4_ProfileInterpretation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de96814c-c4c1-45de-aa94-89bc074c8366",
   "metadata": {},
   "source": [
    "# Data visualization for profile interpretation\n",
    "Written by: Amanda Ng R.H.\n",
    "<br>Language: `python3`\n",
    "<br>Created on: 24 Apr 2023\n",
    "<br>Last updated on: 21 Sep 2023\n",
    "<br>Prior data processing: Required (📜 3a_execute_distanceCalculation.sh and 📔 3b_ProfileCalculations.ipynb)\n",
    "<br>Documentation status: In progress ([Sphinx documentation style](https://sphinx-rtd-tutorial.readthedocs.io/en/latest/docstrings.html))\n",
    "\n",
    "The calculations for profile interpretation were carried out in 📜 3a_execute_distanceCalculation.sh and 📔 3b_ProfileCalculations.ipynb."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c42c2cc-e212-43fb-9a61-719120f056cc",
   "metadata": {},
   "source": [
    "## Import packages & custom modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1758a96f-6676-4ffd-ae9c-e34df079a172",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/research/lab_winter/users/ang/personal_miniconda_setup/miniconda3/envs/isogenicCPA_jupyterlab/lib/python3.11/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  @numba.jit()\n",
      "/research/lab_winter/users/ang/personal_miniconda_setup/miniconda3/envs/isogenicCPA_jupyterlab/lib/python3.11/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  @numba.jit()\n",
      "/research/lab_winter/users/ang/personal_miniconda_setup/miniconda3/envs/isogenicCPA_jupyterlab/lib/python3.11/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  @numba.jit()\n",
      "/research/lab_winter/users/ang/personal_miniconda_setup/miniconda3/envs/isogenicCPA_jupyterlab/lib/python3.11/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  @numba.jit()\n"
     ]
    }
   ],
   "source": [
    "# File management\n",
    "import os\n",
    "\n",
    "# DataFrame management\n",
    "import pandas as pd\n",
    "import openpyxl\n",
    "\n",
    "# Manging static plots\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt\n",
    "from matplotlib import collections\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "# Annotating seaborn plots with significance testing results\n",
    "from statannotations.Annotator import Annotator\n",
    "\n",
    "# Color management\n",
    "import matplotlib.colors as mc\n",
    "import colorsys\n",
    "\n",
    "# UMAP reduction\n",
    "from umap import UMAP\n",
    "\n",
    "# Other math and statistics\n",
    "from numpy.linalg import norm\n",
    "import random\n",
    "import numpy as np\n",
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "# Network graph management\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27a4015c-1437-4e56-a24c-ce0216777c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the paths to the modules to where python will search for modules\n",
    "import sys\n",
    "root = \"/research/lab_winter/users/ang\"\n",
    "module_paths = [\n",
    "    f\"{root}/isogenicCPA_repo/2_feature_analysis\"\n",
    "]\n",
    "for module_path in module_paths:\n",
    "    sys.path.insert(0, module_path)\n",
    "\n",
    "from general_modules import *\n",
    "from profile_interpretation_modules import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba792bd3-bf23-4440-9ba7-553058381801",
   "metadata": {},
   "source": [
    "## Setting up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac82c4fd-a93c-4d65-aea9-f288a3ea2039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following directory already exists:\n",
      "/research/lab_winter/users/ang/projects/GW015_SuFEX_IMiDs/GW015_006__full_cpa/cleanPostFeatureExtraction/output_dir/4_ProfileInterpretation_output\n"
     ]
    }
   ],
   "source": [
    "######################################################################################\n",
    "# Paths to the relevant files and directories prior or unrelated to feature extraction\n",
    "######################################################################################\n",
    "# Path to the parent direcotry with all the data for the experiment\n",
    "parent_dir = \"/research/lab_winter/users/ang/projects/GW015_SuFEX_IMiDs/GW015_006__full_cpa\"\n",
    "\n",
    "# Path to the drug metadata sheet\n",
    "drugMetadata_path = f\"{parent_dir}/drug_metadata_sheet.csv\"\n",
    "\n",
    "# Path to the FP assay data\n",
    "fp_path = f\"{parent_dir}/20220923_FP_CRBNbinding.xlsx\"\n",
    "\n",
    "###########################################################################################\n",
    "# Paths to the relevant files and directories from QC, Profile Assembly & Feature Selection\n",
    "###########################################################################################\n",
    "# Path to the directory for Section 2\n",
    "section2_dir = f\"{parent_dir}/cleanPostFeatureExtraction/output_dir/2_ProfileAssemblyAndFeatureSelection_output\"\n",
    "\n",
    "# Path to the morphology profile after robust Z standardization and baseline feature selection\n",
    "profile_path = f\"{section2_dir}/baseline_output.csv\"\n",
    "\n",
    "# Path to the features selected\n",
    "selectedFeatures_path = f\"{section2_dir}/featuresSelected_output.csv\"\n",
    "\n",
    "# Path to the tau output calculated\n",
    "tauOutput_path = f\"{section2_dir}/tau_output.csv\"\n",
    "\n",
    "###########################################\n",
    "# Path to the profile calculations in julia\n",
    "###########################################\n",
    "reducedProfile_path = f\"{parent_dir}/cleanPostFeatureExtraction/output_dir/3_ProfileInterpretation_output/BioProfiling_output/umapReducedProfile_c662_rko_wt_global_c662_rko_wt.csv\"\n",
    "\n",
    "dmsoDistance_path = f\"{parent_dir}/cleanPostFeatureExtraction/output_dir/3_ProfileInterpretation_output/BioProfiling_output/distance_c662_rko_wt_global_c662_rko_wt_DMSO.csv\"\n",
    "\n",
    "############################################\n",
    "# Path to the profile calculations in python\n",
    "############################################\n",
    "# Path to the directory with the python calculations for profile interpretation\n",
    "section3_python_dir = f\"{parent_dir}/cleanPostFeatureExtraction/output_dir/3_ProfileInterpretation_output/Python_calculations\"\n",
    "\n",
    "# Path to the induction scores calculated\n",
    "inductionScores_path = f\"{section3_python_dir}/inductionScores.csv\"\n",
    "\n",
    "# Path to the corrected U scores calculated\n",
    "correctedU_path = f\"{section3_python_dir}/correctedU.csv\"\n",
    "\n",
    "##########################################\n",
    "# Path to the output directory and make it\n",
    "##########################################\n",
    "output_dir = f\"{parent_dir}/cleanPostFeatureExtraction/output_dir/4_ProfileInterpretation_output\"\n",
    "makeDirectory(output_dir)\n",
    "\n",
    "# Path to save the compound data to\n",
    "compoundData_path = f\"{output_dir}/compoundData.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38d2e406-98f7-4203-af8d-c8353b66065d",
   "metadata": {},
   "source": [
    "## Figure prep set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03fc6b57-a816-4dec-b1a3-d6ffe257e614",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the dictionary to use for the compound types used in the isogenic CPA\n",
    "label2color_dict = {\n",
    "    \"CRBN-dependent\" : \"#67E0E6\",   # blue\n",
    "    \"CRBN-independent\" : \"#E68073\", # pink\n",
    "    \"Morphological\" : \"#E6BC70\",    # brown\n",
    "    \"CRBN binder\" : \"#E2E5DE\"        # light grey\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "492a3b1e-8cde-459d-ba54-0ec5f85bb266",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the example compounds and their colors\n",
    "cpaExamples = {\n",
    "    \"FK-866\" : label2color_dict[\"Morphological\"],\n",
    "    \"TL 13-12\" : label2color_dict[\"CRBN-dependent\"],\n",
    "    \"Pomalidomide\" : \"#D048D6\" # purple\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ed2a04-c278-4689-bdc1-256d4eee0a9b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Data aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "607f371e-38f5-434c-a704-57cdc3b4c7c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPORTED: Compound data exported at\n",
      "/research/lab_winter/users/ang/projects/GW015_SuFEX_IMiDs/GW015_006__full_cpa/cleanPostFeatureExtraction/output_dir/4_ProfileInterpretation_output/compoundData.csv\n",
      "Number of treatments/compounds: 151\n",
      "Preview of five random rows from the compound data:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_580845/4045181631.py:40: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  compoundData.loc[i, \"Compound\"] = compound\n",
      "/tmp/ipykernel_580845/4045181631.py:45: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  compoundData.loc[i, \"Label\"] = \"Morphological\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>Label</th>\n",
       "      <th>Treatment conc. (microM)</th>\n",
       "      <th>FP_CRBN_binding</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Number_Features</th>\n",
       "      <th>Corrected_u</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>FL5-5</td>\n",
       "      <td>CRBN binder</td>\n",
       "      <td>10</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.138636</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5400</td>\n",
       "      <td>0.337507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>FL2-6</td>\n",
       "      <td>CRBN binder</td>\n",
       "      <td>10</td>\n",
       "      <td>4.223</td>\n",
       "      <td>0.247637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>FL4-2</td>\n",
       "      <td>CRBN binder</td>\n",
       "      <td>10</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.784366</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.7875</td>\n",
       "      <td>0.000977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>FL5-16</td>\n",
       "      <td>CRBN binder</td>\n",
       "      <td>10</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.236154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>FL6-5</td>\n",
       "      <td>CRBN binder</td>\n",
       "      <td>10</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.580972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Compound        Label Treatment conc. (microM)  FP_CRBN_binding  Distance  \\\n",
       "113    FL5-5  CRBN binder                       10            0.829  0.138636   \n",
       "51     FL2-6  CRBN binder                       10            4.223  0.247637   \n",
       "89     FL4-2  CRBN binder                       10            0.557  0.784366   \n",
       "124   FL5-16  CRBN binder                       10            4.216  0.236154   \n",
       "134    FL6-5  CRBN binder                       10            0.679  0.580972   \n",
       "\n",
       "     Number_Features  Corrected_u   p-value  \n",
       "113              1.0       0.5400  0.337507  \n",
       "51               0.0          NaN       NaN  \n",
       "89               8.0       0.7875  0.000977  \n",
       "124              0.0          NaN       NaN  \n",
       "134              0.0          NaN       NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def aggregateCalculations(\n",
    "    drugMetadata = pd.read_csv(drugMetadata_path),\n",
    "    correctedU = pd.read_csv(correctedU_path),\n",
    "    dmsoDistance = pd.read_csv(dmsoDistance_path),\n",
    "    selectedFeatures = pd.read_csv(selectedFeatures_path),\n",
    "    fp_path = fp_path,\n",
    "    output_path = compoundData_path\n",
    "):\n",
    "    ##############################################################################\n",
    "    # Rename the CRBN binders (so that the fluorosulfate scaffold is more obvious)\n",
    "    ##############################################################################\n",
    "    # Dictionary for mapping the original fluorosulfate scaffold name to the new name\n",
    "    old2newScaffold_dict = {\n",
    "        \"B\" : \"FL1\",\n",
    "        \"D\" : \"FL2\",\n",
    "        \"F\" : \"FL3\",\n",
    "        \"H\" : \"FL4\",\n",
    "        \"C\" : \"FL5\",\n",
    "        \"E\" : \"FL6\"\n",
    "    }\n",
    "\n",
    "    # Create a new column for the compound names\n",
    "    # and labels that will be used for plotting purposes\n",
    "    for i in compoundData.index:\n",
    "        crbnDependency = compoundData.loc[i, \"CRBN_dependency\"]\n",
    "        moa_type = compoundData.loc[i, \"moa_type\"]\n",
    "        compound = compoundData.loc[i, \"Actual name\"]\n",
    "        if moa_type == \"unknown\" and len(compound) < 4:\n",
    "            scaffold = compound[0]\n",
    "            tailGroup = compound[1:]\n",
    "            newScaffold = old2newScaffold_dict[scaffold]\n",
    "            newName = f\"{newScaffold}-{tailGroup}\"\n",
    "            compoundData.loc[i, \"Compound\"] = newName\n",
    "            compoundData.loc[i, \"Label\"] = \"CRBN binder\"\n",
    "        else:\n",
    "            compoundData.loc[i, \"Compound\"] = compound\n",
    "            if crbnDependency == 1:\n",
    "                compoundData.loc[i, \"Label\"] = \"CRBN-dependent\"\n",
    "            elif crbnDependency == 0:\n",
    "                if moa_type == \"morphology control\":\n",
    "                    compoundData.loc[i, \"Label\"] = \"Morphological\"\n",
    "                else:\n",
    "                    compoundData.loc[i, \"Label\"] = \"CRBN-independent\"\n",
    "\n",
    "    # Drop any rows which do not have a label\n",
    "    compoundData = compoundData[~compoundData[\"Label\"].isnull()]\n",
    "                \n",
    "\n",
    "    # Make the \"Compound\" column the first column\n",
    "    columnOrder = [\"Compound\", \"Label\"] + list(drugMetadata.columns)\n",
    "    compoundData = compoundData[columnOrder]\n",
    "\n",
    "    ##########################\n",
    "    # Add in the FP assay data\n",
    "    ##########################\n",
    "    # Convert the FP assay data into a dictionary for mapping\n",
    "    # the treatments to their FP assay results\n",
    "    treatment2binding_dict = dict()\n",
