scores_to_frac_escape.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deconvolve escape scores to fraction escape for individual mutations and homologs\n",
    "This Python Jupyter notebook estimates the escape fraction for individual mutations from the variant measurements.\n",
    "It does this in two ways: it uses [global epistasis models](https://www.pnas.org/content/115/32/E7550) to deconvolve the effects of individual mutations from the escape scores (many of which are for multiply mutated variants) and it directly gets estimates from just singly mutated variants.\n",
    "It then outputs estimates of the escape-fraction for each mutation and summary site-level metrics.\n",
    "\n",
    "The global epistasis models are those implemented in [dms_variants.globalepistasis](https://jbloomlab.github.io/dms_variants/dms_variants.globalepistasis.html).\n",
    "\n",
    "The notebook also estimates the escape fractions for the homologs by averaging over the relevant variants."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook primarily makes use of the Bloom lab's [dms_variants](https://jbloomlab.github.io/dms_variants) package, and uses [plotnine](https://github.com/has2k1/plotnine) for ggplot2-like plotting syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import itertools\n",
    "import math\n",
    "import multiprocessing\n",
    "import os\n",
    "import pickle\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "import Bio.SeqIO\n",
    "\n",
    "import dms_variants.binarymap\n",
    "import dms_variants.codonvarianttable\n",
    "from dms_variants.constants import CBPALETTE\n",
    "import dms_variants.globalepistasis\n",
    "import dms_variants.plotnine_themes\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set [plotnine](https://github.com/has2k1/plotnine) theme to the gray-grid one defined in [dms_variants](https://jbloomlab.github.io/dms_variants):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "theme_set(dms_variants.plotnine_themes.theme_graygrid())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Versions of key software:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dms_variants version 0.8.5\n"
     ]
    }
   ],
   "source": [
    "print(f\"Using dms_variants version {dms_variants.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore warnings that clutter output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the configuration file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(config['escape_scores_dir'], exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read escape scores\n",
    "Read the escape scores.\n",
    "\n",
    "For the primary target, retain only the ones that passed the pre-count and ACE2-binding / expression filters and do **not** have stop codons.\n",
    "For the homologs, we apply the pre-count filter, but not the binding/ expression ones because the homologs to retain are specified manually in the configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading escape scores for primary target SARS-CoV-2 from results/escape_scores/scores.csv.gz\n",
      "Read 106995 scores for the primary target.\n",
      "\n",
      "Reading escape scores for homologs from results/escape_scores/scores_homologs.csv\n",
      "For homologs, just keeping the following: SARS-CoV-2, GD-Pangolin, RaTG13, SARS-CoV, WIV16, LYRa11\n",
      "Read 36 scores for the homologs.\n"
     ]
    }
   ],
   "source": [
    "primary_target = Bio.SeqIO.read(config['wildtype_sequence'], format='fasta').name\n",
    "print(f\"Reading escape scores for primary target {primary_target} from {config['escape_scores']}\")\n",
    "escape_scores_primary = (\n",
    "    pd.read_csv(config['escape_scores'])\n",
    "    .fillna('')  # empty substitution strings mean no subs, not NA\n",
    "    .query('pass_pre_count_filter == True')\n",
    "    .query('pass_ACE2bind_expr_filter == True')\n",
    "    .query('not aa_substitutions.str.contains(\"\\*\")', engine='python')\n",
    "    )\n",
    "assert all(escape_scores_primary['target'] == primary_target)\n",
    "print(f\"Read {len(escape_scores_primary)} scores for the primary target.\")\n",
    "\n",
    "print(f\"\\nReading escape scores for homologs from {config['escape_scores_homologs']}\")\n",
    "homologs_to_keep = config['homologs_to_keep']\n",
    "print(f\"For homologs, just keeping the following: {', '.join(homologs_to_keep)}\")\n",
    "escape_scores_homologs = (pd.read_csv(config['escape_scores_homologs'])\n",
    "                          .query('pass_pre_count_filter == True')\n",
    "                          .query('target in @homologs_to_keep')\n",
    "                          )\n",
    "print(f\"Read {len(escape_scores_homologs)} scores for the homologs.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit global epistasis models\n",
    "Many of our mutants of the primary target have multiple mutations, and we'd like a model to estimate the effects of individual mutations.\n",
    "We get these estimates by fitting global epistasis models to each selection and library for the mutants of the primary target.\n",
    "We fit both a global epistasis model and a no-epistasis (linear) model.\n",
    "For now we use just a Gaussian likelihood function, which seems to work better than a Cauchy likelihood.\n",
    "Because the fitting takes a while, we do it in a multiprocessing queue.\n",
    "\n",
    "Note also that we filter variants based on how many other variants have the substitutions that they contain, potentially removing variants with substitutions seen only in one or a few variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing variants with substitutions not seen in >=0 single-substitution variants or >=2 variants total.\n",
      "Fitting 12 models using 16 CPUs...\n",
      "Dumping the model fits to results/escape_scores/epistasis_models.pickle\n"
     ]
    },
    {
     "data": {
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zJF8pngycGBHX580vAX8CiIgflIS/IulS4IvAxRExT9I7wMKImF5yzL2BbYGBEfFe3vxtSQcDnyOtjn4ScG1E/FduPy//f752yfm+DVwUEb/O78+Q9AngFOBfS+LGRMStJee/BnhE0oYRMVXS+sCngX1X/ifU/Cq9Ml4NaP+Ptw9pFWWAv5F+65pZdW0F9CWtar4MSZ+T9CdJ03PhPR8Y0sUxR5KK6huS3ml/AUOBzXPMliy58Gq3+L2kDwEbAg+XxTyccy71eOmbiPgz6Wr9qLzpSNKV9x+6yLtHqrQY/xX4mqSPkX6L3Zu3bwi8UY3EzGwp8zprkLQr6U/+caQryx2AC4DVuzjmKsA0YETZa0vgRyVxUX7KDo7VUUz5trkd7HcNS7oqjgX+K6/23utUWozPAE4AHgRujIin8/aDWPa3aNOR9A1JkyXNl/RE/qVj1khtpIL8yQ7aPgq8GhEXRMTjEdEGbFIWswDoU7btSVJ/8cKIeLHsNSvHvEC6H1Rqx/ZvIuItYCqwR1nM7sCkFfhcvwKGSPomsA1wfRfxPVZFfcYR8WDu3/lQRLxZ0nQVHf/2axqSvgiMAb5B+lPrBGCcpK0jYkpDk7NeKyLmS7oIuFjSAtK/zQGkAvYiqaAdCvwFOBD4TNkhXgGGShoBvE664TcBmAjcLukMUuHdkHQz7/aIeBz4CXC1pMeBR0j90NsBL5cc+0fAuZJeAp4iXeGOAA5fgc/1pqRb8zHui4jXV+oH04NUOrTtd8A6ZYUYYDbpzm0zO5V0w+KaiJgUEacAr7HsHWSzejufNCriPNJV502km293AP8B/JRUDHfPsaV+Q+pO/D3wD+Cw3B1wAKmP9hdAK6m7Y1PyKI2I+H+kURyXkK6khwLXAfNLjn15zutS4FlgFHBQvkJfEdeSulR+sYLxPVKloyn2ouP+qDWApv2TXtLqpJsa/17WdB/pH7hZw0TEB6S+4As6aPsO8J2yzWNK2t8jjZAo3+9t4Jv51dl5z6ekuEu6n3Q1XprXeXQyiioiXqHjfuZ2g0n3mu5YTkyPt1LFWNJ2JW+3zmOM2/Uh/Ub8ezUSa5D1SZ9jRtn2GaS+NbNeRdI/AV8jjR1eBBxGGkHV7eFn+dhDge8BP4+IBd09ZjPTyty4lPQBS+6QdvSbbh5wUkQ05Z8bkjYk/TLZPSImlmw/CzgyIv65NL61tTXSuPnqighqcdxK9ZZ8ImLRsGHDKv1rsUeStCZwF/AR0tC6F4AflI4X7saxR5MeKPkD8H8j4p3uHrOZrew/vKGkIvwy6Q7rP0raFgAzI2JRlXJrhFmk3/7lV8EDWfZqGWBRS0tL+R3qbmtra6OlpfzhpcbpLfm0trZW/ZjNLiLmka6Ea3Hs0cDoWhy7Ga1UMY6IV/O3PXKCoYhYIOkJ0p9gt5U07Usv788ys9qq+E8yScNIN/IGUlacI6KZH4e+DBibh/JMBL5KepLpZw3Nysx6tIqKsaTjgStJf9ZPZ+knbYImnpsiIm6S9GHgHNJd3ueAA0r+KjAzq7pKr4zPBs6KiIuqmUxRRMQVwBWNzsPMeo+VGk2xeCfpLWBERLzcZXAP1tbW9lpEeGKkHkLStJaWlo0bnYf1TpUW42uBv0SE+1HNzKqg0m6KF4Hz82xRzwLvlzZGxOXdTczMrDep9Mp48nKaIyI2qzwlM7Pep6JibGZm1dUjH94wM2s23XnoYyPSZPJDKJvBLSJO7WZeZpZNu041+fN18DFRnAlHrOKHPj5JWvduMmmJludIc6CKNOepmZmthEq7KS4ELo2I4aRJpg8BNgYeAv67SrmZmfUalXZTbEWa1xRgIbBmRLwj6RzShDpXViO5omttbR1CmgPZeoZZw4YN89Ja1hCVFuO5pLlNIS1GuDlpxWjoJcUpF+IXSKubWM8wv7W1dcveXpCVJov+OWllkP7AHOC6vAQZkl4BxkTEmE4PUmeSAvhMRNze6FwqVWkxfpS0Iu3zwG+BSyVtC3w2t/UG6+NC3NOsQfrv2quLMWnFnmNIszK+DHxAWjiiQz2hEBZBpcX4VGDt/P3o/P0XSU/mfav7aZlZA20OTIuIR+p5UkmrRcT7XUf2TBXdwIuIlyPimfz9uxHxjYjYLiI+66kmzZqXpOuAnwBDJIWkVyQ9KKnDLoncZQFwW3t8Sdu/SHpC0nxJL0v6vqRVS9pD0tck3SFpLmk2yBXZr0XSH3L785K6vR5fEfihj17oyCOP5IIL0gLDe++9N9ddd93iti233JIJEyY0KDMrgJNJc3m/TprPe6cu4tvbjy2Nl7Q/8CvgcmBr4ARS18dZZfufS7rpvy3wi672k7QKcCtpebRdSYul9oipfFe4m0LSbGBYRMyS9CZLTyi/lIhYrxrJWe3dcsstrLnmmisc//rrr3PFFVfw6KOPMmvWLAYOHMhBBx3E1772NVZfffWuD2CFFhFzJL0NLIqI6cByF3+NiH/k9v9tj8/OAv49Iq7P71+W9G/AxaQC3O6G0gWMJY3tYr99SKO5No2I1/M+ZwLjKv3MRbEyfcbfAt7O359Sg1ysAdZbb+V+b7788stEBOeddx6bbLIJra2t/Nu//Rvz5s3jjDPOqFGW1oRGAjvlldXb9QHWkPRPEfFu3vb4yuxHKsRT2gtxNpEeYIWLcftvqpK+m/FlvwmtCe29994cddRRHHPMMYu3zZw5k+OOO44///nPrL/++px++ul86lOfAmDPPfdkzz33XBy78cYbM3nyZG688UYXYyu1CvB9UpdCufkl389dyf06ukzvEbOdrXSfcUQsJD3U0berWGtOP/7xj9l///254447OOiggzjttNN46aWXOo1/++236devXx0ztIJ5n3T1WupJYMuIeLGD1wfLOVZX+z1Purm4Yck+u1X34zRGpTfwHgN2qGYiVhyjRo3i85//PEOHDuWUU05h+PDhjB07tsPYKVOm8Ktf/YrDDjusw3brFV4BPilpkKT+edt5wFGSRkvaRtJWkr4o6QddHKur/SaQHrb6paTtJX0MuKAWH6reKi3GV5Ae9DhR0m6Stit9VTNBq78ddlj69+yIESM6vDKeMWMGxx133OLibb3WacC+wGvA/wBExHjg03n7X0gPg50KLHfoa1f75avjz5D+Mv8zcA3LjtBoSpU+9HFT/lq6vFKQ+nOCZf9ksSZXfkd9xowZHHXUUYwYMYLzzz+/QVlZLeTHnMeUvN+rrH3Tsvd3AXd1cJzxwPjlnKfDYRorsF8r8LGyzU0/HWilV8ZDO3htVvLVmthTTz211Punn36azTZb8p+1vRBvs802XHjhhayyioerm3VXRVfGfsquZ7v33nsZPnw4I0eO5K677uKZZ55Z/JDIjBkzOPLIIxk8eDBnnHEGs2fPXrzfgAEDGpWyWdOreKUPAElb0/FKH3d257jWWCeddBL33HMP5557LgMGDOCSSy5hiy22AODhhx/m1Vdf5dVXX11qiBvACy+80Ih0zXqESleH3gy4jfQIY3tfMfl7IqLH9xm3trZ+BHii0XlY1Y0cNmxYoVar8bJLvUOlnX0/Ji25tAHwLrANsCfpaZq9qpKZmVkvUmk3xW7A3vm59A+ADyLiT5K+Rxph4THIZmYrodJi3Ad4J38/C9iQNBD7VdICpWZWJe5O6B0qLcbPAduRVgF4DPiOpAXAV/M2MzNbCZUW4x8Aa+XvzwbuBv4IvEFa8cPMzFZCpeOMx5d8/zKwtaT1gDejkuEZZtapL/52Zk3+n7rpwIHu/iiQikZTSDpa0lql2yJidi8rxLManYDVhP+7WkNU2k1xCXCFpLtIS6Tcm6fW7DWGDRs2pbW1ddGQIUOqPqZ6ypQpDBkypNqHrVhvyWfKlCmLhg0b1ttXhrYGqbQYDyYt530Y8GtgnqT/Bn5V7xVlG22NNdao+jEl1eS4lXI+ZrVX6erQCyPi7og4HBhIWoZpE+D3kjqfhdzMLJN0naTb63Ce0ZKe6jqysbo1NwVARLwraTzQn1SQt+p2VmbWNCQ9CDwVESu7NubJ9ICpL6ul4mKcFwf8DHA4acXW14AbAc8ybtZDSFo9IhbU4tgRMacWx21WlY6muBGYCfwHaY6KvSJi84g4OyImVTNBM6sfSQ9K+qmkyyTNAu6XtLWkeyS9I2mGpLGS1s/x1wEfB06WFPm1qaQ+kq6VNFnSPEkvSDq57FxLdVPkc18u6WJJsyVNlzS6bJ9+kq6SNFPSW5J+J2n7spjv5jzflnQt0BQ3GCqdKChID3dsGBH/2ttu2pn1cEcDC4GPAt8FHgKeAnYk3bjfALg5x54MTASuJt3YH0z6K3kV4HXgC8DWpLXtfijpCytw7rnALsB3gHMk7QugtNzMb4FBwAHASNICpg/k5xzIxz+XtBTTjsA04BsV/yTqqNKHPr60InGSngUOiIjXKjmPmTXEixHxHQBJ5wFPRsSZ7Y2Svgy8JmlYRLTmqRDejYjpJcdYBHy/5P1kSbuTivPNdO6ZiDg3f98m6UTgk8D9wCdI0/YOjIj3csy3JR0MfA64ijSY4BcRcU1uP1vSPjTB1XG3b+B1YVNgtRqfw8yq6/GS70cCn5D0TgdxmwOtnR1E0teA40g39tckLULR1aiGZ8reTyON2GrPZW3gjbI1GdfMuUAaQPCzsmNMJBXyQqt1MTaz5jO35PtVSIuNntFB3LTODpC7C/6DtHL0ROBt4HRS98PyvF/2PljSnbpKPudeHez3v10ct/BcjM1seZ4EDgFeWc5TtgtYdkX4jwGPRMQV7RskbU73PEnqL14YEa90EjMJ2BX4Zcm2Xbt53rpwMS6gs1v7QevMive/6cCBXQethJ6WD1Q/px7sP4HjgRsl/Yg0d8cWwKHA8RGxCHgF2EXSpqR5zmcDLwJHSdqfNOLqSGCn/H2lJpCusm+XdAZpDvUNSTfzbo+Ix0mrEF0v6XHgT6Sht9vQBFP7eo11M+tUREwljaroA4wnzWX+Y2AO8EEOu4R0w+554B+kRYp/BtwK3ESa8/zDwBV0Q56I7ADgD8AvSP3Vvybdm5qRY24ijdy4iLRG5SbAld05b734ytjMFouIvTrY1gZ8djn7tJKWYit3bH6V+l7JfseswLkPLnv/NvDN/Oosnx8CPyzb3FGfd6F05wm8T5KGnAyk7Ao7Ir6cvz2B/BvLzMw6V1ExlvR94BzSEJhppDuey4iIGypPzcys96j0yvhrwDERMbaayZiZ9VaV3sBbHfAj0GZmVVJpMb4GWKFHos3MrGuVdlOsAXw1P/P9DGVPzUTEqd1NzMysN6m0GG/HkmfMh5e19aZFSc1qzqs49w6VztpW+Ek3zMyaiZ/AMzMrgEIUY0l7SrpL0tS8UsDBZe3Xlawi0P56tCymr6SfSJolaa6kOyVtVBbTP69SMCe/xkpatyxmSM5lbj7W5ZJWr92nNzMrSDEG1gKeBk5cTsy9LFlJYDDpGfVSY0hr8h0K7EGa9/RuSaWzSd0AjCCtVjAqf794rHSO/W3OZ498rEOASyv8XGZmK6QQc1NExDhgHEDZpNGl3itbSWAxSf2ArwBHRsSEvO0I0vIv+wDjJW1FKsC7RsRjOeZ4YKKkLSPiBWA/0hIxG+cJUpB0GnCdpLMi4q2qfGAzszJFuTJeEXvlRQhbJV0tqXQOxJGkFUXua9+Qi+lzwO55027AnPZCnGMeJc0+VRrzXHshzsYDffM5zMxqohBXxitgHPDfwKvAUOB84HeSRua1sAYBCyLizbL9ZuQ28teOJsGdWRaz1MRGEfFmXuNrUPmOQJ+2trYKPk5X+nVr7+rn1LPygVrkZNY9TVGM8xyl7Z7LE0e/ChxImjO1M2Lpcc8djYGuJKbdopaWlvIVDrqvmxOnt7S0VCmRrIflAx3n1Nra6XJuZjXXTN0Ui0XENFIxbv8/ajqwuqT+ZaEDWXKlO520xHi5AWUxS10B52OuhqcCNbMaaspiLOnDwMYsWRDxCdIj2fuWxAwmPR3YPqHRRKCfpJ1LYnYh/c1bGjM879tuP+C9fA4zs5ooRDeFpLVJ62q1GyppBGktrdnAaOA3pOK7KWkW/1nAbQARMUfStcClkt7I+1wCPEtaN4uImCTpXuBqSSfk81wF3J1HUkC6Aaq2CaoAABlgSURBVPg8MFbS6cB6+ThXeySFmdVSIYoxsCPw+5L3l+Wv1wNfB7YFjgLWJRXk3wNfzEuwtPsWsBC4GVgTeIA05/KikpjDgctZMuriTkrGNkfEIkkHktbqehiYRxqb/O3uf0Qzs84VohhHxIOkm2Sd2X8FjjEfOCm/OouZDRzRxXGmAJ/u6nxmZtXUlH3GZmY9jYuxmVkBuBibmRWAi7GZWQG4GJuZFYCLsZlZAbgYm5kVgIuxmVkBuBibmRWAi7GZWQG4GJuZFYCLsZlZAbgYm5kVgIuxmVkBuBibmRWAi7GZWQG4GJuZFYCLsZlZAbgYm5kVgIuxmVkBuBibmRWAi7GZWQG4GJuZFUAhirGkPSXdJWmqpJB0cFm7JI3O7fMkPShpm7KYvpJ+ImmWpLmS7pS0UVlMf0ljJc3Jr7GS1i2LGZJzmZuPdbmk1Wv36c3MClKMgbWAp4ETO2n/DnBqbt8JmA7cL2mdkpgxwGeAQ4E9gLWBuyX1KYm5ARgBjMqvEcDY9sYc+9uczx75WIcAl3bv45mZLd+qjU4AICLGAeMAJC3VprThFOCCiLg1bzsamAF8Cfi5pH7AV4AjI2JCjjkCeA3YBxgvaStSAd41Ih7LMccDEyVtGREvAPsBWwMbR8TUHHMacJ2ksyLirRr+GMysFyvKlfHyDAUGAfe1b4iI94CHgN3zppHAamUxU4HnSmJ2A+a0F+Ic8ygwpyzmufZCnI0H+uZzmJnVRCGujLswKH+dUbZ9BrBJScyCiHizg5hBJTEzOzj+zLKYpc4TEW9KWlASU6pPW1tblx9g5fXr1t7Vz6ln5QO1yMmse5qhGLeLsvfqYFu58piO4iuJabeopaWlTwfbu6e1o98ZK66lpaVKiWQ9LB/oOKfW1tZuH9esUs3QTTE9fy2/Mh3IkqvY6cDqkvp3EbNBB8cfUBaz1HnyMVdj2StzM7OqaYZiPJlUJPdt35CHmn0ceCRvegJ4vyxmMDC8JGYi0E/SziUxu5D+5i2NGZ73bbcf8F4+h5lZTRSim0LS2sAWJZuGShoBzI6IKZLGAGdKagPagDOBd0lD1YiIOZKuBS6V9AYwG7gEeBaYkGMmSboXuFrSCfk8VwF355EUkG4APg+MlXQ6sF4+ztUeSWFmtVSIYgzsCPy+5P1l+ev1wDHAxcCawBVAf+AxYL+IeLtkn28BC4Gbc+wDwDERsagk5nDgcpaMuriTkrHNEbFI0oH5PA8D80gF/9vd/oRmZstRiGIcEQ+SbpJ11h7A6PzqLGY+cFJ+dRYzGziii1ymAJ9eXoyZWbU1Q5+xmVmP52JsZlYALsZmZgXgYmxmVgAuxmZmBeBibGZWAC7GZmYF4GJsZlYALsZmZgXgYmxmVgAuxmZmBeBibGZWAC7GZmYF4GJsZlYALsZmZgXgYmxmVgAuxmZmBeBibGZWAC7GZmYF4GJsZlYALsZmZgXgYmxmVgBNUYwljZYUZa/pJe3KMVMlzZP0oKRtyo7RV9JPJM2SNFfSnZI2KovpL2mspDn5NVbSuvX6nGbWezVFMc7+CgwueW1b0vYd4FTgRGAnYDpwv6R1SmLGAJ8BDgX2ANYG7pbUpyTmBmAEMCq/RgBja/FhzMxKrdroBFbCwoiYXr5RkoBTgAsi4ta87WhgBvAl4OeS+gFfAY6MiAk55gjgNWAfYLykrUgFeNeIeCzHHA9MlLRlRLxQ809oZr1WM10Zt+RuiMmSfi1ps7x9KDAIuK89MCLeAx4Cds+bRgKrlcVMBZ4ridkNmNNeiHPMo8Cckhgzs5polivjx4CjgFZgA+Bs4JHcLzwox8wo22cGsEn+fhCwICLe7CBmUEnMzA7OPbMkplyftra2Ff0MK6Fft/aufk49Kx+oRU5m3dMUxTgixpW8fVbSROAl4Gjg0fawst3UwbZy5TEdxS/vOItaWlr6dNJWudaOfiesuJaWliolkvWwfKDjnFpbW7t9XLNKNVM3xWIRMRd4Fmgh3ayDZa9eB7Lkank6sLqk/l3EbNDB6Qaw7FW3mVlVNWUxltQX2AqYBkwmFdJ9S9pXBz4OPJI3PQG8XxYzGBheEjMR6Cdp55KYXUh/E7fHmJnVRFN0U0i6BLgLmEK6mj0b+BBwfUSEpDHAmZLagDbgTOBd0lA1ImKOpGuBSyW9AcwGLiFdXU/IMZMk3QtcLemEfOqrgLs9ksLMaq0pijGwEXAjsD7wD1I/8a4R8WpuvxhYE7gC6E+64bdfRLxdcoxvAQuBm3PsA8AxEbGoJOZw4HKWjLq4kzR22cysppqiGEfEoV20BzA6vzqLmQ+clF+dxcwGjqgoSTOzbmjKPmMzs57GxdjMrABcjM3MCsDF2MysAFyMzcwKwMXYzKwAXIzNzArAxdjMrABcjM3MCsDF2MysAFyMzcwKwMXYzKwAXIzNzArAxdjMrABcjM3MCsDF2MysAFyMzcwKwMXYzKwAXIzNzArAxdjMrABcjM3MCsDF2MysAFyMzcwKwMW4A5K+IWmypPmSnpD0sUbnZGY9m4txGUlfBMYAFwA7AH8Exkka0tDEzKxHczFe1qnAtRFxTURMiohTgNeArzc4LzPrwVyMS0haHRgJ3FfWdB+we/0zMrPeQhHR6BwKQ9KGwN+Bj0bEIyXbzwSOjogtS+NbW1tDUtXziAhqcdxK9ZZ8ImLRsGHDVq36gc1WgP/hdaz8N5Q62AawqKWlpU+1T97W1kZLS0u1D1ux3pJPa2tr1Y9ptqLcTbG0WcAiYFDZ9oHAjPqnY2a9hYtxiYhYADwB7FvWtC/wyLJ7mJlVh7splnUZMFbS48BE4KvAEOBnDc3KzHo0F+MyEXGTpA8D5wCDgeeAAyLi1cZmZmY9mUdTdENbW9trETG40XlYdUia1tLSsnGj87DeycXYzKwAfAPPzKwAXIzNzArAxdjMrABcjK3pqEjPZptViYe2NZiktUiTEw0mPf03GXgyfGd1ed6TtH1ETGp0ImbV4mLcIJJWAf4d+FdgjfbN+esUSSdFxF0NyOsbwGeB2cDPIuJ3JW3rA3+OiM3qlMtlnTT1Ab4r6Q2AiDi1HvmY1ZKLceP8EPg08CVgPnAWcDdwZ97235IOiojy6TxrRtI3gQuB/wL6AfdIOjciLswhfYBN6pUPcArwNPC/ZdsFbAXMpeMJnMyajscZN4ikvwOHRsQf8/v/A/wNWD8i3pP0b8CnIqJu8yhL+itwQUTckN/vBtwO/DwizpG0ATA1Iqo+U10n+XwPOB44ruwK/X1g+4h4vh55mNWDb+A1zjqkuZPbTSN1V/TP738DbF/nnIZSMiFSREwE9ga+KunCTveqkXxF/kXgSkmXSFqt3jmY1YuLceM8CxxW8v4LwDsRMT2/XwV4r845zQKWehw4Iv5KKsjHAj+qcz5ExF9INzgHAI9L2hZ3TVgP5D7jxjkH+K2kg0h9xrsDp5e0jwL+p845/Qk4hLQI62IR8bykTwK/r3M+7ed/Bzha0qHA/aS+a7MexX3GDSRpO9Kf4X2B8RFxfwHyGRkR/9VJ+zbA5yLi3PpmtlQOG5GulB/IRdqsR3AxtqYkSR6LbT2J+4wbTFKfsve7SNrTN6tAUl9Jl0p6SNLpedvZkt4B3pF0g6QPNThNs6pwMW4QSYMl/Yn0NNlDkvpLupu0usiDwHOS6jpXsqTVJF0s6UVJf5Z0bFn7BpIW1TGlC4FDgb8Ax0r6KWmo2wnAccBOwA/qmI9ZzbibokEk/RLYnPQU3uGkUQyLSCMsVgH+H/BMRJxYx5xGA18DLgHWBU4EboqIE3L7BsC0iKjLL3FJU4AvR8QESZsBbcBnI+KO3L4vcHVEbFqPfMxqycW4QSRNJRWWRyWtRxpWtm9EPJDbPwFcExGb1zGnNuBbEXF3fr85MA54GPgyaZXsej708S7wzxExJb9fAOyQh9shaVPgrxGxVj3yMasld1M0Tn/yQx8RMRt4FyhdZ+8l0uRB9fR/SGv+kfN6CdgL2A0YS/2HlE3J50bSTqTxxTuXtO/C0g/OmDUtjzNunJmkYvtafv9T0uQ87fqT5l6op+mkrpNX2jdExFRJe5PGGF9f53x+Blwn6TjScLbTgB9K+mfgA+DrwKV1zsmsJnxl3DhPka/6ACLiu/kKud0ewDN1zul3pEmKlhIRU0lP4W1az2QiYgzpRt3fgK9HxE9Jfeq7AfsC/wFcUM+czGrFfcYFlf8snxcRz3UZXL1zbkLqox3fSftgYL+IqPcVslmP52JsZlYA7qYoqDzu+KgGnbvDfxeSVpE0pN75dEbSVpJebnQeZtXgYlxcQ0iTvNeNpA9JuhmYK2mGpHPLnhAcQFoWqihWp76T3ZvVjEdTNMgKPMa7Tl0SWdr5pDmUjyQ99HE2MFLSZyNiQY6p22Kgy1l2qd2AuiRiVgfuM24QSR+w/Hl5BUS9HrAAkPQqcHREPJjffxj4LTAHOIhUoOv50Mci0qiTtzoJWRv4SD1/Rma14mLcIJLmkIZlPdZJSAtpuaN6FuO5wPCImFyybR1gPDCPNMzsxToW478BP4iIX3XSPgJ4wsXYegJ3UzTOkwAR8VBHjZL+lzp2CWSvkRb6XFyMI+JtSfsB9wG31TmfJ0gPe3RYjEl/WdT7Z2RWE76B1zg3kFb46Mx0oN6TuN9HWl5pKXkS9/1Zfr61cBowprPGiHi6XpMWmdWauylsMUn9gQ3bJ+LpoH1t0kogHV7NN5qkw4A7I6Lej5GbdZuLcZOQ9CxwQES81mVwnRQtJ0lvASMiwmOPren4T7zmsSlQtNU/NqVYObn/2JqWi7GZWQG4GJuZFYCLsZlZAbgYm5kVgIux9SSvAu83OgmzSvgJvOZxAjCj0UmUqVtOeerOwaQVtF+JiFnlMRExvB65mNWCxxk3iKTTgFsi4tUug+tI0pqkpY32YEnxmwzc3r5ydZ3z+QZwBrBRWdNE4OSIeKLeOZnVgotxg+RZ2z4gLfR5DXBbyTSVjcppC2ACaTa0BcAg4B5gfWBH4FbgSxGxsE75fBs4FbiI9Cj2ycCNwF9Ia/UdAnw8Ih6vRz5mteRi3CC5GH8ZOBg4gDRN5K+Aa+q57l1ZTvcAU4BvRMQHkr4L7BkRB0hqIc1dcX1EjK5TPpNzLuPy+2HAI8CgiFgo6cfAVhGxXz3yMaslF+MGycV4UETMlDQQOIY0Sc8w0mxlVwO/joi365jTXNLjxG35/erAO8DgiHhD0v8FxkTE0Drms01EvJLfi3TFPiQipknaHvhTRDRiIn6zqvJoigKIiJkRcXFEbAXsBTxPWoZ+Wp1T+V+WXmHkn0g3edu7T54h9SPXSyuwb8n7T+Rcpuf381n+BP1mTcOjKRqnwyISEX8E/ijpm8AX65sS9wOXSfoa8B5wIfBUydX5EGBmHfO5EPiVpH1IhfezwOWx5M+5vYCGdOmYVZu7KRqktJui0bm0y90ldwC7kH5ZTAE+GxH/k9s/R+qy+Ekdc/oUcATQFxgfEVeXtH0YICLeqFc+ZrXiYmzLyDfr+gJ/q9fICbPezsXYuiRJUcB/KJJWJU2GP6XRuZh1l2/gFZSkjSX9otF5ZO9J2qrRSXRgG0rW6zNrZr6BV1zrAUeTxiLXhaTLOmnqA3xX0hsAEXFqvXIy6y1cjBtE0kFdhGxWl0SWdgrwNGmIWymRVo2eSx2Hkkl6souQNeuSiFkduM+4QfJoiq6Wmo+I6FOnlJD0PeB44LiI+F3J9veB7SPi+Xrlks87H/g1nXdFDAaOr+fPyKxWXIwbRNLfgX+NiNs7aR8BPFHvQiNpJ9Jj2XcB34uI9xtYjB8Hro2IKztpb8jPyKwWfAOvcZ4APrKc9q6ummsiIv4CjAQGAI9L2pbGPeX2J2DL5bS/DfyhTrmY1ZSvjBtE0seAtSLi3k7a1wJ2jIiH6pvZUjkcCowhFeZt631lbNabuBjbcknaiHSlPCEi5jY6H7Oeyt0UBSRpFUn/IqnD/uR6iojXI+IOYF69c5LUIulGSR/qoK2fpBskNWLUiVnVuRgXSC4+FwKvAzc3Oh9oeE6nA69FxFvlDRExB3gtx5g1PY8zbrC8zNEXgK8Au5IesPgW8IuIeKeX57QncORy2m8GbqhTLmY15SvjBpG0s6SrSHPzngj8BtiYtBTThEYU4gLmtAnLn7JzFik/s6bnK+PGeQT4CbBzRLzQvjEtZtEwRctpDrA50NmirVuQlqsya3q+Mm6c35G6Ac6RNEoNrsJZ0XL6A3DSctq/CfyxTrmY1ZSLcYPkRTS3AV4ArgSm5QU2oUEPWRQwpwuBT0m6JXeh9MuvXST9Btg/x5g1PY8zLghJ+7JktejXgFuAWyKiq8lyenROkj4N/AL4cFnTG6Q5NO6sVy5mteRiXDCS+pOWGfoysF0R5l1odE55dMcoUh+xSAuV3hcR79YzD7NacjEukPy029SI+CC//0gjr4yLmFN5PmY9hYtxgUh6CxgRES83Opd2RcupaPmYVYtv4BVLo0cvdKRoORUtH7OqcDE2MysAF+Ni+SEwu9FJlClaTkXLx6wq3GdsZlYAvjJuoDwj2iGShub3B0r6g6S/SDqrEU/AFS2nouVjViuem6JBJH2GNOvYB0BI+ipwFfB70nwLo4GFwEW9Naei5WNWUxHhVwNewOPABaTRAccC7wKnlLR/FZjUm3MqWj5++VXLl/uMG0TS26Txsi9JWgVYkN8/l9s3BZ6PiH/qrTkVLR+zWnKfceOsRVrdmEhPk80jXfm1mwf07eU5FS0fs5pxMW6cYOmZ0MrfN0LRcipaPmY1426KBpH0AWny9Pb/AOuSbkq1z7kg4ENRx0l5ipZT0fIxqyWPpmicYxudQAeKllPR8jGrGV8ZF5ikVSNiYaPzKFW0nIqWj1ml3GdcQJK2lnQp8PdG59KuaDkVLR+z7nIxLghJa0s6TtJE4BlgF+DfnVNx8zGrJvcZN5ikPYDjgEOAycDWwMcj4mHnVMx8zGrBV8YNIuk7kv4G/Br4B7BHRGxHGjnwpnMqXj5mteQbeA0iqX1OhXMiYlHJ9veB7SPi+d6eU9HyMaslXxk3zjnA54HJki6SNLzRCVG8nIqWj1nNuBg3SET8MCKGAUcCg4BHJT1NepChv3MqXj5mteRuioKQtA5wOOlBh5HAn4FbIuIy51TMfMyqycW4gPKf418BDo+IgY3OB4qXU9HyMesud1M0iKS9JT0v6UMdNL8G7A98qTfnVLR8zGrJxbhxTgGujoi3yhsiYg7wM+Bfe3lORcvHrGZcjBtne+De5bTfR+oXraei5VS0fMxqxsW4cTYA3l9O+0JgQJ1yaVe0nIqWj1nNuBg3zt+BbZfTvh0wrU65tCtaTkXLx6xmXIwb5x7gPElrlDdIWhM4F7i7l+dUtHzMasZD2xpE0gbAk8Ai4KfAC6Q5F7Yi3ZTqA3wkImb01pyKlo9ZLbkYN5CkTYArSUO0lDcHMB74RkS80ttzKlo+ZrXiYlwAkvoDW5CKTVtENHxGsqLlVLR8zKrNxdjMrAB8A8/MrABcjM3MCsDF2MysAFyMzcwKwMXYzKwAXIx7OEmbSgpJI2p8nusk3V7Lc5j1ZKs2OgFrLpI2BSYDO0TEUyVNJ7PkoQwzW0kuxlYVeX5hM6uQuymahKTPSXpW0jxJb0iaIGmt3HaspEmS5kv6m6RvdHGsrSXdI+kdSTMkjZW0fkn7KpLOkPSipPckTZF0Vm6enL/+T+7+eDDvs1Q3haS+ki6XNDPn9SdJO5W075X3/6SkxyW9K+kRSVtW6Udm1lRcjJuApMHAjcAvSJPk7AXcmpp0PHABcFZuOxM4X9LRyznWQ8BTwI7AKNK8wTeXhF0InAGcD2xNWtqofTKenfPXfYDBwGc7Sfti4BDgaOAjwIvAeEnrlcVdAJyWc1mYP6NZ7xMRfhX8RSpmAWzSQdsU4LCybWcDj+TvN837jsjvzwPGl8VvlGOGAesA84HjOsllqeOVbL8OuD1/vxawAPhSSftqpPmJT8/v98rH+WRJzAF52xqN/pn75Ve9X+4zbg5PAw8Az0oaT1pu6BZSn//GwLWSri6JXxXorA93JPAJSe900LY5sC7QN5+vUpuTiu/D7Rsi4n1JfyZdvZd6puT79oniB5J+yZj1Gi7GTSAiFknaF9gd2A84ifTn/b/kkOOBx8p2W9TJ4VYB7iJ1Q5SbBmzW7YSXnuqyfHv5ttJlldrb3H1mvY7/0TeJSB6OiO8DO5C6AT5K+tN/s4h4sew1uZNDPQlsA7zSwT5zgTZgHvDJTvZfkL/2WU66L+a4Pdo3SFqN1C88acU+sVnv4ivjJiBpF1JxvA+YCexCWohzEjAauFzSW8A4UhfDjkD/iLisg8P9J+lK+kZJPwJmkeYJPhQ4PiLmS7oIuFjSAlJXwwBgm4i4Np9/HjBK0uvA/Cgb1hYRcyVdCfxI0mxSl8N3gH8Crq3Sj8WsR3Exbg5vAXsCpwAfAl4FTouIcQCS3gVOJ41gmAs8C4zp6EARMVXSR4GLSKtl9M3Huxf4IIedTxrZcB6wIan74md5/4WSvgmck9v/SLoZV+67pL+8xpJuCj4O7B+eFN6sQ55c3sysANxnbGZWAC7GZmYF4GJsZlYALsZmZgXgYmxmVgAuxmZmBeBibGZWAC7GZmYF4GJsZlYALsZmZgXgYmxmVgAuxmZmBfD/AXJCsbMMQjspAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 150x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_single_counts = config['global_epistasis_filter_by_subs_observed_single']\n",
    "min_any_counts = config['global_epistasis_filter_by_subs_observed_any']\n",
    "min_and_or = config['global_epistasis_filter_by_subs_observed_and_or']\n",
    "print(f\"Removing variants with substitutions not seen in >={min_single_counts} \"\n",
    "      f\"single-substitution variants {min_and_or} >={min_any_counts} variants total.\")\n",
    "\n",
    "# create a list of models to fit, filtering variants based on criteria above\n",
    "to_fit = []\n",
    "n_filtered_variants = {'selection': [],\n",
    "                       'library': [],\n",
    "                       'retained': [],\n",
