notebooks/custom_model_lme.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom Modelling with BIRDMAn - Paired Data with SingleFeatureModels\n",
    "\n",
    "One of the primary goals of BIRDMAn is to allow users to develop and implement their own statistical models based on their experimental design/question. For example, if you have paired or longitudinal data, you would likely want to specify random intercepts for individual subjects.\n",
    "\n",
    "Here, we will do a fairly in-depth walkthrough of how you can use BIRDMAn to analyze paired data. Additionally, w will show off how to fit single microbes through BIRDMAn rather than the full table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data description\n",
    "\n",
    "We will be using data from the study \"Linking the effects of helminth infection, diet and the gut microbiota with human whole-blood signatures (repeated measurements)\" (Qiita ID: 11913). This study looks at the effect of de-worming on the gut microbiome. Importantly, they collected pre and post-deworming stool samples on several individuals.\n",
    "\n",
    "For our purposes, we would like to consider a linear mixed effects model (LME) where de-worming timepoint is a fixed effect and subject ID is a random effect. We will first process and explore the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/grahman/miniconda3/envs/birdman-demo/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from birdman import __version__\n",
    "print(__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import biom\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import birdman\n",
    "\n",
    "plt.style.use(\"./plot.mplstyle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2948 x 56 <class 'biom.table.Table'> with 8935 nonzero entries (5% dense)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_tbl = biom.load_table(\"../data/helminth/94270_reference-hit.biom\")\n",
    "raw_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>ascaris</th>\n",
       "      <th>collection_device</th>\n",
       "      <th>collection_method</th>\n",
       "      <th>collection_timestamp</th>\n",
       "      <th>date_of_collection</th>\n",
       "      <th>description</th>\n",
       "      <th>dna_extracted</th>\n",
       "      <th>elevation</th>\n",
       "      <th>elevation_units</th>\n",
       "      <th>...</th>\n",
       "      <th>sample_well</th>\n",
       "      <th>scientific_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>site</th>\n",
       "      <th>taxon_id</th>\n",
       "      <th>time_point</th>\n",
       "      <th>title</th>\n",
       "      <th>trichuris</th>\n",
       "      <th>tube_id</th>\n",
       "      <th>villageaf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11913.101</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plastic container</td>\n",
       "      <td>By study subject</td>\n",
       "      <td>2013-05-08</td>\n",
       "      <td>May_13</td>\n",
       "      <td>Selangor, Rural Indigenous, Stool samples</td>\n",
       "      <td>True</td>\n",
       "      <td>not collected</td>\n",
       "      <td>meters</td>\n",
       "      <td>...</td>\n",
       "      <td>p03.C11</td>\n",
       "      <td>human gut metagenome</td>\n",
       "      <td>female</td>\n",
       "      <td>HumanGut</td>\n",
       "      <td>408170</td>\n",
       "      <td>1</td>\n",
       "      <td>Linking the effects of helminth infection, die...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101</td>\n",
       "      <td>BF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.101AF</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plastic container</td>\n",
       "      <td>By study subject</td>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>Jun_13</td>\n",
       "      <td>Selangor, Rural Indigenous, Stool samples</td>\n",
       "      <td>True</td>\n",
       "      <td>not collected</td>\n",
       "      <td>meters</td>\n",
       "      <td>...</td>\n",
       "      <td>p01.E7</td>\n",
       "      <td>human gut metagenome</td>\n",
       "      <td>female</td>\n",
       "      <td>HumanGut</td>\n",
       "      <td>408170</td>\n",
       "      <td>1</td>\n",
       "      <td>Linking the effects of helminth infection, die...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101AF</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.102</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plastic container</td>\n",
       "      <td>By study subject</td>\n",
       "      <td>2013-05-08</td>\n",
       "      <td>May_13</td>\n",
       "      <td>Selangor, Rural Indigenous, Stool samples</td>\n",
       "      <td>True</td>\n",
       "      <td>not collected</td>\n",
       "      <td>meters</td>\n",
       "      <td>...</td>\n",
       "      <td>p03.C12</td>\n",
       "      <td>human gut metagenome</td>\n",
       "      <td>female</td>\n",
       "      <td>HumanGut</td>\n",
       "      <td>408170</td>\n",
       "      <td>1</td>\n",
       "      <td>Linking the effects of helminth infection, die...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102</td>\n",
       "      <td>BF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.102AF</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plastic container</td>\n",
       "      <td>By study subject</td>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>Jun_13</td>\n",
       "      <td>Selangor, Rural Indigenous, Stool samples</td>\n",
       "      <td>True</td>\n",
       "      <td>not collected</td>\n",
       "      <td>meters</td>\n",
       "      <td>...</td>\n",
       "      <td>p01.E8</td>\n",
       "      <td>human gut metagenome</td>\n",
       "      <td>female</td>\n",
       "      <td>HumanGut</td>\n",
       "      <td>408170</td>\n",
       "      <td>2</td>\n",
       "      <td>Linking the effects of helminth infection, die...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102AF</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.103AF</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Plastic container</td>\n",
       "      <td>By study subject</td>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Selangor, Rural Indigenous, Stool samples</td>\n",
       "      <td>True</td>\n",
       "      <td>not collected</td>\n",
       "      <td>meters</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>human gut metagenome</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>408170</td>\n",
       "      <td>2</td>\n",
       "      <td>Linking the effects of helminth infection, die...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103AF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              age  ascaris  collection_device collection_method  \\\n",
       "sample_name                                                       \n",
       "11913.101    15.0      0.0  Plastic container  By study subject   \n",
       "11913.101AF  15.0      0.0  Plastic container  By study subject   \n",
       "11913.102    50.0      0.0  Plastic container  By study subject   \n",
       "11913.102AF  50.0      0.0  Plastic container  By study subject   \n",
       "11913.103AF   NaN      NaN  Plastic container  By study subject   \n",
       "\n",
       "            collection_timestamp date_of_collection  \\\n",
       "sample_name                                           \n",
       "11913.101             2013-05-08             May_13   \n",
       "11913.101AF           2013-06-02             Jun_13   \n",
       "11913.102             2013-05-08             May_13   \n",
       "11913.102AF           2013-06-02             Jun_13   \n",
       "11913.103AF           2013-06-02                NaN   \n",
       "\n",
       "                                           description  dna_extracted  \\\n",
       "sample_name                                                             \n",
       "11913.101    Selangor, Rural Indigenous, Stool samples           True   \n",
       "11913.101AF  Selangor, Rural Indigenous, Stool samples           True   \n",
       "11913.102    Selangor, Rural Indigenous, Stool samples           True   \n",
       "11913.102AF  Selangor, Rural Indigenous, Stool samples           True   \n",
       "11913.103AF  Selangor, Rural Indigenous, Stool samples           True   \n",
       "\n",
       "                 elevation elevation_units  ... sample_well  \\\n",
       "sample_name                                 ...               \n",
       "11913.101    not collected          meters  ...     p03.C11   \n",
       "11913.101AF  not collected          meters  ...      p01.E7   \n",