    "    fp = pd.read_excel(\n",
    "        fp_path,\n",
    "        index_col = 0,\n",
    "        header = 3\n",
    "    )\n",
    "    for prefix in fp.columns:\n",
    "        for number in fp.index:\n",
    "            treatment = prefix + str(number)\n",
    "            binding = fp.loc[number, prefix]\n",
    "            treatment2binding_dict[treatment] = binding\n",
    "    \n",
    "    # Map the FP data to the treatments in the treatmentCalculations\n",
    "    compoundData[\"FP_CRBN_binding\"] = compoundData[\"Actual name\"].map(treatment2binding_dict)\n",
    "\n",
    "    ################################################\n",
    "    # Add in the morphological perturbation strength\n",
    "    ################################################\n",
    "    compoundData = pd.merge(\n",
    "        compoundData,\n",
    "        dmsoDistance[[\"Condition\", \"Distance\"]],\n",
    "        left_on = [\"Actual name\"],\n",
    "        right_on = [\"Condition\"],\n",
    "        how = \"left\"\n",
    "    )\n",
    "\n",
    "    ################################################################\n",
    "    # Add the number of treatment-centric features for each compound\n",
    "    ################################################################\n",
    "    compoundData = pd.merge(\n",
    "        compoundData,\n",
    "        selectedFeatures[[\"Treatment\", \"Number_Features\"]],\n",
    "        left_on = \"Actual name\",\n",
    "        right_on = \"Treatment\",\n",
    "        how = \"left\"\n",
    "    )\n",
    "    \n",
    "    # Fill the Number_Features to ensure that there are no NaN values\n",
    "    compoundData[\"Number_Features\"] = compoundData[\"Number_Features\"].fillna(0)\n",
    "\n",
    "    ############################################################\n",
    "    # Add in the corrected U scores calculated and their p-value\n",
    "    ############################################################\n",
    "    compoundData = pd.merge(\n",
    "        compoundData,\n",
    "        correctedU,\n",
    "        left_on = [\"Actual name\"],\n",
    "        right_on = [\"Metadata_Treatment\"],\n",
    "        how = \"left\"\n",
    "    )\n",
    "\n",
    "    ##############################\n",
    "    # Drop the unnecessary columns\n",
    "    ##############################\n",
    "    columndToDrop = [\n",
    "        \"Actual name\",\n",
    "        \"moa_type\",\n",
    "        \"CRBN_interaction\",\n",
    "        \"CRBN_dependency\",\n",
    "        \"Group\",\n",
    "        \"Transfer vol. (nL)\",\n",
    "        \"Source conc. (mM)\",\n",
    "        \"Synonym(s)\",\n",
    "        \"Condition\",\n",
    "        \"Metadata_Treatment\",\n",
    "        \"Treatment\"\n",
    "    ]\n",
    "    compoundData = compoundData.drop(columns = columndToDrop)\n",
    "\n",
    "    ##########################\n",
    "    # Export the compound data\n",
    "    ##########################\n",
    "    compoundData.to_csv(output_path, index = False)\n",
    "    print(f\"EXPORTED: Compound data exported at\\n{output_path}\")\n",
    "    \n",
    "    # Report the number of treatments retained\n",
    "    print(f\"Number of treatments/compounds: {len(compoundData)}\")\n",
    "    \n",
    "    # Have a look at how the aggregated calculations look like\n",
    "    print(\"Preview of five random rows from the compound data:\")\n",
    "    display(compoundData.sample(n = 5))\n",
    "    \n",
    "    return\n",
    "\n",
    "aggregateCalculations()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b5caf6-406c-4b36-96ac-e9a1eb365af5",
   "metadata": {},
   "source": [
    "## 4.1| Profile interpretations using the \"global\" features and the RKO WT data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18ea7f2-fd75-4eca-8fa2-97d01a205396",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Quality check on the CPA data using \"global\" strategy\n",
    "I carried out two feature selection strategies namely \"global\" for RKO WT and \"treatment-centric\". The \"global\" strategy compares features from all treatments. It is typically used in analysis for cell morphological profiling data. In this sub-section, I'll carry out some of the general data visualizations/interpretations used.\n",
    "\n",
    "I'll start with checking the morphology and the DMSO controls. These controls are present across all plates and should NOT differ drastically if the robust Z standardization (i.e. spherical transformation) was sufficient to negate plate effects (complete negation is not necessary).\n",
    "\n",
    "The morphology controls I've used are recommended by the [Cell Painting-JUMP consortium](https://github.com/jump-cellpainting/JUMP-Target). AMG-900 is excluded from morphological profile assembly as it causes cells to have multiple nuclei (which cannot be handled by the profile assembly approach). The remaining controls can be split into strong and mild morphology perturbers.\n",
    "\n",
    "TC-S-7004, FK-866, NVS-PAK-1 and LY2109761 are strong morphology perturbers, while Quinidine, Aloxistatin and Dexamethasone are mild morphology perturbers.\n",
    "\n",
    "In my experience, strong morphology perturbers form tight clusters which tend to be distinct from that of DMSO controls on 2D UMAPs. Mild morphology perturbers form looser clusters that tend to overlap with DMSO controls on 2D UMAP. The intuition is strong morphology perturbers induce a morphology that isn't usually observed in untreated cells, while mild morphology perturbers shift the distribution of morphology observed in untreated cells toward certain morphology/ies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f842a9aa-902a-4cf2-b8d7-2af5658df1fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_UMAPforTreatmentByPlate(\n",
    "    compounds = [\n",
    "        \"DMSO\",\n",
    "        \"TC-S-7004\",\n",
    "        \"FK-866\",\n",
    "        \"NVS-PAK-1\",\n",
    "        \"LY2109761\",\n",
    "        \"Quinidine\",\n",
    "        \"Aloxistatin\",\n",
    "        \"Dexamethasone\"\n",
    "    ],\n",
    "    profile = pd.read_csv(profile_path),\n",
    "    selectedFeatures = pd.read_csv(selectedFeatures_path),\n",
    "    cell = \"c662_rko_wt\",\n",
    "    output_path = \"\",\n",
    "    suptitle = \"Controls color-coded by plate for RKO WT\",\n",
    "    fontsize = 12,\n",
    "    markerSize = 50,\n",
    "    labelColor = \"black\",\n",
    "    nrows = 2,\n",
    "    figsize = (12, 6)\n",
    "):\n",
    "    # Trim down the profile to the relevant cell line\n",
    "    profile = profile[profile[\"Metadata_Cell\"] == cell].reset_index(drop = True)\n",
    "    \n",
    "    # Retrieve the features selected for the given cell line using the global selection strategy\n",
    "    features = selectedFeatures[selectedFeatures[\"Selection_Strategy\"] == f\"global_{cell}\"].reset_index(drop = True)\n",
    "    features = features.loc[0, \"Features_Selected\"]\n",
    "    features = features.split(\" \")\n",
    "    \n",
    "    # Reduce the profile to 2 UMAP dimensions using \n",
    "    reducer = UMAP()\n",
    "    matrix = profile[features].values\n",
    "    embedding = reducer.fit_transform(matrix)\n",
    "    embedding = pd.DataFrame(embedding, columns = [\"UMAP1\", \"UMAP2\"])\n",
    "    reducedProfile = profile[[\"Metadata_Treatment\", \"Metadata_Cell\", \"UpdatedImageNumber\", \"Metadata_Plate\"]].copy(deep = True)\n",
    "    reducedProfile = pd.concat([reducedProfile, embedding], axis = 1)\n",
    "    \n",
    "    # Set the format of the subplots and the figsize\n",
    "    ncols = int(len(compounds)/nrows)\n",
    "    fig, axes = plt.subplots(ncols = ncols, nrows = nrows, figsize = figsize)\n",
    "    \n",
    "    # For each compound provided by the user\n",
    "    for i, compound in enumerate(compounds):\n",
    "        \n",
    "        # Track which subplot axes to use\n",
    "        rowPosition = 0\n",
    "        colPosition = i\n",
    "        if i + 1> ncols:\n",
    "            rowPosition += 1\n",
    "            colPosition = i - rowPosition*ncols\n",
    "        \n",
    "        # Trim the redcuedProfile to just one compound\n",
    "        compound_reducedProfile = reducedProfile[reducedProfile[\"Metadata_Treatment\"] == compound].reset_index(drop = True)\n",
    "\n",
    "        # Plot the data related to the compound as colored points over a backdrop of grey points\n",
    "        # which constitutes the remaining compounds that are not part of the subset\n",
    "        p = sns.scatterplot(\n",
    "            ax = axes[rowPosition, colPosition],\n",
    "            data = reducedProfile,\n",
    "            x = \"UMAP1\",\n",
    "            y = \"UMAP2\",\n",
    "            color = \"lightgrey\",\n",
    "            s = markerSize\n",
    "        )\n",
    "        p = sns.scatterplot(\n",
    "            ax = axes[rowPosition, colPosition],\n",
    "            data = compound_reducedProfile,\n",
    "            x = \"UMAP1\",\n",
    "            y = \"UMAP2\",\n",
    "            hue = \"Metadata_Plate\",\n",
    "            s = markerSize\n",
    "        )\n",
    "        \n",
    "        # Set the plot title\n",
    "        axes[rowPosition, colPosition].set_title(compound, fontsize = fontsize, color = labelColor)\n",
    "\n",
    "        # Aesthetics to suit a black background\n",
    "        p.tick_params(color = labelColor, labelcolor = labelColor)\n",
    "        for spine in p.spines.values():\n",
    "            spine.set_edgecolor(labelColor)\n",
    "            \n",
    "    # Set the shared legend\n",
    "    handles, labels = axes[rowPosition, colPosition].get_legend_handles_labels()\n",
    "    fig.legend(\n",
    "        handles,\n",
    "        labels,\n",
    "        # title = \"Plate ID\",\n",
    "        # title_fontsize = fontsize,\n",
    "        loc = \"lower left\",\n",
    "        bbox_to_anchor = (0.9, 0.1), # <-- might need to manually change this if figsize is different\n",
    "        ncol = 1,\n",
    "        frameon = False,\n",
    "        fontsize = fontsize,\n",
    "        labelcolor = labelColor\n",
    "    )\n",
    "    \n",
    "    # Only label the x and y axes for the outermost plots\n",
    "    # and remove the legends of the subplots\n",
    "    for ax in axes.flat:\n",
    "        ax.label_outer()\n",
    "        ax.get_legend().remove()\n",
    "        \n",
    "    # Set the suptitle i.e. the title for the whole set of plots\n",
    "    plt.suptitle(suptitle, fontsize = fontsize)\n",
    "\n",
    "    # Save the plot if the user has provided an output_path\n",
    "    savePlot(output_path)\n",
    "\n",
    "    # Display the plot\n",
    "    plt.show()\n",
    "    \n",
    "    return\n",
    "\n",
    "visualize_UMAPforTreatmentByPlate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe82433b-6249-4485-9765-15346c3d9d9d",
   "metadata": {},
   "source": [
    "The controls (which are present across all plates) seem to cluster pretty nicely more by compound than by plate. This clustering pattern indicates that the data handling is sufficient for plate effect correction.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
    "\n",
    "TC-S-7004, FK-866 and NVS-PAK-1 are strong perturbants that induce a pronounced effect in RKO WT that is not observed in DMSO-treated cells. The other morphology controls seem to push cells toward a specific phenotype that has some overlapping features with DMSO-treated cells.\n",
    "\n",
    "Note: I have observed in other instances that the morphological controls might exhibit different clustering patterns depending on the test compounds used. For example, if there are many cytotoxic compounds screened in the Cell Painting assay, TC-S-7004 (which also kills cells) may cluster with the cytotoxic compounds. In this case, many compounds screened are NOT cytotxic.\n",
    "\n",
    "<a name=\"cite_note-1\"></a>[[1]](#cite_ref-1) If the data handling is insufficient for plate effect correction (or batch effect correction), you could use other approaches such as ZCA-sphering (aka Whitening). The Carpenter-Singh lab has an [implementation for ZCA-sphering](https://github.com/broadinstitute/DeepProfilerExperiments/blob/master/profiling/profiling.py#L5)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccec4c5a-61d0-436c-a875-370ac30d779a",
   "metadata": {},
   "source": [
    "### Overview of morphological perturbation strength\n",
    "The difference in morphological perturbation between compound-treated and DMSO-treated cells was calculated in 📜 3a_execute_distanceCalculation.sh. I will plot the results to have an overview of the morphological perturbation strength observed for all compounds screened in the isogenic CPA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e18c3475-5c92-4a21-9954-573f1a9ed5dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_661232/2849148777.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  compoundData[\"color\"] = compoundData[\"Label\"].map(label2color_dict)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def prepFigure(\n",
    "    compoundData = pd.read_csv(compoundData_path),\n",
    "    label2color_dict = label2color_dict,\n",
    "    output_path = \"\",\n",
    "    cpaExamples = cpaExamples,\n",
    "    controlsOnly = False,\n",
    "    fontsize = 12\n",
    "):\n",
    "    \n",
    "    # Discard the SuFeX IMiDs if set to controlsOnly == True and set the width of the bars\n",
    "    width = 1.0\n",
    "    if controlsOnly == True:\n",
    "        compoundData = compoundData[compoundData[\"Label\"]!=\"IMiD\"]\n",
    "        width = 0.4\n",
    "    \n",
    "    # Discard DMSO controls since the distance is 0, which would not appear on the distance plot\n",
    "    compoundData = compoundData[compoundData[\"Compound\"] != \"DMSO\"]\n",
    "    \n",
    "    # Map the categories to their respective colors\n",
    "    compoundData[\"color\"] = compoundData[\"Label\"].map(label2color_dict)\n",
    "    \n",
    "    # Sort the treatmentCalculations by distance in descending order\n",
    "    compoundData = compoundData.sort_values(by = \"Distance\", ascending = False).reset_index(drop = True)\n",
