09_2020-023_diff_abund_vis_BSPSPC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ea467f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8ce27cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11d1dad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following tables were generated in R using ANCOMBC2 package (2018-050_ancomb.r script)\n",
    "res_prim_BSPSPC = pd.read_csv( \"./results/2020-023_res_prim_BSPSPC.csv\", index_col=0)\n",
    "res_prim_PSC = pd.read_csv( \"./results/2020-023_res_prim_PSC.csv\", index_col=0)\n",
    "res_prim_stool = pd.read_csv( \"./results/2020-023_res_prim_stool.csv\", index_col=0)\n",
    "res_prim_seren = pd.read_csv( \"./results/2020-023_res_prim_seren.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc35bc40",
   "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>taxon</th>\n",
       "      <th>lfc_(Intercept)</th>\n",
       "      <th>lfc_Materialstool</th>\n",
       "      <th>se_(Intercept)</th>\n",
       "      <th>se_Materialstool</th>\n",
       "      <th>W_(Intercept)</th>\n",
       "      <th>W_Materialstool</th>\n",
       "      <th>p_(Intercept)</th>\n",
       "      <th>p_Materialstool</th>\n",
       "      <th>q_(Intercept)</th>\n",
       "      <th>q_Materialstool</th>\n",
       "      <th>diff_(Intercept)</th>\n",
       "      <th>diff_Materialstool</th>\n",
       "      <th>passed_ss_(Intercept)</th>\n",
       "      <th>passed_ss_Materialstool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C__Alphaproteobacteria</td>\n",
       "      <td>0.622244</td>\n",
       "      <td>-1.135336</td>\n",
       "      <td>0.084667</td>\n",
       "      <td>0.240215</td>\n",
       "      <td>7.349298</td>\n",
       "      <td>-4.726335</td>\n",
       "      <td>2.339730e-08</td>\n",
       "      <td>4.394869e-05</td>\n",
       "      <td>5.381379e-07</td>\n",
       "      <td>1.538204e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C__Deltaproteobacteria</td>\n",
       "      <td>-0.244857</td>\n",
       "      <td>1.138752</td>\n",
       "      <td>0.087948</td>\n",
       "      <td>0.206243</td>\n",
       "      <td>-2.784116</td>\n",
       "      <td>5.521408</td>\n",
       "      <td>1.549357e-02</td>\n",
       "      <td>9.850289e-05</td>\n",
       "      <td>1.468021e-01</td>\n",
       "      <td>3.349098e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F__Enterobacteriaceae</td>\n",
       "      <td>0.290143</td>\n",
       "      <td>-0.093226</td>\n",
       "      <td>0.047240</td>\n",
       "      <td>0.240240</td>\n",
       "      <td>6.141850</td>\n",
       "      <td>-0.388054</td>\n",
       "      <td>8.439600e-06</td>\n",
       "      <td>7.025268e-01</td>\n",
       "      <td>1.350336e-04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F__Erysipelotrichaceae</td>\n",
       "      <td>-0.910620</td>\n",
       "      <td>1.361404</td>\n",
       "      <td>0.070058</td>\n",
       "      <td>0.267471</td>\n",
       "      <td>-12.998019</td>\n",
       "      <td>5.089918</td>\n",
       "      <td>2.594480e-16</td>\n",
       "      <td>7.946558e-06</td>\n",
       "      <td>9.599576e-15</td>\n",
       "      <td>3.178623e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F__Lachnospiraceae</td>\n",
       "      <td>-0.626429</td>\n",
       "      <td>1.354931</td>\n",
       "      <td>0.121614</td>\n",
       "      <td>0.243401</td>\n",
       "      <td>-5.150980</td>\n",
       "      <td>5.566663</td>\n",
       "      <td>2.282606e-06</td>\n",
       "      <td>4.473948e-07</td>\n",
       "      <td>4.336952e-05</td>\n",
       "      <td>2.013277e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F__Porphyromonadaceae</td>\n",
       "      <td>-0.221284</td>\n",
       "      <td>0.832450</td>\n",
       "      <td>0.051473</td>\n",
       "      <td>0.235775</td>\n",
       "      <td>-4.299020</td>\n",
       "      <td>3.530697</td>\n",
       "      <td>2.135949e-04</td>\n",
       "      <td>1.568833e-03</td>\n",
       "      <td>2.707359e-03</td>\n",
       "      <td>3.922084e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F__Ruminococcaceae</td>\n",
       "      <td>-1.119326</td>\n",
       "      <td>2.054106</td>\n",
       "      <td>0.141956</td>\n",
       "      <td>0.277447</td>\n",
       "      <td>-7.884995</td>\n",
       "      <td>7.403595</td>\n",
       "      <td>3.244568e-11</td>\n",
       "      <td>2.456520e-10</td>\n",
       "      <td>8.435877e-10</td>\n",
       "      <td>1.252825e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>G__Akkermansia</td>\n",
       "      <td>-2.232273</td>\n",
       "      <td>2.760686</td>\n",
       "      <td>0.049511</td>\n",
       "      <td>0.267302</td>\n",
       "      <td>-45.086838</td>\n",
       "      <td>10.327947</td>\n",
       "      <td>3.239265e-26</td>\n",
       "      <td>1.078188e-10</td>\n",
       "      <td>1.457669e-24</td>\n",
       "      <td>5.714397e-09</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>G__Alistipes</td>\n",
       "      <td>-0.899560</td>\n",
       "      <td>1.741509</td>\n",
       "      <td>0.084166</td>\n",
       "      <td>0.241943</td>\n",
       "      <td>-10.687962</td>\n",
       "      <td>7.198011</td>\n",
       "      <td>2.796607e-16</td>\n",
       "      <td>5.815055e-10</td>\n",
       "      <td>1.006778e-14</td>\n",
       "      <td>2.907527e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>G__Anaerostipes</td>\n",
       "      <td>0.093949</td>\n",
       "      <td>0.307571</td>\n",
       "      <td>0.009292</td>\n",
       "      <td>0.238216</td>\n",
       "      <td>10.110716</td>\n",
       "      <td>1.291142</td>\n",
       "      <td>6.173480e-13</td>\n",
       "      <td>2.035526e-01</td>\n",
       "      <td>1.790309e-11</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>G__Anaerotruncus</td>\n",
       "      <td>0.769172</td>\n",
       "      <td>-0.443810</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.251908</td>\n",
       "      <td>38.595829</td>\n",
       "      <td>-1.761791</td>\n",
       "      <td>1.158202e-29</td>\n",
       "      <td>8.709511e-02</td>\n",
       "      <td>5.675188e-28</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G__Anaerovorax</td>\n",
       "      <td>1.245622</td>\n",
       "      <td>-0.857078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.232735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.682629</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.734062e-04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.088197e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>G__Bacteroides</td>\n",
       "      <td>-1.454366</td>\n",
       "      <td>2.658819</td>\n",
       "      <td>0.161801</td>\n",
       "      <td>0.272098</td>\n",
       "      <td>-8.988602</td>\n",
       "      <td>9.771556</td>\n",
       "      <td>2.193241e-13</td>\n",
       "      <td>7.770720e-15</td>\n",
       "      <td>6.799046e-12</td>\n",
       "      <td>4.351603e-13</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>G__Barnesiella</td>\n",
       "      <td>-0.647276</td>\n",
       "      <td>1.125038</td>\n",
       "      <td>0.019386</td>\n",
       "      <td>0.272199</td>\n",
       "      <td>-33.388873</td>\n",
       "      <td>4.133151</td>\n",
       "      <td>2.659573e-30</td>\n",
       "      <td>1.835356e-04</td>\n",
       "      <td>1.329786e-28</td>\n",
       "      <td>6.056674e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>G__Bilophila</td>\n",
       "      <td>-0.222783</td>\n",
       "      <td>0.666338</td>\n",
       "      <td>0.033218</td>\n",
       "      <td>0.241143</td>\n",
       "      <td>-6.706708</td>\n",
       "      <td>2.763252</td>\n",
       "      <td>4.813296e-08</td>\n",
       "      <td>8.607636e-03</td>\n",
       "      <td>1.058925e-06</td>\n",
       "      <td>1.635451e-01</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>G__Blautia</td>\n",
       "      <td>0.466945</td>\n",
       "      <td>-0.161298</td>\n",
       "      <td>0.055301</td>\n",
       "      <td>0.252218</td>\n",
       "      <td>8.443671</td>\n",
       "      <td>-0.639521</td>\n",
       "      <td>1.909158e-11</td>\n",
       "      <td>5.251882e-01</td>\n",
       "      <td>5.154727e-10</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>G__Butyricicoccus</td>\n",
       "      <td>1.339721</td>\n",
       "      <td>-1.049744</td>\n",
       "      <td>0.024509</td>\n",
       "      <td>0.266246</td>\n",
       "      <td>54.663126</td>\n",
       "      <td>-3.942765</td>\n",
       "      <td>4.350283e-42</td>\n",
       "      <td>2.851392e-04</td>\n",
       "      <td>2.479662e-40</td>\n",
       "      <td>8.269038e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>G__Butyricimonas</td>\n",
       "      <td>0.253653</td>\n",
       "      <td>0.134784</td>\n",
       "      <td>0.014645</td>\n",
       "      <td>0.233945</td>\n",
       "      <td>17.320602</td>\n",
       "      <td>0.576135</td>\n",
       "      <td>8.142366e-18</td>\n",
       "      <td>5.685544e-01</td>\n",
       "      <td>3.175523e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>G__Catabacter</td>\n",
       "      <td>1.151947</td>\n",
       "      <td>-0.940244</td>\n",
       "      <td>0.038218</td>\n",
       "      <td>0.222212</td>\n",
       "      <td>30.141802</td>\n",
       "      <td>-4.231295</td>\n",
       "      <td>5.451009e-24</td>\n",
       "      <td>2.015004e-04</td>\n",
       "      <td>2.343934e-22</td>\n",
       "      <td>6.448014e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>G__Clostridium_IV</td>\n",
       "      <td>0.360135</td>\n",
       "      <td>-0.014116</td>\n",
       "      <td>0.041478</td>\n",
       "      <td>0.254641</td>\n",
       "      <td>8.682508</td>\n",
       "      <td>-0.055435</td>\n",
       "      <td>7.930800e-12</td>\n",
       "      <td>9.559966e-01</td>\n",
       "      <td>2.220624e-10</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>G__Clostridium_XVIII</td>\n",
       "      <td>1.301719</td>\n",
       "      <td>-1.137353</td>\n",
       "      <td>0.013978</td>\n",
       "      <td>0.231362</td>\n",
       "      <td>93.125728</td>\n",
       "      <td>-4.915898</td>\n",
       "      <td>1.601076e-39</td>\n",
       "      <td>2.732859e-05</td>\n",
       "      <td>8.966024e-38</td>\n",
       "      <td>9.838294e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>G__Clostridium_XlVa</td>\n",
       "      <td>0.765185</td>\n",
       "      <td>-0.459991</td>\n",
       "      <td>0.023825</td>\n",
       "      <td>0.230229</td>\n",
       "      <td>32.116993</td>\n",
       "      <td>-1.997973</td>\n",
       "      <td>3.759183e-33</td>\n",
       "      <td>5.165201e-02</td>\n",
       "      <td>1.992367e-31</td>\n",
       "      <td>8.264322e-01</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>G__Clostridium_XlVb</td>\n",
       "      <td>0.804096</td>\n",
       "      <td>-0.435544</td>\n",
       "      <td>0.020977</td>\n",
       "      <td>0.234792</td>\n",
       "      <td>38.331445</td>\n",
       "      <td>-1.855022</td>\n",
       "      <td>5.553302e-32</td>\n",
       "      <td>7.136374e-02</td>\n",
       "      <td>2.887717e-30</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>G__Collinsella</td>\n",
       "      <td>0.028014</td>\n",
       "      <td>0.409788</td>\n",
       "      <td>0.080750</td>\n",
       "      <td>0.270557</td>\n",
       "      <td>0.346929</td>\n",
       "      <td>1.514608</td>\n",
       "      <td>7.302269e-01</td>\n",
       "      <td>1.367133e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>G__Coprobacter</td>\n",
       "      <td>0.103560</td>\n",
       "      <td>0.330134</td>\n",
       "      <td>0.020116</td>\n",
       "      <td>0.241223</td>\n",
       "      <td>5.148135</td>\n",
       "      <td>1.368582</td>\n",
       "      <td>9.547220e-06</td>\n",
       "      <td>1.796103e-01</td>\n",
       "      <td>1.432083e-04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>G__Coprococcus</td>\n",
       "      <td>1.060557</td>\n",
       "      <td>-0.841410</td>\n",
       "      <td>0.061618</td>\n",
       "      <td>0.251195</td>\n",
       "      <td>17.211907</td>\n",
       "      <td>-3.349624</td>\n",
       "      <td>3.589974e-22</td>\n",
       "      <td>1.582371e-03</td>\n",
       "      <td>1.471889e-20</td>\n",
       "      <td>3.922084e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>G__Corynebacterium</td>\n",
       "      <td>0.480755</td>\n",
       "      <td>-2.472369</td>\n",
       "      <td>0.109288</td>\n",
       "      <td>0.231263</td>\n",
       "      <td>4.398984</td>\n",
       "      <td>-10.690715</td>\n",
       "      <td>2.082584e-04</td>\n",
       "      <td>2.136538e-10</td>\n",
       "      <td>2.707359e-03</td>\n",
       "      <td>1.111000e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>G__Dialister</td>\n",
       "      <td>-1.475966</td>\n",
       "      <td>2.260728</td>\n",
       "      <td>0.109624</td>\n",
       "      <td>0.352046</td>\n",
       "      <td>-13.463841</td>\n",
       "      <td>6.421676</td>\n",
       "      <td>1.913335e-16</td>\n",
       "      <td>1.207586e-07</td>\n",
       "      <td>7.270674e-15</td>\n",
       "      <td>5.675655e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>G__Dorea</td>\n",
       "      <td>0.480962</td>\n",
       "      <td>-0.131713</td>\n",
       "      <td>0.084707</td>\n",
       "      <td>0.233293</td>\n",
       "      <td>5.677914</td>\n",
       "      <td>-0.564580</td>\n",
       "      <td>1.340167e-06</td>\n",
       "      <td>5.755118e-01</td>\n",
       "      <td>2.814350e-05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>G__Eggerthella</td>\n",
       "      <td>1.797095</td>\n",
       "      <td>-1.412947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.222247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.357542</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.293271e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.566107e-05</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>G__Escherichia.Shigella</td>\n",
       "      <td>0.406961</td>\n",
       "      <td>-0.050195</td>\n",
       "      <td>0.036436</td>\n",
       "      <td>0.321988</td>\n",
       "      <td>11.169347</td>\n",
       "      <td>-0.155892</td>\n",
       "      <td>4.345468e-13</td>\n",
       "      <td>8.770136e-01</td>\n",
       "      <td>1.303640e-11</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>G__Faecalibacterium</td>\n",
       "      <td>-0.353656</td>\n",
       "      <td>0.865413</td>\n",
       "      <td>0.075477</td>\n",
       "      <td>0.268543</td>\n",
       "      <td>-4.685631</td>\n",
       "      <td>3.222620</td>\n",
       "      <td>1.441240e-05</td>\n",
       "      <td>1.974569e-03</td>\n",
       "      <td>2.017736e-04</td>\n",
       "      <td>4.426315e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>G__Flavonifractor</td>\n",
       "      <td>0.395622</td>\n",
       "      <td>-0.042382</td>\n",
       "      <td>0.015049</td>\n",
       "      <td>0.220743</td>\n",
       "      <td>26.288507</td>\n",
       "      <td>-0.191999</td>\n",
       "      <td>1.538129e-25</td>\n",
       "      <td>8.487929e-01</td>\n",
       "      <td>6.767769e-24</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>G__Fusicatenibacter</td>\n",
       "      <td>1.343274</td>\n",
       "      <td>-1.118124</td>\n",
       "      <td>0.043998</td>\n",
       "      <td>0.217692</td>\n",
       "      <td>30.530376</td>\n",
       "      <td>-5.136258</td>\n",
       "      <td>1.014682e-31</td>\n",
