{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Simulate experiment\n",
    "We will simulate data from a deep mutational scanning experiment using the RBD antibody mix in order to provide hypothetical data on which to fit a `Polyclonal` model.\n",
    "\n",
    "We first define a `Polyclonal` object that represents the antibody mix we want to simulate.\n",
    "This mix is again based on the three RBD antibodies targeting class 1, 2, and 3 epitopes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:25:18.398192Z",
     "iopub.status.busy": "2021-11-19T22:25:18.397971Z",
     "iopub.status.idle": "2021-11-19T22:25:19.643886Z",
     "shell.execute_reply": "2021-11-19T22:25:19.643256Z",
     "shell.execute_reply.started": "2021-11-19T22:25:18.398136Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epitopes: ('class 1', 'class 2', 'class 3')\n",
      "Number of mutations: 1932\n",
      "Number of sites: 173\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import polyclonal\n",
    "\n",
    "# read data needed to initialize `Polyclonal` object\n",
    "activity_wt_df = pd.read_csv('RBD_activity_wt_df.csv')\n",
    "mut_escape_df = pd.read_csv('RBD_mut_escape_df.csv')\n",
    "\n",
    "# create `Polyclonal` object with actual antibody mix\n",
    "poly_abs = polyclonal.Polyclonal(\n",
    "                    activity_wt_df=activity_wt_df,\n",
    "                    mut_escape_df=mut_escape_df)\n",
    "\n",
    "print(f\"Epitopes: {poly_abs.epitopes}\")\n",
    "print(f\"Number of mutations: {len(poly_abs.mutations)}\")\n",
    "print(f\"Number of sites: {len(poly_abs.sites)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we simulate libraries of variants with an average of 1, 2, 3, or 4 mutations per gene.\n",
    "These libraries only contain mutations for which we defined mutation escape above.\n",
    "We a [dms_variants](https://jbloomlab.github.io/dms_variants/) `CodonVariantTable` for the RBD with these mutations.\n",
    "Note that because the `CodonVariantTable` requires sequential 1, 2, ... numbering, we have to do some shifting of sites back and forth with an offset of 331 since that is where the RBD starts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:25:19.644796Z",
     "iopub.status.busy": "2021-11-19T22:25:19.644647Z",
     "iopub.status.idle": "2021-11-19T22:25:35.459564Z",
     "shell.execute_reply": "2021-11-19T22:25:35.458832Z",
     "shell.execute_reply.started": "2021-11-19T22:25:19.644776Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1932 allowed amino-acid mutations.\n"
     ]
    }
   ],
   "source": [
    "import Bio.SeqIO\n",
    "\n",
    "import dms_variants.simulate\n",
    "\n",
    "import polyclonal.utils\n",
    "\n",
    "# read coding sequence, then slice to just region of interest\n",
    "# noting that RBD starts at residue 331\n",
    "geneseq = str(Bio.SeqIO.read('RBD_seq.fasta', 'fasta').seq)\n",
    "\n",
    "allowed_aa_muts = poly_abs.mut_escape_df['mutation'].unique()\n",
    "print(f\"There are {len(allowed_aa_muts)} allowed amino-acid mutations.\")\n",
    "\n",
    "variants = dms_variants.simulate.simulate_CodonVariantTable(\n",
    "                        geneseq=geneseq,\n",
    "                        bclen=16,\n",
    "                        library_specs={f\"avg{m}muts\": {'avgmuts': m,\n",
    "                                                       'nvariants': 30000}\n",
    "                                       for m in [1, 2, 3, 4]},\n",
    "                        allowed_aa_muts=[polyclonal.utils.shift_mut_site(m, -330)\n",
    "                                         for m in allowed_aa_muts],\n",
    "                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now look at the distribution of the number of amino-acid mutations per-variant in the library.\n",
    "The mutation rate is relatively high as we pre-screened for tolerated mutations and need multiple mutants to decompose the sera mix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:25:35.461047Z",
     "iopub.status.busy": "2021-11-19T22:25:35.460782Z",
     "iopub.status.idle": "2021-11-19T22:25:36.648727Z",
     "shell.execute_reply": "2021-11-19T22:25:36.648049Z",
     "shell.execute_reply.started": "2021-11-19T22:25:35.461028Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 700x210 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = variants.plotNumMutsHistogram(mut_type='aa', samples=None,\n",
    "                                  libraries=variants.libraries)\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also look at the mutation rate across the gene.\n",
    "Note that this is sequential numbering of the sequence.\n",
    "Also, the per-site mutation frequency is uneven because we only include tolerated mutations, and there are different number of tolerated mutations per site:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:25:36.650326Z",
     "iopub.status.busy": "2021-11-19T22:25:36.650054Z",
