{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "24e71e1d-e45e-4ebe-adfb-8ef52d66018e",
   "metadata": {},
   "source": [
    "# Figure 3d. Cell-type specific colocalization versus fine-mapping eQTL.\n",
    "\n",
    "The fraction of cell-type specific colocalizations (measured at the level of CoS) for different brain cell types recovered by fine-mapping within the cell type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "755ff008-3b22-4f5c-8d42-6ace3b22d29c",
   "metadata": {},
   "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 1.9.4     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.4     \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",
      "\n",
      "Attaching package: ‘cowplot’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:ggpubr’:\n",
      "\n",
      "    get_legend\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:lubridate’:\n",
      "\n",
      "    stamp\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(ggpattern)\n",
    "library(ggpubr)\n",
    "library(cowplot)\n",
    "res <- readRDS(\"cell_type_specific_colocalization.rds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c23fc73-306b-4665-b0e3-8113518a7773",
   "metadata": {},
   "source": [
    "## Organize input data¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "477ccf54-bc18-40a9-8a8b-9d009771ba80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 6 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Microglia</th><th scope=col>Excitatory\n",
       " neuron</th><th scope=col>Astrocytes</th><th scope=col>Oligodendrocytes</th><th scope=col>Oligodendrocyte\n",
       " Progenitor</th><th scope=col>Inhibitory\n",
       " neuron</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>FALSE</th><td>177</td><td> 769</td><td>411</td><td>287</td><td>101</td><td>268</td></tr>\n",
       "\t<tr><th scope=row>TRUE</th><td>182</td><td>1384</td><td>630</td><td>451</td><td>103</td><td>210</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 6 of type int\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & Microglia & Excitatory\n",
       " neuron & Astrocytes & Oligodendrocytes & Oligodendrocyte\n",
       " Progenitor & Inhibitory\n",
       " neuron\\\\\n",
       "\\hline\n",
       "\tFALSE & 177 &  769 & 411 & 287 & 101 & 268\\\\\n",
       "\tTRUE & 182 & 1384 & 630 & 451 & 103 & 210\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 6 of type int\n",
       "\n",
       "| <!--/--> | Microglia | Excitatory\n",
       " neuron | Astrocytes | Oligodendrocytes | Oligodendrocyte\n",
       " Progenitor | Inhibitory\n",
       " neuron |\n",
       "|---|---|---|---|---|---|---|\n",
       "| FALSE | 177 |  769 | 411 | 287 | 101 | 268 |\n",
       "| TRUE | 182 | 1384 | 630 | 451 | 103 | 210 |\n",
       "\n"
      ],
      "text/plain": [
       "      Microglia Excitatory\\n neuron Astrocytes Oligodendrocytes\n",
       "FALSE 177        769                411        287             \n",
       "TRUE  182       1384                630        451             \n",
       "      Oligodendrocyte\\n Progenitor Inhibitory\\n neuron\n",
       "FALSE 101                          268                \n",
       "TRUE  103                          210                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell_types <- c(\"Microglia\", \"Excitatory\\n neuron\", \"Astrocytes\", \"Oligodendrocytes\", \"Oligodendrocyte\\n Progenitor\", \"Inhibitory\\n neuron\")\n",
    "tables <- sapply(res, function(rr) table(rr$finemapping_overlap))\n",
    "colnames(tables) <- cell_types\n",
    "tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbdb6521-4043-48eb-9049-5de696b49af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data <- data.frame(\n",
    "  CellType = c(\"Microglia\", \"Excitatory\\n neuron\", \"Astrocytes\", \"Oligodendrocytes\", \"Oligodendrocyte\\n Progenitor\", \"Inhibitory\\n neuron\"),\n",
    "  cbonly = tables[1,],\n",
    "  both = tables[2,]\n",
    ")\n",
    "# Convert data to long format\n",
    "data_long <- data %>%\n",
    "  pivot_longer(cols = c(cbonly, both), names_to = \"Status\", values_to = \"Count\") %>%\n",
    "  group_by(CellType) %>%\n",
    "  mutate(TotalCount = sum(Count), Percentage = Count / sum(Count) * 100) %>%\n",
