Note-1 ReDeeM development and optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4fa80330-5d05-46f7-ac81-ff8d4f555189",
   "metadata": {},
   "source": [
    "# Note-1: ReDeeM development and optimization\n",
    "---\n",
    "\n",
    "**Author:** Chen Weng\n",
    "\n",
    "**Date:** November 9, 2023\n",
    "\n",
    "## Description\n",
    "\n",
    "This Jupyter Notebook aims reproduce the major analysis in Supplementary Fig. 1\n",
    "\n",
    "## Related Figures\n",
    "- Supplementary Fig. 1 [Section 7, Section 8, Section 9]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd43c7e-c156-4f62-8033-ad16678f0324",
   "metadata": {},
   "source": [
    "# Install redeemR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5daf945-8d23-4422-8a54-c1cfaa54b13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#devtools::install_github(\"chenweng1991/redeemR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5389da-fc76-42bf-b500-b73acc4cd6be",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cb8f423e-b4d3-40d6-9cfa-47632e662cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(plyr)\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "library(gridExtra)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d3d612-e7c3-4fe8-980e-5546cdba96c3",
   "metadata": {},
   "source": [
    "# Set path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1af9d72b-2dea-4014-a04b-2180ebf28846",
   "metadata": {},
   "outputs": [],
   "source": [
    "## set main directory to the reproducibility_pub\n",
    "Dir=\"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/reproducibility_pub\" # Change this to your actual path **/reproducibility_pub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994cb87e-e3f0-41f6-9eee-579a7851c66d",
   "metadata": {},
   "source": [
    "# Test Conditions - full data, NonGDN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1f4200-d0af-44e0-867a-9f2fcc04e11d",
   "metadata": {},
   "source": [
    "|Condition|Fix|Perm  |BSA | \n",
    "|-----|:-----|:---:|:-----:|\n",
    "|A |0.1% FA|0.1% NP40|1% BSA |\n",
    "|B |0.1% FA|0.1% NP40|0.01% BSA |\n",
    "|C |1% PFA|0.1% NP40|1% BSA |\n",
    "|D |1% PFA|0.1% NP40|0.01% BSA |\n",
    "|E |1% PFA|-|1% BSA |0.01% BSA |\n",
    "|F |1% PFA|-|1% BSA |0.01% BSA |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f451b941-bd1f-4779-ad03-5c6e8367a47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read in summary\n",
    "NonGDN.total.summary<-read.table(paste(Dir,\"/data/optimize/atac.fragment.summary.NonGDN\",sep=\"\"))\n",
    "NonGDN.mito.summary<-read.table(paste(Dir,\"/data/optimize/atac.fragment.mito.summary.NonGDN\",sep=\"\"))\n",
    "names(NonGDN.total.summary)<-c(\"cell\",\"Frag\",\"Reads\")\n",
    "names(NonGDN.mito.summary)<-c(\"cell\",\"mitoFrag\",\"mitoReads\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cf9521c7-64bc-4669-bfd1-edaded3926a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read In Hash Call and Hash Hash Dic\n",
    "HashDic<-read.csv(paste(Dir,\"/data/optimize/NonGDN.HashDic.csv\",sep=\"\"),header=F)\n",
    "HashCall<-read.table(paste(Dir,\"/data/optimize/NonGDN_calls.tsv\",sep=\"\"),header=T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1d32350c-3288-4af0-b11f-18b319df454a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Process the Hash table\n",
    "HashCall$Group<-mapvalues(HashCall$call,from=HashDic$V1,to=HashDic$V2)\n",
    "HashCall.non.filtered<-subset(HashCall,sig>=0.6)[,c(\"cell\",\"Group\")]\n",
    "HashCall.non.filtered$cell<-paste(HashCall.non.filtered$cell,\"-1\",sep=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "06d1657e-2215-4413-b1f3-ffa93310b59a",
   "metadata": {},
   "outputs": [],
   "source": [
    "NonGDN.summary<-merge(HashCall.non.filtered,NonGDN.total.summary,by=\"cell\")  %>% merge(.,NonGDN.mito.summary,by=\"cell\")\n",
    "NonGDN.summary$mitopctReads<-NonGDN.summary$mitoReads/NonGDN.summary$Reads\n",
