code/spot_analysis/cDNA_vRNA.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='toc1_'></a>[Sensitivity of cDNA vs vRNA probing](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Table of contents**<a id='toc0_'></a>    \n",
    "- [Sensitivity of cDNA vs vRNA probing](#toc1_)    \n",
    "  - [Summary](#toc1_1_)    \n",
    "  - [Data loading](#toc1_2_)    \n",
    "  - [Split and filter data](#toc1_3_)    \n",
    "    - [Split treated/control and filter infected cells](#toc1_3_1_)    \n",
    "    - [QC: sample dropouts (no infected cells)](#toc1_3_2_)    \n",
    "    - [Summarize spots per (infected) cell](#toc1_3_3_)    \n",
    "    - [QC distribution of number of infected cells](#toc1_3_4_)    \n",
    "    - [QC spot count outliers (failed segmentation):](#toc1_3_5_)    \n",
    "  - [Spots by Sample](#toc1_4_)    \n",
    "  - [Numbers for main text and Table 1](#toc1_5_)    \n",
    "\n",
    "<!-- vscode-jupyter-toc-config\n",
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    "\t/vscode-jupyter-toc-config -->\n",
    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_1_'></a>[Summary](#toc0_)\n",
    "\n",
    "The sensitivity and specificity of cDNA probing, cDNA with RCA primer (cDNA*), and direct vRNA probing is compared.\n",
    "\n",
    "The following settings are used:\n",
    "- only spots from infected cells (at least 5 spots), discarding all other spots - exception: negative controls (cells are not infected so we keep all spots)\n",
    "- include spots from the cytoplasm and the nucleus\n",
    "- include inner cells and border cells in the analysis (see QC below)\n",
    "- do not crop images (neighboring fields of view have a slight overlap so counts from these FOVs are not completely independent, this could be problematic for statistical tests (not performed here))\n",
    "\n",
    "General observations:\n",
    "- there are far more spots for direct vRNA probing, especially with fewer PLPs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_2_'></a>[Data loading](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "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",
    "library(patchwork)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
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   "outputs": [],
   "source": [
    "options(repr.plot.res = 300, repr.plot.width = 6, repr.plot.height = 4)\n",
    "theme_set(theme_light())\n",
    "theme_update(text = element_text(family = \"Calibri\"), strip.text = element_text(colour = 'black'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m318653\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m23\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m  (4): rep, molecule, sample, fov\n",
      "\u001b[32mdbl\u001b[39m (17): intensity, z, y, x, radius, spot_id, z_min, z_max, y_min, y_max, x...\n",
      "\u001b[33mlgl\u001b[39m  (2): nucleus, border_cell\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": [
    "# file generated with code/spot_analysis/combind_csvs.R\n",
    "spots <- read_tsv(\"analysis/spot_detection/cDNA_vRNA/all_spots.tsv.xz\") %>%\n",
    "  mutate(molecule=str_replace(molecule, \"cDNA_RCAprimer\", \"cDNA*\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_3_'></a>[Split and filter data](#toc0_)\n",
    "\n",
    "For negative controls, all spots are used and the number of spots per cell is calculated. For all other samples, only spots from *infected* cells are used and the number of spots per infected cell is calculated.\n",
    "A cell is defined as *infected* if at least 5 spots are assigned to that cell, including the nucleus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_3_1_'></a>[Split treated/control and filter infected cells](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
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   },
   "outputs": [],
   "source": [
    "pos_spots <- spots %>%\n",
    "\tfilter(!str_detect(sample,\"NegCtrl\")) %>%\n",
    "\tgroup_by(rep,molecule,sample,fov,cell) %>%\n",
    "\tfilter(n()>=5) %>%\n",
    "\tgroup_by(rep,molecule,sample,fov) %>%\n",
    "\tmutate(infected_cells=n_distinct(cell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "neg_spots <- spots %>%\n",
    "\tfilter(str_detect(sample,\"NegCtrl\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_3_2_'></a>[QC: sample dropouts (no infected cells)](#toc0_)\n",
    "\n",
    "After filtering cells with less than 5 spots, some samples are lost as they don't contain any infected cells.\n",
    "\n",
    "This affects particularly the cDNA 1PLP samples. Sensitivity in this setting seems to be very low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "get_fovs <- . %>% ungroup %>% select(rep,molecule,sample,fov) %>% unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mJoining with `by = join_by(rep, molecule, sample, fov)`\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 17 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>rep</th><th scope=col>molecule</th><th scope=col>sample</th><th scope=col>fov</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>rep1</td><td>cDNA </td><td>1PLP</td><td>fov_1</td></tr>\n",