    "    \n",
    "    # Plot the distances as a barplot\n",
    "    p = sns.barplot(\n",
    "        data = compoundData,\n",
    "        x = compoundData.index + 1,\n",
    "        y = \"Distance\",\n",
    "        width = width,\n",
    "        palette = compoundData[\"color\"]\n",
    "    )\n",
    "    \n",
    "    # Label the examples\n",
    "    if len(cpaExamples) > 0:\n",
    "        for x in compoundData[compoundData[\"Compound\"].isin(cpaExamples)].index:\n",
    "            plt.annotate(\n",
    "                text = compoundData.loc[x, \"Compound\"],\n",
    "                xy = (x, compoundData.loc[x, \"Distance\"] + 0.05)\n",
    "            )\n",
    "\n",
    "    # Make the x-axis look nicer and set the location of the legend\n",
    "    new_ticks = [i.get_text() for i in p.get_xticklabels()]\n",
    "    if controlsOnly != True:\n",
    "        interval = 20\n",
    "        bbox_to_anchor = (0.50, 0.70)\n",
    "    else:\n",
    "        interval = 5\n",
    "        bbox_to_anchor = (0.45, 0.72)\n",
    "    start_index = interval -1\n",
    "    plt.xticks(range(0, len(new_ticks), interval), [1] + new_ticks[start_index::interval], fontsize = fontsize)\n",
    "    plt.xlabel(\"Treatments ranked by\\nmorphological perturbation strength\", fontsize = fontsize)\n",
    "\n",
    "    # Make the y-axis look nicer\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.ylabel(\"Morphological\\nperturbation strength\", fontsize = fontsize)\n",
    "        \n",
    "    # Add the legend\n",
    "    legend_elements = []\n",
    "    for label, color in label2color_dict.items():\n",
    "        if label != \"IMiD\":\n",
    "            label = f\"{label} control\"\n",
    "        if controlsOnly == True and label == \"IMiD\":\n",
    "            continue\n",
    "        element = Patch(facecolor = color, label = label)\n",
    "        legend_elements.append(element)\n",
    "    p.legend(\n",
    "        handles = legend_elements,\n",
    "        loc = \"lower left\", bbox_to_anchor = bbox_to_anchor, ncol = 1, frameon = False,\n",
    "    )\n",
    "\n",
    "    # Save the plot if the user has provided an output_path\n",
    "    savePlot(output_path)    \n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "       \n",
    "    return\n",
    "prepFigure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8834b90b-ac37-4bf8-b078-835c364b92a9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 4.2| Profile interpretations using the \"treatment-centric\" features and data from isogenic RKO cell lines\n",
    "I developed the \"treatment-centric\" selection strategy, which selects features that correlate with the CRBN expression of cell lines (see 📜 profile_interpretation_modules.py for more details)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94a3f1fc-23d5-482a-836d-f5b32503deb9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Distribution of features selected by treatment-centric selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a50af03f-a3ba-49e6-8b5c-a1e2ae55d530",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def prepFigure(\n",
    "    compoundData = pd.read_csv(compoundData_path),\n",
    "    selectedFeatures = pd.read_csv(selectedFeatures_path),\n",
    "    label2color_dict = label2color_dict,\n",
    "    output_path = \"\",\n",
    "    cpaExamples = [],\n",
    "    controlsOnly = False,\n",
    "    fontsize = 12\n",
    "):\n",
    "    # Discard the SuFeX IMiDs if set to controlsOnly == True and set the width of the bars\n",
    "    width = 1.0\n",
    "    if controlsOnly == True:\n",
    "        compoundData = compoundData[compoundData[\"Label\"]!=\"IMiD\"]\n",
    "        width = 0.4\n",
    "    \n",
    "    # Map the categories to their respective colors\n",
    "    compoundData[\"color\"] = compoundData[\"Label\"].map(label2color_dict)\n",
    "    \n",
    "    # Sort the treatmentCalculations by distance in descending order\n",
    "    compoundData = compoundData.sort_values(by = \"Number_Features\", ascending = False).reset_index(drop = True)\n",
    "    \n",
    "    # Plot the distances as a barplot\n",
    "    p = sns.barplot(\n",
    "        data = compoundData,\n",
    "        x = compoundData.index + 1,\n",
    "        y = \"Number_Features\",\n",
    "        width = width,\n",
    "        palette = compoundData[\"color\"]\n",
    "    )\n",
    "    \n",
    "    # Label the examples\n",
    "    if len(cpaExamples) > 0:\n",
    "        for x in compoundData[compoundData[\"Compound\"].isin(cpaExamples)].index:\n",
    "            plt.annotate(\n",
    "                text = compoundData.loc[x, \"Compound\"],\n",
    "                xy = (x, compoundData.loc[x, \"Number_Features\"] + 5)\n",
    "            )\n",
    "\n",
    "    # Make the x-axis look nicer and set the location of the legend\n",
    "    new_ticks = [i.get_text() for i in p.get_xticklabels()]\n",
    "    if controlsOnly != True:\n",
    "        interval = 20\n",
    "        bbox_to_anchor = (0.50, 0.70)\n",
    "    else:\n",
    "        interval = 5\n",
    "        bbox_to_anchor = (0.45, 0.72)\n",
    "    start_index = interval -1\n",
    "    plt.xticks(range(0, len(new_ticks), interval), [1] + new_ticks[start_index::interval], fontsize = fontsize)\n",
    "    plt.xlabel(\"Treatments ranked by\\nnumber of treatment-centric features\", fontsize = fontsize)\n",
    "\n",
    "    # Make the y-axis look nicer\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.ylabel(\"Number of treatment-centric features\", fontsize = fontsize)\n",
    "        \n",
    "    # Add the legend\n",
    "    legend_elements = []\n",
    "    for label, color in label2color_dict.items():\n",
    "        if label != \"IMiD\":\n",
    "            label = f\"{label} control\"\n",
    "        if controlsOnly == True and label == \"IMiD\":\n",
    "            continue\n",
    "        element = Patch(facecolor = color, label = label)\n",
    "        legend_elements.append(element)\n",
    "    p.legend(\n",
    "        handles = legend_elements,\n",
    "        loc = \"lower left\", bbox_to_anchor = bbox_to_anchor, ncol = 1, frameon = False,\n",
    "    )\n",
    "\n",
    "    # Save the plot if the user has provided an output_path\n",
    "    savePlot(output_path)    \n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "       \n",
    "    return\n",
    "prepFigure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132ac2c0-af98-4933-9a8f-a6751aeea2e0",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Scatterplot: p-value against corrected U scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c8339017-9e6c-4f13-bdca-24ed219699ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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UFf7xQkRUZVT4J26fPn2QmpqKyMhIs/Lw8HC4u7ujdevW+Z7r7u6OkydP5trA9MiRIwCA2rVrl37ARERERJVMhd8T6dGjB7p27YrRo0cjOTkZ3t7eiIiIwK5du7BhwwZ59G3EiBEIDw/H1atX4enpCQB44403EBoaihdffBGjRo2CtbU19u3bhw8//BBdunRB06ZNK7JrRFXOwyPjvr6+HKUjIionFZ7QAcCWLVswY8YMzJ49GwkJCWjYsCEiIiIwcOBAuY7RaITRaIQQQi4bP348atWqhY8//hgjR45ERkYGvLy8MGfOHLzxxhsV0RWiKs1gMODbb78F8GDhkUajqeCIiIiqBkk8nCFVYcnJyXBwcEBSUhLs7e0rOhwii6TX67FgwQIATOgqA36uEVUdvB9CREREZOGY0BERERFZOCZ0RERERBaOCR0RERGRhWNCR0RERGThKsW2JUT0ZFAqlejdu7f8NRERlQ8mdERUapRKJQICAio6DCKiKoe3XImIiIgsHEfoiKjUmEwmXLlyBQDg7e3NR38REZUTftoSUakxGAyIiIhAREQEDAZDRYdDRFRlMKEjIiIisnBM6IiIiIgsHBM6IiIiIgvHhI6IiIjIwjGhIyIiIrJwTOiIiIiILBz3oSOiUqNUKtGjRw/5ayIiKh9M6Iio1CiVSrRq1aqiwyAiqnJ4y5WIiIjIwnGEjohKjclkwo0bNwAAderU4aO/iIjKCT9tiajUGAwGhIeHIzw8nI/+IiIqR0zoiIiIiCwcEzoiIiIiC8eEjoiIiMjCMaEjIiIisnBM6IiIiIgsHBM6IiIiIgvHfeiIqNQolUp06dJF/pqIiMpHsUfoLl68iEGDBsHNzQ0ajQanT58GAMydOxfR0dGlFiARWQ6lUol27dqhXbt2TOiIiMpRsRK6M2fOoGXLljh48CA6duwIo9EoH0tNTcVnn31WpPZSU1MxceJEuLu7Q6fTISAgAJs3by70+d999x06dOgAe3t72NjYwM/PD6tWrSpSDERERESWqlgJ3bRp0+Dv748rV65g/fr1EELIx1q1aoUTJ04Uqb2+ffsiPDwcc+bMwc6dO9GyZUsMGjQImzZteuy5CxcuRN++fdG4cWN8/fXX2LZtG8aMGQO9Xl/kfhFRyZhMJty8eRM3b96EyWSq6HCIiKqMYs2hO3ToEDZs2ABra2uz0TkAqFmzJu7cuVPotnbs2IE9e/Zg06ZNGDRoEAAgMDAQ169fx5QpUzBgwIB8b92cOnUKM2bMwIIFCzB16lS5vHPnzsXoFRGVlMFgwBdffAEAmD59OjQaTQVHRERUNRRrhE4Ike8H9f3796HVagvdVlRUFGxtbdGvXz+z8uDgYNy6dQvHjh3L99xPPvkEWq0W48ePL/T1iIiIiJ40xUro/P39ERUVleexXbt2oXnz5oVu69y5c/D19YVKZT5Y6O/vLx/Pz08//QRfX19ERkaiQYMGUCqVqF27NqZNm/bYW65ZWVlITk42exERERFZomLdcp0wYQJeeeUV2NjY4NVXXwUA3LhxA/v378eaNWvw7bffFrqt+Ph41K1bN1e5k5OTfDw/N2/exD///IPQ0FC8++67aNSoEfbt24eFCxciLi4OGzduzPfcBQsWYO7cuYWOk4iIiKiyKlZCN2DAAFy9ehVhYWFYtmwZAOA///kPVCoV5s6dixdffLFI7UmSVKxjJpMJKSkpiIiIwMCBAwE8mH+XlpaGJUuWYO7cufD29s7z3OnTp2PSpEny98nJyfDw8ChS3ERERESVQbE3Fn777bcxdOhQ7N69G3///TdcXFzQrVs3eHp6FqkdZ2fnPEfhEhISAPw7UpffuXfu3EG3bt3Mynv06IElS5bg9OnT+SZ0Wq22SHP9iIiIiCqrEj0ponbt2hgxYkSJAmjSpAkiIiJgMBjM5tGdPXsWANC4ceN8z/X3989zRW3ONioKBZ9sRkRERE++YmU8N27ceOyrsPr06YPU1FRERkaalYeHh8Pd3R2tW7fO99z//Oc/AICdO3eale/YsQMKhQItW7YsQq+IqKSUSiU6dOiADh068EkRRETlqFgjdF5eXgXObQOQa3+6/PTo0QNdu3bF6NGjkZycDG9vb0RERGDXrl3YsGGD/EthxIgRCA8Px9WrV+XbusHBwVi5ciXGjBmDe/fuoVGjRti7dy+WL1+OMWPGFPn2LxGVjFKpRMeOHSs6DCKiKqdYCd2aNWtyJXT37t3Dtm3b8Ndff2HmzJlFam/Lli2YMWMGZs+ejYSEBDRs2NBsoQPwIEE0Go1mT6VQq9XYs2cP3n77bcyfPx8JCQl46qmnsHDhQrMFD0RERERPMkk8nCGVgn79+qFJkyaYPXt2aTZb5pKTk+Hg4ICkpCTY29tXdDhEFkkIgX/++QcAUL169ceO5FPZ4ucaUdVR6qsGgoKC5Ef/EFHVkp2djRUrVmDFihXIzs6u6HCIiKqMUk/oDAYDEhMTS7tZIiIiIspHibYteVh2djZ+//13zJkzB02bNi2tZomIiIjoMYqV0CkUinznxjg6OmL37t0lCoqIiIiICq9YCd3s2bNzJXQ6nQ5eXl7o2bMn7OzsSiU4IiIiInq8YiV0YWFhpRwGERERERUXn41FREREZOEKPUL3zjvvFLpRSZIwa9asYgVERJZLqVSiTZs28tdERFQ+Cr2xcFEedC9JUqEf/VVZcANOInrS8HONqOoo9AidyWQqyziIiIiIqJhKbR86IiIhBJKSkgAADg4OfPQXEVE54aIIIio12dnZWLp0KZYuXcpHfxERlaNij9D99NNPWLZsGWJiYpCRkWF2TJIkXL16tcTBEREREdHjFWuE7pdffkHnzp2RlJSEmJgYNGzYELVq1cKNGzegUqnw3HPPlXacRERERJSPYiV0c+bMQXBwMHbt2gUAeO+99/Dzzz/j9OnTSE1NRd++fUs1SCIiIiLKX7ESunPnzqFPnz7yhOecLUr8/f0xa9asIu1ZR0REREQlU6yELj09Hba2tlAoFNBqtbh37558rGHDhrhw4UKpBUhEREREBStWQlenTh38/fffAIBGjRrhhx9+kI8dPHgQzs7OpRMdERERET1WsVa5duzYEQcOHMB///tfhISEYMyYMYiJiYFWq8WPP/6IN998s7TjJCILoFAo0KJFC/lrIiIqH4V+9NfD7t27h4SEBPj4+AAAPvroI2zcuBGSJOGFF17AjBkzoFarSz3YssRH5BDRk4afa0RVR7ESuicRP/iI6EnDzzWiqqNY90Q++eQT3L9/v7RjISILJ4RAWloa0tLSwL8ViYjKT7ESutDQULi7u2PAgAHYvXs3P7iJCMCDR38tXrwYixcv5qO/iIjKUbESupiYGISGhuLQoUPo2bMnPDw8MGPGDFy+fLm04yMiIiKixyhWQtegQQMsWrQIN27cwPbt29G2bVt89NFHaNiwIdq3b4+1a9eWdpxERERElI8S7SugUCjQs2dPfP3117h9+zb+7//+D9evX0dISEhpxUdEREREj1EqG0UlJyfj66+/xvr16/HXX39Bp9OVRrNEREREVAglSuj27duHIUOGwM3NDa+//joAYOXKlbh9+3apBEdEREREj1esJ0XMmTMH4eHhiIuLQ82aNTFu3DgEBwejYcOGpR0fERERET1GsUboFi5ciObNm+O7775DXFwcFi1aVKJkLjU1FRMnToS7uzt0Oh0CAgKwefPmIrczc+ZMSJKExo0bFzsWIio+hUKBpk2bomnTpnz0FxFROSrWCN3Nmzfh4uJSakH07dsXJ06cwMKFC+Hj44NNmzZh0KBBMJlMeOWVVwrVxpkzZ7B48WLUrFmz1OIioqJRqVR4+eWXKzoMIqIqp8SP/jKZTPD29sb27dvh5+dX5PN37NiBXr16yUlcjueffx7nz5/HjRs3oFQqC2zDYDCgZcuWeO655/Dbb7/h3r17OHfuXJHi4CNyiOhJw881oqqjxPdEhBCIjY1FVlZWsc6PioqCra0t+vXrZ1YeHByMW7du4dixY49tY+HChUhISMC8efOKFQMRlQ4hBPR6PfR6PZ8gQ0RUjip8ksu5c+fg6+sLlcr87q+/v798vCAXLlzAe++9hxUrVsDW1rbQ183KykJycrLZi4hKJjs7GwsWLMCCBQv46C8ionJU4QldfHw8nJyccpXnlMXHx+d7rslkwvDhw9G3b1/07NmzSNddsGABHBwc5JeHh0fRAiciIiKqJEqc0CkUCsyZMwfu7u7FbkOSpGId++ijj3D58mUsWbKkyNecPn06kpKS5FdcXFyR2yAiIiKqDIq1yhUAjEYjvv76a0RHRyM+Ph6zZ89GYGAg+vXrl+v2aUGcnZ3zHIVLSEgAgDxH7wDgxo0bmD17NhYuXAiNRoPExEQADxZImEwmJCYmQqvVwsrKKs/ztVottFptoeMkIiIiqqyKNUJ37949tG7dGoMHD8a6detw+PBhrFu3DoMHD0br1q1x7969QrfVpEkTxMTEwGAwmJWfPXsWAPLdU+7PP/9ERkYGJkyYAEdHR/l16NAhxMTEwNHREdOnTy9O94iIiIgsSrESujfeeAOXLl3Cxo0bkZGRgdu3byMjIwMbNmzA5cuX8cYbbxS6rT59+iA1NRWRkZFm5eHh4XB3d0fr1q3zPC8gIADR0dG5Xk2bNoWXlxeio6Mxbty44nSPiIiIyKIU65br9u3b8d5775ntG6dUKvHKK6/g7t27CAsLK3RbPXr0QNeuXTF69GgkJyfD29sbERER2LVrFzZs2CDvQTdixAiEh4fj6tWr8PT0RLVq1dCxY8dc7VWrVg0GgyHPY0RERERPomKN0Akh8t1EuHHjxkXef2rLli149dVXMXv2bHTv3h3Hjh1DREQEBg8eLNcxGo0wGo3c24qoElMoFGjUqBEaNWrER38R5WPdunWoVq1aRYdRaF5eXsVafFgVhYWFISAgoEKuXawRui5dumDv3r3o0qVLrmN79uwp8uiYra0tli5diqVLl+ZbZ926dVi3bt1j2zpw4ECRrk1EpUelUuXaJJwsy+vXb5fr9T7zdCvyOXfu3MG8efPwww8/4ObNm6hRowYCAgIwceJEdO7cGcCDJOT69esAAJ1OB09PT4wYMQKTJ0+Wd0+IjY3FU089JberVqtRp04dBAUFYcaMGXK9sLAwzJ07F6NGjcJnn30m1z9z5gyefvppXLt2DV5eXsV9C6qkjh07IiAgoFIkipIkISoqyuIfW1ishG7WrFno27cvjEYjXnnlFbi6uuLOnTvYuHEjtmzZgi1btsirVIH8V6oSEREVRWxsLNq1a4dq1arh/fffh7+/P7Kzs7F7926MHTsWFy9elOu+8847CAkJQWZmJvbu3YvRo0fD3t4eo0aNMmtz79698PPzQ1ZWFn755ReMHDkSbm5uGDFihFxHp9Nh9erVmDRpEnx8fMqtv1Q5ZGdnQ61WV3QYBSrWPZFmzZohNjYWH374IVq0aIHatWujRYsW+OijjxAbG4vmzZujevXq8ouIiKg0jBkzBpIk4fjx4/jvf/8LHx8f+Pn5YdKkSTh69KhZXTs7O7i6usLLywsjR46Ev78/fvzxx1xtOjs7w9XVFZ6enhg8eDDatm2L06dPm9Vp0KABAgMDMXPmzCLHvG7dOtSpUwfW1tbo06dPnlt1bd++Hc2bN4dOp0PdunUxd+5cs90fJEnCihUr0KNHD1hZWeGpp57CN998Y9bGzZs3MWDAADg6OsLZ2Rm9e/dGbGysfDwoKAgvv/wyFi9eDDc3Nzg7O2Ps2LFmT3W5e/cuXnzxRfkaGzduzBVrUlISXnvtNdSoUQP29vbo1KkTfvvtN/l4zm3H9evXw8vLCw4ODhg4cCBSUlLkOA4ePIilS5dCkiRIkmQW58OysrIwdepUeHh4QKvVon79+li9erV8/ODBg2jVqhW0Wi3c3Nwwbdo0s/etY8eOCA0NxdSpU+Hk5ARXV1ezef45I6t9+vSBJEny9zl9WLNmDerWrQutVgshBG7cuIHevXvD1tYW9vb26N+/P/7+++88Yy9vxRqhmz17doEb/hJR1aTX67FgwQIADzbv1mg0FRwRPUkSEhKwa9cuzJs3DzY2NrmO5zcvTQiBgwcPIiYmBvXr1y/wGidPnsTp06cxbNiwXMcWLlyIli1b4sSJE2jZsmWhYj527BiGDx+O+fPno2/fvti1axfmzJljVmf37t0YMmQIli1bhvbt2+Pq1at47bXXAMCs7qxZs7Bw4UIsXboU69evx6BBg9C4cWP4+voiPT0dgYGBaN++PX766SeoVCq899576N69O37//Xf5/2J0dDTc3NwQHR2NK1euYMCAAQgICEBISAiAB8lWXFwc9u/fD41Gg9DQUNy9e9fsvezVqxecnJywY8cOODg4YOXKlejcuTP++OMP+Y7c1atXsXXrVnz//fe4f/8++vfvj4ULF2LevHlYunQp/vjjDzRu3BjvvPMOAOQ7+DN06FAcOXIEy5YtQ9OmTXHt2jV5a7SbN2+iZ8+eCAoKwpdffomLFy8iJCQEOp3OLGkLDw/HpEmTcOzYMRw5cgRBQUFo164dunbtihMnTqBGjRpYu3YtunfvLi/EBIArV67g66+/RmRkpFz+8ssvw8bGBgcPHoTBYMCYMWMwYMCAyjHdS5AQQoikpCQBQCQlJVV0KEQWKysrS4SFhYmwsDCRlZVV0eFUecX5XBsVe6tcX0Vx7NgxAUBs2bLlsXU9PT2FRqMRNjY2Qq1WCwBCp9OJQ4cOyXWuXbsmAAgrKyuzeq+99ppZW3PmzBFNmzYVQggxcOBA0alTJyGEEL/++qsAIK5du5ZvHIMGDRLdu3c3KxswYIBwcHCQv2/fvr2YP3++WZ3169cLNzc3+XsA4vXXXzer07p1azF69GghhBCrV68WDRo0ECaTST6elZUlrKysxO7du4UQQgwbNkx4enoKg8Eg1+nXr58YMGCAEEKIS5cuCQDi6NGj8vGYmBgBQHz88cdCCCH27dsn7O3tRWZmplks9erVEytXrpTfL2tra5GcnCwfnzJlimjdurX8fYcOHcSECRPyeMf+lRPPnj178jz+9ttv5+rz8uXLha2trTAajfJ1nn32WbPzWrZsKd566y35ewAiKirKrM6cOXOEWq0Wd+/elct+/PFHoVQqxY0bN+Sy8+fPCwDi+PHj8nk5/1bKG5ehERGRRRD/2+WgsHeIpkyZgjNnzuDgwYMIDAzEjBkz0LZt21z1vvrqK5w5cwa//fYbvvrqK3z33XeYNm1anm2+9957+Pnnn/O8devn5wdbW1vY2tqiR48eAICYmBi0adPGrN6j3586dQrvvPOOfK6trS1CQkJw+/ZtpKen53temzZtEBMTI7dx5coV2NnZyW04OTkhMzMTV69eNYvx4VEoNzc3eQQuJiYGKpUKLVq0kI83bNjQbOTz1KlTSE1NhbOzs1m8165dM7uOl5cX7Ozs8rxOYZ05cwZKpRIdOnTI83jOe/vwv4d27dohNTUVf/31l1zm7+9vdl5hY/H09DQbOYyJiYGHh4