    "NonGDN.summary$mitopctFrag<-NonGDN.summary$mitoFrag/NonGDN.summary$Frag\n",
    "NonGDN.summary$mitocomplex<-NonGDN.summary$mitoFrag/NonGDN.summary$mitoReads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e396af5d-d388-4b37-9c6f-f5f9a685b6c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Group</th><th scope=col>ave.Frag</th><th scope=col>ave.Reads</th><th scope=col>ave.mitoFrag</th><th scope=col>ave.mitoReads</th><th scope=col>ave.mitopctReads</th><th scope=col>ave.mitopctFrag</th><th scope=col>ave.mitocomplex</th></tr>\n",
       "\t<tr><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;dbl&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>A</td><td>14135.0</td><td>37110.5</td><td>2224.0</td><td>5738.5</td><td>0.1632430</td><td>0.16522285</td><td>0.3852464</td></tr>\n",
       "\t<tr><td>B</td><td>21004.0</td><td>54060.0</td><td>2940.0</td><td>7545.0</td><td>0.1572797</td><td>0.15767486</td><td>0.3853021</td></tr>\n",
       "\t<tr><td>C</td><td> 6297.5</td><td>15717.0</td><td>2794.0</td><td>7091.0</td><td>0.5068349</td><td>0.49397850</td><td>0.3934712</td></tr>\n",
       "\t<tr><td>D</td><td> 6443.5</td><td>16016.5</td><td>2977.5</td><td>7599.0</td><td>0.5181792</td><td>0.50751914</td><td>0.3926778</td></tr>\n",
       "\t<tr><td>E</td><td> 8844.5</td><td>22714.5</td><td> 789.0</td><td>1982.5</td><td>0.1030958</td><td>0.10776873</td><td>0.3921300</td></tr>\n",
       "\t<tr><td>F</td><td>38853.0</td><td>98027.0</td><td>1364.0</td><td>3479.0</td><td>0.0472579</td><td>0.04586108</td><td>0.3877904</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " Group & ave.Frag & ave.Reads & ave.mitoFrag & ave.mitoReads & ave.mitopctReads & ave.mitopctFrag & ave.mitocomplex\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t A & 14135.0 & 37110.5 & 2224.0 & 5738.5 & 0.1632430 & 0.16522285 & 0.3852464\\\\\n",
       "\t B & 21004.0 & 54060.0 & 2940.0 & 7545.0 & 0.1572797 & 0.15767486 & 0.3853021\\\\\n",
       "\t C &  6297.5 & 15717.0 & 2794.0 & 7091.0 & 0.5068349 & 0.49397850 & 0.3934712\\\\\n",
       "\t D &  6443.5 & 16016.5 & 2977.5 & 7599.0 & 0.5181792 & 0.50751914 & 0.3926778\\\\\n",
       "\t E &  8844.5 & 22714.5 &  789.0 & 1982.5 & 0.1030958 & 0.10776873 & 0.3921300\\\\\n",
       "\t F & 38853.0 & 98027.0 & 1364.0 & 3479.0 & 0.0472579 & 0.04586108 & 0.3877904\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 8\n",
       "\n",
       "| Group &lt;chr&gt; | ave.Frag &lt;dbl&gt; | ave.Reads &lt;dbl&gt; | ave.mitoFrag &lt;dbl&gt; | ave.mitoReads &lt;dbl&gt; | ave.mitopctReads &lt;dbl&gt; | ave.mitopctFrag &lt;dbl&gt; | ave.mitocomplex &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| A | 14135.0 | 37110.5 | 2224.0 | 5738.5 | 0.1632430 | 0.16522285 | 0.3852464 |\n",
       "| B | 21004.0 | 54060.0 | 2940.0 | 7545.0 | 0.1572797 | 0.15767486 | 0.3853021 |\n",
       "| C |  6297.5 | 15717.0 | 2794.0 | 7091.0 | 0.5068349 | 0.49397850 | 0.3934712 |\n",
       "| D |  6443.5 | 16016.5 | 2977.5 | 7599.0 | 0.5181792 | 0.50751914 | 0.3926778 |\n",
       "| E |  8844.5 | 22714.5 |  789.0 | 1982.5 | 0.1030958 | 0.10776873 | 0.3921300 |\n",
       "| F | 38853.0 | 98027.0 | 1364.0 | 3479.0 | 0.0472579 | 0.04586108 | 0.3877904 |\n",
       "\n"
      ],
      "text/plain": [
       "  Group ave.Frag ave.Reads ave.mitoFrag ave.mitoReads ave.mitopctReads\n",
       "1 A     14135.0  37110.5   2224.0       5738.5        0.1632430       \n",
       "2 B     21004.0  54060.0   2940.0       7545.0        0.1572797       \n",
       "3 C      6297.5  15717.0   2794.0       7091.0        0.5068349       \n",
       "4 D      6443.5  16016.5   2977.5       7599.0        0.5181792       \n",
       "5 E      8844.5  22714.5    789.0       1982.5        0.1030958       \n",
       "6 F     38853.0  98027.0   1364.0       3479.0        0.0472579       \n",
       "  ave.mitopctFrag ave.mitocomplex\n",
