impute_missing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4c9aa46",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(data.table)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "469d45ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in fread(\"pred_no_na.txt\"):\n",
      "“Detected 60 column names but the data has 61 columns (i.e. invalid file). Added 1 extra default column name for the first column which is guessed to be row names or an index. Use setnames() afterwards if this guess is not correct, or fix the file write command that created the file to create a valid file.”\n"
     ]
    },
    {
     "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>6975</li><li>61</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 6975\n",
       "\\item 61\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 6975\n",
       "2. 61\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 6975   61"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 61</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>X786O_NCI60</th><th scope=col>A498_NCI60</th><th scope=col>A549_NCI60</th><th scope=col>ACHN_NCI60</th><th scope=col>BT549_NCI60</th><th scope=col>CAKI1_NCI60</th><th scope=col>CCRFCEM_NCI60</th><th scope=col>COLO205_NCI60</th><th scope=col>DU145_NCI60</th><th scope=col>⋯</th><th scope=col>SNB19_NCI60</th><th scope=col>SNB75_NCI60</th><th scope=col>SR_NCI60</th><th scope=col>SW620_NCI60</th><th scope=col>T47D_NCI60</th><th scope=col>TK10_NCI60</th><th scope=col>U031_NCI60</th><th scope=col>U251_NCI60</th><th scope=col>UACC257_NCI60</th><th scope=col>UACC62_NCI60</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;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37</td><td>23.82530</td><td>24.89250</td><td>23.46536</td><td>24.26511</td><td>24.99417</td><td>25.46808</td><td>23.91395</td><td>25.65536</td><td>23.57569</td><td>⋯</td><td>25.54598</td><td>26.19636</td><td>22.81692</td><td>24.31618</td><td>25.50504</td><td>25.56392</td><td>23.95363</td><td>25.12832</td><td>25.17307</td><td>25.84836</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2                                  </td><td>26.95893</td><td>28.67702</td><td>29.12149</td><td>29.22591</td><td>24.08905</td><td>27.47051</td><td>22.85227</td><td>32.45874</td><td>28.00763</td><td>⋯</td><td>25.51139</td><td>25.44076</td><td>26.48217</td><td>28.93228</td><td>29.98096</td><td>28.86376</td><td>26.97917</td><td>26.17529</td><td>25.60923</td><td>25.94243</td></tr>\n",
       "\t<tr><td>A0AVF1;A0AVF1-2;A0AVF1-3                         </td><td>23.77847</td><td>24.47958</td><td>23.40375</td><td>26.09918</td><td>24.29845</td><td>24.66992</td><td>25.21604</td><td>24.20237</td><td>24.03991</td><td>⋯</td><td>25.57966</td><td>25.25518</td><td>26.65104</td><td>25.98517</td><td>23.99013</td><td>24.89343</td><td>25.10870</td><td>25.50019</td><td>24.60681</td><td>24.27626</td></tr>\n",
       "\t<tr><td>A0AVT1;A0AVT1-2;A0AVT1-3;A0AVT1-4                </td><td>31.90762</td><td>31.70965</td><td>31.41251</td><td>32.39082</td><td>31.29363</td><td>32.02402</td><td>31.57607</td><td>31.04223</td><td>31.72583</td><td>⋯</td><td>31.48502</td><td>31.35044</td><td>31.52994</td><td>31.32385</td><td>31.54180</td><td>32.04987</td><td>31.81649</td><td>31.61414</td><td>30.10508</td><td>30.35830</td></tr>\n",
       "\t<tr><td>A0FGR8-6;A0FGR8                                  </td><td>29.53564</td><td>26.32843</td><td>26.11850</td><td>24.78599</td><td>26.70081</td><td>25.57264</td><td>27.98287</td><td>27.27502</td><td>26.03777</td><td>⋯</td><td>26.92745</td><td>26.57684</td><td>28.59774</td><td>26.38374</td><td>25.82347</td><td>25.27722</td><td>26.00291</td><td>25.33322</td><td>26.35553</td><td>24.34689</td></tr>\n",
       "\t<tr><td>A0JLT2;A0JLT2-2                                  </td><td>24.02672</td><td>25.56828</td><td>26.18665</td><td>24.93679</td><td>25.16909</td><td>25.26828</td><td>26.73343</td><td>25.75000</td><td>25.72822</td><td>⋯</td><td>25.08475</td><td>24.77894</td><td>25.33499</td><td>25.66370</td><td>26.69872</td><td>24.44849</td><td>25.86691</td><td>25.13800</td><td>25.44573</td><td>25.14074</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 61\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " featureID & X786O\\_NCI60 & A498\\_NCI60 & A549\\_NCI60 & ACHN\\_NCI60 & BT549\\_NCI60 & CAKI1\\_NCI60 & CCRFCEM\\_NCI60 & COLO205\\_NCI60 & DU145\\_NCI60 & ⋯ & SNB19\\_NCI60 & SNB75\\_NCI60 & SR\\_NCI60 & SW620\\_NCI60 & T47D\\_NCI60 & TK10\\_NCI60 & U031\\_NCI60 & U251\\_NCI60 & UACC257\\_NCI60 & UACC62\\_NCI60\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 & 23.82530 & 24.89250 & 23.46536 & 24.26511 & 24.99417 & 25.46808 & 23.91395 & 25.65536 & 23.57569 & ⋯ & 25.54598 & 26.19636 & 22.81692 & 24.31618 & 25.50504 & 25.56392 & 23.95363 & 25.12832 & 25.17307 & 25.84836\\\\\n",
       "\t A0AV96;A0AV96-2                                   & 26.95893 & 28.67702 & 29.12149 & 29.22591 & 24.08905 & 27.47051 & 22.85227 & 32.45874 & 28.00763 & ⋯ & 25.51139 & 25.44076 & 26.48217 & 28.93228 & 29.98096 & 28.86376 & 26.97917 & 26.17529 & 25.60923 & 25.94243\\\\\n",
       "\t A0AVF1;A0AVF1-2;A0AVF1-3                          & 23.77847 & 24.47958 & 23.40375 & 26.09918 & 24.29845 & 24.66992 & 25.21604 & 24.20237 & 24.03991 & ⋯ & 25.57966 & 25.25518 & 26.65104 & 25.98517 & 23.99013 & 24.89343 & 25.10870 & 25.50019 & 24.60681 & 24.27626\\\\\n",
       "\t A0AVT1;A0AVT1-2;A0AVT1-3;A0AVT1-4                 & 31.90762 & 31.70965 & 31.41251 & 32.39082 & 31.29363 & 32.02402 & 31.57607 & 31.04223 & 31.72583 & ⋯ & 31.48502 & 31.35044 & 31.52994 & 31.32385 & 31.54180 & 32.04987 & 31.81649 & 31.61414 & 30.10508 & 30.35830\\\\\n",
       "\t A0FGR8-6;A0FGR8                                   & 29.53564 & 26.32843 & 26.11850 & 24.78599 & 26.70081 & 25.57264 & 27.98287 & 27.27502 & 26.03777 & ⋯ & 26.92745 & 26.57684 & 28.59774 & 26.38374 & 25.82347 & 25.27722 & 26.00291 & 25.33322 & 26.35553 & 24.34689\\\\\n",
       "\t A0JLT2;A0JLT2-2                                   & 24.02672 & 25.56828 & 26.18665 & 24.93679 & 25.16909 & 25.26828 & 26.73343 & 25.75000 & 25.72822 & ⋯ & 25.08475 & 24.77894 & 25.33499 & 25.66370 & 26.69872 & 24.44849 & 25.86691 & 25.13800 & 25.44573 & 25.14074\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 61\n",
       "\n",
       "| featureID &lt;chr&gt; | X786O_NCI60 &lt;dbl&gt; | A498_NCI60 &lt;dbl&gt; | A549_NCI60 &lt;dbl&gt; | ACHN_NCI60 &lt;dbl&gt; | BT549_NCI60 &lt;dbl&gt; | CAKI1_NCI60 &lt;dbl&gt; | CCRFCEM_NCI60 &lt;dbl&gt; | COLO205_NCI60 &lt;dbl&gt; | DU145_NCI60 &lt;dbl&gt; | ⋯ ⋯ | SNB19_NCI60 &lt;dbl&gt; | SNB75_NCI60 &lt;dbl&gt; | SR_NCI60 &lt;dbl&gt; | SW620_NCI60 &lt;dbl&gt; | T47D_NCI60 &lt;dbl&gt; | TK10_NCI60 &lt;dbl&gt; | U031_NCI60 &lt;dbl&gt; | U251_NCI60 &lt;dbl&gt; | UACC257_NCI60 &lt;dbl&gt; | UACC62_NCI60 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 | 23.82530 | 24.89250 | 23.46536 | 24.26511 | 24.99417 | 25.46808 | 23.91395 | 25.65536 | 23.57569 | ⋯ | 25.54598 | 26.19636 | 22.81692 | 24.31618 | 25.50504 | 25.56392 | 23.95363 | 25.12832 | 25.17307 | 25.84836 |\n",
