notebooks/Calculate_relative_mutation_rate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sbn\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import pysam\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseDirectory(path):\n",
    "    \"\"\"Looks for sequencing files in input directory\n",
    "    and opens them.\"\"\"\n",
    "    files = os.listdir(path)\n",
    "    filesFilt = [x for x in files if \"bam\" in x]\n",
    "    if len(filesFilt) == 0:\n",
    "        filesFilt = [x for x in files if \"sam\" in x]\n",
    "        if len(filesFilt) == 0:\n",
    "            raise ValueError(\"No suitable file fount in the hicup directory!\")\n",
    "    filePaths = {i[0:6]: os.path.join(path, i) for i in filesFilt}\n",
    "    return filePaths\n",
    "\n",
    "\n",
    "def countMutations(alignment, mappingq=40, readqual=27, posQual=30, norm=True, reads=100000):\n",
    "    MutDict = {\"AC\": 0.0, \"AG\": 0.0, \"AT\": 0.0, \"CA\": 0.0, \"CG\": 0.0,\n",
    "               \"CT\": 0.0, \"GA\": 0.0, \"GC\": 0.0, \"GT\": 0.0, \"TA\": 0.0, \"TC\": 0.0, \"TG\": 0.0}\n",
    "    BaseNumb = {\"A\": 0.0, \"C\": 0.0, \"T\": 0.0, \"G\": 0.0}\n",
    "    for index, readInst in enumerate(alignment):\n",
    "        if index % 10000 == 0:\n",
    "            print(\"%s reads processed\" % (index))\n",
    "        if index > reads:\n",
    "            break\n",
    "        if readInst.mapping_quality < mappingq:\n",
    "            continue\n",
    "        if np.mean(readInst.query_qualities) < readqual:\n",
    "            continue\n",
    "        mutationCount, baseCount = getMutationlist(readInst, qual=posQual)\n",
    "        for pointMut in MutDict.keys():\n",
    "            MutDict[pointMut] += mutationCount.count(pointMut)\n",
    "        for base in BaseNumb.keys():\n",
    "            BaseNumb[base] += baseCount.get(base, 0)\n",
    "    if norm:\n",
    "        for pointMut in MutDict.keys():\n",
    "            base = pointMut[0]\n",
    "            MutDict[pointMut] = MutDict[pointMut]/BaseNumb[base]\n",
    "    return MutDict\n",
    "\n",
    "\n",
    "def getMutationlist(alignedSegment, qual=30):\n",
    "    \"\"\"Uses methods of pysam.AlignedSegment to get all pointmutations.\n",
    "    Stores them as strings in self.MutationList.\n",
    "    (e.g. AG corresponds to A mutated to G)\"\"\"\n",
    "    pairs = alignedSegment.get_aligned_pairs(matches_only=False, with_seq=True)\n",
    "    ref = np.array(\n",
    "        [x[2].upper() if x[2] is not None else None for x in pairs])\n",
    "    read = [x[0] for x in pairs]\n",
    "    query = alignedSegment.query_sequence\n",
    "    readBase = np.array(\n",
    "        [query[x] if x is not None else None for x in read])\n",
    "    readQuality = np.array(\n",
    "        [alignedSegment.query_qualities[x] if x is not None else None for x in read])\n",
    "    ind = ref == readBase\n",
    "    final = []\n",
    "    count = []\n",
    "    for x, check in enumerate(ind):\n",
    "        if not check and ((ref[x] is not None) and (readBase[x] is not None)):\n",
    "            if readQuality[x] >= qual:\n",
    "                final.append(ref[x] + readBase[x])\n",
    "                count.append(ref[x])\n",
    "        if ((ref[x] is not None) and (readBase[x] is not None)):\n",
    "            if readQuality[x] >= qual:\n",
    "                count.append(ref[x])\n",
    "    return (final, Counter(count))\n",
    "\n",
    "\n",
    "def mapBarcodes(filePaths, barcodefile, sep=\",\"):\n",
    "    \"\"\"Takes in a comma seperated file containing Name and Sequence columns\n",
    "    denoting a sample name under Name and the barcode sequence\n",
    "    under Sequence. Will discard barcodes that are not in the barcodeFile.\n",
    "    \"\"\"\n",
    "    output = {}\n",
    "    barcodes = pd.read_csv(barcodefile, sep=sep)\n",