       "1 0.16522285      0.3852464      \n",
       "2 0.15767486      0.3853021      \n",
       "3 0.49397850      0.3934712      \n",
       "4 0.50751914      0.3926778      \n",
       "5 0.10776873      0.3921300      \n",
       "6 0.04586108      0.3877904      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NonGDN.summary %>% group_by(Group) %>% dplyr::summarise(ave.Frag=median(Frag),ave.Reads=median(Reads),ave.mitoFrag=median(mitoFrag),ave.mitoReads=median(mitoReads),ave.mitopctReads=median(mitopctReads),ave.mitopctFrag=median(mitopctFrag),ave.mitocomplex=median(mitocomplex))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a4e18a-9fe9-4430-ba88-a22baf63201d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "30dafbfe-b72f-4c4f-8cac-b9d16a1f77c0",
   "metadata": {},
   "source": [
    "# Test Conditions - full data, +GDN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b3f9f5b4-0891-48b4-b4d9-36dc3c41dede",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read in summary\n",
    "PlusGDN.total.summary<-read.table(paste(Dir,\"/data/optimize/atac.fragment.summary.PlusGDN\",sep=\"\"))\n",
    "PlusGDN.mito.summary<-read.table(paste(Dir,\"/data/optimize/atac.fragment.mito.summary.PlusGDN\",sep=\"\"))\n",
    "names(PlusGDN.total.summary)<-c(\"cell\",\"Frag\",\"Reads\")\n",
    "names(PlusGDN.mito.summary)<-c(\"cell\",\"mitoFrag\",\"mitoReads\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cc4fd311-b34d-4600-96e4-5e1ab82e654b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read In Hash Call and Hash Hash Dic\n",
    "HashDic<-read.csv(paste(Dir,\"/data/optimize/PlusGDN.HashDic.csv\",sep=\"\"),header=F)\n",
    "HashCall<-read.table(paste(Dir,\"/data/optimize/PlusGDN_calls.tsv\",sep=\"\"),header=T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b8e53f75-ec16-4b4a-8530-960440bd1767",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Process the Hash table\n",
    "HashCall$Group<-mapvalues(HashCall$call,from=HashDic$V1,to=HashDic$V2)\n",
    "HashCall.GDN.filtered<-subset(HashCall,sig>=0.6)[,c(\"cell\",\"Group\")]\n",
    "HashCall.GDN.filtered$cell<-paste(HashCall.GDN.filtered$cell,\"-1\",sep=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a07fcfea-eae1-404c-805e-bbc3afc21098",
   "metadata": {},
   "outputs": [],
   "source": [
    "PlusGDN.summary<-merge(HashCall.GDN.filtered,PlusGDN.total.summary,by=\"cell\")  %>% merge(.,PlusGDN.mito.summary,by=\"cell\")\n",
    "PlusGDN.summary$mitopctReads<-PlusGDN.summary$mitoReads/PlusGDN.summary$Reads\n",
    "PlusGDN.summary$mitopctFrag<-PlusGDN.summary$mitoFrag/PlusGDN.summary$Frag\n",
    "PlusGDN.summary$mitocomplex<-PlusGDN.summary$mitoFrag/PlusGDN.summary$mitoReads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d0fab203-e71f-4d70-912d-0b208ed1607d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Group</th><th scope=col>ave.Frag</th><th scope=col>ave.Reads</th><th scope=col>ave.mitoFrag</th><th scope=col>ave.mitoReads</th><th scope=col>ave.mitopctReads</th><th scope=col>ave.mitopctFrag</th><th scope=col>ave.mitocomplex</th></tr>\n",
       "\t<tr><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;dbl&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>A</td><td>18148.0</td><td>42198.5</td><td>2368.5</td><td>5461.5</td><td>0.14568898</td><td>0.14844472</td><td>0.4350922</td></tr>\n",
       "\t<tr><td>B</td><td>19553.5</td><td>45643.5</td><td>2701.0</td><td>6166.5</td><td>0.15975147</td><td>0.15957085</td><td>0.4320403</td></tr>\n",
       "\t<tr><td>C</td><td> 6318.0</td><td>14134.0</td><td>2891.0</td><td>6463.0</td><td>0.49657133</td><td>0.48510062</td><td>0.4403177</td></tr>\n",
       "\t<tr><td>D</td><td> 6541.5</td><td>14596.5</td><td>3041.0</td><td>6815.0</td><td>0.50942612</td><td>0.50043437</td><td>0.4405335</td></tr>\n",
       "\t<tr><td>E</td><td> 4264.0</td><td> 9640.0</td><td>1252.0</td><td>2782.0</td><td>0.32494220</td><td>0.32563256</td><td>0.4434297</td></tr>\n",