       "11913.102    not collected          meters  ...     p03.C12   \n",
       "11913.102AF  not collected          meters  ...      p01.E8   \n",
       "11913.103AF  not collected          meters  ...         NaN   \n",
       "\n",
       "                  scientific_name     sex      site taxon_id time_point  \\\n",
       "sample_name                                                               \n",
       "11913.101    human gut metagenome  female  HumanGut   408170          1   \n",
       "11913.101AF  human gut metagenome  female  HumanGut   408170          1   \n",
       "11913.102    human gut metagenome  female  HumanGut   408170          1   \n",
       "11913.102AF  human gut metagenome  female  HumanGut   408170          2   \n",
       "11913.103AF  human gut metagenome  female       NaN   408170          2   \n",
       "\n",
       "                                                         title trichuris  \\\n",
       "sample_name                                                                \n",
       "11913.101    Linking the effects of helminth infection, die...       1.0   \n",
       "11913.101AF  Linking the effects of helminth infection, die...       1.0   \n",
       "11913.102    Linking the effects of helminth infection, die...       1.0   \n",
       "11913.102AF  Linking the effects of helminth infection, die...       1.0   \n",
       "11913.103AF  Linking the effects of helminth infection, die...       NaN   \n",
       "\n",
       "             tube_id villageaf  \n",
       "sample_name                     \n",
       "11913.101        101        BF  \n",
       "11913.101AF    101AF        AF  \n",
       "11913.102        102        BF  \n",
       "11913.102AF    102AF        AF  \n",
       "11913.103AF    103AF       NaN  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = pd.read_csv(\"../data/helminth/11913_20191016-112545.txt\", sep=\"\\t\", index_col=0)\n",
    "metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we want to determine which subjects have paired samples (taken at `time_point` 1 and 2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  101,   102,   103,  1097,   119,  1248,  1259,  1287,   135,\n",
       "        1372,  1383,   145, 15104,  1533,  1612,   166,  1719,  1758,\n",
       "         181,  1875,  1889,  1892, 19100,  1998,  1999, 20109,  2093,\n",
       "         218,   220,   221,   351,    37,   442,   445,   463,   511,\n",
       "        5112,  5113,    52,   634,   762,   780,  8107,    88,   930,\n",
       "         941,   953,    96,   969,    98,   995])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata[\"host_subject_id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   37,    52,    96,   102,   119,   135,   145,   166,   181,\n",
       "              218,   220,   221,   351,   445,   463,   511,   780,   930,\n",
       "              941,   953,  1097,  1248,  1259,  1383,  1533,  1612,  1719,\n",
       "             1875,  1998,  8107, 19100, 20109],\n",
       "           dtype='int64', name='host_subject_id')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subj_is_paired = metadata.groupby(\"host_subject_id\").apply(lambda x: (x[\"time_point\"].values == [1, 2]).all())\n",
    "paired_subjs = subj_is_paired[subj_is_paired].index\n",
    "paired_subjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "paired_samps = metadata[metadata[\"host_subject_id\"].isin(paired_subjs)].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will consider only post/pre-deworm status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_keep = [\"time_point\", \"host_subject_id\"]\n",
    "metadata_model = metadata.loc[paired_samps, cols_to_keep].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, we are going to change the encoding of `time_point` to be more explicit. We are also going to prefix each subject ID with \"S\" so that they are read as strings instead of integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_model[\"time_point\"] = metadata_model[\"time_point\"].map({1: \"pre-deworm\", 2: \"post-deworm\"})\n",
    "metadata_model[\"host_subject_id\"] = \"S\" + metadata[\"host_subject_id\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time_point</th>\n",
       "      <th>host_subject_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11913.102</th>\n",
       "      <td>pre-deworm</td>\n",
       "      <td>S102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.102AF</th>\n",
       "      <td>post-deworm</td>\n",
       "      <td>S102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.1097</th>\n",
       "      <td>pre-deworm</td>\n",
       "      <td>S1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.1097AF</th>\n",
       "      <td>post-deworm</td>\n",
       "      <td>S1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.119</th>\n",
       "      <td>pre-deworm</td>\n",
       "      <td>S119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               time_point host_subject_id\n",
       "sample_name                              \n",
       "11913.102      pre-deworm            S102\n",
       "11913.102AF   post-deworm            S102\n",
       "11913.1097     pre-deworm           S1097\n",
       "11913.1097AF  post-deworm           S1097\n",
       "11913.119      pre-deworm            S119"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_model.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering the feature table\n",
    "\n",
    "We want to filter the original BIOM table to include only the samples we've specified. We are also going to reduce the number of microbes in the table to a more manageable number for this demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>11913.20109AF</th>\n",
       "      <th>11913.1383AF</th>\n",
       "      <th>11913.1097</th>\n",
       "      <th>11913.101AF</th>\n",
       "      <th>11913.634AF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCATGCAGGCGGTTCATCAAGTAGGATGTGAAATCCCCGGGCTCAAC</th>\n",
       "      <td>995.0</td>\n",
       "      <td>4355.0</td>\n",
       "      <td>1811.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAAC</th>\n",
       "      <td>917.0</td>\n",
       "      <td>3345.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>5260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAAC</th>\n",
       "      <td>737.0</td>\n",
       "      <td>1761.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>5845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAAC</th>\n",
       "      <td>204.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAAC</th>\n",
       "      <td>122.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    11913.20109AF  \\\n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...          995.0   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...          917.0   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...          737.0   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...          204.0   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...          122.0   \n",
       "\n",
       "                                                    11913.1383AF  11913.1097  \\\n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...        4355.0      1811.0   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...        3345.0       516.0   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...        1761.0       273.0   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...         545.0        71.0   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...          79.0       573.0   \n",
       "\n",
       "                                                    11913.101AF  11913.634AF  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...         28.0       3229.0  \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...        827.0       5260.0  \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...       1298.0       5845.0  \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...        305.0        918.0  \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...        321.0        246.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_tbl_df = raw_tbl.to_dataframe()\n",
    "raw_tbl_df.iloc[:5, :5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samps_to_keep = sorted(list(set(raw_tbl_df.columns).intersection(metadata_model.index)))\n",
    "len(samps_to_keep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To filter out features, we calculate the prevalence of each feature and keep only features that appear in at least 25 samples out of 46."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 46)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt_tbl_df = raw_tbl_df.loc[:, samps_to_keep]\n",
