notebooks/FPR_determination_trans_assignment.ipynb

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  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
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   "version": "3.4.3-final"
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sbn\n",
    "import os\n",
    "import pairlib.scalings\n",
    "import pairlib\n",
    "import bioframe\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pairtools\n",
    "import numpy as np\n",
    "from NGS import HiCTools as HT\n",
    "import matplotlib.ticker as ticker\n",
    "from collections import defaultdict\n",
    "import matplotlib"
   ]
  },
  {
   "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 fuctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree():\n",
    "    return defaultdict(tree)\n",
    "\n",
    "\n",
    "def loadPairsChunks(path: str, chunksize: int = 100000) -> pd.DataFrame:\n",
    "    \"\"\"Function to load a .pairs or .pairsam file\n",
    "    into a pandas dataframe iterator, which returns chunks of size chunksize\"\"\"\n",
    "    # get handels for header and pairs_body\n",
    "    header, pairs_body = pairtools._headerops.get_header(\n",
    "            pairtools._fileio.auto_open(path, 'r'))\n",
    "    # extract column names from header\n",
    "    cols = pairtools._headerops.extract_column_names(header)\n",
    "    # read data into dataframe\n",
    "    frame = pd.read_csv(pairs_body, sep=\"\\t\", names=cols, chunksize=chunksize)\n",
    "    return frame\n",
    "\n",
    "def assignReads(dataFrameChunk, min_mapq, min_right_muts, max_wrong_muts):\n",
    "    \"\"\"assigns read to be either cis-sister or trans-sister or unassigned\n",
    "    based on mutation rates\"\"\"\n",
    "    highQ = dataFrameChunk.loc[(dataFrameChunk[\"mapq1\"] >= min_mapq)\n",
    "                               & (dataFrameChunk[\"mapq2\"] >= min_mapq), :]\n",
    "    highQ.loc[:, \"size\"] = highQ[\"pos2\"] - highQ[\"pos1\"]\n",
    "    # get individual boolean arrays\n",
    "    side1_has_ag = highQ[\"n_AG_muts_phred30_1\"] >= min_right_muts\n",
    "    side1_has_tc = highQ[\"n_TC_muts_phred30_1\"] >= min_right_muts\n",
    "    side2_has_ag = highQ[\"n_AG_muts_phred30_2\"] >= min_right_muts\n",
    "    side2_has_tc = highQ[\"n_TC_muts_phred30_1\"] >= min_right_muts\n",
    "    side1_no_tc = highQ[\"n_TC_muts_phred30_1\"] <= max_wrong_muts\n",
    "    side1_no_ag = highQ[\"n_AG_muts_phred30_1\"] <= max_wrong_muts\n",
    "    side2_no_tc = highQ[\"n_TC_muts_phred30_2\"] <= max_wrong_muts\n",
    "    side2_no_ag = highQ[\"n_AG_muts_phred30_2\"] <= max_wrong_muts\n",
    "    # define criterion for ref and comp\n",
    "    side1_is_ref = side1_has_tc & side1_no_ag\n",
    "    side1_is_comp = side1_has_ag & side1_no_tc\n",
    "    side2_is_ref = side2_has_tc & side2_no_ag\n",
    "    side2_is_comp = side2_has_ag & side2_no_tc\n",
    "    # bin into cisref and ciscomp\n",
    "    is_cis_ref = side1_is_ref & side2_is_ref\n",
    "    is_cis_comp = side1_is_comp & side2_is_comp\n",
    "    is_trans_ref_comp = side1_is_ref & side2_is_comp\n",
    "    is_trans_comp_ref = side1_is_comp & side2_is_ref\n",
    "    # assign cis and trans\n",
    "    is_cis = is_cis_ref | is_cis_comp\n",
    "    is_trans = is_trans_ref_comp | is_trans_comp_ref\n",
    "    # subset dataframe\n",
    "    cisFrame = highQ.loc[is_cis, :]\n",
    "    transFrame = highQ.loc[is_trans, :]\n",
    "    # double\n",
    "    double = pd.concat((cisFrame, transFrame)).drop_duplicates()\n",
    "    return {\"cis\": cisFrame, \"trans\": transFrame, \"total\": len(dataFrameChunk), \"double\": len(double)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set wd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"/groups/gerlich/experiments/Experiments_004800/004812/Sequencing_data/Pooled_FC_1_2_3_4/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Hi-C paths of replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "basePath = \"/groups/gerlich/experiments/Experiments_004800/004812/Sequencing_data/Pooled_FC_1_2_3_4/pairs_dedup/\"\n",
