{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Setup-notebook\" data-toc-modified-id=\"Setup-notebook-1\">Setup notebook</a></span></li><li><span><a href=\"#Align-and-filter-sequences\" data-toc-modified-id=\"Align-and-filter-sequences-2\">Align and filter sequences</a></span></li><li><span><a href=\"#Get-amino-acid-frequencies-and-other-relevant-information\" data-toc-modified-id=\"Get-amino-acid-frequencies-and-other-relevant-information-3\">Get amino-acid frequencies and other relevant information</a></span></li><li><span><a href=\"#Examine-sites-of-strong-immune-selection\" data-toc-modified-id=\"Examine-sites-of-strong-immune-selection-4\">Examine sites of strong immune selection</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup notebook\n",
    "Import Python modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dms_tools2 version 2.4.16\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "import gzip\n",
    "import multiprocessing\n",
    "import os\n",
    "import re\n",
    "import subprocess\n",
    "import warnings\n",
    "\n",
    "import Bio.SeqIO\n",
    "import Bio.SeqRecord\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "import yaml\n",
    "\n",
    "import dms_tools2\n",
    "from dms_tools2 import AAS\n",
    "from dms_tools2.ipython_utils import showPDF\n",
    "from dms_tools2.plot import COLOR_BLIND_PALETTE_GRAY as PALETTE\n",
    "print(f\"Using dms_tools2 version {dms_tools2.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter warnings that clutter output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('config.yaml') as f:\n",
    "    config = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get output directory and create if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsdir = config['natseqs_dir']\n",
    "os.makedirs(resultsdir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CPUs to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncpus = min(config['ncpus'], multiprocessing.cpu_count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Align and filter sequences\n",
    "Read all of the natural sequences and add the reference sequence (the HA used in deep mutational scanning):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing sequences from data/human_H3N2_HA_2007-2018.fasta.gz\n",
      "There are 19094 sequences.\n",
      "Adding reference sequence in data/Perth09_HA_reference.fa\n"
     ]
    }
   ],
   "source": [
    "print(f\"Processing sequences from {config['natseqs']}\")\n",
    "with gzip.open(config['natseqs'], 'rt') as f:\n",
    "    seqs = list(Bio.SeqIO.parse(f, 'fasta'))\n",
    "print(f\"There are {len(seqs)} sequences.\")\n",
    "\n",
    "print(f\"Adding reference sequence in {config['refseq']}\")\n",
    "refseq = Bio.SeqIO.read(config['refseq'], 'fasta')\n",
    "seqs.append(refseq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use [phydms_prepalignment](http://jbloomlab.github.io/phydms/phydms_prepalignment.html) to align the coding sequences from the proteins, only keeping sequences that are unique at the protein level.\n",
    "We also only keep sequences with $\\ge$92% identity with the reference sequence, as this appears to be a good cutoff between true human H3N2 from this timeframe and mis-annotated sequences or ones from non-human H3 lineages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using following version of phydms:\n",
      "phydms_prepalignment 2.3.2\n",
      "\n",
      "Using existing alignment in results/natseqs/all_alignment.fasta\n"
     ]
    }
   ],
   "source": [
    "print('Using following version of phydms:\\n' +\n",
    "      subprocess.check_output(['phydms_prepalignment', '--version']).decode('utf-8'))\n",
    "\n",
    "inseqsfile = os.path.join(resultsdir, 'all_seqs.fasta')\n",
    "Bio.SeqIO.write(seqs, inseqsfile, 'fasta')\n",
    "\n",
    "alignmentfile = os.path.join(resultsdir, 'all_alignment.fasta')\n",
    "\n",
    "if config['use_existing'] == 'yes' and os.path.isfile(alignmentfile):\n",
    "    print(f\"Using existing alignment in {alignmentfile}\")\n",
    "else:\n",
    "    print(f\"Building alignment and writing to {alignmentfile}...\")\n",
    "    _ = subprocess.check_call([\n",
    "        'phydms_prepalignment',\n",
    "        inseqsfile,\n",
    "        alignmentfile,\n",
    "        refseq.name,\n",
    "        '--minuniqueness', '1',\n",
    "        '--minidentity', '0.92',\n",
    "        ])\n",