       "| A0AV96;A0AV96-2                                   | 26.95893 | 28.67702 | 29.12149 | 29.22591 | 24.08905 | 27.47051 | 22.85227 | 32.45874 | 28.00763 | ⋯ | 25.51139 | 25.44076 | 26.48217 | 28.93228 | 29.98096 | 28.86376 | 26.97917 | 26.17529 | 25.60923 | 25.94243 |\n",
       "| A0AVF1;A0AVF1-2;A0AVF1-3                          | 23.77847 | 24.47958 | 23.40375 | 26.09918 | 24.29845 | 24.66992 | 25.21604 | 24.20237 | 24.03991 | ⋯ | 25.57966 | 25.25518 | 26.65104 | 25.98517 | 23.99013 | 24.89343 | 25.10870 | 25.50019 | 24.60681 | 24.27626 |\n",
       "| A0AVT1;A0AVT1-2;A0AVT1-3;A0AVT1-4                 | 31.90762 | 31.70965 | 31.41251 | 32.39082 | 31.29363 | 32.02402 | 31.57607 | 31.04223 | 31.72583 | ⋯ | 31.48502 | 31.35044 | 31.52994 | 31.32385 | 31.54180 | 32.04987 | 31.81649 | 31.61414 | 30.10508 | 30.35830 |\n",
       "| A0FGR8-6;A0FGR8                                   | 29.53564 | 26.32843 | 26.11850 | 24.78599 | 26.70081 | 25.57264 | 27.98287 | 27.27502 | 26.03777 | ⋯ | 26.92745 | 26.57684 | 28.59774 | 26.38374 | 25.82347 | 25.27722 | 26.00291 | 25.33322 | 26.35553 | 24.34689 |\n",
       "| A0JLT2;A0JLT2-2                                   | 24.02672 | 25.56828 | 26.18665 | 24.93679 | 25.16909 | 25.26828 | 26.73343 | 25.75000 | 25.72822 | ⋯ | 25.08475 | 24.77894 | 25.33499 | 25.66370 | 26.69872 | 24.44849 | 25.86691 | 25.13800 | 25.44573 | 25.14074 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID                                         X786O_NCI60 A498_NCI60\n",
       "1 P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 23.82530    24.89250  \n",
       "2 A0AV96;A0AV96-2                                   26.95893    28.67702  \n",
       "3 A0AVF1;A0AVF1-2;A0AVF1-3                          23.77847    24.47958  \n",
       "4 A0AVT1;A0AVT1-2;A0AVT1-3;A0AVT1-4                 31.90762    31.70965  \n",
       "5 A0FGR8-6;A0FGR8                                   29.53564    26.32843  \n",
       "6 A0JLT2;A0JLT2-2                                   24.02672    25.56828  \n",
       "  A549_NCI60 ACHN_NCI60 BT549_NCI60 CAKI1_NCI60 CCRFCEM_NCI60 COLO205_NCI60\n",
       "1 23.46536   24.26511   24.99417    25.46808    23.91395      25.65536     \n",
       "2 29.12149   29.22591   24.08905    27.47051    22.85227      32.45874     \n",
       "3 23.40375   26.09918   24.29845    24.66992    25.21604      24.20237     \n",
       "4 31.41251   32.39082   31.29363    32.02402    31.57607      31.04223     \n",
       "5 26.11850   24.78599   26.70081    25.57264    27.98287      27.27502     \n",
       "6 26.18665   24.93679   25.16909    25.26828    26.73343      25.75000     \n",
       "  DU145_NCI60 ⋯ SNB19_NCI60 SNB75_NCI60 SR_NCI60 SW620_NCI60 T47D_NCI60\n",
       "1 23.57569    ⋯ 25.54598    26.19636    22.81692 24.31618    25.50504  \n",
       "2 28.00763    ⋯ 25.51139    25.44076    26.48217 28.93228    29.98096  \n",
       "3 24.03991    ⋯ 25.57966    25.25518    26.65104 25.98517    23.99013  \n",
       "4 31.72583    ⋯ 31.48502    31.35044    31.52994 31.32385    31.54180  \n",
       "5 26.03777    ⋯ 26.92745    26.57684    28.59774 26.38374    25.82347  \n",
       "6 25.72822    ⋯ 25.08475    24.77894    25.33499 25.66370    26.69872  \n",
       "  TK10_NCI60 U031_NCI60 U251_NCI60 UACC257_NCI60 UACC62_NCI60\n",
       "1 25.56392   23.95363   25.12832   25.17307      25.84836    \n",
       "2 28.86376   26.97917   26.17529   25.60923      25.94243    \n",
       "3 24.89343   25.10870   25.50019   24.60681      24.27626    \n",
       "4 32.04987   31.81649   31.61414   30.10508      30.35830    \n",
       "5 25.27722   26.00291   25.33322   26.35553      24.34689    \n",
       "6 24.44849   25.86691   25.13800   25.44573      25.14074    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_outrider <- fread(\"pred_no_na.txt\")\n",
    "colnames(result_outrider)[1] <- \"featureID\"\n",
    "dim(result_outrider)\n",
    "head(result_outrider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c6ae5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 61</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X786O_NCI60</th><th scope=col>A498_NCI60</th><th scope=col>A549_NCI60</th><th scope=col>ACHN_NCI60</th><th scope=col>BT549_NCI60</th><th scope=col>CAKI1_NCI60</th><th scope=col>CCRFCEM_NCI60</th><th scope=col>COLO205_NCI60</th><th scope=col>DU145_NCI60</th><th scope=col>EKVX_NCI60</th><th scope=col>⋯</th><th scope=col>SNB75_NCI60</th><th scope=col>SR_NCI60</th><th scope=col>SW620_NCI60</th><th scope=col>T47D_NCI60</th><th scope=col>TK10_NCI60</th><th scope=col>U031_NCI60</th><th scope=col>U251_NCI60</th><th scope=col>UACC257_NCI60</th><th scope=col>UACC62_NCI60</th><th scope=col>featureID</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37</th><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>⋯</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37</td></tr>\n",
       "\t<tr><th scope=row>A0A0B4J2F0</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>A0A0B4J2F0                                       </td></tr>\n",
       "\t<tr><th scope=row>A0A0C4DH73</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>A0A0C4DH73                                       </td></tr>\n",
       "\t<tr><th scope=row>A0A5B9;P01850</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>⋯</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>A0A5B9;P01850                                    </td></tr>\n",
       "\t<tr><th scope=row>A0AV96;A0AV96-2</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>⋯</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>A0AV96;A0AV96-2                                  </td></tr>\n",
       "\t<tr><th scope=row>A0AVF1;A0AVF1-2;A0AVF1-3</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>⋯</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>A0AVF1;A0AVF1-2;A0AVF1-3                         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 61\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & X786O\\_NCI60 & A498\\_NCI60 & A549\\_NCI60 & ACHN\\_NCI60 & BT549\\_NCI60 & CAKI1\\_NCI60 & CCRFCEM\\_NCI60 & COLO205\\_NCI60 & DU145\\_NCI60 & EKVX\\_NCI60 & ⋯ & SNB75\\_NCI60 & SR\\_NCI60 & SW620\\_NCI60 & T47D\\_NCI60 & TK10\\_NCI60 & U031\\_NCI60 & U251\\_NCI60 & UACC257\\_NCI60 & UACC62\\_NCI60 & featureID\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\tP0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & ⋯ & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37\\\\\n",
       "\tA0A0B4J2F0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & A0A0B4J2F0                                       \\\\\n",
       "\tA0A0C4DH73 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & A0A0C4DH73                                       \\\\\n",
       "\tA0A5B9;P01850 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & ⋯ & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & A0A5B9;P01850                                    \\\\\n",
       "\tA0AV96;A0AV96-2 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & ⋯ & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & A0AV96;A0AV96-2                                  \\\\\n",
