Hub_species_IS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import scipy\n",
    "import networkx as nx\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import cloudpickle as pickle\n",
    "import copy\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all' #last_expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abb.</th>\n",
       "      <th>normal_hits_pval</th>\n",
       "      <th>obese_hits_pval</th>\n",
       "      <th>nash_hits_pval</th>\n",
       "      <th>obese_intervention_score</th>\n",
       "      <th>nash_intervention_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTU</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f6944e5be5cca661767014ab95867c4dc3848235</th>\n",
       "      <td>P. loveana</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9075bd7b4542aad1faf685d8960946088cca0810</th>\n",
       "      <td>A. indistinctus(OTU57)</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c84919371da9c309c2b7a50ab6ed836c0c9555e6</th>\n",
       "      <td>D. pneumosintes</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4c6d7a5409ae36dbf1be703072de0fc8872933ca</th>\n",
       "      <td>A. caccae</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.281</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d32c3a758a590f2e70b54af0f90eceb9730958bc</th>\n",
       "      <td>Lachnospiraceae(OTU85)</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543e9142bfd56e3312822277113e06cbb521aa91</th>\n",
       "      <td>A. ruminis(OTU2)</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2725fb5dd2001597d9a103735ec633f4fec723af</th>\n",
       "      <td>R. lactaris(OTU0)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0cfa2d530d8bb8deb60488dd1a5ae30b0783b626</th>\n",
       "      <td>B. caecicola(OTU72)</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d8d5f629119e6c50f9e7c95b6240649272c930cd</th>\n",
       "      <td>B. gallinaceum(OTU83)</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20218ad750875d16c9e5642a9a5b42f360b4964d</th>\n",
       "      <td>B. propionicifaciens(OTU29)</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 Abb.  \\\n",
       "OTU                                                                     \n",
       "f6944e5be5cca661767014ab95867c4dc3848235                   P. loveana   \n",
       "9075bd7b4542aad1faf685d8960946088cca0810       A. indistinctus(OTU57)   \n",
       "c84919371da9c309c2b7a50ab6ed836c0c9555e6              D. pneumosintes   \n",
       "4c6d7a5409ae36dbf1be703072de0fc8872933ca                    A. caccae   \n",
       "d32c3a758a590f2e70b54af0f90eceb9730958bc       Lachnospiraceae(OTU85)   \n",
       "...                                                               ...   \n",
       "543e9142bfd56e3312822277113e06cbb521aa91             A. ruminis(OTU2)   \n",
       "2725fb5dd2001597d9a103735ec633f4fec723af            R. lactaris(OTU0)   \n",
       "0cfa2d530d8bb8deb60488dd1a5ae30b0783b626          B. caecicola(OTU72)   \n",
       "d8d5f629119e6c50f9e7c95b6240649272c930cd        B. gallinaceum(OTU83)   \n",
       "20218ad750875d16c9e5642a9a5b42f360b4964d  B. propionicifaciens(OTU29)   \n",
       "\n",
       "                                          normal_hits_pval  obese_hits_pval  \\\n",
       "OTU                                                                           \n",
       "f6944e5be5cca661767014ab95867c4dc3848235             0.013            0.929   \n",
       "9075bd7b4542aad1faf685d8960946088cca0810             0.825            0.962   \n",
       "c84919371da9c309c2b7a50ab6ed836c0c9555e6             0.852            0.001   \n",
       "4c6d7a5409ae36dbf1be703072de0fc8872933ca             0.016            0.001   \n",
       "d32c3a758a590f2e70b54af0f90eceb9730958bc             0.014            0.933   \n",