       "\t<tr><td>F</td><td>42166.0</td><td>95573.5</td><td>1594.5</td><td>3569.5</td><td>0.05002929</td><td>0.04906386</td><td>0.4363187</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " Group & ave.Frag & ave.Reads & ave.mitoFrag & ave.mitoReads & ave.mitopctReads & ave.mitopctFrag & ave.mitocomplex\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t A & 18148.0 & 42198.5 & 2368.5 & 5461.5 & 0.14568898 & 0.14844472 & 0.4350922\\\\\n",
       "\t B & 19553.5 & 45643.5 & 2701.0 & 6166.5 & 0.15975147 & 0.15957085 & 0.4320403\\\\\n",
       "\t C &  6318.0 & 14134.0 & 2891.0 & 6463.0 & 0.49657133 & 0.48510062 & 0.4403177\\\\\n",
       "\t D &  6541.5 & 14596.5 & 3041.0 & 6815.0 & 0.50942612 & 0.50043437 & 0.4405335\\\\\n",
       "\t E &  4264.0 &  9640.0 & 1252.0 & 2782.0 & 0.32494220 & 0.32563256 & 0.4434297\\\\\n",
       "\t F & 42166.0 & 95573.5 & 1594.5 & 3569.5 & 0.05002929 & 0.04906386 & 0.4363187\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 8\n",
       "\n",
       "| Group &lt;chr&gt; | ave.Frag &lt;dbl&gt; | ave.Reads &lt;dbl&gt; | ave.mitoFrag &lt;dbl&gt; | ave.mitoReads &lt;dbl&gt; | ave.mitopctReads &lt;dbl&gt; | ave.mitopctFrag &lt;dbl&gt; | ave.mitocomplex &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| A | 18148.0 | 42198.5 | 2368.5 | 5461.5 | 0.14568898 | 0.14844472 | 0.4350922 |\n",
       "| B | 19553.5 | 45643.5 | 2701.0 | 6166.5 | 0.15975147 | 0.15957085 | 0.4320403 |\n",
       "| C |  6318.0 | 14134.0 | 2891.0 | 6463.0 | 0.49657133 | 0.48510062 | 0.4403177 |\n",
       "| D |  6541.5 | 14596.5 | 3041.0 | 6815.0 | 0.50942612 | 0.50043437 | 0.4405335 |\n",
       "| E |  4264.0 |  9640.0 | 1252.0 | 2782.0 | 0.32494220 | 0.32563256 | 0.4434297 |\n",
       "| F | 42166.0 | 95573.5 | 1594.5 | 3569.5 | 0.05002929 | 0.04906386 | 0.4363187 |\n",
       "\n"
      ],
      "text/plain": [
       "  Group ave.Frag ave.Reads ave.mitoFrag ave.mitoReads ave.mitopctReads\n",
       "1 A     18148.0  42198.5   2368.5       5461.5        0.14568898      \n",
       "2 B     19553.5  45643.5   2701.0       6166.5        0.15975147      \n",
       "3 C      6318.0  14134.0   2891.0       6463.0        0.49657133      \n",
       "4 D      6541.5  14596.5   3041.0       6815.0        0.50942612      \n",
       "5 E      4264.0   9640.0   1252.0       2782.0        0.32494220      \n",
       "6 F     42166.0  95573.5   1594.5       3569.5        0.05002929      \n",
       "  ave.mitopctFrag ave.mitocomplex\n",
       "1 0.14844472      0.4350922      \n",
       "2 0.15957085      0.4320403      \n",
       "3 0.48510062      0.4403177      \n",
       "4 0.50043437      0.4405335      \n",
       "5 0.32563256      0.4434297      \n",
       "6 0.04906386      0.4363187      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlusGDN.summary %>% group_by(Group) %>% dplyr::summarise(ave.Frag=median(Frag),ave.Reads=median(Reads),ave.mitoFrag=median(mitoFrag),ave.mitoReads=median(mitoReads),ave.mitopctReads=median(mitopctReads),ave.mitopctFrag=median(mitopctFrag),ave.mitocomplex=median(mitocomplex))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c56037-c5e2-4fdf-9744-b9200695bca0",
   "metadata": {},
   "source": [
    "# Test condition - Saturation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5e0126a5-0b46-4a93-9c39-317088ed33f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Define atac reading function\n",
    "readSummary<-function(file,nm){\n",
    "summary<-read.table(file)\n",
    "names(summary)<-nm\n",
    "return(summary)\n",
    "}\n",
    "\n",
    "##Define atac Process summary\n",
    "SummaryMergeMedian<-function(HashCall.filtered,total.summary,mito.summary){\n",
    "summary.merge<-merge(HashCall.filtered,total.summary,by=\"cell\")  %>% merge(.,mito.summary,by=\"cell\")\n",
    "summary.merge$mitopctReads<-summary.merge$mitoReads/summary.merge$Reads\n",