       "\tA0AVF1;A0AVF1-2;A0AVF1-3 & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & ⋯ & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & A0AVF1;A0AVF1-2;A0AVF1-3                         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 61\n",
       "\n",
       "| <!--/--> | X786O_NCI60 &lt;dbl&gt; | A498_NCI60 &lt;dbl&gt; | A549_NCI60 &lt;dbl&gt; | ACHN_NCI60 &lt;dbl&gt; | BT549_NCI60 &lt;dbl&gt; | CAKI1_NCI60 &lt;dbl&gt; | CCRFCEM_NCI60 &lt;dbl&gt; | COLO205_NCI60 &lt;dbl&gt; | DU145_NCI60 &lt;dbl&gt; | EKVX_NCI60 &lt;dbl&gt; | ⋯ ⋯ | SNB75_NCI60 &lt;dbl&gt; | SR_NCI60 &lt;dbl&gt; | SW620_NCI60 &lt;dbl&gt; | T47D_NCI60 &lt;dbl&gt; | TK10_NCI60 &lt;dbl&gt; | U031_NCI60 &lt;dbl&gt; | U251_NCI60 &lt;dbl&gt; | UACC257_NCI60 &lt;dbl&gt; | UACC62_NCI60 &lt;dbl&gt; | featureID &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | ⋯ | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 |\n",
       "| A0A0B4J2F0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | A0A0B4J2F0                                        |\n",
       "| A0A0C4DH73 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | A0A0C4DH73                                        |\n",
       "| A0A5B9;P01850 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | ⋯ | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | A0A5B9;P01850                                     |\n",
       "| A0AV96;A0AV96-2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ⋯ | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | A0AV96;A0AV96-2                                   |\n",
       "| A0AVF1;A0AVF1-2;A0AVF1-3 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | ⋯ | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | A0AVF1;A0AVF1-2;A0AVF1-3                          |\n",
       "\n"
      ],
      "text/plain": [
       "                                                  X786O_NCI60 A498_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1           1         \n",
       "A0A0B4J2F0                                        0           0         \n",
       "A0A0C4DH73                                        0           0         \n",
       "A0A5B9;P01850                                     0           0         \n",
       "A0AV96;A0AV96-2                                   1           1         \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1           1         \n",
       "                                                  A549_NCI60 ACHN_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1          1         \n",
       "A0A0B4J2F0                                        0          0         \n",
       "A0A0C4DH73                                        0          0         \n",
       "A0A5B9;P01850                                     0          0         \n",
       "A0AV96;A0AV96-2                                   1          1         \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1          1         \n",
       "                                                  BT549_NCI60 CAKI1_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 0           1          \n",
       "A0A0B4J2F0                                        0           0          \n",
       "A0A0C4DH73                                        0           0          \n",
       "A0A5B9;P01850                                     0           0          \n",
       "A0AV96;A0AV96-2                                   1           1          \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1           0          \n",
       "                                                  CCRFCEM_NCI60 COLO205_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1             1            \n",
       "A0A0B4J2F0                                        0             0            \n",
       "A0A0C4DH73                                        0             0            \n",
       "A0A5B9;P01850                                     1             0            \n",
       "A0AV96;A0AV96-2                                   1             1            \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1             1            \n",
       "                                                  DU145_NCI60 EKVX_NCI60 ⋯\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1           1          ⋯\n",
       "A0A0B4J2F0                                        0           1          ⋯\n",
       "A0A0C4DH73                                        0           0          ⋯\n",
       "A0A5B9;P01850                                     1           0          ⋯\n",
       "A0AV96;A0AV96-2                                   1           1          ⋯\n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1           1          ⋯\n",
       "                                                  SNB75_NCI60 SR_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1           1       \n",
       "A0A0B4J2F0                                        0           0       \n",
       "A0A0C4DH73                                        0           0       \n",
       "A0A5B9;P01850                                     0           1       \n",
       "A0AV96;A0AV96-2                                   1           1       \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1           0       \n",
       "                                                  SW620_NCI60 T47D_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1           1         \n",
       "A0A0B4J2F0                                        0           0         \n",
       "A0A0C4DH73                                        0           0         \n",
       "A0A5B9;P01850                                     0           0         \n",
       "A0AV96;A0AV96-2                                   1           1         \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1           1         \n",
       "                                                  TK10_NCI60 U031_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1          0         \n",
       "A0A0B4J2F0                                        0          0         \n",
       "A0A0C4DH73                                        0          0         \n",
       "A0A5B9;P01850                                     0          0         \n",
       "A0AV96;A0AV96-2                                   1          1         \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1          1         \n",
       "                                                  U251_NCI60 UACC257_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1          1            \n",
       "A0A0B4J2F0                                        0          0            \n",
       "A0A0C4DH73                                        0          0            \n",
       "A0A5B9;P01850                                     1          0            \n",
       "A0AV96;A0AV96-2                                   1          1            \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1          1            \n",
       "                                                  UACC62_NCI60\n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 1           \n",
       "A0A0B4J2F0                                        0           \n",
       "A0A0C4DH73                                        0           \n",
