03_2020-023_avrg_abundances.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a673bbb",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'skbio'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mskbio\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OrdinationResults\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ranksums, kruskal\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'skbio'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from skbio import OrdinationResults\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ranksums, kruskal\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa72e9fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ranksums, kruskal\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bad135de",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2160192",
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiime2\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "58aa4c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiime2 import Artifact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aace40fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load meta\n",
    "meta_df= pd.read_csv('./results/meta_df.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47181bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load genus tables\n",
    "#PSC Seren\n",
    "PSC_genus_seren = pd.read_csv('./results/PSC_genus_seren.csv', index_col=0)\n",
    "\n",
    "#PSC Stool\n",
    "PSC_genus_stool = pd.read_csv('./results/PSC_genus_stool.csv', index_col=0)\n",
    "\n",
    "\n",
    "#BSPSPC Seren\n",
    "BSPSPC_genus_seren = pd.read_csv('./results/BSPSPC_genus_seren.csv', index_col=0)\n",
    "\n",
    "\n",
    "#BSPSPC Stool\n",
    "BSPSPC_genus_stool = pd.read_csv('./results/BSPSPC_genus_stool.csv', index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7018bd45",
   "metadata": {},
   "outputs": [],
   "source": [
    "PSC_genus_seren.index = PSC_genus_seren.index + \"_srn\"\n",
    "PSC_genus_stool.index = PSC_genus_stool.index + \"_stl\"\n",
    "\n",
    "BSPSPC_genus_seren.index = BSPSPC_genus_seren.index + \"_srn\"\n",
    "BSPSPC_genus_stool.index = BSPSPC_genus_stool.index + \"_stl\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c754d7d",
   "metadata": {},
   "source": [
    "# Merging genus tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7919aa",
   "metadata": {},
   "source": [
    "## creating biom tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2725afdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "PSC_genus_seren = PSC_genus_seren.drop(columns = ['phase'], axis=1)\n",
    "PSC_genus_stool= PSC_genus_stool.drop(columns = ['phase'], axis=1)\n",
    "\n",
    "BSPSPC_genus_seren= BSPSPC_genus_seren.drop(columns = ['phase'], axis=1)\n",
    "BSPSPC_genus_stool= BSPSPC_genus_stool.drop(columns = ['phase'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c3626ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create biom objects\n",
    "biom_PSC_genus_seren = qiime2.Artifact.import_data('FeatureTable[Frequency]', PSC_genus_seren)\n",
    "biom_PSC_genus_stool = qiime2.Artifact.import_data('FeatureTable[Frequency]', PSC_genus_stool)\n",
    "\n",
    "biom_BSPSPC_genus_seren = qiime2.Artifact.import_data('FeatureTable[Frequency]', BSPSPC_genus_seren)\n",
    "biom_BSPSPC_genus_stool = qiime2.Artifact.import_data('FeatureTable[Frequency]', BSPSPC_genus_stool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56087877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./results/biom_BSPSPC_genus_stool.qza'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save bioms as qza\n",
    "biom_PSC_genus_seren.save('./results/biom_PSC_genus_seren.qza')\n",
    "biom_PSC_genus_stool.save('./results/biom_PSC_genus_stool.qza')\n",
    "\n",
    "biom_BSPSPC_genus_seren.save('./results/biom_BSPSPC_genus_seren.qza')\n",
    "biom_BSPSPC_genus_stool.save('./results/biom_BSPSPC_genus_stool.qza')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3c48ef1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "PSC_genus_seren = PSC_genus_seren.T.rename_axis('#OTUID')\n",
    "PSC_genus_stool = PSC_genus_stool.T.rename_axis('#OTUID')\n",
    "BSPSPC_genus_seren = BSPSPC_genus_seren.T.rename_axis('#OTUID')\n",
    "BSPSPC_genus_stool = BSPSPC_genus_stool.T.rename_axis('#OTUID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "74692e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define file paths for saving the text files\n",
    "file_paths = ['./results/PSC_genus_seren.txt', './results/PSC_genus_stool.txt', './results/BSPSPC_genus_seren.txt', './results/BSPSPC_genus_stool.txt']\n",
    "\n",
    "# Save each DataFrame as a text file\n",
    "for df, file_path in zip([PSC_genus_seren, PSC_genus_stool, BSPSPC_genus_seren, BSPSPC_genus_stool], file_paths):\n",
    "    df.to_csv(file_path, sep='\\t', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0245a552",
   "metadata": {},
   "outputs": [],
   "source": [
    "!biom convert -i ./results/PSC_genus_seren.txt -o ./results/PSC_genus_seren.biom --table-type=\"OTU table\" --to-hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e8e2199a",
   "metadata": {},
   "outputs": [],
   "source": [
    "!biom convert -i ./results/PSC_genus_stool.txt -o ./results/PSC_genus_stool.biom --table-type=\"OTU table\" --to-hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f447666a",
   "metadata": {},
   "outputs": [],
   "source": [
    "!biom convert -i ./results/BSPSPC_genus_seren.txt -o ./results/BSPSPC_genus_seren.biom --table-type=\"OTU table\" --to-hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "74578809",
   "metadata": {},
   "outputs": [],
   "source": [
    "!biom convert -i ./results/BSPSPC_genus_stool.txt -o ./results/BSPSPC_genus_stool.biom --table-type=\"OTU table\" --to-hdf5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689b03fe",
   "metadata": {},
   "source": [
    "## transform taxonomy files for biom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fafda7d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!biom add-metadata -i ./results/PSC_genus_seren.biom -o ./results/PSC_genus_seren_taxonomy.biom --observation-metadata-fp ./results/PSC_tax_seren.tsv --sc-separated taxonomy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9d1fc007",
   "metadata": {},
   "outputs": [],
   "source": [
    "!biom add-metadata -i ./results/PSC_genus_stool.biom -o ./results/PSC_genus_stool_taxonomy.biom --observation-metadata-fp ./results/PSC_tax_stool.tsv --sc-separated taxonomy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "820510a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "!biom add-metadata -i ./results/BSPSPC_genus_seren.biom -o ./results/BSPSPC_genus_seren_taxonomy.biom --observation-metadata-fp ./results/BSPSPC_tax_seren.tsv --sc-separated taxonomy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d18b8bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "!biom add-metadata -i ./results/BSPSPC_genus_stool.biom -o ./results/BSPSPC_genus_stool_taxonomy.biom --observation-metadata-fp ./results/BSPSPC_tax_stool.tsv --sc-separated taxonomy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bebd061",
