015699334           \n",
       "8  0.000000000            0.000000000            0.000000000           \n",
       "9  0.000000000            0.000000000            0.000000000           \n",
       "10 0.000000000            0.000000000            0.000000000           \n",
       "11 0.000000000            0.000000000            0.000000000           \n",
       "12 0.000000000            0.000000000            0.000000000           \n",
       "   bcn17_remove_ambiguous ... tp50_remove_ambiguous tp51_remove_ambiguous\n",
       "1  0.19579106             ... 0.038900415           0.317476732          \n",
       "2  0.00000000             ... 0.000000000           0.000000000          \n",
       "3  0.00000000             ... 0.000000000           0.007238883          \n",
       "4  0.06426156             ... 0.000000000           0.000000000          \n",
       "5  0.05712138             ... 0.002593361           0.000000000          \n",
       "6  0.08944006             ... 0.579875519           0.027921406          \n",
       "7  0.00000000             ... 0.000000000           0.000000000          \n",
       "8  0.00000000             ... 0.000000000           0.000000000          \n",
       "9  0.00000000             ... 0.000000000           0.000000000          \n",
       "10 0.00000000             ... 0.000000000           0.000000000          \n",
       "11 0.00000000             ... 0.000000000           0.000000000          \n",
       "12 0.00000000             ... 0.000000000           0.000000000          \n",
       "   tp52_remove_ambiguous tp53_remove_ambiguous tp54_remove_ambiguous\n",
       "1  0.037037037           0.01572150            0.8170674            \n",
       "2  0.039094650           0.01909040            0.0000000            \n",
       "3  0.000000000           0.41661988            0.0000000            \n",
       "4  0.022633745           0.00000000            0.0000000            \n",
       "5  0.000000000           0.01066816            0.0000000            \n",
       "6  0.008230453           0.03481190            0.0000000            \n",
       "7  0.008230453           0.01516002            0.0000000            \n",
       "8  0.020576132           0.00000000            0.0000000            \n",
       "9  0.000000000           0.00000000            0.0000000            \n",
       "10 0.000000000           0.00000000            0.0000000            \n",
       "11 0.000000000           0.00000000            0.0000000            \n",
       "12 0.000000000           0.00000000            0.0000000            \n",
       "   tp55_remove_ambiguous tp56_remove_ambiguous tp57_remove_ambiguous\n",
       "1  0.521523179           0.018939394           0.008865248          \n",
       "2  0.004139073           0.000000000           0.000000000          \n",
       "3  0.000000000           0.000000000           0.000000000          \n",
       "4  0.013245033           0.000000000           0.007092199          \n",
       "5  0.000000000           0.126262626           0.015957447          \n",
       "6  0.000000000           0.000000000           0.033687943          \n",
       "7  0.000000000           0.005050505           0.000000000          \n",
       "8  0.000000000           0.000000000           0.000000000          \n",
       "9  0.000000000           0.000000000           0.000000000          \n",
       "10 0.000000000           0.000000000           0.000000000          \n",
       "11 0.000000000           0.000000000           0.000000000          \n",
       "12 0.000000000           0.000000000           0.000000000          \n",
       "   tp58_remove_ambiguous tp59_remove_ambiguous\n",
       "1  0.014909478           0.33632287           \n",
       "2  0.013312034           0.05082212           \n",
       "3  0.000000000           0.00000000           \n",
       "4  0.004259851           0.00000000           \n",
       "5  0.000000000           0.01793722           \n",
       "6  0.054313099           0.01644245           \n",
       "7  0.006389776           0.00000000           \n",
       "8  0.000000000           0.02017937           \n",
       "9  0.000000000           0.00000000           \n",
       "10 0.000000000           0.00000000           \n",
       "11 0.000000000           0.00000000           \n",
       "12 0.000000000           0.00000000           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Analysis ####\n",
    "broad_alignment_results <- sw_annotation_RA_HQ\n",
    "\n",
    "# Summarise frequencies of each virus in all samples\n",
    "freq_summaries <- lapply(broad_alignment_results, function(x) x %>%\n",
