process_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Process the deep mutational scanning data\n",
    "\n",
    "The data come from the following papers from Yunlong Cao's group:\n",
    "\n",
    " - [Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution (2022)](https://www.nature.com/articles/s41586-022-05644-7)\n",
    " \n",
    " - [Repeated Omicron infection alleviates SARS-CoV-2 immune imprinting (2023)](https://www.biorxiv.org/content/10.1101/2023.05.01.538516v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a9757e8-5572-4173-a5c3-025991bccac5",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "import math\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import yaml\n",
    "\n",
    "with open(\"config.yaml\") as f:\n",
    "    config = yaml.safe_load(f)\n",
    "    \n",
    "antibody_reweighting = config[\"antibody_reweighting\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13d8200-2145-4932-9272-a79ae4d935aa",
   "metadata": {},
   "source": [
    "## Process data from [Repeated Omicron infection alleviates SARS-CoV-2 immune imprinting (2023)](https://www.biorxiv.org/content/10.1101/2023.05.01.538516v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a8ed2a8-b997-4a01-8677-86785d5d9080",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:18.396085Z",
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     "shell.execute_reply": "2023-06-20T23:54:18.416907Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# read the data\n",
    "repeated_2023_info = (\n",
    "    pd.read_csv(\"Cao_data/SARS-CoV-2-reinfection-DMS/antibody_info.csv\")\n",
    "    .rename(columns={\"Unnamed: 0\": \"antibody\"})\n",
    ").assign(study=\"repeated_2023\")\n",
    "\n",
    "assert len(repeated_2023_info) == repeated_2023_info[\"antibody\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8f7058b-20ed-49c3-9bdb-10490c589042",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:18.421995Z",
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     "shell.execute_reply": "2023-06-20T23:54:18.447566Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of antibodies from each source:\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>study</th>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">repeated_2023</th>\n",
       "      <th>BA.5 convalescents</th>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA.1 convalescents reinfection</th>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BF.7 convalescents</th>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA.2 convalescents reinfection</th>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA.2 convalescents</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long-term BA.1 convalescents</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA.1 convalescents</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WT convalescents</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARS convalescents</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              n_antibodies\n",
       "study         source                                      \n",
       "repeated_2023 BA.5 convalescents                       445\n",
       "              BA.1 convalescents reinfection           284\n",
       "              BF.7 convalescents                       243\n",
       "              BA.2 convalescents reinfection           232\n",
       "              BA.2 convalescents                        53\n",
       "              long-term BA.1 convalescents              38\n",
       "              BA.1 convalescents                        36\n",
       "              WT convalescents                          12\n",
       "              SARS convalescents                         5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the antibody sources\n",
    "repeated_2023_source = (\n",
    "    repeated_2023_info\n",
    "    [[\"antibody\", \"source\", \"study\"]]\n",
    "    .query(\"source != 'WT mouse'\")  # do not keep mouse antibodies\n",
    ")\n",
    "\n",
    "print(\"Number of antibodies from each source:\")\n",
    "display(\n",
    "    repeated_2023_source\n",
    "    .groupby([\"study\", \"source\"])\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    "    .sort_values(\"n_antibodies\", ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9f0f887-a246-4fd4-8c2d-a9508daa1d28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:18.454263Z",
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     "shell.execute_reply": "2023-06-20T23:54:18.531344Z"
    },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of IC50s per antibody:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>viruses_w_IC50s</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; SARS; BA.2.75; BA.5; BF.7; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; SARS; BA.2.75; BA.5; BF.7; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.3; SARS; BA.1.1; BA.2.12.1; BA.2.75; BA.5; BA.2.13; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; SARS; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.2.75; BA.5; BF.7; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; SARS; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; SARS; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.3; SARS; BA.1.1; BA.2.12.1; BA.2.75; BA.5; BA.2.13; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; SARS; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; SARS; BA.2.12.1; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; SARS; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.2.75; BA.5; BF.7; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; SARS; BA.5; BF.7; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; SARS; BA.2.12.1; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; SARS; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.3; SARS; BA.2.12.1; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; SARS; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.2.12.1; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.5; BF.7; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.3; SARS; BA.2.12.1; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; SARS; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.3; SARS; BA.1.1; BA.2.12.1; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.5; BF.7; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.2; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.5; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.2.75; BA.5; BQ.1.1; XBB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARS; XBB; XBB.1.5; XBB.1.5.10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    n_antibodies\n",
       "viruses_w_IC50s                                                 \n",
       "D614G; BA.1; BA.2; SARS; BA.2.75; BA.5; BF.7; B...           428\n",
       "D614G; BA.1; BA.2; SARS; BA.2.75; BA.5; BF.7; B...           181\n",
       "D614G; BA.1; BA.2; BA.3; SARS; BA.1.1; BA.2.12....           137\n",
       "D614G; BA.1; BA.2; SARS; BA.2.75; BA.5; BQ.1.1;...           130\n",
       "D614G; BA.1; BA.2; BA.2.75; BA.5; BF.7; BQ.1.1;...           129\n",
       "D614G; BA.1; BA.2; SARS; BA.2.75; BA.5; BQ.1.1;...            92\n",
       "D614G; BA.1; BA.2; BA.2.75; BA.5; BQ.1.1; XBB; ...            48\n",
       "D614G; SARS; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1....            42\n",
       "D614G; BA.1; BA.2; BA.2.75; BA.5; BQ.1.1; XBB; ...            34\n",
       "D614G; BA.1; BA.2; BA.3; SARS; BA.1.1; BA.2.12....            17\n",
       "D614G; BA.1; SARS; BQ.1.1; XBB; XBB.1.5                       16\n",
       "D614G; BA.1; BA.2; SARS; BA.2.12.1; BA.2.75; BA...            14\n",
       "D614G; SARS; BA.5; BQ.1.1; XBB; XBB.1.5                       14\n",
       "D614G; BA.5; BQ.1.1; XBB; XBB.1.5; XBB.1.5.10                 12\n",
       "D614G; BA.1; BA.2; BA.2.75; BA.5; BF.7; BQ.1.1;...            10\n",
       "D614G; BA.1; BA.2; SARS; BA.5; BF.7; BQ.1.1; XB...             7\n",
       "D614G; BA.5; BQ.1.1; XBB; XBB.1.5                              5\n",
       "D614G; BA.1; BA.2; SARS; BA.2.12.1; BA.2.75; BA...             4\n",
       "D614G; BA.1; SARS; BA.5; BQ.1.1; XBB; XBB.1.5                  4\n",
       "D614G; BA.1; BA.2; BA.3; SARS; BA.2.12.1; BA.2....             4\n",
       "D614G; BA.1; SARS; BA.5; BQ.1.1; XBB; XBB.1.5; ...             3\n",
       "D614G; BA.1; BA.2; BA.2.12.1; BA.2.75; BA.5; BQ...             3\n",
       "D614G; BA.1; BA.2; BA.5; BF.7; BQ.1.1; XBB; XBB...             3\n",
       "D614G; BA.1; BA.2; BA.3; SARS; BA.2.12.1; BA.2....             3\n",
       "D614G; BA.1; BA.2; SARS; BA.5; BQ.1.1; XBB; XBB...             2\n",
       "D614G; BA.1; BA.2; BA.3; SARS; BA.1.1; BA.2.12....             2\n",
