604 lines (603 with data), 185.3 kB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Targeted Bisuflite Sequencing Alignment Quality \n",
    "- Parse the BSBolt log files to get general alignment statistics \n",
    "- Run *bedtools multicov* to get coverage over regions targeted by probes\n",
    "- Run *samtools flagstat* on the bam files with marked duplicates\n",
    "    - Estimate duplication rate for each sample \n",
    "    - Samtools flagstat is called externally using python built-in library *subprocess* \n",
    "    - Command is run in parallel using *joblib*, a third party multiprocessing library that forms the backend for many projects like *sklearn*\n",
    "- Plot the resulting QC checks using *matplotlib* and *seaborn* libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import os\n",
    "import subprocess\n",
    "\n",
    "import joblib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use latex formatting for figures, latex must be on system path for this to work \n",
    "rc('text', usetex=False)\n",
    "\n",
    "# set environment plotting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "plt.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set working directory\n",
    "wd = '~/working_directory/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = []\n",
    "with open('samples.txt', 'r') as sample_list:\n",
    "    for sample in sample_list:\n",
    "        samples.append(sample.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse BSBolt Alignment logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_log(log_file):\n",
    "    alignment_log = {}\n",
    "    if not os.path.exists(log_file):\n",
    "        return log_file.split('/')[-1], alignment_log\n",
    "    # open log file as plain text file \n",
    "    with open(log_file, 'r') as log:\n",
    "        for line in log:\n",
    "            if 'Alignment Complete: Time ' in line:\n",
    "                alignment_time = line.replace('Alignment Complete: Time ', '').strip().split(':')\n",
    "                alignment_seconds = int(alignment_time[0]) * 60 * 60 + int(alignment_time[1]) * 60 + int(alignment_time[2])\n",
    "                alignment_log['AlignmentTimeSec'] = alignment_seconds\n",
    "            elif 'Total Reads: ' in line:\n",
    "                alignment_log['TotalReads'] = int(line.replace('Total Reads: ', '').strip())\n",
    "            elif 'Mappability' in line:\n",
    "                alignment_log['Mappability'] = float(line.replace('Mappability: ', '').replace('%', '').strip())\n",
    "            elif 'Watson_C2T' in line:\n",
    "                alignment_log['WatsonC2T'] = int(line.split(':')[1].strip())\n",
    "            elif 'Watson_G2A' in line:\n",
    "                alignment_log['WatsonG2A'] = int(line.split(':')[1].strip())\n",
    "            elif 'Crick_C2T' in line:\n",
    "                alignment_log['CrickC2T'] = int(line.split(':')[1].strip())\n",
    "            elif 'Crick_G2A' in line:\n",
    "                alignment_log['CrickG2A'] = int(line.split(':')[1].strip())\n",
    "            elif 'Unmapped' in line:\n",
    "                alignment_log['Unmapped'] = int(line.split(':')[1].strip())\n",
    "            elif 'Ambiguous' in line:\n",
    "                alignment_log['BSAmbiguous'] = int(line.split(':')[1].strip())\n",
    "    return log_file.split('/')[-1], alignment_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44de3d76cf804788bdb7eaf7dc449617",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=48.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# parse all logs and save data in dict\n",
    "\n",
    "sample_stats = {}\n",
    "\n",
    "for sample in tqdm(samples):\n",
    "    _, log = parse_log(f'{wd}alignments/{sample}.log')\n",
    "    sample_stats[sample] = log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform dict into pandas dataframe and plot\n",
    "stats_df = pd.DataFrame(sample_stats).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "plot_cats = ['TotalReads','WatsonC2T','CrickC2T','WatsonG2A','CrickG2A','Unmapped','BSAmbiguous']\n",
    "sns.boxplot(data=stats_df[plot_cats], ax=ax, palette='Set2', linewidth=2.5)\n",
    "ax.set_title('BSBolt Log Stats')\n",
    "#plt.savefig('bsb_log_stats.png', dpi=200, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at coverage over regions targeted by probes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coverage(sample_path, target_bed, sample_name, include_dups=False):\n",
