GLM_model_analysis_shortChain_fattyAcid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49e282a5",
   "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": "markdown",
   "id": "a26fae0d",
   "metadata": {},
   "source": [
    "## Load models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "04989791",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = \"serum\"\n",
    "\n",
    "model_1_name = \"GLM_shortChain_fattyAcid_without_outlier_sample_{}.joblib\".format(sample)\n",
    "model_2_name = \"GLM_shortChain_fattyAcid_without_outlier_ms_treated_sample_{}.joblib\".format(sample)\n",
    "model_3_name = \"GLM_shortChain_fattyAcid_without_outlier_ms_not_treated_sample_{}.joblib\".format(sample)\n",
    "model_4_name = \"GLM_shortChain_fattyAcid_with_outlier_sample_{}.joblib\".format(sample)\n",
    "model_5_name = \"GLM_shortChain_fattyAcid_with_outlier_ms_treated_sample_{}.joblib\".format(sample)\n",
    "model_6_name = \"GLM_shortChain_fattyAcid_with_outlier_ms_not_treated_sample_{}.joblib\".format(sample)\n",
    "\n",
    "model_1 = joblib.load(os.path.join(OUTPUT_PATH, model_1_name))\n",
    "model_2 = joblib.load(os.path.join(OUTPUT_PATH, model_2_name))\n",
    "model_3 = joblib.load(os.path.join(OUTPUT_PATH, model_3_name))\n",
    "model_4 = joblib.load(os.path.join(OUTPUT_PATH, model_4_name))\n",
    "model_5 = joblib.load(os.path.join(OUTPUT_PATH, model_5_name))\n",
    "model_6 = joblib.load(os.path.join(OUTPUT_PATH, model_6_name))\n",
    "\n",
    "model_list = [model_1, model_2, model_3, model_4, model_5, model_6]\n",
    "model_name_list = [\"Without outlier\", \"Without outlier-MS treated\", \"Without outlier-MS not treated\",\n",
    "                  \"With outlier\", \"With outlier-MStreated\", \"With outlier-MS not treated\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9513f825",
   "metadata": {},
   "source": [
    "## Write model results to an excel sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f1196327",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_model_summary_list = []\n",
    "for index, model in enumerate(model_list):\n",
    "    model_summary_list = []\n",
    "    for item in model:\n",
    "        analyte_name = item[\"analyte\"]\n",
    "        try:            \n",
    "            disease_coeff = item[\"model\"].params[\"Disease_label\"]\n",
    "            disease_coeff_pvalue = item[\"model\"].pvalues[\"Disease_label\"]\n",
    "            disease_coeff_CI = tuple(item[\"model\"].conf_int().loc['Disease_label'])\n",
    "            model_converged_flag = item[\"model\"].converged\n",
    "        except:\n",
    "            disease_coeff = None\n",
    "            disease_coeff_pvalue = None\n",
    "            disease_coeff_CI = None\n",
    "            model_converged_flag = None\n",
    "        model_summary_list.append((analyte_name, disease_coeff, disease_coeff_pvalue, disease_coeff_CI, model_converged_flag))        \n",
    "    all_model_summary_list.append(pd.DataFrame(model_summary_list, columns=[\"analyte_name\", \"disease_coeff\", \"pvalue\", \"CI\", \"model_converged_flag\"]))\n",
    "\n",
    "# Create ExcelWriter object\n",
    "writer = pd.ExcelWriter(os.path.join(OUTPUT_PATH, 'GLM_result_targeted_compounds_{}_sample.xlsx'.format(sample)))\n",
    "\n",
    "# Loop through each DataFrame and save to a separate sheet\n",
    "for i, df in enumerate(all_model_summary_list):\n",
    "    sheet_name = model_name_list[i]\n",
    "    df.to_excel(writer, sheet_name=sheet_name, index=False)\n",
    "    \n",
    "# Save and close the ExcelWriter object\n",
    "writer.save()\n",
    "writer.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d96da0",
   "metadata": {},
   "source": [
    "## Parsing model results for downstream analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0fcb5200",
   "metadata": {},
   "outputs": [],
   "source": [
    "disease_coeff = []\n",
    "pval = []\n",
    "pval_thresh = 0.05\n",
    "\n",
    "for model in model_list:\n",
    "    disease_coeff_model = []\n",
    "    pval_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",
    "            pval_model.append(item[\"model\"].pvalues[\"Disease_label\"])\n",
    "        else:\n",
    "            disease_coeff_model.append(None)\n",
    "            pval_model.append(None)\n",
    "    disease_coeff.append(disease_coeff_model)\n",
    "    pval.append(pval_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",
    "\n",
    "pval = np.array(pval).transpose()\n",
    "pval[pval == None] = np.nan\n",
    "pval = np.where(pval == None, np.nan, pval).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9335ba95",
   "metadata": {},
   "source": [
    "## Heat map of disease coeffients for analytes across models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "67fd0910",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving the figure ...\n"
     ]
    }
   ],
   "source": [
    "FIG_SIZE = (8, 6)\n",
    "FONTSIZE = 12\n",
    "LABELSIZE = 15\n",
    "\n",
    "\n",
    "indices = np.where(pval < 10**-5)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = sns.heatmap(disease_coeff_df, annot=True, cmap='coolwarm', vmin=-1, vmax=1, annot_kws={\"fontsize\": FONTSIZE})\n",
    "\n",
    "# Add red outline to the boxes\n",
    "for i, j in zip(indices[0], indices[1]):\n",
    "    rect = plt.Rectangle((j, i), 1, 1, fill=False, edgecolor='red', lw=3)\n",
    "    ax.add_patch(rect)\n",
    "\n",
    "ax.set_xticklabels(ax.get_xticklabels(), fontsize=FONTSIZE)\n",
    "ax.set_yticklabels(ax.get_yticklabels(), fontsize=FONTSIZE)\n",
    "plt.xlabel('Model', fontsize=LABELSIZE)\n",
    "plt.ylabel('Analyte', fontsize=LABELSIZE)\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('Analyte')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print('Saving the figure ...')\n",
    "fig_filename = os.path.join(OUTPUT_PATH, \"figures\", \"heat_map_of_disease_coeff_vs_shortChain_fattyAcid_{}_sample_with_red_box_outline.svg\".format(sample))\n",
    "fig.savefig(fig_filename, format='svg', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d92e2d85",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}