   "metadata": {},
   "source": [
    "## preparing .qza files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2bf16359",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mImported ./results/PSC_genus_seren_taxonomy.biom as BIOMV210Format to ./results/PSC_genus_seren_taxonomy.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!qiime tools import \\\n",
    "  --input-path ./results/PSC_genus_seren_taxonomy.biom \\\n",
    "  --type 'FeatureTable[Frequency]' \\\n",
    "  --input-format BIOMV210Format \\\n",
    "  --output-path ./results/PSC_genus_seren_taxonomy.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c69d3bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mImported ./results/PSC_genus_stool_taxonomy.biom as BIOMV210Format to ./results/PSC_genus_stool_taxonomy.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!qiime tools import \\\n",
    "  --input-path ./results/PSC_genus_stool_taxonomy.biom \\\n",
    "  --type 'FeatureTable[Frequency]' \\\n",
    "  --input-format BIOMV210Format \\\n",
    "  --output-path ./results/PSC_genus_stool_taxonomy.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c3cb23f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mImported ./results/BSPSPC_genus_seren_taxonomy.biom as BIOMV210Format to ./results/BSPSPC_genus_seren_taxonomy.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!qiime tools import \\\n",
    "  --input-path ./results/BSPSPC_genus_seren_taxonomy.biom \\\n",
    "  --type 'FeatureTable[Frequency]' \\\n",
    "  --input-format BIOMV210Format \\\n",
    "  --output-path ./results/BSPSPC_genus_seren_taxonomy.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bb7050ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mImported ./results/BSPSPC_genus_stool_taxonomy.biom as BIOMV210Format to ./results/BSPSPC_genus_stool_taxonomy.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!qiime tools import \\\n",
    "  --input-path ./results/BSPSPC_genus_stool_taxonomy.biom \\\n",
    "  --type 'FeatureTable[Frequency]' \\\n",
    "  --input-format BIOMV210Format \\\n",
    "  --output-path ./results/BSPSPC_genus_stool_taxonomy.qza"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905eedda",
   "metadata": {},
   "source": [
    "# Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "12b25fc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: ./results/norm_BSPSPC_genus_seren_taxonomy.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# Normalization\n",
    "!qiime feature-table rarefy \\\n",
    "  --i-table ./results/BSPSPC_genus_seren_taxonomy.qza \\\n",
    "  --p-sampling-depth 5000 \\\n",
    "  --o-rarefied-table ./results/norm_BSPSPC_genus_seren_taxonomy.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1ffe315c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: ./results/norm_BSPSPC_genus_stool_taxonomy.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# Normalization\n",
    "!qiime feature-table rarefy \\\n",
    "  --i-table ./results/BSPSPC_genus_stool_taxonomy.qza \\\n",
    "  --p-sampling-depth 10000 \\\n",
    "  --o-rarefied-table ./results/norm_BSPSPC_genus_stool_taxonomy.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "db28fdd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: ./results/norm_PSC_genus_seren_taxonomy.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# Normalization\n",
    "!qiime feature-table rarefy \\\n",
    "  --i-table ./results/PSC_genus_seren_taxonomy.qza \\\n",
    "  --p-sampling-depth 2500 \\\n",
    "  --o-rarefied-table ./results/norm_PSC_genus_seren_taxonomy.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c020ec30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: ./results/norm_PSC_genus_stool_taxonomy.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# Normalization\n",
    "!qiime feature-table rarefy \\\n",
    "  --i-table ./results/PSC_genus_stool_taxonomy.qza \\\n",
    "  --p-sampling-depth 10000 \\\n",
    "  --o-rarefied-table ./results/norm_PSC_genus_stool_taxonomy.qza"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91fbd21",
   "metadata": {},
   "source": [
    "# merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "70d591c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: ./results/norm_merged_genus_abundances.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!qiime feature-table merge \\\n",
    "  --i-tables ./results/norm_PSC_genus_seren_taxonomy.qza \\\n",
    "  --i-tables ./results/norm_PSC_genus_stool_taxonomy.qza \\\n",
    "  --i-tables ./results/norm_BSPSPC_genus_seren_taxonomy.qza \\\n",
    "  --i-tables ./results/norm_BSPSPC_genus_stool_taxonomy.qza \\\n",
    "  --o-merged-table ./results/norm_merged_genus_abundances.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5b84385",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load rarefied ab table from the Qiime artifact\n",
    "ab = Artifact.load('./results/norm_merged_genus_abundances.qza')\n",
    "\n",
    "# Convert the PCoA results to a pandas DataFrame\n",
    "ab = ab.view(pd.DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52b5eb92",
   "metadata": {},
   "outputs": [],
   "source": [
    "ab.to_csv('./results/norm_merged_genus_abundances.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "66778626",
   "metadata": {},
   "outputs": [],
   "source": [
    "ab = pd.read_csv('./results/norm_merged_genus_abundances.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "03b2b2b3",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>C__Acidobacteria_Gp4</th>\n",
       "      <th>C__Actinobacteria</th>\n",
       "      <th>C__Alphaproteobacteria</th>\n",
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       "      <th>C__Clostridia</th>\n",
       "      <th>...</th>\n",
       "      <th>P__Cyanobacteria.Chloroplast</th>\n",
       "      <th>P__Firmicutes</th>\n",
       "      <th>P__Latescibacteria</th>\n",
       "      <th>P__Microgenomates</th>\n",
       "      <th>P__Parcubacteria</th>\n",
       "      <th>P__Planctomycetes</th>\n",
       "      <th>P__Proteobacteria</th>\n",
       "      <th>P__SR1</th>\n",
       "      <th>P__Verrucomicrobia</th>\n",
       "      <th>Unassigned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0001_srn</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0006_srn</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0007_srn</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0010_srn</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0015_srn</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0189_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0409_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0515_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0726_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0741_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 814 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       C__Acidobacteria_Gp4  C__Actinobacteria  \\\n",
       "2020-023_PSC_0001_srn                   0.0               36.0   \n",
       "2020-023_PSC_0006_srn                   0.0                0.0   \n",
       "2020-023_PSC_0007_srn                   0.0                0.0   \n",