       "A0A5B9;P01850                                     1           \n",
       "A0AV96;A0AV96-2                                   0           \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          1           \n",
       "                                                  featureID                                        \n",
       "P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37\n",
       "A0A0B4J2F0                                        A0A0B4J2F0                                       \n",
       "A0A0C4DH73                                        A0A0C4DH73                                       \n",
       "A0A5B9;P01850                                     A0A5B9;P01850                                    \n",
       "A0AV96;A0AV96-2                                   A0AV96;A0AV96-2                                  \n",
       "A0AVF1;A0AVF1-2;A0AVF1-3                          A0AVF1;A0AVF1-2;A0AVF1-3                         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "10825"
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       "10825"
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       "10825"
      ],
      "text/plain": [
       "[1] 10825"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "measured <- read.table(\"prot_int.csv\")\n",
    "measured[measured != 0] <- 1\n",
    "measured$featureID <- rownames(measured)\n",
    "head(measured)\n",
    "nrow(measured)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e15d2276",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in melt.data.table(result_outrider):\n",
      "“id.vars and measure.vars are internally guessed when both are 'NULL'. All non-numeric/integer/logical type columns are considered id.vars, which in this case are columns [featureID, ...]. Consider providing at least one of 'id' or 'measure' vars in future.”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>protein_intensity</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37</td><td>X786O_NCI60</td><td>23.82530</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2                                  </td><td>X786O_NCI60</td><td>26.95893</td></tr>\n",
       "\t<tr><td>A0AVF1;A0AVF1-2;A0AVF1-3                         </td><td>X786O_NCI60</td><td>23.77847</td></tr>\n",
       "\t<tr><td>A0AVT1;A0AVT1-2;A0AVT1-3;A0AVT1-4                </td><td>X786O_NCI60</td><td>31.90762</td></tr>\n",
       "\t<tr><td>A0FGR8-6;A0FGR8                                  </td><td>X786O_NCI60</td><td>29.53564</td></tr>\n",
       "\t<tr><td>A0JLT2;A0JLT2-2                                  </td><td>X786O_NCI60</td><td>24.02672</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " featureID & sampleID & protein\\_intensity\\\\\n",
       " <chr> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 & X786O\\_NCI60 & 23.82530\\\\\n",
       "\t A0AV96;A0AV96-2                                   & X786O\\_NCI60 & 26.95893\\\\\n",
       "\t A0AVF1;A0AVF1-2;A0AVF1-3                          & X786O\\_NCI60 & 23.77847\\\\\n",
       "\t A0AVT1;A0AVT1-2;A0AVT1-3;A0AVT1-4                 & X786O\\_NCI60 & 31.90762\\\\\n",
       "\t A0FGR8-6;A0FGR8                                   & X786O\\_NCI60 & 29.53564\\\\\n",
       "\t A0JLT2;A0JLT2-2                                   & X786O\\_NCI60 & 24.02672\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 3\n",
       "\n",
       "| featureID &lt;chr&gt; | sampleID &lt;fct&gt; | protein_intensity &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 | X786O_NCI60 | 23.82530 |\n",
       "| A0AV96;A0AV96-2                                   | X786O_NCI60 | 26.95893 |\n",
       "| A0AVF1;A0AVF1-2;A0AVF1-3                          | X786O_NCI60 | 23.77847 |\n",
       "| A0AVT1;A0AVT1-2;A0AVT1-3;A0AVT1-4                 | X786O_NCI60 | 31.90762 |\n",
       "| A0FGR8-6;A0FGR8                                   | X786O_NCI60 | 29.53564 |\n",
       "| A0JLT2;A0JLT2-2                                   | X786O_NCI60 | 24.02672 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID                                         sampleID   \n",
       "1 P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 X786O_NCI60\n",
       "2 A0AV96;A0AV96-2                                   X786O_NCI60\n",
       "3 A0AVF1;A0AVF1-2;A0AVF1-3                          X786O_NCI60\n",
       "4 A0AVT1;A0AVT1-2;A0AVT1-3;A0AVT1-4                 X786O_NCI60\n",
       "5 A0FGR8-6;A0FGR8                                   X786O_NCI60\n",
       "6 A0JLT2;A0JLT2-2                                   X786O_NCI60\n",
       "  protein_intensity\n",
       "1 23.82530         \n",
       "2 26.95893         \n",
       "3 23.77847         \n",
       "4 31.90762         \n",
       "5 29.53564         \n",
       "6 24.02672         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "418500"
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       "418500"
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      "text/markdown": [
       "418500"
      ],
      "text/plain": [
       "[1] 418500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prot_int_list <- melt(result_outrider)\n",
    "colnames(prot_int_list)[2:3] <- c(\"sampleID\",\"protein_intensity\")\n",
    "head(prot_int_list)\n",
    "nrow(prot_int_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9dec774",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in melt(measured):\n",
      "“The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(measured). In the next version, this warning will become an error.”\n",
      "Using featureID as id variables\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>measured</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37</td><td>X786O_NCI60</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>A0A0B4J2F0                                       </td><td>X786O_NCI60</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>A0A0C4DH73                                       </td><td>X786O_NCI60</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>A0A5B9;P01850                                    </td><td>X786O_NCI60</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>A0AV96;A0AV96-2                                  </td><td>X786O_NCI60</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>A0AVF1;A0AVF1-2;A0AVF1-3                         </td><td>X786O_NCI60</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & featureID & sampleID & measured\\\\\n",
       "  & <chr> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 & X786O\\_NCI60 & 1\\\\\n",
       "\t2 & A0A0B4J2F0                                        & X786O\\_NCI60 & 0\\\\\n",
       "\t3 & A0A0C4DH73                                        & X786O\\_NCI60 & 0\\\\\n",
       "\t4 & A0A5B9;P01850                                     & X786O\\_NCI60 & 0\\\\\n",
       "\t5 & A0AV96;A0AV96-2                                   & X786O\\_NCI60 & 1\\\\\n",
       "\t6 & A0AVF1;A0AVF1-2;A0AVF1-3                          & X786O\\_NCI60 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | featureID &lt;chr&gt; | sampleID &lt;fct&gt; | measured &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 | X786O_NCI60 | 1 |\n",