       "\t<tr><td>rep1</td><td>cDNA </td><td>1PLP</td><td>fov_2</td></tr>\n",
       "\t<tr><td>rep1</td><td>cDNA </td><td>1PLP</td><td>fov_3</td></tr>\n",
       "\t<tr><td>rep1</td><td>cDNA </td><td>1PLP</td><td>fov_4</td></tr>\n",
       "\t<tr><td>rep1</td><td>cDNA </td><td>1PLP</td><td>fov_5</td></tr>\n",
       "\t<tr><td>rep1</td><td>cDNA </td><td>1PLP</td><td>fov_6</td></tr>\n",
       "\t<tr><td>rep1</td><td>cDNA </td><td>1PLP</td><td>fov_8</td></tr>\n",
       "\t<tr><td>rep1</td><td>cDNA </td><td>1PLP</td><td>fov_9</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA*</td><td>5PLP</td><td>fov_0</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA </td><td>1PLP</td><td>fov_0</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA </td><td>1PLP</td><td>fov_1</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA </td><td>1PLP</td><td>fov_2</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA </td><td>1PLP</td><td>fov_5</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA </td><td>1PLP</td><td>fov_6</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA </td><td>1PLP</td><td>fov_7</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA </td><td>1PLP</td><td>fov_8</td></tr>\n",
       "\t<tr><td>rep2</td><td>cDNA </td><td>1PLP</td><td>fov_9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 17 × 4\n",
       "\\begin{tabular}{llll}\n",
       " rep & molecule & sample & fov\\\\\n",
       " <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t rep1 & cDNA  & 1PLP & fov\\_1\\\\\n",
       "\t rep1 & cDNA  & 1PLP & fov\\_2\\\\\n",
       "\t rep1 & cDNA  & 1PLP & fov\\_3\\\\\n",
       "\t rep1 & cDNA  & 1PLP & fov\\_4\\\\\n",
       "\t rep1 & cDNA  & 1PLP & fov\\_5\\\\\n",
       "\t rep1 & cDNA  & 1PLP & fov\\_6\\\\\n",
       "\t rep1 & cDNA  & 1PLP & fov\\_8\\\\\n",
       "\t rep1 & cDNA  & 1PLP & fov\\_9\\\\\n",
       "\t rep2 & cDNA* & 5PLP & fov\\_0\\\\\n",
       "\t rep2 & cDNA  & 1PLP & fov\\_0\\\\\n",
       "\t rep2 & cDNA  & 1PLP & fov\\_1\\\\\n",
       "\t rep2 & cDNA  & 1PLP & fov\\_2\\\\\n",
       "\t rep2 & cDNA  & 1PLP & fov\\_5\\\\\n",
       "\t rep2 & cDNA  & 1PLP & fov\\_6\\\\\n",
       "\t rep2 & cDNA  & 1PLP & fov\\_7\\\\\n",
       "\t rep2 & cDNA  & 1PLP & fov\\_8\\\\\n",
       "\t rep2 & cDNA  & 1PLP & fov\\_9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 17 × 4\n",
       "\n",
       "| rep &lt;chr&gt; | molecule &lt;chr&gt; | sample &lt;chr&gt; | fov &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| rep1 | cDNA  | 1PLP | fov_1 |\n",
       "| rep1 | cDNA  | 1PLP | fov_2 |\n",
       "| rep1 | cDNA  | 1PLP | fov_3 |\n",
       "| rep1 | cDNA  | 1PLP | fov_4 |\n",
       "| rep1 | cDNA  | 1PLP | fov_5 |\n",
       "| rep1 | cDNA  | 1PLP | fov_6 |\n",
       "| rep1 | cDNA  | 1PLP | fov_8 |\n",
       "| rep1 | cDNA  | 1PLP | fov_9 |\n",
       "| rep2 | cDNA* | 5PLP | fov_0 |\n",
       "| rep2 | cDNA  | 1PLP | fov_0 |\n",
       "| rep2 | cDNA  | 1PLP | fov_1 |\n",
       "| rep2 | cDNA  | 1PLP | fov_2 |\n",
       "| rep2 | cDNA  | 1PLP | fov_5 |\n",
       "| rep2 | cDNA  | 1PLP | fov_6 |\n",
       "| rep2 | cDNA  | 1PLP | fov_7 |\n",
       "| rep2 | cDNA  | 1PLP | fov_8 |\n",
       "| rep2 | cDNA  | 1PLP | fov_9 |\n",
       "\n"
      ],
      "text/plain": [
       "   rep  molecule sample fov  \n",
       "1  rep1 cDNA     1PLP   fov_1\n",
       "2  rep1 cDNA     1PLP   fov_2\n",
       "3  rep1 cDNA     1PLP   fov_3\n",
       "4  rep1 cDNA     1PLP   fov_4\n",
       "5  rep1 cDNA     1PLP   fov_5\n",
       "6  rep1 cDNA     1PLP   fov_6\n",
       "7  rep1 cDNA     1PLP   fov_8\n",
       "8  rep1 cDNA     1PLP   fov_9\n",
       "9  rep2 cDNA*    5PLP   fov_0\n",
       "10 rep2 cDNA     1PLP   fov_0\n",
       "11 rep2 cDNA     1PLP   fov_1\n",
       "12 rep2 cDNA     1PLP   fov_2\n",
       "13 rep2 cDNA     1PLP   fov_5\n",
       "14 rep2 cDNA     1PLP   fov_6\n",
       "15 rep2 cDNA     1PLP   fov_7\n",
       "16 rep2 cDNA     1PLP   fov_8\n",
       "17 rep2 cDNA     1PLP   fov_9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fovs_no_infected_cells <- get_fovs(spots %>% filter(!str_detect(sample,\"NegCtrl\"))) %>% anti_join(get_fovs(pos_spots))\n",
    "fovs_no_infected_cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_3_3_'></a>[Summarize spots per (infected) cell](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'rep', 'molecule', 'sample', 'fov'. You can\n",
      "override using the `.groups` argument.\n",
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'rep', 'molecule', 'sample', 'fov'. You can\n",
      "override using the `.groups` argument.\n"
     ]
    }
   ],
   "source": [
    "pos_spots_summary <- pos_spots %>% group_by(rep,molecule,sample,fov,infected_cells) %>% summarize(spots = n(), median_intensity=median(intensity), mean_radius=median(radius)) %>% mutate(spots_per_infected_cell = spots/infected_cells)\n",