       "D614G; BA.1; BA.2; BA.5; BQ.1.1; XBB; XBB.1.5; ...             1\n",
       "D614G; BA.1; BA.2; BA.5; BF.7; BQ.1.1; XBB; XBB...             1\n",
       "D614G; BA.2; BA.2.75; BA.5; BQ.1.1; XBB; XBB.1.5               1\n",
       "D614G; BA.5; XBB; XBB.1.5; XBB.1.5.10                          1\n",
       "D614G; BA.1; BA.2; BA.2.75; BA.5; BQ.1.1; XBB                  1\n",
       "SARS; XBB; XBB.1.5; XBB.1.5.10                                 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the IC50s\n",
    "repeated_2023_ic50s = (\n",
    "    repeated_2023_info\n",
    "    .melt(\n",
    "        id_vars=[\"study\", \"antibody\"],\n",
    "        value_vars=[c for c in repeated_2023_info if c.endswith(\"_IC50\")],\n",
    "        var_name=\"virus\",\n",
    "        value_name=\"IC50\",\n",
    "    )\n",
    "    .query(\"IC50.notnull()\")  # no data, 10 means non-neutralizing\n",
    "    .assign(\n",
    "        virus=lambda x: x[\"virus\"].str.replace(\"_IC50\", \"\").map({\n",
    "            \"D614G\": \"D614G\",\n",
    "            \"BA1\": \"BA.1\",\n",
    "            \"BA2\": \"BA.2\",\n",
    "            \"BA3\": \"BA.3\",\n",
    "            \"SARS\": \"SARS\",\n",
    "            \"BA1_1\": \"BA.1.1\",\n",
    "            \"BA2_12_1\": \"BA.2.12.1\",\n",
    "            \"BA2_75\": \"BA.2.75\",\n",
    "            \"BA5\": \"BA.5\",\n",
    "            \"BF7\": \"BF.7\",\n",
    "            \"BA2_13\": \"BA.2.13\",\n",
    "            \"BQ1_1\": \"BQ.1.1\",\n",
    "            \"XBB\": \"XBB\",\n",
    "            \"XBB1_5\": \"XBB.1.5\",\n",
    "            \"XBB1_5_10\": \"XBB.1.5.10\",\n",
    "        }),\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\"\\nNumber of IC50s per antibody:\")\n",
    "display(\n",
    "    repeated_2023_ic50s\n",
    "    .groupby(\"antibody\", as_index=False)\n",
    "    .aggregate(viruses_w_IC50s=pd.NamedAgg(\"virus\", lambda s: \"; \".join(s)))\n",
    "    .groupby(\"viruses_w_IC50s\")\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    "    .sort_values(\"n_antibodies\", ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b042a20b-fce1-4361-a3d7-066b00344b51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:18.536629Z",
     "iopub.status.busy": "2023-06-20T23:54:18.536261Z",
     "iopub.status.idle": "2023-06-20T23:54:18.603682Z",
     "shell.execute_reply": "2023-06-20T23:54:18.602245Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Binding targets per antibody:\n"
     ]
    },
    {
     "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>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wuhan-Hu-1; Omicron BA.2/BA.5</th>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omicron BA.2/BA.5</th>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wuhan-Hu-1</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               n_antibodies\n",
       "binds                                      \n",
       "Wuhan-Hu-1; Omicron BA.2/BA.5           768\n",
       "Omicron BA.2/BA.5                       562\n",
       "Wuhan-Hu-1                               19"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the binding reactivities\n",
    "repeated_2023_binding = (\n",
    "    repeated_2023_info\n",
    "    .query(\"paper_reactivity != '--'\")\n",
    "    .assign(\n",
    "        binds=lambda x: x[\"paper_reactivity\"].map(\n",
    "            {\n",
    "                \"WT\": [\"Wuhan-Hu-1\"],\n",
    "                \"cross\": [\"Wuhan-Hu-1\", \"Omicron BA.2/BA.5\"],\n",
    "                \"specific\": [\"Omicron BA.2/BA.5\"],\n",
    "            }\n",
    "        )\n",
    "    )\n",
    "    [[\"study\", \"antibody\", \"binds\"]]\n",
    "    .explode(\"binds\")\n",
    ")\n",
    "\n",
    "print(\"\\nBinding targets per antibody:\")\n",
    "display(\n",
    "    repeated_2023_binding\n",
    "    .groupby(\"antibody\", as_index=False)\n",
    "    .aggregate(binds=pd.NamedAgg(\"binds\", lambda s: \"; \".join(s)))\n",
    "    .groupby(\"binds\")\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    "    .sort_values(\"n_antibodies\", ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7965ea9-2c45-48fb-abe3-aa0f43910899",
   "metadata": {},
   "source": [
    "Now read the actual escape data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39e7ab8d-13ab-4a04-a35d-1caed836161b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:18.607891Z",
     "iopub.status.busy": "2023-06-20T23:54:18.607510Z",
     "iopub.status.idle": "2023-06-20T23:54:18.928039Z",
     "shell.execute_reply": "2023-06-20T23:54:18.927087Z"
    },
    "tags": []
   },
   "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>antibody</th>\n",
       "      <th>site</th>\n",
       "      <th>escape</th>\n",
       "      <th>study</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45314</th>\n",
       "      <td>GC1852</td>\n",
       "      <td>390</td>\n",
       "      <td>5.628970e-08</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45311</th>\n",
       "      <td>GC1852</td>\n",
       "      <td>371</td>\n",
       "      <td>6.030392e-08</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45312</th>\n",
       "      <td>GC1852</td>\n",
       "      <td>373</td>\n",
       "      <td>6.448414e-08</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45323</th>\n",
       "      <td>GC1852</td>\n",
       "      <td>514</td>\n",
       "      <td>7.860392e-08</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45313</th>\n",
       "      <td>GC1852</td>\n",
       "      <td>377</td>\n",
       "      <td>9.080642e-08</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7512</th>\n",
       "      <td>GC0281</td>\n",
       "      <td>444</td>\n",
       "      <td>1.611308e+01</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4258</th>\n",
       "      <td>GC0177</td>\n",
       "      <td>346</td>\n",
       "      <td>1.637373e+01</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19760</th>\n",
       "      <td>GC0832</td>\n",
       "      <td>444</td>\n",
       "      <td>1.644617e+01</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4775</th>\n",
       "      <td>GC0191</td>\n",
       "      <td>444</td>\n",
       "      <td>1.648478e+01</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4183</th>\n",
       "      <td>GC0174</td>\n",
       "      <td>444</td>\n",
       "      <td>1.669166e+01</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54344 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      antibody  site        escape          study\n",
       "45314   GC1852   390  5.628970e-08  repeated_2023\n",
       "45311   GC1852   371  6.030392e-08  repeated_2023\n",
       "45312   GC1852   373  6.448414e-08  repeated_2023\n",
       "45323   GC1852   514  7.860392e-08  repeated_2023\n",
       "45313   GC1852   377  9.080642e-08  repeated_2023\n",
       "...        ...   ...           ...            ...\n",
       "7512    GC0281   444  1.611308e+01  repeated_2023\n",
       "4258    GC0177   346  1.637373e+01  repeated_2023\n",
       "19760   GC0832   444  1.644617e+01  repeated_2023\n",
       "4775    GC0191   444  1.648478e+01  repeated_2023\n",
       "4183    GC0174   444  1.669166e+01  repeated_2023\n",
       "\n",
       "[54344 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeated_2023_escape = (\n",
    "    pd.read_csv(\"Cao_data/SARS-CoV-2-reinfection-DMS/antibody_dms_merge_clean.csv.gz\")\n",
    "    .groupby([\"antibody\", \"site\"], as_index=False)\n",
    "    .aggregate(escape=pd.NamedAgg(\"mut_escape\", \"sum\"))\n",
    "    .assign(study=\"repeated_2023\")\n",
    ")\n",
    "\n",
    "repeated_2023_escape.sort_values(\"escape\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709f94b3-33a1-44b8-88a1-fe3dbcc15200",
   "metadata": {},
   "source": [
    "Now get the antibody re-weightings.\n",
    "These are based on the re-weightings described in [Repeated Omicron infection alleviates SARS-CoV-2 immune imprinting (2023)](https://www.biorxiv.org/content/10.1101/2023.05.01.538516v2).\n",
    "We want to re-weight the cross-reactive antibodies by an amount $w$ so that the desired cross reactive fraction $x$ satisfies $x = \\frac{w N_c}{w N_c + N_s}$ where $w$ is the re-weighting assigned to the cross-reactive antibodies, $N_c$ is the number of cross-reactive antibodies, and $N_s$ is the number of specific antibodies.\n",
    "This can be achieved by assigning $w = \\frac{x N_s}{N_c + x N_c}$; we then adjust the weights again so the mean weight over all antibodies for the source equals the number of antibodies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7251b46e-3ec0-40f2-a9f2-ab2fd1d77c80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:18.933023Z",