    "prev = filt_tbl_df.clip(upper=1).sum(axis=1)\n",
    "filt_tbl_df = filt_tbl_df.loc[prev[prev >= 25].index, :]\n",
    "filt_tbl_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we save this DataFrame as a BIOM so we can pass it into BIRDMAn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65 x 46 <class 'biom.table.Table'> with 2239 nonzero entries (74% dense)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt_tbl = biom.table.Table(\n",
    "    filt_tbl_df.values,\n",
    "    sample_ids=filt_tbl_df.columns,\n",
    "    observation_ids=filt_tbl_df.index\n",
    ")\n",
    "filt_tbl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LME Model\n",
    "\n",
    "We have provided a Stan file with the code required to run this model. First, we'll print the contents to the screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data {\n",
      "  int<lower=1> N;\n",
      "  real A;\n",
      "  int<lower=1> p;\n",
      "  array[N] real depth;\n",
      "  array[N] int y;\n",
      "  matrix[N, p] x;\n",
      "  real<lower=0> B_p;\n",
      "  real<lower=0> disp_scale;\n",
      "\n",
      "  // Random Effects\n",
      "  int<lower=1> num_subjs;\n",
      "  array[N] int<lower=1, upper=num_subjs> subj_map;\n",
      "  real<lower=0> re_p;\n",
      "}\n",
      "\n",
      "parameters {\n",
      "  real<offset=A, multiplier=3> beta_0;\n",
      "  vector[p-1] beta_x;\n",
      "  real<lower=0> reciprocal_phi;\n",
      "  vector[num_subjs] subj_re;  // Subject effect on intercept\n",
      "}\n",
      "\n",
      "transformed parameters {\n",
      "  vector[p] beta_var = append_row(beta_0, beta_x);\n",
      "  vector[N] lam;\n",
      "  real phi = inv(reciprocal_phi);\n",
      "\n",
      "  for (n in 1:N) {\n",
      "    lam[n] = depth[n];\n",
      "    for (i in 1:p) {\n",
      "      // when i = 1 -> Intercept (1)\n",
      "      // Add subj effect\n",
      "      lam[n] = lam[n] + (beta_var[i] + subj_re[subj_map[n]]) * x[n, i];\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "model {\n",
      "  beta_0 ~ normal(A, 3);\n",
      "  beta_x ~ normal(0, B_p);\n",
      "  reciprocal_phi ~ lognormal(0, disp_scale);\n",
      "  subj_re ~ normal(0, re_p);\n",
      "\n",
      "  y ~ neg_binomial_2_log(lam, phi);\n",
      "}\n",
      "\n",
      "generated quantities {\n",
      "  vector[N] y_predict;\n",
      "  vector[N] log_lhood;\n",
      "\n",
      "  for (n in 1:N) {\n",
      "    y_predict[n] = neg_binomial_2_log_rng(lam[n], phi);\n",
      "    log_lhood[n] = neg_binomial_2_log_lpmf(y[n] | lam[n], phi);\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"../models/negative_binomial_single_re.stan\", \"r\") as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Description of the model\n",
    "\n",
    "The basic Negative Binomial model for a single microbe is as follows:\n",
    "\n",
    "$$ y_{ij} \\sim \\mathrm{NB}(\\mu_{ij},\\phi_j) $$\n",
    "\n",
    "$$ \\ln(\\mu_{ij}) = X_i \\beta_{j} + \\ln(\\mathrm{Depth}_i)$$\n",
    "\n",
    "where $i$ is an arbitrary sample, $j$ is an arbitrary microbe, $n$ is the sampling depth, and $\\phi$ is the dispersion parameter.\n",
    "\n",
    "For the default NB model, the probability for each microbe in a given sample is a function of only the fixed-effects. $X_i$ is the covariate matrix of sample $i$ and $\\beta$ is the covariate coefficient vector (e.g. $[\\beta_{\\rm{Intercept}}, \\beta_{\\rm{Time}}, ...]^T$).\n",
    "\n",
    "To include a random effect, we add a random intercept for each subject ID.\n",
    "\n",
    "$$ \\ln(\\mu_{ijk}) = X_i \\beta_{j} + \\ln(\\mathrm{Depth}_i) + z_i u_{jk} $$\n",
    "\n",
    "where $z_i$ is the mapping of sample $i$ to subject $k$ and $u$ is the subject coefficient vector (e.g. $[u_{\\textrm{S1}}, u_{\\textrm{S2}}, ...]$)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first compile this model manually. The reason we do this is so that if we are running multiple microbes in parallel, we don't run into issues where multiple processes are trying to compile the same file simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CmdStanModel: name=negative_binomial_single_re\n",
       "\t stan_file=/home/grahman/projects/BIRDMAn-demo/models/negative_binomial_single_re.stan\n",
       "\t exe_file=/home/grahman/projects/BIRDMAn-demo/models/negative_binomial_single_re\n",
       "\t compiler_options=stanc_options={}, cpp_options={}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cmdstanpy\n",
    "cmdstanpy.CmdStanModel(stan_file=\"../models/negative_binomial_single_re.stan\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom class\n",
    "\n",
    "Next, we'll define a custom class that inherits `birdman.SingleFeatureModel`. We will go through the components in this class to describe what each block is doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NBPairedSingle(birdman.SingleFeatureModel):\n",
    "    def __init__(\n",
    "        self,\n",
    "        table: biom.Table,\n",
    "        feature_id: str,\n",
    "        formula: str,\n",
    "        metadata: pd.DataFrame,\n",
    "        num_iter: int = 100,\n",
    "        num_warmup: int = 500,\n",
    "        chains: int = 4,\n",
    "        beta_prior: float = 3.0,\n",
    "        subj_prior: float = 2.0,\n",
    "        disp_scale: float = 0.5,\n",
    "    ):  \n",
    "        super().__init__(\n",
    "            table=table,\n",
    "            feature_id=feature_id,\n",
    "            model_path=\"../models/negative_binomial_single_re.stan\",\n",
    "            num_iter=num_iter,\n",
    "            num_warmup=num_warmup,\n",
    "        )\n",
    "\n",
    "        subj_series = metadata[\"host_subject_id\"].loc[self.sample_names]\n",
    "        samp_subj_map = subj_series.astype(\"category\").cat.codes + 1\n",
    "        self.subjects = np.sort(subj_series.unique())\n",
    "\n",
    "        self.create_regression(formula, metadata)\n",
    "\n",
    "        D = table.shape[0]\n",
    "        A = np.log(1 / D)\n",
    "\n",
    "        param_dict = {\n",
    "            \"depth\": np.log(table.sum(axis=\"sample\")),\n",
    "            \"num_subjs\": len(self.subjects),\n",
    "            \"subj_map\": samp_subj_map.values,\n",
    "            \"B_p\": beta_prior,\n",
    "            \"disp_scale\": disp_scale,\n",
    "            \"re_p\": subj_prior,\n",
    "            \"A\": A\n",
    "        }\n",
    "        self.add_parameters(param_dict)\n",
    "\n",
    "        self.specify_model(\n",
    "            params=[\"beta_var\", \"phi\", \"subj_re\"],\n",
    "            dims={\n",
    "                \"beta_var\": [\"covariate\"],\n",
    "                \"subj_re\": [\"subject\"],\n",
    "                \"log_lhood\": [\"tbl_sample\"],\n",
    "                \"y_predict\": [\"tbl_sample\"]\n",
    "            },\n",
    "            coords={\n",
    "                \"covariate\": self.colnames,\n",
    "                \"tbl_sample\": self.sample_names,\n",
    "                \"subject\": self.subjects\n",
    "            },\n",
    "            include_observed_data=True,\n",
    "            posterior_predictive=\"y_predict\",\n",
    "            log_likelihood=\"log_lhood\"\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Arguments\n",
    "\n",
    "* *self*: Needs to be first arguement since we are creating a class\n",
    "* *table*: BIOM table of counts\n",
    "* *feature_id*: Name of feature to fit\n",
    "* *formula*: Formula for design matrix\n",
    "* *metadata*: Sample metadata\n",
    "* *num_iter*: Number of sampling iterations\n",
    "* *num_warmup*: Number of warmup iterations\n",
    "* *chains*: Number of chains\n",
    "* *beta_prior*: Standard deviation of normally distributed prior around log-fold changes\n",
    "* *subj_prior*: Standard deviation of normally distributed prior around subject offsets\n",
    "* *disp_scale*: Standard deviation of lognormally distributed prior around inverse dispersion parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inherit initialization\n",
    "\n",
    "Since we inherit from `SingleFeatureModel`, we can pass the basic parameters that initialization function.\n",
    "\n",
    "```python\n",
    "super().__init__(\n",
    "    table=table,\n",
    "    feature_id=feature_id,\n",
    "    model_path=\"../models/negative_binomial_single_re.stan\",\n",
    "    num_iter=num_iter,\n",
