sccoda_extended.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scCODA - Modeling options and result analysis\n",
    "\n",
    "This tutorial notebook serves as an extension to the general tutorial and presents ways to alternate the model and perform more in-depth result analysis and diagnostics.\n",
    "We will focus on:\n",
    "\n",
    "- Modifications of the model formula and reference cell type to perform different modeling tasks\n",
    "- Plotting of compositional data and scCODA results\n",
    "- Inference methods available in scCODA\n",
    "- Advanced interpretation and analysis of results\n",
    "- Alternative differential abundance testing using all references\n",
    "\n",
    "We will again analyze the small intestinal epithelium data of mice from [Haber et al., 2017](https://www.nature.com/articles/nature24489).\n",
    "First, we read in the data and perform the same preprocessing steps as in the general tutorial:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pertpy as pt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Note:** If you’re dealing with a continuous covariate, make sure to normalize it (e.g. using min-max normalization) before passing it to scCODA."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MuData object with n_obs × n_vars = 9852 × 15223\n",
      "  3 modalities\n",
      "    rna:\t9842 x 15215\n",
      "      obs:\t'batch', 'barcode', 'condition', 'cell_label'\n",
      "    coda:\t10 x 8\n",
      "      obs:\t'condition'\n",
      "      var:\t'n_cells'\n",
      "    coda_salm:\t6 x 8\n",
      "      obs:\t'condition'\n",
      "      var:\t'n_cells'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "haber_cells = pt.dt.haber_2017_regions()\n",
    "\n",
    "# Convert data to mudata object\n",
    "sccoda_model = pt.tl.Sccoda()\n",
    "sccoda_data = sccoda_model.load(\n",
    "    haber_cells,\n",
    "    type=\"cell_level\",\n",
    "    generate_sample_level=True,\n",
    "    cell_type_identifier=\"cell_label\",\n",
    "    sample_identifier=\"batch\",\n",
    "    covariate_obs=[\"condition\"],\n",
    ")\n",
    "\n",
    "# Select control and salmonella data as one modality\n",
    "sccoda_data.mod[\"coda_salm\"] = sccoda_data[\"coda\"][\n",
    "    sccoda_data[\"coda\"].obs[\"condition\"].isin([\"Control\", \"Salmonella\"])\n",
    "].copy()\n",
    "print(sccoda_data)\n",
    "\n",
    "pt.pl.coda.boxplots(sccoda_data, modality_key=\"coda\", feature_name=\"condition\", add_dots=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can also plot the data as stacked barplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pt.pl.coda.stacked_barplot(sccoda_data, modality_key=\"coda\", feature_name=\"samples\")\n",
    "plt.show()\n",
    "\n",
    "pt.pl.coda.stacked_barplot(sccoda_data, modality_key=\"coda\", feature_name=\"condition\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweaking the model formula and reference cell type\n",
    "\n",
    "First, we take a closer look at how changing the `formula` parameter of the scCODA model influences the results.\n",
    "Internally, the formula string is converted into a linear model-like design matrix via [patsy](https://patsy.readthedocs.io/en/latest/),\n",
    "which has a similar syntax to the `lm` function in the R language.\n",
    "\n",
    "### Multi-level categories\n",
    "\n",
    "Patsy allows us to automatically handle categorical covariates, even with multiple levels.\n",
    "For example, we can model the effect of all three diseases at once, using the `coda` data modality, which contains all 10 samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [01:05<00:00, 166.83it/s, 127 steps of size 2.09e-02. acc. prob=0.75]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                                          Compositional Analysis summary                                           </span>\n",
       "┌─────────────────────────────────────────────┬───────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Name                                        </span>│<span style=\"font-weight: bold\"> Value                                                             </span>│\n",
       "├─────────────────────────────────────────────┼───────────────────────────────────────────────────────────────────┤\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Data                                        </span>│ Data: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span> samples, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span> cell types                                    │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Reference cell type                         </span>│ Endocrine                                                         │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Formula                                     </span>│ condition                                                         │\n",
       "└─────────────────────────────────────────────┴───────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001B[3m                                          Compositional Analysis summary                                           \u001B[0m\n",
       "┌─────────────────────────────────────────────┬───────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mName                                       \u001B[0m\u001B[1m \u001B[0m│\u001B[1m \u001B[0m\u001B[1mValue                                                            \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────┼───────────────────────────────────────────────────────────────────┤\n",
       "│\u001B[36m \u001B[0m\u001B[36mData                                       \u001B[0m\u001B[36m \u001B[0m│ Data: \u001B[1;36m10\u001B[0m samples, \u001B[1;36m8\u001B[0m cell types                                    │\n",
       "│\u001B[36m \u001B[0m\u001B[36mReference cell type                        \u001B[0m\u001B[36m \u001B[0m│ Endocrine                                                         │\n",
       "│\u001B[36m \u001B[0m\u001B[36mFormula                                    \u001B[0m\u001B[36m \u001B[0m│ condition                                                         │\n",
       "└─────────────────────────────────────────────┴───────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Intercepts                                                                                                      </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                        Final Parameter  Expected Sample                                                         │\n",
       "│ Cell Type                                                                                                       │\n",
       "│ Endocrine                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.970</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">46.752</span>                                                              │\n",
       "│ Enterocyte                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.893</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">117.666</span>                                                              │\n",
       "│ Enterocyte.Progenitor      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.328</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">181.790</span>                                                              │\n",
       "│ Goblet                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.458</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">76.161</span>                                                              │\n",
       "│ Stem                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.399</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">195.166</span>                                                              │\n",
       "│ TA                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.857</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">113.505</span>                                                              │\n",
       "│ TA.Early                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.519</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">220.049</span>                                                              │\n",
       "│ Tuft                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.625</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33.111</span>                                                              │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mIntercepts                                                                                                     \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                        Final Parameter  Expected Sample                                                         │\n",
       "│ Cell Type                                                                                                       │\n",
       "│ Endocrine                  \u001B[1;36m0.970\u001B[0m            \u001B[1;36m46.752\u001B[0m                                                              │\n",
       "│ Enterocyte                 \u001B[1;36m1.893\u001B[0m           \u001B[1;36m117.666\u001B[0m                                                              │\n",
       "│ Enterocyte.Progenitor      \u001B[1;36m2.328\u001B[0m           \u001B[1;36m181.790\u001B[0m                                                              │\n",
       "│ Goblet                     \u001B[1;36m1.458\u001B[0m            \u001B[1;36m76.161\u001B[0m                                                              │\n",
       "│ Stem                       \u001B[1;36m2.399\u001B[0m           \u001B[1;36m195.166\u001B[0m                                                              │\n",
       "│ TA                         \u001B[1;36m1.857\u001B[0m           \u001B[1;36m113.505\u001B[0m                                                              │\n",
       "│ TA.Early                   \u001B[1;36m2.519\u001B[0m           \u001B[1;36m220.049\u001B[0m                                                              │\n",
       "│ Tuft                       \u001B[1;36m0.625\u001B[0m            \u001B[1;36m33.111\u001B[0m                                                              │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Effects                                                                                                         </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                               Final Parameter  Expected Sample  log2-fold change                │\n",
       "│ Covariate              Cell Type                                                                                │\n",
       "│ conditionT.Hpoly.Day3  Endocrine                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">46.752</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Enterocyte                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">117.666</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Enterocyte.Progenitor      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">181.790</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Goblet                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">76.161</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Stem                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">195.166</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        TA                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">113.505</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        TA.Early                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">220.049</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Tuft                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33.111</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│ conditionT.Hpoly.Day10 Endocrine                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">46.752</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Enterocyte                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">117.666</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Enterocyte.Progenitor      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">181.790</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Goblet                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">76.161</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Stem                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">195.166</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        TA                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">113.505</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        TA.Early                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">220.049</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│                        Tuft                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33.111</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                     │\n",