    "summary.merge$mitopctFrag<-summary.merge$mitoFrag/summary.merge$Frag\n",
    "summary.merge$mitocomplex<-summary.merge$mitoFrag/summary.merge$mitoReads\n",
    "summary.merge.median<-summary.merge %>% group_by(Group) %>% dplyr::summarise(ave.Frag=median(Frag),ave.Reads=median(Reads),ave.mitoFrag=median(mitoFrag),ave.mitoReads=median(mitoReads),ave.mitopctReads=median(mitopctReads),ave.mitopctFrag=median(mitopctFrag),ave.mitocomplex=median(mitocomplex))\n",
    "return(summary.merge.median)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3eceb1a0-593a-455a-bc12-77d4bef2f8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Define rna reading function\n",
    "readSummary.rna<-function(f,path.gex=path.non.gex){\n",
    "Gene<-read.table(paste(path.gex,\"/tmp_\",f,\".UniqGene\",sep=\"\"),col.names = c(\"cell\",\"Gene\"))\n",
    "UMI<-read.table(paste(path.gex,\"/tmp_\",f,\".UniqUMI\",sep=\"\"),col.names = c(\"cell\",\"UMI\"))\n",
    "Reads<-read.table(paste(path.gex,\"/tmp_\",f,\".TotalReads\",sep=\"\"),col.names = c(\"cell\",\"Reads\"))\n",
    "summary<-merge(merge(Gene,UMI,by=\"cell\"),Reads,by=\"cell\")\n",
    "return(summary)\n",
    "}\n",
    "##Define rna processing function\n",
    "SummaryMergeMedian.rna<-function(HashCall.filtered,GeneSummaries){\n",
    "summary.merge<-merge(HashCall.filtered,GeneSummaries,by=\"cell\") \n",
    "summary.merge.median<-summary.merge %>% group_by(Group) %>% dplyr::summarise(ave.Gene=median(Gene),ave.UMI=median(UMI),ave.Reads=median(Reads))\n",
    "return(summary.merge.median)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bdfcce57-ea88-4fa1-88b3-0b3fc0616ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Define paths\n",
    "Downfactor<-c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99)\n",
    "path.non<-paste(Dir,\"/data/optimize/NonGDN.DownsampleTest/\",sep=\"\")\n",
    "path.GDN<-paste(Dir,\"/data/optimize/PlusGDN.DownsampleTest/\",sep=\"\")\n",
    "path.non.gex<-paste(Dir,\"/data/optimize/NonGDN.DownsampleTest.gex/\",sep=\"\")\n",
    "path.GDN.gex<-paste(Dir,\"/data/optimize/PlusGDN.DownsampleTest.gex/\",sep=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8d9d3288-6432-441a-baa0-e37ceb3a03a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read ATAC and Mito data\n",
    "totals.non<-paste(path.non,\"/tmp_\",Downfactor,\".fragment.0.cut.summary\",sep=\"\")\n",
    "mitos.non<-paste(path.non,\"/tmp_\",Downfactor,\".fragment.0.cut.mito.summary\",sep=\"\")\n",
    "totals.GDN<-paste(path.GDN,\"/tmp_\",Downfactor,\".fragment.0.cut.summary\",sep=\"\")\n",
    "mitos.GDN<-paste(path.GDN,\"/tmp_\",Downfactor,\".fragment.0.cut.mito.summary\",sep=\"\")\n",
    "totalSummaries.non<-lapply(totals.non,readSummary,nm=c(\"cell\",\"Frag\",\"Reads\"))\n",
    "mitoSummaries.non<-lapply(mitos.non,readSummary,nm=c(\"cell\",\"mitoFrag\",\"mitoReads\"))\n",
    "totalSummaries.GDN<-lapply(totals.GDN,readSummary,nm=c(\"cell\",\"Frag\",\"Reads\"))\n",
    "mitoSummaries.GDN<-lapply(mitos.GDN,readSummary,nm=c(\"cell\",\"mitoFrag\",\"mitoReads\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c701fd8b-419d-44df-973d-8d4e59d38776",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read RNA\n",
    "GeneSummaries.non<-lapply(Downfactor,readSummary.rna,path.gex=path.non.gex)\n",
    "GeneSummaries.GDN<-lapply(Downfactor,readSummary.rna,path.gex=path.GDN.gex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "51d198fa-2803-4fac-9a58-a09523c2d1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Non\n",
    "MedianSummaries.non<-c()\n",
    "for(i in 1:10){\n",
    "MedianSummary.non<-SummaryMergeMedian(HashCall.non.filtered,total.summary=totalSummaries.non[[i]],mito.summary =mitoSummaries.non[[i]])\n",
    "MedianSummaries.non<-rbind(MedianSummaries.non,MedianSummary.non)   \n",
    "} \n",
    "\n",
    "##GDN\n",
    "\n",
    "MedianSummaries.GDN<-c()\n",