       "2020-023_PSC_0010_srn                   0.0               26.0   \n",
       "2020-023_PSC_0015_srn                   0.0                0.0   \n",
       "...                                     ...                ...   \n",
       "2020-023_SPC_0189_stl                   0.0                0.0   \n",
       "2020-023_SPC_0409_stl                   0.0                0.0   \n",
       "2020-023_SPC_0515_stl                   0.0                0.0   \n",
       "2020-023_SPC_0726_stl                   0.0                0.0   \n",
       "2020-023_SPC_0741_stl                   0.0                0.0   \n",
       "\n",
       "                       C__Alphaproteobacteria  C__Armatimonadetes_gp5  \\\n",
       "2020-023_PSC_0001_srn                   134.0                     0.0   \n",
       "2020-023_PSC_0006_srn                     8.0                     0.0   \n",
       "2020-023_PSC_0007_srn                    17.0                     0.0   \n",
       "2020-023_PSC_0010_srn                     0.0                     0.0   \n",
       "2020-023_PSC_0015_srn                     4.0                     0.0   \n",
       "...                                       ...                     ...   \n",
       "2020-023_SPC_0189_stl                    44.0                     0.0   \n",
       "2020-023_SPC_0409_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0515_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0726_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0741_stl                     0.0                     0.0   \n",
       "\n",
       "                       C__Bacilli  C__Betaproteobacteria  \\\n",
       "2020-023_PSC_0001_srn         0.0                    0.0   \n",
       "2020-023_PSC_0006_srn         0.0                    0.0   \n",
       "2020-023_PSC_0007_srn         0.0                    0.0   \n",
       "2020-023_PSC_0010_srn         0.0                    0.0   \n",
       "2020-023_PSC_0015_srn         0.0                   10.0   \n",
       "...                           ...                    ...   \n",
       "2020-023_SPC_0189_stl         0.0                    0.0   \n",
       "2020-023_SPC_0409_stl         0.0                    0.0   \n",
       "2020-023_SPC_0515_stl         0.0                    0.0   \n",
       "2020-023_SPC_0726_stl         0.0                    0.0   \n",
       "2020-023_SPC_0741_stl         0.0                    0.0   \n",
       "\n",
       "                       C__Candidatus_Cloacamonas  C__Candidatus_Hydrogenedens  \\\n",
       "2020-023_PSC_0001_srn                        0.0                          0.0   \n",
       "2020-023_PSC_0006_srn                        0.0                          0.0   \n",
       "2020-023_PSC_0007_srn                        0.0                          0.0   \n",
       "2020-023_PSC_0010_srn                        0.0                          0.0   \n",
       "2020-023_PSC_0015_srn                        0.0                          0.0   \n",
       "...                                          ...                          ...   \n",
       "2020-023_SPC_0189_stl                        0.0                          0.0   \n",
       "2020-023_SPC_0409_stl                        0.0                          0.0   \n",
       "2020-023_SPC_0515_stl                        0.0                          0.0   \n",
       "2020-023_SPC_0726_stl                        0.0                          0.0   \n",
       "2020-023_SPC_0741_stl                        0.0                          0.0   \n",
       "\n",
       "                       C__Chloroflexia  C__Clostridia  ...  \\\n",
       "2020-023_PSC_0001_srn              0.0            0.0  ...   \n",
       "2020-023_PSC_0006_srn              0.0            0.0  ...   \n",
       "2020-023_PSC_0007_srn              0.0            0.0  ...   \n",
       "2020-023_PSC_0010_srn              0.0            0.0  ...   \n",
       "2020-023_PSC_0015_srn              0.0            0.0  ...   \n",
       "...                                ...            ...  ...   \n",
       "2020-023_SPC_0189_stl              0.0            0.0  ...   \n",
       "2020-023_SPC_0409_stl              0.0            0.0  ...   \n",
       "2020-023_SPC_0515_stl              0.0           62.0  ...   \n",
       "2020-023_SPC_0726_stl              0.0            0.0  ...   \n",
       "2020-023_SPC_0741_stl              0.0            0.0  ...   \n",
       "\n",
       "                       P__Cyanobacteria.Chloroplast  P__Firmicutes  \\\n",
       "2020-023_PSC_0001_srn                           0.0            0.0   \n",
       "2020-023_PSC_0006_srn                           0.0            0.0   \n",
       "2020-023_PSC_0007_srn                           0.0            0.0   \n",
       "2020-023_PSC_0010_srn                           0.0            0.0   \n",
       "2020-023_PSC_0015_srn                           0.0            0.0   \n",
       "...                                             ...            ...   \n",
       "2020-023_SPC_0189_stl                           0.0           15.0   \n",
       "2020-023_SPC_0409_stl                           0.0          375.0   \n",
       "2020-023_SPC_0515_stl                           0.0           22.0   \n",
       "2020-023_SPC_0726_stl                           0.0           59.0   \n",
       "2020-023_SPC_0741_stl                           0.0          422.0   \n",
       "\n",
       "                       P__Latescibacteria  P__Microgenomates  \\\n",
       "2020-023_PSC_0001_srn                 0.0                0.0   \n",
       "2020-023_PSC_0006_srn                 0.0                0.0   \n",
       "2020-023_PSC_0007_srn                 0.0                0.0   \n",
       "2020-023_PSC_0010_srn                 0.0                0.0   \n",
       "2020-023_PSC_0015_srn                 0.0                0.0   \n",
       "...                                   ...                ...   \n",
       "2020-023_SPC_0189_stl                 0.0                0.0   \n",
       "2020-023_SPC_0409_stl                 0.0                0.0   \n",
       "2020-023_SPC_0515_stl                 0.0                0.0   \n",
       "2020-023_SPC_0726_stl                 0.0                0.0   \n",
       "2020-023_SPC_0741_stl                 0.0                0.0   \n",
       "\n",
       "                       P__Parcubacteria  P__Planctomycetes  P__Proteobacteria  \\\n",
       "2020-023_PSC_0001_srn              23.0                0.0               74.0   \n",
       "2020-023_PSC_0006_srn              78.0                0.0               27.0   \n",
       "2020-023_PSC_0007_srn              15.0                2.0               10.0   \n",
       "2020-023_PSC_0010_srn              22.0                0.0                0.0   \n",
       "2020-023_PSC_0015_srn              90.0                2.0               29.0   \n",
       "...                                 ...                ...                ...   \n",
       "2020-023_SPC_0189_stl               0.0                0.0               33.0   \n",
       "2020-023_SPC_0409_stl               0.0                0.0                3.0   \n",