       "│ conditionT.Salmonella  Endocrine                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32.433</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.528</span>                     │\n",
       "│                        Enterocyte                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.546</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">383.065</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.703</span>                     │\n",
       "│                        Enterocyte.Progenitor      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">126.112</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.528</span>                     │\n",
       "│                        Goblet                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">52.835</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.528</span>                     │\n",
       "│                        Stem                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">135.391</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.528</span>                     │\n",
       "│                        TA                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">78.741</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.528</span>                     │\n",
       "│                        TA.Early                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">152.653</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.528</span>                     │\n",
       "│                        Tuft                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.970</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.528</span>                     │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mEffects                                                                                                        \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                               Final Parameter  Expected Sample  log2-fold change                │\n",
       "│ Covariate              Cell Type                                                                                │\n",
       "│ conditionT.Hpoly.Day3  Endocrine                  \u001B[1;36m0.000\u001B[0m            \u001B[1;36m46.752\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Enterocyte                 \u001B[1;36m0.000\u001B[0m           \u001B[1;36m117.666\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Enterocyte.Progenitor      \u001B[1;36m0.000\u001B[0m           \u001B[1;36m181.790\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Goblet                     \u001B[1;36m0.000\u001B[0m            \u001B[1;36m76.161\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Stem                       \u001B[1;36m0.000\u001B[0m           \u001B[1;36m195.166\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        TA                         \u001B[1;36m0.000\u001B[0m           \u001B[1;36m113.505\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        TA.Early                   \u001B[1;36m0.000\u001B[0m           \u001B[1;36m220.049\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Tuft                       \u001B[1;36m0.000\u001B[0m            \u001B[1;36m33.111\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│ conditionT.Hpoly.Day10 Endocrine                  \u001B[1;36m0.000\u001B[0m            \u001B[1;36m46.752\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Enterocyte                 \u001B[1;36m0.000\u001B[0m           \u001B[1;36m117.666\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Enterocyte.Progenitor      \u001B[1;36m0.000\u001B[0m           \u001B[1;36m181.790\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Goblet                     \u001B[1;36m0.000\u001B[0m            \u001B[1;36m76.161\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Stem                       \u001B[1;36m0.000\u001B[0m           \u001B[1;36m195.166\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        TA                         \u001B[1;36m0.000\u001B[0m           \u001B[1;36m113.505\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        TA.Early                   \u001B[1;36m0.000\u001B[0m           \u001B[1;36m220.049\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│                        Tuft                       \u001B[1;36m0.000\u001B[0m            \u001B[1;36m33.111\u001B[0m             \u001B[1;36m0.000\u001B[0m                     │\n",
       "│ conditionT.Salmonella  Endocrine                  \u001B[1;36m0.000\u001B[0m            \u001B[1;36m32.433\u001B[0m            \u001B[1;36m-0.528\u001B[0m                     │\n",
       "│                        Enterocyte                 \u001B[1;36m1.546\u001B[0m           \u001B[1;36m383.065\u001B[0m             \u001B[1;36m1.703\u001B[0m                     │\n",
       "│                        Enterocyte.Progenitor      \u001B[1;36m0.000\u001B[0m           \u001B[1;36m126.112\u001B[0m            \u001B[1;36m-0.528\u001B[0m                     │\n",
       "│                        Goblet                     \u001B[1;36m0.000\u001B[0m            \u001B[1;36m52.835\u001B[0m            \u001B[1;36m-0.528\u001B[0m                     │\n",
       "│                        Stem                       \u001B[1;36m0.000\u001B[0m           \u001B[1;36m135.391\u001B[0m            \u001B[1;36m-0.528\u001B[0m                     │\n",
       "│                        TA                         \u001B[1;36m0.000\u001B[0m            \u001B[1;36m78.741\u001B[0m            \u001B[1;36m-0.528\u001B[0m                     │\n",
       "│                        TA.Early                   \u001B[1;36m0.000\u001B[0m           \u001B[1;36m152.653\u001B[0m            \u001B[1;36m-0.528\u001B[0m                     │\n",
       "│                        Tuft                       \u001B[1;36m0.000\u001B[0m            \u001B[1;36m22.970\u001B[0m            \u001B[1;36m-0.528\u001B[0m                     │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model all three diseases at once\n",
    "sccoda_data = sccoda_model.prepare(\n",
    "    sccoda_data,\n",
    "    modality_key=\"coda\",\n",
    "    formula=\"condition\",\n",
    "    reference_cell_type=\"Endocrine\",\n",
    ")\n",
    "sccoda_model.run_nuts(sccoda_data, modality_key=\"coda\")\n",
    "sccoda_model.summary(sccoda_data, modality_key=\"coda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different reference levels\n",
    "\n",
    "Per default, categorical variables are encoded via full-rank treatment coding. Hereby, the value of the first sample in the dataset is used as the default (control) category.\n",
    "We can select the default level by changing the model formula to `\"C(<CovariateName>, Treatment('<ReferenceLevelName>'))\"`:\n",
    "\n",
    "For example, we can switch the salmonella model to test diseased versus healthy samples, which switches the sign of the only credible effect (Enterocytes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:47<00:00, 230.14it/s, 255 steps of size 1.80e-02. acc. prob=0.86]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                                          Compositional Analysis summary                                           </span>\n",
       "┌────────────────────────────────────────┬────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Name                                   </span>│<span style=\"font-weight: bold\"> Value                                                                  </span>│\n",
       "├────────────────────────────────────────┼────────────────────────────────────────────────────────────────────────┤\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Data                                   </span>│ Data: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> samples, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span> cell types                                          │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Reference cell type                    </span>│ Goblet                                                                 │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Formula                                </span>│ <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">C</span><span style=\"font-weight: bold\">(</span>condition, <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Treatment</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'Salmonella'</span><span style=\"font-weight: bold\">))</span>                                  │\n",
       "└────────────────────────────────────────┴────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001B[3m                                          Compositional Analysis summary                                           \u001B[0m\n",
       "┌────────────────────────────────────────┬────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mName                                  \u001B[0m\u001B[1m \u001B[0m│\u001B[1m \u001B[0m\u001B[1mValue                                                                 \u001B[0m\u001B[1m \u001B[0m│\n",
       "├────────────────────────────────────────┼────────────────────────────────────────────────────────────────────────┤\n",
       "│\u001B[36m \u001B[0m\u001B[36mData                                  \u001B[0m\u001B[36m \u001B[0m│ Data: \u001B[1;36m6\u001B[0m samples, \u001B[1;36m8\u001B[0m cell types                                          │\n",
       "│\u001B[36m \u001B[0m\u001B[36mReference cell type                   \u001B[0m\u001B[36m \u001B[0m│ Goblet                                                                 │\n",
       "│\u001B[36m \u001B[0m\u001B[36mFormula                               \u001B[0m\u001B[36m \u001B[0m│ \u001B[1;35mC\u001B[0m\u001B[1m(\u001B[0mcondition, \u001B[1;35mTreatment\u001B[0m\u001B[1m(\u001B[0m\u001B[32m'Salmonella'\u001B[0m\u001B[1m)\u001B[0m\u001B[1m)\u001B[0m                                  │\n",
       "└────────────────────────────────────────┴────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Intercepts                                                                                                      </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                        Final Parameter  Expected Sample                                                         │\n",
       "│ Cell Type                                                                                                       │\n",
       "│ Endocrine                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.229</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.334</span>                                                              │\n",
       "│ Enterocyte                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.670</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">325.399</span>                                                              │\n",
       "│ Enterocyte.Progenitor      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.541</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">105.220</span>                                                              │\n",
       "│ Goblet                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.746</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">47.515</span>                                                              │\n",
       "│ Stem                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.594</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">110.947</span>                                                              │\n",
       "│ TA                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.009</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">61.809</span>                                                              │\n",
       "│ TA.Early                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.849</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">143.173</span>                                                              │\n",
       "│ Tuft                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.419</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.604</span>                                                              │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mIntercepts                                                                                                     \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                        Final Parameter  Expected Sample                                                         │\n",
       "│ Cell Type                                                                                                       │\n",
       "│ Endocrine                  \u001B[1;36m1.229\u001B[0m            \u001B[1;36m28.334\u001B[0m                                                              │\n",
       "│ Enterocyte                 \u001B[1;36m3.670\u001B[0m           \u001B[1;36m325.399\u001B[0m                                                              │\n",
       "│ Enterocyte.Progenitor      \u001B[1;36m2.541\u001B[0m           \u001B[1;36m105.220\u001B[0m                                                              │\n",