    "    sample_coverage = []\n",
    "    bed_args = ['bedtools', 'multicov', '-bams', sample_path, '-bed', target_bed]\n",
    "    if include_dups:\n",
    "        bed_args = ['bedtools', 'multicov', '-D', '-bams', sample_path, '-bed', target_bed]\n",
    "    with subprocess.Popen(args=bed_args, stdout=subprocess.PIPE, universal_newlines=True) as bed_process:\n",
    "        while True:\n",
    "            bed_line = bed_process.stdout.readline()\n",
    "            if not bed_line:\n",
    "                break\n",
    "            line_split = bed_line.replace('\\n', '').split('\\t')\n",
    "            sample_coverage.append(dict(site=f'{line_split[0]}:{line_split[1]}-{line_split[2]}', coverage=int(line_split[4]), \n",
    "                                        probe_region=line_split[3], sample=sample_name, dup='De-Duplicated' if not include_dups else 'Duplicates'))\n",
    "    return sample_coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_bed = os.getcwd() + '/combined_target.bed.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "formatted_coverage = []\n",
    "\n",
    "for sample in samples:\n",
    "    formatted_coverage.append([f'{alignment_dir}{sample}.dup.bam', target_bed, sample])\n",
    "    formatted_coverage.append([f'{alignment_dir}{sample}.dup.bam', target_bed, sample, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=16)]: Using backend LokyBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done   9 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=16)]: Done  29 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=16)]: Done  40 tasks      | elapsed:  5.7min\n",
      "[Parallel(n_jobs=16)]: Done  53 tasks      | elapsed:  7.3min\n",
      "[Parallel(n_jobs=16)]: Done  75 out of  96 | elapsed:  9.3min remaining:  2.6min\n",
      "[Parallel(n_jobs=16)]: Done  85 out of  96 | elapsed: 10.2min remaining:  1.3min\n",
      "[Parallel(n_jobs=16)]: Done  96 out of  96 | elapsed: 10.8min finished\n"
     ]
    }
   ],
   "source": [
    "coverages = joblib.Parallel(n_jobs=16, verbose=10)(joblib.delayed(get_coverage)(*run) for run in formatted_coverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "coverages = list(coverages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "dup_cov = {}\n",
    "dedup_cov = {}\n",
    "\n",
    "for coverage in coverages:\n",
    "    sample = coverage[0]['sample']\n",
    "    sample_coverage = {}\n",
    "    for site in coverage:\n",
    "        sample_coverage[site['site']] = site['coverage']\n",
    "    if coverage[0]['dup'] == 'De-Duplicated':\n",
    "        dedup_cov[sample] = sample_coverage\n",
    "    else:\n",
    "        dup_cov[sample] = sample_coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "dup_cov_df = pd.DataFrame(dup_cov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "dedup_cov_df = pd.DataFrame(dedup_cov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "coverage_df = pd.DataFrame([dup_cov_df.mean(axis=1), dedup_cov_df.mean(axis=1)]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "coverage_df.columns = ['Dup Mean', 'DeDup Mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "coverage_df['Log2_cov Dup'] = np.log2(coverage_df['Dup Mean'] + 1)\n",
    "coverage_df['Log2_cov DeDup'] = np.log2(coverage_df['DeDup Mean'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "sns.distplot(coverage_df['Log2_cov Dup'].values, label='Dup. Reads Included', ax=ax)\n",
    "sns.distplot(coverage_df['Log2_cov DeDup'].values, label='Dup. Reads Removed', ax=ax)\n",
    "\n",
    "plt.legend(fontsize=18)\n",
    "ax.set_xlabel(f'$Log_2(coverage + 1)$', fontsize=24)\n",
    "ax.set_ylabel(f'$Density$', fontsize=24)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at alignment flags to get addtional alignment QC info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run samtools flagstat on each sample and parse output \n",
    "# command will fail if samtools is not on path\n",
    "def get_flagstats(sample_path, sample_name):\n",
    "    sam_stats = {}\n",
    "    sam_args = ['samtools', 'flagstat', sample_path]\n",
    "    with subprocess.Popen(args=sam_args, stdout=subprocess.PIPE, universal_newlines=True) as sam_process:\n",
    "        for count, line in enumerate(iter(sam_process.stdout)):\n",
    "            if count == 0:\n",
    "                sam_stats['TotalReads'] = int(line.split(' ')[0])\n",
    "            elif count == 1:\n",
    "                sam_stats['SecondaryAlignment'] = int(line.split(' ')[0])\n",
