select_mutants_for_reconstruction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3444725c-580a-4e68-b919-9ff66eddbc9f",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "# Refer to injected parameters for any run.ipynb (similar cell below)\n",
    "strain = 'BY4741'\n",
    "locus = 'FKS1-HS1'\n",
    "pool_type = 'single'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b875e32-2ec7-4e25-bad9-16f980d37008",
   "metadata": {},
   "source": [
    "# Notebook to select mutants to be reconstructed in the parental strain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28052e1d-fdf4-4093-8b64-ae02c871718a",
   "metadata": {},
   "source": [
    "In this section, we select single mutants of the specified hotspot to recreate in the parental strain. These mutants will be tested in a standard growth assay in isolated wells. The log2FC(AUC) compared to the WT will be compared with selection coefficients to validate the DMS. To make sure we can evaluate correlation with DMS scores properly, we need variants that span the full range of effects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d892dac-a50d-4530-935a-70e94ccac2b6",
   "metadata": {},
   "source": [
    "The entire notebook was meant to be run several times until we were satisfied with the selection of mutants to be reconstructed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a475c357-9eaa-41e5-b3b7-02c97664d622",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83065170-285e-4072-b387-cea5f66c308e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "from upsetplot import from_indicators\n",
    "from upsetplot import UpSet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ab4b24-c8c1-41e2-a8c8-ee9e9a7b75e0",
   "metadata": {},
   "source": [
    "## Specify paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12687ef7-0493-49ab-94b9-b176c4e7a557",
   "metadata": {},
   "outputs": [],
   "source": [
    "classified_path = 'aggregated_data/classified_variants/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decd2137-15dc-4bf8-acfe-1a608565e1ec",
   "metadata": {},
   "source": [
    "## Import single mutants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5beb4fed-2ac4-4531-ba53-ea3c3f864446",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound</th>\n",
       "      <th>seq_type</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>s</th>\n",
       "      <th>cscore</th>\n",
       "      <th>refined_class</th>\n",
       "      <th>sensres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>ortho</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLILSLRDP</td>\n",
       "      <td>-0.223979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>ortho</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLTLSLRDP</td>\n",
       "      <td>0.127139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>-0.062763</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>*LVLSLRDP</td>\n",
       "      <td>0.230635</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ALVLSLRDP</td>\n",
       "      <td>1.756300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>intermediary</td>\n",
       "      <td>resistant</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>none</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLVLSLRDQ</td>\n",
       "      <td>0.007008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>1.445682</td>\n",
       "      <td>1.0</td>\n",
       "      <td>intermediary</td>\n",
       "      <td>resistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>caspofungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>1.040897</td>\n",
       "      <td>1.0</td>\n",
       "      <td>intermediary</td>\n",
       "      <td>resistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>0.903476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>intermediary</td>\n",
       "      <td>resistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>none</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>0.108415</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>734 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          compound seq_type  Nham_aa     aa_seq         s  cscore  \\\n",
       "0    anidulafungin    ortho      1.0  FLILSLRDP -0.223979     1.0   \n",
       "1    anidulafungin    ortho      1.0  FLTLSLRDP  0.127139     1.0   \n",
       "2    anidulafungin   single      0.0  FLVLSLRDP -0.062763     1.0   \n",