       "...                                                    ...              ...   \n",
       "543e9142bfd56e3312822277113e06cbb521aa91             0.001            0.669   \n",
       "2725fb5dd2001597d9a103735ec633f4fec723af             1.000            1.000   \n",
       "0cfa2d530d8bb8deb60488dd1a5ae30b0783b626             0.765            0.308   \n",
       "d8d5f629119e6c50f9e7c95b6240649272c930cd             0.104            0.857   \n",
       "20218ad750875d16c9e5642a9a5b42f360b4964d             0.365            0.948   \n",
       "\n",
       "                                          nash_hits_pval  \\\n",
       "OTU                                                        \n",
       "f6944e5be5cca661767014ab95867c4dc3848235           0.083   \n",
       "9075bd7b4542aad1faf685d8960946088cca0810           0.001   \n",
       "c84919371da9c309c2b7a50ab6ed836c0c9555e6           0.005   \n",
       "4c6d7a5409ae36dbf1be703072de0fc8872933ca           0.664   \n",
       "d32c3a758a590f2e70b54af0f90eceb9730958bc           0.756   \n",
       "...                                                  ...   \n",
       "543e9142bfd56e3312822277113e06cbb521aa91           0.318   \n",
       "2725fb5dd2001597d9a103735ec633f4fec723af           1.000   \n",
       "0cfa2d530d8bb8deb60488dd1a5ae30b0783b626           0.022   \n",
       "d8d5f629119e6c50f9e7c95b6240649272c930cd           0.001   \n",
       "20218ad750875d16c9e5642a9a5b42f360b4964d           0.001   \n",
       "\n",
       "                                          obese_intervention_score  \\\n",
       "OTU                                                                  \n",
       "f6944e5be5cca661767014ab95867c4dc3848235                     0.059   \n",
       "9075bd7b4542aad1faf685d8960946088cca0810                     0.022   \n",
       "c84919371da9c309c2b7a50ab6ed836c0c9555e6                     0.279   \n",
       "4c6d7a5409ae36dbf1be703072de0fc8872933ca                     0.281   \n",
       "d32c3a758a590f2e70b54af0f90eceb9730958bc                     0.025   \n",
       "...                                                            ...   \n",
       "543e9142bfd56e3312822277113e06cbb521aa91                     0.184   \n",
       "2725fb5dd2001597d9a103735ec633f4fec723af                     0.000   \n",
       "0cfa2d530d8bb8deb60488dd1a5ae30b0783b626                     0.025   \n",
       "d8d5f629119e6c50f9e7c95b6240649272c930cd                    -0.059   \n",
       "20218ad750875d16c9e5642a9a5b42f360b4964d                     0.005   \n",
       "\n",
       "                                          nash_intervention_score  \n",
       "OTU                                                                \n",
       "f6944e5be5cca661767014ab95867c4dc3848235                    0.346  \n",
       "9075bd7b4542aad1faf685d8960946088cca0810                    0.206  \n",
       "c84919371da9c309c2b7a50ab6ed836c0c9555e6                    0.294  \n",
       "4c6d7a5409ae36dbf1be703072de0fc8872933ca                    0.042  \n",
       "d32c3a758a590f2e70b54af0f90eceb9730958bc                   -0.039  \n",
       "...                                                           ...  \n",
       "543e9142bfd56e3312822277113e06cbb521aa91                    0.138  \n",
       "2725fb5dd2001597d9a103735ec633f4fec723af                    0.000  \n",
       "0cfa2d530d8bb8deb60488dd1a5ae30b0783b626                    0.056  \n",
       "d8d5f629119e6c50f9e7c95b6240649272c930cd                    0.140  \n",
       "20218ad750875d16c9e5642a9a5b42f360b4964d                    0.181  \n",
       "\n",
       "[97 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Data/Table S2.csv', index_col=0)\n",