     "iopub.status.idle": "2021-11-19T22:25:38.187845Z",
     "shell.execute_reply": "2021-11-19T22:25:38.187291Z",
     "shell.execute_reply.started": "2021-11-19T22:25:36.650303Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x180 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = variants.plotMutFreqs(variant_type='all', mut_type='aa',\n",
    "                          samples=None, libraries=variants.libraries)\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get the data frame with the amino-acid substitutions for each variant, re-adding site offset to get back to RBD numbering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:25:38.189009Z",
     "iopub.status.busy": "2021-11-19T22:25:38.188807Z",
     "iopub.status.idle": "2021-11-19T22:25:38.627092Z",
     "shell.execute_reply": "2021-11-19T22:25:38.626439Z",
     "shell.execute_reply.started": "2021-11-19T22:25:38.188990Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>n_aa_substitutions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAAAAATTTACGCG</td>\n",
       "      <td>F486P</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAAACTATGCACCT</td>\n",
       "      <td>Q498N</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAAAGACGCTGTGC</td>\n",
       "      <td>P337S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAAATAGTTCTGAT</td>\n",
       "      <td>S371F R408V</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAACAGACCTACAA</td>\n",
       "      <td>A372M C391E G482N</td>\n",
       "      <td>3</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>119995</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>TTTTTTGATGCTATTA</td>\n",
       "      <td>S373L T385R D427L S469N N487S</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119996</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>TTTTTTGGTTGCGCCT</td>\n",
       "      <td>T333Q A372S L455C K458S</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119997</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>TTTTTTTCGATATCCT</td>\n",
       "      <td>V367L P384L N439Q G447A Y453K S459P</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119998</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>TTTTTTTTACATCAGC</td>\n",
       "      <td>Q498L</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119999</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>TTTTTTTTGAATTAAC</td>\n",
       "      <td>I332Y R403S L441S N460S A520L</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         library           barcode                     aa_substitutions  \\\n",
       "0       avg1muts  AAAAAAAATTTACGCG                                F486P   \n",
       "1       avg1muts  AAAAAACTATGCACCT                                Q498N   \n",
       "2       avg1muts  AAAAAAGACGCTGTGC                                P337S   \n",
       "3       avg1muts  AAAAAATAGTTCTGAT                          S371F R408V   \n",
       "4       avg1muts  AAAAACAGACCTACAA                    A372M C391E G482N   \n",
       "...          ...               ...                                  ...   \n",
       "119995  avg4muts  TTTTTTGATGCTATTA        S373L T385R D427L S469N N487S   \n",
       "119996  avg4muts  TTTTTTGGTTGCGCCT              T333Q A372S L455C K458S   \n",
       "119997  avg4muts  TTTTTTTCGATATCCT  V367L P384L N439Q G447A Y453K S459P   \n",
       "119998  avg4muts  TTTTTTTTACATCAGC                                Q498L   \n",
       "119999  avg4muts  TTTTTTTTGAATTAAC        I332Y R403S L441S N460S A520L   \n",
       "\n",
       "        n_aa_substitutions  \n",
       "0                        1  \n",
       "1                        1  \n",
       "2                        1  \n",
       "3                        2  \n",
       "4                        3  \n",
       "...                    ...  \n",
       "119995                   5  \n",
       "119996                   4  \n",
       "119997                   6  \n",
       "119998                   1  \n",
       "119999                   5  \n",
       "\n",
       "[120000 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variants_df = (\n",
    "    variants.barcode_variant_df\n",
    "    [['library', 'barcode', 'aa_substitutions', 'n_aa_substitutions']]\n",
    "    .assign(aa_substitutions=lambda x: x['aa_substitutions'].apply(\n",
    "                                polyclonal.utils.shift_mut_site, shift=330)\n",
    "            )\n",
    "    )\n",
    "\n",
    "variants_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use our `Polyclonal` object to compute the predicted probability of escape ($p_v\\left(c\\right)$) at multiple serum concentrations given the mutation escape values $\\beta_{m,e}$ and activities $a_{\\rm{wt},e}$ stored in the `Polyclonal` object.\n",
    "Since we will use these as the \"true\" values in our our simulation, we then rename the column with the predicted probabilities of escape to just be the probabilities of escape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:25:38.628457Z",
