micom_2023_solutions.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0vqP4LJ9y6K"
      },
      "source": [
        "# 🧫🦠 Modeling microbiota-wide metabolism with MICOM\n",
        "\n",
        "This notebook will accompany the second session of the 2023 ISB Microbiome Course. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2023/micom).\n",
        "\n",
        "You can save your own local copy of this notebook by using `File > Save a copy in Drive`. You may be promted to cetify the notebook is safe. We promise that it is 🤞\n",
        "\n",
        "**Disclaimer:**\n",
        "The linear and quadratic programming problems MICOM has to solve are very large and very complicated. There are some very good commercial solvers that are very expensive (even though they are often free for academic use). To make this tutorial as accessible as possible we will use the Open Source solver [OSQP](https://osqp.org/), which is installed along with MICOM. OSQP is amazing with quadratic programming problems (kudos!) but not as accurate for linear problems. Solvers usually only guarantee a solution within a certain numerical tolerance of the real solution. In order to make everything work with OSQP this tolerance has to be relaxed to about 10<sup>-3</sup>. This means that any result with an absolute value smaller than that might very well be zero so we should look at larger values only. Installing cost-free academic versions of commercial solvers like [IBM CPLEX](https://www.ibm.com/analytics/cplex-optimizer) or [Gurobi](https://www.gurobi.com/) would alow you to lower the tolerance to 10<sup>-6</sup>.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgBBl4GtuTuX"
      },
      "source": [
        "# 📝 Setup\n",
        "\n",
        "MICOM installation is is usually pretty straight-forward and can be as easy as typing `pip install micom` into your Terminal.\n",
        "\n",
        "First let's start by downloading the materials again and switching to the folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ckON4xr3_bW5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "875e19ed-d5d3-4075-a408-39e61cd268f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'materials'...\n",
            "remote: Enumerating objects: 755, done.\u001b[K\n",
            "remote: Counting objects: 100% (221/221), done.\u001b[K\n",
            "remote: Compressing objects: 100% (127/127), done.\u001b[K\n",
            "remote: Total 755 (delta 120), reused 187 (delta 92), pack-reused 534\u001b[K\n",
            "Receiving objects: 100% (755/755), 237.34 MiB | 49.34 MiB/s, done.\n",
            "Resolving deltas: 100% (260/260), done.\n",
            "/content/materials\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/gibbons-lab/isb_course_2023 materials\n",
        "%cd materials"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "is6fmBUeorwv"
      },
      "source": [
        "## Basic Installation\n",
        "\n",
        "Installing MICOM is straight-forward in Python. OSQP itself will be installed automatically along with it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oJrxxz6tV9T"
      },
      "source": [
        "## Enable QIIME 2 interactions\n",
        "\n",
        "Before we start, we also need to install packages to read the \"biom\" file format used by QIIME 2 to save tables. This is only necessary if you want to read QIIME 2 FeatureTable artifacts (like the ones we constructed yesterday)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "M_TeC5yrst3h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ecf98a3-0541-4b26-cfb3-7ec880da7227"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m832.5/832.5 kB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.0/12.0 MB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m81.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.4/37.4 MB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.7/75.7 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.3/138.3 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.0/8.0 MB\u001b[0m \u001b[31m91.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.9/112.9 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m81.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m526.7/526.7 kB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDone! 🎉 \n"
          ]
        }
      ],
      "source": [
        "!pip install -q micom Cython biom-format\n",
        "\n",
        "print(\"Done! 🎉 \")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPy1f-WLI0lZ"
      },
      "source": [
        "Okay, all done. So let's get started building some models 🦺🛠d😁.\n",
        "\n",
        "# 💻 MICOM\n",
        "\n",
        "We will use the Python interface to MICOM since it plays nicely with Colaboratory. However, you could run the same steps within the QIIME 2 MICOM plugin ([q2-micom](https://library.qiime2.org/plugins/q2-micom/26/)).\n",
        "\n",
        "Here is an overview of all the steps and functions across both interfaces:\n",
        "![micom overview](https://github.com/micom-dev/q2-micom/raw/706f583a060b91c12c0cec7acea2354fdd0dd320/docs/assets/overview.png)\n",
        "\n",
        "The process of building a metabolic model in MICOM begins with constructing a combined abundance/taxonomy table, referred to hereafter as a taxonomy table. Let's load a sample taxonomy table to see what it looks like:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UV9SObSQkSZh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "523686bc-553d-4546-ce5b-b6b663edd626"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   id        genus             species  reactions  \\\n",
              "0  Escherichia_coli_1  Escherichia  Escherichia coli 0         95   \n",
              "1  Escherichia_coli_2  Escherichia  Escherichia coli 1         95   \n",
              "2  Escherichia_coli_3  Escherichia  Escherichia coli 2         95   \n",
              "3  Escherichia_coli_4  Escherichia  Escherichia coli 3         95   \n",
              "0  Escherichia_coli_1  Escherichia  Escherichia coli 0         95   \n",
              "\n",
              "   metabolites sample_id  abundance  \n",
              "0           72  sample_1        201  \n",
              "1           72  sample_1        916  \n",
              "2           72  sample_1        544  \n",
              "3           72  sample_1        936  \n",
              "0           72  sample_2        803  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0caac5f1-ed94-4efb-ad7a-0377f3a2379d\" class=\"colab-df-container\">\n",
              "    <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>id</th>\n",
              "      <th>genus</th>\n",
              "      <th>species</th>\n",
              "      <th>reactions</th>\n",
              "      <th>metabolites</th>\n",
              "      <th>sample_id</th>\n",
              "      <th>abundance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Escherichia_coli_1</td>\n",
              "      <td>Escherichia</td>\n",
              "      <td>Escherichia coli 0</td>\n",
              "      <td>95</td>\n",
              "      <td>72</td>\n",
              "      <td>sample_1</td>\n",
              "      <td>201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Escherichia_coli_2</td>\n",
              "      <td>Escherichia</td>\n",
              "      <td>Escherichia coli 1</td>\n",
              "      <td>95</td>\n",
              "      <td>72</td>\n",
              "      <td>sample_1</td>\n",
              "      <td>916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Escherichia_coli_3</td>\n",
              "      <td>Escherichia</td>\n",
              "      <td>Escherichia coli 2</td>\n",
              "      <td>95</td>\n",
              "      <td>72</td>\n",
              "      <td>sample_1</td>\n",
              "      <td>544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Escherichia_coli_4</td>\n",
              "      <td>Escherichia</td>\n",
              "      <td>Escherichia coli 3</td>\n",
              "      <td>95</td>\n",
              "      <td>72</td>\n",
              "      <td>sample_1</td>\n",
              "      <td>936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Escherichia_coli_1</td>\n",
              "      <td>Escherichia</td>\n",
              "      <td>Escherichia coli 0</td>\n",
              "      <td>95</td>\n",
              "      <td>72</td>\n",
              "      <td>sample_2</td>\n",
              "      <td>803</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0caac5f1-ed94-4efb-ad7a-0377f3a2379d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0caac5f1-ed94-4efb-ad7a-0377f3a2379d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0caac5f1-ed94-4efb-ad7a-0377f3a2379d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b0a380cd-844f-481c-be07-d9fff27cc1aa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b0a380cd-844f-481c-be07-d9fff27cc1aa')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b0a380cd-844f-481c-be07-d9fff27cc1aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "from micom.data import test_data\n",
        "\n",
        "test_data().head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEk7yfd1lbYp"
      },
      "source": [
        "In this taxonomy table, we see four identical strains of _E. coli_ (1 through 4), across two samples (sample_1 and sample_2). We can see that each row represents a single taxon in a single sample, and the `abundance` column identifies the abundance of that taxon in the sample.\n",
        "\n",
        "The `id` column specifies identifiers for the taxa and should be expressive and not include spaces or special characters. Since we are using a taxonomy database to build our models (more on that soon), we don't need a `file` column.\n",
        "\n",
        "You might notice that this dataframe looks very different from what we generated in yesterday's tutorial, where we ended up with separate QIIME 2 artifacts 😱\n",
        "\n",
        "No worries, we can deal with that.\n",
        "\n",
        "## Importing data from QIIME 2\n",
        "\n",
        "MICOM can read QIIME 2 artifacts. You don't even need to have QIIME 2 installed for that! But before we do so, let's resolve one issue. We discussed that MICOM summarizes genome-scale models into pangenome-scale models as a first step, but our data are on the ASV level...so how will we know what to summarize?\n",
        "\n",
        "Basically, a specific model database can be used to quickly summarize pangenome-scale models for use within MICOM. So, before we read our data we have to decide which model database to use. We will go with the [AGORA database](https://pubmed.ncbi.nlm.nih.gov/27893703/), which is a curated database of more than 800 bacterial strains that commonly live in the human gut. In particular, we will use a version of this database summarized on the genus rank which can be downloaded from the [MICOM data repository](https://doi.org/10.5281/zenodo.3755182), which contains a whole lot of prebuilt databases. This database is available from the materials folder that we previously cloned."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_57iya0D3L6-"
      },
      "source": [
        "Now we're all set to start building models! The data we previously collected can be found in the `treasure_chest` folder, so we can use those files to build our taxonomy for MICOM."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "o0vBAiiqqPLC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "7afd6ce7-4fe1-4e1c-d3e3-24c6339d0544"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sample_id  abundance          genus             id  relative\n",
              "8    ERR1883195        6.0    Akkermansia    Akkermansia  0.000116\n",
              "9    ERR1883207      598.0    Akkermansia    Akkermansia  0.010864\n",
              "10   ERR1883214    54726.0    Akkermansia    Akkermansia  0.821218\n",
              "11   ERR1883240    35554.0    Akkermansia    Akkermansia  0.605979\n",
              "12   ERR1883250    11467.0    Akkermansia    Akkermansia  0.197922\n",
              "..          ...        ...            ...            ...       ...\n",
              "352  ERR1883207        2.0    Abyssivirga    Abyssivirga  0.000036\n",
              "353  ERR1883207        2.0   Dethiobacter   Dethiobacter  0.000036\n",
              "354  ERR1883212        2.0     Finegoldia     Finegoldia  0.000036\n",
              "355  ERR1883250        2.0      Scardovia      Scardovia  0.000035\n",
              "356  ERR1883294        2.0  Mogibacterium  Mogibacterium  0.000370\n",
              "\n",
              "[349 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c9bc632-c0c5-426a-9ea0-91e530849d72\" class=\"colab-df-container\">\n",
              "    <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>sample_id</th>\n",
              "      <th>abundance</th>\n",
              "      <th>genus</th>\n",
              "      <th>id</th>\n",
              "      <th>relative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.000116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>598.0</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.010864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>54726.0</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.821218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>35554.0</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.605979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>11467.0</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.197922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Abyssivirga</td>\n",
              "      <td>Abyssivirga</td>\n",
              "      <td>0.000036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Dethiobacter</td>\n",
              "      <td>Dethiobacter</td>\n",
              "      <td>0.000036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Finegoldia</td>\n",
              "      <td>Finegoldia</td>\n",
              "      <td>0.000036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Scardovia</td>\n",
              "      <td>Scardovia</td>\n",
              "      <td>0.000035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Mogibacterium</td>\n",
              "      <td>Mogibacterium</td>\n",
              "      <td>0.000370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>349 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c9bc632-c0c5-426a-9ea0-91e530849d72')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2c9bc632-c0c5-426a-9ea0-91e530849d72 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2c9bc632-c0c5-426a-9ea0-91e530849d72');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ccbad24b-31c0-4574-be74-5c3bfa055433\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ccbad24b-31c0-4574-be74-5c3bfa055433')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ccbad24b-31c0-4574-be74-5c3bfa055433 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "from micom.taxonomy import qiime_to_micom\n",
        "\n",
        "tax = qiime_to_micom(\n",
        "    \"treasure_chest/dada/table.qza\",\n",
        "    \"treasure_chest/dada/taxa.qza\",\n",
        "    collapse_on=\"genus\"\n",
        ")\n",
        "tax"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TQ6Zp7wouk4"
      },
      "source": [
        "Notice the `collapse_on` argument. That will specify the rank on which to sumarize and can be a list of several ranks. When matching taxonomy you can either match by the particular rank of interest (for example, just comparing genus names here), or you could compare the entire taxonomy, which will require all taxonomic ranks prior to the target rank to match. For that you cloud specify `collapse_on=[\"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\"]`.\n",
        "\n",
        "Taxonomic names will often not match 100% between databases. For instance, the genus name \"Prevotella\" in one database may be \"Prevotella_6\" in another. The more ranks you use for matching the more likely you are to run into these issues. However, the more taxonomic ranks you use to match the more confident you can be that your observed taxon really is the same taxon as the one in the model database.\n",
        "\n",
        "The resulting table will contain the same abundances but it will include more ranks if `collapse_on` is a list. All ranks present in the taxonomy will be used when matching to the database. We will stick with the \"lax\" option of only matching on genus ranks.\n",
        "\n",
        "Let's now take a look at the taxonomy table we generated:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5zwLVFcNTbq"
      },
      "source": [
        "That looks more like the example! Again, we have a row for each taxon in each sample, so we're good to go.\n",
        "\n",
        "One helpful thing to do is to merge in our metadata, so we'll have it at hand for the following steps. In our case, the metadata will include the sample id, disease state, and demographic information for each of the study participants."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "X9hqoO4go0h1",
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "46731dac-1acd-4161-9e1e-754ff9bdbe6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sample_id  abundance               genus                  id  relative  \\\n",
              "0    ERR1883195        6.0         Akkermansia         Akkermansia  0.000116   \n",
              "1    ERR1883195    17971.0         Phocaeicola         Phocaeicola  0.346522   \n",
              "2    ERR1883195        5.0         Veillonella         Veillonella  0.000096   \n",
              "3    ERR1883195     4741.0    Faecalibacterium    Faecalibacterium  0.091417   \n",
              "4    ERR1883195      576.0  Mediterraneibacter  Mediterraneibacter  0.011107   \n",
              "..          ...        ...                 ...                 ...       ...   \n",
              "344  ERR1883294        5.0      Stomatobaculum      Stomatobaculum  0.000926   \n",
              "345  ERR1883294        3.0      Lancefieldella      Lancefieldella  0.000556   \n",
              "346  ERR1883294        3.0        Tumebacillus        Tumebacillus  0.000556   \n",
              "347  ERR1883294        3.0          Tropheryma          Tropheryma  0.000556   \n",
              "348  ERR1883294        2.0       Mogibacterium       Mogibacterium  0.000370   \n",
              "\n",
              "    collection_timestamp day_relative_to_fmt description disease_state  \\\n",
              "0             2011-10-24                  26    Donor 11       healthy   \n",
              "1             2011-10-24                  26    Donor 11       healthy   \n",
              "2             2011-10-24                  26    Donor 11       healthy   \n",
              "3             2011-10-24                  26    Donor 11       healthy   \n",
              "4             2011-10-24                  26    Donor 11       healthy   \n",
              "..                   ...                 ...         ...           ...   \n",
              "344           2011-09-29                   0   Day 0 CD3       Pre-FMT   \n",