    "data = data.loc[:, ['Abb.', 'normal_hits_pval', 'obese_hits_pval', 'nash_hits_pval', \n",
    "                    'obese_intervention_score', 'nash_intervention_score']]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hub_cutoff = 0.05\n",
    "\n",
    "normal_obese_hub = (data['normal_hits_pval']<hub_cutoff)|(data['obese_hits_pval']<hub_cutoff)\n",
    "sum(normal_obese_hub)\n",
    "obese_hub_scores = data.loc[normal_obese_hub, 'obese_intervention_score']\n",
    "obese_nohub_scores = data.loc[-normal_obese_hub, 'obese_intervention_score']\n",
    "\n",
    "normal_nash_hub = (data['normal_hits_pval']<hub_cutoff)|(data['nash_hits_pval']<hub_cutoff)\n",
    "sum(normal_nash_hub)\n",
    "nash_hub_scores = data.loc[normal_nash_hub, 'nash_intervention_score']\n",
    "nash_nohub_scores = data.loc[-normal_nash_hub, 'nash_intervention_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RanksumsResult(statistic=6.450931263304121, pvalue=1.1116491464821352e-10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RanksumsResult(statistic=3.890852068671633, pvalue=9.98928223655761e-05)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 3000x2400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import ranksums, ttest_ind, ks_2samp\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.lines as mlines\n",
    "_ = plt.figure(figsize=(10, 8), dpi=300)\n",
    "gs = gridspec.GridSpec(1, 2)\n",
    "gs.update(left=0.1, right=0.95, wspace=0.2, hspace=0.05)\n",
    "\n",
    "### Obese\n",
    "ranksums(obese_hub_scores, obese_nohub_scores)\n",
    "res = []\n",
    "for i in obese_hub_scores:\n",
    "    res.append(['Hub', i])\n",
    "for i in obese_nohub_scores:\n",
    "    res.append(['Others', i])\n",
    "res = pd.DataFrame(res, columns=['Hub', 'Intervention Score'])\n",
    "ax = plt.subplot(gs[0, 0])\n",
    "p = sns.boxplot(x=\"Hub\", y=\"Intervention Score\", data=res, palette=['#ec6d71', '#89c3eb'], ax=ax)\n",
    "_ = p.set_xticklabels(['Normal or Obese hubs(N='+str(len(obese_hub_scores))+')', 'Others(N='+str(len(obese_nohub_scores))+')'], fontsize=12)\n",
    "_ = p.set_xlabel('Obese', fontsize=15)\n",
    "_ = p.set_ylabel('Intervention Scores', fontsize=15)\n",
    "_ = ax.add_line(mlines.Line2D([0, 1], [0.42, 0.42], lw=1., c='k'))\n",
    "_ = ax.add_line(mlines.Line2D([0, 0], [0.41, 0.42], lw=1., c='k'))\n",
    "_ = ax.add_line(mlines.Line2D([1, 1], [0.29, 0.42], lw=1., c='k'))\n",
    "_ = ax.text(0.5, 0.42, '**', ha=\"center\", fontsize=20)\n",
    "_ = ax.set_ylim([-0.26, 0.46])\n",
    "_ = plt.tick_params(labelsize=12) \n",
    "\n",
    "### NASH\n",
    "ranksums(nash_hub_scores, nash_nohub_scores)\n",
    "res = []\n",
    "for i in nash_hub_scores:\n",
    "    res.append(['Hub', i])\n",
    "for i in nash_nohub_scores:\n",
    "    res.append(['Others', i])\n",
    "res = pd.DataFrame(res, columns=['Hub', 'Intervention Score'])\n",
    "ax = plt.subplot(gs[0, 1])\n",
    "p = sns.boxplot(x=\"Hub\", y=\"Intervention Score\", data=res, palette=['#ec6d71', '#89c3eb'], ax=ax)\n",
    "_ = p.set_xticklabels(['Normal or NASH hubs(N='+str(len(nash_hub_scores))+')', 'Others(N='+str(len(nash_nohub_scores))+')'], fontsize=12)\n",
    "#_ = ax.set_yticklabels(ax.get_yticklabels(),fontsize=12)\n",
    "_ = p.set_xlabel('NASH', fontsize=15)\n",
    "_ = p.set_ylabel('')\n",
    "_ = ax.add_line(mlines.Line2D([0, 1], [0.42, 0.42], lw=1., c='k'))\n",
    "_ = ax.add_line(mlines.Line2D([0, 0], [0.40, 0.42], lw=1., c='k'))\n",
    "_ = ax.add_line(mlines.Line2D([1, 1], [0.26, 0.42], lw=1., c='k'))\n",
    "_ = ax.text(0.5, 0.42, '**', ha=\"center\", fontsize=20)\n",
    "_ = ax.set_ylim([-0.26, 0.46])\n",
    "\n",
    "_ = plt.tick_params(labelsize=12) \n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}