     "iopub.status.busy": "2021-11-19T22:25:38.628229Z",
     "iopub.status.idle": "2021-11-19T22:25:43.421363Z",
     "shell.execute_reply": "2021-11-19T22:25:43.420810Z",
     "shell.execute_reply.started": "2021-11-19T22:25:38.628438Z"
    },
    "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>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>n_aa_substitutions</th>\n",
       "      <th>concentration</th>\n",
       "      <th>prob_escape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAACGCGGTCACTT</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.085566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAAGCACCATAACG</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.085566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAAGGGAGCTAAAC</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.085566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAAGTCGGATGGGC</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.085566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>AAAAAGTCTTAGGGAA</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.085566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719995</th>\n",
       "      <td>avg2muts</td>\n",
       "      <td>CCGGGTACTAAAAAGG</td>\n",
       "      <td>Y508W</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719996</th>\n",
       "      <td>avg3muts</td>\n",
       "      <td>CCCTGCACCCCACATA</td>\n",
       "      <td>Y508W</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719997</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>GTGATTTAGCGTCTCG</td>\n",
       "      <td>Y508W</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719998</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>GGGAAATTAGACTTTC</td>\n",
       "      <td>Y508W C525F</td>\n",
       "      <td>2</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719999</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>GCCACGTTGCAATTCA</td>\n",
       "      <td>Y508W G526L</td>\n",
       "      <td>2</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         library           barcode aa_substitutions  n_aa_substitutions  \\\n",
       "0       avg1muts  AAAAACGCGGTCACTT                                    0   \n",
       "1       avg1muts  AAAAAGCACCATAACG                                    0   \n",
       "2       avg1muts  AAAAAGGGAGCTAAAC                                    0   \n",
       "3       avg1muts  AAAAAGTCGGATGGGC                                    0   \n",
       "4       avg1muts  AAAAAGTCTTAGGGAA                                    0   \n",
       "...          ...               ...              ...                 ...   \n",
       "719995  avg2muts  CCGGGTACTAAAAAGG            Y508W                   1   \n",
       "719996  avg3muts  CCCTGCACCCCACATA            Y508W                   1   \n",
       "719997  avg4muts  GTGATTTAGCGTCTCG            Y508W                   1   \n",
       "719998  avg4muts  GGGAAATTAGACTTTC      Y508W C525F                   2   \n",
       "719999  avg4muts  GCCACGTTGCAATTCA      Y508W G526L                   2   \n",
       "\n",
       "        concentration  prob_escape  \n",
       "0               0.125     0.085566  \n",
       "1               0.125     0.085566  \n",
       "2               0.125     0.085566  \n",
       "3               0.125     0.085566  \n",
       "4               0.125     0.085566  \n",
       "...               ...          ...  \n",
       "719995          4.000     0.000161  \n",
       "719996          4.000     0.000161  \n",
       "719997          4.000     0.000161  \n",
       "719998          4.000     0.000655  \n",
       "719999          4.000     0.000672  \n",
       "\n",
       "[720000 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variants_escape = poly_abs.prob_escape(\n",
    "                    variants_df=variants_df,\n",
    "                    concentrations=[0.125, 0.25, 0.5, 1, 2, 4])\n",
    "\n",
    "variants_escape = (variants_escape\n",
    "                   .rename(columns={'predicted_prob_escape': 'prob_escape'})\n",
    "                   )\n",
    "\n",
    "variants_escape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot some features of the variants, namely the distribution of probability of escape for each number of mutations.\n",
    "As expected, variants with many mutations are more likely to escape the serum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:25:43.422791Z",
     "iopub.status.busy": "2021-11-19T22:25:43.422507Z",
     "iopub.status.idle": "2021-11-19T22:26:02.634338Z",
     "shell.execute_reply": "2021-11-19T22:26:02.633247Z",
     "shell.execute_reply.started": "2021-11-19T22:25:43.422772Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotnine import *\n",
    "\n",
    "p = (ggplot(variants_escape\n",
