code/spot_analysis/specificity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='toc1_'></a>[Specificity PR8 vs StPt](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Table of contents**<a id='toc0_'></a>    \n",
    "- [Specificity PR8 vs StPt](#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 spot count outliers (failed segmentation):](#toc1_3_4_)    \n",
    "  - [Spots per (infected) cell](#toc1_4_)    \n",
    "\n",
    "<!-- vscode-jupyter-toc-config\n",
    "\tnumbering=false\n",
    "\tanchor=true\n",
    "\tflat=false\n",
    "\tminLevel=1\n",
    "\tmaxLevel=6\n",
    "\t/vscode-jupyter-toc-config -->\n",
    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_1_'></a>[Summary](#toc0_)\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",
    "\n",
    "In contrast to other samples we here\n",
    "- do ignore spots from the bottom and left 10% of each FOV (neighboring fields of view have a slight overlap so spots from that region are only considered once), this avoids dependence between FOVs which would be problematic for statistical tests\n",
    "\n",
    "General observations:\n",
    "- probes are highly specific, there is almost no signal from probes designed for strains that are not present\n",
    "- there are higher spot counts for PR8 compared to StPT, both in the single infections and in the co-infection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_2_'></a>[Data loading](#toc0_)"
   ]
  },
  {
   "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",
    "library(patchwork)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "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": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m63128\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, sample, fov, strain\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": [
    "spots <- read_tsv(\"analysis/spot_detection/specificity/all_spots.tsv.xz\", na=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To avoid multiple counting of spots in the overlap of tiles, only the top left 90% of each tile are used (this is not the ideal way to do this, as it also removes regions from border FOVs that have no overlap)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "spots <- spots %>% filter(x<=1800, y<=1800)"
   ]
  },
  {
   "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": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "pos_spots <- spots %>%\n",
    "\tfilter(!str_detect(sample,\"NegCtrl\")) %>%\n",
    "\tgroup_by(rep,sample,fov,cell) %>%\n",
    "\tfilter(n()>=5) %>%\n",
    "\tgroup_by(rep,sample,fov) %>%\n",
    "\tmutate(infected_cells=n_distinct(cell)) %>%\n",
    "\tungroup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 may be lost as they don't contain any infected cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "get_fovs <- . %>% ungroup %>% select(rep,sample,fov) %>% unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mJoining with `by = join_by(rep, sample, fov)`\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 0 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>rep</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 0 × 3\n",
       "\\begin{tabular}{lll}\n",
       " rep & sample & fov\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 0 × 3\n",
       "\n",
       "| rep &lt;chr&gt; | sample &lt;chr&gt; | fov &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "\n"
      ],
      "text/plain": [
       "     rep sample fov"
      ]
     },
     "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": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'rep', 'sample', 'fov', 'strain'. You can\n",
      "override using the `.groups` argument.\n",
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'rep', 'sample', 'fov', 'strain'. You can\n",
      "override using the `.groups` argument.\n"
     ]
    }
   ],
   "source": [
    "pos_spots_summary <- pos_spots %>% group_by(rep,sample,fov,strain,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,sample,fov,strain,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 spot count outliers (failed segmentation):](#toc0_)\n",
    "\n",
    "In some cases the segmentation failed and very few nuclei were detected, therefore the spots/cell count is 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>sample</th><th scope=col>fov</th><th scope=col>strain</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 & sample & fov & strain & 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; | sample &lt;chr&gt; | fov &lt;chr&gt; | strain &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 sample fov strain 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 per (infected) cell](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "pos_plot <- pos_spots_summary %>%\n",
    "\tggplot(aes(sample,spots_per_infected_cell, color=strain))+\n",
    "\t\tgeom_boxplot(outlier.shape = NA) +\n",
    "\t\tgeom_point(position=position_jitterdodge()) +\n",
    "\t\tylim(0,52) +\n",
    "\t\tylab(\"RCPs per infected cell\") +\n",
    "\t\tNULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ggsave(\"figures/fig1e-specificity-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=strain))+\n",
    "\t\tgeom_boxplot(outlier.shape = NA) +\n",
    "\t\tgeom_point(position=position_jitterdodge()) +\n",
    "\t\tylim(0,52) +\n",
    "\t\tylab(\"RCPs per cell\") +\n",
    "\t\tNULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ggsave(\"figures/supp-fig1d-specificity-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": [
    "# only use a single legend\n",
    "pos_plot + neg_plot + plot_layout(widths = c(3, 1), guides=\"collect\")"
   ]
  }
 ],
 "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"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}