    "                       'filtered': []}\n",
    "for (name, library), df in escape_scores_primary.groupby(['name', 'library']):\n",
    "    filtered_df = dms_variants.codonvarianttable.filter_by_subs_observed(\n",
    "                                    df,\n",
    "                                    min_single_counts,\n",
    "                                    min_any_counts,\n",
    "                                    and_vs_or=min_and_or)\n",
    "    n_filtered_variants['selection'].append(name)\n",
    "    n_filtered_variants['library'].append(library)\n",
    "    n_filtered_variants['retained'].append(len(filtered_df))\n",
    "    n_filtered_variants['filtered'].append(len(df) - len(filtered_df))\n",
    "    binarymap = dms_variants.binarymap.BinaryMap(filtered_df,\n",
    "                                                 func_score_col='score',\n",
    "                                                 func_score_var_col='score_var')\n",
    "    for epistasis_func, Model in [\n",
    "            ('global_epistasis', dms_variants.globalepistasis.MonotonicSplineEpistasisGaussianLikelihood),\n",
    "            ('no_epistasis', dms_variants.globalepistasis.NoEpistasisGaussianLikelihood),\n",
    "            ]:\n",
    "        key = (library, name, epistasis_func)\n",
    "        to_fit.append((key, Model(binarymap)))\n",
    "\n",
    "# plot how many variants filtered / retained\n",
    "n_filtered_variants = (\n",
    "    pd.DataFrame(n_filtered_variants)\n",
    "    .melt(id_vars=['selection', 'library'],\n",
    "          value_name='n_variants',\n",
    "          var_name='category')\n",
    "    )\n",
    "p = (ggplot(n_filtered_variants) +\n",
    "     aes('selection', 'n_variants', fill='category') +\n",
    "     geom_bar(stat='identity', position=position_dodge()) +\n",
    "     facet_wrap('~ library', ncol=1) +\n",
    "     theme(axis_text_x=element_text(angle=90),\n",
    "           figure_size=(0.5 * n_filtered_variants['selection'].nunique(),\n",
    "                        2 * n_filtered_variants['library'].nunique()),\n",
    "           ) +\n",
    "     scale_fill_manual(values=CBPALETTE[1:])\n",
    "     )\n",
    "_ = p.draw()\n",
    "\n",
    "# fit the models in a multiprocessing queue\n",
    "ncpus = min(config['max_cpus'], multiprocessing.cpu_count())\n",
    "print(f\"Fitting {len(to_fit)} models using {ncpus} CPUs...\")\n",
    "\n",
    "def fit_func(key_model_tup):\n",
    "    key, model = key_model_tup\n",
    "    start = time.time()\n",
    "    try:\n",
    "        model.fit()\n",
    "    except dms_variants.globalepistasis.EpistasisFittingError:\n",
    "        model = None  # none for model if error fitting\n",
    "    return key, model, time.time() - start\n",
    "\n",
    "models = {}\n",
    "fit_failed = []\n",
    "with multiprocessing.Pool(ncpus) as pool:\n",
    "    for i, (key, model, time_sec) in enumerate(pool.imap_unordered(fit_func, to_fit)):\n",
    "        desc = ', '.join(key)\n",
    "        if model is None:\n",
    "            #print(f\"{i + 1} of {len(to_fit)}: Fit FAILED for {desc} in {time_sec:.1f} sec.\")\n",
    "            fit_failed.append(desc)\n",
    "        else:\n",
    "            #print(f\"{i + 1} of {len(to_fit)}: Fit {desc} in {time_sec:.1f} sec.\")\n",
    "            models[key] = model\n",
    "\n",
    "models_pickle_file = os.path.join(config['escape_scores_dir'], 'epistasis_models.pickle')\n",
    "print(f\"Dumping the model fits to {models_pickle_file}\")\n",
    "with open(models_pickle_file, 'wb') as f:\n",
    "    pickle.dump(models, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we compare the epistasis models fit by AIC.\n",
    "Specifically, we can compare model fits for the same selection.\n",
    "In principle, if the global epistasis models are better than the no-epistasis (linear) models, then they should have a better (lower) AIC than the linear model.\n",
    "We compute the $\\Delta$AIC as the no-epistasis (linear) model AIC minus the global epistasis model AIC.\n",
    "This $\\Delta$AIC should be > 0, and if it's not that indicates that the global epistasis model should be treated with caution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 150x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if fit_failed:\n",
    "    print('Note that the fit FAILED for the following:\\n' + '\\n'.join(fit_failed))\n",
    "\n",
    "loglik_df = (\n",
    "    pd.DataFrame.from_records(\n",
    "        [(name, lib, epistasis_func, model.loglik, model.nparams)\n",
    "         for (lib, name, epistasis_func), model in models.items()],\n",
    "        columns=['selection', 'library', 'epistasis_func', 'log_likelihood', 'nparams']\n",
    "        )\n",
    "    .assign(AIC=lambda x: 2 * x['nparams'] - 2 * x['log_likelihood'])\n",
    "    .pivot_table(index=['selection', 'library'],\n",
    "                 columns='epistasis_func',\n",
    "                 values='AIC',\n",
    "                 aggfunc='first',  # https://stackoverflow.com/a/39229232\n",
    "                 )\n",
    "    .assign(deltaAIC=lambda x: x['no_epistasis'] - x['global_epistasis'])\n",
    "    .reset_index()\n",
    "    )\n",
    "\n",
    "if len(loglik_df.query('deltaAIC < 0')) > 0:\n",
    "    print(\"These are the samples for which the global_epistasis AIC is greater than the no_epistasis AIC.\", \n",
    "          \"\\nThere is probably something wrong with them:\\n\")\n",
    "    display(HTML(loglik_df.query('deltaAIC < 0').to_html(index=False)))\n",
    "\n",
    "p = (ggplot(loglik_df) +\n",
    "     aes('selection', 'deltaAIC', color='library', shape='library') +\n",
    "     geom_point(size=2, position=position_dodge(width=0.35)) +\n",
    "     scale_color_manual(values=CBPALETTE[1:]) +\n",
    "     theme(axis_text_x=element_text(angle=90),\n",
    "           figure_size=(0.5 * loglik_df['selection'].nunique(), 2 * loglik_df['library'].nunique()),\n",
    "           )\n",
    "     )\n",
    "\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the \"shape\" of global epistasis as the latent phenotype of each variant versus the experimentally measured phenotype:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x240 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epistasis_shape_df = []\n",
    "for (lib, selection, epistasis_func), model in models.items():\n",
    "    if epistasis_func != 'global_epistasis':\n",
    "        continue\n",
    "    epistasis_shape_df.append(\n",
    "            model.phenotypes_df.assign(selection=selection,\n",
    "                                       library=lib)\n",
    "            )\n",
    "epistasis_shape_df = pd.concat(epistasis_shape_df, sort=False, ignore_index=True)\n",
    "\n",
    "nfacets = len(epistasis_shape_df.groupby(['selection', 'library']))\n",
    "ncol = min(6, nfacets)\n",
    "nrow = math.ceil(nfacets / ncol)\n",
    "xlim_quantile = 0.0005  # extend x-axis limits to these quintiles of data, cut points beyond\n",
    "p = (ggplot(epistasis_shape_df) +\n",
    "     aes('latent_phenotype', 'func_score') +\n",