     "iopub.status.busy": "2023-06-20T23:54:18.932581Z",
     "iopub.status.idle": "2023-06-20T23:54:19.069656Z",
     "shell.execute_reply": "2023-06-20T23:54:19.068673Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the following reweightings:\n",
      "                           source  desired_cross_reactive_frac\n",
      "0              BA.5 convalescents                         0.89\n",
      "1              BF.7 convalescents                         0.89\n",
      "2  BA.1 convalescents reinfection                         0.51\n",
      "3  BA.2 convalescents reinfection                         0.51\n",
      "\n",
      "Confirm re-weighting worked:\n"
     ]
    },
    {
     "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>source</th>\n",
       "      <th>desired_cross_reactive_frac</th>\n",
       "      <th>cross_reactive_frac</th>\n",
       "      <th>cross_reactive_reweight_frac</th>\n",
       "      <th>n_antibodies</th>\n",
       "      <th>summed_reweights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BA.1 convalescents reinfection</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.345070</td>\n",
       "      <td>0.51</td>\n",
       "      <td>284</td>\n",
       "      <td>284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BA.2 convalescents reinfection</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.439655</td>\n",
       "      <td>0.51</td>\n",
       "      <td>232</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BA.5 convalescents</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.713964</td>\n",
       "      <td>0.89</td>\n",
       "      <td>444</td>\n",
       "      <td>444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BF.7 convalescents</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.728395</td>\n",
       "      <td>0.89</td>\n",
       "      <td>243</td>\n",
       "      <td>243.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           source  desired_cross_reactive_frac  \\\n",
       "0  BA.1 convalescents reinfection                         0.51   \n",
       "1  BA.2 convalescents reinfection                         0.51   \n",
       "2              BA.5 convalescents                         0.89   \n",
       "3              BF.7 convalescents                         0.89   \n",
       "\n",
       "   cross_reactive_frac  cross_reactive_reweight_frac  n_antibodies  \\\n",
       "0             0.345070                          0.51           284   \n",
       "1             0.439655                          0.51           232   \n",
       "2             0.713964                          0.89           444   \n",
       "3             0.728395                          0.89           243   \n",
       "\n",
       "   summed_reweights  \n",
       "0             284.0  \n",
       "1             232.0  \n",
       "2             444.0  \n",
       "3             243.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Here are the re-weightings:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>study</th>\n",
       "      <th>source</th>\n",
       "      <th>cross_reactive</th>\n",
       "      <th>reweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>BA.1 convalescents reinfection</td>\n",
       "      <td>False</td>\n",
       "      <td>0.748172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>BA.1 convalescents reinfection</td>\n",
       "      <td>True</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>BA.2 convalescents reinfection</td>\n",
       "      <td>False</td>\n",
       "      <td>0.874462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>BA.2 convalescents reinfection</td>\n",
       "      <td>True</td>\n",
       "      <td>1.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>BA.5 convalescents</td>\n",
       "      <td>False</td>\n",
       "      <td>0.384567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>BA.5 convalescents</td>\n",
       "      <td>True</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>BF.7 convalescents</td>\n",
       "      <td>False</td>\n",
       "      <td>0.405000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>BF.7 convalescents</td>\n",
       "      <td>True</td>\n",
       "      <td>1.221864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           study                          source  cross_reactive  reweight\n",
       "0  repeated_2023  BA.1 convalescents reinfection           False  0.748172\n",
       "1  repeated_2023  BA.1 convalescents reinfection            True  1.477959\n",
       "2  repeated_2023  BA.2 convalescents reinfection           False  0.874462\n",
       "3  repeated_2023  BA.2 convalescents reinfection            True  1.160000\n",
       "4  repeated_2023              BA.5 convalescents           False  0.384567\n",
       "5  repeated_2023              BA.5 convalescents            True  1.246562\n",
       "6  repeated_2023              BF.7 convalescents           False  0.405000\n",
       "7  repeated_2023              BF.7 convalescents            True  1.221864"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>antibody</th>\n",
       "      <th>study</th>\n",
       "      <th>reweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CP0590</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GC0101</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GC0102</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GC0103</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GC0104</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>GC2052</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>GC2054</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>GC2056</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>GC2057</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>GC2059</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1203 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     antibody          study  reweight\n",
       "0      CP0590  repeated_2023  1.246562\n",
       "1      GC0101  repeated_2023  1.246562\n",
       "2      GC0102  repeated_2023  1.246562\n",
       "3      GC0103  repeated_2023  1.246562\n",
       "4      GC0104  repeated_2023  1.246562\n",
       "...       ...            ...       ...\n",
       "1198   GC2052  repeated_2023  1.477959\n",
       "1199   GC2054  repeated_2023  1.477959\n",
       "1200   GC2056  repeated_2023  1.477959\n",
       "1201   GC2057  repeated_2023  1.477959\n",
       "1202   GC2059  repeated_2023  1.477959\n",
       "\n",
       "[1203 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeated_2023_desired_reweighting = (\n",
    "    pd.Series(antibody_reweighting[\"repeated_2023\"][\"cross_reactive_weights\"])\n",
    "    .rename_axis(\"source\")\n",
    "    .rename(\"desired_cross_reactive_frac\")\n",
    "    .reset_index()\n",
    ")\n",
    "assert set(repeated_2023_desired_reweighting[\"source\"]).issubset(repeated_2023_source[\"source\"])\n",
    "print(f\"Using the following reweightings:\\n{repeated_2023_desired_reweighting}\")\n",
    "\n",
    "repeated_2023_reweighting = (\n",
    "    repeated_2023_binding\n",
    "    .groupby(\"antibody\", as_index=False)\n",
    "    .aggregate(n_binds=pd.NamedAgg(\"binds\", \"nunique\"))\n",
    "    .assign(cross_reactive=lambda x: x[\"n_binds\"] == 2)\n",
    "    .merge(repeated_2023_source, on=\"antibody\", validate=\"one_to_one\")\n",
    "    .merge(repeated_2023_desired_reweighting, on=\"source\", validate=\"many_to_one\")\n",
    "    .assign(\n",
    "        N_c=lambda x: x.groupby(\"source\")[\"cross_reactive\"].transform(\"sum\"),\n",
    "        N=lambda x: x.groupby(\"source\")[\"antibody\"].transform(\"count\"),\n",
    "        N_s=lambda x: x[\"N\"] - x[\"N_c\"],\n",
    "        cross_reactive_frac=lambda x: x[\"N_c\"] / x[\"N\"],\n",
    "        reweight=lambda x: numpy.where(\n",
    "            x[\"cross_reactive\"],\n",
    "            (\n",
    "                x[\"desired_cross_reactive_frac\"] * x[\"N_s\"]\n",
    "                / (x[\"N_c\"] * (1 - x[\"desired_cross_reactive_frac\"]))\n",
    "            ),\n",
    "            1,\n",
    "        ),\n",
    "    )\n",
    "    .assign(\n",
    "        reweight=lambda x: (\n",
    "            x[\"reweight\"] / x.groupby(\"source\")[\"reweight\"].transform(\"mean\")\n",
    "        ),\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\"\\nConfirm re-weighting worked:\")\n",
    "display(\n",
    "    repeated_2023_reweighting\n",
    "    .assign(cross_reactive_reweight=lambda x: x[\"cross_reactive\"] * x[\"reweight\"])\n",
    "    .groupby([\"source\", \"desired_cross_reactive_frac\", \"cross_reactive_frac\"], as_index=False)\n",
