GLM_model_ratio_analysis_shortChain_fattyAcid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f41b002",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from paths import *\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e598e4b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(os.path.join(OUTPUT_PATH, \"GLM_shortChain_fattyAcid_without_outlier_sample_serum_feces_ratio.joblib\"))\n",
    "\n",
    "model_list = [model]\n",
    "model_name_list = [\"Without outlier\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f1416c4",
   "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>analyte_name</th>\n",
       "      <th>disease_coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2-Methylbutyric acid</td>\n",
       "      <td>-0.133243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acetic acid</td>\n",
       "      <td>0.313652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Butyric acid</td>\n",
       "      <td>0.300880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hexanoic acid</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Isobutyric acid</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Isovaleric acid</td>\n",
       "      <td>-0.161534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Propionic acid</td>\n",
       "      <td>0.297206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Valeric acid</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           analyte_name  disease_coeff\n",
       "0  2-Methylbutyric acid      -0.133243\n",
       "1           Acetic acid       0.313652\n",
       "2          Butyric acid       0.300880\n",
       "3         Hexanoic acid            NaN\n",
       "4       Isobutyric acid            NaN\n",
       "5       Isovaleric acid      -0.161534\n",
       "6        Propionic acid       0.297206\n",
       "7          Valeric acid            NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_coeff = []\n",
    "pval_thresh = 0.05\n",
    "\n",
    "for model in model_list:\n",
    "    disease_coeff_model = []\n",
    "    for item in model:\n",
    "        if (item[\"model\"].pvalues[\"Disease_label\"] < pval_thresh) & (item[\"model\"].converged==1):\n",
    "            disease_coeff_model.append(item[\"model\"].params[\"Disease_label\"])\n",
    "        else:\n",
    "            disease_coeff_model.append(None)\n",
    "    disease_coeff.append(disease_coeff_model)\n",
    "        \n",
    "analyte_list = []\n",
    "for item in model_1:\n",
    "    analyte_list.append(item[\"analyte\"])\n",
    "    \n",
    "disease_coeff = np.array(disease_coeff).transpose()\n",
    "disease_coeff[disease_coeff == None] = np.nan\n",
    "disease_coeff = np.where(disease_coeff == None, np.nan, disease_coeff).astype(float)\n",
    "disease_coeff_df = pd.DataFrame(disease_coeff, index=analyte_list, columns=model_name_list)\n",
    "disease_coeff_df.reset_index(inplace=True)\n",
    "disease_coeff_df.rename(columns={\"index\":\"analyte_name\", \"Without outlier\":\"disease_coeff\"}, inplace=True)\n",
    "disease_coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91aa7d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_df = disease_coeff_df.dropna().sort_values(by=\"disease_coeff\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f03e2238",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving the figure ...\n"
     ]
    }
   ],
   "source": [
    "FIG_SIZE = (4, 5)\n",
    "FONTSIZE = 13\n",
    "LABELSIZE = 15\n",
    "\n",
    "filename_suffix_list = [\"combined_population\", \"ms_treated_population\", \"ms_not_treated_population\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=FIG_SIZE)\n",
    "plt.barh(sorted_df[\"analyte_name\"], sorted_df[\"disease_coeff\"])\n",
    "plt.xticks(fontsize=FONTSIZE)\n",
    "plt.yticks(fontsize=FONTSIZE)\n",
    "plt.xlabel('Disease coeff for Serum/Feces ratio', fontsize=LABELSIZE)\n",
    "# plt.ylabel('Analyte name', fontsize=LABELSIZE)\n",
    "plt.show()\n",
    "\n",
    "print('Saving the figure ...')\n",
    "fig_filename = os.path.join(OUTPUT_PATH, \"figures\", \"disease_coeff_plot_for_the_serum_feces_ratio_for_targeted_compounds_using_combined_population.svg\")\n",
    "fig.savefig(fig_filename, format='svg', bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ab70e4",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}