PhenopacketStoreStats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phenopacket store statistics\n",
    "\n",
    "This notebook performs quality assessment and calculate descriptive statistics about a phenopacket-store release.\n",
    "\n",
    "Note: \n",
    "\n",
    "We recommend installing Phenopacket Store Toolkit into the notebook kernel:\n",
    "\n",
    "```shell\n",
    "python3 -m pip install phenopacket-store-toolkit[release]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input file is the ZIP file that is or will be added to each release.\n",
    "\n",
    "The ZIP file can be generated by running:\n",
    "\n",
    "```shell\n",
    "python3 -m ppktstore package --notebook-dir notebooks --release-tag 0.1.19 --output all_phenopackets\n",
    "```\n",
    "\n",
    "assuming `phenopacket-store-toolkit` was installed into the active environment, and `notebooks` points to the folder with Phenopacket store notebook directory.\n",
    "\n",
    "Note, we need to update the `--release-tag` option periodically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "from ppktstore.model import PhenopacketStore\n",
    "\n",
    "release_tag = '0.1.24'\n",
    "\n",
    "input_zip = 'all_phenopackets.zip'\n",
    "with zipfile.ZipFile(input_zip) as zf:\n",
    "    store = PhenopacketStore.from_release_zip(zf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can summarize statistics of the individuals described in the phenopackets, their phenotypic features, diseases, and genotypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ppktstore.release.stats import PPKtStoreStats\n",
    "\n",
    "stats = PPKtStoreStats(store)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>patient_id</th>\n",
       "      <th>cohort</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>disease</th>\n",
       "      <th>gene</th>\n",
       "      <th>allele_1</th>\n",
       "      <th>allele_2</th>\n",
       "      <th>PMID</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Family 3 proband</td>\n",
       "      <td>AAGAB</td>\n",
       "      <td>OMIM:148600</td>\n",
       "      <td>Keratoderma, palmoplantar, punctate type IA</td>\n",
       "      <td>AAGAB</td>\n",
       "      <td>NM_024666.5:c.870+1G&gt;A</td>\n",
       "      <td></td>\n",
       "      <td>PMID:28239884</td>\n",
       "      <td>all_phenopackets.zip/../../..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Family 1 proband</td>\n",
       "      <td>AAGAB</td>\n",
       "      <td>OMIM:148600</td>\n",
       "      <td>Keratoderma, palmoplantar, punctate type IA</td>\n",
       "      <td>AAGAB</td>\n",
       "      <td>NM_024666.5:c.505_506dup</td>\n",
       "      <td></td>\n",
       "      <td>PMID:28239884</td>\n",
       "      <td>all_phenopackets.zip/../../..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Family 2 proband</td>\n",
       "      <td>AAGAB</td>\n",
       "      <td>OMIM:148600</td>\n",
       "      <td>Keratoderma, palmoplantar, punctate type IA</td>\n",
       "      <td>AAGAB</td>\n",
       "      <td>NM_024666.5:c.473del</td>\n",
       "      <td></td>\n",
       "      <td>PMID:28239884</td>\n",
       "      <td>all_phenopackets.zip/../../..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>II.2</td>\n",
       "      <td>ABCA4</td>\n",
       "      <td>OMIM:601718</td>\n",
       "      <td>Retinitis pigmentosa 19</td>\n",
       "      <td>ABCA4</td>\n",
       "      <td>NM_000350.3:c.1938-1G&gt;A</td>\n",
       "      <td></td>\n",
       "      <td>PMID:10874631</td>\n",
       "      <td>all_phenopackets.zip/../../..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>IV-26</td>\n",
       "      <td>ABCB7</td>\n",
       "      <td>OMIM:301310</td>\n",
       "      <td>Anemia, sideroblastic, and spinocerebellar ataxia</td>\n",
       "      <td>ABCB7</td>\n",
       "      <td>NM_001271696.3:c.2044G&gt;A</td>\n",
       "      <td></td>\n",
       "      <td>PMID:26242992</td>\n",
       "      <td>all_phenopackets.zip/../../..</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id cohort   disease_id  \\\n",
       "71  Family 3 proband  AAGAB  OMIM:148600   \n",
       "70  Family 1 proband  AAGAB  OMIM:148600   \n",
       "69  Family 2 proband  AAGAB  OMIM:148600   \n",
       "72              II.2  ABCA4  OMIM:601718   \n",
       "83             IV-26  ABCB7  OMIM:301310   \n",
       "\n",
       "                                              disease   gene  \\\n",
       "71        Keratoderma, palmoplantar, punctate type IA  AAGAB   \n",
       "70        Keratoderma, palmoplantar, punctate type IA  AAGAB   \n",
       "69        Keratoderma, palmoplantar, punctate type IA  AAGAB   \n",
       "72                            Retinitis pigmentosa 19  ABCA4   \n",
       "83  Anemia, sideroblastic, and spinocerebellar ataxia  ABCB7   \n",
       "\n",
       "                    allele_1 allele_2           PMID  \\\n",