fZs98bNWqEatWqyT+HilTsR38RERGVp/r160OSJMTExBRqRaKLiwu8vb3h7e2NyMhIeHt745lnnsm1Q4OHhwe8vb0BAL6+vvjzzz8xa9YshIWFQafTmdWtV68eQkJCMG3aNLO5XACwY8cOeT5azmMnRSG22jKZTJg7dy769u2b69ij139UTjJjMpnQvHnzPOe8PZyUPDqxX5IkmEwms1gLSphNJhPc3NzyvMX4cOJX0HUKK79Hd+YQQuSKNa8+FDeWR2/r53W9gsrLGxM6IiKyCE5OTujWrRuWL1+O0NDQXL9wExMT851H5+joiPHjx2Py5Mn49ddfC/wFrFQqYTAYoNfr80yoZs+ejXr16mHz5s1m5Z6enrnqNmrUKNdijUe/b9asGS5duiQnlfk5evQohg4davb9008/Lbfx1VdfyQsVisPX1xcGgwEnT55Eq1atAACXLl2Sn5Wec507d+5ApVKVaKsWjUYDo9FYYJ0mTZrAZDLh4MGDeW6T1qhRI0RGRpolVIcPH4adnR1q1apV6FjUavVjY8m53o0bNxAXFyeP0l24cAFJSUnw9fUt9PXKCm+5ElVyQgiYTCYYjUZkG7JhNBphMpm4yTZVSZ9++imMRiNatWqFyMhIXL58GTExMVi2bFmuW5KPGjt2LC5dupTrUZPx8fG4c+cO/vrrL+zcuRNLly5FYGBgvolRzZo1MWnSJCxbtuyx8YaGhmLXrl14//338ccff+CTTz7Brl27zOrMnj0bX375JcLCwnD+/HnExMTgq6++yrWi9ptvvsGaNWvwxx9/YM6cOTh+/Lj8iMvBgwfDxcUFvXv3xs8//4xr167h4MGDmDBhgtntx4I0aNAA3bt3R0hICI4dO4ZTp05h5MiRZiNlXbp0QZs2bfDyyy9j9+7diI2NxeHDhzFz5kycPHmyUNcBHtySPXbsGGJjY3Hv3r08R8y8vLwwbNgwDB8+HFu3bsW1a9dw4MABfP311wAerHiOi4vD+PHjcfHiRXz33XeYM2cOJk2aVKSNzb28vLBv3z7cuXMH9+/fz7dely5d4O/vj8GDB+P06dM4fvw4hg4dig4dOpjdpq4wFTJzrxLiogiqjIxGo0jPSBV37t4UN27+Kb/u3L0p0jNS5Ym/lQUXRVQuT+rn2q1bt8TYsWPlhQ+1atUSL730koiOjpbreHp6yhP5HxYSEiL8/PyE0WiUF0XkvJRKpahdu7YICQkxmwyf10T35ORk4eLi8thFEUI8WLBQu3ZtYWVlJV588UWxePFis0URQgixa9cu0bZtW2FlZSXs7e1Fq1atxKpVq+TjAMTy5ctF165dhVarFZ6eniIiIsKsjdu3b4uhQ4cKFxcXodVqRd26dUVISIj88x82bJjo3bu32TkTJkwQHTp0MGujV69eQqvVijp16ogvv/wy13uZnJwsxo8fL9zd3YVarRYeHh5i8ODB8mKBvN6vjz/+WHh6esrfX7p0STzzzDPCysqqwPcwIyNDvPHGG8LNzU1oNBrh7e0t1qxZIx8/cOCAaNmypdBoNMLV1VW89dZbIjs7Wz6e1+KL3r17i2HDhsnfb9u2TXh7ewuVSiXHmN/ihuvXr4uXXnpJ2NjYCDs7O9GvXz9x584d+XhFLoqQhOCf+QCQnJwMBwcHJCUlFXu4mqg0mUwmJKckIiUtKd86djYOsLerVmkes2UwGOS/nvv37w+VirM6KhI/154cT8rTDKjs8NOWqBISQiBLn1FgMgcAKWlJ0Gp10GmtKsekXJUKr7zySkWHQURU5VSOP+uJyIwQAskpBSdzOZJTEzmfjoioiuMIHVElJISAPjurUHX1+iwmdERPOP4fp8dhQkdUCZlE0fZrMgkTlFA+vmIZ0+v1WLx4MQBg8uTJfPQXEVE5YUJHVAkppKLNhihq/bL08IO+iYiofFSe3wJEJJMkCRq1tlB1NRptpVgQQUREFYcJHVElJEkS7O0cClXX3rYaEzoioiqOCR1RJSRJErQaK9jZFJzU2dk4QKvRMaEjIqriOIeOqJJSKBSwt6sGrVaH5NRE6PX/rnrVaLSwt60GrUZXaTYVJiKiisPfBESVmEKhgE5rhepOrnCvWQeuNWrDvWYdVHdyhU5rxWSOqJJbt24dqlWrVuJ2OnbsiIkTJ5a4nfJw4MABSJKExMTEig7FInh5eWHJkiUlbocjdESVnCRJ8i3VyrA1SUEkSYKnp6f8NVme23Mnl+v13OYsLvI5d+7cwbx58/DDDz/g5s2bqFGjBgICAjBx4kR07twZwINfktevXwcA6HQ6eHp6YsSIEZg8ebL8bzM2NhZPPfWU3K5arUadOnUQFBSEGTNmyPXCwsIwd+5cjBo1Cp999plc/8yZM3j66adx7do1eHl55RnrgAED0LNnzyL3kczl/Kx+/fVXBAQEVGgs69atw8SJEytdwsqEjohKjVqtRlBQUEWHQU+w2NhYtGvXDtWqVcP7778Pf39/ZGdnY/fu3Rg7diwuXrwo133nnXcQEhKCzMxM7N27F6NHj4a9vT1GjRpl1ubevXvh5+eHrKws/PLLLxg5ciTc3NwwYsQIuY5Op8Pq1asxadIk+Pj4FDpeKysrWFlZlbzjZHH0en257sXJ+zVERGQxxowZA0mScPz4cfz3v/+Fj48P/Pz8MGnSJBw9etSsrp2dHVxdXeHl5YWRI0fC398fP/74Y642nZ2d4erqCk9PTwwePBht27bF6dOnzeo0aNAAgYGBmDlzZpHiffSWa1hYGAICArB+/Xp4eXnBwcEBAwcOREpKilwnLS0NQ4cOha2tLdzc3PDhhx/malev12Pq1KmoVasWbGxs0Lp1axw4cCDXdbdu3QofHx/odDp07doVcXFxZu1s374dzZs3h06nQ926dTF37lwYDAb5uCRJ+OKLL9CnTx9YW1ujfv362LZtm1kbO3bsgI+PD6ysrBAYGIjY2Nhc8R4+fBjPPfccrKys4OHhgdDQUKSlpcnHvby8MH/+fAwfPhx2dnaoU6cOVq1aJR/PGUl9+umnIUkSOnbsmO97fv78efTq1Qv29vaws7ND+/btcfXqVQCAyWTCO++8g9q1a0Or1SIgIAC7du2Sz42NjYUkSdiyZQsCAwNhbW2Npk2b4siRIwAe3E4ODg5GUlKSfPckLCxM7sN7772HoKAgODg4ICQkBAAQGRkJPz8/aLVaeHl55fnzLA1M6IiIyCIkJCRg165dGDt2LGxsbHIdz2+umhACBw4cQExMDNRqdYHXOHnyJE6fPo3WrVvnOrZw4UJERkbixIkTxYo/x9WrV7F161Z8//33+P7773Hw4EEsXLhQPj5lyhRER0cjKioKP/74Iw4cOIBTp06ZtREcHIxDhw5h8+bN+P3339GvXz90794dly9fluukp6dj3rx5CA8Px6FDh5CcnIyBAwfKx3fv3o0hQ4YgNDQUFy5cwMqVK7Fu3TrMmzfP7Fpz585F//798fvvv6Nnz54YPHgwEhISAABxcXHo27cvevbsiTNnzmDkyJGYNm2a2flnz55Ft27d0LdvX/z+++/46quv8Msvv2DcuHFm9T788EO0aNECv/76K8aMGYPRo0fLI67Hjx8H8GA09fbt29iyZUue7+3Nmzfx3HPPQafTYf/+/Th16hSGDx8uJ6lLly7Fhx9+iMWLF+P3339Ht27d8NJLL5m9bwAwY8YMTJ48GWfOnIGPjw8GDRoEg8GAtm3bYsmSJbC3t8ft27dx+/ZtTJ787zSFDz74AI0bN8apU6cwa9YsnDp1Cv3798fAgQNx9uxZhIWFYdasWVi3bl2e8ZcEEzoiKjV6vR4ffPABPvjgA+j1+ooOh54wV65cgRACDRs2LFT9t956C7a2ttBqtQgMDIQQAqGhobnqtW3bFra2ttBoNGjZsiX69++PoUOH5qrXrFkz9O/fP1fCUlQmkwnr1q1D48aN0b59e7z66qvYt28fACA1NRWrV6/G4sWL0bVrVzRp0gTh4eEwGo3y+VevXkVERAS++eYbtG/fHvXq1cPkyZPx7LPPYu3atXK97OxsfPLJJ2jTpg2aN2+O8PBwHD58WE6O5s2bh2nTpmHYsGGoW7cuunbtinfffRcrV640izcoKAiDBg2Ct7c35s+fj7S0NLmNFStWoG7duvj444/RoEEDDB48ONe0iw8++ACvvPIKJk6ciPr166Nt27ZYtmwZvvzyS2RmZsr1evbsiTFjxsDb2xtvvfUWXFxc5FHH6tWrA/h3NNXJySnP93b58uVwcHDA5s2b0aJFC/j4+CA4OBgNGjQAACxevBhvvfUWBg4ciAYNGmDRokUICAjItShh8uTJ6NWrF3x8fDB37lxcv34dV65cgUajgYODAyRJgqurK1xdXWFrayuf16lTJ0yePBne3t7w9vbGRx99hM6dO2PWrFnw8fFBUFAQxo0bhw8++KDAfyPFwTl0RFSq0tPTKzoEekLlPKC+sAtupkyZgqCgIPzzzz+YMWMGOnXqhLZt2+aq99VXX8HX1xfZ2dk4e/YsQkND4ejoaDZqluO9996Dr68vfvzxR9SoUcPsmJ+fn7wQo3379ti5c2eecXl5ecHOzk7+3s3NDXfv3gXwIFnT6/Vo06aNfNzJyUlOSADg9OnTEELkmsuXlZUFZ2dn+XuVSoUWLVrI3zds2BDVqlVDTEwMWrVqhVOnTuHEiRNmI3JGoxGZmZlIT0+HtbU1AMDf318+bmNjAzs7OznemJgYPPPMM2Y/k4djB4BTp07hypUr2Lhxo1wmhIDJZMK1a9fg6+ub6zo5CVPOdQrrzJkzaN++fZ4jscnJybh16xbatWtnVt6uXTv89ttvZmUPx+Lm5gYAuHv37mP/mHj4/QYevD+9e/fOdb0lS5bAaDRCqSy9hW5M6IgsmBBCfpmECQpJIc/r4CpTetLUr18fkiQhJiYGL7/88mPru7i4yCMlkZGR8Pb2xjPPPIMuXbqY1fPw8IC3tzcAwNfXF3/++SdmzZqFsLAw6HQ6s7r16tVDSEgIpk2bhtWrV5sd27Fjh/ws44IWQjyabEiSBJPJBODfpLUgJpMJSqUSp06dypUQPDxalNP2o3LKTCYT5s6di759++aq83C/SyPeUaNG5Tk6WqdOnUJdp7AKswDl0fdECJGr7OFYHn6/HufRqQB5tV2Y96w4mNARWSiTyYQsfQaSU5Kgz35o02G1FvZ2DtBquE8dPVmcnJzQrVs3LF++HKGhobl+eSYmJuY7j87R0RHjx4/H5MmT8euvvxb4B49SqYTBYIBer8+V0AHA7NmzUa9ePWzevNmsPGfLnpLw9vaGWq3G0aNH5WTn/v37+OOPP9ChQwcADxYGGI1G3L17F+3bt8+3LYPBgJMnT6JVq1YAgEuXLiExMVEeZWrWrBkuXbokJ7PF0ahRI2zdutWs7NHFKc2aNcP58+dLdJ2c1aIP33rOi7+/P8LDw5GdnZ0rQbS3t4e7uzt++eUXPPfcc3L54cOH5feosLE8Lo4cjRo1wi+//GJWdvjwYfj4+JTq6BzAOXREFslkMiE5JRH3Eu6aJXMAoM/Owr2Eu0hOSSzyX7dEld2nn34Ko9GIVq1aITIyEpcvX0ZMTAyWLVuW61bfo8aOHYtLly4hMjLSrDw+Ph537tzBX3/9hZ07d2Lp0qUIDAyEvb19nu3UrFkTkyZNwrJly0qtXzlsbW0xYsQITJkyBfv27cO5c+cQFBRk9seZj48PBg8ejKFDh2LLli24du0aTpw4gUWLFmHHjh1yPbVajfHjx+PYsWM4ffo0goOD8cwzz8jJy+zZs/Hll18iLCwM58+fR0xMDL766qsireR9/fXXcfXqVUyaNAmXLl3Cpk2bck34f+utt3DkyBGMHTsWZ86cweXLl7Ft2zaMHz++0NepUaMGrKyssGvXLvz9999ISkrKs964cePkxR8nT57E5cuXsX79ely6dAnAg9vwixYtwldffYVLly5h2rRpOHPmDCZMmFDoWLy8vJCamop9+/bh3r17BU4zefPNN7Fv3z68++67+OOPPxAeHo5PPvnEbCFFqRGVQEpKipgwYYJwc3MTWq1WNG3aVERERDz2vMjISDFw4EBRr149odPphKenp3jllVfEH3/8UeQYkpKSBACRlJRUnC4QlRuTySTSM1LFjZt/PvaVnpEmTCZTucWWlZUlwsLCRFhYmMjKyiq361LentTPtVu3bomxY8cKT09PodFoRK1atcRLL70koqOj5Tqenp7i448/znVuSEiI8PPzE0ajUVy7dk0AkF9KpVLUrl1bhISEiLt378rnzJkzRzRt2tSsneTkZOHi4iIAiGvXruUb69q1a4WDg0OBbX388cfC09NT/j4lJUUMGTJEWFtbi5o1a4r3339fdOjQQUyYMEGuo9frxezZs4WXl5dQq9XC1dVV9OnTR/z+++9m142MjBR169YVGo1GdOrUScTGxppde9euXaJt27bCyspK2Nvbi1atWolVq1bJxwGIqKgos3McHBzE2rVr5e+3b98uvL29hVarFe3btxdr1qwRAMT9+/flOsePHxddu3YVtra2wsbGRvj7+4t58+bJx/P6eTVt2lTMmTNH/v7zzz8XHh4eQqFQiA4dOuR6r3P89ttv4vnnnxfW1tbCzs5OtG/fXly9elUIIYTRaBRz584VtWrVEmq1WjRt2lTs3LlTPjfn38Svv/4ql92/f18AMPv39frrrwtnZ2cBQI4xv39z3377rWjUqJFQq9WiTp064oMPPjA7nt95RSUJUUY3c4vg+eefx4kTJ7Bw4UL4+Phg06ZN+OKLL7Bx40a88sor+Z7XunVruLq64uWXX0bdunURFxeH+fPnIy4uDkePHoWfn1+hY0hOToaDgwOSkpLy/auMqDIwmUz4J/5OrpG5vGg0WlR3ci23W696vR4LFiwAAEyfPr1cN9Wk3Pi5VnVV1qcZUNmp8Dl0O3bswJ49e7Bp0yYMGjQIABAYGIjr169jypQpGDBgQL73mbdv355rlVGnTp3g5eWFjz/+GF988UWZx09U3oQQhUrmAECvzyqzCbh5kSQJ7u7u8tdERFQ+KnwOXVRUFGxtbdGvXz+z8uDgYNy6dQvHjh3L99xHkzkAcHd3R+3atXPthk30pDCJos2LK2r9klCr1QgJCUFISMhjN3AlIqLSU+EJ3blz5+Dr6wuVynywMGcPmHPnzhWpvT///BPXr19/7O3WrKwsJCcnm72ILIFCKtp/26LWJyLLFxQUxNutVUyFf9LHx8fnueNzTll8fHyh2zIYDBgxYgRsbW3xxhtvFFh3wYIFcHBwkF8eHh5FC5yogkiSBI1aW6i6Go2Wtz6JiKqACk/ogILn2hT2l5EQAiNGjMDPP/+ML7/88rEJ2vTp05GUlCS/eIuWLIUkSbC3cyhUXXvbauWa0GVnZ2PJkiVYsmSJvMEqERGVvQpfFOHs7JznKFzOg3/ze17bw4QQGDlyJDZs2IDw8PBcj9nIi1arhVZbuFEOospEkiRoNVaws3FASlreezEBgJ2NA7QaXbkmdEIIeX+oSrCAnoioyqjwEbomTZogJiYGBoPBrPzs2bMAgMaNGxd4fk4yt3btWnzxxRcYMmRImcVKVFkoFArY21WDi1NNaDTmf5hoNFq4ONWEvV01PimCiKiKqPBP+z59+iA1NTXXzt3h4eFwd3dH69at8z1XCIGQkBCsXbsWK1euRHBwcFmHS1RpKBQK6LRWqO7kCveadeBaozbca9ZBdSdX6LR87BcRUVVS4bdce/Toga5du2L06NFITk6Gt7c3IiIisGvXLmzYsEHeg27EiBEIDw/H1atX5eflhYaGYvXq1Rg+fDiaNGli9vw4rVaLp59+ukL6RFReJEmSb6kqUbrPBSQiIstRKf6E37JlC1599VXMnj0b3bt3x7FjxxAREYHBgwfLdYxGI4xGo9m8nO3btwMA1qxZgzZt2pi9+vTpU+79ICKiqiMoKAgvv/xyidpYt24dqlWrVirx5IiNjYUkSThz5kyptSlJErZu3Vpq7VW0snjfK1qFj9ABDx5GvHTpUixdujTfOuvWrcv1wN/Y2NiyDYyIqIq5va/gLZ9Km1vnj4tUPygoCOHh4Rg1ahQ+++wzs2NjxozBihUrMGzYsFy/L6oSDw8P3L59Gy4uLhUdSqny8vLCxIkTMXHixIoOpVKqFCN0RPRkkCQJ1atXR/Xq1bn/HZUZDw8PbN68GRkZGXJZZmYmIiIiUKdOnRK1LYTItUjP0iiVSri6uubasL8qMBqNMJnK7+k4lQkTOiIqNWq1GmPGjMGYMWP46C8qM82aNUOdOnWwZcsWuWzLli3w8PDINXc6KysLoaGhqFGjBnQ6HZ599lmcOHFCPn7gwAFIkoTdu3ejRYsW0Gq1+PnnnxEWFoaAgACsXLkSHh4esLa2Rr9+/fJ8+sLixYvh5uYGZ2dnjB071mwPxvv372Po0KFwdHSEtbU1evTogcuXLxfYvxUrVqBevXrQaDRo0KAB1q9fb3b84sWLePbZZ6HT6dCoUSPs3bvX7JZoXrdcz58/j169esHe3h52dnZo3749rl69CgA4ceIEunbtChcXFzg4OKBDhw44ffp0gTE+ymQyYdGiRfD29oZWq0WdOnUwb948+fjZs2fRqVMnWFlZwdnZGa+99hpSU1Pl4zm3r/N7Lzt27Ijr16/jjTfeMJs7nHPr9Pvvv0ejRo2g1Wpx/fr1Yr3vlo4JHRERWZzg4GCsXbtW/n7NmjUYPnx4rnpTp05FZGQkwsPDcfr0aXh7e6Nbt27yXqcP11uwYAFiYmLkR09euXIFX3/9NbZv345du3bhzJkzGDt2rNl50dHRuHr1KqKjoxEeHp5relBQUBBOnjyJbdu24ciRIxBCoGfPnvluvB0VFYUJEybgzTffxLlz5zBq1CgEBwcjOjoawIPE6eWXX4a1tTWOHTuGVatWYcaMGQW+Vzdv3sRzzz0HnU6H/fv349SpUxg+fLg8EpmSkoJhw4bh559/xtGjR1G/fn307NkTKSkpBbb7sOnTp2PRokWYNWsWLly4gE2bNqFmzZoAgPT0dHTv3h2Ojo44ceIEvvnmG+zduxfjxo0r9Hu5ZcsW1K5dG++88w5u376N27dvy+elp6djwYIF+OKLL3D+/HnUqFGjyO/7k6DqjccSEZHFe/XVVzF9+nR5NOrQoUPYvHkzDhw4INdJS0vDihUrsG7dOvTo0QMA8Pnnn2PPnj1YvXo1pkyZItd955130LVrV7NrZGZmIjw8HLVr1wYA/N///R969eqFDz/8EK6urgAAR0dHfPLJJ1AqlWjYsCF69eqFffv2ISQkBJcvX8a2bdtw6NAhtG3bFgCwceNGeHh4YOvWrejXr1+ufi1evBhBQUEYM2YMAGDSpEk4evQoFi9ejMDAQPz444+4evUqDhw4IMcwb968XLE/bPny5XBwcMDmzZvlkXMfHx/5eKdOnczqr1y5Eo6Ojjh48CBeeOGFAn4KD6SkpGDp0qX45JNPMGzYMABAvXr18Oyzz8p9zsjIwJdffgkbGxsAwCeffIIXX3wRixYtkhO/gt5LJycnKJVK2NnZyf3OkZ2djU8//RRNmzYFgGK9708CjtARUanJ+WD99NNPn+i/hKniubi4oFevXggPD8fatWvRq1evXIsArl69iuzsbLRr104uU6vVaNWqFWJiYszqtmjRItc16tSpIydzANCmTRuYTCZcunRJLvPz85O31wIANzc33L17FwAQExMDlUpltp+qs7MzGjRokOv6OWJiYsziBYB27drJ9S9dugQPDw+zpKZVq1Z5tpXjzJkzaN++fb7TIO7evYvXX38dPj4+8vPNU1NTcePGjQLbfTjmrKwsdO7cOd/jTZs2lZO5nD4V5b0siEajkUdVc65X1Pf9ScAROiIqNUII/PPPP/LXRGVp+PDh8m275cuX5zqe82/w0QU6QohcZQ8nG/nJOefhcx9NkiRJkifl5/d/IK/r53WdvOo/7ty8WFlZFXg8KCgI//zzD5YsWQJPT09otVq0adMGer2+VNovKObCvpePu/7D7RT3fbd0HKEjIiKL1L17d+j1euj1enTr1i3XcW9vb2g0Gvzyyy9yWXZ2Nk6ePAlfX9/Htn/jxg3cunVL/v7IkSNQKBRmtysL0qhRIxgMBhw7dkwui4+Pxx9//JHv9X19fc3iBYDDhw/L9Rs2bIgbN27g77//lo8/vMgjL/7+/vj555/zHTX/+eefERoaip49e8LPzw9arRb37t0rVB8BoH79+rCyssK+ffvyPN6oUSOcOXMGaWlpctmhQ4eK9F4CD0bijEbjY+sV531/EjChIyIii6RUKhETE4OYmBizW3U5bGxsMHr0aEyZMgW7du3ChQsXEBISgvT0dIwYMeKx7et0OgwbNgy//fabnPT0798/1xyu/NSvXx+9e/dGSEgIfvnlF/z2228YMmQIatWqhd69e+d5zpQpU7Bu3Tp89tlnuHz5Mj766CNs2bIFkydPBgB07doV9erVw7Bhw/D777/j0KFD8qKI/Eafxo0bh+TkZAwcOBAnT57E5cuXsX79evl2p7e3N9avX4+YmBgcO3YMgwcPfuyo26Pv01tvvYWpU6fiyy+/xNWrV3H06FGsXr0aADB48GD5vTx37hyio6Mxfvx4vPrqq/L8ucLw8vLCTz/9hJs3bxaYcBbnfX8S8JYrERHJirrRb0Wzt7cv8PjChQthMpnw6quvIiUlBS1atMDu3bvh6Oj42La9vb3Rt29f9OzZEwkJCejZsyc+/fTTIsW3du1aTJgwAS+88AL0ej2ee+457NixI9/5bC+//DKWLl2KDz74AKGhoXjqqaewdu1adOzYEcCDJHbr1q0YOXIkWrZsibp16+KDDz7Aiy++CJ1Ol2ebzs7O2L9/P6ZMmYIOHTpAqVQiICBAnqu3Zs0avPbaa3j66adRp04dzJ8/X04gC2vWrFlQqVSYPXs2bt26BTc3N7z++usAAGtra+zevRsTJkxAy5YtYW1tjf/85z/46KOPinSNd955B6NGjUK9evWQlZVV4LSOor7vTwJJcKILACA5ORkODg5ISkp67AcEEeVNr9djwYIFAB5sY6DRaCo4oqqNn2vFFxYWhq1bt5bq47PKyqFDh/Dss8/iypUrqFevXkWHQxWEI3REREQWJCoqCra2tqhfvz6uXLmCCRMmoF27dkzmqjgmdERUaiRJgoODg/w1EZW+lJQUTJ06FXFxcXBxcUGXLl3w4YcfVnRYVMF4y/V/eGuCiJ40/Fwjqjq4ypWIiIjIwjGhIyIiIrJwnENHRKUmOztbfph2UFDQE71FABFRZcKEjohKjRBC3lmf03OJiMoPb7kSERERWTgmdEREREQWjgkdERFVGR07dsTEiRMrOgyiUsc5dEREJIu7da1cr+fh/lSRzwkKCkJiYiK2bt2KoKAghIeHY9SoUfjss8/M6o0ZMwYrVqzAsGHD5MU6W7ZsMVus07FjRxw8eBAAoNFo4OLigmbNmiE4OBh9+/YtfseIyhlH6IiIyKJ5eHhg8+bNyMjIkMsyMzMRERGBOnXqmNV1cnKCnZ2dWVlISAhu376NK1euIDIyEo0aNcLAgQPx2muvlUv8RKWBCR0RlSpra2tYW1tXdBhUhTRr1gx16tTBli1b5LItW7bAw8MDTz/9tFndvG65Wltbw9XVFR4eHnjmmWewaNEirFy5Ep9//jn27t1bHl0gKjEmdERUajQaDaZMmYIpU6ZAo9FUdDhUhQQHB2Pt2rXy92vWrMHw4cOL3d6wYcPg6OholiQSVWZM6IioWIQQMJlMMBqNyDZkw2g0wmQycf85qhCvvvoqfvnlF8TGxuL69es4dOgQhgwZUuz2FAoFfHx8EBsbW3pBEpUhLoogoiIzmUzI0mcgOSUJ+uwsuVyj1sLezgFajRUUCv69SOXHxcUFvXr1Qnh4OIQQ6NWrF1xcXErUphACkiSVUoREZYsJHREViclkQnJKIlLSknIdS89Iw9aobVAqVHj11Veh1WorIEKqqoYPH45x48YBAJYvX16itoxGIy5fvoyWLVuWRmhEZY4JHREVmhACWfqMPJO5nON3bv8NAMjMyoBGo+EIB5Wb7t27Q6/XAwC6detWorbCw8Nx//59/Oc//ymN0IjKHBM6Iio0IQSSU/JO5h6VkpYEO1v7Uk3ohBDyyyRMUEgKSJIkvwAgy2SCAhL+yMrCLb0BaklCE2stlJBwQ6+HWlKgnlYDQEBTSreFc2LK/t8tupjMLPydbYS9UoEmVlqoJQUuZmbhTrYBOoUEfysdtBKgAXLFDwDZQkAI4Lpej2tZ2VACaGClhYtKCQhAI4G3tPOgVCoRExMjf11Y6enpuHPnDgwGA27evIktW7bg448/xujRoxEYGFhW4RKVqkqR0KWmpmLmzJn4+uuvkZCQgIYNG2LatGkYOHDgY8+9e/cupk6diu+//x7p6elo2rQp3nvvPXTu3LkcIieqWoQQZnPmCqLXZ5XqAonCzNvLBnA4NR07ktKQYjLJdTbfB3y0GgxydsDptAyExyfiBQdbtLSxgraEiZHJZII+OxNQabE/JR17U9KQbhJwUSnxRk0nHExNx77kNKSZ/n0vIpAMX50GQ50dgPRUaNUqed5hlsmE8xlZiExMQbzB+O+FElNQR6PCQCcHuKqUUJpMJY49L8XZ6Lcysbe3L/I5n3/+OT7//HNoNBo4OzujefPm+Oqrr9CnT58yiJCobEiiEixJe/7553HixAksXLgQPj4+2LRpE7744gts3LgRr7zySr7nZWVloUWLFkhMTMTChQtRo0YNLF++HD/88AP27t2LDh06FDqG5ORkODg4ICkpqVgfCERVQbYhG3fu/pX/8exsrF8XAQB4NWgQPGo9BbVKnW/9wipo3h7wYJTLpWYd7EpKxa7ktHzbsVFImOLqgh8SU3AiPRMvO9iio70NdMVMjHLi0ljbITI5DYdSH2xsqwAw27069iWn4efU9HzPt1cqMMPVGfqke1CrNNDZVcPp9Ex8GZ//KKhaAkJrOKGWWgWdJBU4UsfPNaKqo8JH6Hbs2IE9e/Zg06ZNGDRoEAAgMDAQ169fx5QpUzBgwIB8h85Xr16Nc+fO4fDhw2jTpo18btOmTTF16lQcO3as3PpBVBUopKIlPkWtn5fHzdsDAAd7JyQZjAUmcwCQZhKISEhCP0d7nEjPxNakVLS2tYL2kVueRYnLaDLglsEoJ3MAEGCtQ5LRWGAyBwDJRhM2JSRjsEM1pCXdg8auGjYWkMwBQLYAwuOTMN3VBRlCwMpk4u1XIqr4feiioqJga2uLfv36mZUHBwfj1q1bBSZlUVFRaNCggZzMAYBKpcKQIUNw/Phx3Lx5s8ziJqqKJEmCRl24lasajbZU5s8VZt6eSmeD3SkFJ3M5LmXqoZQk1NM+GDncm5yGLFPRb1TkxKW2tsePKeaJ23N21jiYUnAyl+NsRhYkpQo2ttXwU0oajI8/BfcMRlzT66HighMi+p8KT+jOnTsHX19fqFTmg4X+/v7y8YLOzamX17nnz5/P99ysrCwkJyebvYioYJIkwd7OocA6KpUKKpUKdjYOpZbQFTRvT6VUQSFJiMko3Nw+ALiQkQVPzYOE7lxGFooz7yQnLhuNFhceubanRp2rLD8mABcz9VCotThXxD4kGAqT/hFRVVDht1zj4+NRt27dXOVOTk7y8YLOzalX1HMXLFiAuXPnFjVcoipNkiRoNVaws3HI8xaoWq3G0OBXYGfjUGorXE3CVOBxSaGABMBQhKzMIIQ8umUAUJwwc+KSgFyjaipJgqEI05OzhQAkqUh9yBYoViJKRE+mCh+hA1Dgh/7jfiEU99zp06cjKSlJfsXFxT0+UCKCQqGAvV01uDjVhEZjfvtVo9HCxakm7O2qldq8rsfNwxMmE4xCoLq68NtUVFcpkWR8kJDVUClhKsbasJy49CYjqqvMr51sNKK6uvB/L7uqlRB5tFOQGiolrBQSH7VGRAAqwQids7NzniNpCQkJAJDnCFxpnKvVarmLPVExKRQK6LRW0Gp0Be4JVxpy5u3ld9vVYDRABYGOttb4M+vxe+TZKxVooNPKK0kDba2hLUa8OXFlpKfiOVtrfJuYIh87lpaBZ22t8c39x0/lcFEpUUutRkpSPDrbV8OJ9MzHnqMC8IytFTSSBM6iIyKgEozQNWnSBDExMTAYDGblZ8+eBQA0bty4wHNz6hX1XCIqGel/W2YolUqoVWoolUqYTCZERERg06ZNuf5Pl+Q6j5u3l5aWjABrHWoUYoSru70tTqZnIFMIuKtVaGilhaKYCZ29nQMy0lPQztYK1ZT/fpz+nJKO1jZWcFQ+/iP2RQdbpKenICMzDW5qFby1msee85yddbEWchDRk6vCE7o+ffogNTUVkZGRZuXh4eFwd3dH69atCzz34sWLZithDQYDNmzYgNatW8Pd3b3M4iai3EwmEy5fvozLly/DZCp47lthPTxvLz+paclQmEx4s6ZzgUldN3sb+FlpsS0xBe5qFSbVdIIKj5/aUVBc1jobZKQm4c0aTnJSd99owo6kFITWdIZTAU8s6FvNFo01KqSlJsLOxgFqAGNrOOIpTf5797WyscILDnawVSqgBp8YQUQPVPgt1x49eqBr164YPXo0kpOT4e3tjYiICOzatQsbNmyQ96AbMWIEwsPDcfXqVXh6egJ48CDm5cuXo1+/fvLGwp9++ikuXbqEvXv3VmS3iKgU5czb02p1SE5NhF7/0JMiNFrY2z6Ys2cLYKabC37LyEJ0Shr+yTZCJQF+Vlp0sLOBEMCW+8kY4uQAPystVChZQpQTV5Y+E3p9Oua6ueBkeiYOpmbgRFomrCQJs9xdcCY9EwdT0hFvMEIjSWhipUEXOxtohRFpKQlwdqwBrUYHhUIBKwBv1HTG5aws7ElOwy29AQoJqKfVoLOdDdw1qgeP/yph7ET0ZKnwhA4AtmzZghkzZmD27Nnyo78iIiLMHv1lNBphNBrNJgBrtVrs27cPU6dOxfjx45Geno6AgADs3LmzSE+JIKLKr7Dz9hRCoJmVFo11WiilB9uCmB5a1RrkXO3Bs1BLaa7fo3G1tNaimbWVfG0IgZbWOgRY66DEv2UqISBJatg41sg171CjkOCr1eApFw2U/ys2CkAJQA0AfJYrET2iUjz6qzLgI3KISk6v12PBggUAHqwk12gePx+Myg4/14iqDv6JR0RERGThmNARERERWbhKMYeuMsi588xHgBEVn16vR2bmg33UkpOTecu1guV8nnFmDdGTj3Po/uevv/6Ch4dHRYdBRFTq4uLiULt27YoOg4jKEBO6/zGZTLh16xbs7OwKvfItOTkZHh4eiIuLe2InHD/pfXzS+wc8+X180vsHFL+PQgikpKTA3d2dq2KJnnC85fo/CoWi2H/B2tvbP7G/SHI86X180vsHPPl9fNL7BxSvjw4OBT9lg4ieDPyTjYiIiMjCMaEjIiIisnBM6EpAq9Vizpw50Gq1FR1KmXnS+/ik9w948vv4pPcPqBp9JKKS4aIIIiIiIgvHEToiIiIiC8eEjoiIiMjCMaEjIiIisnBM6PKQmpqKiRMnwt3dHTqdDgEBAdi8eXOhzr179y6CgoLg4uICa2trtGnTBvv27SvjiIuuuH3csmULBg0aBG9vb1hZWcHLywuDBw/G5cuXyyHqwivJz/BhM2fOhCRJaNy4cRlEWTIl7eN3332HDh06wN7eHjY2NvDz88OqVavKMOKiKUn/oqOj0bVrV9SoUQO2trbw9/fHsmXLYDQayzjqwktJScHUqVPx/PPPo3r16pAkCWFhYYU+31I+a4ionAjKpWvXrqJatWris88+E/v37xcjR44UAMTGjRsLPC8zM1M0btxY1K5dW2zYsEH8+OOPonfv3kKlUokDBw6UU/SFU9w+tmrVSrz00ktizZo14sCBA2L9+vXC19dX2NrainPnzpVT9I9X3P497NdffxVarVbUrFlT+Pn5lWG0xVOSPi5YsEAoFAoxZswYsXPnTrF3717xySefiP/7v/8rh8gLp7j927Nnj1AoFKJjx45i69atYs+ePWL8+PECgAgNDS2n6B/v2rVrwsHBQTz33HNy3+bMmVOocy3ps4aIygcTukf88MMPAoDYtGmTWXnXrl2Fu7u7MBgM+Z67fPlyAUAcPnxYLsvOzhaNGjUSrVq1KrOYi6okffz7779zld28eVOo1WoxYsSIUo+1OErSvxzZ2dkiICBAhIaGig4dOlS6hK4kfTx58qRQKBRi0aJFZR1msZWkf4MHDxZarVakpqaalT///PPC3t6+TOItDpPJJEwmkxBCiH/++adICZ2lfNYQUfnhLddHREVFwdbWFv369TMrDw4Oxq1bt3Ds2LECz23QoAHatGkjl6lUKgwZMgTHjx/HzZs3yyzuoihJH2vUqJGrzN3dHbVr10ZcXFypx1ocJelfjoULFyIhIQHz5s0rqzBLpCR9/OSTT6DVajF+/PiyDrPYStI/tVoNjUYDKysrs/Jq1apBp9OVSbzFIUlSoZ8b/ShL+awhovLDhO4R586dg6+vL1Qq88fc+vv7y8cLOjenXl7nnj9/vhQjLb6S9DEvf/75J65fvw4/P79Si7EkStq/Cxcu4L333sOKFStga2tbZnGWREn6+NNPP8HX1xeRkZFo0KABlEolateujWnTpkGv15dp3IVVkv69/vrr0Ov1CA0Nxa1bt5CYmIj169cjKioKU6dOLdO4y4ulfNYQUflhQveI+Ph4ODk55SrPKYuPjy+Tc8tTacZpMBgwYsQI2Nra4o033ii1GEuiJP0zmUwYPnw4+vbti549e5ZZjCVVkj7evHkTly9fRmhoKEJDQ7F3714EBQVh8eLFCA4OLrOYi6Ik/WvdujX279+PqKgo1KpVC46OjggODsa8efPw5ptvllnM5clSPmuIqPyoHl+l6inoNsjjbpGU5NzyVBpxCiEwYsQI/Pzzz4iMjISHh0dphVdixe3fRx99hMuXL2Pbtm1lEVapKm4fTSYTUlJSEBERgYEDBwIAAgMDkZaWhiVLlmDu3Lnw9vYu9XiLqrj9O3XqFPr06YPWrVtj5cqVsLGxwf79+zFz5kxkZmZi1qxZZRFuubOUzxoiKh9M6B7h7Oyc51+3CQkJAJDnX8WlcW55Ko04hRAYOXIkNmzYgPDwcPTu3bvU4yyu4vbvxo0bmD17NhYuXAiNRoPExEQAD0YhTSYTEhMTodVqc83Nqggl/Xd6584ddOvWzay8R48eWLJkCU6fPl3hCV1J+jd27FjUrFkTUVFRUCqVAB4krAqFAmFhYRg8eDDq1q1bNoGXE0v5rCGi8sNbro9o0qQJYmJiYDAYzMrPnj0LAAXuR9akSRO5XlHPLU8l6SPwbzK3du1afPHFFxgyZEiZxVocxe3fn3/+iYyMDEyYMAGOjo7y69ChQ4iJiYGjoyOmT59e5vEXRkl+hnnNvQIe/FwBQKGo+I+FkvTvzJkzaN68uZzM5WjZsiVMJhNiYmJKP+ByZimfNURUfir+k7uS6dOnD1JTUxEZGWlWHh4eDnd3d7Ru3brAcy9evGi2As9gMGDDhg1o3bo13N3dyyzuoihJH4UQCAkJwdq1a7Fy5cpKM+fqYcXtX0BAAKKjo3O9mjZtCi8vL0RHR2PcuHHl0YXHKsnP8D//+Q8AYOfOnWblO3bsgEKhQMuWLUs/4CIqSf/c3d1x8uTJXJsIHzlyBABQu3bt0g+4nFnKZw0RlaMK3TSlkuratatwdHQUq1atEvv37xchISECgNiwYYNcZ/jw4UKpVIrY2Fi5LDMzU/j5+QkPDw+xceNGsWfPHtGnT59Kudlncfs4btw4AUAMHz5cHDlyxOx1+vTpiuhKnorbv7xUxn3ohCh+H/V6vWjWrJlwcHAQS5cuFXv27BFvvfWWUCqVYty4cRXRlTwVt3/Lli0TAESPHj3E1q1bxY8//ijeeustoVKpRJcuXSqiK/nasWOH+Oabb8SaNWsEANGvXz/xzTffiG+++UakpaUJISz/s4aIygcTujykpKSI0NBQ4erqKjQajfD39xcRERFmdYYNGyYAiGvXrpmV37lzRwwdOlQ4OTkJnU4nnnnmGbFnz55yjL5wittHT09PASDPl6enZ/l2ogAl+Rk+qrImdCXpY3x8vBg1apSoWbOmUKvVwsfHR3zwwQfCaDSWYw8KVpL+RUZGimeffVa4uLgIGxsb4efnJ959991cmw1XtIL+P+X0ydI/a4iofEhC/G/iDBERERFZJM6hIyIiIrJwTOiIiIiILBwTOiIiIiILx4SOiIiIyMIxoSMiIiKycEzoiIiIiCwcEzoiIiIiC8eEjizW77//juDgYDz11FPQ6XSwtbVFs2bN8P7778sPKbckt27dQlhYGM6cOVPqba9btw6SJCE2NrbAekFBQbC1tc33uK2tLYKCgko3OCIiKjFVRQdAVByff/45xowZgwYNGmDKlClo1KgRsrOzcfLkSXz22Wc4cuQIoqKiKjrMIrl16xbmzp0LLy8vBAQEVHQ4RERkQZjQkcU5cuQIRo8eja5du2Lr1q3QarXysa5du+LNN9/Erl27SuVa6enpsLa2zlVuNBphMBjMrk1ERFRReMuVLM78+fMhSRJWrVqVZ0Kl0Wjw0ksvyd+bTCa8//77aNiwIbRaLWrUqIGhQ4fir7/+MjuvY8eOaNy4MX766Se0bdsW1tbWGD58OGJjYyFJEt5//3289957eOqpp6DVahEdHQ0AOHnyJF566SU4OTlBp9Ph6aefxtdff50rrps3b+K1116Dh4cHNBoN3N3d8d///hd///03Dhw4gJYtWwIAgoODIUkSJElCWFiYfH5hr3P06FG0a9cOOp0O7u7umD59OrKzs4v1XhMRkWXgCB1ZFKPRiP3796N58+bw8PAo1DmjR4/GqlWrMG7cOLzwwguIjY3FrFmzcODAAZw+fRouLi5y3du3b2PIkCGYOnUq5s+fD4Xi3795li1bBh8fHyxevBj29vaoX78+oqOj0b17d7Ru3RqfffYZHBwcsHnzZgwYMADp6enyfLObN2+iZcuWyM7Oxttvvw1/f3/Ex8dj9+7duH//Ppo1a4a1a9ciODgYM2fORK9evQAAtWvXBoBCX+fChQvo3LkzvLy8sG7dOlhbW+PTTz/Fpk2bSuHdJyKiSksQWZA7d+4IAGLgwIGFqh8TEyMAiDFjxpiVHzt2TAAQb7/9tlzWoUMHAUDs27fPrO61a9cEAFGvXj2h1+vNjjVs2FA8/fTTIjs726z8hRdeEG5ubsJoNAohhBg+fLhQq9XiwoUL+cZ64sQJAUCsXbs217HCXmfAgAHCyspK3LlzR65jMBhEw4YNBQBx7dq1fK8vhBDDhg0TNjY2+R63sbERw4YNK7ANIiIqf7zlSk+0nNuij67MbNWqFXx9fbFv3z6zckdHR3Tq1CnPtl566SWo1Wr5+ytXruDixYsYPHgwAMBgMMivnj174vbt27h06RIAYOfOnQgMDISvr2+R+1CU60RHR6Nz586oWbOmfL5SqcSAAQOKfF0iIrIcvOVKFsXFxQXW1ta4du1aoerHx8cDANzc3HIdc3d3x/Xr183K8qqX37G///4bADB58mRMnjw5z3Pu3bsHAPjnn3/k26dFVZTrxMfHw9XVNdfxvMryolKpYDQa8z1uMBjMkloiIqocmNCRRVEqlejcuTN27tyJv/7667FJkrOzM4AHc+MerXvr1i2z+XMAIElSvm09eizn3OnTp6Nv3755ntOgQQMAQPXq1XMtwiisolzH2dkZd+7cyXU8r7K81KxZE5mZmUhISICTk5PZsfj4eGRlZZmN/hERUeXAW65kcaZPnw4hBEJCQqDX63Mdz87Oxvbt2wFAvn26YcMGszonTpxATEwMOnfuXOw4GjRogPr16+O3335DixYt8nzZ2dkBAHr06IHo6Gj51mheclbsZmRkFPs6gYGB2LdvnzyqBzxYSPLVV18Vqk9dunQBgDzr56yozalDRESVB0foyOK0adMGK1aswJgxY9C8eXOMHj0afn5+yM7Oxq+//opVq1ahcePGePHFF9GgQQO89tpr+L//+z8oFAr06NFDXuXq4eGBN954o0SxrFy5Ej169EC3bt0QFBSEWrVqISEhATExMTh9+jS++eYbAMA777yDnTt34rnnnsPbb7+NJk2aIDExEbt27cKkSZPQsGFD1KtXD1ZWVti4cSN8fX1ha2sLd3d3uLu7F/o6M2fOxLZt29CpUyfMnj0b1tbWWL58OdLS0grVn8DAQLz00kuYMGECYmNj0aFDBwgh8NNPP+Hjjz/GSy+9hI4dO5boPSMiojJQ0asyiIrrzJkzYtiwYaJOnTpCo9EIGxsb8fTTT4vZs2eLu3fvyvWMRqNYtGiR8PHxEWq1Wri4uIghQ4aIuLg4s/Y6dOgg/Pz8cl0nZ5XrBx98kGccv/32m+jfv7+oUaOGUKvVwtXVVXTq1El89tlnZvXi4uLE8OHDhaurq1Cr1cLd3V30799f/P3333KdiIgI0bBhQ6FWqwUAMWfOnCJf59ChQ+KZZ54RWq1WuLq6iilTpohVq1YVapWrEELo9Xoxf/584efnJ7RardBqtcLPz0/Mnz8/1ypfIiKqHCQhhKjgnJKIiIiISoBz6IiIiIgsHBM6IiIiIgvHhI6IiIjIwjGhIyIiIrJwTOiIiIiILBwTOiIiIiILx4SOiIiIyMIxoSMiIiKycEzoiIiIiCwcEzoiIiIiC8eEjoiIiMjCMaEjIiIisnD/DwSsD8ieVjmEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Isogenic CPA output: p-value versus corrected U\n",
    "def prepFigure(\n",
    "    correctedU = pd.read_csv(f\"{output_dir}/compoundData.csv\"),\n",
    "    output_path = \"\",\n",
    "    x = \"Corrected_u\", xlabel = \"Corrected U\",\n",
    "    y = \"p-value\", ylabel = \"p-value\",\n",
    "    correctedUCutoff = 0.7, cutoffLabelHeight = None,\n",
    "    numberFeature_threshold = 5,\n",
    "    pCutoff = None,\n",
    "    markerSize = 80,\n",
    "    fontsize = 12,\n",
    "    label2color_dict = label2color_dict,\n",
    "    figsize = (4, 3)\n",
    "):\n",
    "    # Exclude compounds that have too few treatment-centric features\n",
    "    correctedU = correctedU[correctedU[\"Number_Features\"] >= numberFeature_threshold].reset_index(drop = True)\n",
    "    \n",
    "    # Figure size\n",
    "    plt.figure(figsize = figsize)\n",
    "    \n",
    "    # Plot the SuFEx IMiDs\n",
    "    p = sns.scatterplot(\n",
    "        data = correctedU,\n",
    "        x = x,\n",
    "        y = y,\n",
    "        color = label2color_dict[\"IMiD\"],\n",
    "        s = markerSize\n",
    "    )\n",
    "    \n",
    "    # Plot the controls and color-code them\n",
    "    hue_order = []\n",
    "    palette = []\n",
    "    for label, color in label2color_dict.items():\n",
    "        hue_order.append(label)\n",
    "        palette.append(color)\n",
    "    sns.scatterplot(\n",
    "        data = correctedU[correctedU[\"Label\"] != \"IMiD\"],\n",
    "        x = x,\n",
    "        y = y,\n",
    "        hue = \"Label\",\n",
    "        hue_order = hue_order,\n",
    "        palette = palette,\n",
    "        s = markerSize\n",
    "    )\n",
    "    \n",
    "    # Set the legend\n",
    "    legend_elements = []\n",
    "    for label, color in label2color_dict.items():\n",
    "        if label == \"IMiD\":\n",
    "            label = label\n",
    "        else:\n",
    "            label = f\"{label} control\"\n",
    "        element = Patch(facecolor = color, label = label)\n",
    "        legend_elements.append(element)\n",
    "    p.legend(\n",
    "        handles = legend_elements,\n",
    "        loc = \"lower left\", bbox_to_anchor = (1.0, 0.0), ncol = 1, frameon = False\n",
    "    )\n",
    "    \n",
    "    # X axis aesthetics\n",
    "    plt.xticks(fontsize = fontsize)\n",
    "    plt.xlabel(xlabel, fontsize = fontsize)\n",
    "    \n",
    "    # Y axis aesthetics\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    \n",
    "    # Draw a grey dotted line to mark the tentative cutoff on the corrected U (if any)\n",
    "    if correctedUCutoff != None:\n",
    "        plt.axvline(x = correctedUCutoff, color = \"grey\", linestyle = \"--\")\n",
    "        if cutoffLabelHeight != None:\n",
    "            plt.text(\n",
    "                correctedUCutoff,\n",
    "                cutoffLabelHeight,\n",
    "                f\"Corrected U = {correctedUCutoff}\",\n",
    "                horizontalalignment = \"center\",\n",
    "                color = \"grey\"\n",
    "            )\n",
    "    \n",
    "    # Draw a grey dotted line to mark the tentative cutoff on the p-value (if any)\n",
    "    if pCutoff != None:\n",
    "        plt.axhline(y = pCutoff, color = \"grey\", linestyle = \"--\")\n",
    "        plt.text(\n",
    "            0.70,\n",
    "            pCutoff + 0.02,\n",
    "            f\"p-value\\ncutoff = {pCutoff}\",\n",
    "            horizontalalignment = \"left\",\n",
    "            color = \"grey\"\n",
    "        )\n",
    "    \n",
    "    # Save the plot\n",
    "    savePlot(output_path)\n",
    "    \n",
    "    # Display the plot\n",
    "    plt.show()\n",
    "    return\n",
    "prepFigure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93ec8e30-f945-478c-874f-aae82dc7d95e",
   "metadata": {},
   "source": [
    "### Scatterplot: Number of treatment-centric features against corrected U scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d8f6563-31ce-4d21-ae66-0856f4274b66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepFigure(\n",
    "    x = \"Corrected_u\", xlabel = \"Corrected U\",\n",
    "    y = \"Number_Features\", ylabel = \"Number of\\ntreatment-centric features\",\n",
    "    cutoffLabelHeight = None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "90fc2545-0e68-4da5-9474-84a53dc98585",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_inductionScore(\n",
    "    profile = pd.read_csv(profile_path),\n",
    "    selectedFeatures = pd.read_csv(selectedFeatures_path),\n",
    "    featuresSelectedFor = \"Pomalidomide\",\n",
    "    compounds = [\"Pomalidomide\", \"Methylated pomalidomide\"],\n",
    "    output_path = \"\",\n",
    "    ko = \"c1141_rko_ko\",\n",
    "    oe = \"c1327_rko_oe\",\n",
    "    colors = [\"lightgrey\", \"#42CDDB\"],\n",
    "    figsize = (7, 5)\n",
    "):\n",
    "    # Retrieve the features selected for a specific compound\n",
    "    print(f\"Treatment-centric features selected for: {featuresSelectedFor}\")\n",
    "    selectedFeatures = selectedFeatures[selectedFeatures[\"Treatment\"] == featuresSelectedFor].reset_index(drop = True)\n",
    "    features = selectedFeatures.loc[0, \"Features_Selected\"].split(\" \")\n",
    "    if len(features) == 0:\n",
    "        raise(ValueError(f\"The following given treatment has no features selected by the treatment-centric strategy:\\n{featuresSelectedFor}\"))\n",
    "      \n",
    "    # Calculate the induction scores for image in the profile pertaining to the compounds\n",
    "    inductionScores = []\n",
    "    profile = profile[profile[\"Metadata_Cell\"].isin([ko, oe])]\n",
    "    profile = profile[profile[\"Metadata_Treatment\"].isin(compounds)]\n",
    "    for i in profile.index:\n",
    "        score = 0\n",
    "        for feature in features:\n",
    "            score += abs(profile.loc[i, feature])\n",
    "        score = score/len(features)\n",
    "        inductionScores.append(score)\n",
    "    profile[\"InductionScore\"] = inductionScores\n",
    "    \n",
    "    # Calculate the corrected U\n",
    "    correctedU = compareInduction(profile)\n",
    "    display(correctedU)\n",
    "    treatment2u_dict = dict()\n",
    "    treatment2p_dict = dict()\n",
    "    for i in correctedU.index:\n",
    "        treatment = correctedU.loc[i, \"Metadata_Treatment\"]\n",
    "        u = correctedU.loc[i, \"Corrected_u\"]\n",
    "        p = correctedU.loc[i, \"p-value\"]\n",
    "        treatment2u_dict[treatment] = u\n",
    "        treatment2p_dict[treatment] = p\n",
    "    \n",
    "    # Visualize the induction scores for the RKO CRBN KO and RKO CRBN OE only\n",
    "    plt.figure(figsize = figsize)\n",
    "    p = sns.violinplot(\n",
    "        data = profile,\n",
    "        x = \"Metadata_Treatment\",\n",
    "        y = \"InductionScore\",\n",
    "        hue = \"Metadata_Cell\",\n",
    "        hue_order = [ko, oe],\n",
    "        palette = colors,\n",
    "        order = compounds\n",
    "    )\n",
    "    \n",
    "    # Add the plot title\n",
    "    plt.title(f\"Treatment-centric features selected for\\n {featuresSelectedFor}\")\n",
    "    \n",
    "    # Set the plot legend\n",
    "    legend_elements = [\n",
    "        Patch(facecolor = colors[0], label = \"CRBN KO\"),\n",
    "        Patch(facecolor = colors[1], label = \"CRBN OE\")\n",
    "    ]\n",
    "    p.legend(\n",
    "        handles = legend_elements,\n",
    "        loc = \"lower left\", bbox_to_anchor = (1.0, 0.0), ncol = 1, frameon = False\n",
    "    )\n",
    "    \n",
    "    # Set the y label\n",
    "    plt.ylabel(\"Induction score\")\n",
    "    \n",
    "    # Add the information on the corrected U\n",
    "    plt.xlabel(\"\")\n",
    "    plt.xticks([])\n",
    "    cellText = []\n",