       "3    anidulafungin   single      1.0  *LVLSLRDP  0.230635     1.0   \n",
       "4    anidulafungin   single      1.0  ALVLSLRDP  1.756300     1.0   \n",
       "..             ...      ...      ...        ...       ...     ...   \n",
       "729           none   single      1.0  FLVLSLRDQ  0.007008     1.0   \n",
       "730  anidulafungin   single      1.0  FLWLSLRDP  1.445682     1.0   \n",
       "731    caspofungin   single      1.0  FLWLSLRDP  1.040897     1.0   \n",
       "732     micafungin   single      1.0  FLWLSLRDP  0.903476     1.0   \n",
       "733           none   single      1.0  FLWLSLRDP  0.108415     1.0   \n",
       "\n",
       "    refined_class    sensres  \n",
       "0         WT-like  sensitive  \n",
       "1         WT-like  sensitive  \n",
       "2         WT-like  sensitive  \n",
       "3         WT-like  sensitive  \n",
       "4    intermediary  resistant  \n",
       "..            ...        ...  \n",
       "729       WT-like  sensitive  \n",
       "730  intermediary  resistant  \n",
       "731  intermediary  resistant  \n",
       "732  intermediary  resistant  \n",
       "733       WT-like  sensitive  \n",
       "\n",
       "[734 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BY11nnk = pd.read_csv(f'{classified_path}/{strain}_{locus}_{pool_type}/aa_refined_classification.csv', index_col=0)\n",
    "BY11nnk = BY11nnk[(BY11nnk.Nham_aa <=1)\n",
    "                  & (BY11nnk.cscore < 3)\n",
    "                 ].reset_index(drop=True)\n",
    "BY11nnk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1626402f-1279-4b26-ba54-ff5087cef41d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Annotate MARDy mutants + handpicked mutants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f72d8b4e-1443-4abd-ad77-0183aa4fd23c",
   "metadata": {},
   "source": [
    "Note: one mutant was missing from the DMS dataset (F639C) but was reconstructed individually so we now have an inferred score for it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "737daf44-8237-4b12-bc24-a4c526574f23",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 mutants from MARDy 2.0 in this dataset + F639del\n",
      "In MARDy 2.0 but not in the dataset: []\n",
      "29 single mutants attempted to be recreated in the parental strain\n"
     ]
    }
   ],
   "source": [
    "if locus == 'FKS1-HS1':\n",
    "    mardy_mut = ['CLVLSLRDP','VLVLSLRDP','SLVLSLRDP','FLVYSLRDP','FLVFSLRDP','FLVLPLRDP','FLVLFLRDP','FLVLYLRDP','FLVLCLRDP','FLVLSLRDA']\n",
    "    print(f'{len([x for x in BY11nnk.aa_seq.unique() if x in mardy_mut])}/{len(mardy_mut)} mutants from MARDy 2.0 in this dataset + F639del')\n",
    "    print(f'In MARDy 2.0 but not in the dataset: {[x for x in mardy_mut if x not in BY11nnk.aa_seq.unique()]}')\n",
    "    missing_in_NNK = ['CLVLSLRDP', 'FDVLSLRDP', 'FLVGSLRDP', 'FLVLSLRDQ', 'FLVLSLRDN']\n",
    "    # These mutants are entered manually based on their effect\n",
    "    handpicked_mut = ['FKVLSLRDP', 'FLSLSLRDP', 'FGVLSLRDP', 'FLVLSLRWP', 'DLVLSLRDP', 'ELVLSLRDP', 'FLFLSLRDP', 'FLHLSLRDP', 'FLVKSLRDP', 'FLVLLLRDP', 'FLVLSLDDP', 'FLVLSLSDP', 'FLWLSLRDP', 'FLVLSLQDP', 'QLVLSLRDP']\n",
    "    # We keep the unique variants from both sets\n",
    "    select_mut = set(missing_in_NNK + handpicked_mut + mardy_mut)\n",
    "    print(f'{len(select_mut)} single mutants attempted to be recreated in the parental strain')\n",
    "    reconstructed_mut = [x for x in select_mut if x != 'FLVLSLRDA'] # the only single mutant we were not able to recreate\n",
    "elif locus == 'FKS1-HS2':\n",
    "    mardy_mut = ['DWVRHYTL', 'DWVRGYTL', 'DWVRSYTL']\n",
    "    print(f'{len([x for x in BY11nnk.aa_seq.unique() if x in mardy_mut])}/{len(mardy_mut)} mutants from MARDy 2.0 in this dataset')\n",
    "    print(f'In MARDy 2.0 but not in the dataset: {[x for x in mardy_mut if x not in BY11nnk.aa_seq.unique()]}')\n",
    "    select_mut = ['DWVRRYTF','DWVRPYTL','RWVRRYTL','DWVRRYTK','VWVRRYTL','DWVRRETL','DWVNRYTL','DRVRRYTL','DWVRVYTL']\n",
    "    print(f'{len(select_mut)} single mutants attempted to be recreated in the parental strain')\n",
    "    reconstructed_mut = []\n",
    "else:\n",
    "    print('No data for the specified hotspot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0df93220-53fc-4803-9379-abddddeaae54",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def attribute_selection(x, mardy_list, custom_list):\n",