    "    mapping = barcodes[[\"Name\", \"Sequence\"]]\n",
    "    for barcode in set(mapping[\"Sequence\"]):\n",
    "        newkey = mapping.loc[mapping.Sequence == barcode, \"Name\"]\\\n",
    "            .values[0]\n",
    "        output[newkey] = filePaths[barcode]\n",
    "    return output\n",
    "\n",
    "\n",
    "def normalizeToControl(mutationDict, controlBar):\n",
    "    \"\"\"Sets the mutationrates stored in mutationDict as fold\n",
    "    control.\n",
    "    The control sample is defined through the controlBar parameter.\"\"\"\n",
    "    normDict = {}\n",
    "    for barcode in mutationDict.keys():\n",
    "        normDict[barcode] = {}\n",
    "        for mut in set(mutationDict[barcode].keys()):\n",
    "            if mutationDict[controlBar][mut] == 0:\n",
    "                normDict[barcode][mut] = 0\n",
    "            else:\n",
    "                normDict[barcode][mut] = mutationDict[barcode][mut] / \\\n",
    "                    mutationDict[controlBar][mut]\n",
    "    return normDict\n",
    "\n",
    "\n",
    "def AnnotateMut(x):\n",
    "    if x[\"mut\"] in [\"TC\", \"AG\"]:\n",
    "        return \"4sT\"\n",
    "    return \"Background\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze treated samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n40000 reads processed\n50000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n40000 reads processed\n50000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n40000 reads processed\n50000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n40000 reads processed\n50000 reads processed\n"
    }
   ],
   "source": [
    "# set Directory\n",
    "os.chdir(\"/groups/gerlich/experiments/Experiments_004500/\"\n",
    "         \"004594/Sequencing_data/\")\n",
    "\n",
    "# parse directory\n",
    "\n",
    "files = parseDirectory(\"./\")\n",
    "\n",
    "# map barcodes\n",
    "\n",
    "mapped = mapBarcodes(\n",
    "    files, \"/groups/gerlich/experiments/Experiments_004500/004574/Sample_mapping_exp4574.csv\")\n",
    "\n",
    "# open files and count mutations\n",
    "\n",
    "mutationStats = {}\n",
    "\n",
    "for barcode, fileP in mapped.items():\n",
    "    mutationStats[barcode] = {}\n",
    "    with pysam.AlignmentFile(fileP, \"rb\") as samfile:\n",
    "        mutationStats[barcode] = countMutations(samfile, reads=50000, mappingq=38,\n",
    "                                                readqual=30, posQual=30)\n",
    "\n",
    "# normalize to control\n",
    "\n",
    "normDict = normalizeToControl(mutationStats, controlBar=\"Control old NH4Cl\")\n",
    "HiCNormExp4574 = pd.melt(pd.DataFrame(\n",
    "    normDict).transpose().reset_index(), id_vars=\"index\")\n",
    "HiCNormExp4574.columns = [\"condition\", \"mut\", \"value\"]\n",
    "\n",
    "HiCNormExp4574.loc[:, \"Mutationtype\"] = HiCNormExp4574.apply(AnnotateMut,\n",
    "                                                             axis=1)\n",
    "HiCNormExp4574 = HiCNormExp4574.loc[HiCNormExp4574.condition\n",
    "                                                  .isin([\"4sT new NH4Cl 1\",\n",
    "                                                         \"4sT new NH4Cl 2\",\n",
    "                                                         \"4sT new NH4Cl 3\"]), :]\n",
    "HiCNormExp4574.condition = \"OsO4 modified\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze untreated samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0 reads processed\n10000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n0 reads processed\n10000 reads processed\n"
    }
   ],
   "source": [
    "# define directory\n",
    "seqDir = (\"/groups/gerlich/experiments/Experiments_004500/004561/\"\n",
    "          \"Sequencing_data/000000000-D_demultiplexed/hg19_SNPs/\")\n",
    "sampleMapDir = (\"/groups/gerlich/experiments/Experiments_004500/004557/\")\n",
    "\n",
    "\n",