    "    cellText.append([round(treatment2u_dict[i], 3) for i in compounds])\n",
    "    cellText.append([\"{:.2e}\".format(treatment2p_dict[i]) for i in compounds])\n",
    "    table = plt.table(\n",
    "        cellText = cellText,\n",
    "        cellLoc = \"center\",\n",
    "        rowLabels = [\"Corrected U\", \"p-value\"],\n",
    "        colLabels = compounds,\n",
    "        edges = \"open\",\n",
    "        bbox = (0.00, -0.20, 1.00, 0.15)\n",
    "    )\n",
    "    table.set_fontsize(12)\n",
    "    \n",
    "    # Adjust the layout of the plot to make room for the table\n",
    "    plt.subplots_adjust(left = 0.2, bottom = 0.2)\n",
    "    \n",
    "    # Save the plot\n",
    "    savePlot(output_path)\n",
    "    \n",
    "    # Display the plot\n",
    "    plt.show()\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0767d92f-2406-4327-81ab-870510f32354",
   "metadata": {},
   "source": [
    "#### Compound vs matched CRBN-independent equivalent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "e7db9989-a3b3-4bb1-bbb2-ef982941317a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treatment-centric features selected for: Pomalidomide\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metadata_Treatment</th>\n",
       "      <th>Corrected_u</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pomalidomide</td>\n",
       "      <td>0.835392</td>\n",
       "      <td>1.680885e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Methylated pomalidomide</td>\n",
       "      <td>0.494722</td>\n",
       "      <td>5.407600e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Metadata_Treatment  Corrected_u       p-value\n",
       "0             Pomalidomide     0.835392  1.680885e-10\n",
       "1  Methylated pomalidomide     0.494722  5.407600e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPORTED: The plot has been exported at\n",
      "/research/lab_winter/users/ang/projects/GW015_SuFEX_IMiDs/GW015_006__full_cpa/cleanPostFeatureExtraction/output_dir/4_DataVisualization_output/treatmentcentric/InductionScore_Violin_comparison1.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_inductionScore(\n",
    "    featuresSelectedFor = \"Pomalidomide\",\n",
    "    compounds = [\"Pomalidomide\", \"Methylated pomalidomide\"],\n",
    "    output_path = f\"{output_dir}/treatmentcentric/InductionScore_Violin_comparison1.png\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "4c63704b-e26c-427b-b917-cd7efae43b75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treatment-centric features selected for: dBET1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metadata_Treatment</th>\n",
       "      <th>Corrected_u</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dBET1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.149890e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MZ1</td>\n",
       "      <td>0.074011</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metadata_Treatment  Corrected_u       p-value\n",
       "0              dBET1     1.000000  1.149890e-09\n",
       "1                MZ1     0.074011  1.000000e+00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPORTED: The plot has been exported at\n",
      "/research/lab_winter/users/ang/projects/GW015_SuFEX_IMiDs/GW015_006__full_cpa/cleanPostFeatureExtraction/output_dir/4_DataVisualization_output/treatmentcentric/InductionScore_Violin_comparison2.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_inductionScore(\n",
    "    featuresSelectedFor = \"dBET1\",\n",
    "    compounds = [\"dBET1\", \"MZ1\"],\n",
    "    output_path = f\"{output_dir}/treatmentcentric/InductionScore_Violin_comparison2.png\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2f2a03-d656-45e8-8b71-90e88da80f77",
   "metadata": {},
   "source": [
    "#### Comparing characterized compounds with similar targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "e0a3e2fb-261c-4c94-bc85-87adbd33e539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treatment-centric features selected for: Pomalidomide\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metadata_Treatment</th>\n",
       "      <th>Corrected_u</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lenalidomide</td>\n",
       "      <td>0.875556</td>\n",
       "      <td>6.536427e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pomalidomide</td>\n",
       "      <td>0.835392</td>\n",
       "      <td>1.680885e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DMSO</td>\n",
       "      <td>0.477886</td>\n",
       "      <td>8.478466e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avadomide</td>\n",
       "      <td>0.761944</td>\n",
       "      <td>3.771356e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iberdomide</td>\n",
       "      <td>0.927222</td>\n",
       "      <td>3.521185e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metadata_Treatment  Corrected_u       p-value\n",
       "0       Lenalidomide     0.875556  6.536427e-13\n",
       "1       Pomalidomide     0.835392  1.680885e-10\n",
       "2               DMSO     0.477886  8.478466e-01\n",
       "3          Avadomide     0.761944  3.771356e-07\n",
       "4         Iberdomide     0.927222  3.521185e-16"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPORTED: The plot has been exported at\n",
      "/research/lab_winter/users/ang/projects/GW015_SuFEX_IMiDs/GW015_006__full_cpa/cleanPostFeatureExtraction/output_dir/4_DataVisualization_output/treatmentcentric/InductionScore_Violin_comparison3.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_inductionScore(\n",
    "    featuresSelectedFor = \"Pomalidomide\",\n",
    "    compounds = [\"Pomalidomide\", \"Lenalidomide\", \"Avadomide\", \"Iberdomide\", \"DMSO\"],\n",
    "    output_path = f\"{output_dir}/treatmentcentric/InductionScore_Violin_comparison3.png\",\n",
    "    figsize = (9, 5)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "413d4479-7c73-4fa5-b882-6a7276de363e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treatment-centric features selected for: dBET1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metadata_Treatment</th>\n",
       "      <th>Corrected_u</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dBET1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.149890e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dBET6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.100609e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DMSO</td>\n",
       "      <td>0.494869</td>\n",
       "      <td>5.942491e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metadata_Treatment  Corrected_u       p-value\n",
       "0              dBET1     1.000000  1.149890e-09\n",
       "1              dBET6     1.000000  1.100609e-08\n",
       "2               DMSO     0.494869  5.942491e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_inductionScore(\n",
    "    featuresSelectedFor = \"dBET1\",\n",
    "    compounds = [\"dBET1\", \"dBET6\", \"DMSO\"],\n",
    "    output_path = \"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9be9ec6-3e73-4349-a90f-65122418a3aa",
   "metadata": {},
   "source": [
    "### Scatterplot: Morphological perturbation strength against corrected U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2c3b57cd-9b0f-416e-a9c8-b65000eb1164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepFigure(\n",
    "    x = \"Corrected_u\", xlabel = \"Corrected U\",\n",
    "    y = \"Distance\", ylabel = \"Morphological\\nperturbation strength\",\n",
    "    cutoffLabelHeight = None\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "094c197d-71c4-47a4-b448-30e66bdc6a5b",
   "metadata": {},
   "source": [
    "### Scatterplot: CRBN binding affinity against corrected U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "df924011-a65b-45e9-ba4e-af9f836a6e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepFigure(\n",
    "    x = \"Corrected_u\", xlabel = \"Corrected U\",\n",
    "    y = \"FP_CRBN_binding\", ylabel = \"CRBN binding affinity\",\n",
    "    cutoffLabelHeight = None\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "702a374b-2954-4bea-80be-12d85d4dc1c9",
   "metadata": {},
   "source": [
    "### Example: Viewing the induction scores as a violin plot for TL 13-12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5189586e-a977-41fd-85c7-6e926f2c9364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "c1141_rko_ko vs. c1327_rko_oe: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val:6.380e-21 U_stat=4.000e+00\n",
      "WARNING: The plot has NOT been exported.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def prepFigure(\n",
    "    inductionScores = pd.read_csv(inductionScores_path),\n",
    "    compound = \"TL 13-12\",\n",
    "    output_path = \"\",\n",
    "    pair = [\"c1141_rko_ko\", \"c1327_rko_oe\"],\n",
    "    pairLabels = [\"KO\", \"OE\"],\n",
    "    figsize = (2, 2),\n",
    "    hatch = [None, \"++\"],\n",
    "    color = adjust_colorLuminosity(label2color_dict[\"CRBN-dependent\"], 0.5),\n",
    "    xlim = (-0.5, 1.2)\n",
    "):\n",
    "    # Trim down the inductionScores to those pertaining to the compound\n",
    "    inductionScores = inductionScores[inductionScores[\"Metadata_Treatment\"] == compound]\n",
    "    \n",
    "    # Retrieve the scores corresponding to RKO CRBN KO\n",
    "    ko_scores = inductionScores[inductionScores[\"Metadata_Cell\"] == \"c1141_rko_ko\"][\"InductionScore\"]\n",
    "\n",
    "    # Retrieve the scores corresponding to RKO CRBN OE\n",
    "    oe_scores = inductionScores[inductionScores[\"Metadata_Cell\"] == \"c1327_rko_oe\"][\"InductionScore\"]\n",
    "        \n",
    "    # Define the plotting parameters to use for both the violinplot and the annotator calls\n",
    "    plotting_parameters = {\n",
    "        \"data\" : inductionScores,\n",
    "        \"x\" : \"Metadata_Cell\",\n",
    "        \"y\" : \"InductionScore\",\n",
    "        \"color\" : color,\n",
    "        \"order\" : pair\n",
    "    }\n",
    "    \n",
    "    # Define the pair of data to compare\n",
    "    pairs = [tuple(pair)]\n",
    "    \n",
    "    # Set the size of the figure\n",
    "    if figsize != \"Default\":\n",
    "        plt.figure(figsize = (figsize))\n",
    "\n",
    "    # Seaborn violinplot\n",
    "    p = sns.violinplot(\n",
    "        **plotting_parameters,\n",
    "        inner = None\n",
    "    )\n",
    "    ihatch = iter(hatch)\n",
    "    _ = [i.set_hatch(next(ihatch)) for i in p.get_children() if isinstance(i, collections.PolyCollection)]\n",
    "\n",
    "    # Add the annotations\n",
    "    annotator = Annotator(\n",
    "        p,\n",
    "        pairs,\n",
    "        plot = \"violinplot\",\n",
    "        **plotting_parameters\n",
    "    )\n",
    "    annotator.configure(\n",
    "        test = \"Mann-Whitney\",\n",
    "        comparisons_correction = \"bonferroni\",\n",
    "        text_format = \"star\",\n",
    "        loc = \"outside\"\n",
    "    )\n",
    "    annotator.apply_and_annotate()\n",
    "\n",
    "    # X axis aesthetics\n",
    "    plt.xticks(ticks = [0, 1], labels = pairLabels)\n",
    "    xmin, xmax = p.get_xlim()\n",
    "    plt.xlim(*xlim)\n",
    "    plt.xlabel(\"\")\n",
    "\n",
    "    # Y axis aesthetics\n",
    "    ymin, ymax = p.get_ylim()\n",
    "    plt.ylim(None, ymax*1.5)\n",
    "    plt.ylabel(\"Induction score\")\n",
    "\n",
    "    # Export the plot\n",
    "    savePlot(output_path)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "    \n",
    "    return\n",
    "prepFigure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eff020d-37c9-4f42-9ae2-e5d0da335363",
   "metadata": {},
   "source": [
    "### Force-directed network graph: Comparing the treatment-centric features of compounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17f1c0ee-90fc-45e3-a833-cd4876ee7624",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPORTED: Pairwise cosine similarities between tau values of treatment-centric features has been calculated and exported at:/n/research/lab_winter/users/ang/projects/GW015_SuFEX_IMiDs/GW015_006__full_cpa/cleanPostFeatureExtraction/output_dir/4_ProfileInterpretation_output/cosineSimilarity.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dBET1</th>\n",
       "      <th>Avadomide</th>\n",
       "      <th>FL1-18</th>\n",
       "      <th>CC-90009</th>\n",
       "      <th>Pomalidomide</th>\n",
       "      <th>CC-885</th>\n",
       "      <th>FL1-2</th>\n",
       "      <th>FL4-2</th>\n",
       "      <th>FL4-6</th>\n",
       "      <th>dBET6</th>\n",
       "      <th>FL2-14</th>\n",
       "      <th>FL6-6</th>\n",
       "      <th>Iberdomide</th>\n",
       "      <th>FL1-12</th>\n",
       "      <th>FL1-6</th>\n",
       "      <th>Lenalidomide</th>\n",
       "      <th>TL 13-12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dBET1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.062581</td>\n",
       "      <td>0.636511</td>\n",
       "      <td>0.8662</td>\n",
       "      <td>0.356415</td>\n",
       "      <td>0.808317</td>\n",
       "      <td>0.565888</td>\n",
       "      <td>0.426981</td>\n",
       "      <td>0.079706</td>\n",
       "      <td>0.915383</td>\n",
       "      <td>0.468449</td>\n",
       "      <td>-0.058741</td>\n",
       "      <td>0.455232</td>\n",
       "      <td>0.553289</td>\n",
       "      <td>0.68563</td>\n",
       "      <td>0.477669</td>\n",
       "      <td>0.543142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avadomide</th>\n",
       "      <td>-0.062581</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.36644</td>\n",
       "      <td>-0.27613</td>\n",
       "      <td>0.861959</td>\n",
       "      <td>-0.384018</td>\n",
       "      <td>-0.47169</td>\n",
       "      <td>0.646863</td>\n",
       "      <td>0.571876</td>\n",
       "      <td>-0.107897</td>\n",
       "      <td>0.053011</td>\n",
       "      <td>-0.260575</td>\n",
       "      <td>0.480631</td>\n",
       "      <td>-0.274187</td>\n",
       "      <td>-0.034966</td>\n",
       "      <td>0.09398</td>\n",
       "      <td>-0.166601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL1-18</th>\n",
       "      <td>0.636511</td>\n",
       "      <td>-0.36644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.896998</td>\n",
