code/seq_qc/correctness_seq_qc_A.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.4     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.5\n",
      "\u001b[32m✔\u001b[39m \u001b[34mforcats  \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mstringr  \u001b[39m 1.5.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2  \u001b[39m 3.5.1     \u001b[32m✔\u001b[39m \u001b[34mtibble   \u001b[39m 3.2.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.2     \n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "theme_set(theme_light())\n",
    "theme_update(text = element_text(family = \"Calibri\"), strip.text = element_text(colour = 'black'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m16\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m31\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[32mdbl\u001b[39m (31): total, correct_to_1, additional_to_1, missing_to_1, additionalAndM...\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    }
   ],
   "source": [
    "results_df = read_csv(\"analysis/spot_detection/seq_qc/rep0/A_PB2/results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df %>%\n",
    "\tpivot_longer(cols=contains(\"correct_to\"), names_to=\"correct_to\", values_to=\"correct\") %>%\n",
    "\tselect(fov, total, correct_to, correct) %>%\n",
    "\tmutate(correct_to = parse_number(correct_to), correct=correct/total, fov=as_factor(fov)) %>%\n",
    "\tggplot(aes(x=correct_to, y=correct, group=fov)) +\n",
    "\t\t#geom_point() +\n",
    "\t\tgeom_line(color=\"gray\") +\n",
    "\t\tgeom_smooth(aes(group=1),color=\"black\",method=\"loess\",formula=y~x, se=FALSE) +\n",
    "\t\txlab(\"Round of sequencing\") +\n",
    "\t\tylab(\"Fraction of correct spots (among all spots)\") + \n",
    "\t\tscale_x_continuous(breaks=1:6) +\n",
    "\t\tylim(0,1) +\n",
    "\t\tNULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df %>%\n",
    "\tpivot_longer(cols=contains(\"_to_\"), names_to=\"round\", values_to=\"fraction\") %>%\n",
    "\tmutate(\n",
    "\t\ttype = str_remove(round, \"_to_.*\"),\n",
    "\t\tround = as.factor(parse_number(round)),\n",
    "\t\tfraction = fraction/total\n",
    "\t) %>%\n",
    "\tselect(total, type, round, fraction, fov) %T>%\n",
    "  write_tsv(\"figures/supp-fig6c-correct_by_round-detailed-box.tsv\") %>%\n",
    "\tggplot(aes(x=round, y=fraction, color=type)) +\n",
    "\t\tgeom_boxplot() +\n",
    "    xlab(\"Round of Sequencing\") +\n",
    "    ylab(\"Fraction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ggsave(\"figures/supp-fig6c-correct_by_round-detailed-box.svg\", width = 6, height = 4, dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>round</th><th scope=col>total</th><th scope=col>matching</th><th scope=col>median_fraction</th><th scope=col>fraction</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>29911</td><td>27888</td><td>0.9324921</td><td>0.9323660</td></tr>\n",
       "\t<tr><td>2</td><td>29911</td><td>27665</td><td>0.9236887</td><td>0.9249106</td></tr>\n",
       "\t<tr><td>3</td><td>29911</td><td>27374</td><td>0.9169177</td><td>0.9151817</td></tr>\n",
       "\t<tr><td>4</td><td>29911</td><td>26826</td><td>0.8958773</td><td>0.8968607</td></tr>\n",
       "\t<tr><td>5</td><td>29911</td><td>26052</td><td>0.8804033</td><td>0.8709839</td></tr>\n",
       "\t<tr><td>6</td><td>29911</td><td>25188</td><td>0.8641386</td><td>0.8420982</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " round & total & matching & median\\_fraction & fraction\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 29911 & 27888 & 0.9324921 & 0.9323660\\\\\n",
       "\t 2 & 29911 & 27665 & 0.9236887 & 0.9249106\\\\\n",
       "\t 3 & 29911 & 27374 & 0.9169177 & 0.9151817\\\\\n",
       "\t 4 & 29911 & 26826 & 0.8958773 & 0.8968607\\\\\n",
       "\t 5 & 29911 & 26052 & 0.8804033 & 0.8709839\\\\\n",
       "\t 6 & 29911 & 25188 & 0.8641386 & 0.8420982\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 5\n",
       "\n",
       "| round &lt;fct&gt; | total &lt;dbl&gt; | matching &lt;dbl&gt; | median_fraction &lt;dbl&gt; | fraction &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 29911 | 27888 | 0.9324921 | 0.9323660 |\n",
       "| 2 | 29911 | 27665 | 0.9236887 | 0.9249106 |\n",
       "| 3 | 29911 | 27374 | 0.9169177 | 0.9151817 |\n",
       "| 4 | 29911 | 26826 | 0.8958773 | 0.8968607 |\n",
       "| 5 | 29911 | 26052 | 0.8804033 | 0.8709839 |\n",