    "     geom_point(alpha=0.025) +\n",
    "     geom_line(mapping=aes(y='observed_phenotype'), color=CBPALETTE[1], size=1) +\n",
    "     facet_wrap('~ selection + library', ncol=ncol) +\n",
    "     theme(figure_size=(2.4 * ncol, 2.4 * nrow)) +\n",
    "     scale_x_continuous(limits=epistasis_shape_df\n",
    "                               ['latent_phenotype']\n",
    "                               .quantile([xlim_quantile, 1 - xlim_quantile]),\n",
    "                        )\n",
    "     )\n",
    "\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effects of mutations from epistasis models and direct measurements\n",
    "We now examine the effects of mutations. \n",
    "We can get those effects in two ways: the escape scores measured directly for just the single-mutant variants (averaged across those variants if we have not already grouped by amino acid substitution), or the observed phenotypes (predicted scores) of the single mutants predicted by the global epistasis models.\n",
    "These global epistasis observed phenotypes differ from the single-mutant raw average scores above in that they take advantage of information in the multiple mutants as deconvolved by the global epistasis models, and so in principle should be superior.\n",
    "\n",
    "First, we get a data frame that has all of these measurements for each selection and library, also indicating how many measurements we have of single-mutant variants and multiple-mutant variants for each mutation and filtering mutations with not enough of these measurements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing mutations that do not have EITHER >= 1 single-mutant measurements or >= 2 any-mutant measurements.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 150x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# observed phenotypes of single mutants from global epistasis models\n",
    "effects_df = pd.DataFrame({})\n",
    "for (lib, selection, epistasis_func), model in models.items():\n",
    "    if epistasis_func != 'global_epistasis':\n",
    "        continue  # only get global epistasis values\n",
    "    # We want to determine how many times each mutation was measured as a single mutant\n",
    "    # and in any context, so we make counters that give counts of each variant and\n",
    "    # each mutation in context of any variant:\n",
    "    variant_counter = collections.Counter(model.binarymap.substitution_variants)\n",
    "    muts_counter = collections.Counter(itertools.chain.from_iterable(\n",
    "                        s.split() for s in model.binarymap.substitution_variants))\n",
    "    effects_df = effects_df.append(\n",
    "        pd.DataFrame({'selection': selection,\n",
    "                      'library': lib,\n",
    "                      'mutation': model.binarymap.all_subs})\n",
    "        .assign(n_single_mut_measurements=lambda x: x['mutation'].map(variant_counter),\n",
    "                n_any_mut_measurements=lambda x: x['mutation'].map(muts_counter),\n",
    "                )\n",
    "        .pipe(model.add_phenotypes_to_df, substitutions_col='mutation')\n",
    "        .drop(columns='latent_phenotype')\n",
    "        .rename(columns={'observed_phenotype': 'epistasis_model_score'}),\n",
    "        ignore_index=True, sort=False,\n",
    "        )\n",
    "    \n",
    "# only retain mutations that have sufficient variants in either a single or multi-mutant context\n",
    "print(f\"Removing mutations that do not have EITHER >= {config['escape_frac_min_single_mut_measurements']} \"\n",
    "      f\"single-mutant measurements or >= {config['escape_frac_min_any_mut_measurements']} any-mutant \"\n",
    "      'measurements.')\n",
    "effects_df = (\n",
    "    effects_df\n",
    "    .assign(sufficient_measurements=lambda x: (\n",
    "                (x['n_single_mut_measurements'] >= config['escape_frac_min_single_mut_measurements']) |\n",
    "                (x['n_any_mut_measurements'] >= config['escape_frac_min_any_mut_measurements']))\n",
    "            )\n",
    "    )\n",
    "# plot how many mutations have insufficient measurements\n",
    "p = (ggplot(effects_df) +\n",
    "     aes('selection', fill='sufficient_measurements') +\n",
    "     geom_bar(position=position_dodge()) +\n",
    "     facet_wrap('~ library', ncol=1) +\n",
    "     scale_fill_manual(values=CBPALETTE[1: ]) +\n",
    "     theme(axis_text_x=element_text(angle=90),\n",
    "           figure_size=(0.5 * effects_df['selection'].nunique(), 2 * effects_df['library'].nunique())\n",
    "           ) +\n",
    "     ylab('number of mutations')\n",
    "     )\n",
    "_ = p.draw()\n",
    "# remove mutations with insufficient measurements\n",
    "effects_df = (effects_df\n",
    "              .query('sufficient_measurements == True')\n",
    "              .drop(columns='sufficient_measurements')\n",
    "              )\n",
    "\n",
    "# get raw average single-mutant scores\n",
    "raw_avg_single_mut_scores = (\n",
    "    escape_scores_primary\n",
    "    .query('n_aa_substitutions == 1')\n",
    "    .rename(columns={'name': 'selection',\n",
    "                     'aa_substitutions': 'mutation'})\n",
    "    .groupby(['selection', 'library', 'mutation'])\n",
    "    .aggregate(raw_single_mut_score=pd.NamedAgg('score', 'mean'))\n",
    "    .reset_index()\n",
    "    )\n",
    "    \n",
    "# add single-mutant raw average scores to data frame with global epistasis observed phenotypes\n",
    "effects_df = (\n",
    "    effects_df\n",
    "    .merge(raw_avg_single_mut_scores,\n",
    "           how='outer',\n",
    "           validate='one_to_one',\n",
    "           )\n",
    "    )\n",
    "\n",
    "# some error checks\n",
    "assert len(effects_df) == len(effects_df.drop_duplicates()), 'duplicate rows in `effects_df`'\n",
    "assert all(effects_df['raw_single_mut_score'].notnull() | (effects_df['n_single_mut_measurements'] == 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to compute the escape scores (calculated as [here](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html?highlight=escape_scores#dms_variants.codonvarianttable.CodonVariantTable.escape_scores)) back to escape fractions.\n",
    "We define a function to do this depending on the score type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_to_frac(score):\n",
    "    \"\"\"Convert escape score to escape fraction.\"\"\"\n",
    "    if pd.isnull(score):\n",
    "        return pd.NA\n",
    "    floor = config['escape_score_floor_E']\n",
    "    ceil = config['escape_score_ceil_E']\n",
    "    if config['escape_score_type'] == 'frac_escape':\n",
    "        return min(ceil, max(floor, score))  # just the score after applying ceiling and floor\n",
    "    elif config['escape_score_type'] == 'log_escape':\n",
    "        # convert score back to fraction, apply ceiling, then re-scale so floor is 0\n",
    "        frac = 2**score\n",
    "        frac = min(ceil, max(floor, frac))\n",
    "        frac = (frac - floor) / (ceil - floor)\n",
    "        return frac\n",
    "    else:\n",
    "        raise ValueError(f\"invalid `escape_score_type` of {config['escape_score_type']}\")\n",