    "            .assign(n_aa_substitutions=lambda x: x['n_aa_substitutions'].map(\n",
    "                                            lambda n: str(n) if n <= 6 else '>6'),\n",
    "                    concentration=lambda x: pd.Categorical(x['concentration'])\n",
    "                    )\n",
    "            ) +\n",
    "     aes('n_aa_substitutions', 'prob_escape', fill='concentration') +\n",
    "     geom_boxplot(outlier_size=0.5, outlier_alpha=0.1) +\n",
    "     facet_wrap('~ library', ncol=1) +\n",
    "     theme_classic() +\n",
    "     theme(figure_size=(9, 7)) \n",
    "     )\n",
    "\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, just compute the overall average probability of escape for each library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:26:02.636703Z",
     "iopub.status.busy": "2021-11-19T22:26:02.636352Z",
     "iopub.status.idle": "2021-11-19T22:26:02.681683Z",
     "shell.execute_reply": "2021-11-19T22:26:02.681120Z",
     "shell.execute_reply.started": "2021-11-19T22:26:02.636651Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>prob_escape</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>library</th>\n",
       "      <th>concentration</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">avg1muts</th>\n",
       "      <th>0.125</th>\n",
       "      <td>0.263789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250</th>\n",
       "      <td>0.145056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>0.073018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000</th>\n",
       "      <td>0.035075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.000</th>\n",
       "      <td>0.016744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.000</th>\n",
       "      <td>0.008258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">avg2muts</th>\n",
       "      <th>0.125</th>\n",
       "      <td>0.446783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250</th>\n",
       "      <td>0.305320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>0.193629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000</th>\n",
       "      <td>0.116173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.000</th>\n",
       "      <td>0.067517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.000</th>\n",
       "      <td>0.039012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">avg3muts</th>\n",
       "      <th>0.125</th>\n",
       "      <td>0.603217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250</th>\n",
       "      <td>0.466845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>0.339147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000</th>\n",
       "      <td>0.233266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.000</th>\n",
       "      <td>0.153923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.000</th>\n",
       "      <td>0.099108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">avg4muts</th>\n",
       "      <th>0.125</th>\n",
       "      <td>0.725139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250</th>\n",
       "      <td>0.609332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>0.486684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000</th>\n",
       "      <td>0.370326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.000</th>\n",
       "      <td>0.270021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.000</th>\n",
       "      <td>0.190446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        prob_escape\n",
       "library  concentration             \n",
       "avg1muts 0.125             0.263789\n",
       "         0.250             0.145056\n",
       "         0.500             0.073018\n",
       "         1.000             0.035075\n",
       "         2.000             0.016744\n",
       "         4.000             0.008258\n",
       "avg2muts 0.125             0.446783\n",
       "         0.250             0.305320\n",
       "         0.500             0.193629\n",
       "         1.000             0.116173\n",
       "         2.000             0.067517\n",
       "         4.000             0.039012\n",
       "avg3muts 0.125             0.603217\n",
       "         0.250             0.466845\n",
       "         0.500             0.339147\n",
       "         1.000             0.233266\n",
       "         2.000             0.153923\n",
       "         4.000             0.099108\n",
       "avg4muts 0.125             0.725139\n",
       "         0.250             0.609332\n",
       "         0.500             0.486684\n",
       "         1.000             0.370326\n",
       "         2.000             0.270021\n",
       "         4.000             0.190446"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(variants_escape\n",
    " .groupby(['library', 'concentration'])\n",
    " .aggregate({'prob_escape': 'mean'})\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to acknowledge the fact that a real experiment will have some noise in the data. This noise is likely to come in two forms:\n",