    "  ungroup() %>%\n",
    "  mutate(CellType = fct_reorder(CellType, TotalCount, .desc = TRUE),\n",
    "         Status = recode(Status, \"both\" = \"Shared with Fine-mapping\", \"cbonly\" = \"ColocBoost Only\"))\n",
    "data_long$Status <- factor(data_long$Status, \n",
    "                           levels = c(\"ColocBoost Only\", \"Shared with Fine-mapping\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f4bde6-b327-4d51-98a0-770a013ceec6",
   "metadata": {},
   "source": [
    "## Distribution plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c7272c8c-33c5-48c6-af0a-082105489bb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "library(ggsci)\n",
    "p1 <- ggplot(data_long, aes(y = CellType, x = Count, fill = Status)) +\n",
    "  geom_bar(stat = \"identity\", position = \"stack\") + # Stacked bar chart\n",
    "  scale_fill_npg() +\n",
    "  labs(\n",
    "    x = \"Number of CoS\",\n",
    "    y = NULL,\n",
    "    fill = \"Proportion\",\n",
    "    title = \"Cell-type specific colocalization\"\n",
    "  ) +\n",
    "  theme_minimal(base_size = 15) +\n",
    "  theme(\n",
    "    plot.title = element_text( size = 28, hjust = 0.5 ),\n",
    "    axis.title.x = element_text( margin = margin(t = 0), size = 24), # Adjust x axis title margin\n",
    "    axis.title.y = element_text(margin = margin(r = 10), size = 24), # Adjust y axis title margin\n",
    "    axis.text.x = element_text(margin = margin(t = 0), size = 18), # Adjust x axis text margin\n",
    "    axis.text.y = element_text(margin = margin(r = 5), size = 18), # Adjust y axis text margin\n",
    "    legend.position = \"inside\",\n",
    "    legend.justification = c(0.95, 0.95),\n",
    "    legend.title = element_text(size = 0),\n",
    "    legend.text = element_text(size = 18),\n",
    "    panel.grid.major = element_blank(),\n",
    "    panel.grid.minor = element_blank(),\n",
    "    panel.border = element_rect(color = \"black\", fill = NA, linewidth = 1.5)\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb2896c5-afac-4fea-bfca-203259798ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Microglia</dt><dd>49.3</dd><dt>Excitatory\n",
       " neuron</dt><dd>35.7</dd><dt>Astrocytes</dt><dd>39.5</dd><dt>Oligodendrocytes</dt><dd>38.9</dd><dt>Oligodendrocyte\n",
       " Progenitor</dt><dd>49.5</dd><dt>Inhibitory\n",
       " neuron</dt><dd>56.1</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Microglia] 49.3\n",
       "\\item[Excitatory\n",
       " neuron] 35.7\n",
       "\\item[Astrocytes] 39.5\n",
       "\\item[Oligodendrocytes] 38.9\n",
       "\\item[Oligodendrocyte\n",
       " Progenitor] 49.5\n",
       "\\item[Inhibitory\n",
       " neuron] 56.1\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Microglia\n",
       ":   49.3Excitatory\n",
       " neuron\n",
       ":   35.7Astrocytes\n",
       ":   39.5Oligodendrocytes\n",
       ":   38.9Oligodendrocyte\n",
       " Progenitor\n",
       ":   49.5Inhibitory\n",
       " neuron\n",
       ":   56.1\n",
       "\n"
      ],
      "text/plain": [
       "                   Microglia          Excitatory\\n neuron \n",
       "                        49.3                         35.7 \n",
       "                  Astrocytes             Oligodendrocytes \n",
       "                        39.5                         38.9 \n",
       "Oligodendrocyte\\n Progenitor          Inhibitory\\n neuron \n",
       "                        49.5                         56.1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(tables[1,] / colSums(tables), digits = 3) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56202b49-ac78-4cf1-afe8-4c5389e9c606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LwbIg/U3GhusMEGbUYC8YMbM3fDm3W6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\nM2LECHsf64Qc7l8JHWQUP+6vGbGxl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OOippd7sOAQsRCvM9yeyKDP8qOed4UhFK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\nMuHChQv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o3i+qnE7Hpvq+j4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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73ffc354-0798-4848-96fc-5d6f29100630",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}