cell-painting/3.application/moa components reproducibility vs predictability.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "56617555",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "sys.path.insert(0, \"../../scripts\")\n",
    "from utils import load_data\n",
    "\n",
    "\n",
    "from pycytominer.cyto_utils import infer_cp_features\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from tensorflow import keras\n",
    "\n",
    "from vae import VAE\n",
    "\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "import seaborn\n",
    "import random as python_random\n",
    "import tensorflow as tf\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from scipy import stats\n",
    "from scipy.stats import pearsonr\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1015eb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_splits = [\"complete\"]\n",
    "data_dict = load_data(data_splits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fd59c601",
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_features = infer_cp_features(data_dict[\"complete\"])\n",
    "cp_metadata = infer_cp_features(data_dict[\"complete\"], metadata = True)\n",
    "complete_features_df = data_dict[\"complete\"].reindex(cp_features, axis=\"columns\")\n",
    "complete_metadata_df = data_dict[\"complete\"].reindex(cp_metadata, axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9d439f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "decoder_beta = keras.models.load_model(\"../2.train/models/level5Decoder_beta\")\n",
    "decoder_vanilla = keras.models.load_model(\"../2.train/models/level5Decoder_vanilla\")\n",
    "decoder_mmd = keras.models.load_model(\"../2.train/models/level5Decoder_mmd\")\n",
    "encoder_beta = keras.models.load_model(\"../2.train/models/level5Encoder_beta\")\n",
    "encoder_vanilla = keras.models.load_model(\"../2.train/models/level5Encoder_vanilla\")\n",
    "encoder_mmd = keras.models.load_model(\"../2.train/models/level5Encoder_mmd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6bf567d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "reconstruction_vanilla = decoder_vanilla.predict(encoder_vanilla.predict(complete_features_df)[0])\n",
    "reconstruction_beta = decoder_beta.predict(encoder_beta.predict(complete_features_df)[0])\n",
    "reconstruction_mmd = decoder_mmd.predict(encoder_mmd.predict(complete_features_df)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "918a4e5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "correlation_vanilla = pd.DataFrame(complete_features_df.corrwith(pd.DataFrame(reconstruction_vanilla, columns = cp_features), axis = 1)).assign(moa = pd.read_csv('moa_metadata.csv')['moa'])\n",
    "correlation_beta = pd.DataFrame(complete_features_df.corrwith(pd.DataFrame(reconstruction_beta, columns = cp_features), axis = 1)).assign(moa = pd.read_csv('moa_metadata.csv')['moa'])\n",
    "correlation_mmd = pd.DataFrame(complete_features_df.corrwith(pd.DataFrame(reconstruction_mmd, columns = cp_features), axis = 1)).assign(moa = pd.read_csv('moa_metadata.csv')['moa'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2bae426e",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_vanilla = correlation_vanilla.dropna()\n",
    "# correlation_vanilla = correlation_vanilla[correlation_vanilla.moa.str.contains('\\|')]\n",
    "\n",
    "correlation_beta = correlation_beta.dropna()\n",
    "# correlation_beta = correlation_beta[correlation_beta.moa.str.contains('\\|')]\n",
    "\n",
    "correlation_mmd = correlation_mmd.dropna()\n",
    "# correlation_mmd = correlation_mmd[correlation_mmd.moa.str.contains('\\|')]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7c372910",
   "metadata": {},
   "outputs": [],
   "source": [
    "vanilla_moa_reproducibility = correlation_vanilla.groupby('moa').median()\n",
    "beta_moa_reproducibility = correlation_beta.groupby('moa').median()\n",
    "mmd_moa_reproducibility = correlation_mmd.groupby('moa').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6f4c9ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "vanilla = pd.DataFrame(pd.read_csv('per_moa_performance/level5_vanilla_moa_performance.csv').set_index('MOA')['zscore'])\n",
    "beta = pd.DataFrame(pd.read_csv('per_moa_performance/level5_beta_moa_performance.csv').set_index('MOA')['zscore'])\n",
    "mmd = pd.DataFrame(pd.read_csv('per_moa_performance/level5_mmd_moa_performance.csv').set_index('MOA')['zscore'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "de6dd46d",
   "metadata": {},
   "outputs": [],
   "source": [
    "vanilla = pd.DataFrame(- np.log(stats.norm.sf(-vanilla)), columns = ['Vanilla MOA pvalue']).set_index(vanilla.index)\n",
    "beta = pd.DataFrame(- np.log(stats.norm.sf(-beta)), columns = ['Beta MOA pvalue']).set_index(beta.index)\n",
    "mmd = pd.DataFrame(- np.log(stats.norm.sf(-mmd)), columns = ['MMD MOA pvalue']).set_index(mmd.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c954810b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vanilla MOA pvalue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOA</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NFkB pathway inhibitor|proteasome inhibitor</th>\n",
       "      <td>7.122859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>microtubule inhibitor|tubulin polymerization inhibitor</th>\n",
       "      <td>7.055568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aurora kinase inhibitor|JAK inhibitor</th>\n",