       "71    NM_024666.5:c.870+1G>A           PMID:28239884   \n",
       "70  NM_024666.5:c.505_506dup           PMID:28239884   \n",
       "69      NM_024666.5:c.473del           PMID:28239884   \n",
       "72   NM_000350.3:c.1938-1G>A           PMID:10874631   \n",
       "83  NM_001271696.3:c.2044G>A           PMID:26242992   \n",
       "\n",
       "                         filename  \n",
       "71  all_phenopackets.zip/../../..  \n",
       "70  all_phenopackets.zip/../../..  \n",
       "69  all_phenopackets.zip/../../..  \n",
       "72  all_phenopackets.zip/../../..  \n",
       "83  all_phenopackets.zip/../../..  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = stats.get_summary_df().sort_values(by='gene')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Individual statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>age_in_years</th>\n",
       "      <th>vital_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMID_15266616_35-35</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1095.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMID_15266616_148-148</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id     sex  age_in_days  age_in_years vital_status\n",
       "0    PMID_15266616_35-35    MALE      1095.75           3.0         None\n",
       "1  PMID_15266616_148-148  FEMALE          NaN           NaN         None"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ppktstore.release.stats import summarize_individuals\n",
    "\n",
    "individuals_df = summarize_individuals(store)\n",
    "individuals_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary statistics\n",
    "\n",
    "\n",
    "#### Sex\n",
    "The number of males and females in all case report collections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'males': 3711, 'females': 3372, 'unknown': 1098}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_summary = {\n",
    "    'males': sum(individuals_df.sex=='MALE'),\n",
    "    'females': sum(individuals_df.sex=='FEMALE'),\n",
    "    'unknown': sum(individuals_df.sex=='UNKNOWN_SEX')\n",
    "}\n",
    "sex_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'7083 (86.6%) had the sex specified (52.4% males, 47.6% females)'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_w_sex = sex_summary['males'] + sex_summary['females']\n",
    "perc_w_sex = (100 * n_w_sex) / sum(sex_summary.values())\n",
    "perc_males = (100 * sex_summary['males']) / n_w_sex\n",
    "perc_females = (100 * sex_summary['females']) / n_w_sex\n",
    "\n",
    "f'{n_w_sex} ({perc_w_sex:.1f}%) had the sex specified ({perc_males:.1f}% males, {perc_females:.1f}% females)'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Age\n",
    "The number and percentage of subjects with the age information available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'individuals with no age': '2632 (32.2%)',\n",
       " 'individuals with age': '5550 (67.8%)'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_no_age = sum(individuals_df.age_in_days.isna())\n",
    "n_w_age = len(individuals_df) - n_no_age\n",
    "age_summary = {\n",
    "    'individuals with no age': f'{n_no_age} ({n_no_age * 100 / len(individuals_df):.1f}%)',\n",
    "    'individuals with age': f'{n_w_age} ({n_w_age * 100 / len(individuals_df):.1f}%)',\n",
    "}\n",
    "age_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age at last encounter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (l, r) = plt.subplots(1, 2, figsize=(12, 4), dpi=120, sharey=True,)\n",
    "\n",
    "# round up to nearest 10\n",
    "xmax = math.ceil(math.ceil(max(individuals_df.age_in_years)) / 10) * 10\n",
    "l.hist(\n",
    "    individuals_df.age_in_years, bins=xmax, \n",
    ")\n",
    "\n",
    "l.set(\n",
    "    xlabel='Age in years', xlim=(0, xmax),\n",
    "    ylabel='Number of cases', \n",
    ")\n",
    "l.xaxis.set(major_locator=mpl.ticker.MultipleLocator(10))\n",
    "l.yaxis.set(minor_locator=mpl.ticker.MultipleLocator(10))\n",
    "l.grid(axis='y')\n",
    "\n",
    "r.hist(\n",
    "    individuals_df.loc[individuals_df.sex == 'MALE', 'age_in_years'], bins=xmax, \n",
    "    color='blue', alpha=.4, label='Males',\n",
    ")\n",
    "r.hist(\n",
    "    individuals_df.loc[individuals_df.sex == 'FEMALE', 'age_in_years'], bins=xmax, \n",
    "    color='red', alpha=.4, label='Females',\n",
    ")\n",
    "\n",
    "r.legend()\n",
    "r.set(\n",
    "    xlabel='Age in years', xlim=(0, xmax),\n",
    ")\n",
    "r.xaxis.set(major_locator=mpl.ticker.MultipleLocator(10))\n",
    "r.grid(axis='y')\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.savefig(\"age_dist.pdf\")\n",
    "# fig.savefig(\"age_dist.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>item</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>version</td>\n",
       "      <td>0.1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>phenopackets</td>\n",