              "345           2011-09-29                   0   Day 0 CD3       Pre-FMT   \n",
              "346           2011-09-29                   0   Day 0 CD3       Pre-FMT   \n",
              "347           2011-09-29                   0   Day 0 CD3       Pre-FMT   \n",
              "348           2011-09-29                   0   Day 0 CD3       Pre-FMT   \n",
              "\n",
              "              host_age host_age_units host_body_mass_index        host_height  \\\n",
              "0    Restricted access          years    Restricted access  Restricted access   \n",
              "1    Restricted access          years    Restricted access  Restricted access   \n",
              "2    Restricted access          years    Restricted access  Restricted access   \n",
              "3    Restricted access          years    Restricted access  Restricted access   \n",
              "4    Restricted access          years    Restricted access  Restricted access   \n",
              "..                 ...            ...                  ...                ...   \n",
              "344                 61          years                 32.5              1.727   \n",
              "345                 61          years                 32.5              1.727   \n",
              "346                 61          years                 32.5              1.727   \n",
              "347                 61          years                 32.5              1.727   \n",
              "348                 61          years                 32.5              1.727   \n",
              "\n",
              "    host_height_units host_subject_id        host_weight host_weight_units  \\\n",
              "0                   m           Donor  Restricted access                kg   \n",
              "1                   m           Donor  Restricted access                kg   \n",
              "2                   m           Donor  Restricted access                kg   \n",
              "3                   m           Donor  Restricted access                kg   \n",
              "4                   m           Donor  Restricted access                kg   \n",
              "..                ...             ...                ...               ...   \n",
              "344                 m             CD3               97.3                kg   \n",
              "345                 m             CD3               97.3                kg   \n",
              "346                 m             CD3               97.3                kg   \n",
              "347                 m             CD3               97.3                kg   \n",
              "348                 m             CD3               97.3                kg   \n",
              "\n",
              "                  race                sex  \n",
              "0    Restricted access  Restricted access  \n",
              "1    Restricted access  Restricted access  \n",
              "2    Restricted access  Restricted access  \n",
              "3    Restricted access  Restricted access  \n",
              "4    Restricted access  Restricted access  \n",
              "..                 ...                ...  \n",
              "344              white               male  \n",
              "345              white               male  \n",
              "346              white               male  \n",
              "347              white               male  \n",
              "348              white               male  \n",
              "\n",
              "[349 rows x 19 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3639c89c-691c-4f73-a95e-fbed669114aa\" class=\"colab-df-container\">\n",
              "    <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>sample_id</th>\n",
              "      <th>abundance</th>\n",
              "      <th>genus</th>\n",
              "      <th>id</th>\n",
              "      <th>relative</th>\n",
              "      <th>collection_timestamp</th>\n",
              "      <th>day_relative_to_fmt</th>\n",
              "      <th>description</th>\n",
              "      <th>disease_state</th>\n",
              "      <th>host_age</th>\n",
              "      <th>host_age_units</th>\n",
              "      <th>host_body_mass_index</th>\n",
              "      <th>host_height</th>\n",
              "      <th>host_height_units</th>\n",
              "      <th>host_subject_id</th>\n",
              "      <th>host_weight</th>\n",
              "      <th>host_weight_units</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.000116</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>17971.0</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.346522</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Veillonella</td>\n",
              "      <td>Veillonella</td>\n",
              "      <td>0.000096</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>4741.0</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>0.091417</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>576.0</td>\n",
              "      <td>Mediterraneibacter</td>\n",
              "      <td>Mediterraneibacter</td>\n",
              "      <td>0.011107</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>344</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Stomatobaculum</td>\n",
              "      <td>Stomatobaculum</td>\n",
              "      <td>0.000926</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>345</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Lancefieldella</td>\n",
              "      <td>Lancefieldella</td>\n",
              "      <td>0.000556</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>346</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Tumebacillus</td>\n",
              "      <td>Tumebacillus</td>\n",
              "      <td>0.000556</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Tropheryma</td>\n",
              "      <td>Tropheryma</td>\n",
              "      <td>0.000556</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Mogibacterium</td>\n",
              "      <td>Mogibacterium</td>\n",
              "      <td>0.000370</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>349 rows × 19 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3639c89c-691c-4f73-a95e-fbed669114aa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3639c89c-691c-4f73-a95e-fbed669114aa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3639c89c-691c-4f73-a95e-fbed669114aa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2b39a211-2004-4958-b34b-b957e347ac48\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2b39a211-2004-4958-b34b-b957e347ac48')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2b39a211-2004-4958-b34b-b957e347ac48 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "metadata = pd.read_table(\"data/metadata.tsv\").rename(columns={\"sample-id\": \"sample_id\"})\n",
        "tax = pd.merge(tax, metadata, on=\"sample_id\")\n",
        "tax"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ok, now we want to invade our samples with C. diff. The goal is to predict susceptibility to invasion and see how disease context can influence predicted engraftment. To do this we will introduce 10% C. diff to all the samples! You can read more about this approach and its applications [here](https://www.biorxiv.org/content/10.1101/2023.04.28.538771v2)  "
      ],
      "metadata": {
        "id": "ZsFgo_CRjvsr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "vHyvSZJJ-iIi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2eb76307-43bb-47dd-ce95-aa4b5c95fa85"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sample_id     abundance                   genus                      id  \\\n",
              "1    ERR1883195  17971.000000             Phocaeicola             Phocaeicola   \n",
              "3    ERR1883195   4741.000000        Faecalibacterium        Faecalibacterium   \n",
              "8    ERR1883195   6805.000000             Bacteroides             Bacteroides   \n",
              "11   ERR1883195   3076.000000         Parabacteroides         Parabacteroides   \n",
              "5    ERR1883195   3621.444444          Clostridioides          Clostridioides   \n",
              "71   ERR1883207  16983.000000             Phocaeicola             Phocaeicola   \n",
              "73   ERR1883207   3717.000000        Faecalibacterium        Faecalibacterium   \n",
              "77   ERR1883207   2956.000000             Barnesiella             Barnesiella   \n",
              "78   ERR1883207   6984.000000             Bacteroides             Bacteroides   \n",
              "79   ERR1883207   2901.000000                 Blautia                 Blautia   \n",
              "81   ERR1883207   3413.000000         Parabacteroides         Parabacteroides   \n",
              "7    ERR1883207   4106.000000          Clostridioides          Clostridioides   \n",
              "222  ERR1883212   8393.000000             Phocaeicola             Phocaeicola   \n",
              "224  ERR1883212  10841.000000        Faecalibacterium        Faecalibacterium   \n",
              "227  ERR1883212   4038.000000        Fusicatenibacter        Fusicatenibacter   \n",
              "229  ERR1883212   3286.000000             Bacteroides             Bacteroides   \n",
              "230  ERR1883212   3198.000000                 Blautia                 Blautia   \n",
              "234  ERR1883212   5106.000000           Streptococcus           Streptococcus   \n",
              "7    ERR1883212   3873.555556          Clostridioides          Clostridioides   \n",
              "152  ERR1883214  54726.000000             Akkermansia             Akkermansia   \n",
              "161  ERR1883214   3968.000000             Clostridium             Clostridium   \n",
              "3    ERR1883214   6521.555556          Clostridioides          Clostridioides   \n",
              "291  ERR1883225   3734.000000             Phocaeicola             Phocaeicola   \n",
              "295  ERR1883225    492.000000             Barnesiella             Barnesiella   \n",
              "296  ERR1883225   1475.000000             Bacteroides             Bacteroides   \n",
              "299  ERR1883225    911.000000         Parabacteroides         Parabacteroides   \n",
              "5    ERR1883225    734.666667          Clostridioides          Clostridioides   \n",
              "174  ERR1883240  35554.000000             Akkermansia             Akkermansia   \n",
              "176  ERR1883240  15517.000000             Veillonella             Veillonella   \n",
              "180  ERR1883240   5555.000000           Fusobacterium           Fusobacterium   \n",
              "4    ERR1883240   6291.777778          Clostridioides          Clostridioides   \n",
              "188  ERR1883250  11467.000000             Akkermansia             Akkermansia   \n",
              "190  ERR1883250   9955.000000      Mediterraneibacter      Mediterraneibacter   \n",
              "191  ERR1883250  11923.000000  Erysipelatoclostridium  Erysipelatoclostridium   \n",
              "193  ERR1883250  13975.000000                 Blautia                 Blautia   \n",
              "196  ERR1883250   3282.000000        Ruthenibacterium        Ruthenibacterium   \n",
              "6    ERR1883250   5622.444444          Clostridioides          Clostridioides   \n",
              "321  ERR1883294    619.000000             Veillonella             Veillonella   \n",
              "325  ERR1883294    591.000000           Fusobacterium           Fusobacterium   \n",
              "329  ERR1883294   1866.000000             Anaerosinus             Anaerosinus   \n",
              "330  ERR1883294    580.000000             Megasphaera             Megasphaera   \n",
              "332  ERR1883294    734.000000             Phytobacter             Phytobacter   \n",
              "333  ERR1883294    294.000000              Morganella              Morganella   \n",
              "7    ERR1883294    520.444444          Clostridioides          Clostridioides   \n",
              "\n",
              "     relative collection_timestamp day_relative_to_fmt   description  \\\n",
              "1    0.496238           2011-10-24                  26      Donor 11   \n",
              "3    0.130915           2011-10-24                  26      Donor 11   \n",
              "8    0.187908           2011-10-24                  26      Donor 11   \n",
              "11   0.084938           2011-10-24                  26      Donor 11   \n",
              "5    0.100000           2011-10-24                  26      Donor 11   \n",
              "71   0.413614           2012-01-12                  44      Donor 12   \n",
              "73   0.090526           2012-01-12                  44      Donor 12   \n",
              "77   0.071992           2012-01-12                  44      Donor 12   \n",
              "78   0.170093           2012-01-12                  44      Donor 12   \n",
              "79   0.070653           2012-01-12                  44      Donor 12   \n",
              "81   0.083122           2012-01-12                  44      Donor 12   \n",
              "7    0.100000           2012-01-12                  44      Donor 12   \n",
              "222  0.216674           2012-10-10                 135      Donor 14   \n",
              "224  0.279872           2012-10-10                 135      Donor 14   \n",
              "227  0.104245           2012-10-10                 135      Donor 14   \n",
              "229  0.084832           2012-10-10                 135      Donor 14   \n",
              "230  0.082560           2012-10-10                 135      Donor 14   \n",
              "234  0.131817           2012-10-10                 135      Donor 14   \n",
              "7    0.100000           2012-10-10                 135      Donor 14   \n",
              "152  0.839156           2011-07-26                   0     Day 0 CD1   \n",
              "161  0.060844           2011-07-26                   0     Day 0 CD1   \n",
              "3    0.100000           2011-07-26                   0     Day 0 CD1   \n",
              "291  0.508258           2011-07-26                  54     Donor CD1   \n",
              "295  0.066969           2011-07-26                  54     Donor CD1   \n",
              "296  0.200771           2011-07-26                  54     Donor CD1   \n",
              "299  0.124002           2011-07-26                  54     Donor CD1   \n",
              "5    0.100000           2011-07-26                  54     Donor CD1   \n",
              "174  0.565087           2012-02-14             pre-FMT   CD9 pre-FMT   \n",
              "176  0.246623           2012-02-14             pre-FMT   CD9 pre-FMT   \n",
              "180  0.088290           2012-02-14             pre-FMT   CD9 pre-FMT   \n",
              "4    0.100000           2012-02-14             pre-FMT   CD9 pre-FMT   \n",
              "188  0.203950           2011-12-23             pre-FMT  CD13 pre-FMT   \n",
              "190  0.177058           2011-12-23             pre-FMT  CD13 pre-FMT   \n",
              "191  0.212061           2011-12-23             pre-FMT  CD13 pre-FMT   \n",
              "193  0.248557           2011-12-23             pre-FMT  CD13 pre-FMT   \n",
              "196  0.058373           2011-12-23             pre-FMT  CD13 pre-FMT   \n",
              "6    0.100000           2011-12-23             pre-FMT  CD13 pre-FMT   \n",
              "321  0.118937           2011-09-29                   0     Day 0 CD3   \n",
              "325  0.113557           2011-09-29                   0     Day 0 CD3   \n",
              "329  0.358540           2011-09-29                   0     Day 0 CD3   \n",
              "330  0.111443           2011-09-29                   0     Day 0 CD3   \n",
              "332  0.141033           2011-09-29                   0     Day 0 CD3   \n",
              "333  0.056490           2011-09-29                   0     Day 0 CD3   \n",
              "7    0.100000           2011-09-29                   0     Day 0 CD3   \n",
              "\n",
              "    disease_state           host_age host_age_units host_body_mass_index  \\\n",
              "1         healthy  Restricted access          years    Restricted access   \n",
              "3         healthy  Restricted access          years    Restricted access   \n",
              "8         healthy  Restricted access          years    Restricted access   \n",
              "11        healthy  Restricted access          years    Restricted access   \n",
              "5         healthy  Restricted access          years    Restricted access   \n",
              "71        healthy  Restricted access          years    Restricted access   \n",
              "73        healthy  Restricted access          years    Restricted access   \n",
              "77        healthy  Restricted access          years    Restricted access   \n",
              "78        healthy  Restricted access          years    Restricted access   \n",
              "79        healthy  Restricted access          years    Restricted access   \n",
              "81        healthy  Restricted access          years    Restricted access   \n",
              "7         healthy  Restricted access          years    Restricted access   \n",
              "222       healthy  Restricted access          years    Restricted access   \n",
              "224       healthy  Restricted access          years    Restricted access   \n",
              "227       healthy  Restricted access          years    Restricted access   \n",
              "229       healthy  Restricted access          years    Restricted access   \n",
              "230       healthy  Restricted access          years    Restricted access   \n",
              "234       healthy  Restricted access          years    Restricted access   \n",
              "7         healthy  Restricted access          years    Restricted access   \n",
              "152       Pre-FMT                 39          years                 29.3   \n",
              "161       Pre-FMT                 39          years                 29.3   \n",
              "3         Pre-FMT                 39          years                 29.3   \n",
              "291       healthy  Restricted access          years    Restricted access   \n",
              "295       healthy  Restricted access          years    Restricted access   \n",
              "296       healthy  Restricted access          years    Restricted access   \n",
              "299       healthy  Restricted access          years    Restricted access   \n",
              "5         healthy  Restricted access          years    Restricted access   \n",
              "174       Pre-FMT                 47          years                 35.5   \n",
              "176       Pre-FMT                 47          years                 35.5   \n",
              "180       Pre-FMT                 47          years                 35.5   \n",
              "4         Pre-FMT                 47          years                 35.5   \n",