    "neg_spots_summary <- neg_spots %>% group_by(rep,molecule,sample,fov,n_cells) %>% summarize(spots = n(), median_intensity=median(intensity), mean_radius=median(radius)) %>% mutate(spots_per_cell = spots/n_cells)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_3_4_'></a>[QC distribution of number of infected cells](#toc0_)"
   ]
  },
  {
   "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": 600,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_spots_summary %>% ggplot(aes(x=infected_cells, color=molecule)) + geom_density()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of infected cells should be roughly equal for all molecules. This plot indicates, that the number of cells detected as infected differs. Already hinting at different sensitivities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_3_5_'></a>[QC spot count outliers (failed segmentation):](#toc0_)\n",
    "\n",
    "In some cases the segmentation may fail and very few nuclei are detected, therefore the spots/cell count would be massively inflated. Those samples need to be filtered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 0 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>rep</th><th scope=col>molecule</th><th scope=col>sample</th><th scope=col>fov</th><th scope=col>infected_cells</th><th scope=col>spots</th><th scope=col>median_intensity</th><th scope=col>mean_radius</th><th scope=col>spots_per_infected_cell</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 0 × 9\n",
       "\\begin{tabular}{lllllllll}\n",
       " rep & molecule & sample & fov & infected\\_cells & spots & median\\_intensity & mean\\_radius & spots\\_per\\_infected\\_cell\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <int> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 0 × 9\n",
       "\n",
       "| rep &lt;chr&gt; | molecule &lt;chr&gt; | sample &lt;chr&gt; | fov &lt;chr&gt; | infected_cells &lt;int&gt; | spots &lt;int&gt; | median_intensity &lt;dbl&gt; | mean_radius &lt;dbl&gt; | spots_per_infected_cell &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "\n"
      ],
      "text/plain": [
       "     rep molecule sample fov infected_cells spots median_intensity mean_radius\n",
       "     spots_per_infected_cell"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_spots_summary %>% filter(spots_per_infected_cell>1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "pos_spots_summary <- pos_spots_summary %>% filter(spots_per_infected_cell < 1000)\n",
    "neg_spots_summary <- neg_spots_summary %>% filter(spots_per_cell < 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_4_'></a>[Spots by Sample](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Warning: one outlier sample with 196 spots per cell in 5PLP vRNA not shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "pos_plot <- pos_spots_summary %>%\n",
    "\tmutate(plp = parse_number(sample)) %>%\n",
    "\tggplot(aes(plp,spots_per_infected_cell))+#,color=segment)) +\n",
    "\t\tgeom_jitter(width=.5)+\n",
    "\t\tgeom_boxplot(aes(group=plp), outlier.shape = NA) +\n",
    "\t\tfacet_grid(molecule ~ plp, scale=\"free_x\") +\n",
    "\t\tscale_x_continuous(breaks = c(1,5,10)) +\n",
    "\t\tcoord_cartesian(ylim=c(0,100)) +\n",
    "\t\txlab(\"PLPs per target\") + ylab(\"RCPs per infected cell\") +\n",
    "\t\t# scale_y_log10() +\n",
    "\t\tNULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ggsave(\"figures/fig1c-cDNA_vRNA-spot_counts.svg\", width = 4.5, height = 4, dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "neg_plot <- neg_spots_summary %>%\n",
    "\tggplot(aes(sample,spots_per_cell))+#,color=segment)) +\n",
    "\t\tgeom_jitter()+#aes(color=rep)) +\n",
    "\t\tgeom_boxplot(aes(group=sample), outlier.shape = NA) +\n",
    "\t\tfacet_grid(molecule ~ .) +\n",
    "\t\tcoord_cartesian(ylim=c(0,100)) +\n",
    "\t\tscale_x_discrete(breaks = NULL) +\n",
    "\t\txlab(\"Neg. Control\") + ylab(\"RCPs per cell\") +\n",
    "\t\tNULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ggsave(\"figures/supp-fig1b-cDNA_vRNA-spot_counts-neg_ctrl.svg\", width = 2, height = 4, dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neg_plot + pos_plot + plot_layout(widths = c(1, 3)) #& scale_y_continuous(limits = c(0, 45)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_5_'></a>[Numbers for main text and Table 1](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of spots in the nucleus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'molecule'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    }
   ],
   "source": [
    "percent_nucleus <- spots %>%\n",
    "  filter(sample!=\"NegCtrlPLP\") %>%\n",
    "  group_by(molecule, sample) %>%\n",
    "  summarize(nuc = sum(nucleus)/n()) %>%\n",
    "  ungroup %>%\n",
    "  arrange(molecule, parse_number(sample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of infected cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'rep', 'molecule', 'sample', 'fov'. You can\n",
      "override using the `.groups` argument.\n",
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'molecule'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    }
   ],
   "source": [
    "percent_infected <- spots %>%\n",
    "\tfilter(!str_detect(sample,\"NegCtrl\")) %>%\n",