       "      <td>8182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>diseases</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>genes</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alleles</td>\n",
       "      <td>4469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PMIDs</td>\n",
       "      <td>1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>individuals per gene (max)</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>individuals per gene (min)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>individuals per gene (mean)</td>\n",
       "      <td>17.90372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>individuals per gene (median)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>individuals per disease (max)</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>individuals per disease (min)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>individuals per disease (mean)</td>\n",
       "      <td>15.856589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>individuals per disease (median)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>genes associated with a single disease</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>genes associated with two diseases</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>genes associated with multiple diseases</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gene(s) with maximum number of diseases</td>\n",
       "      <td>FBN1 with 6 associated diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>total HPO terms used</td>\n",
       "      <td>167837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>present HPO terms used</td>\n",
       "      <td>67202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>excluded HPO terms used</td>\n",
       "      <td>100635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>unique HPO terms used</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>present HPO terms per cohort (mean)</td>\n",
       "      <td>147.050328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>present HPO terms per cohort (median)</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>present HPO terms per cohort (min)</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>present HPO terms per cohort (max)</td>\n",
       "      <td>4093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>excluded HPO terms per cohort (mean)</td>\n",
       "      <td>220.207877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>excluded HPO terms per cohort (median)</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>excluded HPO terms per cohort (min)</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>excluded HPO terms per cohort (max)</td>\n",
       "      <td>6689.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>total HPO terms per cohort (mean)</td>\n",
       "      <td>367.258206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>total HPO terms per cohort (median)</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>total HPO terms per cohort (min)</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>total HPO terms per cohort (max)</td>\n",
       "      <td>7953.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       item                            value\n",
       "0                                   version                           0.1.24\n",
       "1                              phenopackets                             8182\n",
       "2                                  diseases                              516\n",
       "3                                     genes                              457\n",
       "4                                   alleles                             4469\n",
       "5                                     PMIDs                             1227\n",
       "6                individuals per gene (max)                              462\n",
       "7                individuals per gene (min)                                1\n",
       "8               individuals per gene (mean)                         17.90372\n",
       "9             individuals per gene (median)                              5.0\n",
       "10            individuals per disease (max)                              462\n",
       "11            individuals per disease (min)                                1\n",
       "12           individuals per disease (mean)                        15.856589\n",
       "13         individuals per disease (median)                              5.0\n",
       "14   genes associated with a single disease                              416\n",
       "15       genes associated with two diseases                               29\n",
       "16  genes associated with multiple diseases                               12\n",
       "17  gene(s) with maximum number of diseases  FBN1 with 6 associated diseases\n",
       "18                     total HPO terms used                           167837\n",