    "    .aggregate(\n",
    "        reweighted_cross_reactive_sum=pd.NamedAgg(\"cross_reactive_reweight\", \"sum\"),\n",
    "        reweighted_sum=pd.NamedAgg(\"reweight\", \"sum\"),\n",
    "    )\n",
    "    .assign(\n",
    "        cross_reactive_reweight_frac=lambda x: x[\"reweighted_cross_reactive_sum\"] / x[\"reweighted_sum\"],\n",
    "    )\n",
    "    .drop(columns=[\"reweighted_cross_reactive_sum\", \"reweighted_sum\"])\n",
    "    .merge(\n",
    "        repeated_2023_reweighting\n",
    "        .groupby(\"source\", as_index=False)\n",
    "        .aggregate(\n",
    "            n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"),\n",
    "            summed_reweights=pd.NamedAgg(\"reweight\", \"sum\"),\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\"\\nHere are the re-weightings:\")\n",
    "display(\n",
    "    repeated_2023_reweighting\n",
    "    [[\"study\", \"source\", \"cross_reactive\", \"reweight\"]]\n",
    "    .drop_duplicates()\n",
    "    .sort_values([\"source\", \"cross_reactive\"])\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "# get the reweightings\n",
    "repeated_2023_reweighting = (\n",
    "    repeated_2023_reweighting\n",
    "    [[\"antibody\", \"study\", \"reweight\"]]\n",
    ")\n",
    "\n",
    "repeated_2023_reweighting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d28d49-bd00-44bb-bd42-4dc6855d001c",
   "metadata": {},
   "source": [
    "## Process data from [Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution (2022)](https://www.nature.com/articles/s41586-022-05644-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b249ed38-a3d0-451d-9380-2dcfae9c405b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:19.074737Z",
     "iopub.status.busy": "2023-06-20T23:54:19.074354Z",
     "iopub.status.idle": "2023-06-20T23:54:20.821511Z",
     "shell.execute_reply": "2023-06-20T23:54:20.820763Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# read the data, which is in multiple sheets\n",
    "imprinted_2022_raw_info = pd.read_excel(\n",
    "    \"Cao_data/convergent_RBD_evolution/antibody_info.xlsx\",\n",
    "    header=1,\n",
    "    sheet_name=None,\n",
    "    usecols=lambda c: not c.startswith(\"Unnamed\"),\n",
    ")\n",
    "\n",
    "common_cols = None\n",
    "\n",
    "for sheetname, sheet in imprinted_2022_raw_info.items():\n",
    "    columns = sheet.columns.tolist()\n",
    "    if common_cols is None:\n",
    "        common_cols = columns\n",
    "    else:\n",
    "        common_cols = [\n",
    "            c for c in columns if c in set(common_cols).intersection(columns)\n",
    "        ]\n",
    "\n",
    "imprinted_2022_info = pd.concat(\n",
    "    [\n",
    "        sheet[common_cols].assign(sheet_name=sheet_name)\n",
    "        for sheet_name, sheet in imprinted_2022_raw_info.items()\n",
    "    ],\n",
    "    ignore_index=True,\n",
    ").rename(columns={\"Antibody  Name\": \"antibody\"}).assign(study=\"imprinted_2022\")\n",
    "\n",
    "assert len(imprinted_2022_info) == imprinted_2022_info[\"antibody\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "706122b4-74af-4cba-9d7a-d256db440fea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:20.825005Z",
     "iopub.status.busy": "2023-06-20T23:54:20.824542Z",
     "iopub.status.idle": "2023-06-20T23:54:20.840191Z",
     "shell.execute_reply": "2023-06-20T23:54:20.839485Z"
    },
    "tags": []
   },
   "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></th>\n",
       "      <th>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>study</th>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">imprinted_2022</th>\n",
       "      <th>BA.1 convalescents</th>\n",
       "      <td>752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA.2 convalescents</th>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA.5 convalescents</th>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARS convalescents</th>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WT convalescents</th>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WT vaccinees</th>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   n_antibodies\n",
       "study          source                          \n",
       "imprinted_2022 BA.1 convalescents           752\n",
       "               BA.2 convalescents           751\n",
       "               BA.5 convalescents           269\n",
       "               SARS convalescents           881\n",
       "               WT convalescents             438\n",
       "               WT vaccinees                 240"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the antibody sources\n",
    "imprinted_2022_source = (\n",
    "    imprinted_2022_info\n",
    "    [[\"antibody\", \"source\", \"study\"]]\n",
    "    .query(\"source != 'WT-engineered'\")\n",
    ")\n",
    "\n",
    "display(\n",
    "    imprinted_2022_source\n",
    "    .groupby([\"study\", \"source\"])\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e7dc881-b3ea-4af6-9e09-95e99b898440",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:20.844204Z",
     "iopub.status.busy": "2023-06-20T23:54:20.843738Z",
     "iopub.status.idle": "2023-06-20T23:54:20.979635Z",
     "shell.execute_reply": "2023-06-20T23:54:20.978186Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of IC50s per antibody:\n"
     ]
    },
    {
     "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>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>viruses_w_IC50s</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.5; BA.2.75; BQ.1.1; XBB</th>\n",
       "      <td>3309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.5; BA.2.75</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.5; BA.2.75; BQ.1.1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BA.1; BA.2; BA.5; BA.2.75; XBB</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BQ.1.1; XBB</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA.1; BA.2; BA.5; BA.2.75; BQ.1.1; XBB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA.1; BQ.1.1; XBB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D614G; BQ.1.1; XBB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               n_antibodies\n",
       "viruses_w_IC50s                                            \n",
       "D614G; BA.1; BA.2; BA.5; BA.2.75; BQ.1.1; XBB          3309\n",
       "D614G; BA.1; BA.2; BA.5; BA.2.75                         13\n",
       "D614G; BA.1; BA.2; BA.5; BA.2.75; BQ.1.1                  3\n",
       "D614G; BA.1; BA.2; BA.5; BA.2.75; XBB                     3\n",
       "BQ.1.1; XBB                                               2\n",
       "BA.1; BA.2; BA.5; BA.2.75; BQ.1.1; XBB                    1\n",
       "BA.1; BQ.1.1; XBB                                         1\n",
       "D614G; BQ.1.1; XBB                                        1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the IC50s\n",
    "imprinted_2022_ic50s = (\n",
    "    imprinted_2022_info\n",
    "    .melt(\n",
    "        id_vars=[\"study\", \"antibody\"],\n",
    "        value_vars=[\"D614G\", \"BA.1\", \"BA.2\", \"BA.5\", \"BA.2.75\", \"BQ.1.1\", \"XBB\"],\n",
    "        var_name=\"virus\",\n",
    "        value_name=\"IC50\",\n",
    "    )\n",
    "    # values of >10 to 10\n",
    "    .query(\"IC50 != '--'\")\n",
    "    .assign(IC50=lambda x: x[\"IC50\"].map(lambda ic50: 10 if ic50 == \">10\" else float(ic50)))\n",
    ")\n",
    "\n",
    "print(\"\\nNumber of IC50s per antibody:\")\n",
    "display(\n",
    "    imprinted_2022_ic50s\n",
    "    .groupby(\"antibody\", as_index=False)\n",
    "    .aggregate(viruses_w_IC50s=pd.NamedAgg(\"virus\", lambda s: \"; \".join(s)))\n",
    "    .groupby(\"viruses_w_IC50s\")\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    "    .sort_values(\"n_antibodies\", ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3068b3f-9ac2-4adf-92f9-4aa3d11090e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:20.984986Z",
     "iopub.status.busy": "2023-06-20T23:54:20.984299Z",
     "iopub.status.idle": "2023-06-20T23:54:21.102811Z",
     "shell.execute_reply": "2023-06-20T23:54:21.102189Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Binding targets per antibody:\n"
     ]
    },
    {
     "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>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wuhan-Hu-1; Omicron BA.2/BA.5</th>\n",
       "      <td>3051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omicron BA.2/BA.5</th>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               n_antibodies\n",
       "binds                                      \n",
       "Wuhan-Hu-1; Omicron BA.2/BA.5          3051\n",