              "188       Pre-FMT                 53          years                 34.4   \n",
              "190       Pre-FMT                 53          years                 34.4   \n",
              "191       Pre-FMT                 53          years                 34.4   \n",
              "193       Pre-FMT                 53          years                 34.4   \n",
              "196       Pre-FMT                 53          years                 34.4   \n",
              "6         Pre-FMT                 53          years                 34.4   \n",
              "321       Pre-FMT                 61          years                 32.5   \n",
              "325       Pre-FMT                 61          years                 32.5   \n",
              "329       Pre-FMT                 61          years                 32.5   \n",
              "330       Pre-FMT                 61          years                 32.5   \n",
              "332       Pre-FMT                 61          years                 32.5   \n",
              "333       Pre-FMT                 61          years                 32.5   \n",
              "7         Pre-FMT                 61          years                 32.5   \n",
              "\n",
              "           host_height host_height_units host_subject_id        host_weight  \\\n",
              "1    Restricted access                 m           Donor  Restricted access   \n",
              "3    Restricted access                 m           Donor  Restricted access   \n",
              "8    Restricted access                 m           Donor  Restricted access   \n",
              "11   Restricted access                 m           Donor  Restricted access   \n",
              "5    Restricted access                 m           Donor  Restricted access   \n",
              "71   Restricted access                 m           Donor  Restricted access   \n",
              "73   Restricted access                 m           Donor  Restricted access   \n",
              "77   Restricted access                 m           Donor  Restricted access   \n",
              "78   Restricted access                 m           Donor  Restricted access   \n",
              "79   Restricted access                 m           Donor  Restricted access   \n",
              "81   Restricted access                 m           Donor  Restricted access   \n",
              "7    Restricted access                 m           Donor  Restricted access   \n",
              "222  Restricted access                 m           Donor  Restricted access   \n",
              "224  Restricted access                 m           Donor  Restricted access   \n",
              "227  Restricted access                 m           Donor  Restricted access   \n",
              "229  Restricted access                 m           Donor  Restricted access   \n",
              "230  Restricted access                 m           Donor  Restricted access   \n",
              "234  Restricted access                 m           Donor  Restricted access   \n",
              "7    Restricted access                 m           Donor  Restricted access   \n",
              "152              165.1                 m             CD1               80.1   \n",
              "161              165.1                 m             CD1               80.1   \n",
              "3                165.1                 m             CD1               80.1   \n",
              "291  Restricted access                 m           Donor  Restricted access   \n",
              "295  Restricted access                 m           Donor  Restricted access   \n",
              "296  Restricted access                 m           Donor  Restricted access   \n",
              "299  Restricted access                 m           Donor  Restricted access   \n",
              "5    Restricted access                 m           Donor  Restricted access   \n",
              "174               1.55                 m             CD9               85.1   \n",
              "176               1.55                 m             CD9               85.1   \n",
              "180               1.55                 m             CD9               85.1   \n",
              "4                 1.55                 m             CD9               85.1   \n",
              "188               1.56                 m            CD13               83.9   \n",
              "190               1.56                 m            CD13               83.9   \n",
              "191               1.56                 m            CD13               83.9   \n",
              "193               1.56                 m            CD13               83.9   \n",
              "196               1.56                 m            CD13               83.9   \n",
              "6                 1.56                 m            CD13               83.9   \n",
              "321              1.727                 m             CD3               97.3   \n",
              "325              1.727                 m             CD3               97.3   \n",
              "329              1.727                 m             CD3               97.3   \n",
              "330              1.727                 m             CD3               97.3   \n",
              "332              1.727                 m             CD3               97.3   \n",
              "333              1.727                 m             CD3               97.3   \n",
              "7                1.727                 m             CD3               97.3   \n",
              "\n",
              "    host_weight_units               race                sex  \n",
              "1                  kg  Restricted access  Restricted access  \n",
              "3                  kg  Restricted access  Restricted access  \n",
              "8                  kg  Restricted access  Restricted access  \n",
              "11                 kg  Restricted access  Restricted access  \n",
              "5                  kg  Restricted access  Restricted access  \n",
              "71                 kg  Restricted access  Restricted access  \n",
              "73                 kg  Restricted access  Restricted access  \n",
              "77                 kg  Restricted access  Restricted access  \n",
              "78                 kg  Restricted access  Restricted access  \n",
              "79                 kg  Restricted access  Restricted access  \n",
              "81                 kg  Restricted access  Restricted access  \n",
              "7                  kg  Restricted access  Restricted access  \n",
              "222                kg  Restricted access  Restricted access  \n",
              "224                kg  Restricted access  Restricted access  \n",
              "227                kg  Restricted access  Restricted access  \n",
              "229                kg  Restricted access  Restricted access  \n",
              "230                kg  Restricted access  Restricted access  \n",
              "234                kg  Restricted access  Restricted access  \n",
              "7                  kg  Restricted access  Restricted access  \n",
              "152                kg              white             female  \n",
              "161                kg              white             female  \n",
              "3                  kg              white             female  \n",
              "291                kg  Restricted access  Restricted access  \n",
              "295                kg  Restricted access  Restricted access  \n",
              "296                kg  Restricted access  Restricted access  \n",
              "299                kg  Restricted access  Restricted access  \n",
              "5                  kg  Restricted access  Restricted access  \n",
              "174                kg              white             female  \n",
              "176                kg              white             female  \n",
              "180                kg              white             female  \n",
              "4                  kg              white             female  \n",
              "188                kg              white             female  \n",
              "190                kg              white             female  \n",
              "191                kg              white             female  \n",
              "193                kg              white             female  \n",
              "196                kg              white             female  \n",
              "6                  kg              white             female  \n",
              "321                kg              white               male  \n",
              "325                kg              white               male  \n",
              "329                kg              white               male  \n",
              "330                kg              white               male  \n",
              "332                kg              white               male  \n",
              "333                kg              white               male  \n",
              "7                  kg              white               male  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62a0a899-4b23-441d-af10-2748b1733f4f\" class=\"colab-df-container\">\n",
              "    <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>sample_id</th>\n",
              "      <th>abundance</th>\n",
              "      <th>genus</th>\n",
              "      <th>id</th>\n",
              "      <th>relative</th>\n",
              "      <th>collection_timestamp</th>\n",
              "      <th>day_relative_to_fmt</th>\n",
              "      <th>description</th>\n",
              "      <th>disease_state</th>\n",
              "      <th>host_age</th>\n",
              "      <th>host_age_units</th>\n",
              "      <th>host_body_mass_index</th>\n",
              "      <th>host_height</th>\n",
              "      <th>host_height_units</th>\n",
              "      <th>host_subject_id</th>\n",
              "      <th>host_weight</th>\n",
              "      <th>host_weight_units</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>17971.000000</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.496238</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>4741.000000</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>0.130915</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>6805.000000</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>0.187908</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>3076.000000</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>0.084938</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>3621.444444</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>16983.000000</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.413614</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>3717.000000</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>0.090526</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2956.000000</td>\n",
              "      <td>Barnesiella</td>\n",
              "      <td>Barnesiella</td>\n",
              "      <td>0.071992</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>6984.000000</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>0.170093</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2901.000000</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>0.070653</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>3413.000000</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>0.083122</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>4106.000000</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>8393.000000</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.216674</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>10841.000000</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>0.279872</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>4038.000000</td>\n",
              "      <td>Fusicatenibacter</td>\n",
              "      <td>Fusicatenibacter</td>\n",
              "      <td>0.104245</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>3286.000000</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>0.084832</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>3198.000000</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>0.082560</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>5106.000000</td>\n",
              "      <td>Streptococcus</td>\n",
              "      <td>Streptococcus</td>\n",
              "      <td>0.131817</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>3873.555556</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>54726.000000</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.839156</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD1</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>39</td>\n",
              "      <td>years</td>\n",
              "      <td>29.3</td>\n",
              "      <td>165.1</td>\n",
              "      <td>m</td>\n",
              "      <td>CD1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>3968.000000</td>\n",
              "      <td>Clostridium</td>\n",
              "      <td>Clostridium</td>\n",
              "      <td>0.060844</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD1</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>39</td>\n",
              "      <td>years</td>\n",
              "      <td>29.3</td>\n",
              "      <td>165.1</td>\n",
              "      <td>m</td>\n",
              "      <td>CD1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>6521.555556</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD1</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>39</td>\n",
              "      <td>years</td>\n",
              "      <td>29.3</td>\n",
              "      <td>165.1</td>\n",
              "      <td>m</td>\n",
              "      <td>CD1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>3734.000000</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.508258</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>Barnesiella</td>\n",
              "      <td>Barnesiella</td>\n",
              "      <td>0.066969</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>1475.000000</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>0.200771</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>911.000000</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>0.124002</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>734.666667</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>35554.000000</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.565087</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>15517.000000</td>\n",
              "      <td>Veillonella</td>\n",
              "      <td>Veillonella</td>\n",
              "      <td>0.246623</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>5555.000000</td>\n",
              "      <td>Fusobacterium</td>\n",
              "      <td>Fusobacterium</td>\n",
              "      <td>0.088290</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>6291.777778</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>11467.000000</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.203950</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>9955.000000</td>\n",
              "      <td>Mediterraneibacter</td>\n",
              "      <td>Mediterraneibacter</td>\n",
              "      <td>0.177058</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>11923.000000</td>\n",
              "      <td>Erysipelatoclostridium</td>\n",
              "      <td>Erysipelatoclostridium</td>\n",
              "      <td>0.212061</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>13975.000000</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>0.248557</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>3282.000000</td>\n",
              "      <td>Ruthenibacterium</td>\n",
              "      <td>Ruthenibacterium</td>\n",
              "      <td>0.058373</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>5622.444444</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>619.000000</td>\n",
              "      <td>Veillonella</td>\n",
              "      <td>Veillonella</td>\n",
              "      <td>0.118937</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>325</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>591.000000</td>\n",
              "      <td>Fusobacterium</td>\n",
              "      <td>Fusobacterium</td>\n",
              "      <td>0.113557</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>329</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>1866.000000</td>\n",
              "      <td>Anaerosinus</td>\n",
              "      <td>Anaerosinus</td>\n",
              "      <td>0.358540</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>330</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>580.000000</td>\n",
              "      <td>Megasphaera</td>\n",
              "      <td>Megasphaera</td>\n",
              "      <td>0.111443</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>332</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>734.000000</td>\n",
              "      <td>Phytobacter</td>\n",
              "      <td>Phytobacter</td>\n",
              "      <td>0.141033</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>Morganella</td>\n",
              "      <td>Morganella</td>\n",
              "      <td>0.056490</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>520.444444</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62a0a899-4b23-441d-af10-2748b1733f4f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-62a0a899-4b23-441d-af10-2748b1733f4f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-62a0a899-4b23-441d-af10-2748b1733f4f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-60dce5d8-f639-455b-82f2-865c66e00598\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60dce5d8-f639-455b-82f2-865c66e00598')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-60dce5d8-f639-455b-82f2-865c66e00598 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "def invade(data,invader,invader_rel): #define a function that will handle community invasion task\n",
        "  invaded=pd.DataFrame()   # set up results dataframe\n",
        "  for smp,df in data.groupby(by='sample_id'): # loop through data, one sample at a time\n",
        "    df=df[df.relative>0.05].copy()            # filter out genera below 5% (this is a high threshold, which will make simulation run faster)\n",
        "    df=df[df.genus!='Clostridioides']         # some samples already have C. diff, so lets remove it and then re-introduce\n",
        "    abund=df.abundance.sum()*invader_rel/(1-invader_rel)  # calculate the abundance needed to achieve desired relative abundance\n",
        "\n",
        "    info=df.iloc[0,:].copy()  # get necessary sample info\n",
        "    info.genus=invader        # add invader name\n",
        "    info.id=invader\n",
        "    info.abundance=abund      # add invader abundance\n",
