make_supp_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make supplementary data files\n",
    "This Python Jupyter notebook makes supplementary files for antibody sets for which doing this is specified in `escape_profiles_config`:\n",
    " - raw data files\n",
    " - [dms-view](https://dms-view.github.io/) input files\n",
    " - correlations between libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up\n",
    "Import Python modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import math\n",
    "import os\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the configuration file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('config.yaml') as f:\n",
    "    config = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create output directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(config['supp_data_dir'], exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract from configuration what we will use as the site- and mutation-level metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At site level, quantifying selection by site_total_escape_frac_single_mut\n",
      "At mutation level, quantify selection by mut_escape_frac_single_mut\n"
     ]
    }
   ],
   "source": [
    "site_metric = config['site_metric']\n",
    "mut_metric = config['mut_metric']\n",
    "\n",
    "print(f\"At site level, quantifying selection by {site_metric}\")\n",
    "print(f\"At mutation level, quantify selection by {mut_metric}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read escape profiles configuration and get sets for which we will be making supp data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making supplementary data for the following condition sets:\n",
      "  B1351\n"
     ]
    }
   ],
   "source": [
    "with open (config['escape_profiles_config']) as f:\n",
    "    escape_profiles_config = yaml.safe_load(f)\n",
    "    \n",
    "condition_sets = {name: specs['conditions'] for name, specs in escape_profiles_config.items()\n",
    "                  if 'make_supp_data' in specs and specs['make_supp_data']}\n",
    "\n",
    "print('Making supplementary data for the following condition sets:\\n  ' + '\\n  '.join(condition_sets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the escape fractions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading escape fractions from results/escape_scores/escape_fracs.csv\n"
     ]
    }
   ],
   "source": [
    "print(f\"Reading escape fractions from {config['escape_fracs']}\")\n",
    "escape_fracs_all = pd.read_csv(config['escape_fracs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the configuration about what data gets output on what PDBs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open (config['output_pdbs_config']) as f:\n",
    "    output_pdbs_config = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations between libraries\n",
    "Plot correlations between libraries for mutation- and site-level escape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plotting correlations of mutation escape for B1351\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1250x500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving plot to results/supp_data/B1351-mutation-corr.pdf\n",
      "\n",
      "Plotting correlations of site escape for B1351\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1250x500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving plot to results/supp_data/B1351-site-corr.pdf\n"
     ]
    }
   ],
   "source": [
    "libraries = [lib for lib in escape_fracs_all['library'].unique() if lib != \"average\"]\n",
    "assert len(libraries) == 2, 'plot only makes sense if 2 libraries'\n",
    "\n",
    "for metric_type, (set_name, condition_set) in itertools.product(['mutation', 'site'],\n",
    "                                                                condition_sets.items()):\n",
    "    \n",
    "    print(f\"\\nPlotting correlations of {metric_type} escape for {set_name}\")\n",
    "    \n",
    "    if metric_type == 'mutation':\n",
    "        metric = mut_metric\n",
    "        pivot_index = ['selection', 'site', 'mutation']\n",
    "    elif metric_type == 'site':\n",
    "        metric = site_metric\n",
    "        pivot_index = ['selection', 'site']\n",
    "    else:\n",
    "        raise ValueError(f\"invalid `metric_type` {metric_type}\")\n",
    "\n",
    "    # wide data frame with each library's score in a different column\n",
    "    df_wide = (\n",
    "        escape_fracs_all\n",
    "        .query('library != \"average\"')\n",
    "        .query('selection in @condition_set')\n",
    "        .assign(selection=lambda x: x['selection'].map(condition_set))\n",
    "        # need to drop duplicate columns if using site metric\n",
    "        [['library', metric] + pivot_index]\n",
    "        .drop_duplicates()\n",
    "        # now make columns for each library, only keep mutants with scores for both libs\n",
    "        .pivot_table(index=pivot_index,\n",
    "                     columns='library',\n",
    "                     values=metric)\n",
    "        .reset_index()\n",
    "        .dropna(axis=0)\n",
    "        )\n",
    "\n",
    "    # correlations between libraries\n",
    "    corrs = (\n",