    "    print(\"Bulding of alignment complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at plot showing divergence from \"reference\" seqeuence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPDF(os.path.splitext(alignmentfile)[0] + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read alignment as proteins:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read all 5382 aligned proteins.\n"
     ]
    }
   ],
   "source": [
    "alignment = [Bio.SeqRecord.SeqRecord(seq.seq.translate(gap='-'), name=seq.name)\n",
    "             for seq in Bio.SeqIO.parse(alignmentfile, 'fasta')\n",
    "             if seq.name != refseq.name]\n",
    "refprot = refseq.seq.translate(to_stop=True)\n",
    "assert all(len(refprot) == len(seq) for seq in alignment)\n",
    "\n",
    "print(f\"Read all {len(alignment)} aligned proteins.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get amino-acid frequencies and other relevant information\n",
    "Get amino-acid frequencies at each site for each year, breaking into 6-month intervals.\n",
    "\n",
    "First, we partition sequences into these 6-month intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are number of sequences in each half-year:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>nsequences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007.5</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008.0</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008.5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009.5</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010.5</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011.0</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011.5</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012.0</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012.5</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013.0</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013.5</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014.0</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014.5</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015.0</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015.5</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016.0</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016.5</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017.0</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017.5</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018.0</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018.5</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unmatched = 0\n",
    "alignment_by_year = collections.defaultdict(list)\n",
    "for seq in alignment:\n",
    "    datematch = re.search('_(?P<year>\\d{4})/(?P<month>\\d{1,2})/\\d*_HA$', seq.name)\n",
    "    if not datematch:\n",
    "        unmatched += 1\n",
    "    else:\n",
    "        year = int(datematch.group('year'))\n",
    "        month = int(datematch.group('month'))\n",
    "        if 1 <= month <= 3:\n",
    "            year = year\n",
    "        elif 3 < month <= 9:\n",
    "            year = year + 0.5\n",
    "        elif 9 < month <= 12:\n",
    "            year = year + 1\n",
    "        else:\n",
    "            raise ValueError('invalid month')\n",
    "        alignment_by_year[year].append(str(seq.seq))\n",
    "\n",
    "print('Here are number of sequences in each half-year:')\n",
    "display(HTML(pd.DataFrame.from_records([(year, len(seqs)) for \n",
    "                                        year, seqs in alignment_by_year.items()],\n",
    "                                       columns=['year', 'nsequences'])\n",
    "             .sort_values('year')\n",
    "             .to_html(index=False)\n",
    "             ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build up data frame giving amino-acid counts at each site in each year, then compute frequencies and add site numbers in H3 numbering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding H3 site numbering from data/H3renumbering_scheme.csv\n"
     ]
    }
   ],
   "source": [
    "aafreqs_df = []\n",
    "for year, year_alignment in alignment_by_year.items():\n",
    "    for r, wt in enumerate(refprot):\n",
    "        counts = collections.Counter([seq[r] for seq in year_alignment])\n",
    "        aafreqs_df.append(\n",
    "            pd.DataFrame({\n",
    "                'year': year,\n",
    "                'isite': r,\n",
    "                'wildtype': wt,\n",
    "                'mutation': [tup[0] for tup in sorted(counts.items())],\n",
    "                'natural_count': [tup[1] for tup in sorted(counts.items())],\n",
    "                })\n",