    "\tgroup_by(rep,molecule,sample,fov,cell) %>%\n",
    "\tsummarize(infected=n()>=5) %>%\n",
    "  group_by(molecule,sample) %>%\n",
    "  summarize(infected=mean(infected)) %>%\n",
    "  ungroup %>%\n",
    "  arrange(molecule, parse_number(sample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spots per infected cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'molecule'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    }
   ],
   "source": [
    "spots_per_infected <- pos_spots_summary %>%\n",
    "  mutate(spots = spots_per_infected_cell*infected_cells) %>% # go back to total spots to aggregate across reps and fovs\n",
    "  group_by(molecule,sample) %>%\n",
    "  summarize(infected_cells = sum(infected_cells), spots = sum(spots), spots_per_infected_cell = spots/infected_cells)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine above tables into one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 9 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>molecule</th><th scope=col>sample</th><th scope=col>nuc</th><th scope=col>infected</th><th scope=col>infected_cells</th><th scope=col>spots</th><th scope=col>spots_per_infected_cell</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><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;int&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>cDNA </td><td>1PLP </td><td>0.8272425</td><td>0.0195122</td><td>   4</td><td>   24</td><td> 6.000000</td></tr>\n",
       "\t<tr><td>cDNA </td><td>5PLP </td><td>0.8353197</td><td>0.4573820</td><td> 601</td><td>11122</td><td>18.505824</td></tr>\n",
       "\t<tr><td>cDNA </td><td>10PLP</td><td>0.6613484</td><td>0.5286526</td><td>1024</td><td>27662</td><td>27.013672</td></tr>\n",
       "\t<tr><td>cDNA*</td><td>1PLP </td><td>0.8511128</td><td>0.1198428</td><td>  61</td><td>  577</td><td> 9.459016</td></tr>\n",
       "\t<tr><td>cDNA*</td><td>5PLP </td><td>0.8576760</td><td>0.3676341</td><td> 418</td><td> 4469</td><td>10.691388</td></tr>\n",
       "\t<tr><td>cDNA*</td><td>10PLP</td><td>0.7774279</td><td>0.6557998</td><td> 865</td><td>22616</td><td>26.145665</td></tr>\n",
       "\t<tr><td>vRNA </td><td>1PLP </td><td>0.6763871</td><td>0.9801061</td><td>1478</td><td>74219</td><td>50.215832</td></tr>\n",
       "\t<tr><td>vRNA </td><td>5PLP </td><td>0.5846468</td><td>0.9534731</td><td>1455</td><td>83430</td><td>57.340206</td></tr>\n",
       "\t<tr><td>vRNA </td><td>10PLP</td><td>0.5754792</td><td>0.9529164</td><td>1356</td><td>87350</td><td>64.417404</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 9 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " molecule & sample & nuc & infected & infected\\_cells & spots & spots\\_per\\_infected\\_cell\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t cDNA  & 1PLP  & 0.8272425 & 0.0195122 &    4 &    24 &  6.000000\\\\\n",
       "\t cDNA  & 5PLP  & 0.8353197 & 0.4573820 &  601 & 11122 & 18.505824\\\\\n",
       "\t cDNA  & 10PLP & 0.6613484 & 0.5286526 & 1024 & 27662 & 27.013672\\\\\n",
       "\t cDNA* & 1PLP  & 0.8511128 & 0.1198428 &   61 &   577 &  9.459016\\\\\n",
       "\t cDNA* & 5PLP  & 0.8576760 & 0.3676341 &  418 &  4469 & 10.691388\\\\\n",
       "\t cDNA* & 10PLP & 0.7774279 & 0.6557998 &  865 & 22616 & 26.145665\\\\\n",
       "\t vRNA  & 1PLP  & 0.6763871 & 0.9801061 & 1478 & 74219 & 50.215832\\\\\n",
       "\t vRNA  & 5PLP  & 0.5846468 & 0.9534731 & 1455 & 83430 & 57.340206\\\\\n",
       "\t vRNA  & 10PLP & 0.5754792 & 0.9529164 & 1356 & 87350 & 64.417404\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 9 × 7\n",
       "\n",
       "| molecule &lt;chr&gt; | sample &lt;chr&gt; | nuc &lt;dbl&gt; | infected &lt;dbl&gt; | infected_cells &lt;int&gt; | spots &lt;dbl&gt; | spots_per_infected_cell &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| cDNA  | 1PLP  | 0.8272425 | 0.0195122 |    4 |    24 |  6.000000 |\n",
       "| cDNA  | 5PLP  | 0.8353197 | 0.4573820 |  601 | 11122 | 18.505824 |\n",
       "| cDNA  | 10PLP | 0.6613484 | 0.5286526 | 1024 | 27662 | 27.013672 |\n",
       "| cDNA* | 1PLP  | 0.8511128 | 0.1198428 |   61 |   577 |  9.459016 |\n",
       "| cDNA* | 5PLP  | 0.8576760 | 0.3676341 |  418 |  4469 | 10.691388 |\n",
       "| cDNA* | 10PLP | 0.7774279 | 0.6557998 |  865 | 22616 | 26.145665 |\n",
       "| vRNA  | 1PLP  | 0.6763871 | 0.9801061 | 1478 | 74219 | 50.215832 |\n",
       "| vRNA  | 5PLP  | 0.5846468 | 0.9534731 | 1455 | 83430 | 57.340206 |\n",
       "| vRNA  | 10PLP | 0.5754792 | 0.9529164 | 1356 | 87350 | 64.417404 |\n",
       "\n"
      ],
      "text/plain": [
       "  molecule sample nuc       infected  infected_cells spots\n",
       "1 cDNA     1PLP   0.8272425 0.0195122    4              24\n",
       "2 cDNA     5PLP   0.8353197 0.4573820  601           11122\n",
       "3 cDNA     10PLP  0.6613484 0.5286526 1024           27662\n",
       "4 cDNA*    1PLP   0.8511128 0.1198428   61             577\n",
       "5 cDNA*    5PLP   0.8576760 0.3676341  418            4469\n",
       "6 cDNA*    10PLP  0.7774279 0.6557998  865           22616\n",
       "7 vRNA     1PLP   0.6763871 0.9801061 1478           74219\n",