    "barcodes=\"ATGTCA_S9 AGTCAA_S7 TTAGGC_S2 CCGTCC_S10 GATCAG_S5 CAGATC_S3 TAGCTT_S6 AGTTCC_S8 ACTTGA_S4\".split(\" \")\n",
    "hiCPath = {}\n",
    "for index, barcode in enumerate(barcodes, start=1):\n",
    "    tempFile = f\"{barcode}.fc_1_2_3_4.all.dedup.pairs.gz\"\n",
    "    hiCPath[f\"rep{index}\"] = os.path.join(basePath, tempFile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get data from the replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix paramters that are not variable\n",
    "mapth = 30\n",
    "max_wrong_muts = 0\n",
    "\n",
    "# initialize result data structures\n",
    "resultDict = tree()\n",
    "doubleDict = tree()\n",
    "cisDict = tree()\n",
    "transDict = tree()\n",
    "\n",
    "for rep in hiCPath:\n",
    "    pairIterator = loadPairsChunks(hiCPath[rep], chunksize=1000000)\n",
    "    tempFrame = next(pairIterator)\n",
    "    for minTH in range(1, 5):\n",
    "        tempResult = assignReads(tempFrame, mapth, minTH, max_wrong_muts)\n",
    "        cis1kb = tempResult[\"cis\"].loc[tempResult[\"cis\"][\"size\"] < 1000, :]\n",
    "        trans1kb = tempResult[\"trans\"].loc[tempResult[\"trans\"][\"size\"] < 1000, :]\n",
    "        resultDict[rep][minTH] = (len(trans1kb))/(len(cis1kb) + len(trans1kb))\n",
    "        doubleDict[rep][minTH] = (tempResult[\"double\"]/tempResult[\"total\"]) * 100\n",
    "        # get Hi-C contacts --> contacts assumed to be further apart than 1kb\n",
    "        cisHiC = tempResult[\"cis\"].loc[tempResult[\"cis\"][\"size\"] > 1000, :]\n",
    "        transHiC = tempResult[\"trans\"].loc[tempResult[\"trans\"][\"size\"] > 1000, :]\n",
    "        cisDict[rep][minTH] = len(cisHiC)\n",
    "        transDict[rep][minTH] = len(transHiC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- resultDict holds the fpr for each replicate and each signature mutation cut-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>rep1</th>\n      <th>rep2</th>\n      <th>rep3</th>\n      <th>rep4</th>\n      <th>rep5</th>\n      <th>rep6</th>\n      <th>rep7</th>\n      <th>rep8</th>\n      <th>rep9</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>0.011144</td>\n      <td>0.011935</td>\n      <td>0.010609</td>\n      <td>0.010411</td>\n      <td>0.011727</td>\n      <td>0.011578</td>\n      <td>0.010970</td>\n      <td>0.012829</td>\n      <td>0.010946</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.000318</td>\n      <td>0.000302</td>\n      <td>0.000253</td>\n      <td>0.000306</td>\n      <td>0.000477</td>\n      <td>0.000340</td>\n      <td>0.000366</td>\n      <td>0.000525</td>\n      <td>0.000323</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.000032</td>\n      <td>0.000016</td>\n      <td>0.000056</td>\n      <td>0.000065</td>\n      <td>0.000038</td>\n      <td>0.000037</td>\n      <td>0.000034</td>\n      <td>0.000044</td>\n      <td>0.000049</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.000000</td>\n      <td>0.000025</td>\n      <td>0.000086</td>\n      <td>0.000050</td>\n      <td>0.000000</td>\n      <td>0.000028</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000026</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "       rep1      rep2      rep3      rep4      rep5      rep6      rep7  \\\n1  0.011144  0.011935  0.010609  0.010411  0.011727  0.011578  0.010970   \n2  0.000318  0.000302  0.000253  0.000306  0.000477  0.000340  0.000366   \n3  0.000032  0.000016  0.000056  0.000065  0.000038  0.000037  0.000034   \n4  0.000000  0.000025  0.000086  0.000050  0.000000  0.000028  0.000000   \n\n       rep8      rep9  \n1  0.012829  0.010946  \n2  0.000525  0.000323  \n3  0.000044  0.000049  \n4  0.000000  0.000026  "
