code/seq_qc/channel_bleed_through_plot.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure for estimated channel bleed-through based on seq_qc data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook creates Supplementary Figure 5b, based on the estimated bleed-through factors calculated in the [corresponding analysis notebook](./channel_bleed_through_plot.ipynb)."
   ]
  },
  {
   "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[34m192\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m8\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (3): sample, fov, source\n",
      "\u001b[32mdbl\u001b[39m (5): round, A, G, T, C\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": [
    "values <- read_csv(\"analysis/bleed_through/lasso_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": [
    "values %>%\n",
    "\tfilter(round == 0) %>%\n",
    "\tpivot_longer(cols = A:C, names_to = \"target\", values_to = \"bleed_through\") %>%\n",
    "\tfilter(source!=target) %>%\n",
    "\tggplot(aes(x = target, y = bleed_through)) +\n",
    "\t\tgeom_boxplot(outlier.shape=NA) +\n",
    "\t\tfacet_grid(source ~ .) +\n",
    "    xlab(\"Target\") +\n",
    "    ylab(\"Bleed through\") +\n",
    "\t\tgeom_jitter() #aes(color=sample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "ggsave(\"figures/supp-fig5b-bleed_through.svg\", width = 6, height = 4, dpi = 300)"
   ]
  }
 ],
 "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"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}