    "# parse directory\n",
    "\n",
    "filesUntreated = parseDirectory(seqDir)\n",
    "\n",
    "# map barcodes\n",
    "\n",
    "mappedUntreated = mapBarcodes(filesUntreated, os.path.join(\n",
    "    sampleMapDir, \"Sample_mapping_exp4557.csv\"))\n",
    "\n",
    "# open files and count mutations\n",
    "\n",
    "mutationStatsUntreated = {}\n",
    "\n",
    "for barcode, fileP in mappedUntreated.items():\n",
    "    mutationStatsUntreated[barcode] = {}\n",
    "    with pysam.AlignmentFile(fileP, \"rb\") as samfile:\n",
    "        mutationStatsUntreated[barcode] = countMutations(samfile, reads=50000, mappingq=38,\n",
    "                                                         readqual=30, posQual=30)\n",
    "\n",
    "# normalize to control\n",
    "\n",
    "normDictUntreated = normalizeToControl(\n",
    "    mutationStatsUntreated, controlBar=\"Control 1 OsO4\")\n",
    "HiCNormExp4557 = pd.melt(pd.DataFrame(\n",
    "    normDictUntreated).transpose().reset_index(), id_vars=\"index\")\n",
    "HiCNormExp4557.columns = [\"condition\", \"mut\", \"value\"]\n",
    "\n",
    "HiCNormExp4557.loc[:, \"Mutationtype\"] = HiCNormExp4557.apply(AnnotateMut,\n",
    "                                                             axis=1)\n",
    "HiCNormExp4557 = HiCNormExp4557.loc[HiCNormExp4557.condition\n",
    "                                                  .isin([\"4sT 1 unmod\",\n",
    "                                                         \"4sT 2 unmod\",\n",
    "                                                         \"4sT 3 unmod\"]), :]\n",
    "HiCNormExp4557.condition = \"Unmodified\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Put both conditions into one dataframe and precalculate summar statistics for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotFrame = pd.concat((HiCNormExp4557, HiCNormExp4574), axis=0)\n",
    "\n",
    "colors = [\"black\", \"white\"]\n",
    "\n",
    "\n",
    "# calculate heights\n",
    "heights = plotFrame.groupby([\"mut\", \"condition\"]).mean()\n",
    "indexFrame = heights.index.to_frame()\n",
    "indexFrame.loc[:, \"value\"] = heights[\"value\"]\n",
    "indexFrame.index = range(len(heights))\n",
    "unmod = indexFrame.loc[indexFrame.condition == \"Unmodified\", :]\n",
    "mod = indexFrame.loc[indexFrame.condition == \"OsO4 modified\", :]\n",
    "\n",
    "\n",
    "# calculate error\n",
    "stde = plotFrame.groupby([\"mut\", \"condition\"]).agg(\n",
    "    lambda x: np.std(x)/np.sqrt(len(x)))\n",
    "# order by condition\n",
    "indError = stde.index.to_frame()\n",
    "indError.loc[:, \"value\"] = stde[\"value\"]\n",
    "indError.index = range(len(stde))\n",
    "unmodE = indError.loc[indError.condition == \"Unmodified\", :]\n",
    "modE = indError.loc[indError.condition == \"OsO4 modified\", :]\n",
    "\n",
    "errorUmod = [np.zeros(len(unmodE)), unmodE[\"value\"].values]\n",
    "errorMod = [np.zeros(len(modE)), modE[\"value\"].values]\n",
    "\n",
    "error_kw = {'capsize': 3, 'capthick': 2, 'ecolor': 'black'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# unmodified samples\n",
    "f, ax = plt.subplots()\n",
    "ax.bar(range(0, len(unmod)), unmod[\"value\"].values, yerr=errorUmod,\n",
    "       error_kw=error_kw, edgecolor=\"black\", color=\"black\")\n",
    "ax.set_ylim(0, 50)\n",
    "sbn.despine()\n",
    "ax.set_ylabel(\"Relative mutation rate\")\n",
    "ax.set_xlabel(\"\")\n",
    "plt.xticks([i for i in range(0, len(unmod))],\n",
    "           unmod[\"mut\"].values, rotation=90)\n",
    "f.set_size_inches(8, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot modified samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x576 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "ax.bar(range(0, len(mod)), mod[\"value\"].values, yerr=errorMod,\n",
    "       error_kw=error_kw, edgecolor=\"black\", color=\"black\")\n",
    "ax.set_ylim(0, 50)\n",