       "8 vRNA     5PLP   0.5846468 0.9534731 1455           83430\n",
       "9 vRNA     10PLP  0.5754792 0.9529164 1356           87350\n",
       "  spots_per_infected_cell\n",
       "1  6.000000              \n",
       "2 18.505824              \n",
       "3 27.013672              \n",
       "4  9.459016              \n",
       "5 10.691388              \n",
       "6 26.145665              \n",
       "7 50.215832              \n",
       "8 57.340206              \n",
       "9 64.417404              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined <- left_join(percent_nucleus, percent_infected, by=c(\"molecule\",\"sample\")) %>%\n",
    "  left_join(spots_per_infected, by=c(\"molecule\",\"sample\"))\n",
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "library(gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!doctype html>\n",
       "<html>\n",
       "\t<head>\n",
       "\t\t<meta charset=\"utf-8\">\n",
       "\t\t\n",
       "\t</head>\n",
       "\t<body>\n",
       "\t\t<div id=\"dkhclvessl\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n",
       "  <style>#dkhclvessl table {\n",
       "  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n",
       "  -webkit-font-smoothing: antialiased;\n",
       "  -moz-osx-font-smoothing: grayscale;\n",
       "}\n",
       "\n",
       "#dkhclvessl thead, #dkhclvessl tbody, #dkhclvessl tfoot, #dkhclvessl tr, #dkhclvessl td, #dkhclvessl th {\n",
       "  border-style: none;\n",
       "}\n",
       "\n",
       "#dkhclvessl p {\n",
       "  margin: 0;\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_table {\n",
       "  display: table;\n",
       "  border-collapse: collapse;\n",
       "  line-height: normal;\n",
       "  margin-left: auto;\n",
       "  margin-right: auto;\n",
       "  color: #333333;\n",
       "  font-size: 16px;\n",
       "  font-weight: normal;\n",
       "  font-style: normal;\n",
       "  background-color: #FFFFFF;\n",
       "  width: auto;\n",
       "  border-top-style: solid;\n",
       "  border-top-width: 2px;\n",
       "  border-top-color: #A8A8A8;\n",
       "  border-right-style: none;\n",
       "  border-right-width: 2px;\n",
       "  border-right-color: #D3D3D3;\n",
       "  border-bottom-style: solid;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #A8A8A8;\n",
       "  border-left-style: none;\n",
       "  border-left-width: 2px;\n",
       "  border-left-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_caption {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_title {\n",
       "  color: #333333;\n",
       "  font-size: 125%;\n",
       "  font-weight: initial;\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  border-bottom-color: #FFFFFF;\n",
       "  border-bottom-width: 0;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_subtitle {\n",
       "  color: #333333;\n",
       "  font-size: 85%;\n",
       "  font-weight: initial;\n",
       "  padding-top: 3px;\n",
       "  padding-bottom: 5px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  border-top-color: #FFFFFF;\n",
       "  border-top-width: 0;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_heading {\n",
       "  background-color: #FFFFFF;\n",
       "  text-align: center;\n",
       "  border-bottom-color: #FFFFFF;\n",
       "  border-left-style: none;\n",
       "  border-left-width: 1px;\n",
       "  border-left-color: #D3D3D3;\n",
       "  border-right-style: none;\n",
       "  border-right-width: 1px;\n",
       "  border-right-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_bottom_border {\n",
       "  border-bottom-style: solid;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_col_headings {\n",
       "  border-top-style: solid;\n",
       "  border-top-width: 2px;\n",
       "  border-top-color: #D3D3D3;\n",
       "  border-bottom-style: solid;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "  border-left-style: none;\n",
       "  border-left-width: 1px;\n",
       "  border-left-color: #D3D3D3;\n",
       "  border-right-style: none;\n",
       "  border-right-width: 1px;\n",
       "  border-right-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_col_heading {\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  font-size: 100%;\n",
       "  font-weight: normal;\n",
       "  text-transform: inherit;\n",
       "  border-left-style: none;\n",
       "  border-left-width: 1px;\n",
       "  border-left-color: #D3D3D3;\n",
       "  border-right-style: none;\n",
       "  border-right-width: 1px;\n",
       "  border-right-color: #D3D3D3;\n",
       "  vertical-align: bottom;\n",
       "  padding-top: 5px;\n",
       "  padding-bottom: 6px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  overflow-x: hidden;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_column_spanner_outer {\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  font-size: 100%;\n",
       "  font-weight: normal;\n",
       "  text-transform: inherit;\n",
       "  padding-top: 0;\n",
       "  padding-bottom: 0;\n",
       "  padding-left: 4px;\n",