       "2020-023_SPC_0515_stl               0.0                0.0               84.0   \n",
       "2020-023_SPC_0726_stl               0.0                0.0                0.0   \n",
       "2020-023_SPC_0741_stl               0.0                0.0                0.0   \n",
       "\n",
       "                       P__SR1  P__Verrucomicrobia  Unassigned  \n",
       "2020-023_PSC_0001_srn     0.0                35.0         0.0  \n",
       "2020-023_PSC_0006_srn     0.0                 0.0        11.0  \n",
       "2020-023_PSC_0007_srn     0.0                 0.0         2.0  \n",
       "2020-023_PSC_0010_srn     0.0                 0.0         0.0  \n",
       "2020-023_PSC_0015_srn     0.0                 0.0        20.0  \n",
       "...                       ...                 ...         ...  \n",
       "2020-023_SPC_0189_stl     0.0                 0.0         0.0  \n",
       "2020-023_SPC_0409_stl     0.0                 0.0         0.0  \n",
       "2020-023_SPC_0515_stl     0.0                 0.0         0.0  \n",
       "2020-023_SPC_0726_stl     0.0                 0.0         0.0  \n",
       "2020-023_SPC_0741_stl     0.0                 0.0         0.0  \n",
       "\n",
       "[721 rows x 814 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65e57c0",
   "metadata": {},
   "source": [
    "# Filtration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "16bcadf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Low count filter: \tminimum count 4 in 20% of samples\n",
    "# Calculate the count threshold for the low count filter\n",
    "min_count_threshold = 4\n",
    "min_samples_threshold = len(ab) * 0.1\n",
    "\n",
    "# Select taxa with count >= min_count_threshold in at least min_samples_threshold samples\n",
    "ab = ab.loc[:, (ab >= min_count_threshold).sum(axis=0) >= min_samples_threshold]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "978ab01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C__Alphaproteobacteria</th>\n",
       "      <th>C__Deltaproteobacteria</th>\n",
       "      <th>C__Gammaproteobacteria</th>\n",
       "      <th>F__Bacillariophyta</th>\n",
       "      <th>F__Clostridiales_Incertae_Sedis_XII</th>\n",
       "      <th>F__Coriobacteriaceae</th>\n",
       "      <th>F__Cryptomonadaceae</th>\n",
       "      <th>F__Enterobacteriaceae</th>\n",
       "      <th>F__Erysipelotrichaceae</th>\n",
       "      <th>F__Lachnospiraceae</th>\n",
       "      <th>...</th>\n",
       "      <th>K__Bacteria</th>\n",
       "      <th>O__Actinomycetales</th>\n",
       "      <th>O__Bacteroidales</th>\n",
       "      <th>O__Burkholderiales</th>\n",
       "      <th>O__Clostridiales</th>\n",
       "      <th>P__Bacteroidetes</th>\n",
       "      <th>P__Candidatus_Saccharibacteria</th>\n",
       "      <th>P__Firmicutes</th>\n",
       "      <th>P__Parcubacteria</th>\n",
       "      <th>P__Proteobacteria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0001_srn</th>\n",
       "      <td>134.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>339.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0006_srn</th>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>...</td>\n",
       "      <td>292.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0007_srn</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>129.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0010_srn</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>362.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0015_srn</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>126.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>29.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0189_stl</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0409_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0515_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0726_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0741_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1255.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       C__Alphaproteobacteria  C__Deltaproteobacteria  \\\n",
       "2020-023_PSC_0001_srn                   134.0                    19.0   \n",
       "2020-023_PSC_0006_srn                     8.0                    15.0   \n",
       "2020-023_PSC_0007_srn                    17.0                     0.0   \n",
       "2020-023_PSC_0010_srn                     0.0                     0.0   \n",
       "2020-023_PSC_0015_srn                     4.0                     0.0   \n",
       "...                                       ...                     ...   \n",
       "2020-023_SPC_0189_stl                    44.0                     0.0   \n",
       "2020-023_SPC_0409_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0515_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0726_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0741_stl                     0.0                     0.0   \n",
       "\n",
       "                       C__Gammaproteobacteria  F__Bacillariophyta  \\\n",
       "2020-023_PSC_0001_srn                    34.0                 9.0   \n",
       "2020-023_PSC_0006_srn                    53.0                 0.0   \n",
       "2020-023_PSC_0007_srn                     5.0                10.0   \n",
       "2020-023_PSC_0010_srn                     0.0                53.0   \n",
       "2020-023_PSC_0015_srn                     9.0                18.0   \n",
       "...                                       ...                 ...   \n",
       "2020-023_SPC_0189_stl                     0.0                 0.0   \n",
       "2020-023_SPC_0409_stl                     0.0                 0.0   \n",
       "2020-023_SPC_0515_stl                     0.0                 0.0   \n",
       "2020-023_SPC_0726_stl                     0.0                 0.0   \n",
       "2020-023_SPC_0741_stl                     0.0                 0.0   \n",
       "\n",
       "                       F__Clostridiales_Incertae_Sedis_XII  \\\n",
       "2020-023_PSC_0001_srn                                  0.0   \n",
       "2020-023_PSC_0006_srn                                  0.0   \n",
       "2020-023_PSC_0007_srn                                  0.0   \n",
       "2020-023_PSC_0010_srn                                  0.0   \n",
       "2020-023_PSC_0015_srn                                  0.0   \n",
       "...                                                    ...   \n",
       "2020-023_SPC_0189_stl                                  0.0   \n",
       "2020-023_SPC_0409_stl                                  0.0   \n",
       "2020-023_SPC_0515_stl                                  0.0   \n",
       "2020-023_SPC_0726_stl                                  0.0   \n",
       "2020-023_SPC_0741_stl                                  9.0   \n",
       "\n",
       "                       F__Coriobacteriaceae  F__Cryptomonadaceae  \\\n",
       "2020-023_PSC_0001_srn                   0.0                  0.0   \n",
       "2020-023_PSC_0006_srn                   0.0                 27.0   \n",