        "    df.loc[df.shape[0]+1,info.index]=info.values  #append invader info\n",
        "    df.relative=df.abundance.apply(lambda x:x/df.abundance.sum()) # re-calculate relative abundance\n",
        "    invaded=pd.concat([invaded,df]) # append results to output dataframe\n",
        "  return invaded\n",
        "\n",
        "invaded=invade(tax,'Clostridioides',0.1) #lets test the function using Clostridioides introducd at 10%\n",
        "invaded"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RTbYBR8cJfup"
      },
      "source": [
        "With our taxonomy table ready to go, and our metadata merged, its finally time to get to the model building! 🎉\n",
        "\n",
        "## Building community models\n",
        "\n",
        "With the data we have now, building our models is pretty easy. We just pass our taxonomy table and model database to MICOM. We will remove all taxa that make up less than 5% of the community to keep the models small and speed up this tutorial. We will also have to specify where to write the models. For simplicity, we'll run this process in parallel over two threads. It should take around 10 minutes to finish."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "kDbSN71SmCZr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
            "8c626fefb268415cb164591c6473a707",
            "553ece32a42b47f4a6b2be9d8016c034"
          ]
        },
        "outputId": "04815630-f662-42c2-e1f0-075ce209f0d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c626fefb268415cb164591c6473a707"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from micom.workflows import build\n",
        "from micom import Community\n",
        "import pandas as pd\n",
        "\n",
        "manifest = build(invaded, \"agora103_genus.qza\", \"models\", solver=\"osqp\",\n",
        "                 cutoff=0.05, threads=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "r9qwglr88Ise",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "0c519896-18b7-4e4d-91e8-8793b2b015f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sample_id collection_timestamp day_relative_to_fmt   description  \\\n",
              "0  ERR1883195           2011-10-24                  26      Donor 11   \n",
              "1  ERR1883207           2012-01-12                  44      Donor 12   \n",
              "2  ERR1883212           2012-10-10                 135      Donor 14   \n",
              "3  ERR1883214           2011-07-26                   0     Day 0 CD1   \n",
              "4  ERR1883225           2011-07-26                  54     Donor CD1   \n",
              "5  ERR1883240           2012-02-14             pre-FMT   CD9 pre-FMT   \n",
              "6  ERR1883250           2011-12-23             pre-FMT  CD13 pre-FMT   \n",
              "7  ERR1883294           2011-09-29                   0     Day 0 CD3   \n",
              "\n",
              "  disease_state           host_age host_age_units host_body_mass_index  \\\n",
              "0       healthy  Restricted access          years    Restricted access   \n",
              "1       healthy  Restricted access          years    Restricted access   \n",
              "2       healthy  Restricted access          years    Restricted access   \n",
              "3       Pre-FMT                 39          years                 29.3   \n",
              "4       healthy  Restricted access          years    Restricted access   \n",
              "5       Pre-FMT                 47          years                 35.5   \n",
              "6       Pre-FMT                 53          years                 34.4   \n",
              "7       Pre-FMT                 61          years                 32.5   \n",
              "\n",
              "         host_height host_height_units host_subject_id        host_weight  \\\n",
              "0  Restricted access                 m           Donor  Restricted access   \n",
              "1  Restricted access                 m           Donor  Restricted access   \n",
              "2  Restricted access                 m           Donor  Restricted access   \n",
              "3              165.1                 m             CD1               80.1   \n",
              "4  Restricted access                 m           Donor  Restricted access   \n",
              "5               1.55                 m             CD9               85.1   \n",
              "6               1.56                 m            CD13               83.9   \n",
              "7              1.727                 m             CD3               97.3   \n",
              "\n",
              "  host_weight_units               race                sex               file  \\\n",
              "0                kg  Restricted access  Restricted access  ERR1883195.pickle   \n",
              "1                kg  Restricted access  Restricted access  ERR1883207.pickle   \n",
              "2                kg  Restricted access  Restricted access  ERR1883212.pickle   \n",
              "3                kg              white             female  ERR1883214.pickle   \n",
              "4                kg  Restricted access  Restricted access  ERR1883225.pickle   \n",
              "5                kg              white             female  ERR1883240.pickle   \n",
              "6                kg              white             female  ERR1883250.pickle   \n",
              "7                kg              white               male  ERR1883294.pickle   \n",
              "\n",
              "   found_taxa  total_taxa  found_fraction  found_abundance_fraction  \n",
              "0         5.0         5.0        1.000000                  1.000000  \n",
              "1         7.0         7.0        1.000000                  1.000000  \n",
              "2         6.0         7.0        0.857143                  0.895755  \n",
              "3         3.0         3.0        1.000000                  1.000000  \n",
              "4         5.0         5.0        1.000000                  1.000000  \n",
              "5         4.0         4.0        1.000000                  1.000000  \n",
              "6         5.0         6.0        0.833333                  0.941627  \n",
              "7         5.0         7.0        0.714286                  0.500427  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-94a2515d-b9d7-4b22-969a-f1550641dcae\" class=\"colab-df-container\">\n",
              "    <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>sample_id</th>\n",
              "      <th>collection_timestamp</th>\n",
              "      <th>day_relative_to_fmt</th>\n",
              "      <th>description</th>\n",
              "      <th>disease_state</th>\n",
              "      <th>host_age</th>\n",
              "      <th>host_age_units</th>\n",
              "      <th>host_body_mass_index</th>\n",
              "      <th>host_height</th>\n",
              "      <th>host_height_units</th>\n",
              "      <th>host_subject_id</th>\n",
              "      <th>host_weight</th>\n",
              "      <th>host_weight_units</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>file</th>\n",
              "      <th>found_taxa</th>\n",
              "      <th>total_taxa</th>\n",
              "      <th>found_fraction</th>\n",
              "      <th>found_abundance_fraction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>ERR1883195.pickle</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>ERR1883207.pickle</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>ERR1883212.pickle</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.895755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD1</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>39</td>\n",
              "      <td>years</td>\n",
              "      <td>29.3</td>\n",
              "      <td>165.1</td>\n",
              "      <td>m</td>\n",
              "      <td>CD1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "      <td>ERR1883214.pickle</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>healthy</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>ERR1883225.pickle</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "      <td>ERR1883240.pickle</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "      <td>ERR1883250.pickle</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.941627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>Pre-FMT</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "      <td>ERR1883294.pickle</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.500427</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94a2515d-b9d7-4b22-969a-f1550641dcae')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-94a2515d-b9d7-4b22-969a-f1550641dcae button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-94a2515d-b9d7-4b22-969a-f1550641dcae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-42a3a671-d486-4c59-ba37-acabf4d67acf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-42a3a671-d486-4c59-ba37-acabf4d67acf')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-42a3a671-d486-4c59-ba37-acabf4d67acf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "manifest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4KAJkhIdspQ"
      },
      "source": [
        "This will tell you how many taxa were found in the database and what fraction of the total abundance was represented by the database. For most samples, this looks okay (i.e., >70% of abundance represented).\n",
        "\n",
        "So we now have our community models and can leverage MICOM fully by simulating community growth - let's discuss what we want to look at.\n",
        "\n",
        "### Microbiome Context\n",
        "\n",
        "Now that our models are ready to go, let's think about some of the insights we might gain from these samples. First and foremost, we want to investigate the invasion potential of C. diff. How does its ability to invade vary in samples from healthy donors versus individuals with dysbiotic gut microbiomes (pre-FMT)?\n",
        "\n",
        "Additionally, we can use MICOM to take a mechanistic look at what metabolic strategies are leveraged by C. diff (e.g., what niche(s) does it occupy) in these different contexts.\n",
        "\n",
        "All that and more, coming up. Stay tuned!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etrvjwBLkKdR"
      },
      "source": [
        "First we need to import our dietary context. For simplicity we will be using a formulation that represents an average western diet, but if information about host diet is known other formulations can be used (e.g., vegetarian or vegan diet). Additional dietary formulations can be found [here]( https://github.com/micom-dev/media/tree/main/media)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "aJwXAR4PPAkA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "655ed379-e8ba-411f-bfbe-663fae18b210"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  reaction metabolite      global_id  flux\n",
              "reaction                                                  \n",
              "EX_4abz_m        EX_4abz_m     4abz_m     EX_4abz(e)   0.1\n",
              "EX_4hbz_m        EX_4hbz_m     4hbz_m     EX_4hbz(e)   0.1\n",
              "EX_ac_m            EX_ac_m       ac_m       EX_ac(e)   0.1\n",
              "EX_acgam_m      EX_acgam_m    acgam_m    EX_acgam(e)   0.1\n",
              "EX_ala_L_m      EX_ala_L_m    ala_L_m    EX_ala_L(e)   0.1\n",
              "...                    ...        ...            ...   ...\n",
              "EX_octa_m        EX_octa_m     octa_m     EX_octa(e)   0.0\n",
              "EX_adpcbl_m    EX_adpcbl_m   adpcbl_m   EX_adpcbl(e)   0.0\n",
              "EX_fe3dcit_m  EX_fe3dcit_m  fe3dcit_m  EX_fe3dcit(e)   0.0\n",
              "EX_pydx5p_m    EX_pydx5p_m   pydx5p_m   EX_pydx5p(e)   0.0\n",
              "EX_glu_D_m      EX_glu_D_m    glu_D_m    EX_glu_D(e)   0.0\n",
              "\n",
              "[171 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5148aa4-c4a1-4a84-ace7-0be0b6de5d11\" class=\"colab-df-container\">\n",
              "    <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>reaction</th>\n",
              "      <th>metabolite</th>\n",
              "      <th>global_id</th>\n",
              "      <th>flux</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reaction</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>EX_4abz_m</th>\n",
              "      <td>EX_4abz_m</td>\n",
              "      <td>4abz_m</td>\n",
              "      <td>EX_4abz(e)</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_4hbz_m</th>\n",
              "      <td>EX_4hbz_m</td>\n",
              "      <td>4hbz_m</td>\n",
              "      <td>EX_4hbz(e)</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_ac_m</th>\n",
              "      <td>EX_ac_m</td>\n",
              "      <td>ac_m</td>\n",
              "      <td>EX_ac(e)</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_acgam_m</th>\n",
              "      <td>EX_acgam_m</td>\n",
              "      <td>acgam_m</td>\n",
              "      <td>EX_acgam(e)</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_ala_L_m</th>\n",
              "      <td>EX_ala_L_m</td>\n",
              "      <td>ala_L_m</td>\n",
              "      <td>EX_ala_L(e)</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_octa_m</th>\n",
              "      <td>EX_octa_m</td>\n",
              "      <td>octa_m</td>\n",
              "      <td>EX_octa(e)</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_adpcbl_m</th>\n",
              "      <td>EX_adpcbl_m</td>\n",
              "      <td>adpcbl_m</td>\n",
              "      <td>EX_adpcbl(e)</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_fe3dcit_m</th>\n",
              "      <td>EX_fe3dcit_m</td>\n",
              "      <td>fe3dcit_m</td>\n",
              "      <td>EX_fe3dcit(e)</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_pydx5p_m</th>\n",
              "      <td>EX_pydx5p_m</td>\n",
              "      <td>pydx5p_m</td>\n",
              "      <td>EX_pydx5p(e)</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_glu_D_m</th>\n",
              "      <td>EX_glu_D_m</td>\n",
              "      <td>glu_D_m</td>\n",
              "      <td>EX_glu_D(e)</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>171 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5148aa4-c4a1-4a84-ace7-0be0b6de5d11')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c5148aa4-c4a1-4a84-ace7-0be0b6de5d11 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c5148aa4-c4a1-4a84-ace7-0be0b6de5d11');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f7a9e4e8-48eb-46cb-a20d-129a9305bdaa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7a9e4e8-48eb-46cb-a20d-129a9305bdaa')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f7a9e4e8-48eb-46cb-a20d-129a9305bdaa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "from micom.qiime_formats import load_qiime_medium\n",
        "medium = load_qiime_medium(\"western_diet_gut_agora.qza\")\n",
        "medium"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4s8R4WYUez4g"
      },
      "source": [
        "### Growing the models\n",
        "Great, now we have our media & our models, it's time to get growing. This will take some time, so we'll use that time as an opportunity to discuss more in depth what these processes do, and what to look for in the results. First, let's run the `grow()` command. This will take the models we've built, and find an optimal solution to the fluxes based upon the medium that's been applied."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3WH8VVrVS4mv"
      },
      "source": [
        "If that takes too long or was aborted, we can read it in from the treasure chest."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "IjDguZEcWGjG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
            "d40d5e3b7f5140db98f2ac398ccbd8a2",
            "034569ffebbe4ca08ff1181db1442375"
          ]
        },
        "outputId": "9c505520-8fa4-426b-b0bf-e9afeb3567e4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d40d5e3b7f5140db98f2ac398ccbd8a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from micom.workflows import grow, save_results\n",
        "\n",
        "growth = grow(manifest, \"models\",medium, tradeoff=0.8, threads=2)\n",
        "\n",
        "# We'll save the results to a file\n",
        "save_results(growth, \"growth.zip\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHedHJxHWkjy"
      },
      "source": [
        "Again, if that takes too long or was aborted, we can read it in from the treasure chest."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rcPNBkDpWGrQ"
      },
      "outputs": [],
      "source": [
        "from micom.workflows import load_results\n",
        "\n",
        "try:               # Will only run if the previous step failed\n",
        "  growth\n",
        "except NameError:\n",
        "  growth = load_results(\"treasure_chest/growth.zip\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69gq9QfAzqxq"
      },
      "source": [
        "What kind of results did we get? Well, `grow` returns a tuple of 3 data sets:\n",
        "\n",
        "1. The predicted growth rate for all taxa in all samples\n",
        "2. The import and export fluxes for each taxon and the external environment\n",
        "3. Annotations for the fluxes mapping to other databases\n",
        "\n",
        "### 📈 Growth Rates\n",
        "\n",
        "The growth rates are pretty straightforward."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "2r_XUm7U-HSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0d31e158-e39c-498a-88d9-8df87bf2b1c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    abundance  growth_rate  reactions  metabolites                   taxon  \\\n",
              "0    0.187908     0.147506       3293         1883             Bacteroides   \n",
              "1    0.100000     0.078513       1844         1401          Clostridioides   \n",
              "2    0.130915     0.102528       1986         1472        Faecalibacterium   \n",
              "3    0.084938     0.066664       2879         1747         Parabacteroides   \n",
              "4    0.496238     0.389568       2712         1656             Phocaeicola   \n",
              "5    0.170093     0.248553       3293         1883             Bacteroides   \n",
              "6    0.071992     0.108048       2235         1371             Barnesiella   \n",
              "7    0.070653     0.101473       2808         1732                 Blautia   \n",
              "8    0.100000     0.143653       1844         1401          Clostridioides   \n",
              "9    0.090526     0.129872       1986         1472        Faecalibacterium   \n",
              "10   0.083122     0.122119       2879         1747         Parabacteroides   \n",
              "11   0.413614     0.607244       2712         1656             Phocaeicola   \n",
              "12   0.839156     0.287210       2274         1386             Akkermansia   \n",
              "13   0.100000     0.124181       1844         1401          Clostridioides   \n",