       "      <td>5.836252e-06</td>\n",
       "      <td>5.174876e-30</td>\n",
       "      <td>2.392863e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>G__Holdemanella</td>\n",
       "      <td>-2.757259</td>\n",
       "      <td>3.337051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.228057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.632561</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.172453e-14</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.044849e-12</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>G__Microbacterium</td>\n",
       "      <td>0.013243</td>\n",
       "      <td>-2.054168</td>\n",
       "      <td>0.091848</td>\n",
       "      <td>0.208891</td>\n",
       "      <td>0.144181</td>\n",
       "      <td>-9.833678</td>\n",
       "      <td>8.882220e-01</td>\n",
       "      <td>1.853296e-06</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.783842e-05</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>G__Odoribacter</td>\n",
       "      <td>0.243962</td>\n",
       "      <td>0.105051</td>\n",
       "      <td>0.048547</td>\n",
       "      <td>0.253371</td>\n",
       "      <td>5.025268</td>\n",
       "      <td>0.414615</td>\n",
       "      <td>6.811668e-06</td>\n",
       "      <td>6.801970e-01</td>\n",
       "      <td>1.157983e-04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>G__Oscillibacter</td>\n",
       "      <td>-0.643314</td>\n",
       "      <td>1.177174</td>\n",
       "      <td>0.064974</td>\n",
       "      <td>0.242239</td>\n",
       "      <td>-9.901075</td>\n",
       "      <td>4.859564</td>\n",
       "      <td>9.711423e-14</td>\n",
       "      <td>1.051709e-05</td>\n",
       "      <td>3.107655e-12</td>\n",
       "      <td>3.996494e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>G__Parabacteroides</td>\n",
       "      <td>-1.367074</td>\n",
       "      <td>2.077438</td>\n",
       "      <td>0.039809</td>\n",
       "      <td>0.236794</td>\n",
       "      <td>-34.341006</td>\n",
       "      <td>8.773173</td>\n",
       "      <td>1.957532e-36</td>\n",
       "      <td>1.085294e-11</td>\n",
       "      <td>1.076643e-34</td>\n",
       "      <td>5.860586e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>G__Paraprevotella</td>\n",
       "      <td>-1.313899</td>\n",
       "      <td>1.802632</td>\n",
       "      <td>0.009292</td>\n",
       "      <td>0.206212</td>\n",
       "      <td>-141.401472</td>\n",
       "      <td>8.741638</td>\n",
       "      <td>1.508202e-27</td>\n",
       "      <td>1.069592e-07</td>\n",
       "      <td>7.088547e-26</td>\n",
       "      <td>5.134040e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>G__Parasutterella</td>\n",
       "      <td>-0.517496</td>\n",
       "      <td>1.030737</td>\n",
       "      <td>0.043307</td>\n",
       "      <td>0.342503</td>\n",
       "      <td>-11.949372</td>\n",
       "      <td>3.009420</td>\n",
       "      <td>2.086981e-15</td>\n",
       "      <td>4.320669e-03</td>\n",
       "      <td>7.095735e-14</td>\n",
       "      <td>8.641339e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>G__Phascolarctobacterium</td>\n",
       "      <td>-0.602502</td>\n",
       "      <td>1.077435</td>\n",
       "      <td>0.011424</td>\n",
       "      <td>0.252123</td>\n",
       "      <td>-52.738554</td>\n",
       "      <td>4.273441</td>\n",
       "      <td>2.487221e-26</td>\n",
       "      <td>2.634938e-04</td>\n",
       "      <td>1.144122e-24</td>\n",
       "      <td>7.904815e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>G__Prevotella</td>\n",
       "      <td>-1.298798</td>\n",
       "      <td>2.274305</td>\n",
       "      <td>0.106783</td>\n",
       "      <td>0.339712</td>\n",
       "      <td>-12.162957</td>\n",
       "      <td>6.694806</td>\n",
       "      <td>1.132405e-14</td>\n",
       "      <td>6.382846e-08</td>\n",
       "      <td>3.736938e-13</td>\n",
       "      <td>3.127594e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>G__Propionibacterium</td>\n",
       "      <td>0.021237</td>\n",
       "      <td>-3.960076</td>\n",
       "      <td>0.220510</td>\n",
       "      <td>0.300951</td>\n",
       "      <td>0.096308</td>\n",
       "      <td>-13.158559</td>\n",
       "      <td>9.238414e-01</td>\n",
       "      <td>6.729462e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.835793e-13</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>G__Pseudoflavonifractor</td>\n",
       "      <td>1.076430</td>\n",
       "      <td>-0.746628</td>\n",
       "      <td>0.041349</td>\n",
       "      <td>0.226903</td>\n",
       "      <td>26.033038</td>\n",
       "      <td>-3.290520</td>\n",
       "      <td>3.590618e-29</td>\n",
       "      <td>1.924485e-03</td>\n",
       "      <td>1.723496e-27</td>\n",
       "      <td>4.426315e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>G__Roseburia</td>\n",
       "      <td>-0.447115</td>\n",
       "      <td>0.937808</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.265483</td>\n",
       "      <td>-5.067303</td>\n",
       "      <td>3.532467</td>\n",
       "      <td>5.656698e-06</td>\n",
       "      <td>8.841901e-04</td>\n",
       "      <td>1.018206e-04</td>\n",
       "      <td>2.298894e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>G__Ruminococcus</td>\n",
       "      <td>-0.594982</td>\n",
       "      <td>1.139950</td>\n",
       "      <td>0.111390</td>\n",
       "      <td>0.308842</td>\n",
       "      <td>-5.341456</td>\n",
       "      <td>3.691042</td>\n",
       "      <td>1.974051e-06</td>\n",
       "      <td>5.282686e-04</td>\n",
       "      <td>3.948101e-05</td>\n",
       "      <td>1.479152e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>G__Ruminococcus2</td>\n",
       "      <td>1.018396</td>\n",
       "      <td>-0.803748</td>\n",
       "      <td>0.053645</td>\n",
       "      <td>0.255557</td>\n",
       "      <td>18.983896</td>\n",
       "      <td>-3.145077</td>\n",
       "      <td>2.142503e-23</td>\n",
       "      <td>2.908175e-03</td>\n",
       "      <td>8.998512e-22</td>\n",
       "      <td>6.107167e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>G__Streptococcus</td>\n",
       "      <td>1.485427</td>\n",
       "      <td>-1.445548</td>\n",
       "      <td>0.033607</td>\n",
       "      <td>0.240776</td>\n",
       "      <td>44.199426</td>\n",
       "      <td>-6.003710</td>\n",
       "      <td>1.415376e-35</td>\n",
       "      <td>4.670014e-07</td>\n",
       "      <td>7.643030e-34</td>\n",
       "      <td>2.054806e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>G__Subdoligranulum</td>\n",
       "      <td>-0.382708</td>\n",
       "      <td>0.877074</td>\n",
       "      <td>0.058513</td>\n",
       "      <td>0.222406</td>\n",
       "      <td>-6.540584</td>\n",
       "      <td>3.943581</td>\n",
       "      <td>1.846993e-08</td>\n",
       "      <td>2.221760e-04</td>\n",
       "      <td>4.432783e-07</td>\n",
       "      <td>6.887456e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>G__Sutterella</td>\n",
       "      <td>-0.904844</td>\n",
       "      <td>1.446651</td>\n",
       "      <td>0.059713</td>\n",
       "      <td>0.273669</td>\n",
       "      <td>-15.153182</td>\n",
       "      <td>5.286140</td>\n",
       "      <td>3.734505e-16</td>\n",
       "      <td>8.642440e-06</td>\n",
       "      <td>1.307077e-14</td>\n",
       "      <td>3.370552e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>G__Veillonella</td>\n",
       "      <td>1.822902</td>\n",
       "      <td>-1.805266</td>\n",
       "      <td>0.043498</td>\n",
       "      <td>0.232927</td>\n",
       "      <td>41.907635</td>\n",
       "      <td>-7.750334</td>\n",
       "      <td>9.976108e-22</td>\n",
       "      <td>1.363889e-07</td>\n",
       "      <td>3.990443e-20</td>\n",
       "      <td>6.273888e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>K__Bacteria</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>-0.698207</td>\n",
       "      <td>0.143344</td>\n",
       "      <td>0.344154</td>\n",
       "      <td>2.383542</td>\n",
       "      <td>-2.028763</td>\n",
       "      <td>1.982421e-02</td>\n",
       "      <td>4.623377e-02</td>\n",
       "      <td>1.585937e-01</td>\n",
       "      <td>7.859740e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>O__Bacteroidales</td>\n",
       "      <td>0.079874</td>\n",
       "      <td>0.169454</td>\n",
       "      <td>0.123975</td>\n",
       "      <td>0.339259</td>\n",
       "      <td>0.644272</td>\n",
       "      <td>0.499481</td>\n",
       "      <td>5.225992e-01</td>\n",
       "      <td>6.198215e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>O__Clostridiales</td>\n",
       "      <td>-0.853735</td>\n",
       "      <td>1.626636</td>\n",
       "      <td>0.105885</td>\n",
       "      <td>0.343306</td>\n",
       "      <td>-8.062889</td>\n",
       "      <td>4.738149</td>\n",
       "      <td>3.428820e-11</td>\n",
       "      <td>1.332160e-05</td>\n",
       "      <td>8.572050e-10</td>\n",
       "      <td>4.928992e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>P__Firmicutes</td>\n",
       "      <td>-0.281443</td>\n",
       "      <td>0.781096</td>\n",
       "      <td>0.079391</td>\n",
       "      <td>0.313618</td>\n",
       "      <td>-3.545026</td>\n",
       "      <td>2.490598</td>\n",
       "      <td>7.838558e-04</td>\n",
       "      <td>1.563809e-02</td>\n",
       "      <td>8.622414e-03</td>\n",
       "      <td>2.814856e-01</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>P__Proteobacteria</td>\n",
       "      <td>0.294002</td>\n",
       "      <td>-0.095627</td>\n",
       "      <td>0.115261</td>\n",
       "      <td>0.292088</td>\n",
       "      <td>2.550755</td>\n",
       "      <td>-0.327389</td>\n",
       "      <td>1.468021e-02</td>\n",
       "      <td>7.450809e-01</td>\n",
       "      <td>1.468021e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       taxon  lfc_(Intercept)  lfc_Materialstool  \\\n",
       "1     C__Alphaproteobacteria         0.622244          -1.135336   \n",
       "2     C__Deltaproteobacteria        -0.244857           1.138752   \n",
       "3      F__Enterobacteriaceae         0.290143          -0.093226   \n",
       "4     F__Erysipelotrichaceae        -0.910620           1.361404   \n",
       "5         F__Lachnospiraceae        -0.626429           1.354931   \n",
       "6      F__Porphyromonadaceae        -0.221284           0.832450   \n",
       "7         F__Ruminococcaceae        -1.119326           2.054106   \n",
       "8             G__Akkermansia        -2.232273           2.760686   \n",
       "9               G__Alistipes        -0.899560           1.741509   \n",
       "10           G__Anaerostipes         0.093949           0.307571   \n",
       "11          G__Anaerotruncus         0.769172          -0.443810   \n",
       "12            G__Anaerovorax         1.245622          -0.857078   \n",
       "13            G__Bacteroides        -1.454366           2.658819   \n",
       "14            G__Barnesiella        -0.647276           1.125038   \n",
       "15              G__Bilophila        -0.222783           0.666338   \n",
       "16                G__Blautia         0.466945          -0.161298   \n",
       "17         G__Butyricicoccus         1.339721          -1.049744   \n",
       "18          G__Butyricimonas         0.253653           0.134784   \n",
       "19             G__Catabacter         1.151947          -0.940244   \n",
       "20         G__Clostridium_IV         0.360135          -0.014116   \n",
       "21      G__Clostridium_XVIII         1.301719          -1.137353   \n",
       "22       G__Clostridium_XlVa         0.765185          -0.459991   \n",
       "23       G__Clostridium_XlVb         0.804096          -0.435544   \n",
       "24            G__Collinsella         0.028014           0.409788   \n",
       "25            G__Coprobacter         0.103560           0.330134   \n",
       "26            G__Coprococcus         1.060557          -0.841410   \n",
       "27        G__Corynebacterium         0.480755          -2.472369   \n",
       "28              G__Dialister        -1.475966           2.260728   \n",
       "29                  G__Dorea         0.480962          -0.131713   \n",
       "30            G__Eggerthella         1.797095          -1.412947   \n",
       "31   G__Escherichia.Shigella         0.406961          -0.050195   \n",
       "32       G__Faecalibacterium        -0.353656           0.865413   \n",
       "33         G__Flavonifractor         0.395622          -0.042382   \n",
       "34       G__Fusicatenibacter         1.343274          -1.118124   \n",
       "35           G__Holdemanella        -2.757259           3.337051   \n",
       "36         G__Microbacterium         0.013243          -2.054168   \n",
       "37            G__Odoribacter         0.243962           0.105051   \n",
       "38          G__Oscillibacter        -0.643314           1.177174   \n",
       "39        G__Parabacteroides        -1.367074           2.077438   \n",
       "40         G__Paraprevotella        -1.313899           1.802632   \n",
       "41         G__Parasutterella        -0.517496           1.030737   \n",
       "42  G__Phascolarctobacterium        -0.602502           1.077435   \n",
       "43             G__Prevotella        -1.298798           2.274305   \n",
       "44      G__Propionibacterium         0.021237          -3.960076   \n",
       "45   G__Pseudoflavonifractor         1.076430          -0.746628   \n",
       "46              G__Roseburia        -0.447115           0.937808   \n",
       "47           G__Ruminococcus        -0.594982           1.139950   \n",
       "48          G__Ruminococcus2         1.018396          -0.803748   \n",
       "49          G__Streptococcus         1.485427          -1.445548   \n",
       "50        G__Subdoligranulum        -0.382708           0.877074   \n",
       "51             G__Sutterella        -0.904844           1.446651   \n",
       "52            G__Veillonella         1.822902          -1.805266   \n",
       "53               K__Bacteria         0.341667          -0.698207   \n",
       "54          O__Bacteroidales         0.079874           0.169454   \n",
       "55          O__Clostridiales        -0.853735           1.626636   \n",
       "56             P__Firmicutes        -0.281443           0.781096   \n",
       "57         P__Proteobacteria         0.294002          -0.095627   \n",
       "\n",
       "    se_(Intercept)  se_Materialstool  W_(Intercept)  W_Materialstool  \\\n",
       "1         0.084667          0.240215       7.349298        -4.726335   \n",
       "2         0.087948          0.206243      -2.784116         5.521408   \n",
       "3         0.047240          0.240240       6.141850        -0.388054   \n",
       "4         0.070058          0.267471     -12.998019         5.089918   \n",
       "5         0.121614          0.243401      -5.150980         5.566663   \n",
       "6         0.051473          0.235775      -4.299020         3.530697   \n",
       "7         0.141956          0.277447      -7.884995         7.403595   \n",
       "8         0.049511          0.267302     -45.086838        10.327947   \n",
       "9         0.084166          0.241943     -10.687962         7.198011   \n",
       "10        0.009292          0.238216      10.110716         1.291142   \n",
       "11        0.019929          0.251908      38.595829        -1.761791   \n",
       "12             NaN          0.232735            NaN        -3.682629   \n",