       "      <td>6.982753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA inhibitor|topoisomerase inhibitor</th>\n",
       "      <td>6.800797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mTOR inhibitor|PI3K inhibitor</th>\n",
       "      <td>6.459888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDK inhibitor|glycogen synthase kinase inhibitor</th>\n",
       "      <td>0.006137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>benzodiazepine receptor agonist|HDAC inhibitor</th>\n",
       "      <td>0.004508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>antimalarial agent|DNA inhibitor</th>\n",
       "      <td>0.001883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cytochrome P450 inhibitor|protein synthesis inhibitor</th>\n",
       "      <td>0.001476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR inhibitor|protein tyrosine kinase inhibitor</th>\n",
       "      <td>0.001371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Vanilla MOA pvalue\n",
       "MOA                                                                   \n",
       "NFkB pathway inhibitor|proteasome inhibitor                   7.122859\n",
       "microtubule inhibitor|tubulin polymerization in...            7.055568\n",
       "Aurora kinase inhibitor|JAK inhibitor                         6.982753\n",
       "DNA inhibitor|topoisomerase inhibitor                         6.800797\n",
       "mTOR inhibitor|PI3K inhibitor                                 6.459888\n",
       "...                                                                ...\n",
       "CDK inhibitor|glycogen synthase kinase inhibitor              0.006137\n",
       "benzodiazepine receptor agonist|HDAC inhibitor                0.004508\n",
       "antimalarial agent|DNA inhibitor                              0.001883\n",
       "cytochrome P450 inhibitor|protein synthesis inh...            0.001476\n",
       "EGFR inhibitor|protein tyrosine kinase inhibitor              0.001371\n",
       "\n",
       "[84 rows x 1 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vanilla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1a7f7f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "vanilla['MOA components reproducibility'] = vanilla_moa_reproducibility.loc[vanilla.index.str.split('\\|').str[0]].set_index(vanilla.index) + vanilla_moa_reproducibility.loc[vanilla.index.str.split('\\|').str[1]].set_index(vanilla.index)\n",
    "beta['MOA components reproducibility'] = beta_moa_reproducibility.loc[beta.index.str.split('\\|').str[0]].set_index(beta.index) + beta_moa_reproducibility.loc[beta.index.str.split('\\|').str[1]].set_index(beta.index)\n",
    "mmd['MOA components reproducibility'] = mmd_moa_reproducibility.loc[mmd.index.str.split('\\|').str[0]].set_index(mmd.index) + mmd_moa_reproducibility.loc[mmd.index.str.split('\\|').str[1]].set_index(mmd.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "70a302b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vanilla = pd.concat([vanilla_moa_reproducibility, pd.DataFrame(- np.log(stats.norm.sf(-vanilla_predictability))).assign(MOA = vanilla_predictability.index).set_index('MOA')], axis = 1)\n",
    "# beta = pd.concat([beta_moa_reproducibility, pd.DataFrame(- np.log(stats.norm.sf(-beta_predictability))).assign(MOA = beta_predictability.index).set_index('MOA')], axis = 1)\n",
    "# mmd = pd.concat([mmd_moa_reproducibility, pd.DataFrame(- np.log(stats.norm.sf(-mmd_predictability))).assign(MOA = mmd_predictability.index).set_index('MOA')], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ce2da648",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vanilla.columns = ['MOA reproducibility', 'Vanilla MOA predictability']\n",
    "# beta.columns = ['MOA reproducibility', 'Beta MOA predictability']\n",
    "# mmd.columns = ['MOA reproducibility', 'MMD MOA predictability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f272bd40",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a08a2a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vanilla MOA pvalue</th>\n",
       "      <th>MOA components reproducibility</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NFkB pathway inhibitor|proteasome inhibitor</th>\n",
       "      <td>7.122859</td>\n",
       "      <td>1.909088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>microtubule inhibitor|tubulin polymerization inhibitor</th>\n",
       "      <td>7.055568</td>\n",
       "      <td>1.661931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aurora kinase inhibitor|JAK inhibitor</th>\n",
       "      <td>6.982753</td>\n",
       "      <td>1.858270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA inhibitor|topoisomerase inhibitor</th>\n",
       "      <td>6.800797</td>\n",
       "      <td>1.907689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mTOR inhibitor|PI3K inhibitor</th>\n",
       "      <td>6.459888</td>\n",
       "      <td>1.811119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDK inhibitor|glycogen synthase kinase inhibitor</th>\n",
       "      <td>0.006137</td>\n",
       "      <td>1.579115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>benzodiazepine receptor agonist|HDAC inhibitor</th>\n",
       "      <td>0.004508</td>\n",
       "      <td>1.842450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>antimalarial agent|DNA inhibitor</th>\n",
       "      <td>0.001883</td>\n",
       "      <td>1.881038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cytochrome P450 inhibitor|protein synthesis inhibitor</th>\n",