       "2020-023_PSC_0007_srn                   0.0                  8.0   \n",
       "2020-023_PSC_0010_srn                   0.0                105.0   \n",
       "2020-023_PSC_0015_srn                   0.0                  0.0   \n",
       "...                                     ...                  ...   \n",
       "2020-023_SPC_0189_stl                  37.0                  0.0   \n",
       "2020-023_SPC_0409_stl                  17.0                  0.0   \n",
       "2020-023_SPC_0515_stl                  16.0                  0.0   \n",
       "2020-023_SPC_0726_stl                   2.0                  0.0   \n",
       "2020-023_SPC_0741_stl                  11.0                  0.0   \n",
       "\n",
       "                       F__Enterobacteriaceae  F__Erysipelotrichaceae  \\\n",
       "2020-023_PSC_0001_srn                    0.0                     0.0   \n",
       "2020-023_PSC_0006_srn                    0.0                     0.0   \n",
       "2020-023_PSC_0007_srn                    7.0                     0.0   \n",
       "2020-023_PSC_0010_srn                    0.0                     0.0   \n",
       "2020-023_PSC_0015_srn                    8.0                     0.0   \n",
       "...                                      ...                     ...   \n",
       "2020-023_SPC_0189_stl                    0.0                     2.0   \n",
       "2020-023_SPC_0409_stl                    0.0                     8.0   \n",
       "2020-023_SPC_0515_stl                   56.0                    55.0   \n",
       "2020-023_SPC_0726_stl                    0.0                    15.0   \n",
       "2020-023_SPC_0741_stl                    0.0                    14.0   \n",
       "\n",
       "                       F__Lachnospiraceae  ...  K__Bacteria  \\\n",
       "2020-023_PSC_0001_srn                 0.0  ...        339.0   \n",
       "2020-023_PSC_0006_srn               227.0  ...        292.0   \n",
       "2020-023_PSC_0007_srn                21.0  ...        129.0   \n",
       "2020-023_PSC_0010_srn                 0.0  ...        362.0   \n",
       "2020-023_PSC_0015_srn                50.0  ...        126.0   \n",
       "...                                   ...  ...          ...   \n",
       "2020-023_SPC_0189_stl               333.0  ...          4.0   \n",
       "2020-023_SPC_0409_stl               499.0  ...          1.0   \n",
       "2020-023_SPC_0515_stl               747.0  ...         68.0   \n",
       "2020-023_SPC_0726_stl               404.0  ...          2.0   \n",
       "2020-023_SPC_0741_stl               469.0  ...          0.0   \n",
       "\n",
       "                       O__Actinomycetales  O__Bacteroidales  \\\n",
       "2020-023_PSC_0001_srn                 0.0               0.0   \n",
       "2020-023_PSC_0006_srn                 4.0               0.0   \n",
       "2020-023_PSC_0007_srn                 2.0               0.0   \n",
       "2020-023_PSC_0010_srn                 0.0               0.0   \n",
       "2020-023_PSC_0015_srn                 0.0               0.0   \n",
       "...                                   ...               ...   \n",
       "2020-023_SPC_0189_stl                 0.0               0.0   \n",
       "2020-023_SPC_0409_stl                 0.0               0.0   \n",
       "2020-023_SPC_0515_stl                 0.0               0.0   \n",
       "2020-023_SPC_0726_stl                 0.0               0.0   \n",
       "2020-023_SPC_0741_stl                 0.0               0.0   \n",
       "\n",
       "                       O__Burkholderiales  O__Clostridiales  P__Bacteroidetes  \\\n",
       "2020-023_PSC_0001_srn                 0.0               0.0               0.0   \n",
       "2020-023_PSC_0006_srn                 0.0               0.0              22.0   \n",
       "2020-023_PSC_0007_srn                 0.0               0.0               6.0   \n",
       "2020-023_PSC_0010_srn                 0.0               0.0               2.0   \n",
       "2020-023_PSC_0015_srn                 0.0               7.0              13.0   \n",
       "...                                   ...               ...               ...   \n",
       "2020-023_SPC_0189_stl                 0.0            1690.0               0.0   \n",
       "2020-023_SPC_0409_stl                 0.0             147.0               0.0   \n",
       "2020-023_SPC_0515_stl                 0.0             606.0               0.0   \n",
       "2020-023_SPC_0726_stl                 0.0              40.0               0.0   \n",
       "2020-023_SPC_0741_stl                 0.0            1255.0               0.0   \n",
       "\n",
       "                       P__Candidatus_Saccharibacteria  P__Firmicutes  \\\n",
       "2020-023_PSC_0001_srn                             0.0            0.0   \n",
       "2020-023_PSC_0006_srn                             0.0            0.0   \n",
       "2020-023_PSC_0007_srn                            12.0            0.0   \n",
       "2020-023_PSC_0010_srn                             0.0            0.0   \n",
       "2020-023_PSC_0015_srn                             5.0            0.0   \n",
       "...                                               ...            ...   \n",
       "2020-023_SPC_0189_stl                             0.0           15.0   \n",
       "2020-023_SPC_0409_stl                             0.0          375.0   \n",
       "2020-023_SPC_0515_stl                             1.0           22.0   \n",
       "2020-023_SPC_0726_stl                             0.0           59.0   \n",
       "2020-023_SPC_0741_stl                             0.0          422.0   \n",
       "\n",
       "                       P__Parcubacteria  P__Proteobacteria  \n",
       "2020-023_PSC_0001_srn              23.0               74.0  \n",
       "2020-023_PSC_0006_srn              78.0               27.0  \n",
       "2020-023_PSC_0007_srn              15.0               10.0  \n",
       "2020-023_PSC_0010_srn              22.0                0.0  \n",
       "2020-023_PSC_0015_srn              90.0               29.0  \n",
       "...                                 ...                ...  \n",
       "2020-023_SPC_0189_stl               0.0               33.0  \n",
       "2020-023_SPC_0409_stl               0.0                3.0  \n",
       "2020-023_SPC_0515_stl               0.0               84.0  \n",
       "2020-023_SPC_0726_stl               0.0                0.0  \n",
       "2020-023_SPC_0741_stl               0.0                0.0  \n",
       "\n",
       "[721 rows x 108 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6e46bf3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Low variance filter: \t5% based on inter-quantile rang\n",
    "# Calculate the 10th and 90th percentile for each taxon\n",
    "percentiles = ab.quantile([0.05, 0.95])\n",
    "\n",
    "# Calculate the inter-quantile range (IQR) for each taxon\n",
    "iqr = percentiles.loc[0.95] - percentiles.loc[0.05]\n",
    "\n",
    "# Filter out taxa with IQR < 5% of the range\n",
    "variance_threshold = 0.1 * (ab.max() - ab.min())\n",