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultFrame = pd.DataFrame(resultDict)\n",
    "resultFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "molten = pd.melt(resultFrame.reset_index(), id_vars=\"index\").rename(columns={\"index\": \"muts\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- this dataframe holds the results of the fpr determination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at doubly labeled reads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>rep1</th>\n      <th>rep2</th>\n      <th>rep3</th>\n      <th>rep4</th>\n      <th>rep5</th>\n      <th>rep6</th>\n      <th>rep7</th>\n      <th>rep8</th>\n      <th>rep9</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>23.3462</td>\n      <td>23.0079</td>\n      <td>22.1688</td>\n      <td>23.4399</td>\n      <td>22.4906</td>\n      <td>21.6799</td>\n      <td>23.3978</td>\n      <td>21.1236</td>\n      <td>23.6086</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>13.5380</td>\n      <td>13.1811</td>\n      <td>12.6642</td>\n      <td>13.6646</td>\n      <td>12.7484</td>\n      <td>12.3752</td>\n      <td>13.5473</td>\n      <td>11.0422</td>\n      <td>13.4767</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.7352</td>\n      <td>8.3520</td>\n      <td>7.9922</td>\n      <td>8.7382</td>\n      <td>7.9688</td>\n      <td>7.9276</td>\n      <td>8.6220</td>\n      <td>6.5166</td>\n      <td>8.4205</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.6751</td>\n      <td>5.3056</td>\n      <td>5.1095</td>\n      <td>5.6245</td>\n      <td>5.0520</td>\n      <td>5.1337</td>\n      <td>5.4932</td>\n      <td>3.8534</td>\n      <td>5.3088</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "      rep1     rep2     rep3     rep4     rep5     rep6     rep7     rep8  \\\n1  23.3462  23.0079  22.1688  23.4399  22.4906  21.6799  23.3978  21.1236   \n2  13.5380  13.1811  12.6642  13.6646  12.7484  12.3752  13.5473  11.0422   \n3   8.7352   8.3520   7.9922   8.7382   7.9688   7.9276   8.6220   6.5166   \n4   5.6751   5.3056   5.1095   5.6245   5.0520   5.1337   5.4932   3.8534   \n\n      rep9  \n1  23.6086  \n2  13.4767  \n3   8.4205  \n4   5.3088  "
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doubleFrame = pd.DataFrame(doubleDict)\n",
    "doubleFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "moltenDouble = pd.melt(doubleFrame.reset_index(), id_vars=\"index\").rename(columns={\"index\": \"muts\"})\n",
    "moltenDouble = moltenDouble.sort_values(by=\"muts\", ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = moltenDouble.groupby(\"muts\").agg({\"value\": np.mean}).reset_index()\n",
    "error = moltenDouble.groupby(\"muts\").agg({\"value\": np.std}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 360x360 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "ax.plot(means.loc[:, \"muts\"],means.loc[:, \"value\"],\n",
    "       color=\"black\", lw=4)\n",
    "ax.errorbar(means.loc[:, \"muts\"], means.loc[:, \"value\"], error.loc[:, \"value\"], fmt=\"o\",\n",
    "       color=\"black\", markersize=10)\n",
    "ax.errorbar(means.loc[means[\"muts\"] == 2, \"muts\"], means.loc[means[\"muts\"] == 2, \"value\"], error.loc[means[\"muts\"] == 2, \"value\"], fmt=\"o\",\n",
    "       color=\"red\", markersize=10)\n",
    "#sbn.barplot(x=\"variable\", y=\"value_log10\", data=molten.loc[molten[\"MappingQuality cut-off\"] == 30, :], color=\"black\")\n",
    "ax.grid(True)\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))\n",
    "majors = [\"\", \"1\", \"2\", \"3\", \"4\"]\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(majors))\n",
    "ax.set(xlabel=\"Number of required mutations\", ylabel=\"Perc doubly labeled reads\", ylim=(0, 40))\n",
    "sbn.despine()\n",