       "13        0.161801          0.272098      -8.988602         9.771556   \n",
       "14        0.019386          0.272199     -33.388873         4.133151   \n",
       "15        0.033218          0.241143      -6.706708         2.763252   \n",
       "16        0.055301          0.252218       8.443671        -0.639521   \n",
       "17        0.024509          0.266246      54.663126        -3.942765   \n",
       "18        0.014645          0.233945      17.320602         0.576135   \n",
       "19        0.038218          0.222212      30.141802        -4.231295   \n",
       "20        0.041478          0.254641       8.682508        -0.055435   \n",
       "21        0.013978          0.231362      93.125728        -4.915898   \n",
       "22        0.023825          0.230229      32.116993        -1.997973   \n",
       "23        0.020977          0.234792      38.331445        -1.855022   \n",
       "24        0.080750          0.270557       0.346929         1.514608   \n",
       "25        0.020116          0.241223       5.148135         1.368582   \n",
       "26        0.061618          0.251195      17.211907        -3.349624   \n",
       "27        0.109288          0.231263       4.398984       -10.690715   \n",
       "28        0.109624          0.352046     -13.463841         6.421676   \n",
       "29        0.084707          0.233293       5.677914        -0.564580   \n",
       "30             NaN          0.222247            NaN        -6.357542   \n",
       "31        0.036436          0.321988      11.169347        -0.155892   \n",
       "32        0.075477          0.268543      -4.685631         3.222620   \n",
       "33        0.015049          0.220743      26.288507        -0.191999   \n",
       "34        0.043998          0.217692      30.530376        -5.136258   \n",
       "35             NaN          0.228057            NaN        14.632561   \n",
       "36        0.091848          0.208891       0.144181        -9.833678   \n",
       "37        0.048547          0.253371       5.025268         0.414615   \n",
       "38        0.064974          0.242239      -9.901075         4.859564   \n",
       "39        0.039809          0.236794     -34.341006         8.773173   \n",
       "40        0.009292          0.206212    -141.401472         8.741638   \n",
       "41        0.043307          0.342503     -11.949372         3.009420   \n",
       "42        0.011424          0.252123     -52.738554         4.273441   \n",
       "43        0.106783          0.339712     -12.162957         6.694806   \n",
       "44        0.220510          0.300951       0.096308       -13.158559   \n",
       "45        0.041349          0.226903      26.033038        -3.290520   \n",
       "46        0.088235          0.265483      -5.067303         3.532467   \n",
       "47        0.111390          0.308842      -5.341456         3.691042   \n",
       "48        0.053645          0.255557      18.983896        -3.145077   \n",
       "49        0.033607          0.240776      44.199426        -6.003710   \n",
       "50        0.058513          0.222406      -6.540584         3.943581   \n",
       "51        0.059713          0.273669     -15.153182         5.286140   \n",
       "52        0.043498          0.232927      41.907635        -7.750334   \n",
       "53        0.143344          0.344154       2.383542        -2.028763   \n",
       "54        0.123975          0.339259       0.644272         0.499481   \n",
       "55        0.105885          0.343306      -8.062889         4.738149   \n",
       "56        0.079391          0.313618      -3.545026         2.490598   \n",
       "57        0.115261          0.292088       2.550755        -0.327389   \n",
       "\n",
       "    p_(Intercept)  p_Materialstool  q_(Intercept)  q_Materialstool  \\\n",
       "1    2.339730e-08     4.394869e-05   5.381379e-07     1.538204e-03   \n",
       "2    1.549357e-02     9.850289e-05   1.468021e-01     3.349098e-03   \n",
       "3    8.439600e-06     7.025268e-01   1.350336e-04     1.000000e+00   \n",
       "4    2.594480e-16     7.946558e-06   9.599576e-15     3.178623e-04   \n",
       "5    2.282606e-06     4.473948e-07   4.336952e-05     2.013277e-05   \n",
       "6    2.135949e-04     1.568833e-03   2.707359e-03     3.922084e-02   \n",
       "7    3.244568e-11     2.456520e-10   8.435877e-10     1.252825e-08   \n",
       "8    3.239265e-26     1.078188e-10   1.457669e-24     5.714397e-09   \n",
       "9    2.796607e-16     5.815055e-10   1.006778e-14     2.907527e-08   \n",
       "10   6.173480e-13     2.035526e-01   1.790309e-11     1.000000e+00   \n",
       "11   1.158202e-29     8.709511e-02   5.675188e-28     1.000000e+00   \n",
       "12   1.000000e+00     7.734062e-04   1.000000e+00     2.088197e-02   \n",
       "13   2.193241e-13     7.770720e-15   6.799046e-12     4.351603e-13   \n",
       "14   2.659573e-30     1.835356e-04   1.329786e-28     6.056674e-03   \n",
       "15   4.813296e-08     8.607636e-03   1.058925e-06     1.635451e-01   \n",
       "16   1.909158e-11     5.251882e-01   5.154727e-10     1.000000e+00   \n",
       "17   4.350283e-42     2.851392e-04   2.479662e-40     8.269038e-03   \n",
       "18   8.142366e-18     5.685544e-01   3.175523e-16     1.000000e+00   \n",
       "19   5.451009e-24     2.015004e-04   2.343934e-22     6.448014e-03   \n",
       "20   7.930800e-12     9.559966e-01   2.220624e-10     1.000000e+00   \n",
       "21   1.601076e-39     2.732859e-05   8.966024e-38     9.838294e-04   \n",
       "22   3.759183e-33     5.165201e-02   1.992367e-31     8.264322e-01   \n",
       "23   5.553302e-32     7.136374e-02   2.887717e-30     1.000000e+00   \n",
       "24   7.302269e-01     1.367133e-01   1.000000e+00     1.000000e+00   \n",
       "25   9.547220e-06     1.796103e-01   1.432083e-04     1.000000e+00   \n",
       "26   3.589974e-22     1.582371e-03   1.471889e-20     3.922084e-02   \n",
       "27   2.082584e-04     2.136538e-10   2.707359e-03     1.111000e-08   \n",
       "28   1.913335e-16     1.207586e-07   7.270674e-15     5.675655e-06   \n",
       "29   1.340167e-06     5.755118e-01   2.814350e-05     1.000000e+00   \n",
       "30   1.000000e+00     8.293271e-07   1.000000e+00     3.566107e-05   \n",
       "31   4.345468e-13     8.770136e-01   1.303640e-11     1.000000e+00   \n",
       "32   1.441240e-05     1.974569e-03   2.017736e-04     4.426315e-02   \n",
       "33   1.538129e-25     8.487929e-01   6.767769e-24     1.000000e+00   \n",
       "34   1.014682e-31     5.836252e-06   5.174876e-30     2.392863e-04   \n",
       "35   1.000000e+00     9.172453e-14   1.000000e+00     5.044849e-12   \n",
       "36   8.882220e-01     1.853296e-06   1.000000e+00     7.783842e-05   \n",
       "37   6.811668e-06     6.801970e-01   1.157983e-04     1.000000e+00   \n",
       "38   9.711423e-14     1.051709e-05   3.107655e-12     3.996494e-04   \n",
       "39   1.957532e-36     1.085294e-11   1.076643e-34     5.860586e-10   \n",
       "40   1.508202e-27     1.069592e-07   7.088547e-26     5.134040e-06   \n",
       "41   2.086981e-15     4.320669e-03   7.095735e-14     8.641339e-02   \n",
       "42   2.487221e-26     2.634938e-04   1.144122e-24     7.904815e-03   \n",
       "43   1.132405e-14     6.382846e-08   3.736938e-13     3.127594e-06   \n",
       "44   9.238414e-01     6.729462e-15   1.000000e+00     3.835793e-13   \n",
       "45   3.590618e-29     1.924485e-03   1.723496e-27     4.426315e-02   \n",
       "46   5.656698e-06     8.841901e-04   1.018206e-04     2.298894e-02   \n",
       "47   1.974051e-06     5.282686e-04   3.948101e-05     1.479152e-02   \n",
       "48   2.142503e-23     2.908175e-03   8.998512e-22     6.107167e-02   \n",
       "49   1.415376e-35     4.670014e-07   7.643030e-34     2.054806e-05   \n",
       "50   1.846993e-08     2.221760e-04   4.432783e-07     6.887456e-03   \n",
       "51   3.734505e-16     8.642440e-06   1.307077e-14     3.370552e-04   \n",
       "52   9.976108e-22     1.363889e-07   3.990443e-20     6.273888e-06   \n",
       "53   1.982421e-02     4.623377e-02   1.585937e-01     7.859740e-01   \n",
       "54   5.225992e-01     6.198215e-01   1.000000e+00     1.000000e+00   \n",
       "55   3.428820e-11     1.332160e-05   8.572050e-10     4.928992e-04   \n",
       "56   7.838558e-04     1.563809e-02   8.622414e-03     2.814856e-01   \n",
       "57   1.468021e-02     7.450809e-01   1.468021e-01     1.000000e+00   \n",
       "\n",
       "    diff_(Intercept)  diff_Materialstool  passed_ss_(Intercept)  \\\n",
       "1               True                True                  False   \n",
       "2              False                True                  False   \n",
       "3               True               False                  False   \n",
       "4               True                True                   True   \n",
       "5               True                True                   True   \n",
       "6               True                True                  False   \n",
       "7               True                True                   True   \n",
       "8               True                True                   True   \n",
       "9               True                True                   True   \n",
       "10              True               False                   True   \n",
       "11              True               False                   True   \n",
       "12             False                True                  False   \n",
       "13              True                True                   True   \n",
       "14              True                True                   True   \n",
       "15              True               False                   True   \n",
       "16              True               False                   True   \n",
       "17              True                True                   True   \n",
       "18              True               False                   True   \n",
       "19              True                True                   True   \n",
       "20              True               False                   True   \n",
       "21              True                True                   True   \n",
       "22              True               False                   True   \n",
       "23              True               False                   True   \n",
       "24             False               False                  False   \n",
       "25              True               False                   True   \n",
       "26              True                True                   True   \n",
       "27              True                True                   True   \n",
       "28              True                True                   True   \n",
       "29              True               False                   True   \n",
       "30             False                True                  False   \n",
       "31              True               False                   True   \n",
       "32              True                True                   True   \n",
       "33              True               False                   True   \n",
       "34              True                True                   True   \n",
       "35             False                True                  False   \n",
       "36             False                True                  False   \n",
       "37              True               False                   True   \n",
       "38              True                True                   True   \n",
       "39              True                True                   True   \n",
       "40              True                True                   True   \n",
       "41              True               False                   True   \n",
       "42              True                True                   True   \n",
       "43              True                True                   True   \n",
       "44             False                True                  False   \n",
       "45              True                True                   True   \n",
       "46              True                True                   True   \n",
       "47              True                True                   True   \n",
       "48              True               False                   True   \n",
       "49              True                True                   True   \n",
       "50              True                True                   True   \n",
       "51              True                True                   True   \n",
       "52              True                True                   True   \n",
       "53             False               False                  False   \n",
       "54             False               False                   True   \n",
       "55              True                True                   True   \n",
       "56              True               False                   True   \n",
       "57             False               False                   True   \n",
       "\n",
       "    passed_ss_Materialstool  \n",
       "1                     False  \n",
       "2                      True  \n",
       "3                     False  \n",
       "4                      True  \n",
       "5                      True  \n",
       "6                     False  \n",
       "7                      True  \n",
       "8                      True  \n",
       "9                      True  \n",
       "10                    False  \n",
       "11                    False  \n",
       "12                     True  \n",
       "13                     True  \n",
       "14                     True  \n",
       "15                    False  \n",
       "16                    False  \n",
       "17                     True  \n",
       "18                    False  \n",
       "19                     True  \n",
       "20                    False  \n",
       "21                     True  \n",
       "22                    False  \n",
       "23                    False  \n",
       "24                    False  \n",
       "25                    False  \n",
       "26                     True  \n",
       "27                     True  \n",
       "28                     True  \n",
       "29                    False  \n",
       "30                     True  \n",
       "31                    False  \n",
       "32                     True  \n",
       "33                    False  \n",
       "34                     True  \n",
       "35                     True  \n",
       "36                    False  \n",
       "37                    False  \n",
       "38                     True  \n",
       "39                     True  \n",
       "40                     True  \n",
       "41                    False  \n",
       "42                     True  \n",
       "43                     True  \n",
       "44                     True  \n",
       "45                     True  \n",
       "46                     True  \n",
       "47                     True  \n",
       "48                    False  \n",
       "49                     True  \n",
       "50                     True  \n",
       "51                     True  \n",
       "52                     True  \n",
       "53                    False  \n",
       "54                     True  \n",
       "55                     True  \n",
       "56                    False  \n",
       "57                    False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_prim_BSPSPC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3bc6fce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_filt = res_prim_BSPSPC[(res_prim_BSPSPC['passed_ss_Materialstool'] == True) & (res_prim_BSPSPC['q_Materialstool'] < 0.1) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7704b864",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_filt = res_prim_filt.sort_values(by='lfc_Materialstool', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e7aeca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 'direct' column based on lfc_age\n",