    "ab = ab.loc[:, iqr >= variance_threshold]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9c15714f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C__Alphaproteobacteria</th>\n",
       "      <th>C__Deltaproteobacteria</th>\n",
       "      <th>F__Clostridiales_Incertae_Sedis_XII</th>\n",
       "      <th>F__Coriobacteriaceae</th>\n",
       "      <th>F__Cryptomonadaceae</th>\n",
       "      <th>F__Enterobacteriaceae</th>\n",
       "      <th>F__Erysipelotrichaceae</th>\n",
       "      <th>F__Lachnospiraceae</th>\n",
       "      <th>F__Porphyromonadaceae</th>\n",
       "      <th>F__Ruminococcaceae</th>\n",
       "      <th>...</th>\n",
       "      <th>G__Streptococcus</th>\n",
       "      <th>G__Subdoligranulum</th>\n",
       "      <th>G__Sutterella</th>\n",
       "      <th>G__Veillonella</th>\n",
       "      <th>K__Bacteria</th>\n",
       "      <th>O__Bacteroidales</th>\n",
       "      <th>O__Clostridiales</th>\n",
       "      <th>P__Firmicutes</th>\n",
       "      <th>P__Parcubacteria</th>\n",
       "      <th>P__Proteobacteria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0001_srn</th>\n",
       "      <td>134.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0006_srn</th>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0007_srn</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0010_srn</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_PSC_0015_srn</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>29.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0189_stl</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>599.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0409_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0515_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>...</td>\n",
       "      <td>156.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0726_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-023_SPC_0741_stl</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1255.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       C__Alphaproteobacteria  C__Deltaproteobacteria  \\\n",
       "2020-023_PSC_0001_srn                   134.0                    19.0   \n",
       "2020-023_PSC_0006_srn                     8.0                    15.0   \n",
       "2020-023_PSC_0007_srn                    17.0                     0.0   \n",
       "2020-023_PSC_0010_srn                     0.0                     0.0   \n",
       "2020-023_PSC_0015_srn                     4.0                     0.0   \n",
       "...                                       ...                     ...   \n",
       "2020-023_SPC_0189_stl                    44.0                     0.0   \n",
       "2020-023_SPC_0409_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0515_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0726_stl                     0.0                     0.0   \n",
       "2020-023_SPC_0741_stl                     0.0                     0.0   \n",
       "\n",
       "                       F__Clostridiales_Incertae_Sedis_XII  \\\n",
       "2020-023_PSC_0001_srn                                  0.0   \n",
       "2020-023_PSC_0006_srn                                  0.0   \n",
       "2020-023_PSC_0007_srn                                  0.0   \n",
       "2020-023_PSC_0010_srn                                  0.0   \n",
       "2020-023_PSC_0015_srn                                  0.0   \n",
       "...                                                    ...   \n",
       "2020-023_SPC_0189_stl                                  0.0   \n",
       "2020-023_SPC_0409_stl                                  0.0   \n",
       "2020-023_SPC_0515_stl                                  0.0   \n",
       "2020-023_SPC_0726_stl                                  0.0   \n",
       "2020-023_SPC_0741_stl                                  9.0   \n",
       "\n",
       "                       F__Coriobacteriaceae  F__Cryptomonadaceae  \\\n",
       "2020-023_PSC_0001_srn                   0.0                  0.0   \n",
       "2020-023_PSC_0006_srn                   0.0                 27.0   \n",
       "2020-023_PSC_0007_srn                   0.0                  8.0   \n",
       "2020-023_PSC_0010_srn                   0.0                105.0   \n",
       "2020-023_PSC_0015_srn                   0.0                  0.0   \n",
       "...                                     ...                  ...   \n",
       "2020-023_SPC_0189_stl                  37.0                  0.0   \n",
       "2020-023_SPC_0409_stl                  17.0                  0.0   \n",
       "2020-023_SPC_0515_stl                  16.0                  0.0   \n",
       "2020-023_SPC_0726_stl                   2.0                  0.0   \n",
       "2020-023_SPC_0741_stl                  11.0                  0.0   \n",
       "\n",
       "                       F__Enterobacteriaceae  F__Erysipelotrichaceae  \\\n",
       "2020-023_PSC_0001_srn                    0.0                     0.0   \n",
       "2020-023_PSC_0006_srn                    0.0                     0.0   \n",
       "2020-023_PSC_0007_srn                    7.0                     0.0   \n",
       "2020-023_PSC_0010_srn                    0.0                     0.0   \n",
       "2020-023_PSC_0015_srn                    8.0                     0.0   \n",
       "...                                      ...                     ...   \n",
       "2020-023_SPC_0189_stl                    0.0                     2.0   \n",
       "2020-023_SPC_0409_stl                    0.0                     8.0   \n",
       "2020-023_SPC_0515_stl                   56.0                    55.0   \n",
       "2020-023_SPC_0726_stl                    0.0                    15.0   \n",
       "2020-023_SPC_0741_stl                    0.0                    14.0   \n",
       "\n",
       "                       F__Lachnospiraceae  F__Porphyromonadaceae  \\\n",
       "2020-023_PSC_0001_srn                 0.0                    0.0   \n",
       "2020-023_PSC_0006_srn               227.0                    0.0   \n",
       "2020-023_PSC_0007_srn                21.0                    0.0   \n",
       "2020-023_PSC_0010_srn                 0.0                    0.0   \n",
       "2020-023_PSC_0015_srn                50.0                    0.0   \n",
       "...                                   ...                    ...   \n",
       "2020-023_SPC_0189_stl               333.0                    0.0   \n",
       "2020-023_SPC_0409_stl               499.0                    0.0   \n",
       "2020-023_SPC_0515_stl               747.0                    0.0   \n",
       "2020-023_SPC_0726_stl               404.0                  248.0   \n",
       "2020-023_SPC_0741_stl               469.0                    0.0   \n",
       "\n",
       "                       F__Ruminococcaceae  ...  G__Streptococcus  \\\n",
       "2020-023_PSC_0001_srn                66.0  ...               0.0   \n",
       "2020-023_PSC_0006_srn                 0.0  ...               0.0   \n",
       "2020-023_PSC_0007_srn                 1.0  ...              12.0   \n",
       "2020-023_PSC_0010_srn                 0.0  ...               0.0   \n",