    "f.set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsdir = \"/groups/gerlich/experiments/Experiments_004800/004882/Results/\"\n",
    "f.savefig(os.path.join(resultsdir, \"Doubly_labeled_allreps_plot_from1mut.png\"), bbox_inches=\"tight\")\n",
    "f.savefig(os.path.join(resultsdir, \"Doubly_labeled_allreps_rom1mut.pdf\"), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at percentage of trans-contacts that would be wrong at each cut-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cisFrame = pd.DataFrame(cisDict)\n",
    "transFrame = pd.DataFrame(transDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanCis = pd.DataFrame(cisFrame.apply(np.nanmean, axis=1)).reset_index().rename(columns={\"index\": \"muts\", 0:\"count\"})\n",
    "meanTrans = pd.DataFrame(transFrame.apply(np.nanmean, axis=1)).reset_index().rename(columns={\"index\": \"muts\", 0:\"count\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate wrong trans contacts based on FPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr = pd.DataFrame(resultFrame.apply(np.nanmean, axis=1)).reset_index().rename(columns={\"index\": \"muts\", 0:\"fpr\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>muts</th>\n      <th>count_cis</th>\n      <th>count_trans</th>\n      <th>fpr</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>73005.777778</td>\n      <td>4634.222222</td>\n      <td>0.011350</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>39509.777778</td>\n      <td>1325.444444</td>\n      <td>0.000357</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>24064.000000</td>\n      <td>611.555556</td>\n      <td>0.000041</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>14957.666667</td>\n      <td>300.111111</td>\n      <td>0.000024</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   muts     count_cis  count_trans       fpr\n0     1  73005.777778  4634.222222  0.011350\n1     2  39509.777778  1325.444444  0.000357\n2     3  24064.000000   611.555556  0.000041\n3     4  14957.666667   300.111111  0.000024"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "readsJoined = pd.merge(meanCis, meanTrans, on=\"muts\", suffixes=(\"_cis\", \"_trans\"))\n",
    "readsJoinedFinal = pd.merge(readsJoined, fpr, on=\"muts\")\n",
    "readsJoinedFinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "readsJoinedFinal.loc[:, \"wrongTrans\"] = readsJoinedFinal[\"count_cis\"] * readsJoinedFinal[\"fpr\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "readsJoinedFinal.loc[:, \"fractionWrongTrans\"] = readsJoinedFinal[\"wrongTrans\"]/(readsJoinedFinal[\"count_trans\"]) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 360x360 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "ax.plot(readsJoinedFinal.loc[:, \"muts\"],readsJoinedFinal.loc[:, \"fractionWrongTrans\"],\n",
    "       color=\"black\", lw=4)\n",
    "ax.plot(readsJoinedFinal.loc[:, \"muts\"],readsJoinedFinal.loc[:, \"fractionWrongTrans\"], \"o\",\n",
    "       color=\"black\", markersize=10)\n",
    "ax.plot(readsJoinedFinal.loc[means[\"muts\"] == 2, \"muts\"],readsJoinedFinal.loc[means[\"muts\"] == 2, \"fractionWrongTrans\"], \"o\",\n",
    "       color=\"red\", markersize=10)\n",
    "ax.grid(True)\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))\n",
    "majors = [\"\", \"1\", \"2\", \"3\", \"4\"]\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(majors))\n",
    "ax.set(xlabel=\"Number of required mutations\", ylabel=\"Perc right trans-contacts\", ylim=(0, 40))\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": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsdir = \"/groups/gerlich/experiments/Experiments_004800/004882/Results/\"\n",
    "f.savefig(os.path.join(resultsdir, \"Perc_wrong_trans_allreps_plot_from1mut.png\"), bbox_inches=\"tight\")\n",
    "f.savefig(os.path.join(resultsdir, \"Perc_wrong_trans_allreps_from1mut.pdf\"), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}