    "res_prim_filt['direct'] = ['Positive LFC' if lfc > 0 else 'Negative LFC' for lfc in res_prim_filt['lfc_Materialstool']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6f38045",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert 'taxon' and 'direct' to categorical variables\n",
    "res_prim_filt['taxon'] = pd.Categorical(res_prim_filt['taxon'], categories=res_prim_filt['taxon'].unique())\n",
    "res_prim_filt['direct'] = pd.Categorical(res_prim_filt['direct'], categories=['Positive LFC', 'Negative LFC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f7aa0b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive_color = 'red'\n",
    "negative_color = 'blue'\n",
    "# Plot\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.bar(x=res_prim_filt['taxon'], height=res_prim_filt['lfc_Materialstool'], width=0.7, color=[positive_color if direct == 'Positive LFC' else negative_color for direct in res_prim_filt['direct']])\n",
    "plt.errorbar(x=res_prim_filt['taxon'], y=res_prim_filt['lfc_Materialstool'], yerr=res_prim_filt['se_Materialstool'], fmt='o', color='black')\n",
    "plt.xlabel(None)\n",
    "plt.ylabel('Log fold change')\n",
    "plt.xticks(rotation=60, ha='right')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
    "# Set y-axis limits\n",
    "plt.ylim(-5, 5)\n",
    "\n",
    "# Customize legend\n",
    "from matplotlib.patches import Patch\n",
    "legend_elements = [Patch(facecolor=positive_color, label='Positive LFC'),\n",
    "                   Patch(facecolor=negative_color, label='Negative LFC')]\n",
    "plt.legend(handles=legend_elements)\n",
    "plt.savefig('./results/diff_abund_primary_BSPSPC.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e29f3ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxon</th>\n",
       "      <th>lfc_(Intercept)</th>\n",
       "      <th>lfc_Materialstool</th>\n",
       "      <th>se_(Intercept)</th>\n",
       "      <th>se_Materialstool</th>\n",
       "      <th>W_(Intercept)</th>\n",
       "      <th>W_Materialstool</th>\n",
       "      <th>p_(Intercept)</th>\n",
       "      <th>p_Materialstool</th>\n",
       "      <th>q_(Intercept)</th>\n",
       "      <th>q_Materialstool</th>\n",
       "      <th>diff_(Intercept)</th>\n",
       "      <th>diff_Materialstool</th>\n",
       "      <th>passed_ss_(Intercept)</th>\n",
       "      <th>passed_ss_Materialstool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C__Alphaproteobacteria</td>\n",
       "      <td>0.749286</td>\n",
       "      <td>-0.466266</td>\n",
       "      <td>0.080182</td>\n",
       "      <td>0.122561</td>\n",
       "      <td>9.344798</td>\n",
       "      <td>-3.804351</td>\n",
       "      <td>8.761606e-18</td>\n",
       "      <td>1.826454e-04</td>\n",
       "      <td>2.102785e-16</td>\n",
       "      <td>3.835553e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F__Coriobacteriaceae</td>\n",
       "      <td>0.079934</td>\n",
       "      <td>-0.028950</td>\n",
       "      <td>0.022673</td>\n",
       "      <td>0.087995</td>\n",
       "      <td>3.525587</td>\n",
       "      <td>-0.329001</td>\n",
       "      <td>5.001696e-04</td>\n",
       "      <td>7.424226e-01</td>\n",
       "      <td>3.501187e-03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F__Enterobacteriaceae</td>\n",
       "      <td>-0.659855</td>\n",
       "      <td>0.875974</td>\n",
       "      <td>0.041884</td>\n",
       "      <td>0.105725</td>\n",
       "      <td>-15.754257</td>\n",
       "      <td>8.285390</td>\n",
       "      <td>3.883405e-39</td>\n",
       "      <td>7.641237e-15</td>\n",
       "      <td>1.631030e-37</td>\n",
       "      <td>2.750845e-13</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F__Erysipelotrichaceae</td>\n",
       "      <td>-0.197106</td>\n",
       "      <td>0.292108</td>\n",
       "      <td>0.036310</td>\n",
       "      <td>0.102134</td>\n",
       "      <td>-5.428362</td>\n",
       "      <td>2.860042</td>\n",
       "      <td>9.122035e-08</td>\n",
       "      <td>4.424006e-03</td>\n",
       "      <td>1.094644e-06</td>\n",
       "      <td>7.078409e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F__Lachnospiraceae</td>\n",
       "      <td>-1.146277</td>\n",
       "      <td>1.554092</td>\n",
       "      <td>0.079867</td>\n",
       "      <td>0.123116</td>\n",
       "      <td>-14.352332</td>\n",
       "      <td>12.622940</td>\n",
       "      <td>3.263666e-40</td>\n",
       "      <td>1.854453e-32</td>\n",
       "      <td>1.403376e-38</td>\n",
       "      <td>8.901376e-31</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>O__Bacteroidales</td>\n",
       "      <td>-1.298481</td>\n",
       "      <td>1.609754</td>\n",
       "      <td>0.039120</td>\n",
       "      <td>0.099323</td>\n",
       "      <td>-33.192457</td>\n",
       "      <td>16.207254</td>\n",
       "      <td>5.417573e-71</td>\n",
       "      <td>8.828245e-35</td>\n",
       "      <td>2.979665e-69</td>\n",
       "      <td>4.502405e-33</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>O__Clostridiales</td>\n",
       "      <td>-0.366019</td>\n",
       "      <td>0.551367</td>\n",
       "      <td>0.062197</td>\n",
       "      <td>0.130723</td>\n",
       "      <td>-5.884793</td>\n",
       "      <td>4.217823</td>\n",
       "      <td>7.815313e-09</td>\n",
       "      <td>2.986346e-05</td>\n",
       "      <td>1.172297e-07</td>\n",
       "      <td>7.167229e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>P__Firmicutes</td>\n",
       "      <td>0.093975</td>\n",
       "      <td>0.049357</td>\n",
       "      <td>0.054733</td>\n",
       "      <td>0.115478</td>\n",
       "      <td>1.716968</td>\n",
       "      <td>0.427413</td>\n",
       "      <td>8.666557e-02</td>\n",
       "      <td>6.692807e-01</td>\n",
       "      <td>4.333278e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>P__Parcubacteria</td>\n",
       "      <td>1.071242</td>\n",
       "      <td>-2.592526</td>\n",
       "      <td>0.075929</td>\n",
       "      <td>0.111306</td>\n",
       "      <td>14.108451</td>\n",
       "      <td>-23.291782</td>\n",
       "      <td>2.964456e-26</td>\n",
       "      <td>6.488376e-44</td>\n",
       "      <td>9.486259e-25</td>\n",
       "      <td>3.828142e-42</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>P__Proteobacteria</td>\n",
       "      <td>1.423529</td>\n",
       "      <td>-1.568030</td>\n",
       "      <td>0.082051</td>\n",
       "      <td>0.125668</td>\n",
       "      <td>17.349302</td>\n",
       "      <td>-12.477514</td>\n",
       "      <td>1.437762e-45</td>\n",
       "      <td>2.033984e-28</td>\n",
       "      <td>6.469928e-44</td>\n",
       "      <td>9.356327e-27</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     taxon  lfc_(Intercept)  lfc_Materialstool  \\\n",
       "1   C__Alphaproteobacteria         0.749286          -0.466266   \n",
       "2     F__Coriobacteriaceae         0.079934          -0.028950   \n",
       "3    F__Enterobacteriaceae        -0.659855           0.875974   \n",
       "4   F__Erysipelotrichaceae        -0.197106           0.292108   \n",
       "5       F__Lachnospiraceae        -1.146277           1.554092   \n",
       "..                     ...              ...                ...   \n",
       "67        O__Bacteroidales        -1.298481           1.609754   \n",
       "68        O__Clostridiales        -0.366019           0.551367   \n",
       "69           P__Firmicutes         0.093975           0.049357   \n",
       "70        P__Parcubacteria         1.071242          -2.592526   \n",
       "71       P__Proteobacteria         1.423529          -1.568030   \n",
       "\n",
       "    se_(Intercept)  se_Materialstool  W_(Intercept)  W_Materialstool  \\\n",
       "1         0.080182          0.122561       9.344798        -3.804351   \n",
       "2         0.022673          0.087995       3.525587        -0.329001   \n",
       "3         0.041884          0.105725     -15.754257         8.285390   \n",
       "4         0.036310          0.102134      -5.428362         2.860042   \n",
       "5         0.079867          0.123116     -14.352332        12.622940   \n",
       "..             ...               ...            ...              ...   \n",
       "67        0.039120          0.099323     -33.192457        16.207254   \n",
       "68        0.062197          0.130723      -5.884793         4.217823   \n",
       "69        0.054733          0.115478       1.716968         0.427413   \n",
       "70        0.075929          0.111306      14.108451       -23.291782   \n",
       "71        0.082051          0.125668      17.349302       -12.477514   \n",
       "\n",
       "    p_(Intercept)  p_Materialstool  q_(Intercept)  q_Materialstool  \\\n",
       "1    8.761606e-18     1.826454e-04   2.102785e-16     3.835553e-03   \n",
       "2    5.001696e-04     7.424226e-01   3.501187e-03     1.000000e+00   \n",
       "3    3.883405e-39     7.641237e-15   1.631030e-37     2.750845e-13   \n",
       "4    9.122035e-08     4.424006e-03   1.094644e-06     7.078409e-02   \n",
       "5    3.263666e-40     1.854453e-32   1.403376e-38     8.901376e-31   \n",
       "..            ...              ...            ...              ...   \n",
       "67   5.417573e-71     8.828245e-35   2.979665e-69     4.502405e-33   \n",
       "68   7.815313e-09     2.986346e-05   1.172297e-07     7.167229e-04   \n",
       "69   8.666557e-02     6.692807e-01   4.333278e-01     1.000000e+00   \n",
       "70   2.964456e-26     6.488376e-44   9.486259e-25     3.828142e-42   \n",
       "71   1.437762e-45     2.033984e-28   6.469928e-44     9.356327e-27   \n",
       "\n",
       "    diff_(Intercept)  diff_Materialstool  passed_ss_(Intercept)  \\\n",
       "1               True                True                   True   \n",
       "2               True               False                  False   \n",
       "3               True                True                  False   \n",
       "4               True               False                   True   \n",
       "5               True                True                   True   \n",
       "..               ...                 ...                    ...   \n",
       "67              True                True                  False   \n",
       "68              True                True                   True   \n",
       "69             False               False                  False   \n",
       "70              True                True                   True   \n",
       "71              True                True                   True   \n",
       "\n",
       "    passed_ss_Materialstool  \n",
       "1                      True  \n",
       "2                     False  \n",
       "3                      True  \n",
       "4                     False  \n",
       "5                      True  \n",
       "..                      ...  \n",
       "67                    False  \n",
       "68                     True  \n",
       "69                    False  \n",
       "70                     True  \n",
       "71                     True  \n",
       "\n",
       "[71 rows x 15 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_prim_PSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "074eef7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_filt = res_prim_PSC[(res_prim_PSC['passed_ss_Materialstool'] == True) & (res_prim_PSC['q_Materialstool'] < 0.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce7dba19",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_filt = res_prim_filt.sort_values(by='lfc_Materialstool', ascending=False)\n",
    "# Create 'direct' column based on lfc_age\n",
    "res_prim_filt['direct'] = ['Positive LFC' if lfc > 0 else 'Negative LFC' for lfc in res_prim_filt['lfc_Materialstool']]\n",
    "# Convert 'taxon' and 'direct' to categorical variables\n",
    "res_prim_filt['taxon'] = pd.Categorical(res_prim_filt['taxon'], categories=res_prim_filt['taxon'].unique())\n",
    "res_prim_filt['direct'] = pd.Categorical(res_prim_filt['direct'], categories=['Positive LFC', 'Negative LFC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "da5ea45f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive_color = 'red'\n",
    "negative_color = 'blue'\n",
    "# Plot\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.bar(x=res_prim_filt['taxon'], height=res_prim_filt['lfc_Materialstool'], width=0.7, color=[positive_color if direct == 'Positive LFC' else negative_color for direct in res_prim_filt['direct']])\n",
    "plt.errorbar(x=res_prim_filt['taxon'], y=res_prim_filt['lfc_Materialstool'], yerr=res_prim_filt['se_Materialstool'], fmt='o', color='black')\n",
    "plt.xlabel(None)\n",
    "plt.ylabel('Log fold change')\n",
    "plt.xticks(rotation=60, ha='right')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
    "# Set y-axis limits\n",
    "plt.ylim(-5, 5)\n",
    "\n",
    "# Customize legend\n",
    "from matplotlib.patches import Patch\n",
    "legend_elements = [Patch(facecolor=positive_color, label='Positive LFC'),\n",
    "                   Patch(facecolor=negative_color, label='Negative LFC')]\n",
    "plt.legend(handles=legend_elements)\n",
    "plt.savefig('./results/diff_abund_primary_PSC.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9031dd7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "common_taxa = set(res_prim_BSPSPC['taxon']).intersection(res_prim_PSC['taxon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20bc76b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       C__Alphaproteobacteria\n",
       "2       C__Deltaproteobacteria\n",
       "3        F__Enterobacteriaceae\n",
       "4       F__Erysipelotrichaceae\n",
       "5           F__Lachnospiraceae\n",
       "6        F__Porphyromonadaceae\n",
       "7           F__Ruminococcaceae\n",
       "8               G__Akkermansia\n",
       "9                 G__Alistipes\n",
       "10             G__Anaerostipes\n",
       "11            G__Anaerotruncus\n",
       "12              G__Anaerovorax\n",
       "13              G__Bacteroides\n",
       "14              G__Barnesiella\n",
       "15                G__Bilophila\n",
       "16                  G__Blautia\n",
       "17           G__Butyricicoccus\n",
       "18            G__Butyricimonas\n",
       "19               G__Catabacter\n",
       "20           G__Clostridium_IV\n",
       "21        G__Clostridium_XVIII\n",
       "22         G__Clostridium_XlVa\n",
       "23         G__Clostridium_XlVb\n",
       "24              G__Collinsella\n",
       "25              G__Coprobacter\n",
       "26              G__Coprococcus\n",
       "27          G__Corynebacterium\n",
       "28                G__Dialister\n",
       "29                    G__Dorea\n",
       "30              G__Eggerthella\n",
       "31     G__Escherichia.Shigella\n",
       "32         G__Faecalibacterium\n",
       "33           G__Flavonifractor\n",
       "34         G__Fusicatenibacter\n",
       "35             G__Holdemanella\n",
       "36           G__Microbacterium\n",
       "37              G__Odoribacter\n",
       "38            G__Oscillibacter\n",
       "39          G__Parabacteroides\n",
       "40           G__Paraprevotella\n",
       "41           G__Parasutterella\n",
       "42    G__Phascolarctobacterium\n",
       "43               G__Prevotella\n",
       "44        G__Propionibacterium\n",
       "45     G__Pseudoflavonifractor\n",