       "      <td>0.001476</td>\n",
       "      <td>1.914585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR inhibitor|protein tyrosine kinase inhibitor</th>\n",
       "      <td>0.001371</td>\n",
       "      <td>1.819712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Vanilla MOA pvalue  \\\n",
       "MOA                                                                      \n",
       "NFkB pathway inhibitor|proteasome inhibitor                   7.122859   \n",
       "microtubule inhibitor|tubulin polymerization in...            7.055568   \n",
       "Aurora kinase inhibitor|JAK inhibitor                         6.982753   \n",
       "DNA inhibitor|topoisomerase inhibitor                         6.800797   \n",
       "mTOR inhibitor|PI3K inhibitor                                 6.459888   \n",
       "...                                                                ...   \n",
       "CDK inhibitor|glycogen synthase kinase inhibitor              0.006137   \n",
       "benzodiazepine receptor agonist|HDAC inhibitor                0.004508   \n",
       "antimalarial agent|DNA inhibitor                              0.001883   \n",
       "cytochrome P450 inhibitor|protein synthesis inh...            0.001476   \n",
       "EGFR inhibitor|protein tyrosine kinase inhibitor              0.001371   \n",
       "\n",
       "                                                    MOA components reproducibility  \n",
       "MOA                                                                                 \n",
       "NFkB pathway inhibitor|proteasome inhibitor                               1.909088  \n",
       "microtubule inhibitor|tubulin polymerization in...                        1.661931  \n",
       "Aurora kinase inhibitor|JAK inhibitor                                     1.858270  \n",
       "DNA inhibitor|topoisomerase inhibitor                                     1.907689  \n",
       "mTOR inhibitor|PI3K inhibitor                                             1.811119  \n",
       "...                                                                            ...  \n",
       "CDK inhibitor|glycogen synthase kinase inhibitor                          1.579115  \n",
       "benzodiazepine receptor agonist|HDAC inhibitor                            1.842450  \n",
       "antimalarial agent|DNA inhibitor                                          1.881038  \n",
       "cytochrome P450 inhibitor|protein synthesis inh...                        1.914585  \n",
       "EGFR inhibitor|protein tyrosine kinase inhibitor                          1.819712  \n",
       "\n",
       "[84 rows x 2 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vanilla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "63e2d25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "vanilla = pd.concat([vanilla, pd.concat([pd.read_csv('moas.csv'), pd.read_csv('L2_distances/level5_PEARSON_general_vanilla.tsv', sep = '\\t').dropna()], axis = 1)[['moa','Unshuffled']].set_index('moa')], axis = 1)\n",
    "beta = pd.concat([beta, pd.concat([pd.read_csv('moas.csv'), pd.read_csv('L2_distances/level5_PEARSON_general_beta.tsv', sep = '\\t').dropna()], axis = 1)[['moa','Unshuffled']].set_index('moa')], axis = 1)\n",
    "mmd = pd.concat([mmd, pd.concat([pd.read_csv('moas.csv'), pd.read_csv('L2_distances/level5_PEARSON_general_mmd.tsv', sep = '\\t').dropna()], axis = 1)[['moa','Unshuffled']].set_index('moa')], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "03802397",
   "metadata": {},
   "outputs": [],
   "source": [
    "vanilla = vanilla.rename(columns={\"Unshuffled\": \"Vanilla MOA predictability\"})\n",
    "beta = beta.rename(columns={\"Unshuffled\": \"Beta MOA predictability\"})\n",
    "mmd = mmd.rename(columns={\"Unshuffled\": \"MMD MOA predictability\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "657f4cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MOA components reproducibility', ylabel='MMD MOA predictability'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x2700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2, ax3)) = plt.subplots(3,1, figsize=(6, 9), dpi=300, sharex = True)\n",
    "sns.scatterplot(ax = ax1, data = vanilla, x = 'MOA components reproducibility', y = 'Vanilla MOA predictability')\n",
    "sns.scatterplot(ax = ax2, data = beta, x = 'MOA components reproducibility', y = 'Beta MOA predictability')\n",
    "sns.scatterplot(ax = ax3, data = mmd, x = 'MOA components reproducibility', y = 'MMD MOA predictability')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f12044c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pearsonr(vanilla['Unshuffled'], vanilla['MOA components reproducibility'])[0])\n",
    "print(pearsonr(beta['Unshuffled'], beta['MOA components reproducibility'])[0])\n",
    "print(pearsonr(mmd['Unshuffled'], mmd['MOA components reproducibility']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "4ffe59b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47759192609389\n",
      "0.335177416480579\n",
      "0.4041211757606379\n"
     ]
    }
   ],
   "source": [
    "print(pearsonr(vanilla['Vanilla MOA predictability'], vanilla['MOA components reproducibility'])[0])\n",
    "print(pearsonr(beta['Beta MOA predictability'], beta['MOA components reproducibility'])[0])\n",
    "print(pearsonr(mmd['MMD MOA predictability'], mmd['MOA components reproducibility'])[0])"
   ]
  }
 ],
 "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}