       "19                   present HPO terms used                            67202\n",
       "20                  excluded HPO terms used                           100635\n",
       "21                    unique HPO terms used                             3500\n",
       "22      present HPO terms per cohort (mean)                       147.050328\n",
       "23    present HPO terms per cohort (median)                             39.0\n",
       "24       present HPO terms per cohort (min)                              2.0\n",
       "25       present HPO terms per cohort (max)                           4093.0\n",
       "26     excluded HPO terms per cohort (mean)                       220.207877\n",
       "27   excluded HPO terms per cohort (median)                             18.0\n",
       "28      excluded HPO terms per cohort (min)                              0.0\n",
       "29      excluded HPO terms per cohort (max)                           6689.0\n",
       "30        total HPO terms per cohort (mean)                       367.258206\n",
       "31      total HPO terms per cohort (median)                             59.0\n",
       "32         total HPO terms per cohort (min)                              2.0\n",
       "33         total HPO terms per cohort (max)                           7953.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_d = stats.get_descriptive_stats(version=release_tag)\n",
    "items = list()\n",
    "for k,v in stats_d.items():\n",
    "    items.append({\"item\": k, \"value\": v})\n",
    "pd.DataFrame(items)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phenotypic features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>cohort</th>\n",
       "      <th>pp_id</th>\n",
       "      <th>term_id</th>\n",
       "      <th>is_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11q_terminal_deletion</td>\n",
       "      <td>PMID_15266616_35</td>\n",
       "      <td>HP:0000348</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11q_terminal_deletion</td>\n",
       "      <td>PMID_15266616_35</td>\n",
       "      <td>HP:0011220</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  cohort             pp_id     term_id  is_present\n",
       "0  11q_terminal_deletion  PMID_15266616_35  HP:0000348        True\n",
       "1  11q_terminal_deletion  PMID_15266616_35  HP:0011220        True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "data = defaultdict(list)\n",
    "\n",
    "for cohort_info in store.cohorts():\n",
    "    for pp_info in cohort_info.phenopackets:\n",
    "        pp = pp_info.phenopacket\n",
    "        for pf in pp.phenotypic_features:\n",
    "            data['cohort'].append(cohort_info.name)\n",
    "            data['pp_id'].append(pp.id)\n",
    "            # data['subject_id'].append(pp.subject.id)\n",
    "            data['term_id'].append(pf.type.id)\n",
    "            data['is_present'].append(not pf.excluded)\n",
    "\n",
    "pf_df = pd.DataFrame(data)\n",
    "\n",
    "pf_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check each phenopacket ID is present at most in one cohort:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert pf_df.groupby('pp_id')['cohort'].nunique().max() == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total phenotypic feature counts per phenopacket:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean       20.533032\n",
       "median     14.000000\n",
       "min         1.000000\n",
       "max       192.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_df.groupby('pp_id').size().agg(['mean', 'median', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phenotypic feature counts by observation status per phenopacket:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>is_present</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.89124</td>\n",
       "      <td>8.295519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>8.00000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>175.00000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_present      False      True \n",
       "mean         14.89124   8.295519\n",
       "median        8.00000   7.000000\n",
       "min           1.00000   1.000000\n",
       "max         175.00000  66.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_df.groupby(['pp_id', 'is_present']).size().unstack('is_present').agg(['mean', 'median', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>pp_id</th>\n",
       "      <th>cnt</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMID_10077612_Family_A_III_10</td>\n",
       "      <td>9</td>\n",
       "      <td>Excluded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMID_10077612_Family_A_III_10</td>\n",
       "      <td>4</td>\n",
       "      <td>Present</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           pp_id  cnt    status\n",
       "0  PMID_10077612_Family_A_III_10    9  Excluded\n",