       "| 2 | A0A0B4J2F0                                        | X786O_NCI60 | 0 |\n",
       "| 3 | A0A0C4DH73                                        | X786O_NCI60 | 0 |\n",
       "| 4 | A0A5B9;P01850                                     | X786O_NCI60 | 0 |\n",
       "| 5 | A0AV96;A0AV96-2                                   | X786O_NCI60 | 1 |\n",
       "| 6 | A0AVF1;A0AVF1-2;A0AVF1-3                          | X786O_NCI60 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID                                         sampleID    measured\n",
       "1 P0DN26;A0A075B759;Q9Y536;F5H284;A0A0B4J2A2;P0DN37 X786O_NCI60 1       \n",
       "2 A0A0B4J2F0                                        X786O_NCI60 0       \n",
       "3 A0A0C4DH73                                        X786O_NCI60 0       \n",
       "4 A0A5B9;P01850                                     X786O_NCI60 0       \n",
       "5 A0AV96;A0AV96-2                                   X786O_NCI60 1       \n",
       "6 A0AVF1;A0AVF1-2;A0AVF1-3                          X786O_NCI60 1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "649500"
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       "649500"
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       "[1] 649500"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "measured_list <- melt(measured)\n",
    "colnames(measured_list)[2:3] <- c(\"sampleID\",\"measured\")\n",
    "head(measured_list)\n",
    "nrow(measured_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09e01037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "418500"
      ],
      "text/latex": [
       "418500"
      ],
      "text/markdown": [
       "418500"
      ],
      "text/plain": [
       "[1] 418500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>protein_intensity</th><th scope=col>measured</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&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>A0AV96;A0AV96-2</td><td>X786O_NCI60</td><td>26.95893</td><td>1</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>A498_NCI60 </td><td>28.67702</td><td>1</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>A549_NCI60 </td><td>29.12149</td><td>1</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>ACHN_NCI60 </td><td>29.22591</td><td>1</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>BT549_NCI60</td><td>24.08905</td><td>1</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>CAKI1_NCI60</td><td>27.47051</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " featureID & sampleID & protein\\_intensity & measured\\\\\n",
       " <chr> & <fct> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t A0AV96;A0AV96-2 & X786O\\_NCI60 & 26.95893 & 1\\\\\n",
       "\t A0AV96;A0AV96-2 & A498\\_NCI60  & 28.67702 & 1\\\\\n",
       "\t A0AV96;A0AV96-2 & A549\\_NCI60  & 29.12149 & 1\\\\\n",
       "\t A0AV96;A0AV96-2 & ACHN\\_NCI60  & 29.22591 & 1\\\\\n",
       "\t A0AV96;A0AV96-2 & BT549\\_NCI60 & 24.08905 & 1\\\\\n",
       "\t A0AV96;A0AV96-2 & CAKI1\\_NCI60 & 27.47051 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 4\n",
       "\n",
       "| featureID &lt;chr&gt; | sampleID &lt;fct&gt; | protein_intensity &lt;dbl&gt; | measured &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| A0AV96;A0AV96-2 | X786O_NCI60 | 26.95893 | 1 |\n",
       "| A0AV96;A0AV96-2 | A498_NCI60  | 28.67702 | 1 |\n",
       "| A0AV96;A0AV96-2 | A549_NCI60  | 29.12149 | 1 |\n",
       "| A0AV96;A0AV96-2 | ACHN_NCI60  | 29.22591 | 1 |\n",
       "| A0AV96;A0AV96-2 | BT549_NCI60 | 24.08905 | 1 |\n",
       "| A0AV96;A0AV96-2 | CAKI1_NCI60 | 27.47051 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID       sampleID    protein_intensity measured\n",
       "1 A0AV96;A0AV96-2 X786O_NCI60 26.95893          1       \n",
       "2 A0AV96;A0AV96-2 A498_NCI60  28.67702          1       \n",
       "3 A0AV96;A0AV96-2 A549_NCI60  29.12149          1       \n",
       "4 A0AV96;A0AV96-2 ACHN_NCI60  29.22591          1       \n",
       "5 A0AV96;A0AV96-2 BT549_NCI60 24.08905          1       \n",
       "6 A0AV96;A0AV96-2 CAKI1_NCI60 27.47051          1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train <- merge(prot_int_list, measured_list)\n",
    "nrow(train)\n",
    "head(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba7efefc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>protein_intensity</th><th scope=col>measured</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&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>A0A0B4J2F0</td><td>X786O_NCI60</td><td>NA</td><td>0</td></tr>\n",
       "\t<tr><td>A0A0B4J2F0</td><td>A498_NCI60 </td><td>NA</td><td>0</td></tr>\n",
       "\t<tr><td>A0A0B4J2F0</td><td>A549_NCI60 </td><td>NA</td><td>0</td></tr>\n",
       "\t<tr><td>A0A0B4J2F0</td><td>ACHN_NCI60 </td><td>NA</td><td>0</td></tr>\n",
       "\t<tr><td>A0A0B4J2F0</td><td>BT549_NCI60</td><td>NA</td><td>0</td></tr>\n",
       "\t<tr><td>A0A0B4J2F0</td><td>CAKI1_NCI60</td><td>NA</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " featureID & sampleID & protein\\_intensity & measured\\\\\n",
       " <chr> & <fct> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t A0A0B4J2F0 & X786O\\_NCI60 & NA & 0\\\\\n",
       "\t A0A0B4J2F0 & A498\\_NCI60  & NA & 0\\\\\n",
       "\t A0A0B4J2F0 & A549\\_NCI60  & NA & 0\\\\\n",
       "\t A0A0B4J2F0 & ACHN\\_NCI60  & NA & 0\\\\\n",
       "\t A0A0B4J2F0 & BT549\\_NCI60 & NA & 0\\\\\n",
       "\t A0A0B4J2F0 & CAKI1\\_NCI60 & NA & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 4\n",
       "\n",
       "| featureID &lt;chr&gt; | sampleID &lt;fct&gt; | protein_intensity &lt;dbl&gt; | measured &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| A0A0B4J2F0 | X786O_NCI60 | NA | 0 |\n",
       "| A0A0B4J2F0 | A498_NCI60  | NA | 0 |\n",
       "| A0A0B4J2F0 | A549_NCI60  | NA | 0 |\n",
       "| A0A0B4J2F0 | ACHN_NCI60  | NA | 0 |\n",
       "| A0A0B4J2F0 | BT549_NCI60 | NA | 0 |\n",
       "| A0A0B4J2F0 | CAKI1_NCI60 | NA | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID  sampleID    protein_intensity measured\n",
       "1 A0A0B4J2F0 X786O_NCI60 NA                0       \n",
       "2 A0A0B4J2F0 A498_NCI60  NA                0       \n",
       "3 A0A0B4J2F0 A549_NCI60  NA                0       \n",
       "4 A0A0B4J2F0 ACHN_NCI60  NA                0       \n",
       "5 A0A0B4J2F0 BT549_NCI60 NA                0       \n",
       "6 A0A0B4J2F0 CAKI1_NCI60 NA                0       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# features that are filtered by PROTRIDER\n",
    "head(merge(prot_int_list, measured_list, all.y=TRUE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8f5ca01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>protein_intensity</th><th scope=col>measured</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&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>A0AV96;A0AV96-2</td><td>LOXIMVI_NCI60</td><td>24.44423</td><td>0</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>MOLT4_NCI60  </td><td>21.11678</td><td>0</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>OVCAR8_NCI60 </td><td>24.50268</td><td>0</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>SF539_NCI60  </td><td>24.81375</td><td>0</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>SN12C_NCI60  </td><td>25.70537</td><td>0</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>UACC62_NCI60 </td><td>25.94243</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " featureID & sampleID & protein\\_intensity & measured\\\\\n",