       "Omicron BA.2/BA.5                       282"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the binding reactivities\n",
    "imprinted_2022_binding = (\n",
    "    imprinted_2022_info\n",
    "    .assign(\n",
    "        binds=lambda x: x[\"sheet_name\"].map(\n",
    "            {\n",
    "                \"cross\": [\"Wuhan-Hu-1\", \"Omicron BA.2/BA.5\"],\n",
    "                \"specific\": [\"Omicron BA.2/BA.5\"],\n",
    "            }\n",
    "        )\n",
    "    )\n",
    "    [[\"study\", \"antibody\", \"binds\"]]\n",
    "    .explode(\"binds\")\n",
    ")\n",
    "\n",
    "print(\"\\nBinding targets per antibody:\")\n",
    "display(\n",
    "    imprinted_2022_binding\n",
    "    .groupby(\"antibody\", as_index=False)\n",
    "    .aggregate(binds=pd.NamedAgg(\"binds\", lambda s: \"; \".join(s)))\n",
    "    .groupby(\"binds\")\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    "    .sort_values(\"n_antibodies\", ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1600244-34ad-4013-ba98-cede879266e2",
   "metadata": {},
   "source": [
    "Now read the actual escape data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ddc06a9-bf6a-43ff-8c70-b3ffc38d3364",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:21.106077Z",
     "iopub.status.busy": "2023-06-20T23:54:21.105783Z",
     "iopub.status.idle": "2023-06-20T23:54:21.363832Z",
     "shell.execute_reply": "2023-06-20T23:54:21.362999Z"
    },
    "tags": []
   },
   "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>antibody</th>\n",
       "      <th>site</th>\n",
       "      <th>escape</th>\n",
       "      <th>study</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-57</td>\n",
       "      <td>338</td>\n",
       "      <td>0.521154</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-57</td>\n",
       "      <td>359</td>\n",
       "      <td>0.140217</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-57</td>\n",
       "      <td>370</td>\n",
       "      <td>0.285110</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-57</td>\n",
       "      <td>394</td>\n",
       "      <td>0.112732</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-57</td>\n",
       "      <td>396</td>\n",
       "      <td>0.194379</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63387</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>465</td>\n",
       "      <td>0.769640</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63388</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>466</td>\n",
       "      <td>0.886558</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63389</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>468</td>\n",
       "      <td>0.313522</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63390</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>484</td>\n",
       "      <td>0.165163</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63391</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>514</td>\n",
       "      <td>0.224545</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63392 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      antibody  site    escape           study\n",
       "0         1-57   338  0.521154  imprinted_2022\n",
       "1         1-57   359  0.140217  imprinted_2022\n",
       "2         1-57   370  0.285110  imprinted_2022\n",
       "3         1-57   394  0.112732  imprinted_2022\n",
       "4         1-57   396  0.194379  imprinted_2022\n",
       "...        ...   ...       ...             ...\n",
       "63387  XGv-422   465  0.769640  imprinted_2022\n",
       "63388  XGv-422   466  0.886558  imprinted_2022\n",
       "63389  XGv-422   468  0.313522  imprinted_2022\n",
       "63390  XGv-422   484  0.165163  imprinted_2022\n",
       "63391  XGv-422   514  0.224545  imprinted_2022\n",
       "\n",
       "[63392 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imprinted_2022_escape = (\n",
    "    pd.read_csv(\"Cao_data/convergent_RBD_evolution/use_res_clean.csv\")\n",
    "    .groupby([\"antibody\", \"site\"], as_index=False)\n",
    "    .aggregate(escape=pd.NamedAgg(\"mut_escape\", \"sum\"))\n",
    "    .assign(study=\"imprinted_2022\")\n",
    ")\n",
    "\n",
    "imprinted_2022_escape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef6347f8-ad27-41a3-a884-6991f91cc450",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Aggregate the antibody information from the different studies\n",
    "\n",
    "First, read the information on duplicated antibodies to get duplicate names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08c9c6a6-2de8-4882-92c2-2738edffe090",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:21.367506Z",
     "iopub.status.busy": "2023-06-20T23:54:21.367227Z",
     "iopub.status.idle": "2023-06-20T23:54:21.624659Z",
     "shell.execute_reply": "2023-06-20T23:54:21.623810Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "antibody_renaming = (\n",
    "    pd.read_excel(\"Cao_data/all-repo-duplicates.xlsx\")\n",
    "    .set_index(\"Antibody  Name\")\n",
    "    [\"BD equivalent name\"]\n",
    "    .to_dict()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40434c66-7697-48ca-942a-6ba0fd4cca9a",
   "metadata": {},
   "source": [
    "Now aggregate the antibody sources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4895396a-b117-4463-8fed-18e512d9e180",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:21.629461Z",
     "iopub.status.busy": "2023-06-20T23:54:21.628778Z",
     "iopub.status.idle": "2023-06-20T23:54:21.672924Z",
     "shell.execute_reply": "2023-06-20T23:54:21.672203Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping the following antibodies duplicated across studies:\n"
     ]
    },
    {
     "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>antibody</th>\n",
       "      <th>source</th>\n",
       "      <th>study</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BD30-604</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BD45-105</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BD45-145</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BD45-147</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BD45-152</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>BD57-0300</td>\n",
       "      <td>BA.5 convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>BD57-0308</td>\n",
       "      <td>BA.5 convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>BD57-0318</td>\n",
       "      <td>BA.5 convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>BD57-0332</td>\n",
       "      <td>BA.5 convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>BD57-0398</td>\n",
       "      <td>BA.5 convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      antibody              source           study\n",
       "0     BD30-604    WT convalescents  imprinted_2022\n",
       "1     BD45-105    WT convalescents  imprinted_2022\n",
       "2     BD45-145    WT convalescents  imprinted_2022\n",
       "3     BD45-147    WT convalescents  imprinted_2022\n",
       "4     BD45-152    WT convalescents  imprinted_2022\n",
       "..         ...                 ...             ...\n",
       "297  BD57-0300  BA.5 convalescents  imprinted_2022\n",
       "298  BD57-0308  BA.5 convalescents  imprinted_2022\n",
       "299  BD57-0318  BA.5 convalescents  imprinted_2022\n",
       "300  BD57-0332  BA.5 convalescents  imprinted_2022\n",
       "301  BD57-0398  BA.5 convalescents  imprinted_2022\n",
       "\n",
       "[302 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Retained sources:\n"
     ]
    },
    {
     "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>antibody</th>\n",
       "      <th>source</th>\n",
       "      <th>study</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-57</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-15</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7D6</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADG-2</td>\n",
       "      <td>SARS convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B38</td>\n",
       "      <td>WT convalescents</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4372</th>\n",
       "      <td>XGv-416</td>\n",
       "      <td>WT vaccinees</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4373</th>\n",
       "      <td>XGv-418</td>\n",
       "      <td>WT vaccinees</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4374</th>\n",
       "      <td>XGv-420</td>\n",
       "      <td>WT vaccinees</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4375</th>\n",
       "      <td>XGv-421</td>\n",