       "| 6 | 29911 | 25188 | 0.8641386 | 0.8420982 |\n",
       "\n"
      ],
      "text/plain": [
       "  round total matching median_fraction fraction \n",
       "1 1     29911 27888    0.9324921       0.9323660\n",
       "2 2     29911 27665    0.9236887       0.9249106\n",
       "3 3     29911 27374    0.9169177       0.9151817\n",
       "4 4     29911 26826    0.8958773       0.8968607\n",
       "5 5     29911 26052    0.8804033       0.8709839\n",
       "6 6     29911 25188    0.8641386       0.8420982"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df %>%\n",
    "\tpivot_longer(cols=contains(\"_to_\"), names_to=\"round\", values_to=\"fraction\") %>%\n",
    "\tmutate(\n",
    "\t\ttype = str_remove(round, \"_to_.*\"),\n",
    "\t\tround = as.factor(parse_number(round)),\n",
    "    matching = fraction,\n",
    "\t\tfraction = fraction/total\n",
    "\t) %>%\n",
    "\tselect(total, matching, type, round, fraction, fov) %>%\n",
    "  filter(type==\"correct\") %>%\n",
    "  group_by(round) %>%\n",
    "  summarize(total=sum(total), matching=sum(matching), median_fraction = median(fraction)) %>%\n",
    "  mutate(fraction = matching/total) %>%\n",
    "\tidentity()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In an experimental setting, this is advantageous as it would allow the identification of spots that may contain multiple RNA molecules. In this control setting, we decoded greater than 92% of spots after 2 rounds of sequencing, and 84% spots after 6 rounds of sequencing defining the background of our assay (Supplementary Fig. 6c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correct spots by round\n",
    "\n",
    "Analysis of correct spots per round of sequencing, only considering spots that are correct up to that round.\n",
    "Either starting with all spots, or restricted to spots, that were correct in round 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "correct_to <- results_df %>%\n",
    "\tpivot_longer(cols=contains(\"correct_to\"), names_to=\"correct_to\", values_to=\"correct\") %>%\n",
    "\tselect(fov, total, correct_to, correct) %>%\n",
    "\tmutate(correct_to = parse_number(correct_to), correct=correct/total, fov=as_factor(fov)) %>%\n",
    "\tgroup_by(fov) %>%\n",
    "\tarrange(fov, correct_to) %>%\n",
    "\tmutate(prev_correct = lag(correct, default=1), factor=correct/prev_correct) %>%\n",
    "\tidentity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "correct_after_1 <- results_df %>%\n",
    "\tmutate(total=correct_to_1) %>%\n",
    "\tpivot_longer(cols=contains(\"correct_to\"), names_to=\"correct_to\", values_to=\"correct\") %>%\n",
    "\tselect(fov, total, correct_to, correct) %>%\n",
    "\tmutate(correct_to = parse_number(correct_to), correct=correct/total, fov=as_factor(fov)) %>%\n",
    "\tgroup_by(fov) %>%\n",
    "\tarrange(fov, correct_to) %>%\n",
    "\tmutate(prev_correct = lag(correct, default=1), factor=correct/prev_correct) %>%\n",
    "\tidentity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correct_to %>%\n",
    "\tggplot(aes(y=factor, x=as_factor(correct_to))) +\n",
    "\t\tgeom_boxplot() +\n",
    "\t\tgeom_point() + #aes(color=fov)) +\n",
    "\t\txlab(\"Round of sequencing\") +\n",
    "\t\tylab(\"Fraction of correct spots up to that round, that remain correct\") +\n",
    "\t\t# ylim(0,1) +\n",
    "\t\tNULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>correct_to</th><th scope=col>median</th><th scope=col>cumprod(median)</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>0.9324921</td><td>0.9324921</td></tr>\n",
       "\t<tr><td>2</td><td>0.9929100</td><td>0.9258808</td></tr>\n",
       "\t<tr><td>3</td><td>0.9930817</td><td>0.9194752</td></tr>\n",
       "\t<tr><td>4</td><td>0.9877467</td><td>0.9082086</td></tr>\n",
       "\t<tr><td>5</td><td>0.9764206</td><td>0.8867936</td></tr>\n",
       "\t<tr><td>6</td><td>0.9795711</td><td>0.8686774</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " correct\\_to & median & cumprod(median)\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 0.9324921 & 0.9324921\\\\\n",
       "\t 2 & 0.9929100 & 0.9258808\\\\\n",
       "\t 3 & 0.9930817 & 0.9194752\\\\\n",
       "\t 4 & 0.9877467 & 0.9082086\\\\\n",
       "\t 5 & 0.9764206 & 0.8867936\\\\\n",
       "\t 6 & 0.9795711 & 0.8686774\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| correct_to &lt;dbl&gt; | median &lt;dbl&gt; | cumprod(median) &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 0.9324921 | 0.9324921 |\n",