    "    num_warmup=num_warmup,\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding subject identifiers\n",
    "\n",
    "We need to map each sample in the dataset to its corresponding subject. We coerce the `host_subject_id` vector into type category which describes each unique entry as a number. We add 1 to each value since Stan starts counting at 1. Then, we tell BIRDMAn that we want to create an attribute `subjects` and have it be an array of the sorted subject IDs (which is the same order used in the mapping).\n",
    "\n",
    "```python\n",
    "subj_series = metadata[\"host_subject_id\"].loc[self.sample_names]\n",
    "samp_subj_map = subj_series.astype(\"category\").cat.codes + 1\n",
    "self.subjects = np.sort(subj_series.unique())\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a design matrix\n",
    "\n",
    "We can easily create a design matrix by passing in a design formula and the metadata into the `create_regression` method. This will automatically create a `colnames` attribute for our model with the names of the design matrix columns.\n",
    "        \n",
    "```python\n",
    "self.create_regression(formula, metadata)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a prior distribution for the Intercept\n",
    "\n",
    "The Intercept of the Negative Binomial model is the log-average proportion of microbial abundance in control samples. We use a prior centered around (log) 1/D, where D is the number of microbes. This weak prior assumes that each microbe has an equal proportion in control samples.\n",
    "\n",
    "```python\n",
    "D = table.shape[0]\n",
    "A = np.log(1 / D)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding our parameters to the model\n",
    "\n",
    "We want to make sure that every piece of input data found in the `data` block of the Stan program is provided. We create a dictionary of the parameters where the key is the name in the Stan file and the value is, well, the value.\n",
    "\n",
    "```python\n",
    "param_dict = {\n",
    "    \"depth\": np.log(table.sum(axis=\"sample\")),\n",
    "    \"num_subjs\": len(self.subjects),\n",
    "    \"subj_map\": samp_subj_map.values,\n",
    "    \"B_p\": beta_prior,\n",
    "    \"disp_scale\": disp_scale,\n",
    "    \"re_p\": subj_prior,\n",
    "    \"A\": A\n",
    "}\n",
    "self.add_parameters(param_dict)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specifying the model for inference\n",
    "\n",
    "By default, a new BIRDMAn model does not know what parameters are of interest or what their coordinates should be. These are critical for convering to an ArviZ `InferenceData` object. We \"specify\" the model by telling BIRDMAn a couple things:\n",
    "\n",
    "1. *params*: Names of the parameters to save.\n",
    "2. *dims*: Named dimensions of each parameter (note that we include the log likelihood and posterior predictive dimensions here).\n",
    "3. *coords*: The names of the entries in each named dimension. For example, we want the `tbl_sample` dimensions to contain the sample names in our feature table.\n",
    "4. *include_observed_data*: Whether or not we want to include the observed data into the `InferenceData` object.\n",
    "5. *posterior_predictive*: Name of the Stan variable representing the posterior predictive values.\n",
    "6. *log_likelihood*: Name of the Stan variable representing the log likelihood values.\n",
    "\n",
    "```python\n",
    "self.specify_model(\n",
    "    params=[\"beta_var\", \"phi\", \"subj_re\"],\n",
    "    dims={\n",
    "        \"beta_var\": [\"covariate\"],\n",
    "        \"subj_re\": [\"subject\"],\n",
    "        \"log_lhood\": [\"tbl_sample\"],\n",
    "        \"y_predict\": [\"tbl_sample\"]\n",
    "    },\n",
    "    coords={\n",
    "        \"covariate\": self.colnames,\n",
    "        \"tbl_sample\": self.sample_names,\n",
    "        \"subject\": self.subjects\n",
    "    },\n",
    "    include_observed_data=True,\n",
    "    posterior_predictive=\"y_predict\",\n",
    "    log_likelihood=\"log_lhood\"\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting each microbe\n",
    "\n",
    "We can fit each microbe individually rather than the whole table at once. We can create a `ModelIterator` instance that tells BIRDMAn what `SingleFeatureModel` architecture to use as well as the necessary arguments. This will create an iterator where at each iteration the feature ID and associated model are yielded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_iter = birdman.ModelIterator(\n",
    "    table=filt_tbl,\n",
    "    model=NBPairedSingle,\n",
    "    metadata=metadata_model,\n",
    "    formula=\"C(time_point, Treatment('pre-deworm'))\",\n",
    "    num_iter=100,\n",
    "    beta_prior=3.0,\n",
    "    subj_prior=2.0,\n",
    "    disp_scale=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "cmdstanpy_logger = logging.getLogger(\"cmdstanpy\")\n",
    "cmdstanpy_logger.disabled = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now fit each model and add the `InferenceData` to a running list.\n",
    "\n",
    "NOTE: Fitting should take about 3 minutes. A better solution would be to run multiple microbes at a time with multiple CPUs!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.72 s, sys: 2.5 s, total: 9.21 s\n",
      "Wall time: 1min 24s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%%capture\n",
    "\n",
    "inferences = []\n",
    "for feature_id, model in model_iter:\n",
    "    model.compile_model()\n",
    "    model.fit_model()\n",
    "    inf = model.to_inference()\n",
    "    inferences.append(inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code block results in a list of `InferenceData` objects. Ideally we would like to concatenate all of these inferences together. BIRDMAn provides such a utility function in the `model_util` module.\n",
    "\n",
    "We also want to mean center the coefficients due to compositionality. This is pretty easy in `xarray`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from birdman.model_util import concatenate_inferences\n",
    "\n",
    "inf_concat = concatenate_inferences(inferences, coords={\"feature\": filt_tbl.ids(\"observation\")})\n",
    "\n",
    "inf_concat.posterior[\"beta_var\"] = (\n",
    "    inf_concat.posterior[\"beta_var\"] - inf_concat.posterior[\"beta_var\"].mean([\"chain\", \"draw\", \"feature\"])\n",
    ")\n",
    "\n",
    "inf_concat.posterior[\"subj_re\"] = (\n",
    "    inf_concat.posterior[\"subj_re\"] - inf_concat.posterior[\"subj_re\"].mean([\"chain\", \"draw\", \"feature\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this notebook we'll look at differential taxa by post-deworm status. First, we want to summarize the posterior distributions of each features. There are a couple ways we can do this, but we'll just take the mean and standard deviation of each feature distribution. BIRDMAn has a convenience function for this purpose. We need to tell BIRDMAn that we are specifically interested in the `post-deworm` covariate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_deworm_mean</th>\n",
       "      <th>post_deworm_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTCTGGTAAGCGTGTTGTGAAATGTCGGGGCTCAAC</th>\n",
       "      <td>1.368741</td>\n",
       "      <td>0.630426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGTGGCAAGGCAAGCCAGAAGTGAAAACCCGGGGCTCAAC</th>\n",
       "      <td>1.324558</td>\n",
       "      <td>0.741344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCATGCAGGCGGTTCATCAAGTAGGATGTGAAATCCCCGGGCTCAAC</th>\n",
       "      <td>1.233840</td>\n",
       "      <td>0.635445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGTATGGAGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGTGTAGGTGGCCATGCAAGTCAGAAGTGAAAATCCGGGGCTCAAC</th>\n",
       "      <td>1.067045</td>\n",
       "      <td>0.762197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAAC</th>\n",
       "      <td>1.046864</td>\n",
       "      <td>0.465206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    post_deworm_mean  \\\n",
       "feature                                                                \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...          1.368741   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...          1.324558   \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...          1.233840   \n",
       "TACGTATGGAGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGT...          1.067045   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...          1.046864   \n",
       "\n",