    "  \n",
    "  1. Inaccuracies in the measurements of the probabilities of escape, $p_v\\left(c\\right)$.\n",
    "  2. Occassional mis-assignment of which mutations are in a variant due to sequencing errors.\n",
    "  \n",
    "We therefore will make a \"noisy\" version of `variants_df` where we have incorporated both of these sources of noise by adding Gaussian measurement error to the escape probabilities (but then truncating them to be between 0 and 1), and by adding or subtracting a mutation to occassional variants to represent sequencing errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:26:02.682676Z",
     "iopub.status.busy": "2021-11-19T22:26:02.682437Z",
     "iopub.status.idle": "2021-11-19T22:26:04.658544Z",
     "shell.execute_reply": "2021-11-19T22:26:04.657948Z",
     "shell.execute_reply.started": "2021-11-19T22:26:02.682656Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>library</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>concentration</th>\n",
       "      <th>prob_escape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.166783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.054978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.059157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.031918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.128836</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>719995</th>\n",
       "      <td>avg2muts</td>\n",
       "      <td>Y508W</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.046410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719996</th>\n",
       "      <td>avg3muts</td>\n",
       "      <td>Y508W</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.022680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719997</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>Y508W</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.030200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719998</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>Y508W C525F</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.036181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719999</th>\n",
       "      <td>avg4muts</td>\n",
       "      <td>Y508W G526L</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         library aa_substitutions  concentration  prob_escape\n",
       "0       avg1muts                           0.125     0.166783\n",
       "1       avg1muts                           0.125     0.054978\n",
       "2       avg1muts                           0.125     0.059157\n",
       "3       avg1muts                           0.125     0.031918\n",
       "4       avg1muts                           0.125     0.128836\n",
       "...          ...              ...            ...          ...\n",
       "719995  avg2muts            Y508W          4.000     0.046410\n",
       "719996  avg3muts            Y508W          4.000     0.022680\n",
       "719997  avg4muts            Y508W          4.000     0.030200\n",
       "719998  avg4muts      Y508W C525F          4.000     0.036181\n",
       "719999  avg4muts      Y508W G526L          4.000     0.000000\n",
       "\n",
       "[720000 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "\n",
    "numpy.random.seed(1)  # seed for reproducible output\n",
    "\n",
    "def add_subtract_mutation(subs):\n",
    "    \"\"\"Sometimes add or subtract a mutation.\"\"\"\n",
    "    subs = subs.split()\n",
    "    sub_sites = [int(sub[1: -1]) for sub in subs]\n",
    "    rand = numpy.random.random()\n",
    "    if rand < 0.01:  # add mutation with 1% probability\n",
    "        mut = numpy.random.choice(poly_abs.mutations)\n",
    "        while int(mut[1: -1]) in sub_sites:\n",
    "            mut = numpy.random.choice(poly_abs.mutations)\n",
    "        subs.append(mut)\n",
    "    elif rand > 0.99 and subs:  # subtract mutation with 1% probability\n",
    "        subs.pop(numpy.random.randint(len(subs)))\n",
    "    return ' '.join(subs)\n",
    "\n",
    "# only keep needed columns in variants_escape\n",
    "variants_escape = (\n",
    "    variants_escape\n",
    "    [['library', 'aa_substitutions', 'concentration', 'prob_escape']]\n",
    "    )\n",
    "\n",
    "# simulate noisy variants\n",
    "noisy_variants_escape = (\n",
    "    variants_escape\n",
    "    .assign(\n",
    "        # add Gaussian noise with standard deviation of 0.05\n",
    "        prob_escape=lambda x: (x['prob_escape'] +\n",
    "                               numpy.random.normal(scale=0.05, size=len(x))\n",
    "                               ).clip(lower=0, upper=1),\n",
    "        # add rare sequencing errors to variants\n",
    "        aa_substitutions=lambda x: x['aa_substitutions'].map(add_subtract_mutation),\n",
    "        )\n",
    "    )\n",
    "\n",
