notebooks/Scaling_Plots_G2_WT_Prometaphase.ipynb

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 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.4-final"
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  "pygments_lexer": "ipython3",
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   "name": "python3",
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 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pairtools\n",
    "import os\n",
    "import pairlib.scalings\n",
    "import pairlib\n",
    "import bioframe\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sbn\n",
    "from NGS import HiCTools as HT\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define plotting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update({\"pdf.fonttype\": 42})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recDict():\n",
    "    return defaultdict(recDict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set WD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"/groups/gerlich/experiments/Experiments_004800/004869/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define samples\n",
    "WT and prometaphase are processed together to be able to downsample the pairs for both conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairDir = (\"/groups/gerlich/experiments/Experiments_004700/004755/\"\n",
    "           \"Sequencing_data/exp4661/pairs/\")\n",
    "\n",
    "# Prometaphase rep1 is from experiments 4638 and rep2 is from exp4615\n",
    "pathMap = {\"G2\": pairDir,\n",
    "           \"Meta_Rep1\": \"/groups/gerlich/experiments/Experiments_004700/004755/Sequencing_data/exp4727/pairs/\",\n",
    "           \"Meta_Rep2\": \"/groups/gerlich/experiments/Experiments_004700/004755/Sequencing_data/exp4727/pairs/\"}\n",
    "barcodeMap = {\"G2\": \"G2\",\n",
    "              \"Meta_Rep1\": \"CAGATC_S8\",\n",
    "              \"Meta_Rep2\": \"GTTTCG\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = {sample: {interType: HT.loadPairs(os.path.join(pathMap[sample], f\"{barcodeMap[sample]}.{interType}.pairs.gz\"))\n",
    "                  for interType in [\"cis\", \"trans\"]}\n",
    "         for sample in [\"G2\", \"Meta_Rep1\", \"Meta_Rep2\"]\n",
    "         }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge Metaphase replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairsMerged = {}\n",
    "pairsMerged[\"Meta\"] = {}\n",
    "pairsMerged[\"Meta\"][\"cis\"] = pd.concat((pairs[\"Meta_Rep1\"][\"cis\"], pairs[\"Meta_Rep2\"][\"cis\"]))\n",
    "pairsMerged[\"Meta\"][\"trans\"] = pd.concat((pairs[\"Meta_Rep1\"][\"trans\"], pairs[\"Meta_Rep2\"][\"trans\"]))\n",
    "pairsMerged[\"G2\"] = {}\n",
    "pairsMerged[\"G2\"][\"cis\"] = pairs[\"G2\"][\"cis\"]\n",
    "pairsMerged[\"G2\"][\"trans\"] = pairs[\"G2\"][\"trans\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downsample pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairsNew = HT.downSamplePairs(pairsMerged, Distance=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get chromosome features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "hg19_chromsizes = bioframe.fetch_chromsizes('hg19')\n",
    "arms = HT.getArmsHg19()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate scaling plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scs = {sample: {rType: pairlib.scalings.compute_scaling(\n",
    "    pairsNew[sample][rType],\n",
    "    arms,\n",
    "    hg19_chromsizes\n",
    ") for rType in [\"cis\", \"trans\"]}\n",
    "    for sample in pairsNew.keys()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 1440x720 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", RuntimeWarning)\n",
    "\n",
    "colorMap = {\"cis\": \"grey\",\n",
    "            \"trans\": \"black\"}\n",
    "\n",
    "f, ax = plt.subplots(1, 2)\n",
    "for index, sample in enumerate([\"G2\", \"Meta\"]):\n",
    "    for rType in [\"cis\", \"trans\"]:\n",
    "        sc = scs[sample][rType]\n",
    "        sc_agg = (sc[0]\n",
    "                  .groupby(['min_dist', 'max_dist'])\n",
    "                  .agg({'n_pairs': 'sum', 'n_bp2': 'sum'})\n",
    "                  .reset_index())\n",
    "        ax[index].loglog(\n",
    "            np.sqrt(sc_agg.min_dist * sc_agg.max_dist),\n",
    "            sc_agg.n_pairs / sc_agg.n_bp2,\n",
    "            label=f'{rType.capitalize()}', color=colorMap[rType], lw=5\n",
    "        )\n",
    "for i in range(0, 2):\n",
    "    ax[i].grid(lw=0.5, color='gray')\n",
    "    ax[i].set_xlim(10000, 100000000)\n",
    "    ax[i].set_ylim(10**(-14), 2 * 10**(-9))\n",
    "    ax[i].legend(frameon=False)\n",
    "ax[0].set_title(\"G2 WT\")\n",
    "ax[1].set_title(\"Prometaphase WT\")\n",
    "f.set_size_inches(20, 10)\n",
    "sbn.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}