    "sbn.despine()\n",
    "ax.set_ylabel(\"Relative mutation rate\")\n",
    "ax.set_xlabel(\"\")\n",
    "plt.xticks([i for i in range(0, len(mod))],\n",
    "           mod[\"mut\"].values, rotation=90)\n",
    "f.set_size_inches(8, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot unmodified samples with individual datapoints overlaid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set plotting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams.update({\"pdf.fonttype\": 42})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "unmod = plotFrame.loc[plotFrame[\"condition\"] == \"Unmodified\", :]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "sbn.barplot(x=\"mut\", y=\"value\", data=unmod, ci=None, order=sorted(set(unmod[\"mut\"])), facecolor=(1, 1, 1, 0), linewidth=2,edgecolor=\"black\")\n",
    "sbn.stripplot(x=\"mut\", y=\"value\", data=unmod, color=\"black\", order=sorted(set(unmod[\"mut\"])), size=10, jitter=1)\n",
    "ax.set_ylim(0, 50)\n",
    "ax.set(xlabel=\"\", ylabel=\"Relative mutation rate\")\n",
    "plt.xticks(*plt.xticks(), rotation=90)\n",
    "sbn.despine()\n",
    "f.set_size_inches(8, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultDir = \"/groups/gerlich/experiments/Experiments_004700/004765/Results/\"\n",
    "f.savefig(os.path.join(resultDir, \"Unmod_wScatter_woEdge.png\"))\n",
    "f.savefig(os.path.join(resultDir, \"Unmod_wScatter_woEdge.pdf\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot modified samples with individual datapoints overlaid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = plotFrame.loc[plotFrame[\"condition\"] == \"OsO4 modified\", :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "sbn.barplot(x=\"mut\", y=\"value\", data=mod, ci=None, order=sorted(set(mod[\"mut\"])), facecolor=(1, 1, 1, 0), linewidth=2,edgecolor=\"black\")\n",
    "sbn.stripplot(x=\"mut\", y=\"value\", data=mod, color=\"black\", order=sorted(set(mod[\"mut\"])), size=10, jitter=1)\n",
    "ax.set_ylim(0, 50)\n",
    "ax.set(xlabel=\"\", ylabel=\"Relative mutation rate\")\n",
    "plt.xticks(*plt.xticks(), rotation=90)\n",
    "sbn.despine()\n",
    "f.set_size_inches(8, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultDir = \"/groups/gerlich/experiments/Experiments_004700/004765/Results/\"\n",
    "f.savefig(os.path.join(resultDir, \"Mod_wScatter_woEdge.png\") ,dpi=300)\n",
    "f.savefig(os.path.join(resultDir, \"Mod_wScatter_woEdge.pdf\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot sequencing based mutation rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load three control replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n40000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n0 reads processed\n10000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n0 reads processed\n10000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n40000 reads processed\n50000 reads processed\n0 reads processed\n10000 reads processed\n20000 reads processed\n30000 reads processed\n"
    }
   ],
   "source": [
    "# set Directory\n",
    "os.chdir(\"/groups/gerlich/experiments/Experiments_004500/\"\n",
    "         \"004561/Sequencing_data/000000000-D_demultiplexed/hg19_SNPs\")\n",
    "\n",
    "# parse directory\n",
    "\n",
    "files = parseDirectory(\"./\")\n",
    "\n",
    "# map barcodes\n",
    "\n",
    "mapped = mapBarcodes(\n",
    "    files, \"/groups/gerlich/experiments/Experiments_004500/004557/Sample_mapping_exp4557.csv\")\n",
    "\n",
    "# open files and count mutations\n",
    "\n",
    "mutationStatsControl = {}\n",
    "\n",
    "for barcode, fileP in mapped.items():\n",
    "    mutationStatsControl[barcode] = {}\n",
    "    with pysam.AlignmentFile(fileP, \"rb\") as samfile:\n",
    "        mutationStatsControl[barcode] = countMutations(samfile, reads=50000, mappingq=38,\n",