       "│ Goblet                     \u001B[1;36m1.746\u001B[0m            \u001B[1;36m47.515\u001B[0m                                                              │\n",
       "│ Stem                       \u001B[1;36m2.594\u001B[0m           \u001B[1;36m110.947\u001B[0m                                                              │\n",
       "│ TA                         \u001B[1;36m2.009\u001B[0m            \u001B[1;36m61.809\u001B[0m                                                              │\n",
       "│ TA.Early                   \u001B[1;36m2.849\u001B[0m           \u001B[1;36m143.173\u001B[0m                                                              │\n",
       "│ Tuft                       \u001B[1;36m0.419\u001B[0m            \u001B[1;36m12.604\u001B[0m                                                              │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Effects                                                                                                         </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                                                       Final Parameter  Expected Sample          │\n",
       "│ log2-fold change                                                                                                │\n",
       "│ Covariate                                      Cell Type                                                        │\n",
       "│ <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">C</span><span style=\"font-weight: bold\">(</span>condition, <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Treatment</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'Salmonella'</span><span style=\"font-weight: bold\">))</span>T.Control Endocrine                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">39.776</span>               │\n",
       "│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.489</span>                                                                                                           │\n",
       "│                                                Enterocyte                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.340</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">119.591</span>               │\n",
       "│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.444</span>                                                                                                          │\n",
       "│                                                Enterocyte.Progenitor       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">147.714</span>               │\n",
       "│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.489</span>                                                                                                           │\n",
       "│                                                Goblet                      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">66.705</span>               │\n",
       "│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.489</span>                                                                                                           │\n",
       "│                                                Stem                        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">155.754</span>               │\n",
       "│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.489</span>                                                                                                           │\n",
       "│                                                TA                          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">86.771</span>               │\n",
       "│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.489</span>                                                                                                           │\n",
       "│                                                TA.Early                    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">200.994</span>               │\n",
       "│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.489</span>                                                                                                           │\n",
       "│                                                Tuft                        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.695</span>               │\n",
       "│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.489</span>                                                                                                           │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mEffects                                                                                                        \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                                                       Final Parameter  Expected Sample          │\n",
       "│ log2-fold change                                                                                                │\n",
       "│ Covariate                                      Cell Type                                                        │\n",
       "│ \u001B[1;35mC\u001B[0m\u001B[1m(\u001B[0mcondition, \u001B[1;35mTreatment\u001B[0m\u001B[1m(\u001B[0m\u001B[32m'Salmonella'\u001B[0m\u001B[1m)\u001B[0m\u001B[1m)\u001B[0mT.Control Endocrine                   \u001B[1;36m0.000\u001B[0m           \u001B[1;36m39.776\u001B[0m               │\n",
       "│ \u001B[1;36m0.489\u001B[0m                                                                                                           │\n",
       "│                                                Enterocyte                 \u001B[1;36m-1.340\u001B[0m          \u001B[1;36m119.591\u001B[0m               │\n",
       "│ \u001B[1;36m-1.444\u001B[0m                                                                                                          │\n",
       "│                                                Enterocyte.Progenitor       \u001B[1;36m0.000\u001B[0m          \u001B[1;36m147.714\u001B[0m               │\n",
       "│ \u001B[1;36m0.489\u001B[0m                                                                                                           │\n",
       "│                                                Goblet                      \u001B[1;36m0.000\u001B[0m           \u001B[1;36m66.705\u001B[0m               │\n",
       "│ \u001B[1;36m0.489\u001B[0m                                                                                                           │\n",
       "│                                                Stem                        \u001B[1;36m0.000\u001B[0m          \u001B[1;36m155.754\u001B[0m               │\n",
       "│ \u001B[1;36m0.489\u001B[0m                                                                                                           │\n",
       "│                                                TA                          \u001B[1;36m0.000\u001B[0m           \u001B[1;36m86.771\u001B[0m               │\n",
       "│ \u001B[1;36m0.489\u001B[0m                                                                                                           │\n",
       "│                                                TA.Early                    \u001B[1;36m0.000\u001B[0m          \u001B[1;36m200.994\u001B[0m               │\n",
       "│ \u001B[1;36m0.489\u001B[0m                                                                                                           │\n",
       "│                                                Tuft                        \u001B[1;36m0.000\u001B[0m           \u001B[1;36m17.695\u001B[0m               │\n",
       "│ \u001B[1;36m0.489\u001B[0m                                                                                                           │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set salmonella infection as \"default\" category\n",
    "\n",
    "sccoda_data = sccoda_model.prepare(\n",
    "    sccoda_data,\n",
    "    modality_key=\"coda_salm\",\n",
    "    formula=\"C(condition, Treatment('Salmonella'))\",\n",
    "    reference_cell_type=\"Goblet\",\n",
    ")\n",
    "sccoda_model.run_nuts(sccoda_data, modality_key=\"coda_salm\")\n",
    "sccoda_model.summary(sccoda_data, modality_key=\"coda_salm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Switching the reference cell type\n",
    "\n",
    "Compositional analysis generally does not allow statements on absolute abundance changes, but only in relation to a reference category,\n",
    "which is assumed to be unchanged in absolute abundance.\n",
    "The reference cell type fixes this category in scCODA. Thus, an interpretation of scCODA's effects should always be formulated like:\n",
    "\n",
    "\"Using cell type xy as a reference, cell types (a, b, c) were found to credibly change in abundance\"\n",
    "\n",
    "Switching the reference cell type might thus produce different results.\n",
    "For example, if we choose a different cell type as the reference (such as Enterocytes in the salmonella infection data),\n",
    "scCODA can find other credible effects on the other cell types.\n",
    "\n",
    "*Note: In this example we have to set the FDR very high, since all cell types except Enterocytes do not show big changes. FDR values of more than 0.2 are not recommended in practice*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:32<00:00, 340.52it/s, 63 steps of size 6.98e-02. acc. prob=0.70]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                                          Compositional Analysis summary                                           </span>\n",
       "┌──────────────────────────────────────────────┬──────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Name                                         </span>│<span style=\"font-weight: bold\"> Value                                                            </span>│\n",
       "├──────────────────────────────────────────────┼──────────────────────────────────────────────────────────────────┤\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Data                                         </span>│ Data: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> samples, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span> cell types                                    │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Reference cell type                          </span>│ Enterocyte                                                       │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Formula                                      </span>│ condition                                                        │\n",
       "└──────────────────────────────────────────────┴──────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001B[3m                                          Compositional Analysis summary                                           \u001B[0m\n",
       "┌──────────────────────────────────────────────┬──────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mName                                        \u001B[0m\u001B[1m \u001B[0m│\u001B[1m \u001B[0m\u001B[1mValue                                                           \u001B[0m\u001B[1m \u001B[0m│\n",
       "├──────────────────────────────────────────────┼──────────────────────────────────────────────────────────────────┤\n",
       "│\u001B[36m \u001B[0m\u001B[36mData                                        \u001B[0m\u001B[36m \u001B[0m│ Data: \u001B[1;36m6\u001B[0m samples, \u001B[1;36m8\u001B[0m cell types                                    │\n",
       "│\u001B[36m \u001B[0m\u001B[36mReference cell type                         \u001B[0m\u001B[36m \u001B[0m│ Enterocyte                                                       │\n",
       "│\u001B[36m \u001B[0m\u001B[36mFormula                                     \u001B[0m\u001B[36m \u001B[0m│ condition                                                        │\n",
       "└──────────────────────────────────────────────┴──────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Intercepts                                                                                                      </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                        Final Parameter  Expected Sample                                                         │\n",
       "│ Cell Type                                                                                                       │\n",
       "│ Endocrine                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.550</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">34.807</span>                                                              │\n",
       "│ Enterocyte                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.075</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">159.941</span>                                                              │\n",
       "│ Enterocyte.Progenitor       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.861</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">129.128</span>                                                              │\n",
