notebooks/HiCRep_Nipbl_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "import os\n",
    "import logging\n",
    "import re\n",
    "import itertools\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sbn\n",
    "import numpy as np\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add path to hicreppy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "env: PATH=/users/michael.mitter/.conda/envs/hicreppy/bin/:/software/2020/software/anaconda3/2019.03/bin;/software/2020/software/anaconda3/2019.03/condabin;/software/2020/software/anaconda3/2019.03/library/bin;/software/2020/software/anaconda3/2019.03/bin;/software/2020/software/anaconda3/2019.03/condabin;/software/2020/software/anaconda3/2019.03/library/bin;/software/2020/software/anaconda3/2019.03/bin;/software/2020/software/anaconda3/2019.03/condabin;/software/2020/software/anaconda3/2019.03/library/bin;/users/michael.mitter/.vscode-server/bin/5763d909d5f12fe19f215cbfdd29a91c0fa9208a/bin:/users/michael.mitter/.conda/envs/gitEnv/bin/:/users/michael.mitter/.vscode-server/bin/5763d909d5f12fe19f215cbfdd29a91c0fa9208a/bin:/software/2020/software/anaconda3/2019.03/condabin:/users/michael.mitter/.conda/envs/gitEnv/bin/:/software/system/utils:/usr/local/bin:/usr/bin:/groups/gerlich/members/MichaelMitter/UCSC_usertools/:/groups/gerlich/members/MichaelMitter/PythonPackages/OnTAD/:/usr/local/sbin:/usr/sbin:/groups/gerlich/members/MichaelMitter/UCSC_usertools/:/groups/gerlich/members/MichaelMitter/PythonPackages/OnTAD/:/users/michael.mitter/.local/bin:/users/michael.mitter/bin\n"
    }
   ],
   "source": [
    "environment_name = 'hicreppy'\n",
    "environment_path = f\"/users/michael.mitter/.conda/envs/{ environment_name }/bin/\"\n",
    "\n",
    "# get pre-set PATH environment variable\n",
    "original_os_path = os.environ.get('PATH')\n",
    "os_path = original_os_path\n",
    "\n",
    "# guard PATH against multiple prepending\n",
    "if environment_path not in original_os_path:\n",
    "    os_path = environment_path + ':' + original_os_path\n",
    "%env PATH=$os_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define plotting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update({\"pdf.fonttype\": 42})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def runSCC(fileIterator, binSize, maxdist=100000, subsample=0):\n",
    "    \"\"\"runs hicreppy scc on a pair of file returned\n",
    "    by fileIterator for files located in fileDir.\"\"\"\n",
    "    # dispatch processes\n",
    "    processList = []\n",
    "    for file1, file2 in fileIterator:\n",
    "        mat1 = f\"{file1}::/resolutions/{binSize}\"\n",
    "        mat2 = f\"{file2}::/resolutions/{binSize}\"\n",
    "        command = f\"hicreppy scc -m {maxdist} -s {subsample} {mat1} {mat2}\"\n",
    "        processList.append((file1, file2, subprocess.Popen(command, stdout=subprocess.PIPE,\n",
    "                                                           stderr=subprocess.STDOUT, shell=True)))\n",
    "    # query if processes are finished and store results in list\n",
    "    resultList = []\n",
    "    while len(processList) != 0:\n",
    "        for index, p in enumerate(processList):\n",
    "            result = p[2].poll()\n",
    "            if result is not None:\n",
    "                output, err = p[2].communicate()\n",
    "                for line in output.decode(\"UTF-8\").split(\"\\n\"):\n",
    "                    logging.info(line)\n",
    "                    try:\n",
    "                        value = float(line.strip())\n",
    "                    except ValueError:\n",
    "                        pass\n",
    "                    else:\n",
    "                        barcode1 = p[0].split(\"/\")[-1].split(\".1000.mcool\")[0]\n",
    "                        barcode2 = p[1].split(\"/\")[-1].split(\".1000.mcool\")[0]\n",
    "                        resultList.append(\n",
    "                            {\"barcode1\": barcode1, \"barcode2\": barcode2, \"sccR\": value})\n",
    "                logging.info(\"\\n\\n\")\n",
    "                processList.pop(index)\n",