    "for(i in 1:10){\n",
    "MedianSummary.GDN<-SummaryMergeMedian(HashCall.GDN.filtered,total.summary=totalSummaries.GDN[[i]],mito.summary =mitoSummaries.GDN[[i]])\n",
    "MedianSummaries.GDN<-rbind(MedianSummaries.GDN,MedianSummary.GDN)   \n",
    "} \n",
    "\n",
    "## Summary for RNA\n",
    "##Non\n",
    "RNA.MedianSummaries.non<-c()\n",
    "for(i in 1:10){\n",
    "tmp<-SummaryMergeMedian.rna(HashCall.non.filtered,GeneSummaries.non[[i]])\n",
    "RNA.MedianSummaries.non<-rbind(RNA.MedianSummaries.non,tmp)   \n",
    "} \n",
    "\n",
    "##GDN\n",
    "RNA.MedianSummaries.GDN<-c()\n",
    "for(i in 1:10){\n",
    "tmp<-SummaryMergeMedian.rna(HashCall.GDN.filtered,GeneSummaries.GDN[[i]])\n",
    "RNA.MedianSummaries.GDN<-rbind(RNA.MedianSummaries.GDN,tmp)   \n",
    "} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "153b9dcc-4020-476b-91b7-6ef633d8237e",
   "metadata": {},
   "outputs": [],
   "source": [
    "MedianSummaries.non$GDN=\"Non\"\n",
    "MedianSummaries.GDN$GDN=\"GDN\"\n",
    "MedianSummaries<-rbind(MedianSummaries.non,MedianSummaries.GDN)\n",
    "RNA.MedianSummaries.combined.BMMC1<-rbind(cbind(RNA.MedianSummaries.non,Exp=\"non\",CellType=\"BMMC\"),cbind(RNA.MedianSummaries.GDN,Exp=\"GDN\",CellType=\"BMMC\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec4f093-3a64-42d8-9002-5e2f358e99b7",
   "metadata": {},
   "source": [
    "## Extended Data Fig. 1a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1f7738eb-c99e-4efd-93ae-9e264950f79d",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1<-ggplot(MedianSummaries)+aes(ave.mitoReads,ave.mitoFrag,color=Group,shape=GDN)+geom_line()+geom_point(size=5)+scale_color_brewer(palette = \"Set2\")+theme_bw()+theme(axis.text=element_text(size=15,color=\"black\"))+labs(x=\"Mito-Reads\",y=\"Mito-Fragment\")+theme(axis.text=element_text(size=15,color=\"black\"),axis.title=element_text(size=20,color=\"black\"))\n",
    "p2<-ggplot(MedianSummaries)+aes(ave.Reads,ave.Frag,color=Group,shape=GDN)+geom_line()+geom_point(size=5)+scale_color_brewer(palette = \"Set2\")+theme_bw()+labs(x=\"ATAC-Reads\",y=\"ATAC-Fragment\")+theme(axis.text=element_text(size=15,color=\"black\"),axis.title=element_text(size=20,color=\"black\"))\n",
    "p3<-ggplot(RNA.MedianSummaries.combined.BMMC1)+aes(ave.Reads,ave.UMI,color=Group,shape=Exp)+geom_line()+geom_point(size=5)+scale_color_brewer(palette = \"Set2\")+theme_bw()+theme(axis.text=element_text(size=15,color=\"black\"))+labs(x=\"RNA-Reads\",y=\"RNA-UMI\")+theme(axis.text=element_text(size=15,color=\"black\"),axis.title=element_text(size=20,color=\"black\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "359e1d51-cd81-4ff1-b0dc-10520d84f624",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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cOHmbfdunWTy+UAEBcXN3LkSADIyMjo1avXnDlzAOCpp5566aWXzp49y+XiSQYA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\nzBe0B7RzAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=20, repr.plot.height=5,res=200)\n",
    "grid.arrange(p1,p2,p3,nrow=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "febee291-3d24-434b-8345-fdfa4c17ec42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1b192996-46f2-4ed3-ac17-902239c282d7",
   "metadata": {},
   "source": [
    "# Mouse/Human species mix analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5041580b-865a-4da3-b766-f40f5c72361b",
   "metadata": {},
   "source": [
    "## Extended Data Fig. 1b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8e56a0a8-6862-4a11-b104-13c0f720884e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Human.nonMT<-read.table(paste(Dir,\"/data/optimize/HumanMouseMix/210522_ATAC_GRCH38.RawBed.Sort.Tag.dedup.summary.Nonmito\",sep=\"\"))[,c(1:2)]\n",
    "Mouse.nonMT<-read.table(paste(Dir,\"/data/optimize/HumanMouseMix/210522_ATAC_Mouse.RawBed.Sort.Tag.dedup.summary.Nonmito\",sep=\"\"))[,c(1:2)]\n",