    "\n",
    "effects_df = (\n",
    "    effects_df\n",
    "    .assign(mut_escape_frac_epistasis_model=lambda x: x['epistasis_model_score'].map(score_to_frac),\n",
    "            mut_escape_frac_single_mut=lambda x: x['raw_single_mut_score'].map(score_to_frac),\n",
    "            )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add rows that are the **average** of the two libraries for the fraction escape for all mutations that are present in both libraries (and if in just one library, the value in that or purge depending on config values printed here), the number of libraries each mutation is measured in, and the sum of the statistics giving the number of measurements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only taking average of mutations with escape fractions in >=2 libraries or with >=2 single-mutant measurements total.\n"
     ]
    }
   ],
   "source": [
    "min_libs = config['escape_frac_avg_min_libraries']\n",
    "min_single = config['escape_frac_avg_min_single']\n",
    "print(f\"Only taking average of mutations with escape fractions in >={min_libs} libraries \"\n",
    "      f\"or with >={min_single} single-mutant measurements total.\")\n",
    "\n",
    "effects_df = (\n",
    "    effects_df\n",
    "    .query('library != \"average\"')  # git rid of averages if already there\n",
    "    .assign(nlibs=1)\n",
    "    .append(effects_df\n",
    "            .query('library != \"average\"')\n",
    "            .groupby(['selection', 'mutation'])\n",
    "            .aggregate(nlibs=pd.NamedAgg('library', 'count'),\n",
    "                       mut_escape_frac_epistasis_model=pd.NamedAgg('mut_escape_frac_epistasis_model',\n",
    "                                                                   lambda s: s.mean(skipna=True)),\n",
    "                       mut_escape_frac_single_mut=pd.NamedAgg('mut_escape_frac_single_mut',\n",
    "                                                              lambda s: s.mean(skipna=True)),\n",
    "                       n_any_mut_measurements=pd.NamedAgg('n_any_mut_measurements', 'sum'),\n",
    "                       n_single_mut_measurements=pd.NamedAgg('n_single_mut_measurements', 'sum'),\n",
    "                       )\n",
    "            .query('(nlibs >= @min_libs) or (n_single_mut_measurements >= @min_single)')\n",
    "            .assign(library=\"average\")\n",
    "            .reset_index(),\n",
    "            ignore_index=True, sort=False,\n",
    "            )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the correlations of the escape fractions among the two libraries for all selections performed on both libraries.\n",
    "Plot correlations for the escape fractions calculated from the raw single-mutant scores, and those from the epistasis models for just mutations present in single-mutant variants (most comparable to the raw single-mutant scores, since the same variants) and for all variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x250 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x250 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x250 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "libraries = [lib for lib in effects_df['library'].unique() if lib != \"average\"]\n",
    "assert len(libraries) == 2, 'plot only makes sense if 2 libraries'\n",
    "\n",
    "for val, has_single_mut_measurement, color in [\n",
    "        ('mut_escape_frac_single_mut', False, CBPALETTE[1]),\n",
    "        ('mut_escape_frac_epistasis_model', True, CBPALETTE[1]),\n",
    "        ('mut_escape_frac_epistasis_model', False, CBPALETTE[2]),\n",
    "        ]:\n",
    "    # wide data frame with each library's score in a different column\n",
    "    effects_df_wide = (\n",
    "        effects_df\n",
    "        .query('library != \"average\"')\n",
    "        .query(f\"n_single_mut_measurements >= {int(has_single_mut_measurement)}\")\n",
    "        # just get selections with 2 libraries\n",
    "        .assign(nlibs=lambda x: x.groupby('selection')['library'].transform('nunique'))\n",
    "        .query('nlibs == 2')\n",
    "        # now make columns for each library, only keep mutants with scores for both libs\n",
    "        [['selection', 'mutation', 'library', val]]\n",
    "        .pivot_table(index=['selection', 'mutation'],\n",
    "                     columns='library',\n",
    "                     values=val,\n",
    "                     aggfunc='first')\n",
    "        .reset_index()\n",
    "        .dropna(axis=0)\n",
    "        )\n",
    "\n",
    "    # correlations between libraries\n",
    "    corrs = (\n",
    "        effects_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(effects_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 = effects_df_wide['selection'].nunique()\n",
    "    ncol = min(nfacets, 5)\n",
    "    nrow = math.ceil(nfacets / ncol)\n",
    "    xmin = effects_df_wide[libraries[0]].min()\n",
    "    xspan = effects_df_wide[libraries[0]].max() - xmin\n",
    "    ymin = effects_df_wide[libraries[1]].min()\n",
    "    yspan = effects_df_wide[libraries[1]].max() - ymin\n",
    "    p = (ggplot(effects_df_wide) +\n",
    "         aes(libraries[0], libraries[1]) +\n",
    "         geom_point(alpha=0.2, color=color) +\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(figure_size=(2.5 * ncol, 2.5 * nrow),\n",
    "               plot_title=element_text(size=14)) +\n",
    "         ggtitle(val + \n",
    "                 (' among mutations with single-mutant variants' if has_single_mut_measurement\n",
    "                  else ' among all mutations in any variant')\n",
    "                 )\n",
    "         )\n",
    "\n",
    "    _ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Escape at the site level\n",
    "The above analysis estimates the effects of mutations.\n",
    "We also compute escape statistics at the **site** level.\n",
    "First, add sites to the data frame of mutational effects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "effects_df = (\n",
    "    effects_df\n",
    "    .assign(site=lambda x: x['mutation'].str[1: -1].astype(int),\n",
    "            wildtype=lambda x: x['mutation'].str[0],\n",
    "            mutant=lambda x: x['mutation'].str[-1],\n",
    "            )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now compute some site-level metrics.\n",
    "These are the average and total escape fraction at each site over all mutations at the site:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>selection</th>\n",
       "      <th>library</th>\n",
       "      <th>site</th>\n",
       "      <th>site_avg_escape_frac_epistasis_model</th>\n",
       "      <th>site_total_escape_frac_epistasis_model</th>\n",
       "      <th>site_avg_escape_frac_single_mut</th>\n",
       "      <th>site_total_escape_frac_single_mut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001017</td>\n",
       "      <td>0.016275</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.003478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001059</td>\n",