              "14   0.060844     0.039351       3532         2001             Clostridium   \n",
              "15   0.094704     0.133863       3293         1883             Bacteroides   \n",
              "16   0.092168     0.130290       2808         1732                 Blautia   \n",
              "17   0.111638     0.157821       1844         1401          Clostridioides   \n",
              "18   0.312443     0.441683       1986         1472        Faecalibacterium   \n",
              "19   0.241890     0.341913       2712         1656             Phocaeicola   \n",
              "20   0.147157     0.208032       2860         1805           Streptococcus   \n",
              "21   0.565087     0.128835       2274         1386             Akkermansia   \n",
              "22   0.100000     0.377698       1844         1401          Clostridioides   \n",
              "23   0.088290     0.323804       2197         1602           Fusobacterium   \n",
              "24   0.246623     0.928642       1652         1308             Veillonella   \n",
              "25   0.200771     0.128531       3293         1883             Bacteroides   \n",
              "26   0.066969     0.042868       2235         1371             Barnesiella   \n",
              "27   0.100000     0.064019       1844         1401          Clostridioides   \n",
              "28   0.124002     0.079385       2879         1747         Parabacteroides   \n",
              "29   0.508258     0.325380       2712         1656             Phocaeicola   \n",
              "30   0.216594     0.253105       2274         1386             Akkermansia   \n",
              "31   0.263966     0.390305       2808         1732                 Blautia   \n",
              "32   0.106199     0.157275       1844         1401          Clostridioides   \n",
              "33   0.225207     0.336170       2826         1639  Erysipelatoclostridium   \n",
              "34   0.188034     0.280992       2681         1578      Mediterraneibacter   \n",
              "35   0.199829     0.324069       1844         1401          Clostridioides   \n",
              "36   0.226920     0.399628       2197         1602           Fusobacterium   \n",
              "37   0.222696     0.492108       1206         1068             Megasphaera   \n",
              "38   0.112884     0.236610       1577         1286              Morganella   \n",
              "39   0.237671     0.422561       1652         1308             Veillonella   \n",
              "\n",
              "    tradeoff   sample_id collection_timestamp day_relative_to_fmt  \\\n",
              "0        0.8  ERR1883195           2011-10-24                  26   \n",
              "1        0.8  ERR1883195           2011-10-24                  26   \n",
              "2        0.8  ERR1883195           2011-10-24                  26   \n",
              "3        0.8  ERR1883195           2011-10-24                  26   \n",
              "4        0.8  ERR1883195           2011-10-24                  26   \n",
              "5        0.8  ERR1883207           2012-01-12                  44   \n",
              "6        0.8  ERR1883207           2012-01-12                  44   \n",
              "7        0.8  ERR1883207           2012-01-12                  44   \n",
              "8        0.8  ERR1883207           2012-01-12                  44   \n",
              "9        0.8  ERR1883207           2012-01-12                  44   \n",
              "10       0.8  ERR1883207           2012-01-12                  44   \n",
              "11       0.8  ERR1883207           2012-01-12                  44   \n",
              "12       0.8  ERR1883214           2011-07-26                   0   \n",
              "13       0.8  ERR1883214           2011-07-26                   0   \n",
              "14       0.8  ERR1883214           2011-07-26                   0   \n",
              "15       0.8  ERR1883212           2012-10-10                 135   \n",
              "16       0.8  ERR1883212           2012-10-10                 135   \n",
              "17       0.8  ERR1883212           2012-10-10                 135   \n",
              "18       0.8  ERR1883212           2012-10-10                 135   \n",
              "19       0.8  ERR1883212           2012-10-10                 135   \n",
              "20       0.8  ERR1883212           2012-10-10                 135   \n",
              "21       0.8  ERR1883240           2012-02-14             pre-FMT   \n",
              "22       0.8  ERR1883240           2012-02-14             pre-FMT   \n",
              "23       0.8  ERR1883240           2012-02-14             pre-FMT   \n",
              "24       0.8  ERR1883240           2012-02-14             pre-FMT   \n",
              "25       0.8  ERR1883225           2011-07-26                  54   \n",
              "26       0.8  ERR1883225           2011-07-26                  54   \n",
              "27       0.8  ERR1883225           2011-07-26                  54   \n",
              "28       0.8  ERR1883225           2011-07-26                  54   \n",
              "29       0.8  ERR1883225           2011-07-26                  54   \n",
              "30       0.8  ERR1883250           2011-12-23             pre-FMT   \n",
              "31       0.8  ERR1883250           2011-12-23             pre-FMT   \n",
              "32       0.8  ERR1883250           2011-12-23             pre-FMT   \n",
              "33       0.8  ERR1883250           2011-12-23             pre-FMT   \n",
              "34       0.8  ERR1883250           2011-12-23             pre-FMT   \n",
              "35       0.8  ERR1883294           2011-09-29                   0   \n",
              "36       0.8  ERR1883294           2011-09-29                   0   \n",
              "37       0.8  ERR1883294           2011-09-29                   0   \n",
              "38       0.8  ERR1883294           2011-09-29                   0   \n",
              "39       0.8  ERR1883294           2011-09-29                   0   \n",
              "\n",
              "     description  ...           host_age host_age_units host_body_mass_index  \\\n",
              "0       Donor 11  ...  Restricted access          years    Restricted access   \n",
              "1       Donor 11  ...  Restricted access          years    Restricted access   \n",
              "2       Donor 11  ...  Restricted access          years    Restricted access   \n",
              "3       Donor 11  ...  Restricted access          years    Restricted access   \n",
              "4       Donor 11  ...  Restricted access          years    Restricted access   \n",
              "5       Donor 12  ...  Restricted access          years    Restricted access   \n",
              "6       Donor 12  ...  Restricted access          years    Restricted access   \n",
              "7       Donor 12  ...  Restricted access          years    Restricted access   \n",
              "8       Donor 12  ...  Restricted access          years    Restricted access   \n",
              "9       Donor 12  ...  Restricted access          years    Restricted access   \n",
              "10      Donor 12  ...  Restricted access          years    Restricted access   \n",
              "11      Donor 12  ...  Restricted access          years    Restricted access   \n",
              "12     Day 0 CD1  ...                 39          years                 29.3   \n",
              "13     Day 0 CD1  ...                 39          years                 29.3   \n",
              "14     Day 0 CD1  ...                 39          years                 29.3   \n",
              "15      Donor 14  ...  Restricted access          years    Restricted access   \n",
              "16      Donor 14  ...  Restricted access          years    Restricted access   \n",
              "17      Donor 14  ...  Restricted access          years    Restricted access   \n",
              "18      Donor 14  ...  Restricted access          years    Restricted access   \n",
              "19      Donor 14  ...  Restricted access          years    Restricted access   \n",
              "20      Donor 14  ...  Restricted access          years    Restricted access   \n",
              "21   CD9 pre-FMT  ...                 47          years                 35.5   \n",
              "22   CD9 pre-FMT  ...                 47          years                 35.5   \n",
              "23   CD9 pre-FMT  ...                 47          years                 35.5   \n",
              "24   CD9 pre-FMT  ...                 47          years                 35.5   \n",
              "25     Donor CD1  ...  Restricted access          years    Restricted access   \n",
              "26     Donor CD1  ...  Restricted access          years    Restricted access   \n",
              "27     Donor CD1  ...  Restricted access          years    Restricted access   \n",
              "28     Donor CD1  ...  Restricted access          years    Restricted access   \n",
              "29     Donor CD1  ...  Restricted access          years    Restricted access   \n",
              "30  CD13 pre-FMT  ...                 53          years                 34.4   \n",
              "31  CD13 pre-FMT  ...                 53          years                 34.4   \n",
              "32  CD13 pre-FMT  ...                 53          years                 34.4   \n",
              "33  CD13 pre-FMT  ...                 53          years                 34.4   \n",
              "34  CD13 pre-FMT  ...                 53          years                 34.4   \n",
              "35     Day 0 CD3  ...                 61          years                 32.5   \n",
              "36     Day 0 CD3  ...                 61          years                 32.5   \n",
              "37     Day 0 CD3  ...                 61          years                 32.5   \n",
              "38     Day 0 CD3  ...                 61          years                 32.5   \n",
              "39     Day 0 CD3  ...                 61          years                 32.5   \n",
              "\n",
              "          host_height host_height_units host_subject_id        host_weight  \\\n",
              "0   Restricted access                 m           Donor  Restricted access   \n",
              "1   Restricted access                 m           Donor  Restricted access   \n",
              "2   Restricted access                 m           Donor  Restricted access   \n",
              "3   Restricted access                 m           Donor  Restricted access   \n",
              "4   Restricted access                 m           Donor  Restricted access   \n",
              "5   Restricted access                 m           Donor  Restricted access   \n",
              "6   Restricted access                 m           Donor  Restricted access   \n",
              "7   Restricted access                 m           Donor  Restricted access   \n",
              "8   Restricted access                 m           Donor  Restricted access   \n",
              "9   Restricted access                 m           Donor  Restricted access   \n",
              "10  Restricted access                 m           Donor  Restricted access   \n",
              "11  Restricted access                 m           Donor  Restricted access   \n",
              "12              165.1                 m             CD1               80.1   \n",
              "13              165.1                 m             CD1               80.1   \n",
              "14              165.1                 m             CD1               80.1   \n",
              "15  Restricted access                 m           Donor  Restricted access   \n",
              "16  Restricted access                 m           Donor  Restricted access   \n",
              "17  Restricted access                 m           Donor  Restricted access   \n",
              "18  Restricted access                 m           Donor  Restricted access   \n",
              "19  Restricted access                 m           Donor  Restricted access   \n",
              "20  Restricted access                 m           Donor  Restricted access   \n",
              "21               1.55                 m             CD9               85.1   \n",
              "22               1.55                 m             CD9               85.1   \n",
              "23               1.55                 m             CD9               85.1   \n",
              "24               1.55                 m             CD9               85.1   \n",
              "25  Restricted access                 m           Donor  Restricted access   \n",
              "26  Restricted access                 m           Donor  Restricted access   \n",
              "27  Restricted access                 m           Donor  Restricted access   \n",
              "28  Restricted access                 m           Donor  Restricted access   \n",
              "29  Restricted access                 m           Donor  Restricted access   \n",
              "30               1.56                 m            CD13               83.9   \n",
              "31               1.56                 m            CD13               83.9   \n",
              "32               1.56                 m            CD13               83.9   \n",
              "33               1.56                 m            CD13               83.9   \n",
              "34               1.56                 m            CD13               83.9   \n",
              "35              1.727                 m             CD3               97.3   \n",
              "36              1.727                 m             CD3               97.3   \n",
              "37              1.727                 m             CD3               97.3   \n",
              "38              1.727                 m             CD3               97.3   \n",
              "39              1.727                 m             CD3               97.3   \n",
              "\n",
              "   host_weight_units               race                sex  \n",
              "0                 kg  Restricted access  Restricted access  \n",
              "1                 kg  Restricted access  Restricted access  \n",
              "2                 kg  Restricted access  Restricted access  \n",
              "3                 kg  Restricted access  Restricted access  \n",
              "4                 kg  Restricted access  Restricted access  \n",
              "5                 kg  Restricted access  Restricted access  \n",
              "6                 kg  Restricted access  Restricted access  \n",
              "7                 kg  Restricted access  Restricted access  \n",
              "8                 kg  Restricted access  Restricted access  \n",
              "9                 kg  Restricted access  Restricted access  \n",
              "10                kg  Restricted access  Restricted access  \n",
              "11                kg  Restricted access  Restricted access  \n",
              "12                kg              white             female  \n",
              "13                kg              white             female  \n",
              "14                kg              white             female  \n",
              "15                kg  Restricted access  Restricted access  \n",
              "16                kg  Restricted access  Restricted access  \n",
              "17                kg  Restricted access  Restricted access  \n",
              "18                kg  Restricted access  Restricted access  \n",
              "19                kg  Restricted access  Restricted access  \n",
              "20                kg  Restricted access  Restricted access  \n",
              "21                kg              white             female  \n",
              "22                kg              white             female  \n",
              "23                kg              white             female  \n",
              "24                kg              white             female  \n",
              "25                kg  Restricted access  Restricted access  \n",
              "26                kg  Restricted access  Restricted access  \n",
              "27                kg  Restricted access  Restricted access  \n",
              "28                kg  Restricted access  Restricted access  \n",
              "29                kg  Restricted access  Restricted access  \n",
              "30                kg              white             female  \n",
              "31                kg              white             female  \n",
              "32                kg              white             female  \n",
              "33                kg              white             female  \n",
              "34                kg              white             female  \n",
              "35                kg              white               male  \n",
              "36                kg              white               male  \n",
              "37                kg              white               male  \n",
              "38                kg              white               male  \n",
              "39                kg              white               male  \n",
              "\n",
              "[40 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d97a82a7-bed8-47fb-9bfb-d902215bba95\" class=\"colab-df-container\">\n",
              "    <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>abundance</th>\n",
              "      <th>growth_rate</th>\n",
              "      <th>reactions</th>\n",
              "      <th>metabolites</th>\n",
              "      <th>taxon</th>\n",
              "      <th>tradeoff</th>\n",
              "      <th>sample_id</th>\n",
              "      <th>collection_timestamp</th>\n",
              "      <th>day_relative_to_fmt</th>\n",
              "      <th>description</th>\n",
              "      <th>...</th>\n",
              "      <th>host_age</th>\n",
              "      <th>host_age_units</th>\n",
              "      <th>host_body_mass_index</th>\n",
              "      <th>host_height</th>\n",
              "      <th>host_height_units</th>\n",
              "      <th>host_subject_id</th>\n",
              "      <th>host_weight</th>\n",
              "      <th>host_weight_units</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.187908</td>\n",
              "      <td>0.147506</td>\n",
              "      <td>3293</td>\n",
              "      <td>1883</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.078513</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.130915</td>\n",
              "      <td>0.102528</td>\n",
              "      <td>1986</td>\n",
              "      <td>1472</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.084938</td>\n",
              "      <td>0.066664</td>\n",
              "      <td>2879</td>\n",
              "      <td>1747</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.496238</td>\n",
              "      <td>0.389568</td>\n",