       "2020-023_PSC_0015_srn                 0.0  ...               0.0   \n",
       "...                                   ...  ...               ...   \n",
       "2020-023_SPC_0189_stl               599.0  ...               4.0   \n",
       "2020-023_SPC_0409_stl               473.0  ...               0.0   \n",
       "2020-023_SPC_0515_stl               488.0  ...             156.0   \n",
       "2020-023_SPC_0726_stl               605.0  ...              38.0   \n",
       "2020-023_SPC_0741_stl               346.0  ...              64.0   \n",
       "\n",
       "                       G__Subdoligranulum  G__Sutterella  G__Veillonella  \\\n",
       "2020-023_PSC_0001_srn                 0.0            0.0             0.0   \n",
       "2020-023_PSC_0006_srn                16.0            0.0             0.0   \n",
       "2020-023_PSC_0007_srn                11.0            0.0             0.0   \n",
       "2020-023_PSC_0010_srn                 0.0            0.0             0.0   \n",
       "2020-023_PSC_0015_srn                 0.0            0.0             0.0   \n",
       "...                                   ...            ...             ...   \n",
       "2020-023_SPC_0189_stl               115.0            0.0             0.0   \n",
       "2020-023_SPC_0409_stl               110.0            0.0             0.0   \n",
       "2020-023_SPC_0515_stl               202.0            0.0             9.0   \n",
       "2020-023_SPC_0726_stl                50.0          477.0            60.0   \n",
       "2020-023_SPC_0741_stl               179.0            0.0            19.0   \n",
       "\n",
       "                       K__Bacteria  O__Bacteroidales  O__Clostridiales  \\\n",
       "2020-023_PSC_0001_srn        339.0               0.0               0.0   \n",
       "2020-023_PSC_0006_srn        292.0               0.0               0.0   \n",
       "2020-023_PSC_0007_srn        129.0               0.0               0.0   \n",
       "2020-023_PSC_0010_srn        362.0               0.0               0.0   \n",
       "2020-023_PSC_0015_srn        126.0               0.0               7.0   \n",
       "...                            ...               ...               ...   \n",
       "2020-023_SPC_0189_stl          4.0               0.0            1690.0   \n",
       "2020-023_SPC_0409_stl          1.0               0.0             147.0   \n",
       "2020-023_SPC_0515_stl         68.0               0.0             606.0   \n",
       "2020-023_SPC_0726_stl          2.0               0.0              40.0   \n",
       "2020-023_SPC_0741_stl          0.0               0.0            1255.0   \n",
       "\n",
       "                       P__Firmicutes  P__Parcubacteria  P__Proteobacteria  \n",
       "2020-023_PSC_0001_srn            0.0              23.0               74.0  \n",
       "2020-023_PSC_0006_srn            0.0              78.0               27.0  \n",
       "2020-023_PSC_0007_srn            0.0              15.0               10.0  \n",
       "2020-023_PSC_0010_srn            0.0              22.0                0.0  \n",
       "2020-023_PSC_0015_srn            0.0              90.0               29.0  \n",
       "...                              ...               ...                ...  \n",
       "2020-023_SPC_0189_stl           15.0               0.0               33.0  \n",
       "2020-023_SPC_0409_stl          375.0               0.0                3.0  \n",
       "2020-023_SPC_0515_stl           22.0               0.0               84.0  \n",
       "2020-023_SPC_0726_stl           59.0               0.0                0.0  \n",
       "2020-023_SPC_0741_stl          422.0               0.0                0.0  \n",
       "\n",
       "[721 rows x 75 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "444545a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ab.to_csv('./results/norm_merged_genus_abundances_filtered.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da20930",
   "metadata": {},
   "source": [
    "# relative abundancies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2b0fbd6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[RelativeFrequency] to: ./results/rel_norm_merged_genus_abundances.qza\u001b[0m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!qiime feature-table relative-frequency \\\n",
    "--i-table ./results/norm_merged_genus_abundances.qza \\\n",
    "--o-relative-frequency-table ./results/rel_norm_merged_genus_abundances.qza"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e4651d1",
   "metadata": {},
   "source": [
    "# abundance barplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2389c3c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load rarefied and relative ab table from the Qiime artifact\n",
    "rel_ab = Artifact.load('./results/rel_norm_merged_genus_abundances.qza')\n",
    "\n",
    "# Convert the PCoA results to a pandas DataFrame\n",
    "rel_ab = rel_ab.view(pd.DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4fcc6a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert row names into a separate column and reset row names\n",
    "rel_ab['sample_id'] = rel_ab.index\n",
    "rel_ab.reset_index(drop=True, inplace=True)\n",
    "# Pivot the dataframe\n",
    "genus_ab = pd.melt(rel_ab , id_vars=['sample_id'], var_name='Genus', value_name='Abundance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7eef7db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "genus_ab['Material'] = np.where(genus_ab['sample_id'].str.contains('srn'), 'seren', 'stool')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a15e7333",
   "metadata": {},
   "outputs": [],
   "source": [
    "genus_ab['Group'] = np.where(genus_ab['sample_id'].str.contains('PSC'), 'PSC', 'BSPSPC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22ef0e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "genus_ab['Combined'] = genus_ab['Group'] + '_' + genus_ab['Material']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "370a6cdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Genus</th>\n",
       "      <th>C__Acidobacteria_Gp4</th>\n",
       "      <th>C__Actinobacteria</th>\n",
       "      <th>C__Alphaproteobacteria</th>\n",
       "      <th>C__Armatimonadetes_gp5</th>\n",
       "      <th>C__Bacilli</th>\n",
       "      <th>C__Betaproteobacteria</th>\n",
       "      <th>C__Candidatus_Cloacamonas</th>\n",
       "      <th>C__Candidatus_Hydrogenedens</th>\n",
       "      <th>C__Chloroflexia</th>\n",
       "      <th>C__Clostridia</th>\n",
       "      <th>...</th>\n",
       "      <th>P__Cyanobacteria.Chloroplast</th>\n",
       "      <th>P__Firmicutes</th>\n",
       "      <th>P__Latescibacteria</th>\n",
       "      <th>P__Microgenomates</th>\n",
       "      <th>P__Parcubacteria</th>\n",
       "      <th>P__Planctomycetes</th>\n",
       "      <th>P__Proteobacteria</th>\n",
       "      <th>P__SR1</th>\n",
       "      <th>P__Verrucomicrobia</th>\n",