       "| 2 | 0.9929100 | 0.9258808 |\n",
       "| 3 | 0.9930817 | 0.9194752 |\n",
       "| 4 | 0.9877467 | 0.9082086 |\n",
       "| 5 | 0.9764206 | 0.8867936 |\n",
       "| 6 | 0.9795711 | 0.8686774 |\n",
       "\n"
      ],
      "text/plain": [
       "  correct_to median    cumprod(median)\n",
       "1 1          0.9324921 0.9324921      \n",
       "2 2          0.9929100 0.9258808      \n",
       "3 3          0.9930817 0.9194752      \n",
       "4 4          0.9877467 0.9082086      \n",
       "5 5          0.9764206 0.8867936      \n",
       "6 6          0.9795711 0.8686774      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correct_to %>%\n",
    "\tgroup_by(correct_to) %>%\n",
    "\t#filter(correct_to > 1) %>%\n",
    "\tsummarize(median=median(factor)) %>%\n",
    "  mutate(cumprod(median))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correct_after_1 %>%\n",
    "\tggplot(aes(y=factor, x=as_factor(correct_to))) +\n",
    "\t\tgeom_boxplot() +\n",
    "\t\tgeom_point() + #aes(color=fov)) +\n",
    "\t\txlab(\"Round of sequencing\") +\n",
    "\t\tylab(\"Fraction of correct spots up to that round, that remain correct\") +\n",
    "\t\t#ylim(0,1) +\n",
    "\t\tNULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ggsave(\"figures/supp-fig6b-factor_correct_by_round.svg\", width = 6, height = 4, dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.989162172208565"
      ],
      "text/latex": [
       "0.989162172208565"
      ],
      "text/markdown": [
       "0.989162172208565"
      ],
      "text/plain": [
       "[1] 0.9891622"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correct_to %>%\n",
    "\tungroup() %>%\n",
    "\tfilter(correct_to > 1) %>%\n",
    "\tsummarize(median(factor)) %>% pull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>correct_to</th><th scope=col>median</th><th scope=col>cumprod(median)</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2</td><td>0.9929100</td><td>0.9929100</td></tr>\n",
       "\t<tr><td>3</td><td>0.9930817</td><td>0.9860407</td></tr>\n",
       "\t<tr><td>4</td><td>0.9877467</td><td>0.9739585</td></tr>\n",
       "\t<tr><td>5</td><td>0.9764206</td><td>0.9509931</td></tr>\n",
       "\t<tr><td>6</td><td>0.9795711</td><td>0.9315654</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 5 × 3\n",
       "\\begin{tabular}{lll}\n",
       " correct\\_to & median & cumprod(median)\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 2 & 0.9929100 & 0.9929100\\\\\n",
       "\t 3 & 0.9930817 & 0.9860407\\\\\n",
       "\t 4 & 0.9877467 & 0.9739585\\\\\n",
       "\t 5 & 0.9764206 & 0.9509931\\\\\n",
       "\t 6 & 0.9795711 & 0.9315654\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 3\n",
       "\n",
       "| correct_to &lt;dbl&gt; | median &lt;dbl&gt; | cumprod(median) &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 2 | 0.9929100 | 0.9929100 |\n",
       "| 3 | 0.9930817 | 0.9860407 |\n",
       "| 4 | 0.9877467 | 0.9739585 |\n",
       "| 5 | 0.9764206 | 0.9509931 |\n",
       "| 6 | 0.9795711 | 0.9315654 |\n",
       "\n"
      ],
      "text/plain": [
       "  correct_to median    cumprod(median)\n",
       "1 2          0.9929100 0.9929100      \n",
       "2 3          0.9930817 0.9860407      \n",
       "3 4          0.9877467 0.9739585      \n",
       "4 5          0.9764206 0.9509931      \n",
       "5 6          0.9795711 0.9315654      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correct_to %>%\n",
    "\tgroup_by(correct_to) %>%\n",
    "\tfilter(correct_to > 1) %>%\n",
    "\tsummarize(median=median(factor)) %>%\n",
    "  mutate(cumprod(median))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> By following RCP spots that were correctly base-called in the first round of sequencing, we found that nearly 99% of spots were correctly called in the next cycle of sequencing (99.3%, 99.3%, 98.8%, 97.6%, and 98.0% for cycles 2, 3, 4, 5 and 6 respectively)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.4.1"
  },
  "vscode": {
   "interpreter": {
    "hash": "ba368e385c9cc9598b2a60a5e4fac7f72009cf9cbc64180bf4bd906d7f9b6ec6"
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  }
 },
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 "nbformat_minor": 4
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