       "1  PMID_10077612_Family_A_III_10    4   Present"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_status_cnts = pf_df.groupby(['pp_id', 'is_present']).size().reset_index(name='cnt')\n",
    "pf_status_cnts['status'] = ''\n",
    "pf_status_cnts.loc[pf_status_cnts.is_present, 'status'] = 'Present'\n",
    "pf_status_cnts.loc[~pf_status_cnts.is_present, 'status'] = 'Excluded'\n",
    "del pf_status_cnts['is_present']\n",
    "pf_status_cnts.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
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       "    }\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>pp_id</th>\n",
       "      <th>cnt</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMID_10077612_Family_A_III_10</td>\n",
       "      <td>13</td>\n",
       "      <td>Total</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMID_10077612_Family_A_III_12</td>\n",
       "      <td>12</td>\n",
       "      <td>Total</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           pp_id  cnt status\n",
       "0  PMID_10077612_Family_A_III_10   13  Total\n",
       "1  PMID_10077612_Family_A_III_12   12  Total"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_tot_cnts = pf_df.groupby('pp_id').size().reset_index(name='cnt')\n",
    "pf_tot_cnts['status'] = 'Total'\n",
    "pf_tot_cnts.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>pp_id</th>\n",
       "      <th>cnt</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMID_10077612_Family_A_III_10</td>\n",
       "      <td>9</td>\n",
       "      <td>Excluded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMID_10077612_Family_A_III_10</td>\n",
       "      <td>4</td>\n",
       "      <td>Present</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14859</th>\n",
       "      <td>PMID_10077612_Family_A_III_10</td>\n",
       "      <td>13</td>\n",
       "      <td>Total</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               pp_id  cnt    status\n",
       "0      PMID_10077612_Family_A_III_10    9  Excluded\n",
       "1      PMID_10077612_Family_A_III_10    4   Present\n",
       "14859  PMID_10077612_Family_A_III_10   13     Total"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_cnts = pd.concat((pf_status_cnts, pf_tot_cnts), ignore_index=True).sort_values(by='pp_id')\n",
    "pf_cnts.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hq/zttr8hjx127fgvh0bq7h4bc80000gn/T/ipykernel_90318/3792679578.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.boxplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4), dpi=120)\n",
    "\n",
    "ax = sns.boxplot(\n",
    "    data=pf_cnts, \n",
    "    x='status', y='cnt', \n",
    "    order=('Total', 'Present', 'Excluded', ),\n",
    "    palette=('#669bbc', '#d62828', '#fcbf49'),\n",
    "    fliersize=2,\n",
    "    ax=ax,\n",
    ")\n",
    "\n",
    "ax.set(\n",
    "    xlabel=None, \n",
    "    ylabel='Count', ylim=(0, 125),\n",
    ")\n",
    "ax.grid(axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show how many phenotypic features include age of onset info.\n",
    "\n",
    "First, count the phenotypic features, and the features where the `onset` is available."
   ]
  },
  {
   "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>age</th>\n",
       "      <th>ontology_class</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11q_terminal_deletion</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAGAB</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       age  ontology_class  total\n",
       "11q_terminal_deletion    0               0   4401\n",
       "AAGAB                    3               0     12"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "count = defaultdict(Counter)\n",
    "total = Counter()\n",
    "\n",
    "for cohort in store.cohorts():\n",
    "    for pp_info in cohort.phenopackets:\n",
    "        for pf in pp_info.phenopacket.phenotypic_features:\n",
    "            total[cohort.name] += 1\n",
    "            element = pf.onset.WhichOneof('element')\n",
    "            if element is not None:\n",
    "                count[element][cohort.name] += 1\n",
    "\n",
    "total_df = pd.Series(total, name='total')\n",
    "\n",
    "onset_df = (\n",
    "    pd.DataFrame(count)\n",
    "    .join(total_df, how=\"outer\")\n",
    "    .fillna(0)\n",
    "    .astype({\"age\": int, \"ontology_class\": int, \"total\": int})\n",
    ")\n",
    "onset_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6,870 of the total of 167,837 (4.09%) phenotypic features had an associated age of onset.\n"
     ]
    }
   ],
   "source": [
    "have_onset = onset_df[['age', 'ontology_class']].sum().sum()\n",
    "n_total_pf = onset_df['total'].sum()\n",
    "perc = 100 * have_onset / n_total_pf\n",
    "print(f'{have_onset:,} of the total of {n_total_pf:,} ({perc:.2f}%) phenotypic features had an associated age of onset.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disease stats\n",