    "    if x in mardy_list:\n",
    "        return 'mardy'\n",
    "    elif x in custom_list:\n",
    "        return 'handpicked'\n",
    "    else:\n",
    "        return 'not'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cacf2bd-3cb0-442e-8956-d3f1fcafebcd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound</th>\n",
       "      <th>seq_type</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>s</th>\n",
       "      <th>cscore</th>\n",
       "      <th>refined_class</th>\n",
       "      <th>sensres</th>\n",
       "      <th>selected</th>\n",
       "      <th>reconstructed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>ortho</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLILSLRDP</td>\n",
       "      <td>-0.223979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "      <td>not</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>ortho</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLTLSLRDP</td>\n",
       "      <td>0.127139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "      <td>not</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>-0.062763</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "      <td>not</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>*LVLSLRDP</td>\n",
       "      <td>0.230635</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "      <td>not</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ALVLSLRDP</td>\n",
       "      <td>1.756300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>intermediary</td>\n",
       "      <td>resistant</td>\n",
       "      <td>not</td>\n",
       "      <td>False</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>none</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLVLSLRDQ</td>\n",
       "      <td>0.007008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>1.445682</td>\n",
       "      <td>1.0</td>\n",
       "      <td>intermediary</td>\n",
       "      <td>resistant</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>caspofungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>1.040897</td>\n",
       "      <td>1.0</td>\n",
       "      <td>intermediary</td>\n",
       "      <td>resistant</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>0.903476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>intermediary</td>\n",
       "      <td>resistant</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>none</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>0.108415</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WT-like</td>\n",
       "      <td>sensitive</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>734 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          compound seq_type  Nham_aa     aa_seq         s  cscore  \\\n",
       "0    anidulafungin    ortho      1.0  FLILSLRDP -0.223979     1.0   \n",
       "1    anidulafungin    ortho      1.0  FLTLSLRDP  0.127139     1.0   \n",
       "2    anidulafungin   single      0.0  FLVLSLRDP -0.062763     1.0   \n",
       "3    anidulafungin   single      1.0  *LVLSLRDP  0.230635     1.0   \n",
       "4    anidulafungin   single      1.0  ALVLSLRDP  1.756300     1.0   \n",
       "..             ...      ...      ...        ...       ...     ...   \n",
       "729           none   single      1.0  FLVLSLRDQ  0.007008     1.0   \n",
       "730  anidulafungin   single      1.0  FLWLSLRDP  1.445682     1.0   \n",
       "731    caspofungin   single      1.0  FLWLSLRDP  1.040897     1.0   \n",
       "732     micafungin   single      1.0  FLWLSLRDP  0.903476     1.0   \n",
       "733           none   single      1.0  FLWLSLRDP  0.108415     1.0   \n",
       "\n",
       "    refined_class    sensres    selected  reconstructed  \n",
       "0         WT-like  sensitive         not          False  \n",
       "1         WT-like  sensitive         not          False  \n",
       "2         WT-like  sensitive         not          False  \n",
       "3         WT-like  sensitive         not          False  \n",
       "4    intermediary  resistant         not          False  \n",
       "..            ...        ...         ...            ...  \n",
       "729       WT-like  sensitive  handpicked           True  \n",
       "730  intermediary  resistant  handpicked           True  \n",
       "731  intermediary  resistant  handpicked           True  \n",
       "732  intermediary  resistant  handpicked           True  \n",