    "        df_wide\n",
    "        .groupby('selection')\n",
    "        [libraries]\n",
    "        .corr(method='pearson')\n",
    "        .reset_index()\n",
    "        .query('library == @libraries[0]')\n",
    "        .assign(correlation=lambda x: 'R=' + x[libraries[1]].round(2).astype(str))\n",
    "        [['selection', 'correlation']]\n",
    "        # add number of mutations measured\n",
    "        .merge(df_wide\n",
    "               .groupby('selection')\n",
    "               .size()\n",
    "               .rename('n')\n",
    "               .reset_index()\n",
    "               )\n",
    "        .assign(correlation=lambda x: x['correlation'] + ', N=' + x['n'].astype(str))\n",
    "        )\n",
    "\n",
    "    # plot correlations\n",
    "    nfacets = df_wide['selection'].nunique()\n",
    "    ncol = min(nfacets, 5)\n",
    "    nrow = math.ceil(nfacets / ncol)\n",
    "    xmin = df_wide[libraries[0]].min()\n",
    "    xspan = df_wide[libraries[0]].max() - xmin\n",
    "    ymin = df_wide[libraries[1]].min()\n",
    "    yspan = df_wide[libraries[1]].max() - ymin\n",
    "    p = (ggplot(df_wide) +\n",
    "         aes(libraries[0], libraries[1]) +\n",
    "         geom_point(alpha=0.2, color='black') +\n",
    "         geom_text(mapping=aes(label='correlation'),\n",
    "                   data=corrs,\n",
    "                   x=0.01 * xspan + xmin,\n",
    "                   y=0.99 * yspan + ymin,\n",
    "                   size=10,\n",
    "                   ha='left',\n",
    "                   va='top',\n",
    "                   ) +\n",
    "         facet_wrap('~ selection', ncol=ncol) +\n",
    "         theme_classic() +\n",
    "         theme(figure_size=(2.5 * ncol, 2.5 * nrow),\n",
    "               plot_title=element_text(size=14)) +\n",
    "         ggtitle(f\"{metric_type}-level escape\")\n",
    "         )\n",
    "\n",
    "    fig = p.draw()\n",
    "    display(fig)\n",
    "    plotfile = os.path.join(config['supp_data_dir'],\n",
    "                            f\"{set_name}-{metric_type}-corr.pdf\")\n",
    "    print(f\"Saving plot to {plotfile}\")\n",
    "    p.save(plotfile, verbose=False)\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw data files\n",
    "Get the \"raw data\" for the effects of each mutation on antibody escape.\n",
    "This has just the columns of most interest for the average of the two libraries, renamed in a sensible way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = (\n",
    "    escape_fracs_all\n",
    "    .query('library == \"average\"')\n",
    "    .drop(columns=['library'])\n",
    "    .rename(columns={mut_metric: 'mut_escape',\n",
    "                     site_metric: 'site_total_escape'})\n",
    "    [['condition', 'site', 'label_site', 'wildtype', 'mutation', 'protein_chain',\n",
    "      'protein_site', 'mut_escape', 'site_total_escape']]\n",
    "    .assign(site_max_escape=lambda x: x.groupby(['condition', 'site'])['mut_escape'].transform('max'))\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now write the raw data for each condition set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Raw data for B1351:\n",
      "Writing to results/supp_data/B1351_raw_data.csv\n"
     ]
    }
   ],
   "source": [
    "for set_name, condition_set in condition_sets.items():\n",
    "    print(f\"\\nRaw data for {set_name}:\")\n",
    "    df = (raw_data\n",
    "          .query('condition in @condition_set')\n",
    "          .assign(condition=lambda x: x['condition'].map(condition_set))\n",
    "          [['condition', 'label_site', 'wildtype', 'mutation', 'mut_escape',\n",
    "            'site_total_escape', 'site_max_escape']]\n",
    "          .rename(columns={'label_site': 'site'})\n",
    "          )\n",
    "    #display(HTML(df.head().to_html(index=False)))\n",
    "    csv_file = os.path.join(config['supp_data_dir'], f\"{set_name}_raw_data.csv\")\n",
    "    print(f\"Writing to {csv_file}\")\n",
    "    df.to_csv(csv_file, index=False, float_format='%.4g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data for `dms-view`:\n",
    "Now we write the data in a format for display by [dms-view](https://dms-view.github.io/docs/dataupload) with several different possible mutation-level escape color schemes and also allowing options to visualize directly the deep mutational scanning data.\n",
    "We write such a file for each condition set for the PDBs specified in `output_pdbs_config`.\n",
    "\n",
    "First, create the different color schemes for the mutation-level escape.\n",
    "The first step is to read these color schemes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading DMS color schemes from results/escape_profiles/escape_profiles_dms_colors.csv\n"
     ]
    }
   ],
   "source": [
    "print(f\"Reading DMS color schemes from {config['escape_profiles_dms_colors']}\")\n",
    "\n",
    "dms_colors = (\n",
    "    pd.read_csv(config['escape_profiles_dms_colors'])\n",
    "    .drop(columns=['bind', 'expr'])\n",
    "    .rename(columns={'site': 'label_site'})\n",
    "    .rename(columns={'bind_color': 'color ACE2 bind',\n",
    "                     'expr_color': 'color RBD expr'})\n",