    "\n",
    "Summary statistics of disease diagnoses per case report collection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean      1.131291\n",
       "median    1.000000\n",
       "min       1.000000\n",
       "max       6.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_by_cohort = df[['cohort', 'disease_id']].drop_duplicates().groupby('cohort').size()\n",
    "disease_by_cohort.agg(['mean', 'median', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of number of cases per disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>disease</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OMIM:147791</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMIM:148600</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       disease  count\n",
       "0  OMIM:147791     69\n",
       "1  OMIM:148600      3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_per_disease = stats.get_counts_per_disease_df()\n",
    "counts_per_disease.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4), dpi=300)\n",
    "\n",
    "disease_counts = counts_per_disease[\"count\"]\n",
    "max_val = 25\n",
    "\n",
    "assert (\n",
    "    isinstance(max_val, int) and max_val > 0\n",
    "), \"`max_val` must be a positive `int`\"\n",
    "\n",
    "bins = list(range(0, max_val + 1)) + [disease_counts.max() + 1]\n",
    "labels = list(map(str, bins[:-2])) + [f\"≥{max_val}\"]\n",
    "cats = pd.cut(\n",
    "    x=disease_counts,\n",
    "    bins=bins,\n",
    "    labels=labels,\n",
    "    right=False,\n",
    ")\n",
    "cat_count = cats.value_counts().sort_index()\n",
    "ax.bar(x=cat_count.index, height=cat_count.values, color='#669bbc',)\n",
    "ax.set(\n",
    "    xlabel=\"Number of cases\", xlim=(-0.5, len(labels) - 0.5),\n",
    "    ylabel=\"Disease count\", ylim=(0, 110),\n",
    ")\n",
    "ax.xaxis.set(major_locator=mpl.ticker.MultipleLocator(5), minor_locator=mpl.ticker.MultipleLocator(1))\n",
    "ax.grid(axis=\"y\")\n",
    "ax.spines['top'].set(visible=False)\n",
    "ax.spines['right'].set(visible=False)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene stats\n",
    "\n",
    "Compute the counts of cases associated with mutations in a gene per case report collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean      1.0\n",
       "median    1.0\n",
       "min       1.0\n",
       "max       1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_by_cohort = df[['cohort', 'gene']].drop_duplicates().groupby('gene').size()\n",
    "disease_by_cohort.agg(['mean', 'median', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Allele stats\n",
    "\n",
    "Compute the counts of distinct alleles per case report collection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>cohort</th>\n",
       "      <th>allele_1</th>\n",
       "      <th>allele_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>AAGAB</td>\n",
       "      <td>NM_024666.5:c.870+1G&gt;A</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>AAGAB</td>\n",
       "      <td>NM_024666.5:c.505_506dup</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cohort                  allele_1 allele_2\n",
       "71  AAGAB    NM_024666.5:c.870+1G>A         \n",
       "70  AAGAB  NM_024666.5:c.505_506dup         "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['cohort', 'allele_1', 'allele_2']].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cohort\n",
       "11q_terminal_deletion    1\n",
       "AAGAB                    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniq_alleles_by_cohort = (\n",
    "    df[['cohort', 'allele_1', 'allele_2']]\n",
    "    .melt(\n",
    "          id_vars=['cohort'], var_name='which', \n",
    "          value_name='allele', value_vars=['allele_1', 'allele_2'],\n",
    "    )\n",
    "    .drop(columns=['which'])\n",
    "    # .sort_values(by=['cohort', 'allele'])\n",
    "    .loc[lambda row: row.allele != '']\n",
    "    .drop_duplicates()\n",
    "    .groupby(['cohort']).size()\n",
    ")\n",
    "uniq_alleles_by_cohort.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean        9.912473\n",
       "median      3.000000\n",
       "min         1.000000\n",
       "max       264.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniq_alleles_by_cohort.agg(['mean', 'median', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Publication statistics\n",
    "\n",
    "How many publications were used to prepare a case report collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean       2.715536\n",
       "median     1.000000\n",
       "min        1.000000\n",
       "max       35.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmid_by_cohort = df[['cohort', 'PMID']].drop_duplicates().groupby('cohort').size()\n",
    "pmid_by_cohort.agg(['mean', 'median', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q/C checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check all disease identifiers\n",