       "      <td>WT vaccinees</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4376</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>WT vaccinees</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4377 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     antibody              source           study\n",
       "0        1-57    WT convalescents  imprinted_2022\n",
       "1        2-15    WT convalescents  imprinted_2022\n",
       "2         7D6    WT convalescents  imprinted_2022\n",
       "3       ADG-2  SARS convalescents  imprinted_2022\n",
       "4         B38    WT convalescents  imprinted_2022\n",
       "...       ...                 ...             ...\n",
       "4372  XGv-416        WT vaccinees  imprinted_2022\n",
       "4373  XGv-418        WT vaccinees  imprinted_2022\n",
       "4374  XGv-420        WT vaccinees  imprinted_2022\n",
       "4375  XGv-421        WT vaccinees  imprinted_2022\n",
       "4376  XGv-422        WT vaccinees  imprinted_2022\n",
       "\n",
       "[4377 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "antibody_source = (\n",
    "    pd.concat(\n",
    "        [repeated_2023_source, imprinted_2022_source],\n",
    "        ignore_index=True,\n",
    "    )\n",
    "    .assign(\n",
    "        antibody=lambda x: x[\"antibody\"].map(\n",
    "            lambda a: antibody_renaming[a] if a in antibody_renaming else a\n",
    "        ),\n",
    "    )\n",
    ")\n",
    "\n",
    "assert (\n",
    "    antibody_source[\"antibody\"].nunique()\n",
    "    == len(antibody_source[[\"antibody\", \"source\"]].drop_duplicates())\n",
    ")\n",
    "\n",
    "print(\"Dropping the following antibodies duplicated across studies:\")\n",
    "display(antibody_source.groupby(\"antibody\").tail(-1).reset_index(drop=True))\n",
    "\n",
    "# keep just one of antibodies repeated across studies\n",
    "antibody_source = antibody_source.groupby(\"antibody\").first().reset_index()\n",
    "\n",
    "print(\"\\nRetained sources:\")\n",
    "display(antibody_source)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcfea729-282d-47e1-a20f-dfba1aa4444f",
   "metadata": {},
   "source": [
    "Now the IC50s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8f51f4d-8fd6-49a9-acd8-832dbcf83334",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:21.676699Z",
     "iopub.status.busy": "2023-06-20T23:54:21.676344Z",
     "iopub.status.idle": "2023-06-20T23:54:21.766248Z",
     "shell.execute_reply": "2023-06-20T23:54:21.765183Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Only keep virus with IC50s against at least 1000 viruses\n",
      "Keeping 9 of 15 viruses\n",
      "\n",
      "Retained IC50s:\n"
     ]
    },
    {
     "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>antibody</th>\n",
       "      <th>virus</th>\n",
       "      <th>study</th>\n",
       "      <th>IC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-57</td>\n",
       "      <td>BA.1</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-57</td>\n",
       "      <td>BA.2</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-57</td>\n",
       "      <td>BA.2.75</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-57</td>\n",
       "      <td>BA.5</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-57</td>\n",
       "      <td>BQ.1.1</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35112</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>BA.2.75</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>0.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35113</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>BA.5</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>0.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35114</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>BQ.1.1</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>1.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35115</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>D614G</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>0.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35116</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>XBB</td>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32764 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      antibody    virus           study    IC50\n",
       "0         1-57     BA.1  imprinted_2022  10.000\n",
       "1         1-57     BA.2  imprinted_2022  10.000\n",
       "2         1-57  BA.2.75  imprinted_2022  10.000\n",
       "3         1-57     BA.5  imprinted_2022  10.000\n",
       "4         1-57   BQ.1.1  imprinted_2022  10.000\n",
       "...        ...      ...             ...     ...\n",
       "35112  XGv-422  BA.2.75  imprinted_2022   0.495\n",
       "35113  XGv-422     BA.5  imprinted_2022   0.681\n",
       "35114  XGv-422   BQ.1.1  imprinted_2022   1.060\n",
       "35115  XGv-422    D614G  imprinted_2022   0.394\n",
       "35116  XGv-422      XBB  imprinted_2022   0.789\n",
       "\n",
       "[32764 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of antibodies with no neutralization on any retained virus:\n"
     ]
    },
    {
     "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>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_neut</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>3188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          n_antibodies\n",
       "non_neut              \n",
       "False             3188\n",
       "True              1193"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "antibody_ic50s = (\n",
    "    pd.concat(\n",
    "        [repeated_2023_ic50s, imprinted_2022_ic50s],\n",
    "        ignore_index=True,\n",
    "    )\n",
    "    .assign(\n",
    "        antibody=lambda x: x[\"antibody\"].map(\n",
    "            lambda a: antibody_renaming[a] if a in antibody_renaming else a\n",
    "        ),\n",
    "    )\n",
    ")\n",
    "\n",
    "# print(\"Dropping the following IC50s duplicated across studies:\")\n",
    "# display(antibody_ic50s.groupby([\"antibody\", \"virus\"]).tail(-1).reset_index(drop=True))\n",
    "\n",
    "# keep just one of IC50s repeated across studies\n",
    "antibody_ic50s = antibody_ic50s.groupby([\"antibody\", \"virus\"]).first().reset_index()\n",
    "\n",
    "# only keep IC50s for viruses w enough antibodies\n",
    "virus_min_antibodies = config[\"virus_min_antibodies_w_ic50\"]\n",
    "print(f\"\\nOnly keep virus with IC50s against at least {virus_min_antibodies} viruses\")\n",
    "n_antibodies_per_virus = (\n",
    "    antibody_ic50s\n",
    "    .groupby(\"virus\")\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    "    .sort_values(\"n_antibodies\", ascending=False)\n",
    "    .reset_index()\n",
    ")\n",
    "viruses_w_enough_antibodies = (\n",
    "    n_antibodies_per_virus\n",
    "    .query(\"n_antibodies >= @virus_min_antibodies\")\n",
    "    [\"virus\"]\n",
    "    .tolist()\n",
    ")\n",
    "print(\n",
    "    f\"Keeping {len(viruses_w_enough_antibodies)} of \"\n",
    "    f\"{len(n_antibodies_per_virus)} viruses\"\n",
    ")\n",
    "antibody_ic50s = antibody_ic50s.query(\"virus in @viruses_w_enough_antibodies\")\n",
    "\n",
    "print(\"\\nRetained IC50s:\")\n",
    "display(antibody_ic50s)\n",
    "\n",
    "print(\"\\nNumber of antibodies with no neutralization on any retained virus:\")\n",
    "assert antibody_ic50s[\"IC50\"].dtype == float\n",
    "assert (antibody_ic50s[\"IC50\"] <= 10).all()\n",
    "display(\n",
    "    antibody_ic50s\n",
    "    .assign(non_neut=lambda x: x[\"IC50\"] == 10)\n",
    "    .groupby(\"antibody\", as_index=False)\n",
    "    .aggregate({\"non_neut\": \"all\"})\n",
    "    .groupby(\"non_neut\")\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056672dd-f7d6-4650-be26-632c6241da77",
   "metadata": {},
   "source": [
    "Now the binding targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "874bf0ec-03ac-4832-a523-4fc1100bfddc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:21.769983Z",
     "iopub.status.busy": "2023-06-20T23:54:21.769621Z",
     "iopub.status.idle": "2023-06-20T23:54:21.932015Z",
     "shell.execute_reply": "2023-06-20T23:54:21.930768Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping the following binding duplicated across studies:\n"
     ]
    },
    {
     "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>study</th>\n",
       "      <th>antibody</th>\n",
       "      <th>binds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD30-604</td>\n",
       "      <td>Wuhan-Hu-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD45-105</td>\n",
       "      <td>Wuhan-Hu-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD45-145</td>\n",
       "      <td>Wuhan-Hu-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD45-147</td>\n",
       "      <td>Wuhan-Hu-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD45-152</td>\n",