    "noisy_variants_escape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For both the exact and noisy data frames, we will also compute the IC90 for each variant based on the simulated antibody mix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:26:04.659548Z",
     "iopub.status.busy": "2021-11-19T22:26:04.659311Z",
     "iopub.status.idle": "2021-11-19T22:26:31.375145Z",
     "shell.execute_reply": "2021-11-19T22:26:31.374672Z",
     "shell.execute_reply.started": "2021-11-19T22:26:04.659529Z"
    },
    "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>library</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>concentration</th>\n",
       "      <th>prob_escape</th>\n",
       "      <th>IC90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.166783</td>\n",
       "      <td>0.112813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.054978</td>\n",
       "      <td>0.112813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.059157</td>\n",
       "      <td>0.112813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.031918</td>\n",
       "      <td>0.112813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td></td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.128836</td>\n",
       "      <td>0.112813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719995</th>\n",
       "      <td>avg2muts</td>\n",
       "      <td>Y473E L518F D427L</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.002918</td>\n",
       "      <td>1.159910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719996</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>Y473S G413Q</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.577999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719997</th>\n",
       "      <td>avg1muts</td>\n",
       "      <td>Y473V P479R F392W</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.160248</td>\n",
       "      <td>1.454528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719998</th>\n",
       "      <td>avg3muts</td>\n",
       "      <td>Y489Q N501Y</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719999</th>\n",
       "      <td>avg2muts</td>\n",
       "      <td>Y505N H519T</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.350485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         library   aa_substitutions  concentration  prob_escape      IC90\n",
       "0       avg1muts                             0.125     0.166783  0.112813\n",
       "1       avg1muts                             0.125     0.054978  0.112813\n",
       "2       avg1muts                             0.125     0.059157  0.112813\n",
       "3       avg1muts                             0.125     0.031918  0.112813\n",
       "4       avg1muts                             0.125     0.128836  0.112813\n",
       "...          ...                ...            ...          ...       ...\n",
       "719995  avg2muts  Y473E L518F D427L          4.000     0.002918  1.159910\n",
       "719996  avg1muts        Y473S G413Q          4.000     0.000000  0.577999\n",
       "719997  avg1muts  Y473V P479R F392W          4.000     0.160248  1.454528\n",
       "719998  avg3muts        Y489Q N501Y          4.000     0.000000  0.588084\n",
       "719999  avg2muts        Y505N H519T          4.000     0.000000  0.350485\n",
       "\n",
       "[720000 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variants_escape = poly_abs.icXX(variants_escape, x=0.9, col='IC90')\n",
    "\n",
    "noisy_variants_escape = poly_abs.icXX(noisy_variants_escape, x=0.9, col='IC90')\n",
    "\n",
    "noisy_variants_escape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will write these data frames giving the variants $v$ and their probabilities of escape $p_v\\left(c\\right)$ at each serum concentration $c$ to CSV files:\n",
    "\n",
    " - [RBD_variants_escape_exact.csv](RBD_variants_escape_exact.csv) for the measurements without noise\n",
    " - [RBD_variants_escape_noisy.csv](RBD_variants_escape_noisy.csv) for the measurements with realistic experimental noise\n",
    "\n",
    "The goal is to infer the properties of the escape mutations and the serum, $a_{\\rm{wt},e}$ and $\\beta_{m,e}$, from these data, which are what would be measured in a deep mutational scanning experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-19T22:26:31.376436Z",
     "iopub.status.busy": "2021-11-19T22:26:31.376183Z",
     "iopub.status.idle": "2021-11-19T22:26:37.145818Z",
     "shell.execute_reply": "2021-11-19T22:26:37.144922Z",
     "shell.execute_reply.started": "2021-11-19T22:26:31.376411Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "variants_escape.to_csv('RBD_variants_escape_exact.csv',\n",
    "                       index=False, float_format='%.4g')\n",
    "\n",
    "noisy_variants_escape.to_csv('RBD_variants_escape_noisy.csv',\n",
    "                             index=False, float_format='%.4g')"
   ]
  }
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