       "733       WT-like  sensitive  handpicked           True  \n",
       "\n",
       "[734 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BY11nnk['selected'] = BY11nnk.aa_seq.apply(lambda x: attribute_selection(x, mardy_mut, select_mut))\n",
    "BY11nnk['reconstructed'] = BY11nnk.aa_seq.apply(lambda x: True if x in reconstructed_mut else False)\n",
    "BY11nnk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8a3570-b5c0-4bc0-854e-ece9fba0ba1f",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visualize on distribution of fitness effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b1184b7-c8d0-4c30-b805-aebfae13ce74",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fde7b069c90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 682.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_l = ['resistant','intermediary','WT-like','slightly deleterious','deleterious']\n",
    "\n",
    "# Mutants not selected\n",
    "fig = sns.catplot(BY11nnk[BY11nnk.selected == 'not'], x='compound', y='s', hue='refined_class',\n",
    "                  hue_order=class_l, palette='icefire_r', alpha=.3)\n",
    "\n",
    "# Mutants selected + reconstructed\n",
    "sns.stripplot(BY11nnk[(BY11nnk.selected == 'mardy')\n",
    "                      & (BY11nnk.reconstructed == True)\n",
    "                     ], x='compound', y='s', hue='refined_class',\n",
    "              hue_order=class_l, palette='icefire_r', marker='o', s=10, alpha=.8,\n",
    "              legend=False)\n",
    "\n",
    "sns.stripplot(BY11nnk[(BY11nnk.selected == 'handpicked')\n",
    "                      & (BY11nnk.reconstructed == True)\n",
    "                     ], x='compound', y='s', hue='refined_class',\n",
    "              hue_order=class_l, palette='icefire_r', marker='^', s=10, alpha=.8,\n",
    "              legend=False)\n",
    "\n",
    "# Mutants selected but not reconstructed\n",
    "sns.stripplot(BY11nnk[(BY11nnk.selected == 'mardy')\n",
    "                      & (BY11nnk.reconstructed == False)\n",
    "                     ], x='compound', y='s',\n",
    "              color='.8', marker='o', s=10, alpha=.8, zorder=100,\n",
    "              legend=False)\n",
    "\n",
    "sns.stripplot(BY11nnk[(BY11nnk.selected == 'handpicked')\n",
    "                      & (BY11nnk.reconstructed == False)\n",
    "                     ], x='compound', y='s',\n",
    "              color='.8', marker='^', s=10, alpha=.8, zorder=100,\n",
    "              legend=False)\n",
    "\n",
    "fig.set(xlabel='', ylabel='median selection coefficient')\n",
    "fig.set_xticklabels(rotation=45)\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "handles, labels = fig.axes[0][0].get_legend_handles_labels()\n",
    "for l in [('reported','o'), ('handpicked','^')]:\n",
    "    labels.append(l[0])\n",
    "    handles.append(Line2D([0], [0], marker=l[1], color='w', markerfacecolor='k', markersize=10))\n",
    "    \n",
    "fig._legend.remove()\n",
    "plt.legend(handles=handles, labels=labels,\n",
    "           bbox_to_anchor=(1, 0.5), loc='lower left', title=None, frameon=True,\n",
    "          )\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463b4c93-a7dc-4193-bdfc-0b8a0223462a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visualize on UpSet plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb456694-c945-4fd3-9faa-2221a0629fec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "parse_dict = {'resistant': True, 'sensitive': False}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37d6d18a-6a3c-4928-b7d7-1804f7982c9d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>AF</th>\n",
       "      <th>CF</th>\n",
       "      <th>MF</th>\n",
       "      <th>ctrl</th>\n",
       "      <th>max_s</th>\n",
       "      <th>selected</th>\n",
       "      <th>reconstructed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.419917</td>\n",
       "      <td>not</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.082255</td>\n",
       "      <td>not</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.139111</td>\n",
       "      <td>not</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.820699</td>\n",
       "      <td>not</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.351538</td>\n",
       "      <td>not</td>\n",
