notebooks/000-AceSeq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Dependencies\" data-toc-modified-id=\"Dependencies-1\">Dependencies</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-2\">Functions</a></span></li><li><span><a href=\"#Paths\" data-toc-modified-id=\"Paths-3\">Paths</a></span></li><li><span><a href=\"#Main\" data-toc-modified-id=\"Main-4\">Main</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(scipen = 999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/OneDrive - UHN/R_src/ggplot2_theme.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/color_schemes.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/plotting_functions.R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Data/\"\n",
    "plotpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Plots/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " C1  C2  C3  C4 \n",
       " 90 279 393 163 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(metadata_IC$immune_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "icgc <- metadata_IC[ metadata_IC$group == \"ICGC\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>195</li><li>24</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 195\n",
       "\\item 24\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 195\n",
       "2. 24\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 195  24"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(icgc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "aceseq <- read.table(file = paste0(datapath, \"ACEseq/ACEseqPurityScore_ImmuneScore_ICGC_pedbrain.txt\"), sep = \"\\t\",\n",
    "                    header = T, stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>197</li><li>10</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 197\n",
       "\\item 10\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 197\n",
       "2. 10\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 197  10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(aceseq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(aceseq)[ colnames(aceseq) == \"SampleID\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "icgc_aceseq <- merge(icgc, aceseq[, c(\"sample_id\", \"ACEseqTumorPurityScore\")], by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>156</li><li>25</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 156\n",
       "\\item 25\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 156\n",
       "2. 25\n",
       "\n",
       "\n"
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      "text/plain": [
       "[1] 156  25"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "dim(icgc_aceseq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.csv(icgc_aceseq, file = \"~/Downloads/icgc_aceseq.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAgL6gX7IQQwrhG/6UXHnhvnzW0ecUi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\nI+tzXyT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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aceseqplot <- ggplot(data = icgc_aceseq, \n",
    "                     aes(x = immune_cluster, y = ACEseqTumorPurityScore, fill = immune_cluster)) + \n",
    "geom_boxplot(outlier.colour = NA) + myaxis + myplot +\n",
    "scale_fill_manual(values = cluster_col) +\n",
    "theme(legend.position = \"bottom\", \n",
    "      axis.text.x = element_text(angle = 0, hjust = 0.5),\n",
    "     axis.title.x = element_blank()) + labs(y = \"Tumour purity score (ACEseq)\") \n",
    "\n",
    "aceseqplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$C2\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.3300  0.6300  0.6900  0.6989  0.8000  1.0000 \n",
       "\n",
       "$C3\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.3200  0.8800  0.9600  0.9077  1.0000  1.0000 \n",
       "\n",
       "$C4\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.8800  0.9925  1.0000  0.9812  1.0000  1.0000 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tapply(icgc_aceseq$ACEseqTumorPurityScore, icgc_aceseq$immune_cluster, summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  icgc_aceseq$ACEseqTumorPurityScore and icgc_aceseq$immune_cluster \n",
       "\n",
       "   C2               C3  \n",
       "C3 0.00000000000055 -   \n",
       "C4 0.00000218215334 0.18\n",
       "\n",
       "P value adjustment method: none "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(icgc_aceseq$ACEseqTumorPurityScore, icgc_aceseq$immune_cluster, \n",
    "                p.adjust.method = \"none\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"ICGC_IC_ACEseq.pdf\"),\n",
    "   width = 8, height = 8,useDingbats = FALSE)\n",
    "\n",
    "aceseqplot\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "R 4.0",
   "language": "R",
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "280px"
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