       "46                G__Roseburia\n",
       "47             G__Ruminococcus\n",
       "48            G__Ruminococcus2\n",
       "49            G__Streptococcus\n",
       "50          G__Subdoligranulum\n",
       "51               G__Sutterella\n",
       "52              G__Veillonella\n",
       "53                 K__Bacteria\n",
       "54            O__Bacteroidales\n",
       "55            O__Clostridiales\n",
       "56               P__Firmicutes\n",
       "57           P__Proteobacteria\n",
       "Name: taxon, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_prim_BSPSPC['taxon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "91ae2d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     C__Alphaproteobacteria\n",
       "2       F__Coriobacteriaceae\n",
       "3      F__Enterobacteriaceae\n",
       "4     F__Erysipelotrichaceae\n",
       "5         F__Lachnospiraceae\n",
       "               ...          \n",
       "67          O__Bacteroidales\n",
       "68          O__Clostridiales\n",
       "69             P__Firmicutes\n",
       "70          P__Parcubacteria\n",
       "71         P__Proteobacteria\n",
       "Name: taxon, Length: 71, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_prim_PSC['taxon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c6c1de3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_PSC_unique = res_prim_PSC[~res_prim_PSC['taxon'].isin(common_taxa)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e777df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxon</th>\n",
       "      <th>lfc_(Intercept)</th>\n",
       "      <th>lfc_Materialstool</th>\n",
       "      <th>se_(Intercept)</th>\n",
       "      <th>se_Materialstool</th>\n",
       "      <th>W_(Intercept)</th>\n",
       "      <th>W_Materialstool</th>\n",
       "      <th>p_(Intercept)</th>\n",
       "      <th>p_Materialstool</th>\n",
       "      <th>q_(Intercept)</th>\n",
       "      <th>q_Materialstool</th>\n",
       "      <th>diff_(Intercept)</th>\n",
       "      <th>diff_Materialstool</th>\n",
       "      <th>passed_ss_(Intercept)</th>\n",
       "      <th>passed_ss_Materialstool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F__Coriobacteriaceae</td>\n",
       "      <td>0.079934</td>\n",
       "      <td>-0.028950</td>\n",
       "      <td>0.022673</td>\n",
       "      <td>0.087995</td>\n",
       "      <td>3.525587</td>\n",
       "      <td>-0.329001</td>\n",
       "      <td>5.001696e-04</td>\n",
       "      <td>7.424226e-01</td>\n",
       "      <td>3.501187e-03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>G__Acidaminococcus</td>\n",
       "      <td>-2.604613</td>\n",
       "      <td>2.913143</td>\n",
       "      <td>0.021455</td>\n",
       "      <td>0.087972</td>\n",
       "      <td>-121.398226</td>\n",
       "      <td>33.114627</td>\n",
       "      <td>5.850284e-98</td>\n",
       "      <td>1.002724e-50</td>\n",
       "      <td>3.510170e-96</td>\n",
       "      <td>6.617975e-49</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>G__Bacillus</td>\n",
       "      <td>1.343719</td>\n",
       "      <td>-4.622232</td>\n",
       "      <td>0.166160</td>\n",
       "      <td>0.193005</td>\n",
       "      <td>8.086910</td>\n",
       "      <td>-23.948726</td>\n",
       "      <td>1.654811e-12</td>\n",
       "      <td>9.599379e-43</td>\n",
       "      <td>2.813179e-11</td>\n",
       "      <td>5.567640e-41</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>G__Bifidobacterium</td>\n",
       "      <td>-0.271837</td>\n",
       "      <td>0.311700</td>\n",
       "      <td>0.023059</td>\n",
       "      <td>0.086819</td>\n",
       "      <td>-11.788992</td>\n",
       "      <td>3.590225</td>\n",
       "      <td>4.442485e-24</td>\n",
       "      <td>4.271121e-04</td>\n",
       "      <td>1.332745e-22</td>\n",
       "      <td>8.542243e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>G__Flavobacterium</td>\n",
       "      <td>1.442258</td>\n",
       "      <td>-5.160314</td>\n",
       "      <td>0.083782</td>\n",
       "      <td>0.118459</td>\n",
       "      <td>17.214321</td>\n",
       "      <td>-43.562075</td>\n",
       "      <td>3.298214e-34</td>\n",
       "      <td>2.160740e-75</td>\n",
       "      <td>1.286304e-32</td>\n",
       "      <td>1.490910e-73</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>G__Fluviicola</td>\n",
       "      <td>1.165715</td>\n",
       "      <td>-4.102614</td>\n",
       "      <td>0.085856</td>\n",
       "      <td>0.119747</td>\n",
       "      <td>13.577497</td>\n",
       "      <td>-34.260585</td>\n",
       "      <td>1.995898e-24</td>\n",
       "      <td>4.606740e-57</td>\n",
       "      <td>6.187285e-23</td>\n",
       "      <td>3.132583e-55</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>G__Legionella</td>\n",
       "      <td>1.140134</td>\n",
       "      <td>-3.187326</td>\n",
       "      <td>0.075870</td>\n",
       "      <td>0.111341</td>\n",
       "      <td>15.027387</td>\n",
       "      <td>-28.626738</td>\n",
       "      <td>6.784470e-24</td>\n",
       "      <td>4.459768e-41</td>\n",
       "      <td>1.967496e-22</td>\n",
       "      <td>2.363677e-39</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>G__Limnohabitans</td>\n",
       "      <td>1.070801</td>\n",
       "      <td>-2.453621</td>\n",
       "      <td>0.073996</td>\n",
       "      <td>0.109707</td>\n",
       "      <td>14.471017</td>\n",
       "      <td>-22.365259</td>\n",
       "      <td>3.964195e-27</td>\n",
       "      <td>1.564345e-42</td>\n",
       "      <td>1.347826e-25</td>\n",
       "      <td>8.916767e-41</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>G__Olsenella</td>\n",
       "      <td>0.248949</td>\n",
       "      <td>-0.129026</td>\n",
       "      <td>0.014193</td>\n",
       "      <td>0.082123</td>\n",
       "      <td>17.539707</td>\n",
       "      <td>-1.571135</td>\n",
       "      <td>2.693741e-36</td>\n",
       "      <td>1.185452e-01</td>\n",
       "      <td>1.077496e-34</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>G__Oxalobacter</td>\n",
       "      <td>-0.442838</td>\n",
       "      <td>0.581044</td>\n",
       "      <td>0.020565</td>\n",
       "      <td>0.082211</td>\n",
       "      <td>-21.533891</td>\n",
       "      <td>7.067727</td>\n",
       "      <td>1.337109e-47</td>\n",
       "      <td>5.649646e-11</td>\n",
       "      <td>6.150699e-46</td>\n",
       "      <td>1.751390e-09</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>G__Polynucleobacter</td>\n",
       "      <td>1.115723</td>\n",
       "      <td>-3.482859</td>\n",
       "      <td>0.081730</td>\n",
       "      <td>0.116204</td>\n",
       "      <td>13.651333</td>\n",
       "      <td>-29.971836</td>\n",
       "      <td>1.516678e-23</td>\n",
       "      <td>2.758807e-48</td>\n",
       "      <td>4.095030e-22</td>\n",
       "      <td>1.765636e-46</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>G__Pseudarcicella</td>\n",
       "      <td>1.140649</td>\n",
       "      <td>-2.331526</td>\n",
       "      <td>0.084587</td>\n",
       "      <td>0.118640</td>\n",
       "      <td>13.484940</td>\n",
       "      <td>-19.652158</td>\n",
       "      <td>1.282664e-22</td>\n",
       "      <td>5.785967e-33</td>\n",
       "      <td>3.334927e-21</td>\n",
       "      <td>2.892983e-31</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>G__Slackia</td>\n",
       "      <td>0.116157</td>\n",
       "      <td>0.051255</td>\n",
       "      <td>0.029561</td>\n",
       "      <td>0.086619</td>\n",
       "      <td>3.929405</td>\n",
       "      <td>0.591724</td>\n",
       "      <td>1.302847e-04</td>\n",
       "      <td>5.549379e-01</td>\n",
       "      <td>1.042277e-03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>G__Sporobacter</td>\n",
       "      <td>-0.004455</td>\n",
       "      <td>0.085783</td>\n",
       "      <td>0.019364</td>\n",
       "      <td>0.085567</td>\n",
       "      <td>-0.230065</td>\n",
       "      <td>1.002523</td>\n",
       "      <td>8.183976e-01</td>\n",
       "      <td>3.179253e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>P__Parcubacteria</td>\n",
       "      <td>1.071242</td>\n",
       "      <td>-2.592526</td>\n",
       "      <td>0.075929</td>\n",
       "      <td>0.111306</td>\n",
       "      <td>14.108451</td>\n",
       "      <td>-23.291782</td>\n",
       "      <td>2.964456e-26</td>\n",
       "      <td>6.488376e-44</td>\n",
       "      <td>9.486259e-25</td>\n",
       "      <td>3.828142e-42</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   taxon  lfc_(Intercept)  lfc_Materialstool  se_(Intercept)  \\\n",
       "2   F__Coriobacteriaceae         0.079934          -0.028950        0.022673   \n",
       "8     G__Acidaminococcus        -2.604613           2.913143        0.021455   \n",
       "14           G__Bacillus         1.343719          -4.622232        0.166160   \n",
       "17    G__Bifidobacterium        -0.271837           0.311700        0.023059   \n",
       "36     G__Flavobacterium         1.442258          -5.160314        0.083782   \n",
       "38         G__Fluviicola         1.165715          -4.102614        0.085856   \n",
       "41         G__Legionella         1.140134          -3.187326        0.075870   \n",
       "42      G__Limnohabitans         1.070801          -2.453621        0.073996   \n",
       "45          G__Olsenella         0.248949          -0.129026        0.014193   \n",
       "47        G__Oxalobacter        -0.442838           0.581044        0.020565   \n",
       "52   G__Polynucleobacter         1.115723          -3.482859        0.081730   \n",
       "55     G__Pseudarcicella         1.140649          -2.331526        0.084587   \n",
       "60            G__Slackia         0.116157           0.051255        0.029561   \n",
       "61        G__Sporobacter        -0.004455           0.085783        0.019364   \n",
       "70      P__Parcubacteria         1.071242          -2.592526        0.075929   \n",
       "\n",
       "    se_Materialstool  W_(Intercept)  W_Materialstool  p_(Intercept)  \\\n",
       "2           0.087995       3.525587        -0.329001   5.001696e-04   \n",
       "8           0.087972    -121.398226        33.114627   5.850284e-98   \n",
       "14          0.193005       8.086910       -23.948726   1.654811e-12   \n",
       "17          0.086819     -11.788992         3.590225   4.442485e-24   \n",
       "36          0.118459      17.214321       -43.562075   3.298214e-34   \n",
       "38          0.119747      13.577497       -34.260585   1.995898e-24   \n",
       "41          0.111341      15.027387       -28.626738   6.784470e-24   \n",
       "42          0.109707      14.471017       -22.365259   3.964195e-27   \n",
       "45          0.082123      17.539707        -1.571135   2.693741e-36   \n",
       "47          0.082211     -21.533891         7.067727   1.337109e-47   \n",
       "52          0.116204      13.651333       -29.971836   1.516678e-23   \n",
       "55          0.118640      13.484940       -19.652158   1.282664e-22   \n",
       "60          0.086619       3.929405         0.591724   1.302847e-04   \n",
       "61          0.085567      -0.230065         1.002523   8.183976e-01   \n",
       "70          0.111306      14.108451       -23.291782   2.964456e-26   \n",
       "\n",
       "    p_Materialstool  q_(Intercept)  q_Materialstool  diff_(Intercept)  \\\n",
       "2      7.424226e-01   3.501187e-03     1.000000e+00              True   \n",
       "8      1.002724e-50   3.510170e-96     6.617975e-49              True   \n",
       "14     9.599379e-43   2.813179e-11     5.567640e-41              True   \n",
       "17     4.271121e-04   1.332745e-22     8.542243e-03              True   \n",
       "36     2.160740e-75   1.286304e-32     1.490910e-73              True   \n",
       "38     4.606740e-57   6.187285e-23     3.132583e-55              True   \n",
       "41     4.459768e-41   1.967496e-22     2.363677e-39              True   \n",
       "42     1.564345e-42   1.347826e-25     8.916767e-41              True   \n",
       "45     1.185452e-01   1.077496e-34     1.000000e+00              True   \n",
       "47     5.649646e-11   6.150699e-46     1.751390e-09              True   \n",
       "52     2.758807e-48   4.095030e-22     1.765636e-46              True   \n",
       "55     5.785967e-33   3.334927e-21     2.892983e-31              True   \n",
       "60     5.549379e-01   1.042277e-03     1.000000e+00              True   \n",
       "61     3.179253e-01   1.000000e+00     1.000000e+00             False   \n",
       "70     6.488376e-44   9.486259e-25     3.828142e-42              True   \n",
       "\n",
       "    diff_Materialstool  passed_ss_(Intercept)  passed_ss_Materialstool  \n",
       "2                False                  False                    False  \n",
       "8                 True                  False                    False  \n",
       "14                True                   True                     True  \n",
       "17                True                  False                    False  \n",
       "36                True                   True                     True  \n",
       "38                True                   True                     True  \n",
       "41                True                   True                     True  \n",
       "42                True                   True                     True  \n",
       "45               False                  False                    False  \n",
       "47                True                  False                    False  \n",
       "52                True                   True                     True  \n",
       "55                True                   True                     True  \n",
       "60               False                  False                    False  \n",
       "61               False                  False                    False  \n",
       "70                True                   True                     True  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_prim_PSC_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3c04ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_PSC_common = res_prim_PSC[res_prim_PSC['taxon'].isin(common_taxa)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69f5a806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxon</th>\n",
       "      <th>lfc_(Intercept)</th>\n",
       "      <th>lfc_Materialstool</th>\n",
       "      <th>se_(Intercept)</th>\n",
       "      <th>se_Materialstool</th>\n",
       "      <th>W_(Intercept)</th>\n",
       "      <th>W_Materialstool</th>\n",
       "      <th>p_(Intercept)</th>\n",
       "      <th>p_Materialstool</th>\n",
       "      <th>q_(Intercept)</th>\n",
       "      <th>q_Materialstool</th>\n",
       "      <th>diff_(Intercept)</th>\n",
       "      <th>diff_Materialstool</th>\n",
       "      <th>passed_ss_(Intercept)</th>\n",
       "      <th>passed_ss_Materialstool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C__Alphaproteobacteria</td>\n",
       "      <td>0.749286</td>\n",
       "      <td>-0.466266</td>\n",
       "      <td>0.080182</td>\n",
       "      <td>0.122561</td>\n",
       "      <td>9.344798</td>\n",
       "      <td>-3.804351</td>\n",
       "      <td>8.761606e-18</td>\n",
       "      <td>1.826454e-04</td>\n",
       "      <td>2.102785e-16</td>\n",
       "      <td>3.835553e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F__Enterobacteriaceae</td>\n",
       "      <td>-0.659855</td>\n",
       "      <td>0.875974</td>\n",
       "      <td>0.041884</td>\n",