    "    return pd.DataFrame(resultList)\n",
    "\n",
    "def recDict():\n",
    "    return defaultdict(recDict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define HiC samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleFiles = {}\n",
    "for rType in [\"cis\", \"trans\", \"all\"]:\n",
    "    sampleFiles[rType] = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "HICDIR1 = \"/groups/gerlich/experiments/Experiments_004800/004869/Sequencing_data/Pooled/cooler\"\n",
    "\n",
    "barcodes = [\"CAGATC\", \"ACTTGA\"]\n",
    "\n",
    "for barcode in barcodes:\n",
    "    for rType in [\"cis\", \"trans\", \"all\"]:\n",
    "        tempFile = os.path.join(HICDIR1, f\"{barcode}.{rType}.1000.mcool\")\n",
    "        sampleFiles[rType].append(tempFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "HICDIR2 = \"/groups/gerlich/experiments/Experiments_004600/004638/Sequencing_data/Novaseq/cooler/\"\n",
    "\n",
    "barcodes = [\"GGCTAC\", \"GTGAAA\"]\n",
    "\n",
    "for barcode in barcodes:\n",
    "    for rType in [\"cis\", \"trans\", \"all\"]:\n",
    "        tempFile = os.path.join(HICDIR2, f\"{barcode}.{rType}.1000.mcool\")\n",
    "        sampleFiles[rType].append(tempFile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define HiCRep parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxdist = 1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "allFilesComb = itertools.combinations(sampleFiles[\"all\"], r=2)\n",
    "allResultsAll = runSCC(allFilesComb, 100000, maxdist, subsample=0)\n",
    "allResultsAll.loc[:, \"type\"] = \"all\"\n",
    "allResultsAll.loc[:, \"maxdist\"] = maxdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cisFilesComb = itertools.combinations(sampleFiles[\"cis\"],r=2)\n",
    "cisResultsAll = runSCC(cisFilesComb, 100000, maxdist, subsample=0)\n",
    "cisResultsAll.loc[:, \"type\"] = \"cis\"\n",
    "cisResultsAll.loc[:, \"maxdist\"] = maxdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "transFilesComb = itertools.combinations(sampleFiles[\"trans\"], r=2)\n",
    "transResultsAll = runSCC(transFilesComb, 100000, maxdist, subsample=0)\n",
    "transResultsAll.loc[:, \"type\"] = \"trans\"\n",
    "transResultsAll.loc[:, \"maxdist\"] = maxdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cisTransFilesComb = itertools.product(sampleFiles[\"cis\"], sampleFiles[\"trans\"])\n",
    "cisTransResultsAll = runSCC(cisTransFilesComb, 100000, maxdist, subsample=0)\n",
    "cisTransResultsAll.loc[:, \"type\"] = \"cis/trans\"\n",
    "cisTransResultsAll.loc[:, \"maxdist\"] = maxdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "        barcode1      barcode2      sccR       type  maxdist\n0     GGCTAC.cis    GTGAAA.cis  0.900601        cis  1000000\n1     ACTTGA.cis    GTGAAA.cis  0.962313        cis  1000000\n2     ACTTGA.cis    GGCTAC.cis  0.948291        cis  1000000\n3     CAGATC.cis    GGCTAC.cis  0.951933        cis  1000000\n4     CAGATC.cis    GTGAAA.cis  0.958662        cis  1000000\n5     CAGATC.cis    ACTTGA.cis  0.995364        cis  1000000\n0   GGCTAC.trans  GTGAAA.trans  0.827684      trans  1000000\n1   ACTTGA.trans  GGCTAC.trans  0.885088      trans  1000000\n2   ACTTGA.trans  GTGAAA.trans  0.899438      trans  1000000\n3   CAGATC.trans  GTGAAA.trans  0.895498      trans  1000000\n4   CAGATC.trans  GGCTAC.trans  0.893496      trans  1000000\n5   CAGATC.trans  ACTTGA.trans  0.983930      trans  1000000\n0     GTGAAA.cis  GGCTAC.trans  0.705572  cis/trans  1000000\n1     GGCTAC.cis  GTGAAA.trans  0.888166  cis/trans  1000000\n2     GGCTAC.cis  GGCTAC.trans  0.864824  cis/trans  1000000\n3     GTGAAA.cis  GTGAAA.trans  0.874843  cis/trans  1000000\n4     CAGATC.cis  GGCTAC.trans  0.799661  cis/trans  1000000\n5     CAGATC.cis  ACTTGA.trans  0.900891  cis/trans  1000000\n6     GGCTAC.cis  CAGATC.trans  0.891312  cis/trans  1000000\n7     GTGAAA.cis  ACTTGA.trans  0.808321  cis/trans  1000000\n8     ACTTGA.cis  GGCTAC.trans  0.797036  cis/trans  1000000\n9     GGCTAC.cis  ACTTGA.trans  0.884668  cis/trans  1000000\n10    ACTTGA.cis  GTGAAA.trans  0.904775  cis/trans  1000000\n11    