       "  padding-right: 4px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_column_spanner_outer:first-child {\n",
       "  padding-left: 0;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_column_spanner_outer:last-child {\n",
       "  padding-right: 0;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_column_spanner {\n",
       "  border-bottom-style: solid;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "  vertical-align: bottom;\n",
       "  padding-top: 5px;\n",
       "  padding-bottom: 5px;\n",
       "  overflow-x: hidden;\n",
       "  display: inline-block;\n",
       "  width: 100%;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_spanner_row {\n",
       "  border-bottom-style: hidden;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_group_heading {\n",
       "  padding-top: 8px;\n",
       "  padding-bottom: 8px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  font-size: 100%;\n",
       "  font-weight: initial;\n",
       "  text-transform: inherit;\n",
       "  border-top-style: solid;\n",
       "  border-top-width: 2px;\n",
       "  border-top-color: #D3D3D3;\n",
       "  border-bottom-style: solid;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "  border-left-style: none;\n",
       "  border-left-width: 1px;\n",
       "  border-left-color: #D3D3D3;\n",
       "  border-right-style: none;\n",
       "  border-right-width: 1px;\n",
       "  border-right-color: #D3D3D3;\n",
       "  vertical-align: middle;\n",
       "  text-align: left;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_empty_group_heading {\n",
       "  padding: 0.5px;\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  font-size: 100%;\n",
       "  font-weight: initial;\n",
       "  border-top-style: solid;\n",
       "  border-top-width: 2px;\n",
       "  border-top-color: #D3D3D3;\n",
       "  border-bottom-style: solid;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "  vertical-align: middle;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_from_md > :first-child {\n",
       "  margin-top: 0;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_from_md > :last-child {\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_row {\n",
       "  padding-top: 8px;\n",
       "  padding-bottom: 8px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  margin: 10px;\n",
       "  border-top-style: solid;\n",
       "  border-top-width: 1px;\n",
       "  border-top-color: #D3D3D3;\n",
       "  border-left-style: none;\n",
       "  border-left-width: 1px;\n",
       "  border-left-color: #D3D3D3;\n",
       "  border-right-style: none;\n",
       "  border-right-width: 1px;\n",
       "  border-right-color: #D3D3D3;\n",
       "  vertical-align: middle;\n",
       "  overflow-x: hidden;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_stub {\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  font-size: 100%;\n",
       "  font-weight: initial;\n",
       "  text-transform: inherit;\n",
       "  border-right-style: solid;\n",
       "  border-right-width: 2px;\n",
       "  border-right-color: #D3D3D3;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_stub_row_group {\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  font-size: 100%;\n",
       "  font-weight: initial;\n",
       "  text-transform: inherit;\n",
       "  border-right-style: solid;\n",
       "  border-right-width: 2px;\n",
       "  border-right-color: #D3D3D3;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_row_group_first td {\n",
       "  border-top-width: 2px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_row_group_first th {\n",
       "  border-top-width: 2px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_summary_row {\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  text-transform: inherit;\n",
       "  padding-top: 8px;\n",
       "  padding-bottom: 8px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_first_summary_row {\n",
       "  border-top-style: solid;\n",
       "  border-top-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_first_summary_row.thick {\n",
       "  border-top-width: 2px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_last_summary_row {\n",
       "  padding-top: 8px;\n",
       "  padding-bottom: 8px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  border-bottom-style: solid;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_grand_summary_row {\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  text-transform: inherit;\n",
       "  padding-top: 8px;\n",
       "  padding-bottom: 8px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_first_grand_summary_row {\n",
       "  padding-top: 8px;\n",
       "  padding-bottom: 8px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  border-top-style: double;\n",
       "  border-top-width: 6px;\n",
       "  border-top-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_last_grand_summary_row_top {\n",
       "  padding-top: 8px;\n",
       "  padding-bottom: 8px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "  border-bottom-style: double;\n",