    "                                                readqual=30, posQual=30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "HiCNorm = pd.DataFrame(mutationStats).T.reset_index().rename(columns={\"index\": \"condition\"})\n",
    "control = pd.DataFrame(mutationStatsControl).T.reset_index().rename(columns={\"index\": \"condition\"})\n",
    "molten4sT = pd.melt(HiCNorm, id_vars=\"condition\")\n",
    "moltenCont = pd.melt(control, id_vars=\"condition\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "molten4sT.columns = [\"condition\", \"mut\", \"value\"]\n",
    "moltenCont.columns = [\"condition\", \"mut\", \"value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "molten4sT.loc[:, \"Condition\"] = [i.split(\" \")[0] + \" \" + i.split(\" \")[1]\n",
    "                                 for i in molten4sT.condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "moltenCont.loc[:, \"Condition\"] = [i.split(\" \")[0]\n",
    "                                     for i in moltenCont.condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cont = moltenCont.loc[moltenCont[\"Condition\"] == \"Control\", :]\n",
    "Cont.columns = [\"Cont_\" + i for i in Cont.columns]\n",
    "\n",
    "fourTT = molten4sT.loc[molten4sT[\"Condition\"] == \"4sT new\", :]\n",
    "fourTT.columns = [\"fourTT_\" + i for i in fourTT.columns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  Name     value\n0  4sT  0.025083\n1  4sT  0.024439\n2  4sT  0.024504",
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     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "labelled = fourTT.groupby(\"fourTT_condition\").apply(lambda x: x.loc[x[\"fourTT_mut\"] == \"AG\", \"fourTT_value\"].values[0] + x.loc[x[\"fourTT_mut\"] == \"TC\", \"fourTT_value\"].values[0]).reset_index()\n",
    "labelled.iloc[:, 0] = \"4sT\"\n",
    "labelled.columns = [\"Name\", \"value\"]\n",
    "labelled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      Name     value\n0  Control  0.000908\n1  Control  0.000918\n2  Control  0.000909",
      "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>Name</th>\n      <th>value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Control</td>\n      <td>0.000908</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Control</td>\n      <td>0.000918</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Control</td>\n      <td>0.000909</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "unlabelled = Cont.groupby(\"Cont_condition\").apply(lambda x: x.loc[x[\"Cont_mut\"] == \"AG\", \"Cont_value\"].values[0] + x.loc[x[\"Cont_mut\"] == \"TC\", \"Cont_value\"].values[0]).reset_index()\n",
    "unlabelled.iloc[:, 0] = \"Control\"\n",
    "unlabelled.columns = [\"Name\", \"value\"]\n",
    "unlabelled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotFrame = pd.concat((labelled, unlabelled))\n",
    "plotFrame.loc[:, \"perc\"] = plotFrame[\"value\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "sbn.barplot(x=\"Name\", y=\"perc\", data=plotFrame, ax=ax, color=\"black\",facecolor=(1, 1, 1, 0) , linewidth=2,edgecolor=\"black\", ci=None, order=[\"Control\", \"4sT\"])\n",
    "sbn.stripplot(x=\"Name\", y=\"perc\", data=plotFrame, color=\"black\", size=10, jitter=0.02, ax=ax, order=[\"Control\", \"4sT\"])\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Percent 4sT\")\n",
    "sbn.despine()\n",
    "plt.xticks(*plt.xticks(), rotation=90)\n",
    "sbn.despine()\n",
    "ax.set_ylim(0, 3.1)\n",
    "f.set_size_inches(4, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultDir = \"/groups/gerlich/experiments/Experiments_004700/004765/Results\"\n",
    "f.savefig(os.path.join(resultDir, \"4sT_percentage_exp4594_black_wPoints.png\"), bbox_inches=\"tight\")\n",
    "f.savefig(os.path.join(resultDir, \"4sT_percentage_exp4594_black_wPoints.pdf\"), bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.4-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "ml",
   "display_name": "ml"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}