       "                                                    post_deworm_std  \n",
       "feature                                                              \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...         0.630426  \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...         0.741344  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...         0.635445  \n",
       "TACGTATGGAGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGT...         0.762197  \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...         0.465206  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from birdman.summary import summarize_posterior\n",
    "\n",
    "coords = {\"covariate\": \"C(time_point, Treatment('pre-deworm'))[T.post-deworm]\"}\n",
    "\n",
    "deworm_mean = summarize_posterior(inf_concat.posterior, \"beta_var\", coords)\n",
    "deworm_std = summarize_posterior(inf_concat.posterior, \"beta_var\", coords, estimator=\"std\")\n",
    "\n",
    "deworm_df = pd.concat([deworm_mean, deworm_std], axis=1)\n",
    "deworm_df.columns = [\"post_deworm_mean\", \"post_deworm_std\"]\n",
    "deworm_df = deworm_df.sort_values(by=\"post_deworm_mean\", ascending=False)\n",
    "deworm_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll take the top and bottom 10 microbes associated with post-deworm status to create per-sample log-ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10\n",
    "bottom_feats = deworm_df.tail(n).index\n",
    "top_feats = deworm_df.head(n).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>denom</th>\n",
       "      <th>log_ratio</th>\n",
       "      <th>time_point</th>\n",
       "      <th>host_subject_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11913.102</th>\n",
       "      <td>2132.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.903616</td>\n",
       "      <td>pre-deworm</td>\n",
       "      <td>S102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.102AF</th>\n",
       "      <td>3226.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.102265</td>\n",
       "      <td>post-deworm</td>\n",
       "      <td>S102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.1097</th>\n",
       "      <td>2110.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>3.019714</td>\n",
       "      <td>pre-deworm</td>\n",
       "      <td>S1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.1097AF</th>\n",
       "      <td>2489.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.385649</td>\n",
       "      <td>post-deworm</td>\n",
       "      <td>S1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913.1248</th>\n",
       "      <td>442.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.865563</td>\n",
       "      <td>pre-deworm</td>\n",
       "      <td>S1248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 num  denom  log_ratio   time_point host_subject_id\n",
       "11913.102     2132.0   43.0   3.903616   pre-deworm            S102\n",
       "11913.102AF   3226.0  145.0   3.102265  post-deworm            S102\n",
       "11913.1097    2110.0  103.0   3.019714   pre-deworm           S1097\n",
       "11913.1097AF  2489.0   31.0   4.385649  post-deworm           S1097\n",
       "11913.1248     442.0  186.0   0.865563   pre-deworm           S1248"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def log_ratio(table, top_feats, bot_feats):\n",
    "    num_df = table.loc[:, top_feats].sum(axis=1).to_frame()\n",
    "    num_df.columns = [\"num\"]\n",
    "    num_df = num_df[num_df[\"num\"] > 0]\n",
    "    denom_df = table.loc[:, bot_feats].sum(axis=1).to_frame()\n",
    "    denom_df.columns = [\"denom\"]\n",
    "    denom_df = denom_df[denom_df[\"denom\"] > 0]\n",
    "    lr_df = num_df.join(denom_df, how=\"inner\")\n",
    "    lr_df[\"log_ratio\"] = np.log(lr_df[\"num\"]/lr_df[\"denom\"])\n",
    "    return lr_df\n",
    "\n",
    "lr_df = log_ratio(filt_tbl_df.T, top_feats, bottom_feats).join(metadata_model, how=\"inner\")\n",
    "print(lr_df.shape)\n",
    "lr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have paired samples, we want to look at the differences from pre-deworm to post-deworm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_subject_id\n",
       "S102    -0.801351\n",
       "S1097    1.365935\n",
       "S1248    3.301400\n",
       "S1259    0.373832\n",
       "S1383    3.672885\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func = lambda x: (\n",
    "    x.query(\"time_point == 'post-deworm'\")[\"log_ratio\"][0] \n",
    "    - x.query(\"time_point == 'pre-deworm'\")[\"log_ratio\"][0]\n",
    ")\n",
    "\n",
    "lr_diffs = (\n",
    "    lr_df\n",
    "    .groupby(\"host_subject_id\")\n",
    "    .apply(func)\n",
    ")\n",
    "lr_diffs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll plot the log-ratio changes from pre-deworm to post-deworm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "for subj_id, subj_df in lr_df.groupby(\"host_subject_id\"):\n",
    "    _d = lr_diffs[subj_id]\n",
    "    \n",
    "    args = {\n",
    "        \"data\": subj_df,\n",
    "        \"x\": \"time_point\",\n",
    "        \"y\": \"log_ratio\",\n",
    "        \"color\": \"black\" if _d > 0 else \"red\",\n",
    "    }\n",
    "    sns.pointplot(**args, ax=ax)\n",
    "\n",
    "ax.set_xlim([-0.4, 1.4])\n",
    "\n",
    "pre_deworm_lrs = lr_df.query(\"time_point == 'pre-deworm'\")[\"log_ratio\"].values\n",
    "post_deworm_lrs = lr_df.query(\"time_point == 'post-deworm'\")[\"log_ratio\"].values\n",
    "\n",
    "bplot = ax.boxplot(\n",
    "    [pre_deworm_lrs, post_deworm_lrs],\n",
    "    positions=[-0.2, 1.2],\n",
    "    patch_artist=True,\n",
    "    showfliers=False\n",
    ")\n",
    "\n",
    "for patch in bplot[\"boxes\"]:\n",
    "    patch.set_facecolor(\"lightgray\")\n",
    "for med in bplot[\"medians\"]:\n",
    "    med.set_color(\"black\")\n",
    "\n",
    "    \n",
    "ax.set_xticks([0, 1])\n",
    "ax.set_xticklabels([\"Pre-Deworm\", \"Post-Deworm\"], fontsize=16)\n",
    "ax.tick_params(\"y\", labelsize=14)\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Log-Ratio\", fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WilcoxonResult(statistic=25.0, pvalue=0.0002148151397705078)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as ss\n",
    "\n",
    "ss.wilcoxon(lr_diffs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that, indeed, this auto-selected log-ratio separates post-deworm from pre-deworm paired samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may want to generate a rank plot summarizing the log-fold changes of all microbes. BIRDMAn has a simple function to visualize the sorted log-fold changes with the standard deviations as error bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tt3uF8/b/weOB8q6+Wd6SVfJ569rHTJYM3Xj9fN12y4KIAwIA8S3bnqLyxRf2dRkABjhCAgAAAABA3HA1tOjOv3+iF9+v7nKeMSlVw+fcob1//YX219WEnBfJ2/8cDwQAAPojjhsCAAAAAAw6Ab9PLbsr1FT1iVp2Vyjg92nVplqdd8+b3QYEkjTEbNIvr5qmt999N+bNgTkeCAAA9Cd8SQAAAAAAGDTq6z1yr18ub8nKDo2HTRa7bn57pqwTZ8uYlNrlHuccN0x3XnySsu0pqnQ18vY/AAAY1AgJAAAAAACDQmlpqWZNnyFP+bZOYz6vS551y9SwabWGz7lD5vRRneZkWhJ121dO1FdOGSWDwdBh7ODb/wAAAIMNxw0BAAAAAAY8j8ej/Px8bQ8SEByqzV2rXSsWyd/csWHxFWdk640fn6PZpx7VKSAAAAAYzPiSAAAAAADQ5wJ+n1rrquRvbpAxKVXmjKzw1gUCqtq7Xz/52R0qKysLa02bu1b1G15RWt5cOYel6q5LTtaXnBndLwQAABiECAkAAEDcqXQ1asqSNUHH1i6cqmw750sDQG/pqofAfab5uu2WBbLZbO3XG5rb9GGVR+9v36uN290qqdyr3fX7Vf30YxHdt+GDVVr0s5/ph9OPU1ICjYMBAED8IiQAAGCA4kE3AGCg666HwL2L79Tzy5fp5vue0I5AmjZud2tLbb38gY5zW+uqOgQM4WjbV6eZowMEBAAAIO4REgAAAAAAel24PQQqK7bpx9fM0ah598uYFDwA9zc3RFXD3r17o1oHAAAwmNC4GAAAAADQ64qKiiLuIRCKMSk1qhrS09OjWgcAADCY8CUBAAAAACBi0TQaDgQCKt3doH9trtVvf/9ARPfzlqySbdIcGYydjwcyZ2TJZLFHdOSQw+FQbm5uRDUAwJHKtqeofPGFfV1Gr4in3xUY6AgJAACDCuf0AwDQsyJtNOxubNH6rXVa+/lurf18j6rd+9Wyu0KN7t0R3dfnrVOrq1qJmaM7jRmMJg0dP1PudcvC3q+goEAmE/0IAAAACAkAAAAAAGEJp9Hwyyue0d2PrtC2Fov+/fkefVjlVuCwRsPR9hDwN3nb/z4txazTstN02uh0TRidrqN/9CWde9bbYR1h5HQ6VVhYGFUNAAAAgw0hAQAAAACgW+E2Gi4rK9Pll1zUZaPhaHsIXPyl45Sfd6pOG52uMRkpMhgMHcaLi4s1bfqMLmt0Op0qLi6W1WqNqgYAAIDBhsbFAAAAAIBuxbLR8MEeApFwOBz64/yL9LUJWRqbmdopIJCknJwcvbZmvWyTr5LJktFhzGTJ0E23LtLGjRuVk5MT0b0BAAAGM74kAAAAAAB0yefz6U8PPRTRmq4aDRuMJlnGz5SnB3oIWK02peXNlW3SHLW6quVv8sqYbJHZ7lDhgumyWulPBAAAcChCAgAAAACIAwG/T611VfI3N8iYlCpzRlaX8/3+gD6s9uifn+7US//8j2p37Ijofl01GjYapMkXX613Sv+tuh2V3e4VTQ8Bg9EU9N4A+p9se4rKF1/Y12UAQNwiJAAAAACAfijSh/qh1Nd75F6/XN6SlfJ5Xe3XTRa77jPN1223LJDNZpMkNTS3ad3WPVr96U79c/Nu7fE2S5KaqnZGde9DGw070obo7OOG6exjM3VWTqZsKWaVXryGHgIAAAB9jJAAAAAAAPqRSB7qd6e0tFSzps+QJ8hDeJ/XpXsX36kXnl2mHy55TJv2DdHbZXVqafN3mhtto+G8caM1+9wTdfZxw4I2Gj7YQyDv6pvlLVkln7eufcxkydCN1x/4fQkIAACxxhcswBcICSK0bds2lZSUqKamRl6vV6NGjdLRRx+ts846S2azua/LAwAAADCAhfNQ/+UVz6i4uLjb5rsej0f5+fldvqUvSdvLt+mn37tCo+bdL2NS8PP6DzYaPjS06I7D4dCKn1zabR8BeggAAAD0LUKCML3wwgu699579fbbbwcdt9vtuvzyy/WrX/1KmZmZvVwdAAAAgP7gSI4ICvehfllZmc46Z5rueOLvSkhKVavPr1afXy2+wIG/b/OrzR/QP5c/qLKysrDu3eauVf2GV5SWNzfoeE82Gj70HvQQAHCkeDscACJHSNANr9er7373u3r22We7nOdyufSnP/1JL774op544gmdf/75vVQhAAAAgL4W7RFBTa0+bXc1qnxPgx64Z3HYD/V3VW/Xwl8uDvlQP+D3qfrVrv8Z5nDeklWyTZojgzH4g33rxNlq2LRabe7abveKptEwAAAA+gYhQRd8Pp8uv/xyvfbaax2uDxs2TKeddppsNptKS0u1ceNGBQIBSdLOnTv11a9+VW+88YYmT57cF2UDABCVSlejpixZE3Rs7cKpyrZz3AMABBPOEUHPL39av/zTcrWmDFN53YFQoKKuQTvqmxQI/O+h/vNPRXTfrh7qt9ZVRXQ00IFa69Tqqu70Nn+iyagv52Ro+gknauw3Xtc3Lp1No2EAAIBBhJCgC7fcckuHgMBsNuvee+/V9773PSUmJrZf/+STT/Sd73yn/Sii5uZmXXzxxfroo480atSoXq8bAAAAQO8I94igyopyFVx5Schz/2P5UF+S/M0NEe3Vvq7JK0nKtCRp2vHDNO34EZpybKZSk774R0caDQMAAAwuhAQhlJWVqaioqMO1559/Xl/96lc7zR03bpxWr16t8847rz0oqKur0+23366HHnqoV+oFAAAA0PuKiopicu7/kT7UP5wxKTWq/a4+Z5yuuiBPpzhsMhoNQefQaBgAAGBwMfZ1Af3V7bffrtbW1vaf582bFzQgOGjIkCF6/PHHO3xh8Oijj4b9DwwAAAAABo5AIKC3Pt+lJfc9ENE6b8kqBfy+TtejfahvTLYEvW7OyJLJYo9oL4fDocXXXqDx2WkhA4JDHWw0nJw1TomZo0P2MgAAAED/RkgQxP79+/XCCy90uPaTn/yk23XHHXecLr744vaf29ra9Mwzz8S6PAAAAAB9xNXQokfWlum8e/+lSxe/oIa9uyNaf/CIoMNF81A/yZapk8cdr5MdNk0YnaYvjbVryrGZmnb8cF1w8lE6/fzLItqvoKBAJhMP+gEAAOINxw0F8Y9//EONjY3tP0+aNEnHH398WGuvueYarVixov3nF198UT//+c9jXiMAAACA3hEIBPROmUvL392uVZtq1eLzS4rtEUEmk0mOSbO1/fXHw97nZzfdoEU/nhpy3DP7WE3Y+HpYXzc7nU4VFhaGfW8AA0u2PUXliy/s6zIAAP0UIUEQq1at6vDzueeeG/baKVOmKCEhQW1tbZKkjRs3aufOnRoxYkQsSwQAAAAQAwG/T611VfI3N8iYlCpzRlb7WJ23WX99v0rL363Utj2dA4Fojwj60vHZOu2UMTo6I0VjMlI1JjNVjrQh2t8wWRMm/DtmD/VtNpuKi4s1bfqMLhsrO51OFRcX02wYAAAgThESBLFp06YOP0+aNCnstampqTr55JO1cePG9msff/wxIQGAQaPS1agpS9YEHVu7cKqy7YOnWWE8/a4AMFB09VA/EvX1HrnXL5e3ZKV8Xlf7dZPFroX7rlHq6V/Rv7Y1qNUXCLnHwSOCDl3fHYfDoRdvvSzosT6JPfBQPycnR6+tWa+8q2+Wt2SVfN669jGTJUM3Xj9ft92ygIAAAAAgjhESBPHpp592+PmYY46JaH1OTk6HkOCTTz7RtGnTYlIbAAAAMJD0xkP9+0wHHnTbbLaw9iotLdWs6TPkCfIg3ud16dmH7lFC2jINn3OHzOmjQu5jMJpkGT9TnnXLwv49ujv3vyce6lutNqXlzZVt0hy1uqrlb/LKmGyR2e5Q4YLpsloJvQEAAOIZIcFhXC6XXK6ObwKNHj06oj0On//5558fcV0AAABAbxiID/XvXXynXl7xjIqLi5WTkxNyH58/oG01uzTtvOmqrCjv8p5t7lrtWrFIo+bdL2NS6IfoaRNny7/lTe3b3bkZ8eHCPfe/px7qG4wmJWZG9s82AAAAGPwICQ7jdrs7/JySkqLU1MjOGh0+fHiHnz0ez5GWBQAAAPSo/vhQXzrw/6Xz8/O7PH5HksrKynT21Om6e9lKNQQStcfbrN37mrX7f3/u8TbL1dAi17rl8nQTEBzU5q5V/YZXlJY3t9OYI22IrjgjW5dNzFbDD0/vkXP/eagPAACA3kBIcBiv19vh5yFDhkS8x+Fr9u3bd0Q1SdKuXbu0e/fuiNZs3br1iO8LAACA/u9I3/6P1UP9QCCgPS63ps8I76F+3rnTdNeTrykhOVXNbX61+PxqbQuoxedTS5tfLW1+/XP5g2E18pWkmspyzf/pnUEf6ksH/nXylqwMa6+DvCWrZJs0RwajSSajQdNPGK65Z47WlGOHyWQ0HJhk49x/AAAADFyEBIc5PCRITk6OeI/DQ4LD94zGgw8+qNtvv/2I9wEAAMDgEYu3/yN5U3/8pHM0/aePy5+QrOY2v5pafWpq9au57Ys/XeuWy7MtvIf6O6u260e33dXlQ/3qvz8X1l4HHfpQ/3CtdVURNRmWJJ+3Tpm+Pbp25tm67PQsDbcG/+cDzv0HAADAQEVI0A2DwdArawAAABA/YnHuf6Rv/7e0+VXt3q/trkZVHvxrb6NWLwv/TX3v7mqt+evjPfam/uGifajf6qoOekyPv7khor0Ouu/i4zRlyjFhzeWIIAAAAAw0hASHsVgsHX7ev39/xHscvubwPQEAABCfYnXufyRv/5/65bN1wvw/aU+LSYFAx/GA36fqtS9H9DsMhIf6/qbgX/IakyLrNXZQRoY9qnUAAADAQEBIcJj+GhLMnz9fl112WURrtm7dqosvvviI7w0AQDQqXY2asmRN0LG1C6cq287RG4gvkb753+rzy93YeqDZbkOL9jb+78+GFr34l6Kw3/5v2FOjz/+5Iujb/4P1ob4xOfj//zZnZMlksUf0OzscDuXm5kZVBwAAADAQEBIc5vA3txobG9XQ0KDU1PD/AWXXrl0dfk5LSzviuoYPH67hw4cf8T4AAADofZG8+X/SmVPk/N4DaggkBZ0T8PtU/bflEd0/1Nv/g/GhvtmaqRNPyNUwa4qGDU1SpiWpw5/PBwr0x9/dFfZ+BQUFMpk6fzUBAAAADBaEBIfJyMhQenq69u7d235t+/btOuGEE8Leo6KiosPPxx57bMzqAwAAQO+LtodAIBBQ1d79uuXnd4b95n+Ta4eq170Y8tz/WL793x8e6idaM5Wbe5ySzGYlJRiVePAv04E/Sy6Yo3dfeCjs/RYtuEGLbpoWcnz8z3+ilS8+F9a/H06nU4WFhWHfGwAAABiICAmCOOGEE/TWW2+1/7x169aIQoLD/4EjkrUAAADoPyLtIeBpbFVJlVsfVLpVUnngzz379qv6qb9EdN+uzv2P5dv/0TzUt2WO0E/nTlNKUqKSzSYlJRiVbDYp2WxUUoJJT7V9Tw//fnHY+/18wQ1adPN5Icc9s4/VhPeLY/ZQ32azqbi4WNOmz+jyyw6n06ni4mJZrdZu7wsAAAAMZIQEQZx00kkdQoK3335bX/nKV8Ja29DQoA8//LDTfgAAABhYwukhsOKZp3XD3Y+rJmBTSaVb2/Z0foAf63P/Y/H2//ChSRptT1G2PUWffu0q/ePJP4S9z003/EA/PC/0Gf0n/+IWvfHKin79UD8nJ0evrVmvvKtvlrdklXzeuvYxkyVDN15/IAAiIAAAAEA8ICQI4oILLtDDDz/c/vObb74Z9tq1a9eqra2t/efTTjtNI0aMiGV5AAAA6GHh9hCo2l6unxZcoVHz7pcxKXgz7lif+x/N2//pw0bqyR9drDHDLMpKT1Gy+YsvFDwX3KEJ6/4edw/1rVab0vLmyjZpjlpd1fI3eWVMtshsd6hwwXRZrTRXBwAAQHwgJAji/PPP15AhQ7R//35JB74k2Lx5s44//vhu1z7++OMdfr7kkkt6okQAAAD0oKKiorB7CLS5a1W/4ZWQPQRide7/0KQEpacmKj01UfbpX1fJy38Oe68fXT9f008cFXQs3h/qG4ymoF9sAAAAAPGCkCCIlJQUXXrppXrqqafar/32t7/VY4891uW6zz77TC+99FL7zwkJCbryyit7rE4AAADEns/n00MPLY1oTVc9BKJ58z9j+Eg9d/PXlGkdIntqotJTEpWYYGwf93zjJE2YsDpmb//zUB8AAACIX4QEIfzyl7/Us88+q9bWVkkHvhC45JJLNHv27KDzm5qadM0116ilpaX92re//W3l5OT0Sr0AAAA4cpWuRv12+RvasaMmonVd9RAwGE2yjJ8pz7plYe9X+MP5mpIb+sjKnnj7n4f6AAAAQHwiJAjh4BtXv/vd79qvXXrppbr33nv1ve99T4mJie3XP/30U33nO9/p0Ow4IyNDv/jFL3q1ZqA/qXQ1asqSNUHH1i6cqmw75/z2Fv69AIDufVDp1p/Xlum1j3aosXJLVHsc2kMg05Kk8dlpOm10mk7NStPRP/qSzj3r7Zi9+S/1XPNdHuoDAAAA8YWQoAuLFy/Wxx9/rJUrV0qSWltbdf311+uOO+7QhAkTNHToUJWVlen9999XIBBoX5eYmKiXXnpJo0YFP/cVAAAAfc/vD+ifm3fp4bVlenfbF0cBRdtD4IrJx2vGWRM0fnSajrIly2AwdBiP9Zv/Es13AQAAABw5QoIumEwmrVixQt/5znf03HPPtV/ftWuXVq1aFXTN8OHD9cQTT2jKlCm9VSYAAAAOE/D71FpXJX9zg4xJqTJnZLWPNbX69OL71XpkXZnKdjd0WhtNDwGHw6F7vjtLJlPnngQH9dSb/xJv/wMAAACIHiFBNywWi5599lldeumluueee/TOO+8EnWe323X55Zfr9ttv17Bhw3q5SgAAAEhSfb1H7vXL5S1Z2eEhv8li113+AmVP+bpe+NCluoaWkHtE00OgoKCgy4DgIN78BwAAANDfEBKE6dJLL9Wll16qbdu26f3331dNTY0aGho0cuRIHX300crLy+vQpwAAAAC9q7S0VLOmz5AnyHE+Pq9Lf7rnLiU8+piGz7lD5vSuj4W0Tpythk2r1eau7fa+4fYQOBRv/gMAAADoLwgJIjR27FiNHTu2r8sAAADAITwej/Lz87s871+S2ty12rVikUbNu1/GpNBv7R+TNVw/emSFfn/TN1VZUR5yXqQ9BAAAAACgvzH2dQEAAADAkSoqKlJZWVlYc9vctarf8ErQsS+NteuRb07UGz86Rz/6+hStfPMt2SZfJZMlo8M8kyVDN926SBs3blROTs4R1w8AAAAAfYUvCQAAADCg+Xw+LV26NKI13pJVsk2aI4PRJJPRoJknjdR3pzh1anZah3n0EAAAAAAw2BESAIhLla5GTVmyJujY2oVTlW3noQ8ADBTvvP+hampqIlrj89bJvG+Hvjlriq7JG9Ptf+/TQwAAAADAYEVIAAAAgAFpf4tPf1m/Tfc8GTz07c4fLzte508bF+OqAAAAAGBgISQAAADAgOLzB/TX96p0z+tbtLO+WS3G5Kj2yR45LMaVAQAAAMDAQ0gAAACAASEQCOjNLbu1eOVmbdm5r/26OSNLJotdPq8r7L0cDodyc3N7okwAAAAAGFAICQAAfYr+EADC8WGVW3e9tllvl9V1GjMYTbKMnynPumVh71dQUCCTyRTLEgEAAABgQCIkAAAAQJ8L+H1qrauSv7lBxqRUmTOyJB0IEu/+xxb97YOuGxNbJ85Ww6bVanPXdnsvp9OpwsLCmNQNAAAAAAMdIQEAAAD6TH29R+71y+UtWdnhuCCTxa65FZdrR9Y0+cxDut3HmJSqsVf/RnXP/0Ku2sqQ85xOp4qLi2W1WmNSPwAAAAAMdIQEAHCEOC4HAKJTWlqqWdNnyFO+rdOYz+vS+hV/UkLaSxo+5w6Z00eF3CfRZNQ3Jx2tH0ydoZofX6C8q2+Wt2SVfN4vjiYyWTJ04/XzddstCwgIAAA4Qtn2FJUvvrCvywAAxAghAQAAAHqdx+NRfn6+tgcJCA7V5q7VrhWLNGre/TImdQ5dZ596lG4+P7c9kPU225SWN1e2SXPU6qqWv8krY7JFZrtDhQumy2oluAUAAACAQxESAAAAICqh+gh0Z29Di25a9BuVlZWFNb/NXav6Da8oLW9u+7UvO+366awTdEpWWtA1BqNJiZmjw9ofAAAAAOIZIQEAAAAi0lUfgftMB470sdlskiR3Y4s2Vdfrw2q3NlV79GGVR5V1XlU/9khE9/SWrJJt0hzljrLp1pkn6NzcYTIYDDH9vQAAAAAgHhESAEA/Q48DAP1Zd30E7l18p5588gmdf9MfVNE2VJWu/Z3mtdZVdQgXwuHz1umG01N042Vny2QkHAAAAACAWDH2dQEAAAAYGMLtI7CnplLP3XGdKnbUBR33NzdEdf8vZw0hIAAAAACAGCMkAAAAQFjuvuf3EfcRCMaYlBrV/dPT06NaBwAAAAAIjeOGAAAAEFK1e79Wf7pTxR/v0LO/fyCitQf7CBiMpg7XzRlZMlnsER055HA4lJubG9H9AQAAAADdIyQAAACIEwG/T611VfI3N8iYlCpzRlanOX5/QB9We7T6051649Nd+nRHvSSpZXeFfN7gxweF4vPWqdVVrcTM0R2uG4wmWcbPlGfdsrD3KigokMlk6n4iAAAAACAihAQAAACDXH29R+71y+UtWdnh7X2Txa77TPN1849/pI92t2r1pzu1evMu7d7X3GmPaPsI+Ju8kqRMS6JOcth0isOmkxw2jZk/UTOnvh3W8UVOp1OFhYVR3R8AAAAA0DVCAgAAgEGstLRUs6bPkCdIs2Gf16V7F9+pPzz0iIbNuUPm9FEh94m2j8Cv5pypr5xzpkZak2UwdGw6XFxcrGnTZ3TZCNnpdKq4uFhWqzWq+wMAAAAAukbjYgAAgEHK4/EoPz+/y4fwktTqrtWuFYvkb24MOedgH4FIOBwOffvCyRplG9IpIJCknJwcvbZmvWyTr5LJktFhzGTJ0E23LtLGjRuVk5MT0X0BAAAAAOHjSwIAAIBBqqioKKzjfCSpzV2r+g2vKC1vbtDxnuojYLXalJY3V7ZJc9Tqqpa/yStjskVmu0OFC6bLak0J+34AAAAAgMgREgAAAAxC7oYm/f4PD0a0xluySrZJc2Qwdn6wP26UVXk/vlEPbV+vqu3l3e4VaR8Bg9HUqcExAAADRbY9ReWLL+zrMgAAiAohAQAAwCCyva5Rj79VridfWyv3np0RrfV569TqqlZi5mglmoyalJOh6ScM17QTRsiRNkSSdMUpb9BHAAAAAAAGEUICAACAAS4QCOidMpf+sn6b3vh0pwIBqWlffVR7nXP0EH3rkgmafOwwWZI6/1/Fg30E8q6+Wd6SVfJ569rHTJYM3Xj9fN12ywICAgAAAAAYIAgJAAAA+qmA36fWuir5mxtkTEqVOSOrw3hTq09/+6BGf1m3TZtr93UYMyalRnXPRV8/U+PGjepyDn0EAAAAAGDwICQAAADoZ+rrPXKvXy5vyUr5vK726yaLXfeZ5uv783+ov33q1rL/bFddQ0vQPcwZWTJZ7B3Wd8fhcCg3Nzfs+fQRAIDBpb+fq9/f6wMAYKAy9nUBAAAA+EJpaalmTc2TZ92yTg/4fV6X7l18p044Zbzu+evakAGBdOABvmX8zIjuXVBQIJOpc9NiAAAAAMDgRUgAAADQT3g8HuXn53fZFFiS2ty12rVikfzNjV3Os06crcT0ro8OOsjpdKqwsDDsWgEAAAAAgwPHDQGQJFW6GjVlyZqgY2sXTlW2nfOlAaCnFRUVqaysLKy5be5a1W94RWl5c4OO544YqmvyTtYpBW9q5swLugwenE6niouLaTYMAAAAAHGILwkAAAD6AZ/Pp6VLl0a0xluySgG/r/1ng0GafsJwLfvOl7Tqxim64szRGnf8cXptzXrZJl8lkyWjw3qTJUM33bpIGzduVE5OTkx+DwAAAADAwMKXBAAAAH2szefXo/+3TjU1NRGt83nr1OqqVvpRY3XZxGx966wxGpuZ2mme1WpTWt5c2SbNUaurWv4mr4zJFpntDhUumC6rla/FAADxhSbIAAB8gZAAAACgj7gbW/Tsfyv11NsVKt30XlR7fHNChn567XmyJpu7nWswmpSYOTqq+wAAAAAABidCAgAAgF726Y56PfFWuV4uqVZTq1+SZEzq/AVAOL557klhBQQAAAAAAARDSAAAABAjAb9PrXVV8jc3yJiUKnNGVvtYm8+vNz7dqcfWl+s/21yd1pozsmSy2OXzdh4LxeFwKDc3Nya1AwAAAADiEyEBMEBVuho1ZcmaoGNrF05Vtp3zpQGgt9TXe+Rev1zekpUdHvKbLHYt9hco59w5euGjOtV4mkLuYTCaZBk/U551y8K+b0FBgUwm0xHVDgAAAACIb8a+LgAAAGAgKy0t1aypefKsW9bpKwCf16UH77lLP7l6pirKt3W7