       " <chr> & <fct> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t A0AV96;A0AV96-2 & LOXIMVI\\_NCI60 & 24.44423 & 0\\\\\n",
       "\t A0AV96;A0AV96-2 & MOLT4\\_NCI60   & 21.11678 & 0\\\\\n",
       "\t A0AV96;A0AV96-2 & OVCAR8\\_NCI60  & 24.50268 & 0\\\\\n",
       "\t A0AV96;A0AV96-2 & SF539\\_NCI60   & 24.81375 & 0\\\\\n",
       "\t A0AV96;A0AV96-2 & SN12C\\_NCI60   & 25.70537 & 0\\\\\n",
       "\t A0AV96;A0AV96-2 & UACC62\\_NCI60  & 25.94243 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 4\n",
       "\n",
       "| featureID &lt;chr&gt; | sampleID &lt;fct&gt; | protein_intensity &lt;dbl&gt; | measured &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| A0AV96;A0AV96-2 | LOXIMVI_NCI60 | 24.44423 | 0 |\n",
       "| A0AV96;A0AV96-2 | MOLT4_NCI60   | 21.11678 | 0 |\n",
       "| A0AV96;A0AV96-2 | OVCAR8_NCI60  | 24.50268 | 0 |\n",
       "| A0AV96;A0AV96-2 | SF539_NCI60   | 24.81375 | 0 |\n",
       "| A0AV96;A0AV96-2 | SN12C_NCI60   | 25.70537 | 0 |\n",
       "| A0AV96;A0AV96-2 | UACC62_NCI60  | 25.94243 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID       sampleID      protein_intensity measured\n",
       "1 A0AV96;A0AV96-2 LOXIMVI_NCI60 24.44423          0       \n",
       "2 A0AV96;A0AV96-2 MOLT4_NCI60   21.11678          0       \n",
       "3 A0AV96;A0AV96-2 OVCAR8_NCI60  24.50268          0       \n",
       "4 A0AV96;A0AV96-2 SF539_NCI60   24.81375          0       \n",
       "5 A0AV96;A0AV96-2 SN12C_NCI60   25.70537          0       \n",
       "6 A0AV96;A0AV96-2 UACC62_NCI60  25.94243          0       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing values in the prot_int\n",
    "head(train[train$measured ==0, ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74106150",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg <- glm(measured~protein_intensity, family=binomial, data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a93ee907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = measured ~ protein_intensity, family = binomial, \n",
       "    data = train)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-5.7732   0.0848   0.1765   0.3303   2.1366  \n",
       "\n",
       "Coefficients:\n",
       "                   Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)       -13.55501    0.10194  -133.0   <2e-16 ***\n",
       "protein_intensity   0.63870    0.00413   154.7   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 169375  on 418499  degrees of freedom\n",
       "Residual deviance: 131939  on 418498  degrees of freedom\n",
       "AIC: 131943\n",
       "\n",
       "Number of Fisher Scoring iterations: 7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(log_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "70d7d419",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train$predprob<-round(fitted(log_reg),3)\n",
    "train$predprob <- fitted(log_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c174ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>protein_intensity</th><th scope=col>measured</th><th scope=col>predprob</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>X786O_NCI60</td><td>26.95893</td><td>1</td><td>0.9749996</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>A498_NCI60 </td><td>28.67702</td><td>1</td><td>0.9915146</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>A549_NCI60 </td><td>29.12149</td><td>1</td><td>0.9935983</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>ACHN_NCI60 </td><td>29.22591</td><td>1</td><td>0.9940088</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>BT549_NCI60</td><td>24.08905</td><td>1</td><td>0.8618288</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>CAKI1_NCI60</td><td>27.47051</td><td>1</td><td>0.9818415</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " featureID & sampleID & protein\\_intensity & measured & predprob\\\\\n",
       " <chr> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t A0AV96;A0AV96-2 & X786O\\_NCI60 & 26.95893 & 1 & 0.9749996\\\\\n",
       "\t A0AV96;A0AV96-2 & A498\\_NCI60  & 28.67702 & 1 & 0.9915146\\\\\n",
       "\t A0AV96;A0AV96-2 & A549\\_NCI60  & 29.12149 & 1 & 0.9935983\\\\\n",
       "\t A0AV96;A0AV96-2 & ACHN\\_NCI60  & 29.22591 & 1 & 0.9940088\\\\\n",
       "\t A0AV96;A0AV96-2 & BT549\\_NCI60 & 24.08905 & 1 & 0.8618288\\\\\n",
       "\t A0AV96;A0AV96-2 & CAKI1\\_NCI60 & 27.47051 & 1 & 0.9818415\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 5\n",
       "\n",
       "| featureID &lt;chr&gt; | sampleID &lt;fct&gt; | protein_intensity &lt;dbl&gt; | measured &lt;dbl&gt; | predprob &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| A0AV96;A0AV96-2 | X786O_NCI60 | 26.95893 | 1 | 0.9749996 |\n",
       "| A0AV96;A0AV96-2 | A498_NCI60  | 28.67702 | 1 | 0.9915146 |\n",
       "| A0AV96;A0AV96-2 | A549_NCI60  | 29.12149 | 1 | 0.9935983 |\n",
       "| A0AV96;A0AV96-2 | ACHN_NCI60  | 29.22591 | 1 | 0.9940088 |\n",
       "| A0AV96;A0AV96-2 | BT549_NCI60 | 24.08905 | 1 | 0.8618288 |\n",
       "| A0AV96;A0AV96-2 | CAKI1_NCI60 | 27.47051 | 1 | 0.9818415 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID       sampleID    protein_intensity measured predprob \n",
       "1 A0AV96;A0AV96-2 X786O_NCI60 26.95893          1        0.9749996\n",
       "2 A0AV96;A0AV96-2 A498_NCI60  28.67702          1        0.9915146\n",
       "3 A0AV96;A0AV96-2 A549_NCI60  29.12149          1        0.9935983\n",
       "4 A0AV96;A0AV96-2 ACHN_NCI60  29.22591          1        0.9940088\n",
       "5 A0AV96;A0AV96-2 BT549_NCI60 24.08905          1        0.8618288\n",
       "6 A0AV96;A0AV96-2 CAKI1_NCI60 27.47051          1        0.9818415"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "575b5d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>protein_intensity</th><th scope=col>measured</th><th scope=col>predprob</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>LOXIMVI_NCI60</td><td>24.44423</td><td>0</td><td>0.8866952</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>MOLT4_NCI60  </td><td>21.11678</td><td>0</td><td>0.4830533</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>OVCAR8_NCI60 </td><td>24.50268</td><td>0</td><td>0.8903918</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>SF539_NCI60  </td><td>24.81375</td><td>0</td><td>0.9083315</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>SN12C_NCI60  </td><td>25.70537</td><td>0</td><td>0.9459819</td></tr>\n",
       "\t<tr><td>A0AV96;A0AV96-2</td><td>UACC62_NCI60 </td><td>25.94243</td><td>0</td><td>0.9532165</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " featureID & sampleID & protein\\_intensity & measured & predprob\\\\\n",
       " <chr> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t A0AV96;A0AV96-2 & LOXIMVI\\_NCI60 & 24.44423 & 0 & 0.8866952\\\\\n",