              "      <td>2712</td>\n",
              "      <td>1656</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.170093</td>\n",
              "      <td>0.248553</td>\n",
              "      <td>3293</td>\n",
              "      <td>1883</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.071992</td>\n",
              "      <td>0.108048</td>\n",
              "      <td>2235</td>\n",
              "      <td>1371</td>\n",
              "      <td>Barnesiella</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.070653</td>\n",
              "      <td>0.101473</td>\n",
              "      <td>2808</td>\n",
              "      <td>1732</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.143653</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.090526</td>\n",
              "      <td>0.129872</td>\n",
              "      <td>1986</td>\n",
              "      <td>1472</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.083122</td>\n",
              "      <td>0.122119</td>\n",
              "      <td>2879</td>\n",
              "      <td>1747</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.413614</td>\n",
              "      <td>0.607244</td>\n",
              "      <td>2712</td>\n",
              "      <td>1656</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.839156</td>\n",
              "      <td>0.287210</td>\n",
              "      <td>2274</td>\n",
              "      <td>1386</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>39</td>\n",
              "      <td>years</td>\n",
              "      <td>29.3</td>\n",
              "      <td>165.1</td>\n",
              "      <td>m</td>\n",
              "      <td>CD1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.124181</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>39</td>\n",
              "      <td>years</td>\n",
              "      <td>29.3</td>\n",
              "      <td>165.1</td>\n",
              "      <td>m</td>\n",
              "      <td>CD1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.060844</td>\n",
              "      <td>0.039351</td>\n",
              "      <td>3532</td>\n",
              "      <td>2001</td>\n",
              "      <td>Clostridium</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>39</td>\n",
              "      <td>years</td>\n",
              "      <td>29.3</td>\n",
              "      <td>165.1</td>\n",
              "      <td>m</td>\n",
              "      <td>CD1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.094704</td>\n",
              "      <td>0.133863</td>\n",
              "      <td>3293</td>\n",
              "      <td>1883</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.092168</td>\n",
              "      <td>0.130290</td>\n",
              "      <td>2808</td>\n",
              "      <td>1732</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.111638</td>\n",
              "      <td>0.157821</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.312443</td>\n",
              "      <td>0.441683</td>\n",
              "      <td>1986</td>\n",
              "      <td>1472</td>\n",
              "      <td>Faecalibacterium</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.241890</td>\n",
              "      <td>0.341913</td>\n",
              "      <td>2712</td>\n",
              "      <td>1656</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.147157</td>\n",
              "      <td>0.208032</td>\n",
              "      <td>2860</td>\n",
              "      <td>1805</td>\n",
              "      <td>Streptococcus</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.565087</td>\n",
              "      <td>0.128835</td>\n",
              "      <td>2274</td>\n",
              "      <td>1386</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.377698</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.088290</td>\n",
              "      <td>0.323804</td>\n",
              "      <td>2197</td>\n",
              "      <td>1602</td>\n",
              "      <td>Fusobacterium</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.246623</td>\n",
              "      <td>0.928642</td>\n",
              "      <td>1652</td>\n",
              "      <td>1308</td>\n",
              "      <td>Veillonella</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.200771</td>\n",
              "      <td>0.128531</td>\n",
              "      <td>3293</td>\n",
              "      <td>1883</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.066969</td>\n",
              "      <td>0.042868</td>\n",
              "      <td>2235</td>\n",
              "      <td>1371</td>\n",
              "      <td>Barnesiella</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.064019</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.124002</td>\n",
              "      <td>0.079385</td>\n",
              "      <td>2879</td>\n",
              "      <td>1747</td>\n",
              "      <td>Parabacteroides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.508258</td>\n",
              "      <td>0.325380</td>\n",
              "      <td>2712</td>\n",
              "      <td>1656</td>\n",
              "      <td>Phocaeicola</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.216594</td>\n",
              "      <td>0.253105</td>\n",
              "      <td>2274</td>\n",
              "      <td>1386</td>\n",
              "      <td>Akkermansia</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.263966</td>\n",
              "      <td>0.390305</td>\n",
              "      <td>2808</td>\n",
              "      <td>1732</td>\n",
              "      <td>Blautia</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.106199</td>\n",
              "      <td>0.157275</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.225207</td>\n",
              "      <td>0.336170</td>\n",
              "      <td>2826</td>\n",
              "      <td>1639</td>\n",
              "      <td>Erysipelatoclostridium</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.188034</td>\n",
              "      <td>0.280992</td>\n",
              "      <td>2681</td>\n",
              "      <td>1578</td>\n",
              "      <td>Mediterraneibacter</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.199829</td>\n",
              "      <td>0.324069</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>...</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.226920</td>\n",
              "      <td>0.399628</td>\n",
              "      <td>2197</td>\n",
              "      <td>1602</td>\n",
              "      <td>Fusobacterium</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>...</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.222696</td>\n",
              "      <td>0.492108</td>\n",
              "      <td>1206</td>\n",
              "      <td>1068</td>\n",
              "      <td>Megasphaera</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>...</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.112884</td>\n",
              "      <td>0.236610</td>\n",
              "      <td>1577</td>\n",
              "      <td>1286</td>\n",
              "      <td>Morganella</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>...</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.237671</td>\n",
              "      <td>0.422561</td>\n",
              "      <td>1652</td>\n",
              "      <td>1308</td>\n",
              "      <td>Veillonella</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>...</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>40 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d97a82a7-bed8-47fb-9bfb-d902215bba95')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d97a82a7-bed8-47fb-9bfb-d902215bba95 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d97a82a7-bed8-47fb-9bfb-d902215bba95');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3a79e749-cbea-404a-95b3-bad3062b68c4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a79e749-cbea-404a-95b3-bad3062b68c4')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3a79e749-cbea-404a-95b3-bad3062b68c4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "growth_rates=pd.merge(growth.growth_rates,metadata,on='sample_id')\n",
        "growth_rates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5BK7DDv0UfA"
      },
      "source": [
        "### ↔️ Exchange Fluxes\n",
        "\n",
        "More interesting are the exchange fluxes. These reactions represent the import and export of metabolites from the system Let's look at those now:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "lQW2BBS10jdN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "18b7cff8-c6a5-4da1-891a-01b47d910737"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            taxon   sample_id  tolerance    reaction      flux  abundance  \\\n",
              "5          medium  ERR1883195     0.0001    EX_thm_m -0.000327        NaN   \n",
              "9          medium  ERR1883195     0.0001    EX_gly_m -0.000549        NaN   \n",
              "10         medium  ERR1883195     0.0001  EX_leu_L_m -0.000586        NaN   \n",
              "11         medium  ERR1883195     0.0001   EX_mqn8_m  0.000381        NaN   \n",
              "15         medium  ERR1883195     0.0001  EX_dad_2_m -0.000799        NaN   \n",
              "...           ...         ...        ...         ...       ...        ...   \n",
              "25233  Morganella  ERR1883294     0.0001  EX_succ(e) -1.773372   0.112884   \n",
              "25237  Morganella  ERR1883294     0.0001   EX_nmn(e) -0.881175   0.112884   \n",
              "25239  Morganella  ERR1883294     0.0001  EX_acac(e) -0.161820   0.112884   \n",
              "25241  Morganella  ERR1883294     0.0001   EX_akg(e) -0.010816   0.112884   \n",
              "25249  Morganella  ERR1883294     0.0001  EX_xtsn(e) -0.001976   0.112884   \n",
              "\n",
              "      metabolite direction  \n",
              "5          thm_m    import  \n",
              "9          gly_m    import  \n",
              "10       leu_L_m    import  \n",
              "11        mqn8_m    export  \n",
              "15       dad_2_m    import  \n",
              "...          ...       ...  \n",
              "25233    succ[e]    import  \n",
              "25237     nmn[e]    import  \n",
              "25239    acac[e]    import  \n",
              "25241     akg[e]    import  \n",
              "25249    xtsn[e]    import  \n",
              "\n",
              "[8390 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c658239-98e8-4242-8555-3252c0ae4329\" class=\"colab-df-container\">\n",
              "    <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>taxon</th>\n",
              "      <th>sample_id</th>\n",
              "      <th>tolerance</th>\n",
              "      <th>reaction</th>\n",
              "      <th>flux</th>\n",
              "      <th>abundance</th>\n",
              "      <th>metabolite</th>\n",
              "      <th>direction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>medium</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_thm_m</td>\n",
              "      <td>-0.000327</td>\n",
              "      <td>NaN</td>\n",
              "      <td>thm_m</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>medium</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_gly_m</td>\n",
              "      <td>-0.000549</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gly_m</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>medium</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_leu_L_m</td>\n",
              "      <td>-0.000586</td>\n",
              "      <td>NaN</td>\n",
              "      <td>leu_L_m</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>medium</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_mqn8_m</td>\n",
              "      <td>0.000381</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mqn8_m</td>\n",
              "      <td>export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>medium</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_dad_2_m</td>\n",
              "      <td>-0.000799</td>\n",
              "      <td>NaN</td>\n",
              "      <td>dad_2_m</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25233</th>\n",
              "      <td>Morganella</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_succ(e)</td>\n",
              "      <td>-1.773372</td>\n",
              "      <td>0.112884</td>\n",
              "      <td>succ[e]</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25237</th>\n",
              "      <td>Morganella</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_nmn(e)</td>\n",
              "      <td>-0.881175</td>\n",
              "      <td>0.112884</td>\n",
              "      <td>nmn[e]</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25239</th>\n",
              "      <td>Morganella</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_acac(e)</td>\n",
              "      <td>-0.161820</td>\n",
              "      <td>0.112884</td>\n",
              "      <td>acac[e]</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25241</th>\n",
              "      <td>Morganella</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_akg(e)</td>\n",
              "      <td>-0.010816</td>\n",
              "      <td>0.112884</td>\n",
              "      <td>akg[e]</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25249</th>\n",
              "      <td>Morganella</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>EX_xtsn(e)</td>\n",
              "      <td>-0.001976</td>\n",
              "      <td>0.112884</td>\n",
              "      <td>xtsn[e]</td>\n",
              "      <td>import</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8390 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c658239-98e8-4242-8555-3252c0ae4329')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8c658239-98e8-4242-8555-3252c0ae4329 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8c658239-98e8-4242-8555-3252c0ae4329');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1bd82e5a-af14-4f0b-bda5-840e5df3949e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1bd82e5a-af14-4f0b-bda5-840e5df3949e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1bd82e5a-af14-4f0b-bda5-840e5df3949e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "growth.exchanges"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pu5XtkUl1YG1"
      },
      "source": [
        "So we see how much of each metabolite is either consumed or produced by each taxon in each sample. `tolerance` denotes the accuracy of the solver and tells you the smallest absolute flux that is likely different form zero (i.e., substantial flux). *All of the fluxes are normalized to 1g dry weight of bacteria*. So, you can directly compare fluxes between taxa, even if they are present at very different abundances.\n",
        "\n",
        "If you're curious what the abbreviation for each of these metabolites represents, that can be found in the annotations dataframe. For instance, let's find out what `\"tre[e]\"` represents."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "DphXa9hw1yxM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "5efd5e77-0751-4171-fe5f-e77b9194237b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          metabolite       name  molecular_weight  C_number  N_number  \\\n",
              "reaction                                                                \n",
              "EX_tre(e)     tre[e]  Trehalose         342.29648        12         0   \n",
              "\n",
              "                hmdb inchi kegg.compound pubchem.compound chebi   reaction  \n",
              "reaction                                                                    \n",
              "EX_tre(e)  HMDB00975   NaN        C01083             7427   NaN  EX_tre(e)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6542b606-f16f-4d73-8351-1d11a3a0233d\" class=\"colab-df-container\">\n",
              "    <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>metabolite</th>\n",
              "      <th>name</th>\n",
              "      <th>molecular_weight</th>\n",
              "      <th>C_number</th>\n",
              "      <th>N_number</th>\n",
              "      <th>hmdb</th>\n",
              "      <th>inchi</th>\n",
              "      <th>kegg.compound</th>\n",
              "      <th>pubchem.compound</th>\n",
              "      <th>chebi</th>\n",
              "      <th>reaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reaction</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>EX_tre(e)</th>\n",
              "      <td>tre[e]</td>\n",
              "      <td>Trehalose</td>\n",
              "      <td>342.29648</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>HMDB00975</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C01083</td>\n",
              "      <td>7427</td>\n",
              "      <td>NaN</td>\n",
              "      <td>EX_tre(e)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6542b606-f16f-4d73-8351-1d11a3a0233d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6542b606-f16f-4d73-8351-1d11a3a0233d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6542b606-f16f-4d73-8351-1d11a3a0233d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "anns = growth.annotations\n",
        "anns[anns.metabolite == \"tre[e]\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVHLD2dm4a6B"
      },
      "source": [
        "Trehalose! Interesting, [that's an important metabolite](https://pubmed.ncbi.nlm.nih.gov/34277467/) in the context of CDI! All of these annotations and more information at are also available at https://vmh.life, maintained by Dr. Ines Thiele's lab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CImtzqRJbbGj"
      },
      "source": [
        "\n",
        "# 📊 Visualizations\n",
        "\n",
        "Let's visualize our results. Because of the rich output of these models, it can be overwhelming to represent it all, but don't worry! There are tools in place for this already.\n",
        "\n",
        "We will use the standard visualizations included in MICOM. These tools take in the growth results we obtained before and create visualizations in standalone HTML files that bundle the plots and raw data and can be viewed directly in your browser.\n",
        "\n",
        "First, let's look at the growth rates of each taxon across samples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "EaplMHFLcMT7"
      },
      "outputs": [],
      "source": [
        "from micom.viz import *\n",
        "\n",
        "viz = plot_growth(growth, filename=\"growthrates.html\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normally, we could call `viz.view()` afterwards and it would open it in our web browser. However, this will not work in Colab. However, the plot function creates the file `growth_rates_[DATE].html` in your `materials` folder. To open it, simply download that file and view it in your web browser. We can see that there are many things going on, but it's not super clear. Let's continue."