    "    # add color schemes by functional group and all gray\n",
    "    .assign(**{'color gray': '#696969',\n",
    "               'color func group': 'functional'})\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now write a [dms-view](https://dms-view.github.io/docs/dataupload) input file that allows different mutation-level coloring schemes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>condition</th>\n",
       "      <th>site</th>\n",
       "      <th>label_site</th>\n",
       "      <th>wildtype</th>\n",
       "      <th>mutation</th>\n",
       "      <th>protein_site</th>\n",
       "      <th>mut_escape color ACE2 bind</th>\n",
       "      <th>site_total escape</th>\n",
       "      <th>site_max escape</th>\n",
       "      <th>color_for_mutation</th>\n",
       "      <th>mut_escape color RBD expr</th>\n",
       "      <th>mut_escape color gray</th>\n",
       "      <th>mut_escape color func group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>K007_500</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>331</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.03074</td>\n",
       "      <td>0.01674</td>\n",
       "      <td>#662505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>K007_500</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>C</td>\n",
       "      <td>331</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.03074</td>\n",
       "      <td>0.01674</td>\n",
       "      <td>#7a2b05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>K007_500</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>D</td>\n",
       "      <td>331</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.03074</td>\n",
       "      <td>0.01674</td>\n",
       "      <td>#662505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>K007_500</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>331</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.03074</td>\n",
       "      <td>0.01674</td>\n",
       "      <td>#662505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>K007_500</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>F</td>\n",
       "      <td>331</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.03074</td>\n",
       "      <td>0.01674</td>\n",
       "      <td>#762905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dms_view_df = (\n",
    "    pd.concat([raw_data.merge(dms_colors[['label_site', 'mutation', color]],\n",
    "                              how='left',\n",
    "                              validate='many_to_one')\n",
    "                       .rename(columns={color: 'color_for_mutation',\n",
    "                                        'mut_escape': 'mut_escape ' + color})\n",
    "               for color in dms_colors.drop(columns=['label_site', 'mutation']).columns.tolist()\n",
    "               ])\n",
    "    .rename(columns={'site_max_escape': 'site_max escape',\n",
    "                     'site_total_escape': 'site_total escape'})\n",
    "    .drop(columns='protein_chain')\n",
    "    )\n",
    "\n",
    "display(HTML(dms_view_df.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, write `dms-view` input files for each condition set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing `dms-view` input file for B1351 mapped to PDB 6m0j to results/supp_data/B1351_6m0j_dms-view_data.csv\n",
      "Writing `dms-view` input file for B1351 mapped to PDB 7LYQ to results/supp_data/B1351_7LYQ_dms-view_data.csv\n"
     ]
    }
   ],
   "source": [
    "for set_name, condition_set in condition_sets.items():\n",
    "    \n",
    "    for pdb_name, pdb_specs in output_pdbs_config.items():\n",
    "        \n",
    "        # get all conditions for this set to be written to this PDB\n",
    "        if isinstance(pdb_specs['conditions'], str) and pdb_specs['conditions'].upper() == 'ALL':\n",
    "            pdb_conditions = condition_set\n",
    "        else:\n",
    "            assert isinstance(pdb_specs['conditions'], list)\n",
    "            pdb_conditions = [condition for condition in condition_set\n",
    "                              if condition in pdb_specs['conditions']]\n",
    "        if not pdb_conditions:\n",
    "            continue\n",
    "            \n",
    "        df = (dms_view_df\n",
    "              .query('condition in @pdb_conditions')\n",
    "              .assign(condition=lambda x: x['condition'].map(condition_set))  # re-name to shorter names\n",
    "              )\n",
    "        \n",
    "        # assign all relevant protein chains\n",
    "        cols = df.columns.tolist()\n",
    "        i = cols.index('protein_site')  # add protein chain column here\n",
    "        newcols = cols[: i] + ['protein_chain'] + cols[i: ]\n",
    "        chains = ' '.join(pdb_specs['chains'])\n",
    "        df = df.assign(protein_chain=chains)[newcols]\n",
    "   \n",
    "        csv_file = os.path.join(config['supp_data_dir'], f\"{set_name}_{pdb_name}_dms-view_data.csv\")\n",
    "        print(f\"Writing `dms-view` input file for {set_name} mapped to PDB {pdb_name} to {csv_file}\")\n",
    "        df.to_csv(csv_file, index=False, float_format='%.4g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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