    "            elif count == 2:\n",
    "                sam_stats['Supplementary']= int(line.split(' ')[0])\n",
    "            elif count == 3:\n",
    "                sam_stats['Duplicate']= int(line.split(' ')[0])\n",
    "            elif count == 4:\n",
    "                sam_stats['Mapped']= int(line.split(' ')[0])\n",
    "            elif count == 5:\n",
    "                sam_stats['Paired']= int(line.split(' ')[0])\n",
    "            elif count == 6:\n",
    "                sam_stats['Read1']= int(line.split(' ')[0])\n",
    "            elif count == 7:\n",
    "                sam_stats['Read2']= int(line.split(' ')[0])\n",
    "            elif count == 8:\n",
    "                sam_stats['ProperPair']= int(line.split(' ')[0])\n",
    "            elif count == 9:\n",
    "                sam_stats['MateMapped']= int(line.split(' ')[0])\n",
    "            elif count == 10:\n",
    "                sam_stats['Singletons']= int(line.split(' ')[0])\n",
    "            elif count == 11:\n",
    "                sam_stats['MateMappedToDiffChrom']= int(line.split(' ')[0])\n",
    "            elif count == 12:\n",
    "                sam_stats['MateMappedToDiffChromMapq>=5']= int(line.split(' ')[0])\n",
    "    return sample_name, sam_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=14)]: Using backend LokyBackend with 14 concurrent workers.\n",
      "[Parallel(n_jobs=14)]: Done   4 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=14)]: Done  13 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=14)]: Done  26 out of  48 | elapsed:  3.8min remaining:  3.2min\n",
      "[Parallel(n_jobs=14)]: Done  31 out of  48 | elapsed:  4.9min remaining:  2.7min\n",
      "[Parallel(n_jobs=14)]: Done  36 out of  48 | elapsed:  5.2min remaining:  1.7min\n",
      "[Parallel(n_jobs=14)]: Done  41 out of  48 | elapsed:  5.7min remaining:   58.3s\n",
      "[Parallel(n_jobs=14)]: Done  46 out of  48 | elapsed:  6.0min remaining:   15.6s\n",
      "[Parallel(n_jobs=14)]: Done  48 out of  48 | elapsed:  6.0min finished\n"
     ]
    }
   ],
   "source": [
    "# use multiple processing threads to parallelize\n",
    "flag_stats = joblib.Parallel(n_jobs=14, verbose=10)(joblib.delayed(get_flagstats)(*[f'{wd}alignments/{sample}.dup.bam', sample]) for sample in samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# format flagstat results\n",
    "\n",
    "processed_flag_stats = {}\n",
    "\n",
    "for sample, f_stats in flag_stats:\n",
    "    processed_flag_stats[sample] = f_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make pandas data frame \n",
    "flag_df = pd.DataFrame(processed_flag_stats).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add duplication rate statistic\n",
    "flag_df['DuplicationRate'] = flag_df['Duplicate'] / flag_df['Mapped'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get number of reads mapping to coverage region\n",
    "def get_on_target_coverage(sample_name):\n",
    "    dup_removed_count =  dedup_cov_df[sample_name].sum()\n",
    "    dup_count = dup_cov_df[sample_name].sum()\n",
    "    return dup_count, dup_removed_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "on_target_count_dedup = []\n",
    "on_target_count_dup = []\n",
    "\n",
    "for sample in flag_df.index:\n",
    "    dup_count, dup_removed_count = get_on_target_coverage(sample)\n",
    "    on_target_count_dup.append(dup_count)\n",
    "    on_target_count_dedup.append(dup_removed_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "flag_df['OnTarget-All'] = on_target_count_dup\n",
    "flag_df['OnTarget-DeDuplicated'] = on_target_count_dedup\n",
    "flag_df['Mapped-Deduplicated'] = flag_df['Mapped'] - flag_df['Duplicate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "plot_cats = ['TotalReads', 'Duplicate', 'Mapped', 'Mapped-Deduplicated', 'Paired', 'ProperPair', 'OnTarget-All', 'OnTarget-DeDuplicated']\n",
    "sns.boxplot(data=flag_df[plot_cats], ax=ax, palette='Set2', linewidth=2.5)\n",
    "ax.set_title('Samtools Flagstat', fontsize=20)\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation=30)\n",
    "#plt.savefig('flagstats.png', dpi=200, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "plot_cats = ['DuplicationRate']\n",
    "sns.swarmplot(data=flag_df[plot_cats], ax=ax, palette='Set2', linewidth=2.5)\n",
    "ax.set_title('Observed Duplication')\n",
    "#plt.savefig('duplication.png', dpi=200, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}