       "      <td>Wuhan-Hu-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD57-0300</td>\n",
       "      <td>Omicron BA.2/BA.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD57-0308</td>\n",
       "      <td>Omicron BA.2/BA.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD57-0318</td>\n",
       "      <td>Omicron BA.2/BA.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD57-0332</td>\n",
       "      <td>Omicron BA.2/BA.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>imprinted_2022</td>\n",
       "      <td>BD57-0398</td>\n",
       "      <td>Omicron BA.2/BA.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>547 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              study   antibody              binds\n",
       "0    imprinted_2022   BD30-604         Wuhan-Hu-1\n",
       "1    imprinted_2022   BD45-105         Wuhan-Hu-1\n",
       "2    imprinted_2022   BD45-145         Wuhan-Hu-1\n",
       "3    imprinted_2022   BD45-147         Wuhan-Hu-1\n",
       "4    imprinted_2022   BD45-152         Wuhan-Hu-1\n",
       "..              ...        ...                ...\n",
       "542  imprinted_2022  BD57-0300  Omicron BA.2/BA.5\n",
       "543  imprinted_2022  BD57-0308  Omicron BA.2/BA.5\n",
       "544  imprinted_2022  BD57-0318  Omicron BA.2/BA.5\n",
       "545  imprinted_2022  BD57-0332  Omicron BA.2/BA.5\n",
       "546  imprinted_2022  BD57-0398  Omicron BA.2/BA.5\n",
       "\n",
       "[547 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Binding summary:\n"
     ]
    },
    {
     "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>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Omicron BA.2/BA.5</th>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omicron BA.2/BA.5; Wuhan-Hu-1</th>\n",
       "      <td>3574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wuhan-Hu-1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               n_antibodies\n",
       "binds                                      \n",
       "Omicron BA.2/BA.5                       804\n",
       "Omicron BA.2/BA.5; Wuhan-Hu-1          3574\n",
       "Wuhan-Hu-1                                2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "antibody_binding = (\n",
    "    pd.concat(\n",
    "        [repeated_2023_binding, imprinted_2022_binding],\n",
    "        ignore_index=True,\n",
    "    )\n",
    "    .assign(\n",
    "        antibody=lambda x: x[\"antibody\"].map(\n",
    "            lambda a: antibody_renaming[a] if a in antibody_renaming else a\n",
    "        ),\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\"Dropping the following binding duplicated across studies:\")\n",
    "display(antibody_binding.groupby([\"antibody\", \"binds\"]).tail(-1).reset_index(drop=True))\n",
    "\n",
    "# keep just one of binding repeated across studies\n",
    "antibody_binding = antibody_binding.groupby([\"antibody\", \"binds\"]).first().reset_index()\n",
    "\n",
    "# print(\"\\nRetained bindings:\")\n",
    "# display(antibody_binding)\n",
    "\n",
    "print(\"\\nBinding summary:\")\n",
    "assert antibody_binding[\"binds\"].notnull().all()\n",
    "display(\n",
    "    antibody_binding\n",
    "    .groupby(\"antibody\", as_index=False)\n",
    "    .aggregate({\"binds\": lambda s: \"; \".join(s)})\n",
    "    .groupby(\"binds\")\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"nunique\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9465462b-17ea-4ddc-b9a5-1ebd911a394c",
   "metadata": {},
   "source": [
    "Now combine the escape data, keeping only the escape data in the antibody source data frame for that study.\n",
    "We normalize so that the max site escape for each antibody is 1.\n",
    "We also determine how many sites have significant escape, and drop antibodies with too many such sites:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2e953763-edf0-45e4-81a5-4dc87f214b3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:21.936754Z",
     "iopub.status.busy": "2023-06-20T23:54:21.936414Z",
     "iopub.status.idle": "2023-06-20T23:54:25.025949Z",
     "shell.execute_reply": "2023-06-20T23:54:25.024103Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>antibody</th>\n",
       "      <th>site</th>\n",
       "      <th>escape</th>\n",
       "      <th>study</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BD30-604</td>\n",
       "      <td>335</td>\n",
       "      <td>0.049379</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BD30-604</td>\n",
       "      <td>340</td>\n",
       "      <td>0.051323</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BD30-604</td>\n",
       "      <td>346</td>\n",
       "      <td>0.032896</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BD30-604</td>\n",
       "      <td>365</td>\n",
       "      <td>0.072947</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BD30-604</td>\n",
       "      <td>370</td>\n",
       "      <td>0.039942</td>\n",
       "      <td>repeated_2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109577</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>465</td>\n",
       "      <td>0.229740</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109578</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>466</td>\n",
       "      <td>0.264641</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109579</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>468</td>\n",
       "      <td>0.093587</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109580</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>484</td>\n",
       "      <td>0.049302</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109581</th>\n",
       "      <td>XGv-422</td>\n",
       "      <td>514</td>\n",
       "      <td>0.067027</td>\n",
       "      <td>imprinted_2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109582 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        antibody  site    escape           study\n",
       "0       BD30-604   335  0.049379   repeated_2023\n",
       "1       BD30-604   340  0.051323   repeated_2023\n",
       "2       BD30-604   346  0.032896   repeated_2023\n",
       "3       BD30-604   365  0.072947   repeated_2023\n",
       "4       BD30-604   370  0.039942   repeated_2023\n",
       "...          ...   ...       ...             ...\n",
       "109577   XGv-422   465  0.229740  imprinted_2022\n",
       "109578   XGv-422   466  0.264641  imprinted_2022\n",
       "109579   XGv-422   468  0.093587  imprinted_2022\n",
       "109580   XGv-422   484  0.049302  imprinted_2022\n",
       "109581   XGv-422   514  0.067027  imprinted_2022\n",
       "\n",
       "[109582 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Escape measurements for each study:\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>study</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>imprinted_2022</th>\n",
       "      <td>57010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repeated_2023</th>\n",
       "      <td>52572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    n\n",
       "study                \n",
       "imprinted_2022  57010\n",
       "repeated_2023   52572"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "escape = (\n",
    "    pd.concat(\n",
    "        [\n",
    "            # get just antibodies from that study\n",
    "            (\n",
    "                escape_df\n",
    "                .assign(\n",
    "                    antibody=lambda x: x[\"antibody\"].map(\n",
    "                        lambda a: antibody_renaming[a] if a in antibody_renaming else a\n",
    "                    ),\n",
    "                )\n",
    "                .merge(antibody_source.query(\"study == @study\")[[\"antibody\"]])\n",
    "            )\n",
    "            for study, escape_df in [\n",
    "                (\"repeated_2023\", repeated_2023_escape),\n",
    "                (\"imprinted_2022\", imprinted_2022_escape),\n",
    "            ]\n",
    "        ],\n",
    "        ignore_index=True,\n",
    "    )\n",
    "    .assign(\n",
    "        escape=lambda x: (\n",
    "            x[\"escape\"] / x.groupby([\"antibody\", \"study\"])[\"escape\"].transform(\"max\")\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "assert len(escape) == len(escape.groupby([\"antibody\", \"site\"]))\n",
    "\n",
    "# get and plot n sites with significant escape, then eliminate antibodies with\n",
    "# too many sites of substantial escape\n",
    "sig_escape_threshold = config[\"filter_antibodies\"][\"sig_escape_threshold\"]\n",
    "max_sig_sites = config[\"filter_antibodies\"][\"max_sig_sites\"]\n",
    "n_sig_escape = (\n",