       "  border-bottom-width: 6px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_striped {\n",
       "  background-color: rgba(128, 128, 128, 0.05);\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_table_body {\n",
       "  border-top-style: solid;\n",
       "  border-top-width: 2px;\n",
       "  border-top-color: #D3D3D3;\n",
       "  border-bottom-style: solid;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_footnotes {\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  border-bottom-style: none;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "  border-left-style: none;\n",
       "  border-left-width: 2px;\n",
       "  border-left-color: #D3D3D3;\n",
       "  border-right-style: none;\n",
       "  border-right-width: 2px;\n",
       "  border-right-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_footnote {\n",
       "  margin: 0px;\n",
       "  font-size: 90%;\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_sourcenotes {\n",
       "  color: #333333;\n",
       "  background-color: #FFFFFF;\n",
       "  border-bottom-style: none;\n",
       "  border-bottom-width: 2px;\n",
       "  border-bottom-color: #D3D3D3;\n",
       "  border-left-style: none;\n",
       "  border-left-width: 2px;\n",
       "  border-left-color: #D3D3D3;\n",
       "  border-right-style: none;\n",
       "  border-right-width: 2px;\n",
       "  border-right-color: #D3D3D3;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_sourcenote {\n",
       "  font-size: 90%;\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "  padding-left: 5px;\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_left {\n",
       "  text-align: left;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_center {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_right {\n",
       "  text-align: right;\n",
       "  font-variant-numeric: tabular-nums;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_font_normal {\n",
       "  font-weight: normal;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_font_bold {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_font_italic {\n",
       "  font-style: italic;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_super {\n",
       "  font-size: 65%;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_footnote_marks {\n",
       "  font-size: 75%;\n",
       "  vertical-align: 0.4em;\n",
       "  position: initial;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_asterisk {\n",
       "  font-size: 100%;\n",
       "  vertical-align: 0;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_indent_1 {\n",
       "  text-indent: 5px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_indent_2 {\n",
       "  text-indent: 10px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_indent_3 {\n",
       "  text-indent: 15px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_indent_4 {\n",
       "  text-indent: 20px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .gt_indent_5 {\n",
       "  text-indent: 25px;\n",
       "}\n",
       "\n",
       "#dkhclvessl .katex-display {\n",
       "  display: inline-flex !important;\n",
       "  margin-bottom: 0.75em !important;\n",
       "}\n",
       "\n",
       "#dkhclvessl div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {\n",
       "  height: 0px !important;\n",
       "}\n",
       "</style>\n",
       "  <table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n",
       "  <thead>\n",
       "    <tr class=\"gt_col_headings gt_spanner_row\">\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"2\" colspan=\"1\" scope=\"col\" id=\"\"></th>\n",
       "      <th class=\"gt_center gt_columns_top_border gt_column_spanner_outer\" rowspan=\"1\" colspan=\"3\" scope=\"colgroup\" id=\"cDNA\">\n",
       "        <span class=\"gt_column_spanner\">cDNA</span>\n",
       "      </th>\n",
       "      <th class=\"gt_center gt_columns_top_border gt_column_spanner_outer\" rowspan=\"1\" colspan=\"3\" scope=\"colgroup\" id=\"cDNA with RCA primer\">\n",
       "        <span class=\"gt_column_spanner\">cDNA with RCA primer</span>\n",
       "      </th>\n",
       "      <th class=\"gt_center gt_columns_top_border gt_column_spanner_outer\" rowspan=\"1\" colspan=\"3\" scope=\"colgroup\" id=\"direct RNA\">\n",
       "        <span class=\"gt_column_spanner\">direct RNA</span>\n",
       "      </th>\n",
       "    </tr>\n",
       "    <tr class=\"gt_col_headings\">\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Percentage of RCP spots in the nucleus\">Percentage of RCP spots in the nucleus</th>\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Percentage of infected cells\">Percentage of infected cells</th>\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Mean RCP spots per infected cells\">Mean RCP spots per infected cells</th>\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Percentage of RCP spots in the nucleus\">Percentage of RCP spots in the nucleus</th>\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Percentage of infected cells\">Percentage of infected cells</th>\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Mean RCP spots per infected cells\">Mean RCP spots per infected cells</th>\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Percentage of RCP spots in the nucleus\">Percentage of RCP spots in the nucleus</th>\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Percentage of infected cells\">Percentage of infected cells</th>\n",