       "      <td>0.105725</td>\n",
       "      <td>-15.754257</td>\n",
       "      <td>8.285390</td>\n",
       "      <td>3.883405e-39</td>\n",
       "      <td>7.641237e-15</td>\n",
       "      <td>1.631030e-37</td>\n",
       "      <td>2.750845e-13</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F__Erysipelotrichaceae</td>\n",
       "      <td>-0.197106</td>\n",
       "      <td>0.292108</td>\n",
       "      <td>0.036310</td>\n",
       "      <td>0.102134</td>\n",
       "      <td>-5.428362</td>\n",
       "      <td>2.860042</td>\n",
       "      <td>9.122035e-08</td>\n",
       "      <td>4.424006e-03</td>\n",
       "      <td>1.094644e-06</td>\n",
       "      <td>7.078409e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F__Lachnospiraceae</td>\n",
       "      <td>-1.146277</td>\n",
       "      <td>1.554092</td>\n",
       "      <td>0.079867</td>\n",
       "      <td>0.123116</td>\n",
       "      <td>-14.352332</td>\n",
       "      <td>12.622940</td>\n",
       "      <td>3.263666e-40</td>\n",
       "      <td>1.854453e-32</td>\n",
       "      <td>1.403376e-38</td>\n",
       "      <td>8.901376e-31</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F__Porphyromonadaceae</td>\n",
       "      <td>-0.053173</td>\n",
       "      <td>0.475713</td>\n",
       "      <td>0.032855</td>\n",
       "      <td>0.096273</td>\n",
       "      <td>-1.618408</td>\n",
       "      <td>4.941314</td>\n",
       "      <td>1.084915e-01</td>\n",
       "      <td>2.853253e-06</td>\n",
       "      <td>4.339662e-01</td>\n",
       "      <td>7.989107e-05</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F__Ruminococcaceae</td>\n",
       "      <td>-1.656900</td>\n",
       "      <td>2.004716</td>\n",
       "      <td>0.079653</td>\n",
       "      <td>0.130579</td>\n",
       "      <td>-20.801393</td>\n",
       "      <td>15.352545</td>\n",
       "      <td>2.846032e-71</td>\n",
       "      <td>1.660157e-44</td>\n",
       "      <td>1.593778e-69</td>\n",
       "      <td>9.960941e-43</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>G__Akkermansia</td>\n",
       "      <td>-1.047560</td>\n",
       "      <td>1.127729</td>\n",
       "      <td>0.031614</td>\n",
       "      <td>0.103058</td>\n",
       "      <td>-33.135702</td>\n",
       "      <td>10.942647</td>\n",
       "      <td>1.717094e-92</td>\n",
       "      <td>5.955449e-23</td>\n",
       "      <td>9.959146e-91</td>\n",
       "      <td>2.620398e-21</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>G__Alistipes</td>\n",
       "      <td>-1.785403</td>\n",
       "      <td>1.972635</td>\n",
       "      <td>0.066695</td>\n",
       "      <td>0.125467</td>\n",
       "      <td>-26.769843</td>\n",
       "      <td>15.722287</td>\n",
       "      <td>3.376742e-97</td>\n",
       "      <td>3.255340e-45</td>\n",
       "      <td>1.992278e-95</td>\n",
       "      <td>1.985758e-43</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>G__Anaerostipes</td>\n",
       "      <td>0.488069</td>\n",
       "      <td>-0.383382</td>\n",
       "      <td>0.057552</td>\n",
       "      <td>0.110033</td>\n",
       "      <td>8.480514</td>\n",
       "      <td>-3.484235</td>\n",
       "      <td>3.930438e-16</td>\n",
       "      <td>5.458186e-04</td>\n",
       "      <td>8.253919e-15</td>\n",
       "      <td>9.824734e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G__Anaerotruncus</td>\n",
       "      <td>0.465191</td>\n",
       "      <td>-0.404871</td>\n",
       "      <td>0.035424</td>\n",
       "      <td>0.105937</td>\n",
       "      <td>13.131997</td>\n",
       "      <td>-3.821796</td>\n",
       "      <td>1.317242e-33</td>\n",
       "      <td>1.509934e-04</td>\n",
       "      <td>5.005521e-32</td>\n",
       "      <td>3.321855e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>G__Anaerovorax</td>\n",
       "      <td>0.461634</td>\n",
       "      <td>-0.410179</td>\n",
       "      <td>0.025309</td>\n",
       "      <td>0.089557</td>\n",
       "      <td>18.239771</td>\n",
       "      <td>-4.580090</td>\n",
       "      <td>1.840925e-51</td>\n",
       "      <td>6.666072e-06</td>\n",
       "      <td>9.020535e-50</td>\n",
       "      <td>1.733179e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>G__Bacteroides</td>\n",
       "      <td>-3.099484</td>\n",
       "      <td>3.541105</td>\n",
       "      <td>0.080601</td>\n",
       "      <td>0.123467</td>\n",
       "      <td>-38.454776</td>\n",
       "      <td>28.680688</td>\n",
       "      <td>1.637308e-157</td>\n",
       "      <td>4.952611e-111</td>\n",
       "      <td>1.096996e-155</td>\n",
       "      <td>3.516354e-109</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>G__Barnesiella</td>\n",
       "      <td>-1.561982</td>\n",
       "      <td>1.744369</td>\n",
       "      <td>0.039423</td>\n",
       "      <td>0.102934</td>\n",
       "      <td>-39.621185</td>\n",
       "      <td>16.946545</td>\n",
       "      <td>1.322155e-131</td>\n",
       "      <td>1.714367e-47</td>\n",
       "      <td>8.594004e-130</td>\n",
       "      <td>1.080051e-45</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>G__Bilophila</td>\n",
       "      <td>-1.423377</td>\n",
       "      <td>1.507611</td>\n",
       "      <td>0.015543</td>\n",
       "      <td>0.096276</td>\n",
       "      <td>-91.577074</td>\n",
       "      <td>15.659309</td>\n",
       "      <td>3.557457e-241</td>\n",
       "      <td>5.730570e-42</td>\n",
       "      <td>2.525794e-239</td>\n",
       "      <td>3.094508e-40</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>G__Blautia</td>\n",
       "      <td>0.389574</td>\n",
       "      <td>-0.207130</td>\n",
       "      <td>0.078956</td>\n",
       "      <td>0.123903</td>\n",
       "      <td>4.934081</td>\n",
       "      <td>-1.671711</td>\n",
       "      <td>1.061450e-06</td>\n",
       "      <td>9.513600e-02</td>\n",
       "      <td>1.061450e-05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>G__Butyricicoccus</td>\n",
       "      <td>0.644231</td>\n",
       "      <td>-0.578315</td>\n",
       "      <td>0.035675</td>\n",
       "      <td>0.096986</td>\n",
       "      <td>18.058506</td>\n",
       "      <td>-5.962859</td>\n",
       "      <td>1.245677e-54</td>\n",
       "      <td>5.183207e-09</td>\n",
       "      <td>6.228383e-53</td>\n",
       "      <td>1.554962e-07</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>G__Butyricimonas</td>\n",
       "      <td>0.132367</td>\n",
       "      <td>-0.042212</td>\n",
       "      <td>0.016806</td>\n",
       "      <td>0.086941</td>\n",
       "      <td>7.876222</td>\n",
       "      <td>-0.485524</td>\n",
       "      <td>1.151900e-13</td>\n",
       "      <td>6.277456e-01</td>\n",
       "      <td>2.303800e-12</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>G__Catabacter</td>\n",
       "      <td>0.956068</td>\n",
       "      <td>-0.937550</td>\n",
       "      <td>0.029588</td>\n",
       "      <td>0.086811</td>\n",
       "      <td>32.312984</td>\n",
       "      <td>-10.799901</td>\n",
       "      <td>4.369139e-86</td>\n",
       "      <td>3.832401e-22</td>\n",
       "      <td>2.490409e-84</td>\n",
       "      <td>1.647932e-20</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>G__Clostridium_IV</td>\n",
       "      <td>-0.187514</td>\n",
       "      <td>0.263455</td>\n",
       "      <td>0.035458</td>\n",
       "      <td>0.104639</td>\n",
       "      <td>-5.288361</td>\n",
       "      <td>2.517749</td>\n",
       "      <td>1.888218e-07</td>\n",
       "      <td>1.213925e-02</td>\n",
       "      <td>2.077039e-06</td>\n",
       "      <td>1.578103e-01</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>G__Clostridium_XVIII</td>\n",
       "      <td>0.745241</td>\n",
       "      <td>-0.723921</td>\n",
       "      <td>0.022195</td>\n",
       "      <td>0.097349</td>\n",
       "      <td>33.577197</td>\n",
       "      <td>-7.436313</td>\n",
       "      <td>1.078037e-109</td>\n",
       "      <td>8.599767e-13</td>\n",
       "      <td>6.683831e-108</td>\n",
       "      <td>2.751926e-11</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>G__Clostridium_XlVa</td>\n",
       "      <td>-0.904893</td>\n",
       "      <td>1.011502</td>\n",
       "      <td>0.026348</td>\n",
       "      <td>0.106622</td>\n",
       "      <td>-34.343582</td>\n",
       "      <td>9.486775</td>\n",
       "      <td>5.580074e-133</td>\n",
       "      <td>1.007048e-19</td>\n",
       "      <td>3.682849e-131</td>\n",
       "      <td>3.726078e-18</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>G__Clostridium_XlVb</td>\n",
       "      <td>0.403878</td>\n",
       "      <td>-0.362998</td>\n",
       "      <td>0.037557</td>\n",
       "      <td>0.103414</td>\n",
       "      <td>10.753622</td>\n",
       "      <td>-3.510142</td>\n",
       "      <td>1.272916e-23</td>\n",
       "      <td>5.037455e-04</td>\n",
       "      <td>3.564164e-22</td>\n",
       "      <td>9.571164e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>G__Collinsella</td>\n",
       "      <td>0.154269</td>\n",
       "      <td>0.048084</td>\n",
       "      <td>0.052680</td>\n",
       "      <td>0.108804</td>\n",
       "      <td>2.928424</td>\n",
       "      <td>0.441930</td>\n",
       "      <td>3.601198e-03</td>\n",
       "      <td>6.587784e-01</td>\n",
       "      <td>2.160719e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>G__Coprobacter</td>\n",
       "      <td>-0.239859</td>\n",
       "      <td>0.312056</td>\n",
       "      <td>0.017998</td>\n",
       "      <td>0.092437</td>\n",
       "      <td>-13.327242</td>\n",
       "      <td>3.375879</td>\n",
       "      <td>5.895467e-32</td>\n",
       "      <td>8.358316e-04</td>\n",
       "      <td>2.122368e-30</td>\n",
       "      <td>1.420914e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>G__Coprococcus</td>\n",
       "      <td>0.190747</td>\n",
       "      <td>-0.064358</td>\n",
       "      <td>0.026099</td>\n",
       "      <td>0.095660</td>\n",
       "      <td>7.308695</td>\n",
       "      <td>-0.672783</td>\n",
       "      <td>1.507772e-12</td>\n",
       "      <td>5.014789e-01</td>\n",
       "      <td>2.713990e-11</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>G__Corynebacterium</td>\n",
       "      <td>1.651079</td>\n",
       "      <td>-2.798056</td>\n",
       "      <td>0.079806</td>\n",
       "      <td>0.115340</td>\n",
       "      <td>20.688625</td>\n",
       "      <td>-24.259166</td>\n",
       "      <td>9.164096e-37</td>\n",
       "      <td>3.017516e-42</td>\n",
       "      <td>3.757279e-35</td>\n",
       "      <td>1.659634e-40</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>G__Dialister</td>\n",
       "      <td>-1.473886</td>\n",
       "      <td>1.700811</td>\n",
       "      <td>0.069192</td>\n",
       "      <td>0.131913</td>\n",
       "      <td>-21.301475</td>\n",
       "      <td>12.893430</td>\n",
       "      <td>1.784864e-68</td>\n",
       "      <td>3.353613e-32</td>\n",
       "      <td>9.459781e-67</td>\n",
       "      <td>1.576198e-30</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>G__Dorea</td>\n",
       "      <td>0.593298</td>\n",
       "      <td>-0.463139</td>\n",
       "      <td>0.052048</td>\n",
       "      <td>0.102159</td>\n",
       "      <td>11.399020</td>\n",
       "      <td>-4.533515</td>\n",
       "      <td>2.378678e-26</td>\n",
       "      <td>7.614415e-06</td>\n",
       "      <td>7.849637e-25</td>\n",
       "      <td>1.903604e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>G__Eggerthella</td>\n",
       "      <td>1.340480</td>\n",
       "      <td>-1.307152</td>\n",
       "      <td>0.017783</td>\n",
       "      <td>0.087493</td>\n",
       "      <td>75.381789</td>\n",
       "      <td>-14.940020</td>\n",
       "      <td>7.614271e-184</td>\n",
       "      <td>3.153278e-37</td>\n",
       "      <td>5.253847e-182</td>\n",
       "      <td>1.639704e-35</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>G__Escherichia.Shigella</td>\n",
       "      <td>-0.756726</td>\n",
       "      <td>1.138050</td>\n",
       "      <td>0.087715</td>\n",
       "      <td>0.149218</td>\n",
       "      <td>-8.627125</td>\n",
       "      <td>7.626740</td>\n",
       "      <td>8.052218e-17</td>\n",
       "      <td>1.191098e-13</td>\n",
       "      <td>1.852010e-15</td>\n",
       "      <td>4.168842e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>G__Faecalibacterium</td>\n",
       "      <td>-0.938421</td>\n",
       "      <td>1.284573</td>\n",
       "      <td>0.074254</td>\n",
       "      <td>0.129672</td>\n",
       "      <td>-12.638031</td>\n",
       "      <td>9.906328</td>\n",
       "      <td>5.179429e-32</td>\n",
       "      <td>2.904826e-21</td>\n",
       "      <td>1.916389e-30</td>\n",
       "      <td>1.190979e-19</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>G__Flavonifractor</td>\n",
       "      <td>-0.214936</td>\n",
       "      <td>0.277568</td>\n",
       "      <td>0.037082</td>\n",
       "      <td>0.102182</td>\n",
       "      <td>-5.796211</td>\n",
       "      <td>2.716402</td>\n",
       "      <td>1.295101e-08</td>\n",
       "      <td>6.859674e-03</td>\n",
       "      <td>1.813142e-07</td>\n",
       "      <td>1.028951e-01</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>G__Fusicatenibacter</td>\n",
       "      <td>0.233017</td>\n",
       "      <td>-0.155624</td>\n",
       "      <td>0.038886</td>\n",
       "      <td>0.096069</td>\n",
       "      <td>5.992319</td>\n",
       "      <td>-1.619922</td>\n",
       "      <td>4.566878e-09</td>\n",
       "      <td>1.060255e-01</td>\n",
       "      <td>7.307005e-08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>G__Holdemanella</td>\n",
       "      <td>-0.118229</td>\n",
       "      <td>0.369545</td>\n",
       "      <td>0.027946</td>\n",
       "      <td>0.086238</td>\n",
       "      <td>-4.230565</td>\n",
       "      <td>4.285171</td>\n",
       "      <td>4.127420e-05</td>\n",
       "      <td>3.323039e-05</td>\n",
       "      <td>3.714678e-04</td>\n",
       "      <td>7.642990e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>G__Microbacterium</td>\n",
       "      <td>1.165516</td>\n",
       "      <td>-3.003028</td>\n",
       "      <td>0.069230</td>\n",
       "      <td>0.105924</td>\n",
       "      <td>16.835505</td>\n",
       "      <td>-28.350792</td>\n",
       "      <td>5.872502e-29</td>\n",
       "      <td>2.086985e-45</td>\n",
       "      <td>2.055376e-27</td>\n",
       "      <td>1.293931e-43</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>G__Odoribacter</td>\n",
       "      <td>-0.628331</td>\n",
       "      <td>0.726436</td>\n",
       "      <td>0.028031</td>\n",
       "      <td>0.094349</td>\n",
       "      <td>-22.415470</td>\n",
       "      <td>7.699435</td>\n",
       "      <td>4.301377e-70</td>\n",
       "      <td>1.355957e-13</td>\n",
       "      <td>2.322744e-68</td>\n",
       "      <td>4.610253e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>G__Oscillibacter</td>\n",
       "      <td>-0.962897</td>\n",
       "      <td>1.085317</td>\n",
       "      <td>0.051650</td>\n",
       "      <td>0.109863</td>\n",
       "      <td>-18.642647</td>\n",
       "      <td>9.878848</td>\n",
       "      <td>4.245237e-59</td>\n",
       "      <td>4.039766e-21</td>\n",
       "      <td>2.207523e-57</td>\n",
       "      <td>1.615906e-19</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>G__Parabacteroides</td>\n",
       "      <td>-1.499618</td>\n",