    "    escape\n",
    "    .query(\"escape >= @sig_escape_threshold\")\n",
    "    .groupby([\"antibody\", \"study\"], as_index=False)\n",
    "    .aggregate(n_sites=pd.NamedAgg(\"site\", \"nunique\"))\n",
    ")\n",
    "\n",
    "ncols = 2\n",
    "nrows = int(math.ceil(n_sig_escape[\"study\"].nunique()) / ncols)\n",
    "axes = n_sig_escape.hist(\n",
    "    column=\"n_sites\",\n",
    "    by=\"study\",\n",
    "    bins=range(1, n_sig_escape[\"n_sites\"].max() + 1, 1),\n",
    "    grid=False,\n",
    "    layout=(nrows, ncols),\n",
    "    figsize=(4 * ncols, 3 * nrows),\n",
    ")\n",
    "for ax in axes:\n",
    "    ax.axvline(max_sig_sites, color=\"red\")\n",
    "fig = plt.gcf()\n",
    "fig.suptitle(f\"number of sites with escape >={sig_escape_threshold}\")\n",
    "fig.tight_layout()\n",
    "plt.show(plt.gcf())\n",
    "\n",
    "# drop antibodies with too many sites of escape\n",
    "escape = (\n",
    "    escape\n",
    "    .merge(\n",
    "        n_sig_escape.query(\"n_sites <= @max_sig_sites\")[[\"antibody\", \"study\"]],\n",
    "        how=\"inner\",\n",
    "        validate=\"many_to_one\",\n",
    "    )\n",
    ")\n",
    "    \n",
    "assert escape[\"antibody\"].nunique() == len(escape.groupby([\"antibody\", \"study\"]))\n",
    "\n",
    "display(escape)\n",
    "\n",
    "print(\"Escape measurements for each study:\")\n",
    "display(escape.groupby(\"study\").aggregate(n=pd.NamedAgg(\"site\", \"count\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b96b0fc0-3123-4c19-80be-5d2a46923aeb",
   "metadata": {},
   "source": [
    "Only keep the antibodies that have all types of measurements when writing files in next cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9d526e0c-4e59-4ee0-91bf-d2f163ba069f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:25.034593Z",
     "iopub.status.busy": "2023-06-20T23:54:25.033804Z",
     "iopub.status.idle": "2023-06-20T23:54:25.273285Z",
     "shell.execute_reply": "2023-06-20T23:54:25.272362Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This many antibodies have all measurements:\n"
     ]
    },
    {
     "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>n_antibodies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_source</th>\n",
       "      <th>has_ic50</th>\n",
       "      <th>has_binding</th>\n",
       "      <th>has_escape</th>\n",
       "      <th>has_all</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>False</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">True</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">True</th>\n",
       "      <th>False</th>\n",
       "      <th>False</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th>True</th>\n",
       "      <td>4089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    n_antibodies\n",
       "has_source has_ic50 has_binding has_escape has_all              \n",
       "False      True     True        False      False               4\n",
       "True       True     False       True       False               1\n",
       "                    True        False      False             287\n",
       "                                True       True             4089"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "antibodies = (\n",
    "    antibody_source\n",
    "    [[\"antibody\"]]\n",
    "    .assign(has_source=True)\n",
    "    .drop_duplicates()\n",
    "    .merge(\n",
    "        antibody_ic50s[[\"antibody\"]].assign(has_ic50=True).drop_duplicates(),\n",
    "        how=\"outer\",\n",
    "        validate=\"one_to_one\",\n",
    "    )\n",
    "    .merge(\n",
    "        antibody_binding[[\"antibody\"]].assign(has_binding=True).drop_duplicates(),\n",
    "        how=\"outer\",\n",
    "        validate=\"one_to_one\",\n",
    "    )\n",
    "    .merge(\n",
    "        escape[[\"antibody\"]].assign(has_escape=True).drop_duplicates(),\n",
    "        how=\"outer\",\n",
    "        validate=\"one_to_one\",\n",
    "    )\n",
    "    .set_index(\"antibody\")\n",
    "    .fillna(False)\n",
    "    .assign(has_all=lambda x: x.all(axis=1))\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "print(\"This many antibodies have all measurements:\")\n",
    "display(\n",
    "    antibodies\n",
    "    .groupby([c for c in antibodies.columns if c != \"antibody\"])\n",
    "    .aggregate(n_antibodies=pd.NamedAgg(\"antibody\", \"count\"))\n",
    ")\n",
    "\n",
    "antibodies_to_keep = set(antibodies.query(\"has_all\")[\"antibody\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d58139-be75-4b1c-a94d-04199cdde12e",
   "metadata": {},
   "source": [
    "Get the antibody reweightings.\n",
    "Unlike for other parameters, we only expect reweightings for some antibodies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ba27a03c-aa27-42f4-b2bf-347f4799b931",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:25.277004Z",
     "iopub.status.busy": "2023-06-20T23:54:25.276403Z",
     "iopub.status.idle": "2023-06-20T23:54:25.291384Z",
     "shell.execute_reply": "2023-06-20T23:54:25.290294Z"
    },
    "tags": []
   },
   "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>antibody</th>\n",
       "      <th>study</th>\n",
       "      <th>reweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CP0590</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GC0101</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GC0102</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GC0103</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GC0104</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.246562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>GC2052</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>GC2054</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>GC2056</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>GC2057</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>GC2059</td>\n",
       "      <td>repeated_2023</td>\n",
       "      <td>1.477959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1203 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     antibody          study  reweight\n",
       "0      CP0590  repeated_2023  1.246562\n",
       "1      GC0101  repeated_2023  1.246562\n",
       "2      GC0102  repeated_2023  1.246562\n",
       "3      GC0103  repeated_2023  1.246562\n",
       "4      GC0104  repeated_2023  1.246562\n",
       "...       ...            ...       ...\n",
       "1198   GC2052  repeated_2023  1.477959\n",
       "1199   GC2054  repeated_2023  1.477959\n",
       "1200   GC2056  repeated_2023  1.477959\n",
       "1201   GC2057  repeated_2023  1.477959\n",
       "1202   GC2059  repeated_2023  1.477959\n",
       "\n",
       "[1203 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "antibody_reweighting = repeated_2023_reweighting\n",
    "\n",
    "antibody_reweighting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05dcf7f7-db55-4113-a3ac-3ab855de56c3",
   "metadata": {},
   "source": [
    "Write the output files, only with antibodies to keep:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d0db434b-838d-49f4-9d54-cb54e28b651b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T23:54:25.297572Z",
     "iopub.status.busy": "2023-06-20T23:54:25.296742Z",
     "iopub.status.idle": "2023-06-20T23:54:25.822663Z",
     "shell.execute_reply": "2023-06-20T23:54:25.821832Z"
    }
   },
   "outputs": [],
   "source": [
    "os.makedirs(\"results\", exist_ok=True)\n",
    "\n",
    "(\n",
    "    antibody_source\n",
    "    .query(\"antibody in @antibodies_to_keep\")\n",
    "    .to_csv(\"results/antibody_sources.csv\", index=False)\n",
    ")\n",
    "\n",
    "(\n",
    "    antibody_ic50s\n",
    "    .query(\"antibody in @antibodies_to_keep\")\n",
    "    .drop(columns=\"study\")\n",
    "    .to_csv(\"results/antibody_IC50s.csv\", index=False)\n",
    ")\n",
    "\n",
    "(\n",
    "    antibody_binding\n",
    "    .query(\"antibody in @antibodies_to_keep\")\n",
    "    .drop(columns=\"study\")\n",
    "    .to_csv(\"results/antibody_binding.csv\", index=False)\n",
    ")\n",
    "\n",
    "(\n",
    "    escape\n",
    "    .query(\"antibody in @antibodies_to_keep\")\n",
    "    .drop(columns=\"study\")\n",
    "    .to_csv(\"results/escape.csv\", float_format=\"%.4g\", index=False)\n",
    ")\n",
    "\n",
    "(\n",
    "    antibody_reweighting\n",
    "    .query(\"antibody in @antibodies_to_keep\")\n",
    "    .drop(columns=\"study\")\n",
    "    .to_csv(\"results/antibody_reweighting.csv\", float_format=\"%.4g\", index=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa5d193d-0598-414f-8641-eba126a2fa6f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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