       "\t A0AV96;A0AV96-2 & MOLT4\\_NCI60   & 21.11678 & 0 & 0.4830533\\\\\n",
       "\t A0AV96;A0AV96-2 & OVCAR8\\_NCI60  & 24.50268 & 0 & 0.8903918\\\\\n",
       "\t A0AV96;A0AV96-2 & SF539\\_NCI60   & 24.81375 & 0 & 0.9083315\\\\\n",
       "\t A0AV96;A0AV96-2 & SN12C\\_NCI60   & 25.70537 & 0 & 0.9459819\\\\\n",
       "\t A0AV96;A0AV96-2 & UACC62\\_NCI60  & 25.94243 & 0 & 0.9532165\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 5\n",
       "\n",
       "| featureID &lt;chr&gt; | sampleID &lt;fct&gt; | protein_intensity &lt;dbl&gt; | measured &lt;dbl&gt; | predprob &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| A0AV96;A0AV96-2 | LOXIMVI_NCI60 | 24.44423 | 0 | 0.8866952 |\n",
       "| A0AV96;A0AV96-2 | MOLT4_NCI60   | 21.11678 | 0 | 0.4830533 |\n",
       "| A0AV96;A0AV96-2 | OVCAR8_NCI60  | 24.50268 | 0 | 0.8903918 |\n",
       "| A0AV96;A0AV96-2 | SF539_NCI60   | 24.81375 | 0 | 0.9083315 |\n",
       "| A0AV96;A0AV96-2 | SN12C_NCI60   | 25.70537 | 0 | 0.9459819 |\n",
       "| A0AV96;A0AV96-2 | UACC62_NCI60  | 25.94243 | 0 | 0.9532165 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID       sampleID      protein_intensity measured predprob \n",
       "1 A0AV96;A0AV96-2 LOXIMVI_NCI60 24.44423          0        0.8866952\n",
       "2 A0AV96;A0AV96-2 MOLT4_NCI60   21.11678          0        0.4830533\n",
       "3 A0AV96;A0AV96-2 OVCAR8_NCI60  24.50268          0        0.8903918\n",
       "4 A0AV96;A0AV96-2 SF539_NCI60   24.81375          0        0.9083315\n",
       "5 A0AV96;A0AV96-2 SN12C_NCI60   25.70537          0        0.9459819\n",
       "6 A0AV96;A0AV96-2 UACC62_NCI60  25.94243          0        0.9532165"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(train[train$measured ==0,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "633467e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>protein_intensity</th><th scope=col>measured</th><th scope=col>predprob</th><th scope=col>missing</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>P80723;P80723-2                                    </td><td>MOLT4_NCI60   </td><td>47.31490</td><td>0</td><td>0.9999999</td><td>5.788424e-08</td></tr>\n",
       "\t<tr><td>O14910                                             </td><td>K562_NCI60    </td><td>45.96735</td><td>0</td><td>0.9999999</td><td>1.368819e-07</td></tr>\n",
       "\t<tr><td>P80723;P80723-2                                    </td><td>CCRFCEM_NCI60 </td><td>44.29144</td><td>0</td><td>0.9999996</td><td>3.992209e-07</td></tr>\n",
       "\t<tr><td>P29034                                             </td><td>K562_NCI60    </td><td>41.09334</td><td>0</td><td>0.9999969</td><td>3.078285e-06</td></tr>\n",
       "\t<tr><td>P00918                                             </td><td>RPMI8226_NCI60</td><td>40.50324</td><td>0</td><td>0.9999955</td><td>4.487351e-06</td></tr>\n",
       "\t<tr><td>CON__P05787;P05787;P05787-2;CON__H-INV:HIT000016045</td><td>NCIH322M_NCI60</td><td>40.19136</td><td>1</td><td>0.9999945</td><td>5.476469e-06</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " featureID & sampleID & protein\\_intensity & measured & predprob & missing\\\\\n",
       " <chr> & <fct> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t P80723;P80723-2                                     & MOLT4\\_NCI60    & 47.31490 & 0 & 0.9999999 & 5.788424e-08\\\\\n",
       "\t O14910                                              & K562\\_NCI60     & 45.96735 & 0 & 0.9999999 & 1.368819e-07\\\\\n",
       "\t P80723;P80723-2                                     & CCRFCEM\\_NCI60  & 44.29144 & 0 & 0.9999996 & 3.992209e-07\\\\\n",
       "\t P29034                                              & K562\\_NCI60     & 41.09334 & 0 & 0.9999969 & 3.078285e-06\\\\\n",
       "\t P00918                                              & RPMI8226\\_NCI60 & 40.50324 & 0 & 0.9999955 & 4.487351e-06\\\\\n",
       "\t CON\\_\\_P05787;P05787;P05787-2;CON\\_\\_H-INV:HIT000016045 & NCIH322M\\_NCI60 & 40.19136 & 1 & 0.9999945 & 5.476469e-06\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 6\n",
       "\n",
       "| featureID &lt;chr&gt; | sampleID &lt;fct&gt; | protein_intensity &lt;dbl&gt; | measured &lt;dbl&gt; | predprob &lt;dbl&gt; | missing &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| P80723;P80723-2                                     | MOLT4_NCI60    | 47.31490 | 0 | 0.9999999 | 5.788424e-08 |\n",
       "| O14910                                              | K562_NCI60     | 45.96735 | 0 | 0.9999999 | 1.368819e-07 |\n",
       "| P80723;P80723-2                                     | CCRFCEM_NCI60  | 44.29144 | 0 | 0.9999996 | 3.992209e-07 |\n",
       "| P29034                                              | K562_NCI60     | 41.09334 | 0 | 0.9999969 | 3.078285e-06 |\n",
       "| P00918                                              | RPMI8226_NCI60 | 40.50324 | 0 | 0.9999955 | 4.487351e-06 |\n",
       "| CON__P05787;P05787;P05787-2;CON__H-INV:HIT000016045 | NCIH322M_NCI60 | 40.19136 | 1 | 0.9999945 | 5.476469e-06 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID                                           sampleID      \n",
       "1 P80723;P80723-2                                     MOLT4_NCI60   \n",
       "2 O14910                                              K562_NCI60    \n",
       "3 P80723;P80723-2                                     CCRFCEM_NCI60 \n",
       "4 P29034                                              K562_NCI60    \n",
       "5 P00918                                              RPMI8226_NCI60\n",
       "6 CON__P05787;P05787;P05787-2;CON__H-INV:HIT000016045 NCIH322M_NCI60\n",
       "  protein_intensity measured predprob  missing     \n",
       "1 47.31490          0        0.9999999 5.788424e-08\n",
       "2 45.96735          0        0.9999999 1.368819e-07\n",
       "3 44.29144          0        0.9999996 3.992209e-07\n",
       "4 41.09334          0        0.9999969 3.078285e-06\n",
       "5 40.50324          0        0.9999955 4.487351e-06\n",
       "6 40.19136          1        0.9999945 5.476469e-06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train$missing <- 1- train$predprob\n",
    "train <- train[order(train$missing),]\n",
    "head(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ebed5c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train$measured <- as.logical(train$measured)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dc4aec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GqsNYEgIIIIBA5QJvv/129s5R1NLyG8mJmoZhCCBQukBqAyT1qzJ9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\ngeoL5FZfrf7SWSICCCCAAAIIIFBdg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mmXbyySebnvt555137IgjjiiyXknf6Evg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\nEEAgWwQIkLLlTFFOBDJU4NBDDw0t2R577BGaTyYCCCCAAAIII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QyAcBfVcqSFKaNWuWqQU+NqlFSdMvRCIRu/nmm+0///mP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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(train, aes(x=measured, y=protein_intensity)) + geom_boxplot() + ylab(\"Predicted protein intensity\") + \n",