       "      <td>False</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.001425</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.109278</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.978596</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.151452</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.885922</td>\n",
       "      <td>handpicked</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AF     CF     MF   ctrl     max_s    selected  reconstructed\n",
       "0    False  False  False  False  0.419917         not          False\n",
       "119   True   True   True  False  2.082255         not          False\n",
       "111   True   True   True  False  2.139111         not          False\n",
       "112   True   True   True  False  1.820699         not          False\n",
       "113   True   True  False  False  1.351538         not          False\n",
       "..     ...    ...    ...    ...       ...         ...            ...\n",
       "39   False  False  False  False  0.001425  handpicked           True\n",
       "83   False  False   True  False  2.109278  handpicked           True\n",
       "53    True   True   True  False  1.978596  handpicked           True\n",
       "124   True   True   True  False  2.151452  handpicked           True\n",
       "42    True   True   True  False  1.885922  handpicked           True\n",
       "\n",
       "[180 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BYnnkCompWide = BY11nnk.pivot_table(index=['aa_seq','selected','reconstructed'],\n",
    "                                    columns='compound',\n",
    "                                    values=['s','sensres'],\n",
    "                                    aggfunc='first'\n",
    "                                   ).reset_index()\n",
    "BYnnkCompWide['max_s'] = BYnnkCompWide['s'].max(axis=1)\n",
    "bincat = BYnnkCompWide['sensres'].replace(parse_dict)\n",
    "bincat.rename(columns={'anidulafungin':'AF',\n",
    "                       'caspofungin':'CF',\n",
    "                       'micafungin':'MF',\n",
    "                       'none':'ctrl'\n",
    "                      }, inplace=True)\n",
    "compounds = bincat.columns.tolist()\n",
    "bincat = pd.concat([bincat, BYnnkCompWide[['max_s', 'selected', 'reconstructed']].droplevel(1, axis=1)], axis=1)\n",
    "bincat.sort_values(by='selected', ascending=False, inplace=True)\n",
    "bincat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a242e62-5c35-4312-9f94-1c3184de9ea0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bincat.ctrl.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eca05911-ea11-430e-b200-e480bbf97e32",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'maximum\\nresistance score')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 6))\n",
    "\n",
    "upset_obj = UpSet(from_indicators(compounds, data=bincat),\n",
    "                   #show_percentages=True,\n",
    "                   show_counts=True,\n",
    "                   #min_subset_size=10,\n",
    "                   sort_by='cardinality',\n",
    "                   element_size=None,\n",
    "                   intersection_plot_elements=2, # height of intersection barplot in matrix elements # needs to be 0 to be able to draw stacked_barplot\n",
    "                   totals_plot_elements = 2, # width of totals barplot in matrix elements\n",
    "                  )\n",
    "\n",
    "upset_obj.add_catplot(value='max_s', kind='strip',\n",
    "                      hue='selected', hue_order=['not','mardy','handpicked'], palette=['lightgrey','blue','red'], alpha=.9, s=5,\n",
    "                      elements=3 # height in number of matrix elements\n",
    "                      )\n",
    "\n",
    "d = upset_obj.plot(fig=fig) # Assigns all plots to a dictionary containing axes subplots - same keys as gridspec returned by upset_obj.make_grid()\n",
    "ax0 = d['intersections'] # Key corresponding to stacked barplot ('intersections' = intersection barplot)\n",
    "ax1 = d['extra1'] # Key corresponding to additional plot, in this case catplot\n",
    "ax1.get_legend().remove()\n",
    "\n",
    "ax0.set_ylabel('# variants') # (unique aminoacid sequences)\n",
    "ax1.set_ylabel('maximum\\nresistance score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de872b75-3a70-4173-84d7-be0be72c0bb0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dms-env",
   "language": "python",
   "name": "dms-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}