       "      <td>-0.026192</td>\n",
       "      <td>0.933002</td>\n",
       "      <td>0.931628</td>\n",
       "      <td>-0.169751</td>\n",
       "      <td>-0.593286</td>\n",
       "      <td>0.744885</td>\n",
       "      <td>0.864715</td>\n",
       "      <td>0.517926</td>\n",
       "      <td>0.206487</td>\n",
       "      <td>0.964441</td>\n",
       "      <td>0.780743</td>\n",
       "      <td>0.17973</td>\n",
       "      <td>0.619421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC-90009</th>\n",
       "      <td>0.8662</td>\n",
       "      <td>-0.27613</td>\n",
       "      <td>0.896998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.105754</td>\n",
       "      <td>0.957125</td>\n",
       "      <td>0.833691</td>\n",
       "      <td>0.211852</td>\n",
       "      <td>-0.042999</td>\n",
       "      <td>0.924637</td>\n",
       "      <td>0.817251</td>\n",
       "      <td>0.374775</td>\n",
       "      <td>0.366034</td>\n",
       "      <td>0.852774</td>\n",
       "      <td>0.799217</td>\n",
       "      <td>0.371792</td>\n",
       "      <td>0.719769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pomalidomide</th>\n",
       "      <td>0.356415</td>\n",
       "      <td>0.861959</td>\n",
       "      <td>-0.026192</td>\n",
       "      <td>0.105754</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.042774</td>\n",
       "      <td>-0.226386</td>\n",
       "      <td>0.853705</td>\n",
       "      <td>0.569002</td>\n",
       "      <td>0.184224</td>\n",
       "      <td>0.240973</td>\n",
       "      <td>-0.37597</td>\n",
       "      <td>0.864552</td>\n",
       "      <td>-0.020435</td>\n",
       "      <td>0.261943</td>\n",
       "      <td>0.646758</td>\n",
       "      <td>0.052014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC-885</th>\n",
       "      <td>0.808317</td>\n",
       "      <td>-0.384018</td>\n",
       "      <td>0.933002</td>\n",
       "      <td>0.957125</td>\n",
       "      <td>-0.042774</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.898831</td>\n",
       "      <td>0.020642</td>\n",
       "      <td>-0.39166</td>\n",
       "      <td>0.910503</td>\n",
       "      <td>0.782558</td>\n",
       "      <td>0.463259</td>\n",
       "      <td>0.239874</td>\n",
       "      <td>0.89747</td>\n",
       "      <td>0.806027</td>\n",
       "      <td>0.284655</td>\n",
       "      <td>0.742565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL1-2</th>\n",
       "      <td>0.565888</td>\n",
       "      <td>-0.47169</td>\n",
       "      <td>0.931628</td>\n",
       "      <td>0.833691</td>\n",
       "      <td>-0.226386</td>\n",
       "      <td>0.898831</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.350393</td>\n",
       "      <td>-0.534406</td>\n",
       "      <td>0.695687</td>\n",
       "      <td>0.734281</td>\n",
       "      <td>0.566224</td>\n",
       "      <td>0.068955</td>\n",
       "      <td>0.914583</td>\n",
       "      <td>0.711146</td>\n",
       "      <td>0.168508</td>\n",
       "      <td>0.672555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL4-2</th>\n",
       "      <td>0.426981</td>\n",
       "      <td>0.646863</td>\n",
       "      <td>-0.169751</td>\n",
       "      <td>0.211852</td>\n",
       "      <td>0.853705</td>\n",
       "      <td>0.020642</td>\n",
       "      <td>-0.350393</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.736193</td>\n",
       "      <td>0.248188</td>\n",
       "      <td>0.235827</td>\n",
       "      <td>-0.899763</td>\n",
       "      <td>0.83225</td>\n",
       "      <td>-0.328492</td>\n",
       "      <td>0.391451</td>\n",
       "      <td>0.713396</td>\n",
       "      <td>0.284029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL4-6</th>\n",
       "      <td>0.079706</td>\n",
       "      <td>0.571876</td>\n",
       "      <td>-0.593286</td>\n",
       "      <td>-0.042999</td>\n",
       "      <td>0.569002</td>\n",
       "      <td>-0.39166</td>\n",
       "      <td>-0.534406</td>\n",
       "      <td>0.736193</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.107956</td>\n",
       "      <td>-0.34605</td>\n",
       "      <td>-0.647547</td>\n",
       "      <td>0.272893</td>\n",
       "      <td>-0.625519</td>\n",
       "      <td>-0.049395</td>\n",
       "      <td>0.31092</td>\n",
       "      <td>0.036555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dBET6</th>\n",
       "      <td>0.915383</td>\n",
       "      <td>-0.107897</td>\n",
       "      <td>0.744885</td>\n",
       "      <td>0.924637</td>\n",
       "      <td>0.184224</td>\n",
       "      <td>0.910503</td>\n",
       "      <td>0.695687</td>\n",
       "      <td>0.248188</td>\n",
       "      <td>-0.107956</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.680975</td>\n",
       "      <td>0.32903</td>\n",
       "      <td>0.381409</td>\n",
       "      <td>0.729541</td>\n",
       "      <td>0.783517</td>\n",
       "      <td>0.352765</td>\n",
       "      <td>0.627983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL2-14</th>\n",
       "      <td>0.468449</td>\n",
       "      <td>0.053011</td>\n",
       "      <td>0.864715</td>\n",
       "      <td>0.817251</td>\n",
       "      <td>0.240973</td>\n",
       "      <td>0.782558</td>\n",
       "      <td>0.734281</td>\n",
       "      <td>0.235827</td>\n",
       "      <td>-0.34605</td>\n",
       "      <td>0.680975</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.451272</td>\n",
       "      <td>0.303465</td>\n",
       "      <td>0.839685</td>\n",
       "      <td>0.789177</td>\n",
       "      <td>0.082928</td>\n",
       "      <td>0.541167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL6-6</th>\n",
       "      <td>-0.058741</td>\n",
       "      <td>-0.260575</td>\n",
       "      <td>0.517926</td>\n",
       "      <td>0.374775</td>\n",
       "      <td>-0.37597</td>\n",
       "      <td>0.463259</td>\n",
       "      <td>0.566224</td>\n",
       "      <td>-0.899763</td>\n",
       "      <td>-0.647547</td>\n",
       "      <td>0.32903</td>\n",
       "      <td>0.451272</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.283349</td>\n",
       "      <td>0.629541</td>\n",
       "      <td>0.071268</td>\n",
       "      <td>-0.180943</td>\n",
       "      <td>0.102699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iberdomide</th>\n",
       "      <td>0.455232</td>\n",
       "      <td>0.480631</td>\n",
       "      <td>0.206487</td>\n",
       "      <td>0.366034</td>\n",
       "      <td>0.864552</td>\n",
       "      <td>0.239874</td>\n",
       "      <td>0.068955</td>\n",
       "      <td>0.83225</td>\n",
       "      <td>0.272893</td>\n",
       "      <td>0.381409</td>\n",
       "      <td>0.303465</td>\n",
       "      <td>-0.283349</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.171458</td>\n",
       "      <td>0.490409</td>\n",
       "      <td>0.942752</td>\n",
       "      <td>0.350251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL1-12</th>\n",
       "      <td>0.553289</td>\n",
       "      <td>-0.274187</td>\n",
       "      <td>0.964441</td>\n",
       "      <td>0.852774</td>\n",
       "      <td>-0.020435</td>\n",
       "      <td>0.89747</td>\n",
       "      <td>0.914583</td>\n",
       "      <td>-0.328492</td>\n",
       "      <td>-0.625519</td>\n",
       "      <td>0.729541</td>\n",
       "      <td>0.839685</td>\n",
       "      <td>0.629541</td>\n",
       "      <td>0.171458</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.827962</td>\n",
       "      <td>0.162755</td>\n",
       "      <td>0.648748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL1-6</th>\n",
       "      <td>0.68563</td>\n",
       "      <td>-0.034966</td>\n",
       "      <td>0.780743</td>\n",
       "      <td>0.799217</td>\n",
       "      <td>0.261943</td>\n",
       "      <td>0.806027</td>\n",
       "      <td>0.711146</td>\n",
       "      <td>0.391451</td>\n",
       "      <td>-0.049395</td>\n",
       "      <td>0.783517</td>\n",
       "      <td>0.789177</td>\n",
       "      <td>0.071268</td>\n",
       "      <td>0.490409</td>\n",
       "      <td>0.827962</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.471169</td>\n",
       "      <td>0.73353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lenalidomide</th>\n",
       "      <td>0.477669</td>\n",
       "      <td>0.09398</td>\n",
       "      <td>0.17973</td>\n",
       "      <td>0.371792</td>\n",
       "      <td>0.646758</td>\n",
       "      <td>0.284655</td>\n",
       "      <td>0.168508</td>\n",
       "      <td>0.713396</td>\n",
       "      <td>0.31092</td>\n",
       "      <td>0.352765</td>\n",
       "      <td>0.082928</td>\n",
       "      <td>-0.180943</td>\n",
       "      <td>0.942752</td>\n",
       "      <td>0.162755</td>\n",
       "      <td>0.471169</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.479391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TL 13-12</th>\n",
       "      <td>0.543142</td>\n",
       "      <td>-0.166601</td>\n",
       "      <td>0.619421</td>\n",
       "      <td>0.719769</td>\n",
       "      <td>0.052014</td>\n",
       "      <td>0.742565</td>\n",
       "      <td>0.672555</td>\n",
       "      <td>0.284029</td>\n",
       "      <td>0.036555</td>\n",
       "      <td>0.627983</td>\n",
       "      <td>0.541167</td>\n",
       "      <td>0.102699</td>\n",
       "      <td>0.350251</td>\n",
       "      <td>0.648748</td>\n",
       "      <td>0.73353</td>\n",
       "      <td>0.479391</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dBET1 Avadomide    FL1-18  CC-90009 Pomalidomide    CC-885  \\\n",
       "dBET1              1.0 -0.062581  0.636511    0.8662     0.356415  0.808317   \n",
       "Avadomide    -0.062581       1.0  -0.36644  -0.27613     0.861959 -0.384018   \n",
       "FL1-18        0.636511  -0.36644       1.0  0.896998    -0.026192  0.933002   \n",
       "CC-90009        0.8662  -0.27613  0.896998       1.0     0.105754  0.957125   \n",
       "Pomalidomide  0.356415  0.861959 -0.026192  0.105754          1.0 -0.042774   \n",
       "CC-885        0.808317 -0.384018  0.933002  0.957125    -0.042774       1.0   \n",
       "FL1-2         0.565888  -0.47169  0.931628  0.833691    -0.226386  0.898831   \n",
       "FL4-2         0.426981  0.646863 -0.169751  0.211852     0.853705  0.020642   \n",
       "FL4-6         0.079706  0.571876 -0.593286 -0.042999     0.569002  -0.39166   \n",
       "dBET6         0.915383 -0.107897  0.744885  0.924637     0.184224  0.910503   \n",
       "FL2-14        0.468449  0.053011  0.864715  0.817251     0.240973  0.782558   \n",
       "FL6-6        -0.058741 -0.260575  0.517926  0.374775     -0.37597  0.463259   \n",
       "Iberdomide    0.455232  0.480631  0.206487  0.366034     0.864552  0.239874   \n",
       "FL1-12        0.553289 -0.274187  0.964441  0.852774    -0.020435   0.89747   \n",
       "FL1-6          0.68563 -0.034966  0.780743  0.799217     0.261943  0.806027   \n",
       "Lenalidomide  0.477669   0.09398   0.17973  0.371792     0.646758  0.284655   \n",
       "TL 13-12      0.543142 -0.166601  0.619421  0.719769     0.052014  0.742565   \n",
       "\n",
       "                 FL1-2     FL4-2     FL4-6     dBET6    FL2-14     FL6-6  \\\n",
       "dBET1         0.565888  0.426981  0.079706  0.915383  0.468449 -0.058741   \n",
       "Avadomide     -0.47169  0.646863  0.571876 -0.107897  0.053011 -0.260575   \n",
       "FL1-18        0.931628 -0.169751 -0.593286  0.744885  0.864715  0.517926   \n",
       "CC-90009      0.833691  0.211852 -0.042999  0.924637  0.817251  0.374775   \n",
       "Pomalidomide -0.226386  0.853705  0.569002  0.184224  0.240973  -0.37597   \n",
       "CC-885        0.898831  0.020642  -0.39166  0.910503  0.782558  0.463259   \n",
       "FL1-2              1.0 -0.350393 -0.534406  0.695687  0.734281  0.566224   \n",
       "FL4-2        -0.350393       1.0  0.736193  0.248188  0.235827 -0.899763   \n",
       "FL4-6        -0.534406  0.736193       1.0 -0.107956  -0.34605 -0.647547   \n",
       "dBET6         0.695687  0.248188 -0.107956       1.0  0.680975   0.32903   \n",
       "FL2-14        0.734281  0.235827  -0.34605  0.680975       1.0  0.451272   \n",
       "FL6-6         0.566224 -0.899763 -0.647547   0.32903  0.451272       1.0   \n",
       "Iberdomide    0.068955   0.83225  0.272893  0.381409  0.303465 -0.283349   \n",
       "FL1-12        0.914583 -0.328492 -0.625519  0.729541  0.839685  0.629541   \n",
       "FL1-6         0.711146  0.391451 -0.049395  0.783517  0.789177  0.071268   \n",
       "Lenalidomide  0.168508  0.713396   0.31092  0.352765  0.082928 -0.180943   \n",
       "TL 13-12      0.672555  0.284029  0.036555  0.627983  0.541167  0.102699   \n",
       "\n",
       "             Iberdomide    FL1-12     FL1-6 Lenalidomide  TL 13-12  \n",
       "dBET1          0.455232  0.553289   0.68563     0.477669  0.543142  \n",
       "Avadomide      0.480631 -0.274187 -0.034966      0.09398 -0.166601  \n",
       "FL1-18         0.206487  0.964441  0.780743      0.17973  0.619421  \n",
       "CC-90009       0.366034  0.852774  0.799217     0.371792  0.719769  \n",
       "Pomalidomide   0.864552 -0.020435  0.261943     0.646758  0.052014  \n",
       "CC-885         0.239874   0.89747  0.806027     0.284655  0.742565  \n",
       "FL1-2          0.068955  0.914583  0.711146     0.168508  0.672555  \n",
       "FL4-2           0.83225 -0.328492  0.391451     0.713396  0.284029  \n",
       "FL4-6          0.272893 -0.625519 -0.049395      0.31092  0.036555  \n",
       "dBET6          0.381409  0.729541  0.783517     0.352765  0.627983  \n",
       "FL2-14         0.303465  0.839685  0.789177     0.082928  0.541167  \n",
       "FL6-6         -0.283349  0.629541  0.071268    -0.180943  0.102699  \n",
       "Iberdomide          1.0  0.171458  0.490409     0.942752  0.350251  \n",
       "FL1-12         0.171458       1.0  0.827962     0.162755  0.648748  \n",
       "FL1-6          0.490409  0.827962       1.0     0.471169   0.73353  \n",
       "Lenalidomide   0.942752  0.162755  0.471169          1.0  0.479391  \n",