       "      <td>0.020120</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.021175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000993</td>\n",
       "      <td>0.017872</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.029781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001024</td>\n",
       "      <td>0.018439</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>0.017606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000991</td>\n",
       "      <td>0.018820</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>0.012251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "site_effects_df = (\n",
    "    effects_df\n",
    "    .groupby(['selection', 'library', 'site'])\n",
    "    .aggregate(\n",
    "        site_avg_escape_frac_epistasis_model=pd.NamedAgg('mut_escape_frac_epistasis_model',\n",
    "                                                         lambda s: s.mean(skipna=True)),\n",
    "        site_total_escape_frac_epistasis_model=pd.NamedAgg('mut_escape_frac_epistasis_model', 'sum'),\n",
    "        site_avg_escape_frac_single_mut=pd.NamedAgg('mut_escape_frac_single_mut',\n",
    "                                                    lambda s: s.mean(skipna=True)),\n",
    "        site_total_escape_frac_single_mut=pd.NamedAgg('mut_escape_frac_single_mut', 'sum'),\n",
    "        )\n",
    "    .reset_index()\n",
    "    )\n",
    "\n",
    "display(HTML(site_effects_df.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot correlations between libraries of the same selection for the site-level statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x250 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x250 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x250 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x250 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "libraries = [lib for lib in effects_df['library'].unique() if lib != \"average\"]\n",
    "assert len(libraries) == 2, 'plot only makes sense if 2 libraries'\n",
    "\n",
    "for val, color in [\n",
    "        ('site_avg_escape_frac_epistasis_model', CBPALETTE[1]),\n",
    "        ('site_avg_escape_frac_single_mut', CBPALETTE[1]),\n",
    "        ('site_total_escape_frac_epistasis_model', CBPALETTE[2]),\n",
    "        ('site_total_escape_frac_single_mut', CBPALETTE[2]),\n",
    "        ]:\n",
    "    # wide data frame with each library's score in a different column\n",
    "    site_effects_df_wide = (\n",
    "        site_effects_df\n",
    "        .query('library != \"average\"')\n",
    "        # just get selections with 2 libraries\n",
    "        .assign(nlibs=lambda x: x.groupby('selection')['library'].transform('nunique'))\n",
    "        .query('nlibs == 2')\n",
    "        # now make columns for each library, only keep sites with scores for both libs\n",
    "        .pivot_table(index=['selection', 'site'],\n",
    "                     columns='library',\n",
    "                     values=val)\n",
    "        .reset_index()\n",
    "        .dropna(axis=0)\n",
    "        )\n",
    "\n",
    "    # correlations between libraries\n",
    "    corrs = (\n",
    "        site_effects_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(site_effects_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 = site_effects_df_wide['selection'].nunique()\n",
    "    ncol = min(nfacets, 5)\n",
    "    nrow = math.ceil(nfacets / ncol)\n",
    "    xmin = site_effects_df_wide[libraries[0]].min()\n",
    "    xspan = site_effects_df_wide[libraries[0]].max() - xmin\n",
    "    ymin = site_effects_df_wide[libraries[1]].min()\n",
    "    yspan = site_effects_df_wide[libraries[1]].max() - ymin\n",
    "    p = (ggplot(site_effects_df_wide) +\n",
    "         aes(libraries[0], libraries[1]) +\n",
    "         geom_point(alpha=0.2, color=color) +\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(figure_size=(2.5 * ncol, 2.5 * nrow),\n",
    "               plot_title=element_text(size=14)) +\n",
    "         ggtitle(val)\n",
    "         )\n",
    "\n",
    "    _ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write file with escape fractions at mutation and site levels\n",
    "We write a files that has the mutation- and site-level escape fractions.\n",
    "This file has both the separate measurements for each library **plus** the average across libraries for all mutations measured in both libraries.\n",
    "We name the columns in such a way that this file can be used as [dms-view data file](https://dms-view.github.io/docs/dataupload/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the first few lines that will be written to the escape-fraction file:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>selection</th>\n",
       "      <th>library</th>\n",
       "      <th>condition</th>\n",
       "      <th>site</th>\n",
       "      <th>label_site</th>\n",
       "      <th>wildtype</th>\n",
       "      <th>mutation</th>\n",
       "      <th>protein_chain</th>\n",
       "      <th>protein_site</th>\n",
       "      <th>mut_escape_frac_epistasis_model</th>\n",
       "      <th>mut_escape_frac_single_mut</th>\n",
       "      <th>site_total_escape_frac_epistasis_model</th>\n",
       "      <th>site_total_escape_frac_single_mut</th>\n",
       "      <th>site_avg_escape_frac_epistasis_model</th>\n",
       "      <th>site_avg_escape_frac_single_mut</th>\n",
       "      <th>nlibs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>331</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.0001792</td>\n",
       "      <td>0.016275</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.001017</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>331</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.0001672</td>\n",
       "      <td>0.016275</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.001017</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>331</td>\n",
       "      <td>0.000979</td>\n",
       "      <td>0.00023675</td>\n",
       "      <td>0.016275</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.001017</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>F</td>\n",
       "      <td>E</td>\n",
       "      <td>331</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>9.52817e-05</td>\n",
       "      <td>0.016275</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.001017</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>average</td>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>N</td>\n",
       "      <td>G</td>\n",
       "      <td>E</td>\n",
       "      <td>331</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>0.000108917</td>\n",
       "      <td>0.016275</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.001017</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Writing to results/escape_scores/escape_fracs.csv\n"
     ]
    }
   ],
   "source": [
    "escape_fracs_to_write = (\n",
    "    effects_df\n",
    "    .merge(site_effects_df,\n",
    "           how='left',\n",
    "           validate='many_to_one',\n",
    "           on=['selection', 'library', 'site'])\n",
    "    .assign(protein_chain=config['escape_frac_protein_chain'],\n",