    "                           group_by(antigen.species) %>%\n",
    "                           dplyr::summarise(freq_sum = sum(freq)))\n",
    "\n",
    "frequency_summaries_full <- freq_summaries %>% \n",
    "  Reduce(function(dtf1,dtf2) full_join(dtf1,dtf2,by=\"antigen.species\"), .) \n",
    "names(frequency_summaries_full) <- c(\"antigen.species\", names(freq_summaries))\n",
    "\n",
    "#frequency_summaries_full <- frequency_summaries_full %>% as.list %>% map(na.rm) %>% as.tibble # Not sure why i did this..\n",
    "#names(frequency_summaries_full) <- c(\"antigen.species\", names(broad_alignment_results))\n",
    "frequency_summaries_full[is.na(frequency_summaries_full)] <- 0\n",
    "frequency_summaries_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PuVxRxsAEIQJECAD2QBAYIECPSHqaja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\nCBIgQJAAgf8DuE2VSB1Omm8AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Coverage of TCR sequences across samples\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EBV_TCR_pcents <- frequency_summaries_full %>% filter(antigen.species == \"EBV\") %>% select(-antigen.species) %>% as_vector\n",
    "names(EBV_TCR_pcents) %<>% str_remove(\"_remove_ambiguous\")\n",
    "\n",
    "# How much of the TCR repertoir was I able to recover?\n",
    "par(bg = rgb(7, 44, 98, max = 255), \n",
    "    col.main = \"white\", col.lab = \"white\", col.axis = \"white\", \n",
    "    cex.axis = 1.2, cex.main = 1)\n",
    "hist(colSums(frequency_summaries_full %>% select(-antigen.species)), col = \"red2\", \n",
    "     main = \"Coverage of TCR sequences across samples\", \n",
    "     xlab = \"Coverage\")\n",
    "\n",
    "# Summarise Counts\n",
    "count_summaries <- lapply(broad_alignment_results, function(x) x %>% group_by(antigen.species) %>% dplyr::summarise(freq_sum = sum(count)))\n",
    "count_summaries_full <- count_summaries %>% \n",
    "  Reduce(function(dtf1,dtf2) full_join(dtf1,dtf2,by=\"antigen.species\"), .) \n",
    "names(count_summaries_full) <- c(\"antigen.species\", names(count_summaries))\n",
    "count_summaries_full[is.na(count_summaries_full)] <- 0 # Change NAs to 0s\n",
    "\n",
    "EBV_TCR_counts <- count_summaries_full %>% filter(antigen.species == \"EBV\") %>% select(-antigen.species) %>% as_vector\n",
    "names(EBV_TCR_counts) %<>% str_remove(\"_remove_ambiguous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  sample = col_character(),\n",
      "  subtype = col_character(),\n",
      "  WES = col_character(),\n",
      "  EBV_K5 = col_character(),\n",
      "  EBV_K4 = col_character(),\n",
      "  tot_viral_load_group = col_character(),\n",
      "  lyt_lat_cmv = col_character(),\n",
      "  lyt_lab_ebv = col_character(),\n",
      "  lyt_lat_hhv6b = col_character(),\n",
      "  lyt_lat_kshv = col_character()\n",
      ")\n"
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error in rownames(VRPMHR): object 'VRPMHR' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in rownames(VRPMHR): object 'VRPMHR' not found\nTraceback:\n",
      "1. tibble(sample = rownames(VRPMHR)) %>% mutate(EBV = VRPMHR[sample, \n .     ]$EBV) %>% mutate(TCR_pcent = EBV_TCR_pcents[sample]) %>% \n .     mutate(TCR_count = EBV_TCR_counts[sample]) %>% mutate(lgEBV = log(EBV + \n .     1)) %>% mutate(lgTCR_count = log(TCR_count + 1)) %>% mutate(subtype = subtypes[sample])",
      "2. eval(lhs, parent, parent)",
      "3. eval(lhs, parent, parent)",
      "4. tibble(sample = rownames(VRPMHR))",
      "5. as_tibble(lst_quos(xs, expand = TRUE))",
      "6. lst_quos(xs, expand = TRUE)",
      "7. eval_tidy(xs[[i]], unique_output)",
      "8. rownames(VRPMHR)"
     ]
    }
   ],
   "source": [
    "# Load in Patient Metadata\n",
    "setwd(\"C:/Users/jee12/Dropbox/Rabadan_Lab/tcr/\")\n",
    "metadata <- read_tsv(\"metadata.tsv\")\n",
    "\n",
    "subtypes <- metadata$subtype\n",
    "names(subtypes) <- metadata$sample\n",
    "\n",
    "EBV_TCR <- tibble(sample = rownames(VRPMHR)) %>% \n",
    "  mutate(EBV = VRPMHR[sample,]$EBV) %>%\n",
    "  mutate(TCR_pcent = EBV_TCR_pcents[sample]) %>%\n",
    "  mutate(TCR_count = EBV_TCR_counts[sample]) %>%\n",
    "  mutate(lgEBV = log(EBV + 1)) %>% \n",
    "  mutate(lgTCR_count = log(TCR_count + 1)) %>%\n",
    "  mutate(subtype = subtypes[sample])\n",
    "\n",
    "t <- ggplot(EBV_TCR, aes(y = lgTCR_count, x = lgEBV, colour = subtype)) +\n",
    "  geom_point(size = 3) +\n",
    "  theme(axis.text.x=element_text(angle=90,size=12,colour=\"black\"), \n",
    "        panel.background = element_rect(fill = 'gray90', colour=\"black\"),\n",