       "TL 13-12       0.350251  0.648748   0.73353     0.479391       1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def test(\n",
    "    selectedFeatures = pd.read_csv(selectedFeatures_path),\n",
    "    tauValues = pd.read_csv(tauOutput_path),\n",
    "    compoundData_path = compoundData_path,\n",
    "    output_path = f\"{output_dir}/cosineSimilarity.csv\"\n",
    "):\n",
    "    \"\"\"\n",
    "    Function for quantifying the pairwise similarities of the treatment-centric features of compounds\n",
    "    predicted to have CRBN-dependent bioactivity (number of treatment-centric features >= 5, corrected\n",
    "    U >= 0.7). The first part consists of renaming the CRBN binders so that it matches the manuscript\n",
    "    and the second part uses the actual function for the quantification. The quantification is done\n",
    "    using the cosine similarity metric.\n",
    "\n",
    "    :param selectedFeatures: Dataframe with the features selected using different strategies.\n",
    "    :type selectedFeatures: pd.DataFrame\n",
    "    :param tauValues: Dataframe with the tau values calculated during treatment-centric feature\n",
    "        selection. The tau values loosely reflect the strength and type of correlation (positive or\n",
    "        negative) between the change in a given feature and the expression of CRBN (or another protein)\n",
    "        across the isogenic cell lines used.\n",
    "    :type tauValues: pd.DataFrame\n",
    "    :param compoundData_path: Full path to the dataframe with calculations/data for each compound\n",
    "    (Corrected U scores and number of treatment-centric features required).\n",
    "    :type compoundData: pd.DataFrame\n",
    "    :param output_path: Path to export the dataframe of cosine similarity values to.\n",
    "    :type output_path: str\n",
    "    \"\"\"\n",
    "    #############################################################################\n",
    "    # Rename the CRBN binders in the selectedFeatures_path and the tauOutput_path\n",
    "    #############################################################################\n",
    "    # Dictionary for mapping the original fluorosulfate scaffold name to the new name\n",
    "    old2newScaffold_dict = {\n",
    "        \"B\" : \"FL1\",\n",
    "        \"D\" : \"FL2\",\n",
    "        \"F\" : \"FL3\",\n",
    "        \"H\" : \"FL4\",\n",
    "        \"C\" : \"FL5\",\n",
    "        \"E\" : \"FL6\"\n",
    "    }\n",
    "\n",
    "    # Update the names in the selectedFeatures and tauValues\n",
    "    renamed_df_list = []\n",
    "    for df, column in zip(\n",
    "        [selectedFeatures, tauValues],\n",
    "        [\"Treatment\", \"Metadata_Treatment\"]\n",
    "    ):\n",
    "        for i in df.index:\n",
    "            compound = df.loc[i, column]\n",
    "            if len(compound) in [2, 3] and compound[0] in old2newScaffold_dict:\n",
    "                scaffold = compound[0]\n",
    "                tailGroup = compound[1:]\n",
    "                newScaffold = old2newScaffold_dict[scaffold]\n",
    "                compound = f\"{newScaffold}-{tailGroup}\"\n",
    "            df.loc[i, column] = compound\n",
    "        renamed_df_list.append(df)\n",
    "\n",
    "    #################################################################################################\n",
    "    # Calculate the cosine similarities of tau values for treatment-centric features for all pairs of\n",
    "    # compounds that have a corrected U score of >= 0.7 and at least 5 treatment-centric features\n",
    "    #################################################################################################\n",
    "    cosine_df = calculate_cosineSimilarity(\n",
    "        selectedFeatures = renamed_df_list[0],\n",
    "        tauValues = renamed_df_list[1],\n",
    "        compoundData = pd.read_csv(compoundData_path),\n",
    "        output_path = output_path,\n",
    "        correctedU_threshold = 0.7,\n",
    "        featureNumber_threshold = 5\n",
    "    )\n",
    "                \n",
    "    return(cosine_df)\n",
    "\n",
    "cosine_df = test()\n",
    "\n",
    "# Have a look at the cosine similarities\n",
    "cosine_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "efa0fe5a-77de-4ebf-ac94-8c807739d832",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for visualizing all cosine similarities as a network\n",
    "# Note: The layout of the network is set to spring_layout which I have found to be the best\n",
    "# for visualizing separate bioactivity groups. This layout format may not be able to cope with\n",
    "# very large datasets.\n",
    "def visualizeAs_network(\n",
    "    cosine_df,\n",
    "    correctedU_threshold,\n",
    "    featureNumber_threshold,\n",
    "    highlightCompounds = [], # names of the compounds to highlight\n",
    "    highlightColor = \"black\",\n",
    "    controls = [], # names of the controls\n",
    "    cosine_threshold = 0.85,\n",
    "    showNodeLabels = True,\n",
    "    output_path = \"\",\n",
    "    marginSpace_xy = (0.0, 0.0), # squeeze the network to ensure all labels can be seen\n",
    "    springLayout_seed = 500,\n",
    "    footnote_position = (0.01, 0.01),\n",
    "    showFootnote = True,\n",
    "    label2color_dict = label2color_dict\n",
    "):\n",
    "    \"\"\"\n",
    "    Visualize the cosine similarities calculated for all pairs of compounds as a force-directed network\n",
    "    graph (i.e. the spring layout using NetworkX).\n",
    "\n",
    "    :param cosine_df: Dataframe of cosine similarity values calculated.\n",
    "    :type cosine_df: pd.DataFrame\n",
    "    :param correctedU_threshold: Minimum corrected U score for a compound to be predicted as having\n",
    "        bioactivity dependent on an effector protein of interest.\n",
    "    :type correctedU_threshold: float (can be from 0 to 1)\n",
    "    :param featureNumber_threshold: Minimum number of treatment-centric features for a compound to have\n",
    "        a reliable corrected U score. In theory, a higher number should reflect higher reliability.\n",
    "    :type featureNumber_threshold: int\n",
    "    :param highlightCompounds: List of compounds to highlight in the network graph by changing the\n",
    "        border color of the compounds' node.\n",
    "    :type highlightCompounds: list\n",
    "    :param highlightColor: Color to use for the border of nodes of compounds to highlight.\n",
    "    :type highlightColor: str\n",
    "    :param controls: List of compound names corresponding to the CRBN-dependent controls.\n",
    "    :type controls: list\n",
    "    :param cosine_threshold: The minimum cosine similarity score for the edge between two nodes to be\n",
    "        thick, representing that two nodes (compounds) have similar treatment-centric features.\n",
    "    :param showNodeLabels: Option to show the labels of the nodes. Defaults to True.\n",
    "    :type showNodeLabels: bool\n",
    "    :param output_path: Path to export the network graph to. Defaults to \"\" which means the network\n",
    "        graph is NOT exported.\n",
    "    :type output_path: str\n",
    "    :param marginSpace_xy: The amount of margin space to give. Adjusting the (x, y) values helps with\n",
    "        ensuring that all labels/nodes of the network graph are fully visible. Defaults to (0.0, 0.0).\n",
    "    :type marginSpace_xy: tuple\n",
    "    :param springLayout_seed: Number of seeds to use for generating the force directed graph. Defaults\n",
    "        to 500.\n",
    "    :type springLayout_seed: int\n",
    "    :param footnote_position: The position of the footnote (if any) on the network graph. Defaults to\n",
    "        (0.01, 0.01).\n",
    "    :type footnote_position: tuple\n",
    "    :param showFootnote: Option to show the footnote that describes the threshold and network layout\n",
    "        used. Defaults to True.\n",
    "    :type showFootnote: bool\n",
    "    :param label2color_dict: Dictionary for color-coding the nodes by their label.\n",
    "    :type label2color_dict: dict\n",
    "    \"\"\"\n",
    "    ####################################################################\n",
    "    # Plot all the compounds as nodes\n",
    "    # and cosine similarities as edges if they pass the cosine_threshold\n",
    "    ####################################################################\n",
    "    # Initialize an empty graph\n",
    "    G = nx.Graph()\n",
    "    \n",
    "    #  Add the compounds as nodes\n",
    "    for treatment in cosine_df.index:\n",
    "        if treatment in controls:\n",
    "            color = label2color_dict[\"CRBN-dependent\"]\n",
    "        else:\n",
    "            color = label2color_dict[\"CRBN binder\"]\n",
    "        G.add_node(treatment, node_color = color)\n",
    "        \n",
    "    # Add the edges\n",
    "    treatmentsWithEdgesAdded = []\n",
    "    for treatment_a in cosine_df.index:\n",
    "        treatmentsWithEdgesAdded.append(treatment_a)\n",
    "        for treatment_b in cosine_df.columns:\n",
    "            if treatment_b not in treatmentsWithEdgesAdded:\n",
    "                cosine = cosine_df.loc[treatment_a, treatment_b]\n",
    "                if cosine >= cosine_threshold:\n",
    "                    if treatment_a in controls or treatment_b in controls:\n",
    "                        passColor = label2color_dict[\"CRBN-dependent\"]\n",
    "                    else:\n",
    "                        passColor = \"black\"\n",
    "                    edgeAesthetics = {\n",
    "                        \"color\" : passColor,\n",
    "                        \"weight\" : 3\n",
    "                    }\n",
    "                else:\n",
    "                    edgeAesthetics = {\n",
    "                        \"color\" : \"lightgrey\",\n",
    "                        \"weight\" : 0.2\n",
    "                    }\n",
    "                G.add_edge(\n",
    "                    treatment_a,\n",
    "                    treatment_b,\n",
    "                    color = edgeAesthetics[\"color\"],\n",
    "                    weight = edgeAesthetics[\"weight\"]\n",
    "                )\n",
    "    \n",
    "    #################################################################################\n",
    "    # Draw the graph and export it if the use has provided the output_path\n",
    "    # Note: Increasing the number of seeds improves how spaced out the nodes can be\n",
    "    # which can be useful when you have more compounds/nodes\n",
    "    #################################################################################\n",
    "    pos = nx.spring_layout(G, iterations = 200, seed = springLayout_seed)\n",
    "    nodeColors = list(nx.get_node_attributes(G, \"node_color\").values())\n",
    "    colors = nx.get_edge_attributes(G, \"color\").values()\n",
    "    weights = nx.get_edge_attributes(G,\"weight\").values()\n",
    "    nx.draw(\n",
    "        G,\n",
    "        pos,\n",
    "        with_labels = showNodeLabels,\n",
    "        font_size = 10,\n",
    "        node_color = nodeColors,\n",
    "        edge_color = colors,\n",
    "        width = list(weights),\n",
    "        node_size = 1600,\n",
    "        # bbox = {\n",
    "        #     \"facecolor\" : \"white\",\n",
    "        #     \"alpha\" : 0.5,\n",
    "        #     \"linewidth\" : 0\n",
    "        # }\n",
    "    )\n",
    "    \n",
    "    # Highlight compounds if any\n",
    "    nodeColors_subset = []\n",
    "    for color, node in zip(nodeColors, list(pos.keys())):\n",
    "        if node in highlightCompounds:\n",
    "            nodeColors_subset.append(color)\n",
    "    if len(highlightCompounds) > 0:\n",
    "        nx.draw_networkx_nodes(\n",
    "            G,\n",
    "            pos, nodelist = highlightCompounds,\n",
    "            node_color = nodeColors_subset,\n",
    "            node_size = 1620,\n",
    "            linewidths = 5,\n",
    "            edgecolors = highlightColor\n",
    "        )\n",
    "    \n",
    "    # Squeeze the network graph to fit the margins and make space for the labels\n",
    "    # either horizontally (increase x)\n",
    "    # or vertically (increse y)\n",
    "    x, y = marginSpace_xy\n",
    "    plt.margins(x = x, y = y)\n",
    "    \n",
    "    # Add footnote describing the settings used\n",
    "    footnote = f\"\"\"Network layout: spring\n",
    "Minimum number of treatment-centric features: {featureNumber_threshold}\n",
    "Minimum corrected U score: {correctedU_threshold}\n",
    "Cosine similarity threshold: {cosine_threshold}\n",
    "    \"\"\"\n",
    "    x, y = footnote_position\n",
    "    if showFootnote == True:\n",
    "        plt.figtext(x, y, footnote, ha = \"left\")\n",
    "    else:\n",
    "        print(footnote)\n",
    "    \n",
    "    # Save the plot if the user has provided the output_path\n",
    "    savePlot(output_path)\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c885677-26cb-45c6-97f9-c57233cf08c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network layout: spring\n",
      "Minimum number of treatment-centric features: 5\n",
      "Minimum corrected U score: 0.7\n",
      "Cosine similarity threshold: 0.85\n",
      "    \n",
      "EXPORTED: The plot has been exported at\n",
      "/research/lab_winter/users/ang/projects/GW015_SuFEX_IMiDs/GW015_006__full_cpa/cleanPostFeatureExtraction/output_dir/4_ProfileInterpretation_output/networkGraph.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizeAs_network(\n",
    "    cosine_df = cosine_df,\n",
    "    correctedU_threshold = 0.7,\n",
    "    featureNumber_threshold = 5,\n",
    "    highlightCompounds = [\"FL2-14\"],\n",
    "    controls = [\n",
    "        \"Pomalidomide\", \"Iberdomide\", \"Lenalidomide\", \"Avadomide\",\n",
    "        \"dBET1\", \"dBET6\", \"CC-885\", \"CC-90009\", \"TL 13-12\"\n",
    "    ],\n",
    "    cosine_threshold = 0.85,\n",
    "    output_path = f\"{output_dir}/networkGraph.png\",\n",
    "    marginSpace_xy = (0.1, 0.1),\n",
    "    footnote_position = (0.01, 0.01),\n",
    "    showFootnote = False\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}