    "xlab(\"Whether the protein was measured\") + theme_bw() + ggtitle(\"Predicted protein intensities for measured and missing values\") +\n",
    "theme(text=element_text(size=13), axis.text.x = element_text(size = 14),\n",
    "         axis.title.x = element_text(size = 14),\n",
    "         axis.text.y = element_text(size = 14),\n",
    "         axis.title.y = element_text(size = 14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7d5bfa8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/zpyybtNdzws4BlLdnAVLmfw/46iFtHUpO3dfGnuQaT1G5LdjANqh37ZH5ytWJn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wKAGLe7vttptMnTp1TPx2/REMOEBTBRPm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\nDz12Q9jxuEk8AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(train, aes(x=protein_intensity, y=predprob, color=measured)) + \n",
    "geom_point(size=0.8) + \n",
    "xlab(\"Predicted protein intensity\") + theme_bw() +\n",
    "ylab(\"Predicted probability to be measured\") + labs(color='Measured') +\n",
    "   ggtitle(\"Modelling the probability of being measured based on expected \\nprotein intensities from PROTRIDER\")+\n",
    "   theme(plot.title = element_text(size = 14),\n",
    "         axis.text.x = element_text(size = 13),\n",
    "         axis.title.x = element_text(size = 13),\n",
    "         axis.title.y = element_text(size = 13),\n",
    "         axis.text.y = element_text(size = 13),\n",
    "         legend.title=element_text(size=13),\n",
    "         legend.text=element_text(size=13)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3e51d3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FBAglRCXFUTIECAAAECBAgQIFBZAhKk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\nvvvuG6lv0osvvtiwzAsCBAgQIECAAAECBAhUgkDuBGnXXXeN+++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wTCJkCAQMkF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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(train, aes(x=measured, y=predprob)) + geom_boxplot() + ylab(\"Predicted probability to be measured\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe61c6b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ycCBAgMRUCCNJQzqR8ECBAgsGuB1mvqdXbdUMcnQIAAgYMFWn+YH7wHawgQIECA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\nEEAAAQQ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AABBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCwwP8D\nmQwitXWgFWUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(train$protein_intensity, train$predprob, pch = 16, cex = 1, ylab = \"Probability to be measured\", xlab = \"Predicted protein intensity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "31fa9731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0SdWxTUU4UAAQIECLRSQILUysOiUwQIEGilwAOiVyWX2f1jK3uvUwQIECBAoEBA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\nAm0RoEBqCyMLQQABBBAosYAvTfeBWyig0oqoeEF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BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(train$protein_intensity, train$missing, pch = 16, cex = 1, ylab = \"Probability to be missing\", xlab = \"Protein Intensity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f7f0bda6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>featureID</th><th scope=col>sampleID</th><th scope=col>protein_intensity</th><th scope=col>measured</th><th scope=col>predprob</th><th scope=col>missing</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&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>P80723;P80723-2</td><td>MOLT4_NCI60   </td><td>47.31490</td><td>FALSE</td><td>0.9999999</td><td>5.788424e-08</td></tr>\n",
       "\t<tr><td>O14910         </td><td>K562_NCI60    </td><td>45.96735</td><td>FALSE</td><td>0.9999999</td><td>1.368819e-07</td></tr>\n",
       "\t<tr><td>P80723;P80723-2</td><td>CCRFCEM_NCI60 </td><td>44.29144</td><td>FALSE</td><td>0.9999996</td><td>3.992209e-07</td></tr>\n",
       "\t<tr><td>P29034         </td><td>K562_NCI60    </td><td>41.09334</td><td>FALSE</td><td>0.9999969</td><td>3.078285e-06</td></tr>\n",
       "\t<tr><td>P00918         </td><td>RPMI8226_NCI60</td><td>40.50324</td><td>FALSE</td><td>0.9999955</td><td>4.487351e-06</td></tr>\n",
       "\t<tr><td>P29034         </td><td>NCIH522_NCI60 </td><td>38.98237</td><td>FALSE</td><td>0.9999881</td><td>1.185360e-05</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " featureID & sampleID & protein\\_intensity & measured & predprob & missing\\\\\n",
       " <chr> & <fct> & <dbl> & <lgl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t P80723;P80723-2 & MOLT4\\_NCI60    & 47.31490 & FALSE & 0.9999999 & 5.788424e-08\\\\\n",
       "\t O14910          & K562\\_NCI60     & 45.96735 & FALSE & 0.9999999 & 1.368819e-07\\\\\n",
       "\t P80723;P80723-2 & CCRFCEM\\_NCI60  & 44.29144 & FALSE & 0.9999996 & 3.992209e-07\\\\\n",
       "\t P29034          & K562\\_NCI60     & 41.09334 & FALSE & 0.9999969 & 3.078285e-06\\\\\n",
       "\t P00918          & RPMI8226\\_NCI60 & 40.50324 & FALSE & 0.9999955 & 4.487351e-06\\\\\n",
       "\t P29034          & NCIH522\\_NCI60  & 38.98237 & FALSE & 0.9999881 & 1.185360e-05\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 6\n",
       "\n",
       "| featureID &lt;chr&gt; | sampleID &lt;fct&gt; | protein_intensity &lt;dbl&gt; | measured &lt;lgl&gt; | predprob &lt;dbl&gt; | missing &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| P80723;P80723-2 | MOLT4_NCI60    | 47.31490 | FALSE | 0.9999999 | 5.788424e-08 |\n",
       "| O14910          | K562_NCI60     | 45.96735 | FALSE | 0.9999999 | 1.368819e-07 |\n",
       "| P80723;P80723-2 | CCRFCEM_NCI60  | 44.29144 | FALSE | 0.9999996 | 3.992209e-07 |\n",
       "| P29034          | K562_NCI60     | 41.09334 | FALSE | 0.9999969 | 3.078285e-06 |\n",
       "| P00918          | RPMI8226_NCI60 | 40.50324 | FALSE | 0.9999955 | 4.487351e-06 |\n",
       "| P29034          | NCIH522_NCI60  | 38.98237 | FALSE | 0.9999881 | 1.185360e-05 |\n",
       "\n"
      ],
      "text/plain": [
       "  featureID       sampleID       protein_intensity measured predprob \n",
       "1 P80723;P80723-2 MOLT4_NCI60    47.31490          FALSE    0.9999999\n",
       "2 O14910          K562_NCI60     45.96735          FALSE    0.9999999\n",
       "3 P80723;P80723-2 CCRFCEM_NCI60  44.29144          FALSE    0.9999996\n",
       "4 P29034          K562_NCI60     41.09334          FALSE    0.9999969\n",
       "5 P00918          RPMI8226_NCI60 40.50324          FALSE    0.9999955\n",
       "6 P29034          NCIH522_NCI60  38.98237          FALSE    0.9999881\n",
       "  missing     \n",
       "1 5.788424e-08\n",
       "2 1.368819e-07\n",
       "3 3.992209e-07\n",
       "4 3.078285e-06\n",
       "5 4.487351e-06\n",
       "6 1.185360e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(train[train$measured == 0,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "02fc551a",
   "metadata": {},
   "outputs": [],
   "source": [
    "write.csv(train[train$measured == 0,], row.names=FALSE, \"missing_pred.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c5a235c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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