      ],
      "metadata": {
        "id": "_sjNIuCSXkyb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We're interested in understanding the invasion potential of C. diff so lets extract the predicted C. diff growth rates. In addition to C. diff growth rate we can also look at what fraction of the community growth rate this represents.\n",
        "\n"
      ],
      "metadata": {
        "id": "qPTCyw-7RqFm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "9YlWytSp-yKI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "915edd34-0cd4-461b-9fbc-fe5f9a2726b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    abundance  growth_rate  reactions  metabolites           taxon  tradeoff  \\\n",
              "1    0.100000     0.078513       1844         1401  Clostridioides       0.8   \n",
              "8    0.100000     0.143653       1844         1401  Clostridioides       0.8   \n",
              "13   0.100000     0.124181       1844         1401  Clostridioides       0.8   \n",
              "17   0.111638     0.157821       1844         1401  Clostridioides       0.8   \n",
              "22   0.100000     0.377698       1844         1401  Clostridioides       0.8   \n",
              "27   0.100000     0.064019       1844         1401  Clostridioides       0.8   \n",
              "32   0.106199     0.157275       1844         1401  Clostridioides       0.8   \n",
              "35   0.199829     0.324069       1844         1401  Clostridioides       0.8   \n",
              "\n",
              "     sample_id collection_timestamp day_relative_to_fmt   description  ...  \\\n",
              "1   ERR1883195           2011-10-24                  26      Donor 11  ...   \n",
              "8   ERR1883207           2012-01-12                  44      Donor 12  ...   \n",
              "13  ERR1883214           2011-07-26                   0     Day 0 CD1  ...   \n",
              "17  ERR1883212           2012-10-10                 135      Donor 14  ...   \n",
              "22  ERR1883240           2012-02-14             pre-FMT   CD9 pre-FMT  ...   \n",
              "27  ERR1883225           2011-07-26                  54     Donor CD1  ...   \n",
              "32  ERR1883250           2011-12-23             pre-FMT  CD13 pre-FMT  ...   \n",
              "35  ERR1883294           2011-09-29                   0     Day 0 CD3  ...   \n",
              "\n",
              "             host_age host_age_units host_body_mass_index        host_height  \\\n",
              "1   Restricted access          years    Restricted access  Restricted access   \n",
              "8   Restricted access          years    Restricted access  Restricted access   \n",
              "13                 39          years                 29.3              165.1   \n",
              "17  Restricted access          years    Restricted access  Restricted access   \n",
              "22                 47          years                 35.5               1.55   \n",
              "27  Restricted access          years    Restricted access  Restricted access   \n",
              "32                 53          years                 34.4               1.56   \n",
              "35                 61          years                 32.5              1.727   \n",
              "\n",
              "   host_height_units host_subject_id        host_weight host_weight_units  \\\n",
              "1                  m           Donor  Restricted access                kg   \n",
              "8                  m           Donor  Restricted access                kg   \n",
              "13                 m             CD1               80.1                kg   \n",
              "17                 m           Donor  Restricted access                kg   \n",
              "22                 m             CD9               85.1                kg   \n",
              "27                 m           Donor  Restricted access                kg   \n",
              "32                 m            CD13               83.9                kg   \n",
              "35                 m             CD3               97.3                kg   \n",
              "\n",
              "                 race                sex  \n",
              "1   Restricted access  Restricted access  \n",
              "8   Restricted access  Restricted access  \n",
              "13              white             female  \n",
              "17  Restricted access  Restricted access  \n",
              "22              white             female  \n",
              "27  Restricted access  Restricted access  \n",
              "32              white             female  \n",
              "35              white               male  \n",
              "\n",
              "[8 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec42e703-5d3c-4740-a34f-3c22057523a4\" class=\"colab-df-container\">\n",
              "    <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>abundance</th>\n",
              "      <th>growth_rate</th>\n",
              "      <th>reactions</th>\n",
              "      <th>metabolites</th>\n",
              "      <th>taxon</th>\n",
              "      <th>tradeoff</th>\n",
              "      <th>sample_id</th>\n",
              "      <th>collection_timestamp</th>\n",
              "      <th>day_relative_to_fmt</th>\n",
              "      <th>description</th>\n",
              "      <th>...</th>\n",
              "      <th>host_age</th>\n",
              "      <th>host_age_units</th>\n",
              "      <th>host_body_mass_index</th>\n",
              "      <th>host_height</th>\n",
              "      <th>host_height_units</th>\n",
              "      <th>host_subject_id</th>\n",
              "      <th>host_weight</th>\n",
              "      <th>host_weight_units</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.078513</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883195</td>\n",
              "      <td>2011-10-24</td>\n",
              "      <td>26</td>\n",
              "      <td>Donor 11</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.143653</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883207</td>\n",
              "      <td>2012-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>Donor 12</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.124181</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883214</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>39</td>\n",
              "      <td>years</td>\n",
              "      <td>29.3</td>\n",
              "      <td>165.1</td>\n",
              "      <td>m</td>\n",
              "      <td>CD1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.111638</td>\n",
              "      <td>0.157821</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883212</td>\n",
              "      <td>2012-10-10</td>\n",
              "      <td>135</td>\n",
              "      <td>Donor 14</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.377698</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883240</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD9 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>years</td>\n",
              "      <td>35.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>m</td>\n",
              "      <td>CD9</td>\n",
              "      <td>85.1</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.064019</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883225</td>\n",
              "      <td>2011-07-26</td>\n",
              "      <td>54</td>\n",
              "      <td>Donor CD1</td>\n",
              "      <td>...</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>years</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>m</td>\n",
              "      <td>Donor</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>kg</td>\n",
              "      <td>Restricted access</td>\n",
              "      <td>Restricted access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.106199</td>\n",
              "      <td>0.157275</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883250</td>\n",
              "      <td>2011-12-23</td>\n",
              "      <td>pre-FMT</td>\n",
              "      <td>CD13 pre-FMT</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>years</td>\n",
              "      <td>34.4</td>\n",
              "      <td>1.56</td>\n",
              "      <td>m</td>\n",
              "      <td>CD13</td>\n",
              "      <td>83.9</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.199829</td>\n",
              "      <td>0.324069</td>\n",
              "      <td>1844</td>\n",
              "      <td>1401</td>\n",
              "      <td>Clostridioides</td>\n",
              "      <td>0.8</td>\n",
              "      <td>ERR1883294</td>\n",
              "      <td>2011-09-29</td>\n",
              "      <td>0</td>\n",
              "      <td>Day 0 CD3</td>\n",
              "      <td>...</td>\n",
              "      <td>61</td>\n",
              "      <td>years</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.727</td>\n",
              "      <td>m</td>\n",
              "      <td>CD3</td>\n",
              "      <td>97.3</td>\n",
              "      <td>kg</td>\n",
              "      <td>white</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec42e703-5d3c-4740-a34f-3c22057523a4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ec42e703-5d3c-4740-a34f-3c22057523a4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ec42e703-5d3c-4740-a34f-3c22057523a4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1c71cb8e-c920-46e0-9c16-8655e377ad41\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c71cb8e-c920-46e0-9c16-8655e377ad41')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1c71cb8e-c920-46e0-9c16-8655e377ad41 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "cdiff = growth_rates[growth_rates.taxon=='Clostridioides'].copy()\n",
        "cdiff"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we've extracted the C. diff specific growth rates lets take a look at how they compare between patients with healthy and disbiotic gut microbiomes"
      ],
      "metadata": {
        "id": "bQKYxSZTSPk1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "AWG3U094-_OI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "46f94117-736f-4ddc-85c1-3cd2f88be940"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='disease_state', ylabel='growth_rate'>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "sns.boxplot(x='disease_state',y='growth_rate',data=cdiff)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looks like C. diff is predicted to grow in all samples but its predicted growth rate is ~2x higher in the Pre-FMT samples. You can see there is also a decent amount of variation in the Pre-FMT results."
      ],
      "metadata": {
        "id": "MEuym_PVui-k"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1JbbKrLcVye"
      },
      "source": [
        "## Growth niches\n",
        "\n",
        "Another thing we can look at is whether individual taxa inhabit different growth niches across different disease contexts. Here we can use the `plot_exchanges_per_taxon` function to see how exchanges differ within and between taxa, within and across human populations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "NlZrfv38esj8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "852f653a-aa94-4684-b355-f29e53672cf6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<micom.viz.core.Visualization at 0x7f6cfc671090>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "plot_exchanges_per_taxon(growth, perplexity=4, direction=\"import\", filename=\"niche.html\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXnbUCCs2yVG"
      },
      "source": [
        "\n",
        "This function projects the full set of import or export fluxes onto a two dimensional plane, and arranges taxa so that more similar flux patterns lie nearer together. Taxa closer to one another compete for a more similar set of resources (and/or produce a more similar set of metabolites). The center of the plot signifies a more competitive nutrient space, whereas clusters on the outskirts denote more isolated niches.\n",
        "\n",
        "You can tune [TSNE parameters](https://distill.pub/2016/misread-tsne/), such as perplexity, to get a more meaningful grouping. We will lower the perplexity here since we don't have a lot of data points.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One small take away from this analysis is the speration between C. diff pre- and post- FMT samples, suggesting that C. diff may leverage different metabolic strategies in these contexts. Lets take a closer look at this..."
      ],
      "metadata": {
        "id": "weoSaLDPZYAK"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Y_XfHkB4sO8"
      },
      "source": [
        "## Comparative flux analysis\n",
        "\n",
        "Now let's compare the metabolite imports between the two disease contexts. We're interested to see how the consumption profile of the microbiome changes when the disease state changes, as changes in microbiome context can lead to changes in host succeptibility to infection.  To look into this deeper, we'll transform the microbiome import data and then plot the metabolite imports on a heatmap."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAtec3I78DJJ"
      },
      "source": [
        "We can use the `consumption_rates` function in MICOM to calculate consumption rates from the growth results. This will tell us what the patient microbiomes are consuming and provide additional insight into available niches. To visualize the results we'll  run a centered log ratio transformation on the data, to account for the compositional nature of these data and compare all the fluxes against each other. Importantly, here we consider the consumption rates for samples with no C. diff present to understand how the initial state of the patient microbiomes may influence invasion potential."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "DJAsxjnFdajN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "953a4f92-9eaf-402c-d3d8-aab9d710d341"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disease_state    Pre-FMT                                     healthy  \\\n",
              "sample_id     ERR1883214 ERR1883240 ERR1883250 ERR1883294 ERR1883195   \n",
              "name                                                                   \n",
              "D-glucose      -3.253101  -3.564477  -2.064038  -3.972573   4.450353   \n",
              "acetate         2.572101   3.594739   4.306373   4.539524  -2.642172   \n",
              "Water           2.966225  -3.584166   3.043172  -3.972573   4.497614   \n",
              "D-Fructose      3.219760  -3.584166  -0.695070  -3.972573   3.268810   \n",
              "pyruvate       -3.404889   3.505223  -3.497278   3.022735  -2.642172   \n",
              "...                  ...        ...        ...        ...        ...   \n",
              "Thiamin        -3.404889  -2.773089  -2.982110  -3.403379  -2.388275   \n",
              "Spermidine     -3.404889  -2.445104  -2.992604  -3.250518  -2.374584   \n",
              "Fe2+           -2.838896  -2.091063  -2.203376  -2.370595  -1.778976   \n",
              "Putrescine     -3.404889  -2.848543  -3.087577  -3.295817  -2.353273   \n",
              "Menaquinone 8  -2.873638  -3.295815  -2.979719  -3.351557  -2.276149   \n",
              "\n",
              "disease_state                                   \n",
              "sample_id     ERR1883207 ERR1883212 ERR1883225  \n",
              "name                                            \n",
              "D-glucose       4.493531   3.416515   4.569671  \n",
              "acetate        -2.772490  -3.668369  -2.625105  \n",
              "Water           4.280483   3.763363   4.354428  \n",
              "D-Fructose      3.253561   2.612354   3.276231  \n",
              "pyruvate       -2.772490  -3.668369  -2.625105  \n",
              "...                  ...        ...        ...  \n",
              "Thiamin        -2.444364  -2.939826  -2.378889  \n",
              "Spermidine     -2.445049  -2.936479  -2.376260  \n",
              "Fe2+           -1.820472  -2.611296  -1.668659  \n",
              "Putrescine     -2.438953  -2.912851  -2.400161  \n",
              "Menaquinone 8  -2.471199  -3.185775  -2.625105  \n",
              "\n",
              "[647 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-571da5d4-bc5a-4459-842f-bfea075ae9bb\" class=\"colab-df-container\">\n",
              "    <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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>disease_state</th>\n",
              "      <th colspan=\"4\" halign=\"left\">Pre-FMT</th>\n",
              "      <th colspan=\"4\" halign=\"left\">healthy</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sample_id</th>\n",
              "      <th>ERR1883214</th>\n",
              "      <th>ERR1883240</th>\n",
              "      <th>ERR1883250</th>\n",
              "      <th>ERR1883294</th>\n",
              "      <th>ERR1883195</th>\n",
              "      <th>ERR1883207</th>\n",
              "      <th>ERR1883212</th>\n",
              "      <th>ERR1883225</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>D-glucose</th>\n",
              "      <td>-3.253101</td>\n",
              "      <td>-3.564477</td>\n",
              "      <td>-2.064038</td>\n",
              "      <td>-3.972573</td>\n",
              "      <td>4.450353</td>\n",
              "      <td>4.493531</td>\n",
              "      <td>3.416515</td>\n",
              "      <td>4.569671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acetate</th>\n",
              "      <td>2.572101</td>\n",
              "      <td>3.594739</td>\n",
              "      <td>4.306373</td>\n",
              "      <td>4.539524</td>\n",
              "      <td>-2.642172</td>\n",
              "      <td>-2.772490</td>\n",
              "      <td>-3.668369</td>\n",
              "      <td>-2.625105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Water</th>\n",
              "      <td>2.966225</td>\n",
              "      <td>-3.584166</td>\n",
              "      <td>3.043172</td>\n",
              "      <td>-3.972573</td>\n",
              "      <td>4.497614</td>\n",
              "      <td>4.280483</td>\n",
              "      <td>3.763363</td>\n",
              "      <td>4.354428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D-Fructose</th>\n",
              "      <td>3.219760</td>\n",
              "      <td>-3.584166</td>\n",
              "      <td>-0.695070</td>\n",
              "      <td>-3.972573</td>\n",
              "      <td>3.268810</td>\n",
              "      <td>3.253561</td>\n",
              "      <td>2.612354</td>\n",
              "      <td>3.276231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pyruvate</th>\n",
              "      <td>-3.404889</td>\n",
              "      <td>3.505223</td>\n",
              "      <td>-3.497278</td>\n",
              "      <td>3.022735</td>\n",
              "      <td>-2.642172</td>\n",
              "      <td>-2.772490</td>\n",
              "      <td>-3.668369</td>\n",
              "      <td>-2.625105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thiamin</th>\n",
              "      <td>-3.404889</td>\n",
              "      <td>-2.773089</td>\n",
              "      <td>-2.982110</td>\n",
              "      <td>-3.403379</td>\n",
              "      <td>-2.388275</td>\n",
              "      <td>-2.444364</td>\n",
              "      <td>-2.939826</td>\n",