    "            .assign(natural_frequency=lambda x: x['natural_count'] / len(year_alignment)) \n",
    "            )\n",
    "\n",
    "aafreqs_df = pd.concat(aafreqs_df)\n",
    "\n",
    "print(f\"Adding H3 site numbering from {config['renumbering_scheme']}\")\n",
    "\n",
    "aafreqs_df = aafreqs_df.merge(\n",
    "                pd.read_csv(config['renumbering_scheme'])\n",
    "                .rename(columns={'original': 'isite', 'new': 'site'})\n",
    "                .assign(isite=lambda x: x['isite'] - 1)\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are what the first few lines of this data frame look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>isite</th>\n",
       "      <th>wildtype</th>\n",
       "      <th>mutation</th>\n",
       "      <th>natural_count</th>\n",
       "      <th>natural_frequency</th>\n",
       "      <th>site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>731</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>161</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017.5</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>187</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(aafreqs_df.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now write this amino-acid frequency data frame to a file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing amino-acid frequencies to results/natseqs/aafreqs.csv\n"
     ]
    }
   ],
   "source": [
    "aafreqs_file = os.path.join(resultsdir, 'aafreqs.csv')\n",
    "print(f\"Writing amino-acid frequencies to {aafreqs_file}\")\n",
    "aafreqs_df.to_csv(aafreqs_file, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine sites of strong immune selection\n",
    "Now we examine the amino-acid frequency changes at sites of strong immune selection.\n",
    "\n",
    "First, we merge the amino-acid frequencies with the data frame containing the immune-selection values.\n",
    "We keep only sites of major selection from human sera, which we define as:\n",
    "\n",
    " - being significanly selected in either the \"VIDD sera\" or \"Hensley sera\" set.\n",
    " - being \"significant\" sites according to the criterion used when analyzing the mutational antigenic profiling, which means that they have a value of `True` in the *zoom_site* column.\n",
    " \n",
    "We also only keep amino acids at each site that have frequencies that exceed 5% in at least one year, and the re-normalize the frequencies for each site / year to sum to one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merging with selection values in results/avgdiffsel/avg_sel_tidy.csv\n"
     ]
    }
   ],
   "source": [
    "sel_df_file = os.path.join(config['avgdiffseldir'], 'avg_sel_tidy.csv')\n",
    "print(f\"Merging with selection values in {sel_df_file}\")\n",
    "sel_df = pd.read_csv(sel_df_file, low_memory=False)\n",
    "\n",
    "# add immune selection values and get sites of major selection\n",
    "df = (pd.merge(aafreqs_df, sel_df)\n",
    "      .query('serum_group in [\"Hensley_sera\", \"VIDD_sera\"]')\n",
    "      .query('zoom_site')\n",
    "      [['year', 'site', 'mutation', 'natural_frequency']]\n",
    "      .drop_duplicates()\n",
    "      .rename(columns={'mutation': 'amino acid'})\n",
    "      )\n",
    "\n",
    "# subset to amino acids with high natural frequencies in at least one year\n",
    "df = (df.merge(df\n",
    "               .groupby(['site', 'amino acid'])\n",
    "               ['natural_frequency']\n",
    "               .max()\n",
    "               .rename('max_freq')\n",
    "               .reset_index()\n",
    "               )\n",
    "      .query('max_freq > 0.05')\n",
    "      .drop(columns='max_freq')\n",
    "      )\n",
    "\n",
    "# re-normalize frequencies\n",
    "df = (df.merge(df\n",
    "               .groupby(['site', 'year'])\n",
    "               ['natural_frequency']\n",
    "               .sum()\n",
    "               .rename('tot_freq')\n",
    "               .reset_index()\n",
    "               )\n",
    "      .assign(natural_frequency=lambda x: x['natural_frequency'] / x['tot_freq'])\n",
    "      .drop(columns='tot_freq')\n",
    "      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot frequencies per year for these sites:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = (\n",
    " ggplot(df, aes('year', 'natural_frequency', color='amino acid')) +\n",
    " geom_line() +\n",
    " facet_wrap('site')\n",
    " )\n",
    "\n",
    "_ = p.draw()\n",
    "plotfile = os.path.join(resultsdir, 'natural_freqs_plot.pdf')\n",
    "p.save(plotfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}