       "      <td>1.765147</td>\n",
       "      <td>0.052992</td>\n",
       "      <td>0.105184</td>\n",
       "      <td>-28.298968</td>\n",
       "      <td>16.781508</td>\n",
       "      <td>1.301822e-102</td>\n",
       "      <td>1.302478e-49</td>\n",
       "      <td>7.941116e-101</td>\n",
       "      <td>8.466107e-48</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>G__Paraprevotella</td>\n",
       "      <td>-0.888148</td>\n",
       "      <td>0.990687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.086742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.421052</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.492040e-22</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.306657e-20</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>G__Parasutterella</td>\n",
       "      <td>-1.352367</td>\n",
       "      <td>1.485843</td>\n",
       "      <td>0.018291</td>\n",
       "      <td>0.112539</td>\n",
       "      <td>-73.938224</td>\n",
       "      <td>13.202889</td>\n",
       "      <td>6.552589e-217</td>\n",
       "      <td>1.265734e-32</td>\n",
       "      <td>4.586812e-215</td>\n",
       "      <td>6.202094e-31</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>G__Phascolarctobacterium</td>\n",
       "      <td>-1.110394</td>\n",
       "      <td>1.271482</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.097023</td>\n",
       "      <td>-67.188513</td>\n",
       "      <td>13.105019</td>\n",
       "      <td>7.344034e-125</td>\n",
       "      <td>1.299875e-27</td>\n",
       "      <td>4.700181e-123</td>\n",
       "      <td>5.849438e-26</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>G__Prevotella</td>\n",
       "      <td>-0.506821</td>\n",
       "      <td>1.152740</td>\n",
       "      <td>0.090627</td>\n",
       "      <td>0.150160</td>\n",
       "      <td>-5.592402</td>\n",
       "      <td>7.676769</td>\n",
       "      <td>5.482748e-08</td>\n",
       "      <td>2.998071e-13</td>\n",
       "      <td>7.127572e-07</td>\n",
       "      <td>9.893636e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>G__Propionibacterium</td>\n",
       "      <td>2.199396</td>\n",
       "      <td>-3.647220</td>\n",
       "      <td>0.122585</td>\n",
       "      <td>0.153279</td>\n",
       "      <td>17.941870</td>\n",
       "      <td>-23.794712</td>\n",
       "      <td>1.341954e-41</td>\n",
       "      <td>6.862173e-57</td>\n",
       "      <td>5.904598e-40</td>\n",
       "      <td>4.597656e-55</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>G__Pseudoflavonifractor</td>\n",
       "      <td>0.247398</td>\n",
       "      <td>-0.200079</td>\n",
       "      <td>0.028828</td>\n",
       "      <td>0.096929</td>\n",
       "      <td>8.581858</td>\n",
       "      <td>-2.064177</td>\n",
       "      <td>1.900862e-16</td>\n",
       "      <td>3.962343e-02</td>\n",
       "      <td>4.181896e-15</td>\n",
       "      <td>4.754812e-01</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>G__Roseburia</td>\n",
       "      <td>-0.366153</td>\n",
       "      <td>0.478151</td>\n",
       "      <td>0.035640</td>\n",
       "      <td>0.103414</td>\n",
       "      <td>-10.273742</td>\n",
       "      <td>4.623647</td>\n",
       "      <td>1.431385e-22</td>\n",
       "      <td>4.820375e-06</td>\n",
       "      <td>3.578463e-21</td>\n",
       "      <td>1.301501e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>G__Ruminococcus</td>\n",
       "      <td>-0.769735</td>\n",
       "      <td>0.991923</td>\n",
       "      <td>0.046153</td>\n",
       "      <td>0.102620</td>\n",
       "      <td>-16.677915</td>\n",
       "      <td>9.666009</td>\n",
       "      <td>6.109456e-48</td>\n",
       "      <td>4.977610e-20</td>\n",
       "      <td>2.871445e-46</td>\n",
       "      <td>1.891492e-18</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>G__Ruminococcus2</td>\n",
       "      <td>0.058369</td>\n",
       "      <td>0.086164</td>\n",
       "      <td>0.057901</td>\n",
       "      <td>0.109301</td>\n",
       "      <td>1.008093</td>\n",
       "      <td>0.788321</td>\n",
       "      <td>3.139024e-01</td>\n",
       "      <td>4.308866e-01</td>\n",
       "      <td>9.417071e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>G__Streptococcus</td>\n",
       "      <td>0.470973</td>\n",
       "      <td>-0.326826</td>\n",
       "      <td>0.064091</td>\n",
       "      <td>0.126615</td>\n",
       "      <td>7.348483</td>\n",
       "      <td>-2.581251</td>\n",
       "      <td>7.722814e-13</td>\n",
       "      <td>1.011440e-02</td>\n",
       "      <td>1.467335e-11</td>\n",
       "      <td>1.416015e-01</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>G__Subdoligranulum</td>\n",
       "      <td>-0.868035</td>\n",
       "      <td>1.088496</td>\n",
       "      <td>0.052982</td>\n",
       "      <td>0.111653</td>\n",
       "      <td>-16.383582</td>\n",
       "      <td>9.748937</td>\n",
       "      <td>1.837635e-48</td>\n",
       "      <td>1.195411e-20</td>\n",
       "      <td>8.820646e-47</td>\n",
       "      <td>4.662102e-19</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>G__Sutterella</td>\n",
       "      <td>-1.523812</td>\n",
       "      <td>1.665414</td>\n",
       "      <td>0.020153</td>\n",
       "      <td>0.101536</td>\n",
       "      <td>-75.613507</td>\n",
       "      <td>16.402137</td>\n",
       "      <td>7.482313e-182</td>\n",
       "      <td>2.963096e-42</td>\n",
       "      <td>5.087973e-180</td>\n",
       "      <td>1.659334e-40</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>G__Veillonella</td>\n",
       "      <td>0.672107</td>\n",
       "      <td>-0.551456</td>\n",
       "      <td>0.033739</td>\n",
       "      <td>0.107685</td>\n",
       "      <td>19.920848</td>\n",
       "      <td>-5.121022</td>\n",
       "      <td>1.555352e-55</td>\n",
       "      <td>5.673939e-07</td>\n",
       "      <td>7.932294e-54</td>\n",
       "      <td>1.645442e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>K__Bacteria</td>\n",
       "      <td>2.431057</td>\n",
       "      <td>-3.136319</td>\n",
       "      <td>0.066505</td>\n",
       "      <td>0.116555</td>\n",
       "      <td>36.554643</td>\n",
       "      <td>-26.908535</td>\n",
       "      <td>4.787625e-114</td>\n",
       "      <td>7.233241e-83</td>\n",
       "      <td>3.016204e-112</td>\n",
       "      <td>5.063268e-81</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>O__Bacteroidales</td>\n",
       "      <td>-1.298481</td>\n",
       "      <td>1.609754</td>\n",
       "      <td>0.039120</td>\n",
       "      <td>0.099323</td>\n",
       "      <td>-33.192457</td>\n",
       "      <td>16.207254</td>\n",
       "      <td>5.417573e-71</td>\n",
       "      <td>8.828245e-35</td>\n",
       "      <td>2.979665e-69</td>\n",
       "      <td>4.502405e-33</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>O__Clostridiales</td>\n",
       "      <td>-0.366019</td>\n",
       "      <td>0.551367</td>\n",
       "      <td>0.062197</td>\n",
       "      <td>0.130723</td>\n",
       "      <td>-5.884793</td>\n",
       "      <td>4.217823</td>\n",
       "      <td>7.815313e-09</td>\n",
       "      <td>2.986346e-05</td>\n",
       "      <td>1.172297e-07</td>\n",
       "      <td>7.167229e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>P__Firmicutes</td>\n",
       "      <td>0.093975</td>\n",
       "      <td>0.049357</td>\n",
       "      <td>0.054733</td>\n",
       "      <td>0.115478</td>\n",
       "      <td>1.716968</td>\n",
       "      <td>0.427413</td>\n",
       "      <td>8.666557e-02</td>\n",
       "      <td>6.692807e-01</td>\n",
       "      <td>4.333278e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>P__Proteobacteria</td>\n",
       "      <td>1.423529</td>\n",
       "      <td>-1.568030</td>\n",
       "      <td>0.082051</td>\n",
       "      <td>0.125668</td>\n",
       "      <td>17.349302</td>\n",
       "      <td>-12.477514</td>\n",
       "      <td>1.437762e-45</td>\n",
       "      <td>2.033984e-28</td>\n",
       "      <td>6.469928e-44</td>\n",
       "      <td>9.356327e-27</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       taxon  lfc_(Intercept)  lfc_Materialstool  \\\n",
       "1     C__Alphaproteobacteria         0.749286          -0.466266   \n",
       "3      F__Enterobacteriaceae        -0.659855           0.875974   \n",
       "4     F__Erysipelotrichaceae        -0.197106           0.292108   \n",
       "5         F__Lachnospiraceae        -1.146277           1.554092   \n",
       "6      F__Porphyromonadaceae        -0.053173           0.475713   \n",
       "7         F__Ruminococcaceae        -1.656900           2.004716   \n",
       "9             G__Akkermansia        -1.047560           1.127729   \n",
       "10              G__Alistipes        -1.785403           1.972635   \n",
       "11           G__Anaerostipes         0.488069          -0.383382   \n",
       "12          G__Anaerotruncus         0.465191          -0.404871   \n",
       "13            G__Anaerovorax         0.461634          -0.410179   \n",
       "15            G__Bacteroides        -3.099484           3.541105   \n",
       "16            G__Barnesiella        -1.561982           1.744369   \n",
       "18              G__Bilophila        -1.423377           1.507611   \n",
       "19                G__Blautia         0.389574          -0.207130   \n",
       "20         G__Butyricicoccus         0.644231          -0.578315   \n",
       "21          G__Butyricimonas         0.132367          -0.042212   \n",
       "22             G__Catabacter         0.956068          -0.937550   \n",
       "23         G__Clostridium_IV        -0.187514           0.263455   \n",
       "24      G__Clostridium_XVIII         0.745241          -0.723921   \n",
       "25       G__Clostridium_XlVa        -0.904893           1.011502   \n",
       "26       G__Clostridium_XlVb         0.403878          -0.362998   \n",
       "27            G__Collinsella         0.154269           0.048084   \n",
       "28            G__Coprobacter        -0.239859           0.312056   \n",
       "29            G__Coprococcus         0.190747          -0.064358   \n",
       "30        G__Corynebacterium         1.651079          -2.798056   \n",
       "31              G__Dialister        -1.473886           1.700811   \n",
       "32                  G__Dorea         0.593298          -0.463139   \n",
       "33            G__Eggerthella         1.340480          -1.307152   \n",
       "34   G__Escherichia.Shigella        -0.756726           1.138050   \n",
       "35       G__Faecalibacterium        -0.938421           1.284573   \n",
       "37         G__Flavonifractor        -0.214936           0.277568   \n",
       "39       G__Fusicatenibacter         0.233017          -0.155624   \n",
       "40           G__Holdemanella        -0.118229           0.369545   \n",
       "43         G__Microbacterium         1.165516          -3.003028   \n",
       "44            G__Odoribacter        -0.628331           0.726436   \n",
       "46          G__Oscillibacter        -0.962897           1.085317   \n",
       "48        G__Parabacteroides        -1.499618           1.765147   \n",
       "49         G__Paraprevotella        -0.888148           0.990687   \n",
       "50         G__Parasutterella        -1.352367           1.485843   \n",
       "51  G__Phascolarctobacterium        -1.110394           1.271482   \n",
       "53             G__Prevotella        -0.506821           1.152740   \n",
       "54      G__Propionibacterium         2.199396          -3.647220   \n",
       "56   G__Pseudoflavonifractor         0.247398          -0.200079   \n",
       "57              G__Roseburia        -0.366153           0.478151   \n",
       "58           G__Ruminococcus        -0.769735           0.991923   \n",
       "59          G__Ruminococcus2         0.058369           0.086164   \n",
       "62          G__Streptococcus         0.470973          -0.326826   \n",
       "63        G__Subdoligranulum        -0.868035           1.088496   \n",
       "64             G__Sutterella        -1.523812           1.665414   \n",
       "65            G__Veillonella         0.672107          -0.551456   \n",
       "66               K__Bacteria         2.431057          -3.136319   \n",
       "67          O__Bacteroidales        -1.298481           1.609754   \n",
       "68          O__Clostridiales        -0.366019           0.551367   \n",
       "69             P__Firmicutes         0.093975           0.049357   \n",
       "71         P__Proteobacteria         1.423529          -1.568030   \n",
       "\n",
       "    se_(Intercept)  se_Materialstool  W_(Intercept)  W_Materialstool  \\\n",
       "1         0.080182          0.122561       9.344798        -3.804351   \n",
       "3         0.041884          0.105725     -15.754257         8.285390   \n",
       "4         0.036310          0.102134      -5.428362         2.860042   \n",
       "5         0.079867          0.123116     -14.352332        12.622940   \n",
       "6         0.032855          0.096273      -1.618408         4.941314   \n",
       "7         0.079653          0.130579     -20.801393        15.352545   \n",
       "9         0.031614          0.103058     -33.135702        10.942647   \n",
       "10        0.066695          0.125467     -26.769843        15.722287   \n",
       "11        0.057552          0.110033       8.480514        -3.484235   \n",
       "12        0.035424          0.105937      13.131997        -3.821796   \n",
       "13        0.025309          0.089557      18.239771        -4.580090   \n",
       "15        0.080601          0.123467     -38.454776        28.680688   \n",
       "16        0.039423          0.102934     -39.621185        16.946545   \n",
       "18        0.015543          0.096276     -91.577074        15.659309   \n",
       "19        0.078956          0.123903       4.934081        -1.671711   \n",
       "20        0.035675          0.096986      18.058506        -5.962859   \n",
       "21        0.016806          0.086941       7.876222        -0.485524   \n",
       "22        0.029588          0.086811      32.312984       -10.799901   \n",
       "23        0.035458          0.104639      -5.288361         2.517749   \n",
       "24        0.022195          0.097349      33.577197        -7.436313   \n",
       "25        0.026348          0.106622     -34.343582         9.486775   \n",
       "26        0.037557          0.103414      10.753622        -3.510142   \n",
       "27        0.052680          0.108804       2.928424         0.441930   \n",
       "28        0.017998          0.092437     -13.327242         3.375879   \n",
       "29        0.026099          0.095660       7.308695        -0.672783   \n",
       "30        0.079806          0.115340      20.688625       -24.259166   \n",
       "31        0.069192          0.131913     -21.301475        12.893430   \n",
       "32        0.052048          0.102159      11.399020        -4.533515   \n",
       "33        0.017783          0.087493      75.381789       -14.940020   \n",
       "34        0.087715          0.149218      -8.627125         7.626740   \n",
       "35        0.074254          0.129672     -12.638031         9.906328   \n",
       "37        0.037082          0.102182      -5.796211         2.716402   \n",
       "39        0.038886          0.096069       5.992319        -1.619922   \n",
       "40        0.027946          0.086238      -4.230565         4.285171   \n",
       "43        0.069230          0.105924      16.835505       -28.350792   \n",