       "│ Goblet                      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.089</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">59.669</span>                                                              │\n",
       "│ Stem                        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.086</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">161.710</span>                                                              │\n",
       "│ TA                          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.472</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">87.515</span>                                                              │\n",
       "│ TA.Early                    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.211</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">183.242</span>                                                              │\n",
       "│ Tuft                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.056</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.988</span>                                                              │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mIntercepts                                                                                                     \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                        Final Parameter  Expected Sample                                                         │\n",
       "│ Cell Type                                                                                                       │\n",
       "│ Endocrine                   \u001B[1;36m0.550\u001B[0m           \u001B[1;36m34.807\u001B[0m                                                              │\n",
       "│ Enterocyte                  \u001B[1;36m2.075\u001B[0m          \u001B[1;36m159.941\u001B[0m                                                              │\n",
       "│ Enterocyte.Progenitor       \u001B[1;36m1.861\u001B[0m          \u001B[1;36m129.128\u001B[0m                                                              │\n",
       "│ Goblet                      \u001B[1;36m1.089\u001B[0m           \u001B[1;36m59.669\u001B[0m                                                              │\n",
       "│ Stem                        \u001B[1;36m2.086\u001B[0m          \u001B[1;36m161.710\u001B[0m                                                              │\n",
       "│ TA                          \u001B[1;36m1.472\u001B[0m           \u001B[1;36m87.515\u001B[0m                                                              │\n",
       "│ TA.Early                    \u001B[1;36m2.211\u001B[0m          \u001B[1;36m183.242\u001B[0m                                                              │\n",
       "│ Tuft                       \u001B[1;36m-0.056\u001B[0m           \u001B[1;36m18.988\u001B[0m                                                              │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Effects                                                                                                         </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                                Final Parameter  Expected Sample  log2-fold change               │\n",
       "│ Covariate               Cell Type                                                                               │\n",
       "│ condition<span style=\"font-weight: bold\">[</span>T.Salmonella<span style=\"font-weight: bold\">]</span> Endocrine                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41.619</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.258</span>                    │\n",
       "│                         Enterocyte                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">191.246</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.258</span>                    │\n",
       "│                         Enterocyte.Progenitor       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">154.402</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.258</span>                    │\n",
       "│                         Goblet                      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">71.347</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.258</span>                    │\n",
       "│                         Stem                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.468</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">121.082</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.417</span>                    │\n",
       "│                         TA                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.380</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">71.579</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.290</span>                    │\n",
       "│                         TA.Early                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.308</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">161.019</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.187</span>                    │\n",
       "│                         Tuft                        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.704</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.258</span>                    │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mEffects                                                                                                        \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                                Final Parameter  Expected Sample  log2-fold change               │\n",
       "│ Covariate               Cell Type                                                                               │\n",
       "│ condition\u001B[1m[\u001B[0mT.Salmonella\u001B[1m]\u001B[0m Endocrine                   \u001B[1;36m0.000\u001B[0m           \u001B[1;36m41.619\u001B[0m             \u001B[1;36m0.258\u001B[0m                    │\n",
       "│                         Enterocyte                  \u001B[1;36m0.000\u001B[0m          \u001B[1;36m191.246\u001B[0m             \u001B[1;36m0.258\u001B[0m                    │\n",
       "│                         Enterocyte.Progenitor       \u001B[1;36m0.000\u001B[0m          \u001B[1;36m154.402\u001B[0m             \u001B[1;36m0.258\u001B[0m                    │\n",
       "│                         Goblet                      \u001B[1;36m0.000\u001B[0m           \u001B[1;36m71.347\u001B[0m             \u001B[1;36m0.258\u001B[0m                    │\n",
       "│                         Stem                       \u001B[1;36m-0.468\u001B[0m          \u001B[1;36m121.082\u001B[0m            \u001B[1;36m-0.417\u001B[0m                    │\n",
       "│                         TA                         \u001B[1;36m-0.380\u001B[0m           \u001B[1;36m71.579\u001B[0m            \u001B[1;36m-0.290\u001B[0m                    │\n",
       "│                         TA.Early                   \u001B[1;36m-0.308\u001B[0m          \u001B[1;36m161.019\u001B[0m            \u001B[1;36m-0.187\u001B[0m                    │\n",
       "│                         Tuft                        \u001B[1;36m0.000\u001B[0m           \u001B[1;36m22.704\u001B[0m             \u001B[1;36m0.258\u001B[0m                    │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sccoda_data = sccoda_model.prepare(\n",
    "    sccoda_data,\n",
    "    modality_key=\"coda_salm\",\n",
    "    formula=\"condition\",\n",
    "    reference_cell_type=\"Enterocyte\",\n",
    ")\n",
    "sccoda_model.run_nuts(sccoda_data, modality_key=\"coda_salm\")\n",
    "sccoda_model.summary(sccoda_data, modality_key=\"coda_salm\", est_fdr=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding a reference cell type\n",
    "The scCODA model requires a cell type to be set as the reference category. However, choosing this cell type is often difficult.\n",
    "A good first choice is a referenece cell type that closely preserves the changes in relative abundance during the compositional analysis.\n",
    "\n",
    "For this, it is important that the reference cell type is not rare, to avoid large relative changes being caused by small absolute changes.\n",
    "Also, the relative abundance of the reference should vary as little as possible across all samples.\n",
    "\n",
    "The visualization `rel_abundance_dispersion_plot` shows the presence (share of non-zero samples) over all samples for each cell type\n",
    "versus its dispersion in relative abundance. Cell types that have a higher presence than a certain threshold (default 0.9) are suitable candidates for the reference and thus colored.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Presence', ylabel='Total dispersion'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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58uS8Ll9E8oqbG7zyCrRpk3Yzha0tlCoFevKCiNW5fv06CxYswM3NjfLly7NhwwbKlCmDm5tbhr6urq589913VK9enQsXLphusrj9OvvPP/+cKlWq8MgjjzBp0iSaNm2Kl5cXqamplC1bls8++4yhQ4fy22+/8f333/PEE09kq86XX36ZSZMmUbVqVby9vQkJCTGdYSxWrBiFChVi06ZNBAYG8scff5gmYM7ODaHp9wRERkbi5uaGk5NTtmrKjEWDXZEiRZg+fTrTp0/PsOzPP//MdJ3OnTtnGvayaheRAsrePu3onI7QiVi17t27c/78eUaMGMHVq1epXr068+fPz/Rh91OnTmXixIm0adOG0qVL07VrVwoXLsyRI0do1KgRAH369GH27NmcPn2aRo0a8c477wBQqFAhQkJCmDx5Mq1bt6ZBgwYMHDiQH3/8MVt1dujQgdjYWCZPnkxCQgL9+/cnMjISgDJlyjBx4kTmzZvHrFmz8Pb2ZuzYsYwaNYrDhw9nec1/uhIlStC+fXuGDRvG22+/nemcvdllY+TmrHgFVFxcHP7+/oSHh+t6OxERkWy40+/OhIQEjh8/jre3933f5Sk5G0/dWiYiIiJiJRTsRERERKyEgp2IiIiIlVCwExEREbESCnYiIiIiVkLBTkRERMRKKNiJiIiIWAkFOxERERErYdEnT4iIiIjkR6NHj2bdunVZLl+8eDH169d/gBVlj4KdiIiIyL8EBwfz1ltvAbB582bCwsJYvXq1aXnx4sUtVdodKdiJiIiI/IuLiwsuLi6m94ULF77rM1/zA11jJyIiIgVDSgp8/z0sW5b235QUi5Rx+vRpfHx8mDdvHnXr1uWdd94hNDSUHj16mPULDAxk7dq1ABiGwbx58wgICKBOnToMHDiQs2fP5nptCnYiIiKS/61dCxUrQtOm8NJLaf+tWDGt3UL279/PmjVr6Nmz5137fvnll2zYsIH333+fFStW4O7uTt++fUlKSsrVmhTsREREJH9buxaeew5OnzZvP3Mmrd1C4a5Xr1488sgjVKxY8a59P/30U0aOHEn9+vWpXLky77zzDlevXuWnn37K1Zp0jZ2IiIjkXykp8MYbYBgZlxkG2NjAsGHQoQMULvxAS/P09MxWvxs3bnD+/HnefPNNChX63zG1hIQEoqOjc7UmBTsRERHJv376KeORutsZBpw6ldavSZMHVhaAg4OD6b2NjU2G5cnJyQCk/PdawA8//BBvb2+zPrl9d61OxYqIiEj+de5c7vbLI3Z2dty4ccP0+caNG1y+fBmAYsWK4e7uTkxMDF5eXnh5eVG2bFnee+89jh8/nqt1KNiJiIhI/lW2bO72yyM1atQgMjKSLVu2cPz4ccaPH2922rV3797Mnj2bnTt3Eh0dzdixY9m/fz+VKlXK1Tp0KlZERETyr6efhvLl026UyOw6OxubtOVPP/3ga7tNgwYN6N27tynQ9enTh4sXL5qW9+vXjxs3bjB+/Hji4uKoXr06n332Wa6firUxjMxG6eESFxeHv78/4eHhODs7W7ocERGRfO9OvzsTEhI4fvw43t7eODo63v+Xpd8VC+bhLv26ttWroXPn+/+efCon46lTsSIiIpK/de6cFt7+fRdq+fJWH+pySqdiRUREJP/r3DltSpOffkq7UaJs2bTTrw94ipP8TsFORERECobChR/4lCYFjU7FioiIiFgJBTsRERERK6FgJyIiInlCE2/kjpyMo4KdiIiI5Co7OzsA4uPjLVyJdUgfx/RxvRPdPCEiIiK5qnDhwri6upom6C1atGimz1KVOzMMg/j4eC5evIirqyuFs3EHsIKdiIiI5LoyZcoAmD19Qe6Nq6uraTzvRsFOREREcp2NjQ1ly5alVKlSJCUlWbqcAsvOzi5bR+rSKdiJiIhInilcuHCOgoncH908ISIiImIlFOxERERErISCnYiIiIiVULATERERsRIKdiIiIiJWQsFORERExEoo2ImIiIhYCQU7ERERESuhYCciIiJiJRTsRERERKyEgp2IiIiIlVCwExEREbESCnYiIiIiVkLBTkRERMRKKNiJiIiIWAkFOxEREREroWAnIiIiYiUU7ERERESshIKdiIiIiJVQsBMRERGxEgp2IiIiIlZCwU5ERETESijYiYiIiFgJBTsRERERK6FgJyIiImIlFOxERERErISCnYiIiIiVULATERERsRIKdiIiIiJWQsFORERExEoo2ImIiIhYCQU7ERERESuhYCciIiJiJRTsRERERKyEgp2IiIiIlVCwExEREbESFg12t27dIigoiDp16hAQEEBYWFiWfQ8fPkzXrl3x9fWlS5cuHDp0yLTMMAxCQ0Np1KgRdevWZdiwYVy+fPlB7IKIiIhIvmHRYDdjxgwOHTrEokWLmDBhAnPnzmXr1q0Z+sXHx9O/f3/q1KnD2rVr8fPzY8CAAcTHxwOwYsUKVq9ezcyZM1m6dCkXL14kODj4Qe+OiIiIiEVZLNjFx8ezatUqgoODqVatGi1atOCVV15h6dKlGfpu3rwZBwcHRo4cSeXKlQkODsbJyckUAn/44Qdat25NvXr1qFKlCq+88gq//vrrg94lEREREYuyWLCLjIwkOTkZPz8/U5u/vz8RERGkpqaa9Y2IiMDf3x8bGxsAbGxsqF27NgcPHgTA1dWV77//ngsXLpCQkMCmTZuoWrXqA9sXERERkfzAYsEuJiYGNzc37O3tTW0eHh7cunWLK1euZOhbqlQpszZ3d3fOnz8PwODBg7G1taVRo0bUrl2bffv2MWvWrDzfBxEREZH8xGLB7ubNm2ahDjB9TkxMzFbf9H5nzpzB0dGRBQsWsGTJEsqUKUNQUFCW352YmEhcXJzZS0RERKSgs7XUFzs4OGQIcOmfHR0ds9XX0dERwzAYNWoUI0eOpGnTpgDMnj2bpk2bEhERga+vb4bvXrhwIXPnzs3N3RERERGxOIsdsStdujSxsbEkJyeb2mJiYnB0dKRYsWIZ+l66dMms7dKlS5QqVYrLly9z7tw5fHx8TMvKli2Lm5sbZ86cyfS7BwwYQHh4uOn1ww8/5OKeiYiIiFiGxYJd1apVsbW1Nd0AARAeHk6NGjUoVMi8LF9fXw4cOIBhGEDavHX79+/H19eX4sWLY29vT1RUlKn/5cuXuXLlCuXLl8/0u+3t7XF2djZ7iYiIiBR0Fgt2RYoUoWPHjkycOJHff/+d7du3ExYWRs+ePYG0o3cJCQkAPPvss1y7do2QkBD+/vtvQkJCuHnzJq1atcLW1pbOnTszffp0fvvtN/766y9GjBiBr68vNWrUsNTuiYiIiDxwFp2geMyYMVSrVo1evXoxadIkhg4dSsuWLQEICAhg8+bNADg7O7Nw4ULCw8Pp3LkzERERfPzxxxQtWhSAoKAgWrZsyVtvvUWPHj0oVqwYH330kWl6FBEREZGHgY2Rfn7zIRYXF4e/vz/h4eE6LSsiIpIN+t2ZP1n0iJ2IiIiI5B4FOxEREREroWAnIiIiYiUU7ERERESshIKdiIiIiJVQsBMRERGxEgp2IiIiIlZCwU5ERETESijYiYiIiFgJBTsRERERK6FgJyIiImIlFOxERERErISCnYiIiIiVULATERERsRIKdiIiIiJWQsFORERExEoo2ImIiIhYCQU7ERERESuhYCciIiJiJRTsRERERKyEgp2IiIiIlVCwExEREbESCnYiIiIiVkLBTkRERMRKKNiJiIiIWAkFOxEREREroWAnIiIiYiUU7ERERESshIKdiIiIiJVQsBMRERGxEgp2IiIiIlZCwU5ERETESijYiYiIiFgJBTsRERERK6FgJyIiImIlFOxERERErISCnYiIiIiVULATERERsRIKdiIiIiJWQsFORERExEoo2ImIiIhYCQU7ERERESuhYCciIiJiJRTsRERERKyEgp2IiIiIlVCwExEREbESCnYiIiIiVkLBTkRERMRKKNiJiIiIWAkFOxEREREroWAnIiIiYiUU7ERERESshIKdiIiIiJWwzekKSUlJrF+/nj/++IPk5GQMwzBbPm3atFwrTkRERESyL8dH7IKDgwkJCSE2NjZDqBMRERERy8nxEbtt27Yxb948GjZsmBf1iIiIiMg9yvEROxcXF0qXLp0XtYiIiIjIfchxsBs0aBAhISFERUWRnJycFzWJiIiIyD3I8anYTz75hIsXL9K2bdtMlx85cuS+ixIRERGRnMtxsHv33Xfzog4RERERuU85Dnb16tUDIDo6mqioKFJTU/H29ubRRx/N9eJEREREJPtyHOyuXbvGmDFj2LFjB8WLFyclJYUbN25Qt25d5s2bh4uLS17UKSIiIiJ3keObJ6ZMmcL58+fZvHkze/bsYd++fWzYsIH4+HhNTiwiIiJiQTkOdjt37mTixIlUqlTJ1Pboo48yfvx4duzYkavFiYiIiEj25TjYOTg4UKhQxtVsbGxISUnJlaJEREREJOdyHOwCAwOZNGkSJ0+eNLVFR0czZcoUGjdunKvFiYiIiEj25fjmiREjRjB48GCeeeYZihUrBqTdUPH0008zbty4XC9QRERERLInx8GuWLFiLFmyhMjISI4dO4aDgwPe3t5m19yJiIiIyIOXrVOxZ8+exTAM0/uzZ89SrFgxatWqRdWqVXF0dDS158StW7cICgqiTp06BAQEEBYWlmXfw4cP07VrV3x9fenSpQuHDh0yW75161aeeeYZatWqRd++fTlz5kyOahEREREp6LJ1xC4wMJCff/4Zd3d3AgMDsbGxMVueHvpsbGxy9EixGTNmcOjQIRYtWsTZs2cZNWoU5cqV49lnnzXrFx8fT//+/WnXrh3vvvsuy5YtY8CAAWzbto2iRYuyf/9+3nrrLcaNG0e9evWYMWMGw4cPZ8WKFdmuRURERKSgy1aw27FjByVKlDC9zw3x8fGsWrWKTz75hGrVqlGtWjWOHj3K0qVLMwS7zZs34+DgwMiRI7GxsSE4OJgff/yRrVu30rlzZ8LCwmjfvj0vvvgiAMHBwfTq1YvLly+b6hYRERGxdtk6Fevp6Wk6Sufp6YmLiwseHh54enpy/fp1tmzZwsmTJ/H09Mz2F0dGRpKcnIyfn5+pzd/fn4iICFJTU836RkRE4O/vb6rBxsaG2rVrc/DgQQD27t1LixYtTP0rVKjAzp07FepERETkoZLj6U62b99Oo0aNCA8P58SJE3Tv3p1169bx2muv8eWXX2Z7OzExMbi5uWFvb29q8/Dw4NatW1y5ciVD31KlSpm1ubu7c/78ea5du8bVq1dJSUmhX79+NGzYkEGDBnHhwoUsvzsxMZG4uDizl4iIiEhBl+NgN3v2bF5//XWeeuopVq1aRdmyZdm0aROzZs26480P/3bz5k2zUAeYPicmJmarb2JiIvHx8UDao87atWvH/PnzSUxMZMCAARmO/KVbuHAh/v7+ppfm3xMRERFrkOPpTk6ePEmrVq2AtOvt0q+He+yxx7h8+XK2t+Pg4JAhwKV/dnR0zFZfR0dHChcuDEDXrl3p2LEjADNnzqRhw4YcPHiQ2rVrZ/juAQMG0KdPH9PnuLg4hTsREREp8HIc7MqVK8eePXsoXbo0x48fJzAwEIANGzZQsWLFbG+ndOnSxMbGkpycjK1tWhkxMTE4OjqaJj6+ve+lS5fM2i5dukSpUqVwc3PDzs7ObB49Nzc3XF1dOX/+fKbfbW9vn+EIoIiIiEhBl+NTsa+//jpjx46lb9++NGnShBo1ajB9+nQ+/vhjRowYke3tVK1aFVtbW9MNEADh4eHUqFEjw7NofX19OXDggGlaFcMw2L9/P76+vtja2lKtWjUiIyNN/S9fvkxsbGyObuYQERERKehyHOyqV6/Ojz/+yNq1a1mwYAGQdhp027ZtNGzYMNvbKVKkCB07dmTixIn8/vvvbN++nbCwMHr27AmkHb1LSEgA4Nlnn+XatWuEhITw999/ExISws2bN02nhPv06cOSJUvYsmULUVFRBAUFUbVqVWrWrJnT3RMREREpsHIc7Lp168bZs2epWrWqqa1SpUp4eHjk+MvHjBlDtWrV6NWrF5MmTWLo0KG0bNkSgICAADZv3gyAs7MzCxcuJDw8nM6dOxMREcHHH39M0aJFgbTgN2bMGN577z06d+5MSkoKH330UYaJlEVERESsmY2Rfn4zmzp06MDw4cOt6maDuLg4/P39CQ8Px9nZ2dLliIiI5Hv63Zk/5fjmiSeeeILXXnuNGjVq4OnpmeEmhGnTpuVacSIiIiKSfTkOdgDt27fP7TpERERE5D7lONjpiJyIiIhI/pTjmycgbVqS119/nQ4dOnDu3Dk+/vhjNm3alNu1iYiIiEgO5DjYffvtt/Tv3x9PT0+OHz9ummB49OjRfPXVV3lRo4iIiIhkQ46D3dy5c5k4cSKjRo0yPc6rb9++TJ06lc8//zzXCxQRERGR7MlxsDtx4gS1atXK0F6zZk0uXLiQGzWJiIiIyD3IcbB79NFH+emnnzK0r1u3jkcffTRXihIRERGRnMvxXbFjxoxh4MCB/PrrryQlJbFgwQJOnDjBoUOHmD9/fl7UKCIiIiLZkOMjdnXq1GHLli1UrlyZwMBArly5Qq1atdi8eTMNGjTIixpFREREJBvuaYLikiVL0q9fP6KjoylUqBDe3t4UKVIkt2sTERERkRzIcbCLj49nwoQJbNmyhZSUFADs7Ozo1KkTY8eOxc7OLteLFBEREZG7y/Gp2PHjxxMZGclnn33Gvn372Lt3LwsWLGDfvn16KoWIiIiIBeU42O3cuZN3332X+vXr4+TkhIuLC0899RRTp05l48aNeVGjiIiIiGRDjoOdu7s7//zzT4b2xMREnJ2dc6UoEREREcm5HF9jN2DAAIKDgxkwYAB+fn7Y2tpy5MgR5syZQ6dOnfjtt99MfevWrZurxYqIiIhI1mwMwzByssLjjz+evQ3b2HDkyJF7KupBi4uLw9/fn/DwcB11FBERyQb97syfcnzELjIyMi/qEBEREZH7lONr7ACioqK4fv06AD/99BOTJk1i1apVuVqYiIiIiORMjoPdihUraN++PUeOHOHw4cMMGjSIU6dO8eGHH/Lhhx/mRY0iIiIikg05Dnaffvop06dPp169eqxZs4aqVavy6aef8sEHH+ionYiIiIgF5TjYXbhwAX9/fwC+++47mjdvDkCZMmW4ceNG7lYnIiIiItmW45snKlWqxIYNGyhRogRnz56lefPmJCUlERYWlu07ZkVEREQk9+U42I0aNYphw4Zx9epVXnrpJSpXrsw777zDtm3bWLBgQV7UKCIiIiLZkON57ABSU1O5fv06xYsXB+DSpUsUL14cOzu7XC/wQdBcPCIiIjmj3535U7aO2P3222+mp0zc/mSJzOhpEyIiIiKWka1g16NHD37++Wfc3d3p0aNHlv0K0tMmRERERKxNtoLd7U+b0JMnRERERPKnbAW7s2fPZnuD5cqVu+diREREROTeZSvYBQYGYmNjk6E9/b6L25fpVKyIiIiIZWQr2O3YscP0/vvvv2fJkiWMGTOGGjVqYG9vz//93//x7rvv8vzzz+dZoSIiIiJyZ9kKdp6enqb3n3zyCR9++CG+vr6mtvr16/POO+8waNAgunXrlvtVioiIiMhd5fiRYjdu3CA5OTlDe1xcHElJSblSlIiIiIjkXI6fPNG+fXtGjhzJsGHDePzxxzEMgz/++IM5c+bw4osv5kWNIiIiIpINOQ52Y8aMwcnJiWnTpnH58mUAPDw86N69OwMHDsz1AkVEREQke3Ic7GxtbRk+fDjDhw83BbsSJUrkemEiIiIikjM5Dna3U6ATERERyT9yfPOEiIiIiORPCnYiIiIiVkLBTkRERMRKZOsau7lz52Z7g0OGDLnnYkRERETk3mUr2O3ZsydbG8vsebIiIiIi8mBkK9gtWbIkr+sQERERkft0T9OdHDlyhKNHj5KamgqAYRgkJiZy+PBhJk2alKsFioiIiEj25DjYzZ07l7lz5+Lh4cE///xD6dKluXTpEikpKbRo0SIvahQRERGRbMjxXbErVqxg0qRJ7Nq1i7Jly7JkyRJ2797NU089xSOPPJIXNYqIiIhINuQ42MXGxvL0008DULVqVQ4cOECxYsV488032bx5c64XKCIiIiLZk+NgV7p0aU6dOgVA5cqVOXz4MADOzs6mZ8eKiIiIyIOX42vsunbtyvDhw5k6dSrNmzend+/elCpVit27d/P444/nRY0iIiIikg05DnYDBw6kTJkyFClShJo1azJmzBiWL1+Oq6sr06ZNy4saRURERCQbchzs1q9fT+vWrbG3twfSjuB17dqV+Ph4Vq9ejbe3d64XKSIiIiJ3l61gd/nyZRISEgAYM2YMjz32GG5ubmZ9IiMjmTlzJj179sz9KkVERETkrrIV7Pbu3cuwYcNMjwx77rnngLSJiW/Xvn37XC5PRERERLIrW8Hu2WefZefOnaSmptK8eXNWrVpFiRIlTMttbGwoUqRIhqN4IiIiIvLgZPsau3LlygFpp1wBbt68yYkTJ0hNTeWRRx7B2dk5byoUERERkWzJ8c0TSUlJvPfee3z11VckJyenbcTWlnbt2jFp0iTTTRUiIiIi8mDleILi6dOn89133zF//nz27dvH3r17mTdvHvv27eODDz7IixpFREREJBtyfMRu48aNfPjhh9SvX9/U1rhxYxwcHHj77bcZNWpUrhYoIiIiItmT4yN2hmHg7u6eob1EiRLcuHEjV4oSERERkZzLcbB78sknmTlzJnFxcaa2a9euMWvWLLOjeCIiIiLyYGXrVOxvv/2Gn58ftra2BAUF0bNnT55++mnTUyaOHz9OhQoVmD9/fp4WKyIiIiJZy1aw69mzJ7t27cLd3Z3SpUuzceNGfvzxR44dO4aDgwPe3t40bNiQQoVyfABQRERERHJJtoLdv58wYWdnR7NmzWjWrFmeFCUiIiIiOZftQ2zpjxMTERERkfwp29OddOnSJVunWnfs2HFfBYmIiIjIvcl2sOvTpw8uLi55WYuIiIiI3IdsBTsbGxvatGmT6fx1IiIiIpI/ZOsau3/fPCEiIiIi+U+2gl2nTp1wcHDI9S+/desWQUFB1KlTh4CAAMLCwrLse/jwYbp27Yqvry9dunTh0KFDmfbbsmULPj4+uV6riIiISH6XrWA3bdo0nJ2dc/3LZ8yYwaFDh1i0aBETJkxg7ty5bN26NUO/+Ph4+vfvT506dVi7di1+fn4MGDCA+Ph4s37Xrl0jJCQk1+sUERERKQgsNqNwfHw8q1atIjg4mGrVqtGiRQteeeUVli5dmqHv5s2bcXBwYOTIkVSuXJng4GCcnJwyhMAZM2ZQoUKFB7ULIiIiIvmKxYJdZGQkycnJ+Pn5mdr8/f2JiIggNTXVrG9ERAT+/v6mufRsbGyoXbs2Bw8eNPXZu3cve/fuZeDAgQ+kfhEREZH8xmLBLiYmBjc3N+zt7U1tHh4e3Lp1iytXrmToW6pUKbM2d3d3zp8/D0BiYiLjxo1j/PjxODo63vW7ExMTiYuLM3uJiIiIFHTZnscut928edMs1AGmz4mJidnqm95v3rx5VKtWjYCAAPbs2XPX7164cCFz5869n/JFRERE8h2LBTsHB4cMAS7987+PumXV19HRkb/++ouVK1eyYcOGbH/3gAED6NOnj+lzXFwcjRs3zukuiIiIiOQrFgt2pUuXJjY2luTkZGxt08qIiYnB0dGRYsWKZeh76dIls7ZLly5RqlQpvv32W65evUqLFi0ASElJAcDPz49JkybRvn37DN9tb2+f4QigiIiISEFnsWBXtWpVbG1tOXjwIHXq1AEgPDycGjVqZHgmra+vL5988gmGYWBjY4NhGOzfv5+BAwfSrFkz2rVrZ+obERHBiBEjWL9+vZ6UISIiIg8Vi908UaRIETp27MjEiRP5/fff2b59O2FhYfTs2RNIO3qXkJAAwLPPPmuao+7vv/8mJCSEmzdv0qpVK1xdXfHy8jK9SpcuDYCXl1eezL0nIiIikl9ZLNgBjBkzhmrVqtGrVy8mTZrE0KFDadmyJQABAQFs3rwZAGdnZxYuXEh4eDidO3cmIiKCjz/+mKJFi1qyfBEREZF8xcbQg2CJi4vD39+f8PBwHeUTERHJBv3uzJ8sesRORERERHKPxW6eEBHJidGjR7Nu3bosly9evJj69etnuTw0NJQvvvgCGxsbvv/+e3766Sfq1aunm6xExKroVCw6nCxSEFy/ft10Q9XmzZsJCwtj9erVpuXFixfPchqjq1evUq9ePSZPnkzDhg0BCAwMZMeOHZQvXz7vixexQvrdmT/piJ2IFAguLi64uLiY3hcuXJiSJUtma930xwY2aNAAT09PTp8+nWd1iohYkq6xE5EC7/Tp0/j4+JgFttDQUHr06MHp06cJDAwEoHnz5owePZpmzZoB0KxZM9auXWuRmkVE8oKCnYhYtbJly7Jq1SoAVq1aRXBwsNnn1q1bW7I8EZFcpWAnIlatcOHClChRAoASJUrg4uJi9vnfz6YWESnIFOxERERErISCnYgUeDY2NhnakpOTLVCJiIhlKdiJSIFnZ2cHwI0bN0xtd7rzNbMgKCJiDRTsRKTA8/DwoGzZsnz22WecOnWKtWvX8v3332fZv0iRIgBERkaahUERkYJOwU5ECrxChQoREhLC77//TuvWrdm6dSsDBw7Msn+JEiVo3749w4YNM90hKyJiDfTkCTR7tki+dPUqXLwIf/wBTk7g4wPlykEWT5cQkQdLvzvzJz15QkTyn4sXISQEQkMh/d+eRYrA8uXQokXaexERyUCnYkUk/9m2DebM+V+oA7h5Ezp1ghMnLFeXiEg+p2AnIvnLhQswZUrmy1JTYenSB1uPiEgBomAnIvlLUhKcOpX18sOH0wKeiIhkoGAnIvmLkxP4+WW9vEULKKQfXSIimdFPRxHJX9zcYPr0zJeVKAHPPvtg6xERKUAU7EQk/6lRA77+Gjw9/9dWty78+CN4eVmuLhGRfE7TnYhI/uPiAu3aQZ06cPky2NmBuzt4eFi6MhGRfE3BTkTyJxubtAmJy5WzdCUiIgWGTsWKiIiIWAkFOxEREREroWAnIiIiYiUU7ERERESshIKdiIiIiJVQsBMRERGxEgp2IiIiIlZCwU5ERETESijYiYiIiFgJBTsRERERK6FgJyIiImIlFOxERERErISCnYiIiIiVULATERERsRIKdiIiIiJWQsFORERExEoo2ImIiIhYCQU7ERERESuhYCciIiJiJRTsRERERKyEgp2IiIiIlVCwExEREbESCnYiIiIiVkLBTkRERMRKKNiJiIiIWAkFOxEREREroWAnIiIiYiUU7ERERESshIKdiIiIiJVQsBMRERGxEgp2IiIiIlZCwU5ERETESijYiYiIiFgJBTsRERERK6FgJyIiImIlFOxERERErISCnYiIiIiVULATERERsRIKdiJSYCQlJREaGkqzZs2oXr06TZo0Ydq0acTFxQHwzz//sGXLFgtXKSJiObaWLkBEJLtmzpzJ7t27mTJlChUqVODUqVOEhIRw4sQJFixYwMyZMzEMg1atWlm6VBERi1CwE5ECY926dUydOpUGDRoAUL58eSZOnEj37t25ePEihmFYuEIREcvSqVgRKTBsbGz49ddfSU1NNbX5+fmxadMmli5dyrp161i3bh2BgYEAXLt2jREjRlC7dm0CAgKYPHkyCQkJAOzZs4fAwEBWr15Nw4YNqVu3Lp988gm//fYbzz77LH5+fowcOdLsu0RE8jsdsRORAqNnz57MmTOH7du307hxY5566ikCAgJ49NFH6d+/PydOnABg/PjxAAQHB5OUlMSyZcu4desWU6ZM4Z133mHq1KkAXLx4ke3bt7NkyRK+++47Zs6cyeOPP867775LbGwsQ4cOpUWLFrRo0cJi+ywikhMKdiJSYAwePJgKFSrw1VdfsXLlSpYvX46TkxPBwcF06dIFR0dHAEqUKMHJkyfZvn07e/fuxcXFBYDJkyfTsWNHxowZA6TdjDFq1Ci8vb0pV64cM2bMoHv37tSqVQuAqlWrcuzYMYvsq4jIvVCwE5ECpX379rRv357Y2Fh27drFl19+SXBwMD4+Pmb9oqKiSE1NpVGjRmbtqamppiN7ABUqVAAwhUJPT0/TMkdHRxITE/NqV0REcp2CnYgUCJGRkaxfv57Ro0cD4ObmRrt27XjmmWdo2bIlv/76q1n/lJQUXFxcWLNmTYZtlS5dmoiICABsbc1/DBYqpEuPRaTgsuhPsFu3bhEUFESdOnUICAggLCwsy76HDx+ma9eu+Pr60qVLFw4dOmRaZhgGH3/8MYGBgdSuXZtevXrx999/P4hdEJEHJCUlhc8//5zDhw+btdvb2+Po6EiJEiWwsbExtXt7e3P9+nVsbGzw8vLCy8uLhIQEZsyYoaNwImK1LBrsZsyYwaFDh1i0aBETJkxg7ty5bN26NUO/+Ph4+vfvT506dVi7di1+fn4MGDCA+Ph4AJYvX05YWBjjxo1jzZo1lC9fnldffZWbN28+6F0SkTxSrVo1mjRpwmuvvcaGDRs4ffo0Bw8eZMKECSQmJtKyZUuKFCnCmTNnuHDhApUrV+bpp5/m7bff5vfff+f//u//GDNmDPHx8RQrVszSuyMikicsFuzi4+NZtWoVwcHBVKtWjRYtWvDKK6+wdOnSDH03b96Mg4MDI0eOpHLlygQHB+Pk5GQKgevWraNv3740bdoUb29vJk6cyJUrV9i/f/+D3i0RyUOzZ8+mQ4cOzJ07l1atWjFgwADi4uL48ssvcXZ2pkOHDhw/fpz27dtjGAYzZsygfPny9O7dmz59+uDt7c2sWbMsvRsiInnGYtfYRUZGkpycjJ+fn6nN39+fBQsWkJqaanadS0REBP7+/qbTLDY2NtSuXZuDBw/SuXNnRo4cSfny5U39bWxsMAyD69evP7gdEpHcde4c/PEHLFsGxYtDz54UqViRN998kzfffDPTVXx9fdm1a5fpc4kSJbIMcvXr1+fPP/80a/v35yVLltznToiIPFgWC3YxMTG4ublhb29vavPw8ODWrVtcuXKFEiVKmPV99NFHzdZ3d3fn6NGjANSpU8ds2apVq0hOTsbf3z/T705MTDS7xib9OZMikk+cOQOdOsFvv/2v7cMPYcwYePttuO3ng4iI/I/Fgt3NmzfNQh1g+vzvC5uz6pvZBdARERFMnz6dfv36UbJkyUy/e+HChcydO/d+yheRvJKSAl98YR7q0k2bBl26KNiJiGTBYsHOwcEhQzBL/5w+n9Td+v6734EDB3j11Vdp1KgRb7zxRpbfPWDAAPr06WP6HBcXR+PGje9pP0Qkl124APPmZb38008hi6PxIiIPO4vdPFG6dGliY2NJTk42tcXExODo6JjhjrXSpUtz6dIls7ZLly5RqlQp0+c9e/bQt29fnnzySd5///07zkVlb2+Ps7Oz2UvkYRMfH8/s2bN59tlnqVmzJvXr1+f11183XeJwN6NHjzbNKZeZwMBA1q5dm61tnTp1ih9++CHtQ2oq3OnyiH/+SesjIiIZWCzYVa1aFVtbWw4ePGhqCw8Pp0aNGhlCma+vLwcOHMAwDCBt3rr9+/fj6+sLwF9//cWgQYN4+umnmT17NnZ2dg9sP0QKohs3btCtWzc2bdrEiBEj2LJlC5999hlOTk68+OKLnDp16oHWExQUxO+//572wc0NWrfOunP37qBJhEVEMmWxU7FFihShY8eOTJw4kalTp3Lx4kXCwsKYNm0akHb0zsXFBUdHR5599lnef/99QkJCePHFF1m+fDk3b96kVatWQNoDv8uWLcuYMWOIjY01fUf6+iJibt68efzzzz9s3rzZdITc09OTadOmce7cOb744gvGjRtnmeKcnGDiRIiOhj59wMMDCheGAwfghx/gXzdLiYjI/1j0n71jxoyhWrVq9OrVi0mTJjF06FBatmwJQEBAAJs3bwbA2dmZhQsXEh4eTufOnYmIiODjjz+maNGixMTEcODAAf7++2+aNGlCQECA6ZW+voj8T2pqKuvWraNPnz6ZTtQ7Y8YMRowYAaRdt9qtWzdq1apFYGAgy5YtM+sbFxfHa6+9Ro0aNWjXrl2Gx3qlMwyDefPmERAQQJ06dRg4cCBnz54F0k7p7t27l7lz59KjR4+0FcqWheDgtJslnnsu7Q7ZbdsgNBRue5ariIiYs+izYosUKcL06dOZPn16hmX/nk+qZs2arFu3LkO/kiVLZugrIlk7efIkly9fzjBNULr0a1ejoqLo1asXvXv3JiQkhIiICCZNmoSHhwctWrQAYNu2bQwaNIi33nqLVatWMWTIEL777jtcXFzMtvnll1+yYcMG3n//fTw8PAgLC6Nv375s2LCB4OBgoqOjTU+UASAyEjp0gP9efgHAL79AYCDs2wdeXrk/MCIiVkAXqog8ZNIvVyhevLipbffu3fj5+Zlebdq0YeXKlTzxxBMMHz6cSpUq0alTJ15++WU+/fRT03rVq1dn2LBhVK5cmZEjR+Lq6srGjRszfOenn37KyJEjqV+/PpUrV+add97h6tWr/PTTT7i4uGBnZ0fRokVxdXWF2FgYPdo81KW7dAn+859cHxMREWth0SN2IvLgpZ9+vXbtmqnNz8+P9evXA/Dtt9+ybNkyoqKiqFmzptm6fn5+LF++3PT59uWFChWiatWqREVFma1z48YNzp8/z5tvvml2Y1RCQgLR0dEZC7xxI/M57NJ9+y288opuoBARyYSCnchDxsvLC1dXVw4cOGAKZkWKFMHrv6c33d3dgbT5I/8tNTWVlJQU0+fChQtnWP7vu9LT+3/44Yd4e3ubLbv9qKGJnV3adXR//ZX5Dvj4KNSJiGRBPx1FHjK2trZ06dKFRYsWZfo4vQsXLgDg7e1NRESE2bIDBw6YhbPbr29NTk7m8OHDVKpUyWydYsWK4e7uTkxMDF5eXnh5eVG2bFnee+89jh8/nrHA0qUhKChD8+jSpfGpUgWf1avx8fHJ8NqzZw+QNn9e06ZNTdMjiYg8TBTsRB5CQ4cOpWTJkrz44ots3bqVU6dO8fvvvzNu3DjmzJmDv78/L730EkeOHGHWrFkcP36cdevW8dVXX9G9e3fTdvbt28f8+fOJiopiypQpJCUl0bZt2wzf17t3b2bPns3OnTuJjo5m7Nix7N+/3xQCixYtSnR0NP/880/aCq1aQfqNFP8VfPUqu0aPZte2bQQFBVGmTBl27dplevn5+XHgwAESEhK4evWqKeiJiDxMdCpW5CFUpEgRlixZwqJFi/joo484ceIE9vb21KxZk9DQUJo3bw6kPVd5xowZhIWFUa5cOUaPHk2XLl1M2+nYsSP79u1j3rx5VKlShYULF1KkSJEM39evXz9u3LjB+PHjiYuLo3r16nz22WemU7Fdu3YlKCiIV155Je3u91Kl4N134c03Yf9+cHbGpXp1XMqWBUdHXFxcKFy4cIbnQW/atIk6deqQlJTE+vXrefLJJ/NwFEVE8h8bQ+criIuLw9/fn/DwcD1eTKQAWLt2LXPnzmXnzp2mttTUVBo1akT//v1xdHRk2rRp/PzzzxQtWtSClYpYL/3uzJ90KlZErMKePXuIiYmhadOmNG3alISEBL799ltLlyUi8kAp2ImIVdi4cSM+Pj5UqFCBkiVLUqtWrUwnNRcRsWYKdiJS4CUmJrJt2zbTtYEALVu2ZM+ePaZHl4mIPAwU7ESkwPvpp5+4evUq8+fP54knnuCJJ55gxowZGIbB119/benyREQeGN0VKyIF3ubNm6lUqRIffvihWfs777zD+vXrGTRokIUqExF5sHTETkQKtJs3b7Jz506ee+45qlSpYvbq3r070dHRHDhwwNJliog8EAp2IlKg7dy5k6SkJDp27JhhWfPmzSlZsqRuohCRh4ZOxYpIvhUYGMiZM2cyXbZ48WIA2rRpQ5s2bTLtY2dnx65du0yfQ0ND2bt3L0uWLMnVOk+fPk2zZs3YsWMH5cuXz9Vti4jkhIKdiORrQUFBtG7dOkN7+lMr8oOyZcuya9cuSpQoYelSROQhp2AnIvmai4tLhkeH5TeZPd5MRMQSdI2diBRYgYGBLF26lOeff54aNWrQoUMHDh06ZFr+999/061bN3x9fenZsyexsbFm6x84cIBu3bpRq1YtAgMDWbZsmdnyzz//nMDAQPz8/OjXrx+nTp0CoEePHkyePJlmzZrRpEkT/vzzT3x8fDh9+jQAPj4+fP3117Rt25bq1avz0ksvmdYF+Ouvv+jRowc1a9bkmWeeYenSpXk1RCLykFGwE5ECLTQ0lP79+/PNN9/g4uLClClTgLRJi/v370+FChVYu3YtzzzzDCtWrDCtFxUVRa9evahbty5r165l6NChTJ8+nW3btgGwfPly5s6dy9tvv826detwcnLijTfeMK2/du1a3nvvPebOnYuTk1OmdQUHB7N27VpiY2OZPXs2AAkJCbz66qv4+/vzzTffMGrUKD766CPWr1+fd4MkIg8NnYoVkXxtwoQJTJ482aytXLlybNq0CYBOnTqZnjjRp08fU/javXs3V65cYeLEiRQtWpTKlSuzd+9eLl++DMDKlSt54oknGD58OACVKlUiKiqKTz/9lBYtWrBixQp69+5tur5v/PjxfPbZZyQkJADQpEkTateuDWA6Une7Pn360KBBAwC6detmOiq3YcMG3N3dGTZsGAAVK1bkzJkzLF68ONM7e0VEckLBTkTytddff52WLVuatdna/u9HV8WKFU3vnZ2dSUpKAtJOw1asWJGiRYualteoUYMffvgBSDtiV7NmTbPt+vn5sXz5cgCOHz9OtWrVTMs8PDwYNWqU6bOnp+cd6/by8sq0rmPHjhEZGYmfn59peUpKCoULF77j9kREskPBTkTyNXdnZ7zKlAEHh0yX29nZZbmuYRhZ9nXIZHupqamkpKQA5uExM5mtn526kpOTadCgAePHj7/j+iIi90LX2IlI/nTqFMTFwZw58OKLsHMnXLyY7dUfe+wxoqOjuX79uqntyJEjpvfe3t5ERESYrXPgwAG8vb2BtCNukZGRpmWxsbE8+eSTmZ52zQlvb2+OHz9O+fLl8fLywsvLi4MHD+b63Hoi8nBSsBOR/OfkSQgMhEuXuB4ZScyGDcS0bEnMsGHEREYSExNDfHz8HTfx1FNPUbZsWYKDg4mKimLt2rVs3rzZtPyll17iyJEjzJo1i+PHj7Nu3Tq++uorunfvDqTd+bpo0SK2b9/O8ePHmTBhAuXLl7/vCYjbt29PQkIC48ePJyoqih9++IGQkBDc3d3va7siIqBTsSKS3yQlwUcfwd9/g7c3U0uVYmqpUmnLwsOhQwcAsztUM2NnZ8fChQsZO3YsnTp1wsfHh+7du5umQylXrhwLFy5kxowZhIWFUa5cOUaPHk2XLl0A6NChAxcuXGDSpEnExcVRr1495syZc9+75+zszCeffMLUqVPp2LEjrq6udO/enQEDBtz3tkVEbIx/X4TyEIqLi8Pf35/w8HCcnZ0tXY7Iw+3MGfDzg5iYzJf36QNhYQ+2JhHJQL878yedihWR/OXWLUhMzHr5zZugf4+KiGRKwU5E8hdHR+jcOevlL76oYCcikgUFOxHJXxwdYeBAKFEi47KnnoJKlaCQfnSJiGRGN0+ISP6SkABvvQXLlsGaNfCf/4CTE7z0ElSuDAsXwty5lq5SRCRf0j97RSR/SU2F33+HNm3g0iUYMiQt1K1fD926pd1UkZpq6SpFRPIlHbETkfzFzQ3atoWvvoK1a9Net3v5ZZ2KFRHJgn46ikUFBgbi4+OT6WvPnj13Xf/IkSPs37//AVSaM7/88gtRUVH3tG5oaKjZOFStWpX69eszZswYLubgyQsPUo8ePQgNDQUgMTGRlStX3vvGnJxg/HjIbPoEX1/w97/3bYuIWDkFO7G4oKAgdu3aleF1+0PSszJ48GCio6Pzvsgc6t27N5cuXbrn9f38/Ezj8MMPP/Dpp5/yxx9/8Pbbb+dilbknNDSUvn37ArBp0yYWLFhwfxt89FHYty/tFKyLC5QpA5MmwcaNUK5cLlQsImKddCpWLM7FxYWSJUtauox8xc7OzmxMSpUqxaBBgxg+fDhXr16lePHiFqwuI1dXV9P7XJnzvHBh8PGBjz+G2Ni0U6+lS6e1i4hIlnTETvK1wMBAli5dyvPPP0+NGjXo0KGD6ZFQPXr04MyZM4wZM4bRo0cD8Ndff9GjRw9q1qzJM888w9KlS03bCg0N5bXXXqN79+7Uq1ePvXv3cuvWLd577z0aN25MrVq1GDhwIOfOnTOtc+LECfr164efnx9NmjRh8eLFAPTp04cpU6aY1Tpw4EBmz55NYGAgAD179jSdnty3bx+dO3emZs2atGvXjv/85z85HovChQtjY2ODnZ0da9eu5cUXX2Tw4MH4+/vzzTffkJqayqeffkqzZs2oWbMmPXr04M8//zStHxsby5AhQ/Dz86NZs2YsW7YMHx8f0/K7jd1bb73FhAkTqF27Ng0aNOCTTz4xLU8/Fbtnzx7GjBnDmTNn8PHx4fTp03ety8fHhw8//JD69eszcOBA8512coLy5dOO0inUiYjclYKd5HuhoaH079+fb775BhcXF1OgCg0NpUyZMgQFBREcHExCQgKvvvqqKeiMGjWKjz76iPXr15u2tWPHDtq2bcuiRYuoWbMmEyZMYNu2bUyfPp3ly5eTnJzMa6+9RmpqKrdu3aJv3744OTmxcuVKxo8fzwcffMB3331HmzZt+Pbbb01Hp65fv86uXbto06YNq1evNtXXt29fYmJiGDBgAJ07d2bDhg288sorjB49mn379mV7DKKjo/n4449p0KABRYsWBeDAgQM8+uijrFy5koCAAObNm0dYWBhBQUGsW7cOT09PXnnlFeLj4wEYPnw4ly9fZtmyZYwfP5558+aZtp+dsfvPf/6Dg4MD69ato1+/fsycOZPjx4+b1enn50dQUBBlypRh165dlC1b9q51AXz33XcsW7Ys355qFhEpMAwxrl+/blSpUsW4fv26pUt56DRt2tSoXr26UatWLbNX69atTcvfffddU//t27cb1apVM1t/zZo1hmEYxsqVK41OnTqZbX/x4sWmtjlz5hhPPfWUadmVK1eMxx9/3Pjpp59MbbGxsYavr6/x448/Gtu3bzdq1apl9vdi9erVxvfff29cvXrVqFatmrFv3z7DMAxj7dq1Rtu2bU39qlSpYvz666+GYRjGBx98YAwZMsSsrmnTpmVoSzdnzhzj8ccfN41FtWrVDB8fH6N79+7G6dOnDcMwjDVr1hg+Pj7GzZs3DcMwjNTUVKNevXrG8uXLTdtJTEw0GjdubCxbtsw4duyYUaVKFePkyZOm5cuWLTOqVKmS7bFr2LChkZycbFper14945tvvjEMwzBefvllY86cOabamjZtmq260sfqq6++ynQsRCT/0u/O/EnX2InFvf7667Rs2dKszdb2f381K1asaHrv7OxMUlJSpts5duwYkZGRZjddpKSkUPi2U3ienp6m99HR0aSmpuLr62tqc3V1xdvbm6ioKJKTk/H29jZ7uHWXLl1M7xs1asTWrVvx9/dny5YttG7dOsu6vvvuO7O6kpKS8Pb2zrQ/QPXq1Zk5cyYAhQoVokSJEjg5OZn1cXd3x9HREYB//vmHK1eumO2LnZ0d1atXJyoqCldXV1xdXalQoYJpea1atcxqvNvYlS9f3uyzk5MTycnJWe5DdupKd/ufi4iI3DsFO7E4d3d3vLy8slxuZ2eXre0kJyfToEEDxo8fn2UfBweHTN/fLiUlhdTUVLNwmZm2bdsyffp0hg4dyu7duxk7dmyWdbVr1y7D9WN32r6jo+Mdx+Tf9WdnX4w73NSQnbHL7M/hTtvMTl136yciIjmja+zEanh7e3P8+HHKly+Pl5cXXl5eHDx4kCVLlmTav0KFCtja2nLw4EFTW2xsLCdOnMDb25uKFSty4sQJbt68aVo+ffp00zV+gYGBXLt2jc8++wwfHx8eeeSRLOs6ceKEqSYvLy927NjBhg0bcm3fXVxc8PDwMNuXpKQk/u///g9vb28qV67M1atXOXXqlGl5+k0o6TXmZOzuxMbGJtt13dGFC/DLLzB2LMyYAUeOwLVrOa5HRORhomAnFnf9+nViYmIyvG6/uD4rRYsW5dixY1y5coX27duTkJDA+PHjiYqK4ocffiAkJAR3d/dM13VycqJr165MnjyZPXv2EBkZyYgRIyhTpgwNGzYkICAADw8P0/Z27NjB8uXLCQgIANKOqjVr1ozPP/+cNm3aZKjr6NGjXL9+nZdeeolDhw7xwQcfEB0dzYYNG5g1axbl/jsfW2JiIjExMaSkpNzXOPbu3Zs5c+awc+dOoqKiGDduHLdu3aJ169Z4e3sTEBBAUFAQkZGR/Pzzz8yZM8e0bk7H7k6KFCnC1atXiY6OJjk5+Y51ZencubQnTDz1FISEwKhR8MQT8MkncOXKPYyOiMjDQadixeKmTp3K1KlTM7S/8cYbd123W7duzJw5k+joaObOncsnn3zC1KlT6dixI66urnTv3p0BAwZkuf6oUaOYPn06r7/+OomJiTz11FN88cUX2NvbA/DRRx/xzjvv0KlTJzw8PBg5ciRNmjQxrd+6dWs2btyYIaT06NGDGTNmcPLkSYKCgliwYAEzZ87ks88+o3Tp0owePZr27dsDaXe39uzZkx07dlC+fPnsDFmm+vbtS1xcHOPGjSMuLg4/Pz+WLFlCiRIlAJg2bRrjxo3j+eefp3Tp0nTu3JlPP/0USLt2Madjl5Unn3wSLy8v2rVrx1dffXXXujJITYXly2H79ozL3n4bmjeH2+bNExGR/7Ex7naRzEMgLi4Of39/wsPDzS6UF7mblStX8s033/Dll19aupQ7unnzJrt376ZRo0ama+W2bNnCe++9x86dOy1c3b+cOwdPPgknT2a+fOhQuO1oo4hYhn535k86FStyD06cOMGmTZuYP38+Xbt2tXQ5d+Xg4EBQUBDz5s3j1KlTHDhwgHnz5vHMM89YurSMUlLg8uWsl587l3ZUT0REMlCwE7kHp0+fJjg4mNq1a9OuXTtLl3NXhQoVYt68eezevZu2bdsyZMgQnn76ad58801Ll5ZRsWLw36d3ZOq559IeMSYiIhnoVCw6nCyS7/zxB/j7w7/nLPT2hh9/THvMmIhYlH535k+6eUJE8h8fH/j1V3jzzbQgZ2/P6Hr1WHfxIjRrlukqixcvpn79+gQGBmIYBjt37jSbeuXf9uzZQ8+ePTNd5unpec/XHgYGBjJkyBA6d+58T+uLiNwPBTsRyX/s7aF2bVi/Hq5ehUKFCHZ05K3/BrXNmzcTFhZmei4vQPHixTlw4AAJCQkkJCSwZ88ennzyybt+1a5duzK03f6EDRGRgkTBTkTyLze3tBfg8t8XpE18XLhwYUqWLGnWfdOmTdSpU4ekpCTWr1+frWD3722IiBRkugJZRKxCamoqW7dupU6dOjRt2pT//Oc/2Zrk+m527NhBx44dqVGjBnXq1GH48OHcuHEDgNDQUF577TW6d+9OvXr12Lt3r2m98PBwnnjiCS7fdofvoUOH8PX1JS4u7r7rEhHJjIKdiFiFPXv2EBMTQ9OmTWnatCkJCQl8++2397XNkydP8sYbb/DSSy+xZcsWZs+eze7du1m5cqWpz44dO2jbti2LFi2iZs2apvbatWtTunRptm3bZmrbsmULjRs31oXmIpJndCpWRKzCxo0b8fHxoUKFCgDUqlWLdevW0bFjxzuu5+fnl6FtwIABDBw4kNTUVMaOHcvzzz8PQPny5Xnqqac4evSoqa+HhwfdunXLsA0bGxtat27N1q1beeGFFwDYunUrI0eOvNddFBG5KwU7ESnwEhMT2bZtGy+//LKprWXLlkyfPp2zZ8+ansubmfXr12doK168OAAVK1bE3t6e+fPnc/ToUY4ePcrff/9Nhw4dTH09PT2z3Hbbtm354osviI2N5dSpU8TGxpo9kk5EJLcp2IlIgffTTz9x9epV5s+fz4IFCwAwDAPDMPj6668ZNGhQlut6eXlluSwyMpJu3boRGBhInTp16N27N4sWLTLr4+DgkOX6VatW5ZFHHmH79u1ER0fTrFmzO/YXEblfCnYiUuBt3ryZSpUq8eGHH5q1v/POO6xfv/6Owe5Ovv76a+rWrcv7779vajtx4gSVK1fO9jbatm3Ld999x8mTJ3n77bfvqQ4RkexSsBORAu3mzZvs3LmTIUOGUKVKFbNl3bt3Z9iwYRw4cAA/Pz9iYmJwcXHB0dHR1CcmJibT7bq7u+Pq6sqff/7J77//jouLCytWrOCPP/4wXceXHW3btmXhwoUUKVKEhg0b3ttOiohkk4KdiBRoO3fuJCkpKdObJJo3b07JkiVZt24dfn5+BAQEMG3aNLOnQgQEBGS63R9++IEePXpw+PBhevfujYODA3Xr1mXw4MFs2rQp2/V5eXnx6KOP8sQTT2BnZ5fj/RMRyQk9KxY9705E8k5qaipNmzZl+vTp2ZowWaSg0O/O/ElH7ERE8sj333/Prl27cHR0pF69epYuR0QeAgp2IiJ55LPPPuP48ePMnj2bQoU0H7yI5D0FOxGRPLJkyRJLlyAiDxkFO9LmuwL0/EYREZFsSv+dqUv18xcFOzA90Ltx48YWrkRERKRguXHjBi4uLpYuQ/5Ld8WSdtfaxYsXcXJywsbGxtLlPHBxcXE0btyYH374QXc25QKNZ+7SeOYujWfuepjH0zAMbty4QalSpXQNaT6iI3ZAoUKFKFOmjKXLsDhnZ+eH7gdTXtJ45i6NZ+7SeOauh3U8daQu/1HEFhEREbESCnYiIiIiVkLBTrC3t2fIkCHY29tbuhSroPHMXRrP3KXxzF0aT8lvdPOEiIiIiJXQETsRERERK6FgJyIiImIlFOys1K1btwgKCqJOnToEBAQQFhaWZd9du3bRvn17/Pz86N27N8eOHTNbvnXrVp555hlq1apF3759OXPmTF6Xn+/k1ngahkFoaCiNGjWibt26DBs2jMuXLz+IXciXEhMTadu2LXv27Mmyz+HDh+natSu+vr506dKFQ4cOmS3fuHEjzZs3x9fXl8GDB2s872M8DcPg448/JjAwkNq1a9OrVy/+/vvvB1F6vpQbfz/TbdmyBR8fn7wqVcREwc5KzZgxg0OHDrFo0SImTJjA3Llz2bp1a4Z+R48eZcCAATRr1ow1a9bwxBNP0KtXL9PTOPbv389bb71Fnz59WLt2Lfb29gwfPvxB747F5dZ4rlixgtWrVzNz5kyWLl3KxYsXCQ4OftC7ky/cunWL4cOHc/To0Sz7xMfH079/f+rUqcPatWvx8/NjwIABxMfHA/D7778THBzMkCFDWLFiBdeuXWPMmDEPahfyldwYz+XLlxMWFsa4ceNYs2YN5cuX59VXX+XmzZsPajfyjdwYz3TXrl0jJCQkr0sWSWOI1blx44ZRo0YN49dffzW1zZs3z3j55Zcz9J00aZLRvXt30+fU1FSjVatWxrJlywzDMIzBgwcbo0ePNi0/efKk0bRpU+Off/7Jwz3IX3JzPAcOHGi8++67puU7duwwatWqlYfV509Hjx412rdvb7Rr186oUqWK2djebtWqVUZgYKCRmppqGEbaeLZo0cJYs2aNYRiGMWLECGPUqFGm/mfPnjV8fHyMkydP5v1O5CO5NZ5du3Y1Fi5caOqfmJho1KpVy9i1a1fe70Q+klvjmS44ONh48cUXjSpVquR57SI6YmeFIiMjSU5Oxs/Pz9Tm7+9PREQEqampZn1PnTpFzZo1TZ9tbGyoUqUKBw8eBGDv3r20aNHCtLxChQrs3LmTEiVK5O1O5CO5OZ6urq58//33XLhwgYSEBDZt2kTVqlUfyH7kJ3v37qV+/fqsWLHijv0iIiLw9/c3PerPxsaG2rVrm8YzIiKCOnXqmPqXLVuWcuXKERERkWe150e5NZ4jR46kffv2pv42NjYYhsH169fzrPb8KLfGM31be/fuZeDAgXlZsoiJHilmhWJiYnBzczObV8nDw4Nbt25x5coVs1Dm4eHBhQsXzNY/f/48xYsX59q1a1y9epWUlBT69etHZGQkNWvWZOLEiZQuXfqB7Y+l5dZ4AgwePJhBgwbRqFEjChcuTMmSJe/6y8MavfTSS9nqFxMTw6OPPmrW5u7ubjo9dvHiRUqVKpVh+fnz53On0AIit8bz9pAMsGrVKpKTk/H398+dQguI3BrPxMRExo0bx/jx47Gzs8v1OkUyoyN2VujmzZsZJstM/5yYmGjW3qpVK/7zn//w3XffkZyczLp16/jjjz9ISkoyXScyZcoU2rVrx/z580lMTGTAgAEZjlRZs9waT4AzZ87g6OjIggULWLJkCWXKlCEoKOjB7EgBlNXYp497QkLCHZeLubuN5+0iIiKYPn06/fr1o2TJkg+qxALlbuM5b948qlWrRkBAgCXKk4eUjthZIQcHhww/qNM/Ozo6mrU3atSIwYMHM3ToUFJSUqhfvz4dOnQgLi6OwoULA9C1a1c6duwIwMyZM2nYsCEHDx6kdu3aeb8z+UBujadhGIwaNYqRI0fStGlTAGbPnk3Tpk2JiIjA19f3wexQAZLV2KePe1bLixQp8sBqLEjuNp7pDhw4wKuvvkqjRo144403HmSJBcqdxvOvv/5i5cqVbNiwwULVycNKR+ysUOnSpYmNjSU5OdnUFhMTg6OjI8WKFcvQf9CgQezfv59du3bxxRdfcOPGDTw9PXFzc8POzo5KlSqZ+rq5ueHq6vpQnerKrfG8fPky586dM5vyoGzZsri5uT2UU8hkR+nSpbl06ZJZ26VLl0ynX7NariNMmbvbeALs2bOHvn378uSTT/L+++9TqJB+TWTlTuP57bffcvXqVVq0aIGfnx+vvvoqAH5+fnzzzTeWKFceEvo/1gpVrVoVW1tbswt4w8PDqVGjRoYf0hs3biQkJAR7e3vc3d1JSEhgz5491K9fH1tbW6pVq0ZkZKSp/+XLl4mNjcXT0/NB7Y7F5dZ4Fi9eHHt7e6Kiokz9L1++zJUrVyhfvvyD2p0CxdfXlwMHDmD898mHhmGwf/9+09FNX19fwsPDTf3PnTvHuXPndPQzC3cbz7/++otBgwbx9NNPM3v2bF0Xdhd3Gs+XX36ZLVu2sH79etavX8+UKVMAWL9+PYGBgZYsW6ycgp0VKlKkCB07dmTixIn8/vvvbN++nbCwMHr27AmkHW1KSEgAoGLFiixfvpxvv/2W6Oho3nrrLcqWLUujRo0A6NOnD0uWLGHLli1ERUURFBRE1apVze78tHa5NZ62trZ07tyZ6dOn89tvv/HXX38xYsQIfH19qVGjhiV3MV+5fTyfffZZ0xxgf//9NyEhIdy8eZNWrVoB0K1bN77++mtWrVpFZGQkI0eOpEmTJlSoUMGSu5Cv5GQ8x48fT9myZRkzZgyxsbHExMSYrS/ZH09XV1e8vLxMr/Qbzry8vHB2drbkLoi1s9xMK5KX4uPjjZEjRxq1atUyAgICjM8//9y0rEqVKmbzLK1evdpo2rSp4efnZ7z22mvGhQsXzLa1YsUKo2nTpkbNmjWNV155xTh37tyD2o18I7fGMyEhwXj33XeNp59+2qhXr54xbNiwh2pOwMz8e56wf49nRESE0bFjR6NGjRrGc889Z/zf//2f2fpr1qwxGjdubNSqVcsYPHiwcfny5QdWe350r+N58eJFo0qVKpm+/j0v28Pkfv9+pvv11181j508EDaG8d9jyCIiIiJSoOlUrIiIiIiVULATERERsRIKdiIiIiJWQsFORERExEoo2ImIiIhYCQU7ERERESuhYCciIiJiJRTsRERERKyEraULEJGCJzAwkDNnzpg+29raUqFCBV588UV69+5tucJERB5yCnYick+CgoJo3bo1AMnJyfz6668EBwfj6upKx44dLVuciMhDSqdiReSeuLi4ULJkSUqWLEnZsmXp1KkTDRo04Ntvv7V0aSIiDy0FOxHJNba2ttjZ2dGjRw8mT55Ms2bNaNKkCXFxcZw7d46BAwfi6+tLYGAgc+fOJSUlBYCkpCTGjh1L/fr18fPzY+DAgVy4cMG03W3bttG6dWt8fX157rnn2Lt3r2lZjx49mD9/Pv369aNmzZo888wz/PTTT6bl//zzD8OGDaN27do0bNiQWbNmkf6I7DvVJCJSECnYich9S0pK4ttvv+Xnn3+mWbNmAKxdu5b33nuPuXPn4uTkxJAhQ3B3d2fdunVMmzaNDRs2sGDBAgCWLl3Kb7/9RlhYGKtXr+bGjRtMnToVgMjISEaNGsWgQYP45ptvaN++Pa+++ionTpwwff+CBQto06YNGzdu5PHHH2fcuHGkpqYCMHjwYGJiYvjyyy+ZPXs2a9euZenSpRiGcceaREQKIhsj/Z+uIiLZFBgYSExMDLa2aZfpJiQk4OjoyEsvvcSIESPo0aMHHh4efPDBBwD88ssvDB8+nJ9//plChdL+Pblz507GjBnDnj17mDJlCnv37mXx4sW4urpy5swZrly5QrVq1RgxYgTu7u6MHj3a9P1Dhw7F09OT0aNH06NHD9zc3JgzZw6QFgQ7dOjAjz/+SGxsLB06dGD79u1UqFABgO3btxMfH0/JkiXvWJOISEGkmydE5J68/vrrtGzZEgAHBwdKlixJ4cKFTcs9PT1N76Oiorhy5Qr+/v6mttTUVBISEoiNjeWFF15g06ZNBAQEUK9ePZo3b07nzp1N627ZsoUVK1aY1k1KSiIgIMD0uWLFiqb3zs7OQNoNHcePH8fV1dUU6gCaN28OwJdffnnHmtzc3O5rfERELEHBTkTuibu7O15eXlkud3BwML1PTk6mUqVKfPTRRxn6ubi44Obmxs6dO/n+++/5/vvvmTVrFhs3bmTp0qWkpKTw6quvZrjT1tHR0fTezs4uw3YNw8i0Pbs1iYgURLrGTkTynLe3N2fPnqVEiRJ4eXnh5eXF6dOnmTNnDjY2Nqxfv57vvvuOVq1aMX36dD799FPCw8P5559/8Pb25vTp06b1vLy8WLFiBT/++ONdv9fLy4srV65w7tw5U9vixYt57bXX7lqTiEhBpGAnInkuICAAT09PRowYwZ9//sm+ffsYN24cRYoUoXDhwly/fp2QkBB++eUXTp06xYYNGyhTpgxubm707t2bzZs3s3jxYk6ePMkXX3zBF198YXb6NSuPPfYYTz75JMHBwfz555/s2bOHjz/+mIYNG961JhGRgkinYkUkzxUuXJj58+czefJknn/+eYoWLcqzzz7LqFGjAOjevTvnz59nxIgRXL16lerVqzN//nwKFy5MrVq1mDFjBqGhocyYMYNHHnmE999/n7p162bru9977z0mTZrECy+8gLOzMy+88AIvvfQSNjY2d6xJRKQg0l2xIiIiIlZCp2JFRERErISCnYiIiIiVULATERERsRIKdiIiIiJWQsFORERExEoo2ImIiIhYCQU7ERERESuhYCciIiJiJRTsRERERKyEgp2IiIiIlVCwExEREbESCnYiIiIiVuL/AZwIVH860xcbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pt.pl.coda.rel_abundance_dispersion_plot(sccoda_data, modality_key=\"coda\", abundant_threshold=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference algorithms in scCODA\n",
    "\n",
    "scCODA performs parameter inference via Markov-chain Monte Carlo (MCMC) methods.\n",
    "There are three different MCMC sampling methods available for scCODA:\n",
    "\n",
    "- HMC sampling with Dual-averaging step size adaptation ([Nesterov, 2009](https://link.springer.com/article/10.1007/s10107-007-0149-x)): `run_hmc()`\n",
    "- No-U-Turn sampling ([Hoffman and Gelman, 2014](https://jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf)): `run_nuts()`\n",
    "\n",
    "Generally, it is recommended to use NUTS sampling. Other methods, such as variational inference, are in consideration.\n",
    "\n",
    "For all MCMC sampling methods, properties such as the MCMC chain length and the number of burn-in samples are directly adjustable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result analysis and diagnostics\n",
    "\n",
    "The \"getting started\" tutorial explains how to do interpret the basic output of scCODA.\n",
    "To follow this up, we now take a look at how MCMC diagnostics and more advanced result analysis in scCODA can be performed.\n",
    "\n",
    "For this section, we again use the model of salmonella infection versus control group, with a reference cell type of Goblet cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:39<00:00, 275.96it/s, 255 steps of size 2.61e-02. acc. prob=0.82]\n"
     ]
    }
   ],
   "source": [
    "sccoda_data = sccoda_model.prepare(\n",
    "    sccoda_data,\n",
    "    modality_key=\"coda_salm\",\n",
    "    formula=\"condition\",\n",
    "    reference_cell_type=\"Goblet\",\n",
    ")\n",
    "sccoda_model.run_nuts(sccoda_data, modality_key=\"coda_salm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extended model summary\n",
    "\n",
    "`sccoda_model.summary()` can give us, apart from the properties already explained in the basic tutorial, more information about the posterior inferred by the model by setting `extended=True`.\n",
    "The extended summary also includes some information on the MCMC sampling procedure (chain length, burn-in, acceptance rate, duration).\n",
    "\n",
    "For both effects and intercepts, we also get the standard deviation (SD) and high density interval endpoints of the posterior density of the generated Markov chain.\n",
    "\n",
    "The effects summary also includes the spike-and-slab inclusion probability for each effect, i.e. the share of MCMC samples, for which this effect was not set to 0 by the spike-and-slab prior.\n",
    "A threshold on this value serves as the deciding factor whether an effect is considered statistically credible\n",
    "\n",
    "We can also use the summary tables from `summary()` as pandas DataFrames to tweak them further.\n",
    "They are also accessible via `sccoda_model.get_intercept_df()` and `sccoda_model.get_effect_df()`, respectively.\n",
    "Furthermore, the tables are generated through the `summary()` function in [arviz](https://arviz-devs.github.io/arviz/index.html) and support all its functionality.\n",