    "        panel.grid.major = element_line(colour = \"white\"),\n",
    "        axis.text.y=element_text(size=12,colour=\"black\"), \n",
    "        axis.title.x=element_text(size=15,colour=\"black\"), \n",
    "        axis.title.y=element_text(size=15,colour=\"black\"),\n",
    "        legend.title=element_text(size=15,colour=\"black\"),\n",
    "        legend.text=element_text(size=15,colour=\"black\"),\n",
    "        legend.position=\"left\") +\n",
    "  ggtitle(\"Observed EBV and TCR comparison\")\n",
    "\n",
    "t\n",
    "\n",
    "EBV_TCR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x <- EBV_TCR %>% filter(subtype %in% c(\"ALCL\", \"AITL\")) %>% select(TCR_pcent, subtype)\n",
    "t.test(TCR_pcent ~ subtype, x)#### Summary of all Found Epitope Species ####\n",
    "epitope_summary <- frequency_summaries_full %>% data.frame(row.names = 1) %>% t %>% colSums\n",
    "epitope_summary <- tibble(virus = names(epitope_summary), total = epitope_summary) %>% arrange(desc(total))\n",
    "epitope_summary$virus <- with(epitope_summary, factor(epitope_summary$virus, epitope_summary$virus, ordered=T))\n",
    "epitope_summary %<>% mutate(pcent = total / sum(total))\n",
    "\n",
    "db_virus_totals <- vdjdb_human_beta$antigen.species %>% table %>% as.tibble\n",
    "names(db_virus_totals) <- c(\"virus\", \"db_totals\")\n",
    "\n",
    "epitope_summary %<>% left_join(db_virus_totals)\n",
    "epitope_summary %<>% mutate(db_pcent = db_totals / sum(db_totals) %>% as.numeric())\n",
    "\n",
    "# Plot the barplot\n",
    "par(bg = rgb(7, 44, 98, max = 255), \n",
    "    col.main = \"white\", col.lab = \"white\", col.axis = \"white\", \n",
    "    cex.axis = .8, cex.main = 1, fg = \"white\", mar = c(7,3,2,2))\n",
    "barplot(epitope_summary %>% select(pcent, db_pcent) %>% as.matrix %>% t,\n",
    "        beside = TRUE, col = c(\"firebrick\", \"yellow2\"), \n",
    "        main = \"TCR recovery vs. Database\",\n",
    "        names.arg = epitope_summary$virus, \n",
    "        legend.text = c(\"Observed\", \"Expected\"),\n",
    "        las = 2)\n",
    "\n",
    "ggplot(epitope_summary, aes(x = virus, y = total)) +\n",
    "  geom_bar(stat=\"identity\", fill=\"steelblue\")+\n",
    "  geom_text(aes(label=total), vjust=1.6, color=\"white\", size=3.5)+\n",
    "  theme_minimal() +\n",
    "  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + \n",
    "  ggtitle(\"Summary of Identified Epitope Species Across Samples\")\n",
    "\n",
    "# How does this compare to normal, non PTCL samples?\n",
    "setwd(\"parsed/\")\n",
    "control_tcr_tables <- lapply(list.files(), function(x) x %>%\n",
    "                               read_tsv %>% \n",
    "                               dplyr::rename(cdr3aa = CDR3.amino.acid.sequence) %>%\n",
    "                               mutate(freq = Read.count / sum(Read.count)) %>%\n",
    "                               dplyr::rename(count = Read.count))\n",
    "names(control_tcr_tables) <- tools::file_path_sans_ext(list.files())\n",
    "\n",
    "control_alignment <- batch_vdjdb_align(control_tcr_tables, vdjdb_human, strand = \"beta\", retain_extras = \"remove_ambiguous\")\n",
    "antigen_summary <- control_alignment %>% map(\"antigen.species\") %>% unlist %>% table %>% as.tibble\n",
    "names(antigen_summary) <- c(\"virus\", \"control_total\")\n",
    "epitope_summary %<>% left_join(antigen_summary)\n",
    "epitope_summary[is.na(epitope_summary)] <- 0\n",
    "epitope_summary %<>% mutate(control_pcent = control_total / sum(control_total))\n",
    "\n",
    "barplot(epitope_summary %>% select(pcent, control_pcent, db_pcent) %>% as.matrix %>% t,\n",
    "        beside = TRUE, col = c(\"firebrick\", \"blue3\", \"yellow2\"), \n",
    "        main = \"TCR recovery (Control Dataset) vs. Database\",\n",
    "        names.arg = epitope_summary$virus, \n",
    "        legend.text = c(\"Observed\", \"Observed (control)\", \"Expected\"),\n",
    "        las = 2)\n",
    "\n",
    "\n",
    "#### Boxplot of TCR counts per sample ####\n",
    "EBV_TCR_o <- EBV_TCR %>% mutate(subtype = subtype %>% factor(levels = c(\"ALCL\", \"AITL\", \"NK\", \"NOS\")))\n",
    "EBV_TCR_o <- EBV_TCR_o %>% gather(key = measure, lgCount, lgEBV,lgTCR_count)\n",
    "\n",
    "ggplot(EBV_TCR_o, aes(x = measure, y = lgCount, fill = measure)) +\n",
    "  geom_boxplot() + facet_wrap(\"subtype\", nrow = 1) +\n",
    "  theme(axis.text.x = element_blank()) +\n",
    "  scale_fill_discrete(labels = c(\"log EBV reads\", \"log EBV epitope\"),\n",
    "                      name = \"Measure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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