    "            protein_site=lambda x: x['site'] + config['site_number_offset'],\n",
    "            label_site=lambda x: x['protein_site'],\n",
    "            condition=lambda x: x['selection'].where(x['library'] == \"average\", x['selection'] + '_' + x['library']),\n",
    "            mutation=lambda x: x['mutant'],  # dms-view uses mutation to refer to mutant amino acid\n",
    "            )\n",
    "    [['selection', 'library', 'condition', 'site', 'label_site', 'wildtype', 'mutation',\n",
    "      'protein_chain', 'protein_site', 'mut_escape_frac_epistasis_model', 'mut_escape_frac_single_mut',\n",
    "      'site_total_escape_frac_epistasis_model', 'site_total_escape_frac_single_mut',\n",
    "      'site_avg_escape_frac_epistasis_model', 'site_avg_escape_frac_single_mut', 'nlibs',\n",
    "      ]]\n",
    "    .sort_values(['library', 'selection', 'site', 'mutation'])\n",
    "    )\n",
    "\n",
    "print('Here are the first few lines that will be written to the escape-fraction file:')\n",
    "display(HTML(escape_fracs_to_write.head().to_html(index=False)))\n",
    "\n",
    "print(f\"\\nWriting to {config['escape_fracs']}\")\n",
    "escape_fracs_to_write.to_csv(config['escape_fracs'], index=False, float_format='%.4g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze and write homolog escape fractions\n",
    "Now examine the escape scores and fractions for the homologs.\n",
    "Recall these homologs are not mutagenized, so there is just a single estimate for each homolog.\n",
    "\n",
    "First, plot the distribution of escape scores for the homologs.\n",
    "If we grouped by amino-acid sequence when calculating escape scores, these will just be point estimates.\n",
    "If we grouped by barcode, these will be distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "homolog_order = ['SARS-CoV-2','GD-Pangolin','RaTG13','SARS-CoV','WIV16','LYRa11']\n",
    "escape_scores_homologs['target'] = pd.Categorical(escape_scores_homologs['target'], homolog_order, ordered=True)\n",
    "\n",
    "nfacets = escape_scores_homologs['name'].nunique()\n",
    "ncol = min(4, nfacets)\n",
    "nrow = math.ceil(nfacets / ncol)\n",
    "nhomologs = escape_scores_homologs['target'].nunique()\n",
    "\n",
    "p = (ggplot(escape_scores_homologs) +\n",
    "     aes('target', 'score', color='library') +\n",
    "     geom_boxplot(outlier_size=1, outlier_alpha=0.2) +\n",
    "     facet_wrap('~ name', ncol=ncol) +\n",
    "     theme(axis_text_x=element_text(angle=90),\n",
    "           panel_grid_major_x=element_blank(),\n",
    "           figure_size=(0.6 * nhomologs * ncol, 2 * nrow),\n",
    "           ) +\n",
    "     scale_color_manual(values=CBPALETTE[1:])\n",
    "     )\n",
    "\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now compute our best estimate of the escape score for each homolog in each library.\n",
    "If these were already grouped by amino-acid sequence, we already have these estimates.\n",
    "Otherwise, if they were grouped by barcode we take the **median** of all of the variant-level measurements for each homolog.\n",
    "\n",
    "We then convert the escape scores to escape fractions as above, and plot them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "homolog_avgs = (\n",
    "    escape_scores_homologs\n",
    "    .rename(columns={'target': 'homolog',\n",
    "                     'name': 'selection'})\n",
    "    .groupby(['selection', 'homolog', 'library'])\n",
    "    .aggregate(escape_score=pd.NamedAgg('score', 'median'))\n",
    "    .assign(escape_frac=lambda x: x['escape_score'].map(score_to_frac))\n",
    "    .query('escape_frac.notnull()')\n",
    "    .assign(escape_frac=lambda x: x['escape_frac'].astype(float))\n",
    "    .reset_index()\n",
    "    )\n",
    "\n",
    "nfacets = homolog_avgs['selection'].nunique()\n",
    "ncol = min(5, nfacets)\n",
    "nrow = math.ceil(nfacets / ncol)\n",
    "p = (ggplot(homolog_avgs) +\n",
    "     aes('homolog', 'escape_frac', color='library', shape='library') +\n",
    "     geom_point(size=2.5, position=position_dodge(width=0.4)) +\n",
    "     facet_wrap('~ selection', ncol=ncol) +\n",
    "     theme(axis_text_x=element_text(angle=90),\n",
    "           figure_size=(0.5 * homolog_avgs['homolog'].nunique() * ncol, 2 * nrow),\n",
    "           ) +\n",
    "     scale_color_manual(values=CBPALETTE[1:])\n",
    "     )\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we write the homolog escape fractions to a file, first adding an \"average\" column for each homolog measured in both libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only taking average of mutations with escape fractions in at least 2 libraries.\n",
      "Here are the first few lines of the the homolog escape fraction:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>selection</th>\n",
       "      <th>homolog</th>\n",
       "      <th>library</th>\n",
       "      <th>escape_frac</th>\n",
       "      <th>nlibs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>average</td>\n",
       "      <td>0.001031</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>GD-Pangolin</td>\n",
       "      <td>average</td>\n",
       "      <td>0.005420</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>RaTG13</td>\n",
       "      <td>average</td>\n",
       "      <td>0.003762</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>SARS-CoV</td>\n",
       "      <td>average</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAB-A17_139</td>\n",
       "      <td>WIV16</td>\n",
       "      <td>average</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Writing the homolog escape fractons to results/escape_scores/escape_fracs_homologs.csv\n"
     ]
    }
   ],
   "source": [
    "min_libs = config['escape_frac_avg_min_libraries']\n",
    "print(f\"Only taking average of mutations with escape fractions in at least {min_libs} libraries.\")\n",
    "\n",
    "homolog_fracs = (\n",
    "    homolog_avgs\n",
    "    .assign(nlibs=1)\n",
    "    .append(homolog_avgs\n",
    "            .groupby(['selection', 'homolog'])\n",
    "            .aggregate(nlibs=pd.NamedAgg('library', 'count'),\n",
    "                       escape_frac=pd.NamedAgg('escape_frac', lambda s: s.mean(skipna=True))\n",
    "                       )\n",
    "            .assign(library='average')\n",
    "            .reset_index()\n",
    "            )\n",
    "    .query('nlibs >= @min_libs')\n",
    "    .sort_values(['library', 'selection', 'homolog'])\n",
    "    [['selection', 'homolog', 'library', 'escape_frac', 'nlibs']]\n",
    "    )\n",
    "\n",
    "print('Here are the first few lines of the the homolog escape fraction:')\n",
    "display(HTML(\n",
    "    homolog_fracs\n",
    "    .head()\n",
    "    .to_html(index=False)\n",
    "    ))\n",
    "\n",
    "print(f\"\\nWriting the homolog escape fractons to {config['escape_fracs_homologs']}\")\n",
    "homolog_fracs.to_csv(config['escape_fracs_homologs'], index=False, float_format='%.4g')"
   ]
  }
 ],
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   "name": "python"
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