    "Human.mito<-read.table(paste(Dir,\"/data/optimize/HumanMouseMix/210522_ATAC_GRCH38.RawBed.Sort.Tag.dedup.summary.mito\",sep=\"\"))[,c(1:2)]\n",
    "Mouse.mito<-read.table(paste(Dir,\"/data/optimize/HumanMouseMix/210522_ATAC_Mouse.RawBed.Sort.Tag.dedup.summary.mito\",sep=\"\"))[,c(1:2)]\n",
    "\n",
    "names(Human.nonMT)<-c(\"Cell\",\"Fragments.human\")\n",
    "names(Mouse.nonMT)<-c(\"Cell\",\"Fragments.mouse\")\n",
    "names(Human.mito)<-c(\"Cell\",\"Fragments.human\")\n",
    "names(Mouse.mito)<-c(\"Cell\",\"Fragments.mouse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "eec4f90d-a5ff-41d6-a94d-8f82cd685bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "HumanMouse.nonMT<-merge(Human.nonMT,Mouse.nonMT,by=\"Cell\",all.x=T,all.y=T)\n",
    "HumanMouse.nonMT[is.na(HumanMouse.nonMT)]<-1\n",
    "HumanMouse.nonMT<-subset(HumanMouse.nonMT,Fragments.human>2000 | Fragments.mouse>2000)\n",
    "HumanMouse.nonMT$Specificity<-log10(HumanMouse.nonMT$Fragments.human/HumanMouse.nonMT$Fragments.mouse)\n",
    "HumanMouse.nonMT$SingleDouble<-with(HumanMouse.nonMT,ifelse(abs(Specificity)<=1,\"Doublets\",\"Singlest\"))\n",
    "CollisionRate=length(which(HumanMouse.nonMT$SingleDouble==\"Doublets\"))/nrow(HumanMouse.nonMT)\n",
    "p1<-ggplot(HumanMouse.nonMT)+aes(Fragments.human,Fragments.mouse,color=SingleDouble)+geom_point()+theme_bw()+ggtitle(paste(\"Doublets rate: \",CollisionRate))\n",
    "\n",
    "HumanMouse.mito<-merge(Human.mito,Mouse.mito,by=\"Cell\",all.x=T,all.y=T)\n",
    "HumanMouse.mito[is.na(HumanMouse.mito)]<-1\n",
    "HumanMouse.mito<-subset(HumanMouse.mito,Cell %in% as.character(HumanMouse.nonMT$Cell))\n",
    "HumanMouse.mito<-merge(HumanMouse.mito,HumanMouse.nonMT[,c(\"Cell\",\"Specificity\",\"SingleDouble\")],by=\"Cell\")\n",
    "HumanMouse.mito$MitoSpecificity<-log10(HumanMouse.mito$Fragments.human/HumanMouse.mito$Fragments.mouse)\n",
    "\n",
    "p2<-ggplot(HumanMouse.mito)+aes(Fragments.human,Fragments.mouse,color=SingleDouble)+geom_point()+theme_bw()+scale_color_manual(values=c(\"red\",\"steelblue\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b0e52122-8825-4957-ab83-c4bb794cbf68",
   "metadata": {},
   "outputs": [],
   "source": [
    "RNAWhiteList<-read.table(paste(Dir,\"/data/optimize/RNA.737K-arc-v1.txt\",sep=\"\"))\n",
    "ATACWhiteList<-read.table(paste(Dir,\"/data/optimize/ATAC.737K-arc-v1.txt\",sep=\"\"))\n",
    "ATAC2RNA<-data.frame(row.names=ATACWhiteList$V1,RNABC=RNAWhiteList$V1)\n",
    "## Translate the atac doublet names into RNA\n",
    "Doublets.rnaNames<-ATAC2RNA[as.character(subset(HumanMouse.nonMT,SingleDouble==\"Doublets\")$Cell),]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d6106096-6dc8-4b77-8f83-40b793ae6de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Below is map to a hg18_mm9 mix STAR index\n",
    "HumanMouse.table<-read.table(paste(Dir,\"/data/optimize/HumanMouseMix/HumanMouse.table\",sep=\"\"))\n",
    "names(HumanMouse.table)<-c(\"Chr\",\"CellBC\",\"UMI\")\n",
    "HumanMouse.table$species<-strsplit(as.character(HumanMouse.table$Chr),\"_\") %>% sapply(.,function(x){x[2]})\n",
    "HumanMouse.summmary<-as.matrix(table(HumanMouse.table$CellBC,HumanMouse.table$species))\n",
    "HumanMouse.summmary.toplot<-data.frame(CellBC=row.names(HumanMouse.summmary),Human=HumanMouse.summmary[,\"hg18\"],Mouse=HumanMouse.summmary[,\"mm9\"])\n",
    "HumanMouse.summmary.toplot$Cells<-strsplit(row.names(HumanMouse.summmary.toplot),\":\") %>% sapply(.,function(x){x[3]})\n",
    "HumanMouse.summmary.toplot$SingleDouble<-ifelse(HumanMouse.summmary.toplot$Cells %in% Doublets.rnaNames,\"Doublets\",\"Singlets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "88cadecb-1f16-4069-9d2c-bef87fe21329",
   "metadata": {},
   "outputs": [],
   "source": [
    "p3<-ggplot(HumanMouse.summmary.toplot)+aes(Human,Mouse,color=SingleDouble)+geom_point(size=0.25)+xlim(0,2500)+ylim(0,12000)+theme_bw()+scale_color_manual(values=c(\"red\",\"black\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6c665d05-6560-4359-9d5b-30b022f281a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).”\n"
     ]
    },
    {
     "data": {
      "image/png": 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vSoS5cu9RdPk1XNcxc71QpXT0Zx+93m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YBAdj0yaFQq+PDw4dQseOxo9ShsePsWIF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\nF4UahAiCIAiCIAiCIF3U/wOT78yJhacD3AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=20, repr.plot.height=5,res=200)\n",
    "grid.arrange(p1,p2,p3,nrow=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da290ccd-e711-438f-bef2-871139e44884",
   "metadata": {},
   "source": [
    "# ATAC/Mito Fragment sizes distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14879844-ddcd-4398-8c68-2ba3e3f2d788",
   "metadata": {},
   "source": [
    "## Extended Data Fig. 1c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "68ef83e3-9834-4cce-ba68-ae21ef887393",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a big file, make sure sufficient memory\n",
    "Frag<-read.table(paste(Dir,\"/data/optimize/NonGDN.atac_fragments.tsv.gz\",sep=\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "45d44c91-c1a9-4859-b1fc-23547bfbe08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "SelectCells<-subset(NonGDN.summary,Group ==\"B\")$cell\n",
    "SelectCells.monocloneal.bed<-subset(Frag,V4 %in% SelectCells)\n",
    "SelectCells.monocloneal.bed$size<-SelectCells.monocloneal.bed$V3-SelectCells.monocloneal.bed$V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "228cf539-0c4c-4a58-b0c1-f2ae6d8ffc1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mRemoved 27738 rows containing non-finite values (`stat_density()`).”\n"
     ]
    },
    {
     "data": {
      "image/png": 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PT7J8+VQ0a34+AhCjxzvMrcFWF0lvXt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\nhwnscJjADocJ7HCYwA7n/wN8o9WA8J6VwgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 240
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=8,repr.plot.res=60)\n",
    "ggplot(SelectCells.monocloneal.bed)+aes(size)+geom_density()+xlim(0,1000)+theme_classic()+theme(text=element_text(size=40))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad78aff-2db8-4b25-b405-dd51fece5e78",
   "metadata": {},
   "source": [
    "## Extended Data Fig. 1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f47e5c36-1552-4f20-9ea7-f534806e276e",
   "metadata": {},
   "outputs": [],
   "source": [
    "MitoFragment<-read.table(paste(Dir,\"/data/optimize/NonGDN.atac_fragments.mito.tsv.gz\",sep=\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "be9bb089-665b-43b9-925e-4990915a8d70",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mRemoved 105 rows containing non-finite values (`stat_density()`).”\n"
     ]
    },
    {
     "data": {
      "image/png": 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QOHJsHbo/2/fGUb50JhBpoE/4ipbeZrGAYBTYLXtsK8nldWBg5NglfiJuJ4znrl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\nwREOFxzh/D/K0c2LQ91p6gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 240
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MitoFragment$size<-MitoFragment$V3-MitoFragment$V2\n",
    "ggplot(MitoFragment)+aes(size)+geom_density()+xlim(0,1000)+theme_classic()+theme(text=element_text(size=40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88014be5-18dd-434a-a501-607c7961ab17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b00afb0f-42a6-463c-b317-bb5ce2f30f9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9194c331-6d5b-45f8-b39f-7976b3849305",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae5cf03e-8c1c-45ef-8268-e9d8459257d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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