    "They should all be CURIEs with the prefixes OMIM or MONDO. There should be no whitespace between the colon and the suffix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No problems found.\n"
     ]
    }
   ],
   "source": [
    "_ = stats.check_disease_id()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for duplicates\n",
    "In some cases, duplicate phenopackets were inadvertently added to some of the initial cohorts at earlier stages of the project. This function lists phenopacket identifers arranged according to variant and allows us to more easily check for this kind of error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No candidate duplicates found for ERI1\n"
     ]
    }
   ],
   "source": [
    "_ = stats.show_possible_duplicates_by_variant(\"ERI1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All phenopackets had at least one variant\n"
     ]
    }
   ],
   "source": [
    "_ = stats.find_phenopackets_with_no_variants()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No candidate duplicates found for ARPC5\n"
     ]
    }
   ],
   "source": [
    "_ = stats.show_possible_duplicates_by_variant( \"ARPC5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All phenopackets had a disease diagnosis\n"
     ]
    }
   ],
   "source": [
    "_ = stats.find_phenopackets_with_no_disease()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hq/zttr8hjx127fgvh0bq7h4bc80000gn/T/ipykernel_90318/2876177173.py:43: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x1400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(8, 7), dpi=200)\n",
    "\n",
    "## Top left\n",
    "ll, lr = axs[0, :]\n",
    "\n",
    "## Lower left\n",
    "# round up to nearest 10\n",
    "xmax = math.ceil(math.ceil(max(individuals_df.age_in_years)) / 10) * 10\n",
    "ll.hist(\n",
    "    individuals_df.age_in_years, bins=xmax, color='#669bbc'\n",
    ")\n",
    "\n",
    "ll.set(\n",
    "    xlabel='Age in years', xlim=(0, xmax),\n",
    "    ylabel='Number of phenopackets', ylim=(0, 275),\n",
    ")\n",
    "ll.xaxis.set(major_locator=mpl.ticker.MultipleLocator(10))\n",
    "ll.yaxis.set(minor_locator=mpl.ticker.MultipleLocator(10))\n",
    "ll.grid(axis='y')\n",
    "\n",
    "\n",
    "## Lower right\n",
    "lr.hist(\n",
    "    individuals_df.loc[individuals_df.sex == 'MALE', 'age_in_years'], bins=xmax, \n",
    "    color='blue', alpha=.4, label='Males',\n",
    ")\n",
    "lr.hist(\n",
    "    individuals_df.loc[individuals_df.sex == 'FEMALE', 'age_in_years'], bins=xmax, \n",
    "    color='red', alpha=.4, label='Females',\n",
    ")\n",
    "\n",
    "lr.legend()\n",
    "lr.set(\n",
    "    xlabel='Age in years', xlim=(0, xmax),\n",
    "    ylim=(0, 275),\n",
    ")\n",
    "lr.xaxis.set(major_locator=mpl.ticker.MultipleLocator(10))\n",
    "lr.yaxis.set(minor_locator=mpl.ticker.MultipleLocator(10))\n",
    "lr.grid(axis='y')\n",
    "\n",
    "## Lower row\n",
    "l, r = axs[1, :]\n",
    "sns.boxplot(\n",
    "    data=pf_cnts, \n",
    "    x='status', y='cnt', \n",
    "    order=('Total', 'Present', 'Excluded', ),\n",
    "    palette=('#669bbc', '#d62828', '#fcbf49'),\n",
    "    fliersize=2,\n",
    "    ax=l, \n",
    ")\n",
    "\n",
    "l.set(\n",
    "    xlabel='Phenotype feature count', \n",
    "    ylabel='Counts', ylim=(0, 125),\n",
    ")\n",
    "l.yaxis.set(minor_locator=mpl.ticker.MultipleLocator(10))\n",
    "l.grid(axis='y')\n",
    "\n",
    "\n",
    "## Top right\n",
    "disease_counts = counts_per_disease[\"count\"]\n",
    "max_val = 25\n",
    "\n",
    "assert (\n",
    "    isinstance(max_val, int) and max_val > 0\n",
    "), \"`max_val` must be a positive `int`\"\n",
    "\n",
    "bins = list(range(0, max_val + 1)) + [disease_counts.max() + 1]\n",
    "labels = list(map(str, bins[:-2])) + [f\"≥{max_val}\"]\n",
    "cats = pd.cut(\n",
    "    x=disease_counts,\n",
    "    bins=bins,\n",
    "    labels=labels,\n",
    "    right=False,\n",
    ")\n",
    "cat_count = cats.value_counts().sort_index()\n",
    "r.bar(x=cat_count.index, height=cat_count.values, color='#669bbc',)\n",
    "r.set(\n",
    "    # title='Disease count', \n",
    "    xlabel=\"Number of phenopackets\", xlim=(-0.5, len(labels) - 0.5),\n",
    "    ylabel=None, ylim=(0, 125),\n",
    ")\n",
    "r.xaxis.set(major_locator=mpl.ticker.MultipleLocator(5), minor_locator=mpl.ticker.MultipleLocator(1))\n",
    "r.yaxis.set(minor_locator=mpl.ticker.MultipleLocator(10))\n",
    "r.grid(axis=\"y\")\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.savefig('Figure1.png')\n",
    "# fig.savefig('Figure1.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After generating the figure, we need to add the panel identifiers (A, B, C, D) in a vector graphics processor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*-* EOF *-*"
   ]
  }
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