    "This means that we can, for example, easily change the credible interval of parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                                          Compositional Analysis summary                                           </span>\n",
       "┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Name                                  </span>│<span style=\"font-weight: bold\"> Value                                                                   </span>│\n",
       "├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Data                                  </span>│ Data: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> samples, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span> cell types                                           │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Reference cell type                   </span>│ Goblet                                                                  │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Formula                               </span>│ condition                                                               │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Reference index                       </span>│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>                                                                       │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Spike-and-slab threshold              </span>│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000</span>                                                                   │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Spike-and-slab threshold              </span>│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000</span>                                                                   │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> MCMC Sampling                         </span>│ Sampled <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10000</span> chain states <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1000</span> burnin samples<span style=\"font-weight: bold\">)</span>                        │\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> Acceptance rate                       </span>│ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">81.8</span>%                                                                   │\n",
       "└───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001B[3m                                          Compositional Analysis summary                                           \u001B[0m\n",
       "┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mName                                 \u001B[0m\u001B[1m \u001B[0m│\u001B[1m \u001B[0m\u001B[1mValue                                                                  \u001B[0m\u001B[1m \u001B[0m│\n",
       "├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│\u001B[36m \u001B[0m\u001B[36mData                                 \u001B[0m\u001B[36m \u001B[0m│ Data: \u001B[1;36m6\u001B[0m samples, \u001B[1;36m8\u001B[0m cell types                                           │\n",
       "│\u001B[36m \u001B[0m\u001B[36mReference cell type                  \u001B[0m\u001B[36m \u001B[0m│ Goblet                                                                  │\n",
       "│\u001B[36m \u001B[0m\u001B[36mFormula                              \u001B[0m\u001B[36m \u001B[0m│ condition                                                               │\n",
       "│\u001B[36m \u001B[0m\u001B[36mReference index                      \u001B[0m\u001B[36m \u001B[0m│ \u001B[1;36m3\u001B[0m                                                                       │\n",
       "│\u001B[36m \u001B[0m\u001B[36mSpike-and-slab threshold             \u001B[0m\u001B[36m \u001B[0m│ \u001B[1;36m1.000\u001B[0m                                                                   │\n",
       "│\u001B[36m \u001B[0m\u001B[36mSpike-and-slab threshold             \u001B[0m\u001B[36m \u001B[0m│ \u001B[1;36m1.000\u001B[0m                                                                   │\n",
       "│\u001B[36m \u001B[0m\u001B[36mMCMC Sampling                        \u001B[0m\u001B[36m \u001B[0m│ Sampled \u001B[1;36m10000\u001B[0m chain states \u001B[1m(\u001B[0m\u001B[1;36m1000\u001B[0m burnin samples\u001B[1m)\u001B[0m                        │\n",
       "│\u001B[36m \u001B[0m\u001B[36mAcceptance rate                      \u001B[0m\u001B[36m \u001B[0m│ \u001B[1;36m81.8\u001B[0m%                                                                   │\n",
       "└───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Intercepts                                                                                                      </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                        Final Parameter  HDI <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>%  HDI <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">90</span>%   SD   Expected Sample                                 │\n",
       "│ Cell Type                                                                                                       │\n",
       "│ Endocrine                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.118</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.699</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.590</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.357</span>      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">34.667</span>                                      │\n",
       "│ Enterocyte                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.329</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.961</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.741</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.310</span>     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">116.372</span>                                      │\n",
       "│ Enterocyte.Progenitor      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.519</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.129</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.915</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.311</span>     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">140.723</span>                                      │\n",
       "│ Goblet                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.747</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.355</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.158</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.321</span>      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">65.027</span>                                      │\n",
       "│ Stem                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.703</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.335</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.101</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.304</span>     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">169.152</span>                                      │\n",
       "│ TA                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.109</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.705</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.532</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.326</span>      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">93.391</span>                                      │\n",
       "│ TA.Early                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.861</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.482</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.244</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.301</span>     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">198.105</span>                                      │\n",
       "│ Tuft                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.438</span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.036</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.946</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.394</span>      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.563</span>                                      │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mIntercepts                                                                                                     \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                        Final Parameter  HDI \u001B[1;36m10\u001B[0m%  HDI \u001B[1;36m90\u001B[0m%   SD   Expected Sample                                 │\n",
       "│ Cell Type                                                                                                       │\n",
       "│ Endocrine                  \u001B[1;36m1.118\u001B[0m         \u001B[1;36m0.699\u001B[0m   \u001B[1;36m1.590\u001B[0m   \u001B[1;36m0.357\u001B[0m      \u001B[1;36m34.667\u001B[0m                                      │\n",
       "│ Enterocyte                 \u001B[1;36m2.329\u001B[0m         \u001B[1;36m1.961\u001B[0m   \u001B[1;36m2.741\u001B[0m   \u001B[1;36m0.310\u001B[0m     \u001B[1;36m116.372\u001B[0m                                      │\n",
       "│ Enterocyte.Progenitor      \u001B[1;36m2.519\u001B[0m         \u001B[1;36m2.129\u001B[0m   \u001B[1;36m2.915\u001B[0m   \u001B[1;36m0.311\u001B[0m     \u001B[1;36m140.723\u001B[0m                                      │\n",
       "│ Goblet                     \u001B[1;36m1.747\u001B[0m         \u001B[1;36m1.355\u001B[0m   \u001B[1;36m2.158\u001B[0m   \u001B[1;36m0.321\u001B[0m      \u001B[1;36m65.027\u001B[0m                                      │\n",
       "│ Stem                       \u001B[1;36m2.703\u001B[0m         \u001B[1;36m2.335\u001B[0m   \u001B[1;36m3.101\u001B[0m   \u001B[1;36m0.304\u001B[0m     \u001B[1;36m169.152\u001B[0m                                      │\n",
       "│ TA                         \u001B[1;36m2.109\u001B[0m         \u001B[1;36m1.705\u001B[0m   \u001B[1;36m2.532\u001B[0m   \u001B[1;36m0.326\u001B[0m      \u001B[1;36m93.391\u001B[0m                                      │\n",
       "│ TA.Early                   \u001B[1;36m2.861\u001B[0m         \u001B[1;36m2.482\u001B[0m   \u001B[1;36m3.244\u001B[0m   \u001B[1;36m0.301\u001B[0m     \u001B[1;36m198.105\u001B[0m                                      │\n",
       "│ Tuft                       \u001B[1;36m0.438\u001B[0m        \u001B[1;36m-0.036\u001B[0m   \u001B[1;36m0.946\u001B[0m   \u001B[1;36m0.394\u001B[0m      \u001B[1;36m17.563\u001B[0m                                      │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Effects                                                                                                         </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                                Final Parameter  Expected Sample  log2-fold change               │\n",
       "│ Covariate               Cell Type                                                                               │\n",
       "│ condition<span style=\"font-weight: bold\">[</span>T.Salmonella<span style=\"font-weight: bold\">]</span> Endocrine                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24.752</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.486</span>                    │\n",
       "│                         Enterocyte                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.354</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">321.913</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.468</span>                    │\n",
       "│                         Enterocyte.Progenitor      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.474</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.486</span>                    │\n",
       "│                         Goblet                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">46.428</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.486</span>                    │\n",
       "│                         Stem                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">120.771</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.486</span>                    │\n",
       "│                         TA                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">66.680</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.486</span>                    │\n",
       "│                         TA.Early                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">141.443</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.486</span>                    │\n",
       "│                         Tuft                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.540</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.486</span>                    │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mEffects                                                                                                        \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                                Final Parameter  Expected Sample  log2-fold change               │\n",
       "│ Covariate               Cell Type                                                                               │\n",