l3XibCWkjQzrvk6nU4WFhVHVDAAAAADAQYQEAAAAUfJ4PMrPz9f2bgKANnetdq1YJH9zY5fzklOG6pu3/1mjso/ucp7T6VRxcbGsVmvENQMAAAAAcCiOGwIwIHC8EoCe0FUPgXD8+rf3qKysLKy5be5a1W94RWl5czuNjbAm6eovH60rzhytTEuSPp59uvKuvlneklXyeeva55ksGbrx+vm67ZYFBAQAAAAAgJggJAAAAHGnqx4C95kOPIS32Wwd1rT5/Npcu0/vVezVexV7tWHbHr37hz9GdF9vySrZJs2RwXigj8DEo9M1L2+Mzj9xpMymLz7wtFptSsubK9ukOWp1Vcvf5JUx2SKz3aHCBdNltRKMAgAAAABig5AAAAAMGEf65r/0vx4C02fIE+SIIJ/XpXsX36mXVzyjv/7t73In2NtDgZJKtxpbfO1zW3ZXdOpB0B2ft07y7NCl07+seWeN0UkOW5fzDUaTEjNHR3QPAAAAAAAiQUgAAAD6vWje/A8m3B4CZWVlOmPyVI2ad7+MScHf2vc3N0T2S/zPny49TrNmnBrVWgAADpVtT1H54gv7ugwAADDAERIAAIB+Ldw3/4uLi5WTkxN0j0AgoL2Nrbrtzt/GpIeAJBmTUsP/JQ4xxjEiqnUAgPD05wfn/bk2AAAQvwgJAABAvxXJm//nTJuue5evUr3PrFpPk2rrm7Sz/uCfzWpuaVX1Q0sjuv/hPQQOZc7Ikslij+jIIYfDodzc3IhqAABgoCIUAQBgYCAkAAAA/VZRUVHYb/5Xby/X935yR8g3/1vrqqLqIdDqqg7aF8BgNMkyfqY865aFvV9BQYFMps6BAwCg/+JBNwAAGOwICQAAQL/k8/m0dGns3vyPtoeAv8nb4efEBKNOzbJpwtHpOv7i2/TjK99S+bauv3SQJKfTqcLCwqhqAAAA8YVwCgDQmwgJAABAv7R582bV1NREtKarN/+j7SGQYbdryokjdfrR6Tp9TLpOPMqqpIQvQohTXn9d06bP6PJIJKfTqeLiYlmt1qhqAAAAAACgpxASAACAfqep1aen/vVxVGsPf/P/oGh6CIwcdZQ+uPdbSkgI/X+ZcnJy9Nqa9cq7+mZ5S1bJ561rHzNZMnTj9fN12y0LCAgAAAAAAP0SIQEAAOg32nx+vfBelYpWf66KrXui2sOYbAl6PZoeAvO/f12XAcFBVqtNaXlzZZs0R62uavmbvDImW2S2O1S4YLqs1pSw7wkAAAAAQG8iJAAAAH3O7w/o1Y926Pevf6Ztew70Dojmzf+EoRnKPe44jUpP1UhrskbYkg/8aU3WSFuyUuZP1Mypb4fVDDmaHgIGoynoUUcAAAAAAPRXhAQAAKDPBAIBrdmyS3f/4zN9uqO+w1g0b/4vWnCDblt4XpdziouL6SEAAAAAAMD/EBIAvajS1agpS9YEHVu7cKqy7RxHAWDwCfh9aq2rkr+5QcakVJkzsiRJ75TV6e5/bNF7FXtDrrVOnK2GTavV5q7t9j5Op1M33nhjt/PoIQAAAAAAwBcICQAAQI+or/fIvX65vCUrOxwZZLLYdcGnl2jfMfkyJqV2uYcxKVXD59yh3SsWqbWLoCDSN//pIQAAAAAAwAHGvi4AAAAMPqWlpZo1NU+edcs69RTweV365NVHtePxQrXu3dHlPokmo7574ST9c907sk2+SiZLRodxkyVDN926SBs3blROTk7EdR7sIZCcNU6JmaNlMJoi3gMAAAAAgIGMLwkAAEAHoY4HCpfH41F+fn6XZ/5LUpu7VrtWLNKoeffLmNTxzX2jQfr6hCwVTj9WWekpqnQ18uY/AAAAAAA9gJAAAABI6vp4oPtMB87pt9lsnda1tPlVtbdR5XUN2ranUU/+8XcqKysL655t7lrVb3hFaXlz26/NOnmkfjwjV8cMt3Saf/DNfwAAAAAAEBuEBAAA4MDxQNNnyBPk7X+f16V7F9+pF55dpl8//KxaU4apfE+DttU1qnxPg6rd++XzByQd+Aqh+tVnI7q3t2SVbJPm6NzjR2pBfq5OzuocRAAAAAAAgJ5BSAAAQJwL93ig7eXbdM2crwY9Huig1rqqTj0IuuPz1um352XqivPPjGgdAGBwyLanqHzxhX1dBgAAQNwiJAAAII4FAgH98q67j+h4oEP5mxuiqiPbEohqHQD0FzzoBgAAwEBFSAAAwAAXaaPhXfua9NbWOq3fukfrPtup//7xTxHd7+DxQAajqdOYMSk1or0OSk9Pj2odAKB7BBgAAADoCiEBAAADVLiNhvc1teo/ZS6t27pHb5Xu0Wc7ve1zW3ZXRHU8UKurOmgDYXNGlkwWe0R7OhwO5ebmRlQDAAAAAACIDUICAAAGoHAaDT/xxBOaUPA7lTZb2hsLHy7a44H8Td5O1wwGyWG3yDbta/rob4+EvVdBQYFMps5fJQAAAAAAgJ5HSAAAwAATbqPhuh2VWnNfYZeNhqM9HmjCMVk69ZRsjclI1ZjMVI3NTNVoe4qSzSZ5PKdrwoR/htXnwOl0qrCwMKoaAAAAAADAkSMkAACgl0XaQ+BQ+1t8+smvFses0XC0xwP936I5Id/+t9lsKi4u1rTpM7oMMpxOp4qLi2W1WsO+NwAAAAAAiC1CAgAAekm4PQQO8jS26uMajz6uqdfHNR5tqqlX6U6PKh9+OKL7dtVo2GA0yTJ+pjzrloW9XzjHA+Xk5Oi1NeuVd/XN8pasks9b1z5msmToxusP/L4EBAAAAAAA9C1CAgAAekE4PQSWL3tK3/n1o9opmz6uqVfV3v2d5rbsqYpZo2FnZqrOOiZDp311kRZc9ZbKt3V9fJEU2fFAVqtNaXlzZZs0R62uavmbvDImW2S2O1S4YLqs1uBHIAEAAAAAgN5DSAAAQA8Lt4fAjsoK3XXDN7rsIXAkjYaHDU3S5GMydVZOhvKOydRRaUPax8e//nqPHQ9kMJo6BRQAAAAAAKB/ICQAAKCH/fZ398ash0C0jYYf+965umDKRBkMhqDjHA8EAAAAAEB8IiQAAKAbkTYa3tfUqne3ufR2aZ3e2rpLxfc9ENH9uuohEG2j4fy8CSEDgoM4HggAAAAAgPhDSAAAQAjhNhpubGnTf8v36u3SOr1dVqePqtzyBw7MbdldEbMeAlLPNRo+/B4cDwQAAAAAQHwgJAAAIIhwGg0//sQTmnjd71TaZFHbwVTgMEfSQ+Age2qiTjzKqnFHWXXSUTaN/u54fS3/7bCOMIqk0TAAAAAAAIg/hAQAABwm3EbDrh2V+ufvC7tsNBxtD4F5U0/UeZNO14lHWTXKltzpqKDi4uIeazQMAAAAAADiByEBAACHKSoqilmj4Wh7CPx63vldHhFEo2EAsZZtT1H54gv7ugwAAAAAvYyQAACAQ/h8Pi1dujSiNV01Gu7JHgI0GgYAAAAAAEfK2NcFAADQn6z5z0bV1NREtOZgo+HDDRuapNmnHqV7br9Fo8eMDWuvaHoIHGw0nJw1TomZo4OGFQAAAAAAAMHwJQEAAJL2eJv1wD+36pEX3oxqvb/JK3tqor7stGuSM0OTcjKUM8zS3kvg3Ddep4cAAAAAAADodwgJAABxzdvcpj//u0yPrC1TQ4tPfnN0R/Q88t1zNGvKGTIaDUHH6SEAAAAAAAD6I0ICAEBcam7z6Zn/bNcD/9yquoaW9uvRNhqeOfn0kAHBQfQQAAYemvkCAAAAGOwICQAAg1LA71NrXZX8zQ0yJqXKnJElSfL7A3rlg2rdU/yZqvbu77SuJxsNH3qPxMzRYc8HAAAAAADoKYQEAIB+IdRD/UjV13vkXr9c3pKVHb4GMFnsuskzT3VjzlOpJ9DlHtaJs9WwabXa3LXd3i+aRsMAAAAAAAD9BSEB0IVKV6OmLFkTdGztwqnKtnM0CHCkunqof5/pwDn9NpstrL1KS0s1a/oMeYI0B/Z5XXr+4XuVkPaMhs+5Q+b0USH3MSal6qRvL1HV8p9pT01lyHk0GgYAAAAAAAOdsa8LAAAMTAG/Ty27K9RU9Yladlco4PdFvEdpaalmTc2TZ92yTj0AfF6X7l18pyZMmKDS0tJu9/J4PMrPz9f2IAHBodrctdq1YpH8zY1Bx21DzPrprOP19l1X6s31/5Ft8lUyWTI6zDFZMnTTrYu0ceNG5eTkdFsbAAAAAABAf8WXBACAiMTqzf/a3S5NO2+Gtld0/VC/rKxMp591rq646xmZh1jkDwQUCATk90v+QED+wIE/N77yZ5WVlYX1O7S5a1W/4RWl5c1tv5ZsNuravLEqOCdHtiFmSTQaBgAAAAAAgx8hAQAgbN0d53Pv4jv18opn9Ne//V1DMhza4dmvWk+TauubVOtp0g5Pk3bWH/iz4o0n5ekmIDjIs6tKzz62tMND/UMF/D5Vv/HXiH4Xb8kq2SbNUUJCgi4/I1uF5x2rEdbkoHNpNAwAAAAAAAYrQgIAiBNH2hg43ON8ysrKdMbkqRo1734Zk4K/aR/w++QtWRnR/Q8+1Dc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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from birdman import visualization as viz\n",
    "\n",
    "viz.plot_parameter_estimates(\n",
    "    inf_concat,\n",
    "    \"beta_var\",\n",
    "    coords=coords,\n",
    "    num_std=1  # Number of standard deviations to plot\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far this is very similar to what you could do with other differential abundance tools. Remember that we included the subject level effects as well! We can summarize these differentials in a similar way as the deworm status differentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>subject</th>\n",
       "      <th>S102</th>\n",
       "      <th>S1097</th>\n",
       "      <th>S1248</th>\n",
       "      <th>S1259</th>\n",
       "      <th>S1383</th>\n",
       "      <th>S1533</th>\n",
       "      <th>S1612</th>\n",
       "      <th>S1719</th>\n",
       "      <th>S19100</th>\n",
       "      <th>S1998</th>\n",
       "      <th>...</th>\n",
       "      <th>S221</th>\n",
       "      <th>S351</th>\n",
       "      <th>S37</th>\n",
       "      <th>S445</th>\n",
       "      <th>S463</th>\n",
       "      <th>S511</th>\n",
       "      <th>S780</th>\n",
       "      <th>S930</th>\n",
       "      <th>S953</th>\n",
       "      <th>S96</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCATGCAGGCGGTTCATCAAGTAGGATGTGAAATCCCCGGGCTCAAC</th>\n",
       "      <td>-1.507705</td>\n",
       "      <td>0.369061</td>\n",
       "      <td>-0.543341</td>\n",
       "      <td>-2.689872</td>\n",
       "      <td>0.827533</td>\n",
       "      <td>1.013385</td>\n",
       "      <td>0.206595</td>\n",
       "      <td>1.215848</td>\n",
       "      <td>-0.010188</td>\n",
       "      <td>0.037085</td>\n",
       "      <td>...</td>\n",
       "      <td>1.545671</td>\n",
       "      <td>-0.657633</td>\n",
       "      <td>-0.219805</td>\n",
       "      <td>0.180835</td>\n",
       "      <td>-2.037848</td>\n",
       "      <td>1.299663</td>\n",
       "      <td>1.009818</td>\n",
       "      <td>0.198065</td>\n",
       "      <td>1.100489</td>\n",
       "      <td>0.266816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAAC</th>\n",
       "      <td>0.748216</td>\n",
       "      <td>0.085338</td>\n",
       "      <td>-0.269733</td>\n",
       "      <td>-1.298420</td>\n",
       "      <td>0.517194</td>\n",
       "      <td>1.229552</td>\n",
       "      <td>-0.545617</td>\n",
       "      <td>-0.201165</td>\n",
       "      <td>-0.244254</td>\n",
       "      <td>0.045168</td>\n",
       "      <td>...</td>\n",
       "      <td>0.331023</td>\n",
       "      <td>-0.506983</td>\n",
       "      <td>-0.573405</td>\n",
       "      <td>0.702037</td>\n",
       "      <td>0.305558</td>\n",
       "      <td>1.361021</td>\n",
       "      <td>0.015776</td>\n",
       "      <td>0.300960</td>\n",
       "      <td>0.991631</td>\n",
       "      <td>0.946283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAAC</th>\n",
       "      <td>0.323448</td>\n",
       "      <td>-0.052525</td>\n",
       "      <td>-0.049939</td>\n",
       "      <td>0.399084</td>\n",
       "      <td>-0.077430</td>\n",
       "      <td>1.022092</td>\n",
       "      <td>-0.990644</td>\n",
       "      <td>-0.819817</td>\n",
       "      <td>-0.163476</td>\n",
       "      <td>0.142354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.256360</td>\n",
       "      <td>-0.179118</td>\n",
       "      <td>-0.452383</td>\n",
       "      <td>0.344067</td>\n",
       "      <td>0.466715</td>\n",
       "      <td>1.298575</td>\n",
       "      <td>-0.252527</td>\n",
       "      <td>0.116617</td>\n",
       "      <td>0.787400</td>\n",
       "      <td>0.324611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAAC</th>\n",
       "      <td>0.485864</td>\n",
       "      <td>0.124242</td>\n",
       "      <td>0.582437</td>\n",
       "      <td>-0.417812</td>\n",
       "      <td>0.149319</td>\n",
       "      <td>-0.570761</td>\n",