              "      <td>-2.378889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spermidine</th>\n",
              "      <td>-3.404889</td>\n",
              "      <td>-2.445104</td>\n",
              "      <td>-2.992604</td>\n",
              "      <td>-3.250518</td>\n",
              "      <td>-2.374584</td>\n",
              "      <td>-2.445049</td>\n",
              "      <td>-2.936479</td>\n",
              "      <td>-2.376260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fe2+</th>\n",
              "      <td>-2.838896</td>\n",
              "      <td>-2.091063</td>\n",
              "      <td>-2.203376</td>\n",
              "      <td>-2.370595</td>\n",
              "      <td>-1.778976</td>\n",
              "      <td>-1.820472</td>\n",
              "      <td>-2.611296</td>\n",
              "      <td>-1.668659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Putrescine</th>\n",
              "      <td>-3.404889</td>\n",
              "      <td>-2.848543</td>\n",
              "      <td>-3.087577</td>\n",
              "      <td>-3.295817</td>\n",
              "      <td>-2.353273</td>\n",
              "      <td>-2.438953</td>\n",
              "      <td>-2.912851</td>\n",
              "      <td>-2.400161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Menaquinone 8</th>\n",
              "      <td>-2.873638</td>\n",
              "      <td>-3.295815</td>\n",
              "      <td>-2.979719</td>\n",
              "      <td>-3.351557</td>\n",
              "      <td>-2.276149</td>\n",
              "      <td>-2.471199</td>\n",
              "      <td>-3.185775</td>\n",
              "      <td>-2.625105</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-571da5d4-bc5a-4459-842f-bfea075ae9bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-571da5d4-bc5a-4459-842f-bfea075ae9bb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-571da5d4-bc5a-4459-842f-bfea075ae9bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8991d7ae-99c1-40b6-b20d-c4126061c9e8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8991d7ae-99c1-40b6-b20d-c4126061c9e8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8991d7ae-99c1-40b6-b20d-c4126061c9e8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "from micom.workflows import load_results\n",
        "from micom.measures import consumption_rates\n",
        "import numpy as np\n",
        "no_cdiff_growth = load_results(\"treasure_chest/no-cdiff-growth.zip\")  # Load growth results with no C. diff invasion\n",
        "exchanges = consumption_rates(no_cdiff_growth)         # extract consumption rates\n",
        "exchanges=pd.merge(exchanges,metadata,on='sample_id')  # add metadata\n",
        "exchanges = pd.pivot_table(                            # convert to a matrix of samples vs. metabolites\n",
        "    exchanges,                                         # that contains the production rates\n",
        "    index = ['disease_state','sample_id'],\n",
        "    columns = 'name',\n",
        "    values = 'flux'\n",
        ")\n",
        "exchanges = exchanges.T.fillna(0.0)                    # if a metabolite is not produced its flux is zero\n",
        "exchanges = exchanges.apply(                           # ...and a CLR transform again, normalizes the fluxes\n",
        "    lambda xs: np.log(xs + 0.001) - np.log(xs.mean() + 0.001),\n",
        "    axis=0)\n",
        "exchanges = exchanges.reindex(                         # sort by variance, highest variance fluxes first\n",
        "    exchanges.var(axis = 1).sort_values(ascending=False).index\n",
        ")\n",
        "\n",
        "exchanges"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nYuc7Wu38nYd"
      },
      "source": [
        "We can use seaborn to plot our heatmap:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "YHnKeFuF3qAt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c610f0ac-d2cd-4043-956b-7ea6cbefe472"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.matrix.ClusterGrid at 0x7f6cfa4f4e20>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x1200 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "sns.clustermap(\n",
        "    exchanges.head(50),  # take 50 highest fluxes\n",
        "    cmap = 'viridis',\n",
        "    yticklabels = True,  # show all metabolite names\n",
        "    figsize = (8, 12)    # size of the heatmap\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1i2LStZiUst"
      },
      "source": [
        "We can see here that the disease context is important - there are apparent differences in consumption rates between the healthy and pre-fmt microbiomes. These differences may be why C. diff can exploit the pre-FMT microbiomes and achieve higher predicted growth rates."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8maOr3w2bOo"
      },
      "source": [
        "# 🏫 Exercises"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time for you to try your hand at some analysis, lets take a closer looks at the metabolic strategies used by C. diff"
      ],
      "metadata": {
        "id": "XZt3ojssnCzz"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VjUustQG2_bX"
      },
      "source": [
        "## Metabolic strategies used by C. diff\n",
        "We've alread looked at the community wide consumption fluxes in the absencence of C. diff and found that they differ between disease contexts. What about the import fluxes of C. diff specifically? Can you develop a visualization to look at those?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exchanges=growth.exchanges                                           # extract exchanges from growth data\n",
        "cdiff_exchanges=exchanges[exchanges.taxon=='Clostridioides']         # get cdiff specific exchanges\n",
        "cdiff_imports=cdiff_exchanges[cdiff_exchanges.direction=='import']   # specifically look at imports\n",
        "cdiff_imports=pd.merge(cdiff_imports,metadata,on='sample_id')        # add metadata\n",
        "cdiff_imports = pd.pivot_table(                                      # convert to a matrix of samples vs. metabolites\n",
        "    cdiff_imports,                                                   # that contains the production rates\n",
        "    index = ['disease_state','sample_id'],\n",
        "    columns = 'reaction',\n",
        "    values = 'flux'\n",
        ")\n",
        "cdiff_imports = abs(cdiff_imports.T.fillna(0.0))                     # fill nans with 0s\n",
        "\n",
        "annot=growth.annotations                                             # optionally map reactions to metabolite names\n",
        "annot.index=annot.reaction\n",
        "cdiff_imports.index=cdiff_imports.index.map(annot.name.to_dict())    # not necessary but makes results more human readible\n",
        "\n",
        "cdiff_imports = cdiff_imports.apply(                                 # ...and a CLR transform again, normalizes the fluxes\n",
        "    lambda xs: np.log(xs + 0.001) - np.log(xs.mean() + 0.001),\n",
        "    axis=0)\n",
        "cdiff_imports = cdiff_imports.reindex(                               # sort by variance, highest variance fluxes first\n",
        "    cdiff_imports.var(axis = 1).sort_values(ascending=False).index\n",
        ")\n",
        "\n",
        "cdiff_imports"
      ],
      "metadata": {
        "id": "zrUZSSp62uUt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "151638da-065f-479a-86cc-32642d3401e0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disease_state            Pre-FMT                                     healthy  \\\n",
              "sample_id             ERR1883214 ERR1883240 ERR1883250 ERR1883294 ERR1883195   \n",
              "reaction                                                                       \n",
              "D-Mannitol             -5.808786  -4.296840  -3.630109  -3.920776   3.634470   \n",
              "D-glucose              -5.808786  -4.296840  -3.630109  -3.920776   3.304489   \n",
              "D-Glucosamine          -5.808786  -4.296840  -3.630109   2.271824   2.377617   \n",
              "(R)-3-hydroxybutyrate  -5.808786   3.558166  -2.120361   3.868590  -2.667913   \n",
              "L-glutamate(1-)        -5.808786   3.604962   3.894522   3.513705   0.794090   \n",
              "...                          ...        ...        ...        ...        ...   \n",
              "L-tryptophan           -3.637679  -1.236494  -1.357196  -0.986176  -1.000820   \n",
              "Spermidine             -5.215470  -3.544686  -3.108385  -3.095548  -2.396104   \n",
              "(R)-Pantothenate       -5.032868  -4.296840  -3.630109  -3.920776  -2.667913   \n",
              "L-valine                1.067311   0.591860   0.434674   0.848274   0.704993   \n",
              "Hypoxanthine           -0.248423  -0.153093  -0.392121   0.157093   0.070047   \n",
              "\n",
              "disease_state                                           \n",
              "sample_id             ERR1883207 ERR1883212 ERR1883225  \n",
              "reaction                                                \n",
              "D-Mannitol              3.610574   3.142424   3.379778  \n",
              "D-glucose               3.192248   2.785788   3.034983  \n",
              "D-Glucosamine           2.498763   3.095175   1.521825  \n",
              "(R)-3-hydroxybutyrate  -3.225646   1.879563  -2.827799  \n",
              "L-glutamate(1-)         1.163559   2.796392  -2.827799  \n",
              "...                          ...        ...        ...  \n",
              "L-tryptophan           -1.042780  -0.960229  -1.323975  \n",
              "Spermidine             -2.783785  -2.736675  -2.593484  \n",
              "(R)-Pantothenate       -3.225646  -3.226607  -2.827799  \n",
              "L-valine                0.740112   0.827624   0.343975  \n",
              "Hypoxanthine           -0.057532   0.028589  -0.422760  \n",
              "\n",
              "[269 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ee59d08-99d3-457d-a0d6-0c9bd473832c\" class=\"colab-df-container\">\n",
              "    <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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>disease_state</th>\n",
              "      <th colspan=\"4\" halign=\"left\">Pre-FMT</th>\n",
              "      <th colspan=\"4\" halign=\"left\">healthy</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sample_id</th>\n",
              "      <th>ERR1883214</th>\n",
              "      <th>ERR1883240</th>\n",
              "      <th>ERR1883250</th>\n",
              "      <th>ERR1883294</th>\n",
              "      <th>ERR1883195</th>\n",
              "      <th>ERR1883207</th>\n",
              "      <th>ERR1883212</th>\n",
              "      <th>ERR1883225</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reaction</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>D-Mannitol</th>\n",
              "      <td>-5.808786</td>\n",
              "      <td>-4.296840</td>\n",
              "      <td>-3.630109</td>\n",
              "      <td>-3.920776</td>\n",
              "      <td>3.634470</td>\n",
              "      <td>3.610574</td>\n",
              "      <td>3.142424</td>\n",
              "      <td>3.379778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D-glucose</th>\n",
              "      <td>-5.808786</td>\n",
              "      <td>-4.296840</td>\n",
              "      <td>-3.630109</td>\n",
              "      <td>-3.920776</td>\n",
              "      <td>3.304489</td>\n",
              "      <td>3.192248</td>\n",
              "      <td>2.785788</td>\n",
              "      <td>3.034983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D-Glucosamine</th>\n",
              "      <td>-5.808786</td>\n",
              "      <td>-4.296840</td>\n",
              "      <td>-3.630109</td>\n",
              "      <td>2.271824</td>\n",
              "      <td>2.377617</td>\n",
              "      <td>2.498763</td>\n",
              "      <td>3.095175</td>\n",
              "      <td>1.521825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(R)-3-hydroxybutyrate</th>\n",
              "      <td>-5.808786</td>\n",
              "      <td>3.558166</td>\n",
              "      <td>-2.120361</td>\n",
              "      <td>3.868590</td>\n",
              "      <td>-2.667913</td>\n",
              "      <td>-3.225646</td>\n",
              "      <td>1.879563</td>\n",
              "      <td>-2.827799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L-glutamate(1-)</th>\n",
              "      <td>-5.808786</td>\n",
              "      <td>3.604962</td>\n",
              "      <td>3.894522</td>\n",
              "      <td>3.513705</td>\n",
              "      <td>0.794090</td>\n",
              "      <td>1.163559</td>\n",
              "      <td>2.796392</td>\n",
              "      <td>-2.827799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L-tryptophan</th>\n",
              "      <td>-3.637679</td>\n",
              "      <td>-1.236494</td>\n",
              "      <td>-1.357196</td>\n",
              "      <td>-0.986176</td>\n",
              "      <td>-1.000820</td>\n",
              "      <td>-1.042780</td>\n",
              "      <td>-0.960229</td>\n",
              "      <td>-1.323975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spermidine</th>\n",
              "      <td>-5.215470</td>\n",
              "      <td>-3.544686</td>\n",
              "      <td>-3.108385</td>\n",
              "      <td>-3.095548</td>\n",
              "      <td>-2.396104</td>\n",
              "      <td>-2.783785</td>\n",
              "      <td>-2.736675</td>\n",
              "      <td>-2.593484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(R)-Pantothenate</th>\n",
              "      <td>-5.032868</td>\n",
              "      <td>-4.296840</td>\n",
              "      <td>-3.630109</td>\n",
              "      <td>-3.920776</td>\n",
              "      <td>-2.667913</td>\n",
              "      <td>-3.225646</td>\n",
              "      <td>-3.226607</td>\n",
              "      <td>-2.827799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L-valine</th>\n",
              "      <td>1.067311</td>\n",
              "      <td>0.591860</td>\n",
              "      <td>0.434674</td>\n",
              "      <td>0.848274</td>\n",
              "      <td>0.704993</td>\n",
              "      <td>0.740112</td>\n",
              "      <td>0.827624</td>\n",
              "      <td>0.343975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hypoxanthine</th>\n",
              "      <td>-0.248423</td>\n",
              "      <td>-0.153093</td>\n",
              "      <td>-0.392121</td>\n",
              "      <td>0.157093</td>\n",
              "      <td>0.070047</td>\n",
              "      <td>-0.057532</td>\n",
              "      <td>0.028589</td>\n",
              "      <td>-0.422760</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>269 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ee59d08-99d3-457d-a0d6-0c9bd473832c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9ee59d08-99d3-457d-a0d6-0c9bd473832c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9ee59d08-99d3-457d-a0d6-0c9bd473832c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6cf99301-dcf4-4f3c-9876-244e3c877598\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6cf99301-dcf4-4f3c-9876-244e3c877598')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6cf99301-dcf4-4f3c-9876-244e3c877598 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.clustermap(\n",
        "    cdiff_imports.head(50),  # take 50 highest fluxes\n",
        "    cmap = 'viridis',\n",
        "    yticklabels = True,  # show all metabolite names\n",
        "    figsize = (8, 12)    # size of the heatmap\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1gB6X3DJanii",
        "outputId": "a7ab5aba-2946-4a19-a7e1-40872925d159"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.matrix.ClusterGrid at 0x7f6cfa5e2ef0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x1200 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CsqIRTbC7doD"
      },
      "source": [
        "# 🔵 Addendum\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hycoXNTi5xsH"
      },
      "source": [
        "## Choosing a tradeoff value\n",
        "\n",
        "Even if you don't have growth rates available you can still use your data to choose a decent tradeoff value. This can be done by choosing the largest tradeoff value that still allows growth for the majority of the taxa that you observed in the sample (if they are present at an appreciable abundance, they should be able to grow). This can be done with the `tradeoff` workflow in MICOM that will run cooperative tradeoff with varying tradeoff values, which can be visualized with the `plot_tradeoff` function."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "manifest"
      ],
      "metadata": {
        "id": "I8NnkbnymxoE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8_1jesZTHYra"
      },
      "outputs": [],
      "source": [
        "from micom.workflows import tradeoff\n",
        "import micom\n",
        "\n",
        "tradeoff_results = tradeoff(manifest, \"models\", medium, threads=2)\n",
        "tradeoff_results.to_csv(\"tradeoff.csv\", index=False)\n",
        "\n",
        "plot_tradeoff(tradeoff_results, tolerance=1e-4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y9703vhK6d6c"
      },
      "source": [
        "After opeing `tradeoff_[DATE].html` you will see that, for our example here, all tradeoff values work great. This is because we modeled very few taxa, which keeps the compettion down. If you would allow for fewer abundant taxa in the models, this would change drastically. For instance, here is an example from a colorectal cancer data set:\n",
        "\n",
        "[![tradeoff example](https://micom-dev.github.io/micom/_images/tradeoff.png)](https://micom-dev.github.io/micom/_static/tradeoff.html)\n",
        "\n",
        "You can see how not using the cooperative tradeoff would give you nonsense results where only 10% of all observed taxa grew. A tradeoff value of 0.6-0.8 would probably be a good choice for this particular data set."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "vscode": {
      "interpreter": {
        "hash": "c991a7ed881363492957ff225bb30af9d5174cd8515a21cbef71fcaa303e4050"
      }
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "8c626fefb268415cb164591c6473a707": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_553ece32a42b47f4a6b2be9d8016c034",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "Running \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:02:41\u001b[0m\n",
                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Running <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:41</span>\n</pre>\n"
                },
                "metadata": {}
              }
            ]
          }
        },
        "553ece32a42b47f4a6b2be9d8016c034": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d40d5e3b7f5140db98f2ac398ccbd8a2": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_034569ffebbe4ca08ff1181db1442375",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "Running \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:04:44\u001b[0m\n",
                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Running <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:04:44</span>\n</pre>\n"
                },
                "metadata": {}
              }
            ]
          }
        },
        "034569ffebbe4ca08ff1181db1442375": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}