       "│ condition\u001B[1m[\u001B[0mT.Salmonella\u001B[1m]\u001B[0m Endocrine                  \u001B[1;36m0.000\u001B[0m            \u001B[1;36m24.752\u001B[0m            \u001B[1;36m-0.486\u001B[0m                    │\n",
       "│                         Enterocyte                 \u001B[1;36m1.354\u001B[0m           \u001B[1;36m321.913\u001B[0m             \u001B[1;36m1.468\u001B[0m                    │\n",
       "│                         Enterocyte.Progenitor      \u001B[1;36m0.000\u001B[0m           \u001B[1;36m100.474\u001B[0m            \u001B[1;36m-0.486\u001B[0m                    │\n",
       "│                         Goblet                     \u001B[1;36m0.000\u001B[0m            \u001B[1;36m46.428\u001B[0m            \u001B[1;36m-0.486\u001B[0m                    │\n",
       "│                         Stem                       \u001B[1;36m0.000\u001B[0m           \u001B[1;36m120.771\u001B[0m            \u001B[1;36m-0.486\u001B[0m                    │\n",
       "│                         TA                         \u001B[1;36m0.000\u001B[0m            \u001B[1;36m66.680\u001B[0m            \u001B[1;36m-0.486\u001B[0m                    │\n",
       "│                         TA.Early                   \u001B[1;36m0.000\u001B[0m           \u001B[1;36m141.443\u001B[0m            \u001B[1;36m-0.486\u001B[0m                    │\n",
       "│                         Tuft                       \u001B[1;36m0.000\u001B[0m            \u001B[1;36m12.540\u001B[0m            \u001B[1;36m-0.486\u001B[0m                    │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│<span style=\"font-weight: bold\"> Effects Extended                                                                                                </span>│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                                HDI <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>%  HDI <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">90</span>%   SD   Inclusion probability                    │\n",
       "│ Covariate               Cell Type                                                                               │\n",
       "│ condition<span style=\"font-weight: bold\">[</span>T.Salmonella<span style=\"font-weight: bold\">]</span> Endocrine              <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.136</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.848</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.319</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.452</span>                             │\n",
       "│                         Enterocyte              <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.010</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.676</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.263</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000</span>                             │\n",
       "│                         Enterocyte.Progenitor  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.185</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.461</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.159</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.321</span>                             │\n",
       "│                         Goblet                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>                             │\n",
       "│                         Stem                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.589</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.040</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.212</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.388</span>                             │\n",
       "│                         TA                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.613</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.134</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.232</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.385</span>                             │\n",
       "│                         TA.Early               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.289</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.274</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.127</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.283</span>                             │\n",
       "│                         Tuft                   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0.506</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.658</span>   <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.308</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.398</span>                             │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│\u001B[1m \u001B[0m\u001B[1mEffects Extended                                                                                               \u001B[0m\u001B[1m \u001B[0m│\n",
       "├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│                                                HDI \u001B[1;36m10\u001B[0m%  HDI \u001B[1;36m90\u001B[0m%   SD   Inclusion probability                    │\n",
       "│ Covariate               Cell Type                                                                               │\n",
       "│ condition\u001B[1m[\u001B[0mT.Salmonella\u001B[1m]\u001B[0m Endocrine              \u001B[1;36m-0.136\u001B[0m   \u001B[1;36m0.848\u001B[0m   \u001B[1;36m0.319\u001B[0m         \u001B[1;36m0.452\u001B[0m                             │\n",
       "│                         Enterocyte              \u001B[1;36m1.010\u001B[0m   \u001B[1;36m1.676\u001B[0m   \u001B[1;36m0.263\u001B[0m         \u001B[1;36m1.000\u001B[0m                             │\n",
       "│                         Enterocyte.Progenitor  \u001B[1;36m-0.185\u001B[0m   \u001B[1;36m0.461\u001B[0m   \u001B[1;36m0.159\u001B[0m         \u001B[1;36m0.321\u001B[0m                             │\n",
       "│                         Goblet                  \u001B[1;36m0.000\u001B[0m   \u001B[1;36m0.000\u001B[0m   \u001B[1;36m0.000\u001B[0m         \u001B[1;36m0.000\u001B[0m                             │\n",
       "│                         Stem                   \u001B[1;36m-0.589\u001B[0m   \u001B[1;36m0.040\u001B[0m   \u001B[1;36m0.212\u001B[0m         \u001B[1;36m0.388\u001B[0m                             │\n",
       "│                         TA                     \u001B[1;36m-0.613\u001B[0m   \u001B[1;36m0.134\u001B[0m   \u001B[1;36m0.232\u001B[0m         \u001B[1;36m0.385\u001B[0m                             │\n",
       "│                         TA.Early               \u001B[1;36m-0.289\u001B[0m   \u001B[1;36m0.274\u001B[0m   \u001B[1;36m0.127\u001B[0m         \u001B[1;36m0.283\u001B[0m                             │\n",
       "│                         Tuft                   \u001B[1;36m-0.506\u001B[0m   \u001B[1;36m0.658\u001B[0m   \u001B[1;36m0.308\u001B[0m         \u001B[1;36m0.398\u001B[0m                             │\n",
       "└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sccoda_model.summary(sccoda_data, modality_key=\"coda_salm\", hdi_prob=0.8, extended=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Diagnostics and plotting\n",
    "\n",
    "Similarly to the summary dataframes being compatible with [arviz](https://arviz-devs.github.io/arviz/index.html),\n",
    "e can convert the result to an extension of arviz's `Inference Data` class via `make_arviz`. This means that we can use all its MCMC diagnostic and plotting functionality.\n",
    "As an example, looking at the MCMC trace plots and kernel density estimates, we see that they are indicative of a well sampled MCMC chain:\n",
    "\n",
    "Note: Due to the spike-and-slab priors, the `beta` parameters have many values at 0, which looks like a convergence issue, but is actually not.\n",
    "\n",
    "**Caution: Trying to plot a kernel density estimate for an effect on the reference cell type results in an error, since it is constant at 0 for the entire chain.\n",
    "To avoid this, add** `coords={\"cell_type\": salm_results.posterior.coords[\"cell_type_nb\"]}` **as an argument to `az.plot_trace`, which causes the plots for the reference cell type to be skipped.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "salm_arviz = sccoda_model.make_arviz(sccoda_data, modality_key=\"coda_salm\")\n",
    "az.plot_trace(\n",
    "    salm_arviz,\n",
    "    divergences=False,\n",
    "    var_names=[\"alpha\", \"beta\"],\n",
    "    coords={\"cell_type\": salm_arviz.posterior.coords[\"cell_type_nb\"]},\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using all cell types as reference alternatively to reference selection\n",
    "\n",
    "scCODA uses a reference cell type that is considered to be unchanged over the experiment to guarantee the unique identifiability of results.\n",
    "If no such cell type is known beforehand, setting `reference_cell_type=\"automatic\"` will find a suited reference.\n",
    "Alternatively, it is possible to find credible effects on cell types that are mostly independent of the reference.\n",
    "By sequentially running scCODA and selecting each cell type as the reference once, we can then use a majority vote to find the cell types that were credibly changing more than half of the time.\n",
    "\n",
    "Below, an example code for this procedure on the Salmonella infection data shows that only Enterocytes were found to be credible more than half of the time.\n",
    "Indeed, they are credibly changing for every reference cell type except themselves.\n",
    "All other cell types were not found to change with any reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: Endocrine\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:41<00:00, 264.03it/s, 255 steps of size 2.40e-02. acc. prob=0.83]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: Enterocyte\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:29<00:00, 375.11it/s, 31 steps of size 9.02e-02. acc. prob=0.63]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: Enterocyte.Progenitor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:41<00:00, 264.37it/s, 127 steps of size 2.26e-02. acc. prob=0.87]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: Goblet\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:37<00:00, 294.89it/s, 127 steps of size 2.88e-02. acc. prob=0.75]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: Stem\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:43<00:00, 254.13it/s, 127 steps of size 2.02e-02. acc. prob=0.87]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: TA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:36<00:00, 301.89it/s, 127 steps of size 3.26e-02. acc. prob=0.71]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: TA.Early\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:38<00:00, 282.39it/s, 127 steps of size 2.59e-02. acc. prob=0.81]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: Tuft\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 11000/11000 [00:46<00:00, 238.00it/s, 255 steps of size 1.76e-02. acc. prob=0.90]\n"
     ]
    }
   ],
   "source": [
    "# Run scCODA with each cell type as the reference\n",
    "cell_types = sccoda_data[\"coda_salm\"].var.index\n",
    "results_cycle = pd.DataFrame(index=cell_types, columns=[\"times_credible\"]).fillna(0)\n",
    "\n",
    "for ct in cell_types:\n",
    "    print(f\"Reference: {ct}\")\n",
    "\n",
    "    # Run inference\n",
    "    sccoda_data = sccoda_model.prepare(\n",
    "        sccoda_data,\n",
    "        modality_key=\"coda_salm\",\n",
    "        formula=\"condition\",\n",
    "        reference_cell_type=ct,\n",
    "    )\n",
    "    sccoda_model.run_nuts(sccoda_data, modality_key=\"coda_salm\")\n",
    "\n",
    "    # Select credible effects\n",
    "    cred_eff = sccoda_model.credible_effects(sccoda_data, modality_key=\"coda_salm\")\n",
    "    cred_eff.index = cred_eff.index.droplevel(level=0)\n",
    "\n",
    "    # add up credible effects\n",
    "    results_cycle[\"times_credible\"] += cred_eff.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       times_credible  pct_credible  is_credible\n",
      "cell_label                                                      \n",
      "Endocrine                           0         0.000        False\n",
      "Enterocyte                          7         0.875         True\n",
      "Enterocyte.Progenitor               0         0.000        False\n",
      "Goblet                              0         0.000        False\n",
      "Stem                                0         0.000        False\n",
      "TA                                  0         0.000        False\n",
      "TA.Early                            0         0.000        False\n",
      "Tuft                                0         0.000        False\n"
     ]
    }
   ],
   "source": [
    "# Calculate percentages\n",
    "results_cycle[\"pct_credible\"] = results_cycle[\"times_credible\"] / len(cell_types)\n",
    "results_cycle[\"is_credible\"] = results_cycle[\"pct_credible\"] > 0.5\n",
    "print(results_cycle)"
   ]
  }
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