       "      <th>Unassigned</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Combined</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BSPSPC_seren</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.013764</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.004969</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001996</td>\n",
       "      <td>0.003573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.911111e-04</td>\n",
       "      <td>0.040511</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006133</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BSPSPC_stool</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004166</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSC_seren</th>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>0.009793</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.001421</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>4.137931e-05</td>\n",
       "      <td>0.023593</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.013686</td>\n",
       "      <td>0.000514</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSC_stool</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007659</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.898635e-07</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001892</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 814 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Genus         C__Acidobacteria_Gp4  C__Actinobacteria  C__Alphaproteobacteria  \\\n",
       "Combined                                                                        \n",
       "BSPSPC_seren              0.000000           0.000209                0.013764   \n",
       "BSPSPC_stool              0.000000           0.000000                0.002034   \n",
       "PSC_seren                 0.000086           0.000655                0.009793   \n",
       "PSC_stool                 0.000000           0.000000                0.003024   \n",
       "\n",
       "Genus         C__Armatimonadetes_gp5  C__Bacilli  C__Betaproteobacteria  \\\n",
       "Combined                                                                  \n",
       "BSPSPC_seren                0.000000    0.000213               0.004969   \n",
       "BSPSPC_stool                0.000000    0.000000               0.000002   \n",
       "PSC_seren                   0.000107    0.000745               0.001421   \n",
       "PSC_stool                   0.000000    0.000061               0.000004   \n",
       "\n",
       "Genus         C__Candidatus_Cloacamonas  C__Candidatus_Hydrogenedens  \\\n",
       "Combined                                                               \n",
       "BSPSPC_seren                   0.000000                     0.000000   \n",
       "BSPSPC_stool                   0.000000                     0.000000   \n",
       "PSC_seren                      0.000041                     0.000003   \n",
       "PSC_stool                      0.000000                     0.000000   \n",
       "\n",
       "Genus         C__Chloroflexia  C__Clostridia  ...  \\\n",
       "Combined                                      ...   \n",
       "BSPSPC_seren         0.000000       0.000000  ...   \n",
       "BSPSPC_stool         0.000000       0.000334  ...   \n",
       "PSC_seren            0.000083       0.000003  ...   \n",
       "PSC_stool            0.000000       0.000153  ...   \n",
       "\n",
       "Genus         P__Cyanobacteria.Chloroplast  P__Firmicutes  P__Latescibacteria  \\\n",
       "Combined                                                                        \n",
       "BSPSPC_seren                      0.001996       0.003573            0.000000   \n",
       "BSPSPC_stool                      0.000000       0.018062            0.000000   \n",
       "PSC_seren                         0.000421       0.001834            0.000055   \n",
       "PSC_stool                         0.000000       0.007659            0.000000   \n",
       "\n",
       "Genus         P__Microgenomates  P__Parcubacteria  P__Planctomycetes  \\\n",
       "Combined                                                               \n",
       "BSPSPC_seren       3.911111e-04          0.040511           0.000000   \n",
       "BSPSPC_stool       0.000000e+00          0.000000           0.000000   \n",
       "PSC_seren          4.137931e-05          0.023593           0.000179   \n",
       "PSC_stool          3.898635e-07          0.000009           0.000000   \n",
       "\n",
       "Genus         P__Proteobacteria    P__SR1  P__Verrucomicrobia  Unassigned  \n",
       "Combined                                                                   \n",
       "BSPSPC_seren           0.006133  0.000098            0.000427    0.000000  \n",
       "BSPSPC_stool           0.004166  0.000000            0.000000    0.000000  \n",
       "PSC_seren              0.013686  0.000514            0.000214    0.000524  \n",
       "PSC_stool              0.001892  0.000000            0.000000    0.000000  \n",
       "\n",
       "[4 rows x 814 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create table for diagnosis\n",
    "pivot_data = genus_ab.pivot_table(index='Combined', columns='Genus', values='Abundance', fill_value=0)\n",
    "pivot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "961a6538",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sort table\n",
    "pivot_data= pivot_data.reindex(pivot_data.mean().sort_values(ascending=False).index, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a224250b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_data.to_csv('./results/relative_abundance_avrg.csv', index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "30c6dd10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate proportions for each sample type\n",
    "cross_tab_prop = pivot_data * 100\n",
    "\n",
    "# Filter out genera with abundance < 1% and sum them into a single category\n",
    "low_abundance_threshold = 1\n",
    "low_abundance_genera = cross_tab_prop.columns[(cross_tab_prop <= low_abundance_threshold).all()]\n",
    "low_abundance_data = cross_tab_prop[low_abundance_genera].sum(axis=1)\n",
    "cross_tab_prop.drop(columns=low_abundance_genera, inplace=True)\n",
    "cross_tab_prop['Low Abundance Bacteria'] = low_abundance_data\n",
    "\n",
    "ax = cross_tab_prop.plot(kind='bar', stacked=True, cmap='Spectral', width=0.8)\n",
    "\n",
    "# Customize legend\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "filtered_handles = []\n",
    "filtered_labels = []\n",
    "for handle, label in zip(handles, labels):\n",
    "    if label in low_abundance_genera:\n",
    "        filtered_labels.append(\"Low Abundance Bacteria\")\n",
    "    else:\n",
    "        filtered_handles.append(handle)\n",
    "        filtered_labels.append(label)\n",
    "\n",
    "plt.xlabel('Sample Type')\n",
    "plt.ylabel('Abundance (%)')\n",
    "legend = plt.legend(filtered_handles, filtered_labels, title='Genus', loc='upper center', bbox_to_anchor=(0.5, -0.69), ncol=4)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./results/stacked_abundance_mock.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d1ce963",
   "metadata": {},
   "outputs": [],
   "source": [
    "ab = "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}