       "      <td>0.578297</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>0.133136</td>\n",
       "      <td>0.349377</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.387139</td>\n",
       "      <td>0.513691</td>\n",
       "      <td>0.168261</td>\n",
       "      <td>0.518822</td>\n",
       "      <td>0.429666</td>\n",
       "      <td>0.422526</td>\n",
       "      <td>0.133397</td>\n",
       "      <td>0.264310</td>\n",
       "      <td>0.001767</td>\n",
       "      <td>0.463247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAAC</th>\n",
       "      <td>1.361978</td>\n",
       "      <td>0.340287</td>\n",
       "      <td>0.872990</td>\n",
       "      <td>0.152384</td>\n",
       "      <td>-0.455136</td>\n",
       "      <td>-0.598039</td>\n",
       "      <td>0.365386</td>\n",
       "      <td>-0.509052</td>\n",
       "      <td>-0.392172</td>\n",
       "      <td>0.026007</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.615039</td>\n",
       "      <td>0.434025</td>\n",
       "      <td>0.415165</td>\n",
       "      <td>0.207571</td>\n",
       "      <td>0.610918</td>\n",
       "      <td>-0.054466</td>\n",
       "      <td>0.284741</td>\n",
       "      <td>0.493301</td>\n",
       "      <td>-1.380215</td>\n",
       "      <td>0.962201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "subject                                                 S102     S1097  \\\n",
       "feature                                                                  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT... -1.507705  0.369061   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.748216  0.085338   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.323448 -0.052525   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.485864  0.124242   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...  1.361978  0.340287   \n",
       "\n",
       "subject                                                S1248     S1259  \\\n",
       "feature                                                                  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT... -0.543341 -2.689872   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.269733 -1.298420   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.049939  0.399084   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.582437 -0.417812   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...  0.872990  0.152384   \n",
       "\n",
       "subject                                                S1383     S1533  \\\n",
       "feature                                                                  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...  0.827533  1.013385   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.517194  1.229552   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.077430  1.022092   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.149319 -0.570761   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC... -0.455136 -0.598039   \n",
       "\n",
       "subject                                                S1612     S1719  \\\n",
       "feature                                                                  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...  0.206595  1.215848   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.545617 -0.201165   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.990644 -0.819817   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.578297  0.487805   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...  0.365386 -0.509052   \n",
       "\n",
       "subject                                               S19100     S1998  ...  \\\n",
       "feature                                                                 ...   \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT... -0.010188  0.037085  ...   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.244254  0.045168  ...   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.163476  0.142354  ...   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.133136  0.349377  ...   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC... -0.392172  0.026007  ...   \n",
       "\n",
       "subject                                                 S221      S351  \\\n",
       "feature                                                                  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...  1.545671 -0.657633   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.331023 -0.506983   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.256360 -0.179118   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC... -4.387139  0.513691   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC... -0.615039  0.434025   \n",
       "\n",
       "subject                                                  S37      S445  \\\n",
       "feature                                                                  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT... -0.219805  0.180835   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.573405  0.702037   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.452383  0.344067   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.168261  0.518822   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...  0.415165  0.207571   \n",
       "\n",
       "subject                                                 S463      S511  \\\n",
       "feature                                                                  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT... -2.037848  1.299663   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.305558  1.361021   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.466715  1.298575   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.429666  0.422526   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...  0.610918 -0.054466   \n",
       "\n",
       "subject                                                 S780      S930  \\\n",
       "feature                                                                  \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...  1.009818  0.198065   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.015776  0.300960   \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC... -0.252527  0.116617   \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.133397  0.264310   \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC...  0.284741  0.493301   \n",
       "\n",
       "subject                                                 S953       S96  \n",
       "feature                                                                 \n",
       "TACGGAGGGTGCAAGCGTTAATCGGAATAACTGGGCGTAAAGGGCAT...  1.100489  0.266816  \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.991631  0.946283  \n",
       "TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC...  0.787400  0.324611  \n",
       "AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGC...  0.001767  0.463247  \n",
       "TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGC... -1.380215  0.962201  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subj_re_df = summarize_posterior(inf_concat.posterior, \"subj_re\")\n",
    "subj_re_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This DataFrame contains the mean subject intercept offset for each feature. We expect the microbial abundances to vary by subject so we can visualize these parameter estimates as a heatmap. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x3000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.clustermap(subj_re_df, yticklabels=False, cmap=\"vlag\", center=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do see that microbial abundance does vary by subject. By accounting for this, we can hopefully get better estimates for the signal that arises from deworming by accounting for the subject \"noise\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it for this notebook. There's a lot of possibilities with BIRDMAn depending on your specific experimental design and questions.\n",
    "\n",
    "For instance, here we decided that each subject should have an individual random intercept, but what if we wanted to see how each microbe changes with each subject? We could augment the model by changing the subject effect parameters to include both random intercepts *and* random slopes. This way, we would assert that the rate of change associated with deworming status may differ between subjects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (birdman-demo)",
   "language": "python",
   "name": "birdman_demo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}