GTGAAA.cis  CAGATC.trans  0.802628  cis/trans  1000000\n12    ACTTGA.cis  CAGATC.trans  0.901188  cis/trans  1000000\n13    CAGATC.cis  CAGATC.trans  0.904016  cis/trans  1000000\n14    CAGATC.cis  GTGAAA.trans  0.903057  cis/trans  1000000\n15    ACTTGA.cis  ACTTGA.trans  0.903358  cis/trans  1000000\n0     GGCTAC.all    GTGAAA.all  0.914945        all  1000000\n1     ACTTGA.all    GTGAAA.all  0.964876        all  1000000\n2     ACTTGA.all    GGCTAC.all  0.943996        all  1000000\n3     CAGATC.all    GTGAAA.all  0.931698        all  1000000\n4     CAGATC.all    GGCTAC.all  0.960448        all  1000000\n5     CAGATC.all    ACTTGA.all  0.990495        all  1000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>barcode1</th>\n      <th>barcode2</th>\n      <th>sccR</th>\n      <th>type</th>\n      <th>maxdist</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GGCTAC.cis</td>\n      <td>GTGAAA.cis</td>\n      <td>0.900601</td>\n      <td>cis</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ACTTGA.cis</td>\n      <td>GTGAAA.cis</td>\n      <td>0.962313</td>\n      <td>cis</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ACTTGA.cis</td>\n      <td>GGCTAC.cis</td>\n      <td>0.948291</td>\n      <td>cis</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CAGATC.cis</td>\n      <td>GGCTAC.cis</td>\n      <td>0.951933</td>\n      <td>cis</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CAGATC.cis</td>\n      <td>GTGAAA.cis</td>\n      <td>0.958662</td>\n      <td>cis</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CAGATC.cis</td>\n      <td>ACTTGA.cis</td>\n      <td>0.995364</td>\n      <td>cis</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>GGCTAC.trans</td>\n      <td>GTGAAA.trans</td>\n      <td>0.827684</td>\n      <td>trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ACTTGA.trans</td>\n      <td>GGCTAC.trans</td>\n      <td>0.885088</td>\n      <td>trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ACTTGA.trans</td>\n      <td>GTGAAA.trans</td>\n      <td>0.899438</td>\n      <td>trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CAGATC.trans</td>\n      <td>GTGAAA.trans</td>\n      <td>0.895498</td>\n      <td>trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CAGATC.trans</td>\n      <td>GGCTAC.trans</td>\n      <td>0.893496</td>\n      <td>trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CAGATC.trans</td>\n      <td>ACTTGA.trans</td>\n      <td>0.983930</td>\n      <td>trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>GTGAAA.cis</td>\n      <td>GGCTAC.trans</td>\n      <td>0.705572</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GGCTAC.cis</td>\n      <td>GTGAAA.trans</td>\n      <td>0.888166</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GGCTAC.cis</td>\n      <td>GGCTAC.trans</td>\n      <td>0.864824</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>GTGAAA.cis</td>\n      <td>GTGAAA.trans</td>\n      <td>0.874843</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CAGATC.cis</td>\n      <td>GGCTAC.trans</td>\n      <td>0.799661</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CAGATC.cis</td>\n      <td>ACTTGA.trans</td>\n      <td>0.900891</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>GGCTAC.cis</td>\n      <td>CAGATC.trans</td>\n      <td>0.891312</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>GTGAAA.cis</td>\n      <td>ACTTGA.trans</td>\n      <td>0.808321</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>ACTTGA.cis</td>\n      <td>GGCTAC.trans</td>\n      <td>0.797036</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>GGCTAC.cis</td>\n      <td>ACTTGA.trans</td>\n      <td>0.884668</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>ACTTGA.cis</td>\n      <td>GTGAAA.trans</td>\n      <td>0.904775</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>GTGAAA.cis</td>\n      <td>CAGATC.trans</td>\n      <td>0.802628</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>ACTTGA.cis</td>\n      <td>CAGATC.trans</td>\n      <td>0.901188</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CAGATC.cis</td>\n      <td>CAGATC.trans</td>\n      <td>0.904016</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CAGATC.cis</td>\n      <td>GTGAAA.trans</td>\n      <td>0.903057</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>ACTTGA.cis</td>\n      <td>ACTTGA.trans</td>\n      <td>0.903358</td>\n      <td>cis/trans</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>GGCTAC.all</td>\n      <td>GTGAAA.all</td>\n      <td>0.914945</td>\n      <td>all</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ACTTGA.all</td>\n      <td>GTGAAA.all</td>\n      <td>0.964876</td>\n      <td>all</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ACTTGA.all</td>\n      <td>GGCTAC.all</td>\n      <td>0.943996</td>\n      <td>all</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CAGATC.all</td>\n      <td>GTGAAA.all</td>\n      <td>0.931698</td>\n      <td>all</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CAGATC.all</td>\n      <td>GGCTAC.all</td>\n      <td>0.960448</td>\n      <td>all</td>\n      <td>1000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CAGATC.all</td>\n      <td>ACTTGA.all</td>\n      <td>0.990495</td>\n      <td>all</td>\n      <td>1000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "finFrameAll = pd.concat((cisResultsAll, transResultsAll, cisTransResultsAll, allResultsAll))\n",
    "finFrameAll"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "#sbn.swarmplot(x=\"type\", y=\"sccR\", data=finFrameAll, ax=ax, color=\"black\", alpha=0.2, order=[\"all\", \"cis\", \"trans\", \"cis/trans\"])\n",
    "sbn.barplot(x=\"type\", y=\"sccR\", data=finFrameAll, ax=ax, color=\"black\", alpha=1, order=[\"all\", \"cis\", \"trans\", \"cis/trans\"], errcolor=\"black\", capsize=0.1)\n",
    "sbn.despine()\n",
    "f.set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsdir = \"/groups/gerlich/experiments/Experiments_004800/004869/Results/Nipbl\"\n",
    "f.savefig(os.path.join(resultsdir, \"HiC_Rep_Nipbl.png\"), bbox_inches=\"tight\")\n",
    "f.savefig(os.path.join(resultsdir, \"HiC_Rep_Nipbl.pdf\"), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 360x360 with 1 Axes>"
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     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "#sbn.swarmplot(x=\"type\", y=\"sccR\", data=finFrameAll, ax=ax, color=\"black\", alpha=0.2, order=[\"all\", \"cis\", \"trans\", \"cis/trans\"])\n",
    "sbn.barplot(x=\"type\", y=\"sccR\", data=finFrameAll, ax=ax, color=\"black\", errcolor=\"black\", capsize=0.1, order=[\"all\", \"cis\", \"trans\"])\n",
    "sbn.despine()\n",
    "ax.set(title=\"Nipbl-AID wAuxin\")\n",
    "f.set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsdir = \"/groups/gerlich/experiments/Experiments_004800/004869/Results/Nipbl/\"\n",
    "f.savefig(os.path.join(resultsdir, \"HiC_Rep_Nipbl.png\"), bbox_inches=\"tight\")\n",
    "f.savefig(os.path.join(resultsdir, \"HiC_Rep_Nipbl.pdf\"), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot barplots with individual points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "sbn.barplot(x=\"type\", y=\"sccR\", data=finFrameAll.loc[finFrameAll[\"type\"] != \"cis/trans\", :], ax=ax, color=\"black\",facecolor=(1, 1, 1, 0) , linewidth=2,edgecolor=\"black\", order=[\"all\", \"cis\", \"trans\"], ci=None)\n",
    "sbn.stripplot(x=\"type\", y=\"sccR\", data=finFrameAll.loc[finFrameAll[\"type\"] != \"cis/trans\", :], color=\"black\", size=10, jitter=0.1, ax=ax, order=[\"all\", \"cis\", \"trans\"])\n",
    "sbn.despine()\n",
    "sbn.despine()\n",
    "f.set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsdir = \"/groups/gerlich/experiments/Experiments_004800/004869/Results/Nipbl/\"\n",
    "f.savefig(os.path.join(resultsdir, \"HiC_Rep_Nipbl_indSamples.png\"), bbox_inches=\"tight\")\n",
    "f.savefig(os.path.join(resultsdir, \"HiC_Rep_Nipbl_indSamples.pdf\"), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.5-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python37564bithicreppyconda751433cdac1c4fa4a5020f25484c7aab",
   "display_name": "Python 3.7.5 64-bit ('hicreppy': conda)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}