       "      <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Mean RCP spots per infected cells\">Mean RCP spots per infected cells</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody class=\"gt_table_body\">\n",
       "    <tr><th id=\"stub_1_1\" scope=\"row\" class=\"gt_row gt_left gt_stub\">1 PLP</th>\n",
       "<td headers=\"stub_1_1 cDNA_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.827</td>\n",
       "<td headers=\"stub_1_1 cDNA_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.020</td>\n",
       "<td headers=\"stub_1_1 cDNA_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">6.0</td>\n",
       "<td headers=\"stub_1_1 cDNA*_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.851</td>\n",
       "<td headers=\"stub_1_1 cDNA*_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.120</td>\n",
       "<td headers=\"stub_1_1 cDNA*_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">9.5</td>\n",
       "<td headers=\"stub_1_1 vRNA_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.676</td>\n",
       "<td headers=\"stub_1_1 vRNA_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.980</td>\n",
       "<td headers=\"stub_1_1 vRNA_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">50.2</td></tr>\n",
       "    <tr><th id=\"stub_1_2\" scope=\"row\" class=\"gt_row gt_left gt_stub\">5 PLPs</th>\n",
       "<td headers=\"stub_1_2 cDNA_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.835</td>\n",
       "<td headers=\"stub_1_2 cDNA_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.457</td>\n",
       "<td headers=\"stub_1_2 cDNA_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">18.5</td>\n",
       "<td headers=\"stub_1_2 cDNA*_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.858</td>\n",
       "<td headers=\"stub_1_2 cDNA*_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.368</td>\n",
       "<td headers=\"stub_1_2 cDNA*_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">10.7</td>\n",
       "<td headers=\"stub_1_2 vRNA_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.585</td>\n",
       "<td headers=\"stub_1_2 vRNA_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.953</td>\n",
       "<td headers=\"stub_1_2 vRNA_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">57.3</td></tr>\n",
       "    <tr><th id=\"stub_1_3\" scope=\"row\" class=\"gt_row gt_left gt_stub\">10 PLPs</th>\n",
       "<td headers=\"stub_1_3 cDNA_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.661</td>\n",
       "<td headers=\"stub_1_3 cDNA_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.529</td>\n",
       "<td headers=\"stub_1_3 cDNA_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">27.0</td>\n",
       "<td headers=\"stub_1_3 cDNA*_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.777</td>\n",
       "<td headers=\"stub_1_3 cDNA*_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.656</td>\n",
       "<td headers=\"stub_1_3 cDNA*_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">26.1</td>\n",
       "<td headers=\"stub_1_3 vRNA_Percentage_of_RCP_spots_in_the_nucleus\" class=\"gt_row gt_right\">0.575</td>\n",
       "<td headers=\"stub_1_3 vRNA_Percentage_of_infected_cells\" class=\"gt_row gt_right\">0.953</td>\n",
       "<td headers=\"stub_1_3 vRNA_Mean_RCP_spots_per_infected_cells\" class=\"gt_row gt_right\">64.4</td></tr>\n",
       "  </tbody>\n",
       "  \n",
       "  \n",
       "</table>\n",
       "</div>\n",
       "\t</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "Shiny tags cannot be represented in plain text (need html)"
      ]
     },
     "metadata": {
      "text/html": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table1 <- combined %>%\n",
    "  select(-infected_cells, -spots) %>%\n",
    "  rename(\n",
    "    Percentage_of_RCP_spots_in_the_nucleus=nuc,\n",
    "    Percentage_of_infected_cells=infected,\n",
    "    Mean_RCP_spots_per_infected_cells=spots_per_infected_cell) %>%\n",
    "  mutate(sample=str_replace(sample, \"PLP\", \" PLPs\"), sample=str_replace(sample, \"1 PLPs\", \"1 PLP\")) %>%\n",
    "  pivot_longer(3:5) %>%\n",
    "  pivot_wider(names_from = c(molecule,name), values_from = value) %>%\n",
    "  gt(rowname_col = \"sample\") %>%\n",
    "  tab_spanner(\"cDNA\", starts_with(\"cDNA_\")) %>%\n",
    "  tab_spanner(\"cDNA with RCA primer\", starts_with(\"cDNA*\")) %>%\n",
    "  tab_spanner(\"direct RNA\", starts_with(\"vRNA\")) %>%\n",
    "  cols_label_with(fn=\\(x){str_remove(x,pattern=\"..NA.?_\") |> str_replace_all(\"_\",\" \")} ) %>%\n",
    "  fmt_number(decimals=3) %>%\n",
    "  fmt_number(columns = contains(\"Mean\"), decimals = 1)\n",
    "\n",
    "table1 %>% gt:::as.tags.gt_tbl()"
   ]
  }
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