       "44        0.028031          0.094349     -22.415470         7.699435   \n",
       "46        0.051650          0.109863     -18.642647         9.878848   \n",
       "48        0.052992          0.105184     -28.298968        16.781508   \n",
       "49             NaN          0.086742            NaN        11.421052   \n",
       "50        0.018291          0.112539     -73.938224        13.202889   \n",
       "51        0.016527          0.097023     -67.188513        13.105019   \n",
       "53        0.090627          0.150160      -5.592402         7.676769   \n",
       "54        0.122585          0.153279      17.941870       -23.794712   \n",
       "56        0.028828          0.096929       8.581858        -2.064177   \n",
       "57        0.035640          0.103414     -10.273742         4.623647   \n",
       "58        0.046153          0.102620     -16.677915         9.666009   \n",
       "59        0.057901          0.109301       1.008093         0.788321   \n",
       "62        0.064091          0.126615       7.348483        -2.581251   \n",
       "63        0.052982          0.111653     -16.383582         9.748937   \n",
       "64        0.020153          0.101536     -75.613507        16.402137   \n",
       "65        0.033739          0.107685      19.920848        -5.121022   \n",
       "66        0.066505          0.116555      36.554643       -26.908535   \n",
       "67        0.039120          0.099323     -33.192457        16.207254   \n",
       "68        0.062197          0.130723      -5.884793         4.217823   \n",
       "69        0.054733          0.115478       1.716968         0.427413   \n",
       "71        0.082051          0.125668      17.349302       -12.477514   \n",
       "\n",
       "    p_(Intercept)  p_Materialstool  q_(Intercept)  q_Materialstool  \\\n",
       "1    8.761606e-18     1.826454e-04   2.102785e-16     3.835553e-03   \n",
       "3    3.883405e-39     7.641237e-15   1.631030e-37     2.750845e-13   \n",
       "4    9.122035e-08     4.424006e-03   1.094644e-06     7.078409e-02   \n",
       "5    3.263666e-40     1.854453e-32   1.403376e-38     8.901376e-31   \n",
       "6    1.084915e-01     2.853253e-06   4.339662e-01     7.989107e-05   \n",
       "7    2.846032e-71     1.660157e-44   1.593778e-69     9.960941e-43   \n",
       "9    1.717094e-92     5.955449e-23   9.959146e-91     2.620398e-21   \n",
       "10   3.376742e-97     3.255340e-45   1.992278e-95     1.985758e-43   \n",
       "11   3.930438e-16     5.458186e-04   8.253919e-15     9.824734e-03   \n",
       "12   1.317242e-33     1.509934e-04   5.005521e-32     3.321855e-03   \n",
       "13   1.840925e-51     6.666072e-06   9.020535e-50     1.733179e-04   \n",
       "15  1.637308e-157    4.952611e-111  1.096996e-155    3.516354e-109   \n",
       "16  1.322155e-131     1.714367e-47  8.594004e-130     1.080051e-45   \n",
       "18  3.557457e-241     5.730570e-42  2.525794e-239     3.094508e-40   \n",
       "19   1.061450e-06     9.513600e-02   1.061450e-05     1.000000e+00   \n",
       "20   1.245677e-54     5.183207e-09   6.228383e-53     1.554962e-07   \n",
       "21   1.151900e-13     6.277456e-01   2.303800e-12     1.000000e+00   \n",
       "22   4.369139e-86     3.832401e-22   2.490409e-84     1.647932e-20   \n",
       "23   1.888218e-07     1.213925e-02   2.077039e-06     1.578103e-01   \n",
       "24  1.078037e-109     8.599767e-13  6.683831e-108     2.751926e-11   \n",
       "25  5.580074e-133     1.007048e-19  3.682849e-131     3.726078e-18   \n",
       "26   1.272916e-23     5.037455e-04   3.564164e-22     9.571164e-03   \n",
       "27   3.601198e-03     6.587784e-01   2.160719e-02     1.000000e+00   \n",
       "28   5.895467e-32     8.358316e-04   2.122368e-30     1.420914e-02   \n",
       "29   1.507772e-12     5.014789e-01   2.713990e-11     1.000000e+00   \n",
       "30   9.164096e-37     3.017516e-42   3.757279e-35     1.659634e-40   \n",
       "31   1.784864e-68     3.353613e-32   9.459781e-67     1.576198e-30   \n",
       "32   2.378678e-26     7.614415e-06   7.849637e-25     1.903604e-04   \n",
       "33  7.614271e-184     3.153278e-37  5.253847e-182     1.639704e-35   \n",
       "34   8.052218e-17     1.191098e-13   1.852010e-15     4.168842e-12   \n",
       "35   5.179429e-32     2.904826e-21   1.916389e-30     1.190979e-19   \n",
       "37   1.295101e-08     6.859674e-03   1.813142e-07     1.028951e-01   \n",
       "39   4.566878e-09     1.060255e-01   7.307005e-08     1.000000e+00   \n",
       "40   4.127420e-05     3.323039e-05   3.714678e-04     7.642990e-04   \n",
       "43   5.872502e-29     2.086985e-45   2.055376e-27     1.293931e-43   \n",
       "44   4.301377e-70     1.355957e-13   2.322744e-68     4.610253e-12   \n",
       "46   4.245237e-59     4.039766e-21   2.207523e-57     1.615906e-19   \n",
       "48  1.301822e-102     1.302478e-49  7.941116e-101     8.466107e-48   \n",
       "49   1.000000e+00     5.492040e-22   1.000000e+00     2.306657e-20   \n",
       "50  6.552589e-217     1.265734e-32  4.586812e-215     6.202094e-31   \n",
       "51  7.344034e-125     1.299875e-27  4.700181e-123     5.849438e-26   \n",
       "53   5.482748e-08     2.998071e-13   7.127572e-07     9.893636e-12   \n",
       "54   1.341954e-41     6.862173e-57   5.904598e-40     4.597656e-55   \n",
       "56   1.900862e-16     3.962343e-02   4.181896e-15     4.754812e-01   \n",
       "57   1.431385e-22     4.820375e-06   3.578463e-21     1.301501e-04   \n",
       "58   6.109456e-48     4.977610e-20   2.871445e-46     1.891492e-18   \n",
       "59   3.139024e-01     4.308866e-01   9.417071e-01     1.000000e+00   \n",
       "62   7.722814e-13     1.011440e-02   1.467335e-11     1.416015e-01   \n",
       "63   1.837635e-48     1.195411e-20   8.820646e-47     4.662102e-19   \n",
       "64  7.482313e-182     2.963096e-42  5.087973e-180     1.659334e-40   \n",
       "65   1.555352e-55     5.673939e-07   7.932294e-54     1.645442e-05   \n",
       "66  4.787625e-114     7.233241e-83  3.016204e-112     5.063268e-81   \n",
       "67   5.417573e-71     8.828245e-35   2.979665e-69     4.502405e-33   \n",
       "68   7.815313e-09     2.986346e-05   1.172297e-07     7.167229e-04   \n",
       "69   8.666557e-02     6.692807e-01   4.333278e-01     1.000000e+00   \n",
       "71   1.437762e-45     2.033984e-28   6.469928e-44     9.356327e-27   \n",
       "\n",
       "    diff_(Intercept)  diff_Materialstool  passed_ss_(Intercept)  \\\n",
       "1               True                True                   True   \n",
       "3               True                True                  False   \n",
       "4               True               False                   True   \n",
       "5               True                True                   True   \n",
       "6              False                True                  False   \n",
       "7               True                True                   True   \n",
       "9               True                True                   True   \n",
       "10              True                True                   True   \n",
       "11              True                True                   True   \n",
       "12              True                True                   True   \n",
       "13              True                True                  False   \n",
       "15              True                True                   True   \n",
       "16              True                True                   True   \n",
       "18              True                True                   True   \n",
       "19              True               False                   True   \n",
       "20              True                True                   True   \n",
       "21              True               False                   True   \n",
       "22              True                True                  False   \n",
       "23              True               False                   True   \n",
       "24              True                True                   True   \n",
       "25              True                True                   True   \n",
       "26              True                True                   True   \n",
       "27              True               False                   True   \n",
       "28              True                True                   True   \n",
       "29              True               False                   True   \n",
       "30              True                True                   True   \n",
       "31              True                True                   True   \n",
       "32              True                True                   True   \n",
       "33              True                True                  False   \n",
       "34              True                True                   True   \n",
       "35              True                True                   True   \n",
       "37              True               False                   True   \n",
       "39              True               False                   True   \n",
       "40              True                True                  False   \n",
       "43              True                True                   True   \n",
       "44              True                True                   True   \n",
       "46              True                True                   True   \n",
       "48              True                True                   True   \n",
       "49             False                True                  False   \n",
       "50              True                True                   True   \n",
       "51              True                True                  False   \n",
       "53              True                True                   True   \n",
       "54              True                True                   True   \n",
       "56              True               False                   True   \n",
       "57              True                True                   True   \n",
       "58              True                True                   True   \n",
       "59             False               False                  False   \n",
       "62              True               False                   True   \n",
       "63              True                True                   True   \n",
       "64              True                True                   True   \n",
       "65              True                True                   True   \n",
       "66              True                True                   True   \n",
       "67              True                True                  False   \n",
       "68              True                True                   True   \n",
       "69             False               False                  False   \n",
       "71              True                True                   True   \n",
       "\n",
       "    passed_ss_Materialstool  \n",
       "1                      True  \n",
       "3                      True  \n",
       "4                     False  \n",
       "5                      True  \n",
       "6                     False  \n",
       "7                      True  \n",
       "9                      True  \n",
       "10                     True  \n",
       "11                     True  \n",
       "12                     True  \n",
       "13                     True  \n",
       "15                     True  \n",
       "16                     True  \n",
       "18                     True  \n",
       "19                    False  \n",
       "20                     True  \n",
       "21                    False  \n",
       "22                    False  \n",
       "23                    False  \n",
       "24                     True  \n",
       "25                     True  \n",
       "26                     True  \n",
       "27                    False  \n",
       "28                     True  \n",
       "29                    False  \n",
       "30                     True  \n",
       "31                     True  \n",
       "32                     True  \n",
       "33                     True  \n",
       "34                     True  \n",
       "35                     True  \n",
       "37                    False  \n",
       "39                    False  \n",
       "40                    False  \n",
       "43                     True  \n",
       "44                     True  \n",
       "46                     True  \n",
       "48                     True  \n",
       "49                     True  \n",
       "50                     True  \n",
       "51                     True  \n",
       "53                    False  \n",
       "54                     True  \n",
       "56                    False  \n",
       "57                     True  \n",
       "58                     True  \n",
       "59                    False  \n",
       "62                    False  \n",
       "63                     True  \n",
       "64                     True  \n",
       "65                     True  \n",
       "66                     True  \n",
       "67                    False  \n",
       "68                     True  \n",
       "69                    False  \n",
       "71                     True  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_prim_PSC_common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "148b6550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxon</th>\n",
       "      <th>lfc_(Intercept)</th>\n",
       "      <th>lfc_GroupPSC</th>\n",
       "      <th>se_(Intercept)</th>\n",
       "      <th>se_GroupPSC</th>\n",
       "      <th>W_(Intercept)</th>\n",
       "      <th>W_GroupPSC</th>\n",
       "      <th>p_(Intercept)</th>\n",
       "      <th>p_GroupPSC</th>\n",
       "      <th>q_(Intercept)</th>\n",
       "      <th>q_GroupPSC</th>\n",
       "      <th>diff_(Intercept)</th>\n",
       "      <th>diff_GroupPSC</th>\n",
       "      <th>passed_ss_(Intercept)</th>\n",
       "      <th>passed_ss_GroupPSC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C__Alphaproteobacteria</td>\n",
       "      <td>-0.280237</td>\n",
       "      <td>0.228478</td>\n",
       "      <td>0.180359</td>\n",
       "      <td>0.178853</td>\n",
       "      <td>-1.553773</td>\n",
       "      <td>1.277461</td>\n",
       "      <td>1.224186e-01</td>\n",
       "      <td>2.034810e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F__Clostridiales_Incertae_Sedis_XII</td>\n",
       "      <td>1.007926</td>\n",
       "      <td>-1.174295</td>\n",
       "      <td>0.131312</td>\n",
       "      <td>0.122364</td>\n",
       "      <td>7.675824</td>\n",
       "      <td>-9.596738</td>\n",
       "      <td>8.913345e-12</td>\n",
       "      <td>4.928430e-16</td>\n",
       "      <td>5.437140e-10</td>\n",
       "      <td>3.055627e-14</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F__Coriobacteriaceae</td>\n",
       "      <td>0.366674</td>\n",
       "      <td>-0.480108</td>\n",
       "      <td>0.189552</td>\n",
       "      <td>0.187581</td>\n",
       "      <td>1.934427</td>\n",
       "      <td>-2.559470</td>\n",
       "      <td>5.403327e-02</td>\n",
       "      <td>1.099049e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.286290e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F__Enterobacteriaceae</td>\n",
       "      <td>-0.354231</td>\n",
       "      <td>0.326151</td>\n",
       "      <td>0.195322</td>\n",
       "      <td>0.198003</td>\n",
       "      <td>-1.813577</td>\n",
       "      <td>1.647206</td>\n",
       "      <td>7.098150e-02</td>\n",
       "      <td>1.008136e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F__Erysipelotrichaceae</td>\n",
       "      <td>0.071204</td>\n",
       "      <td>-0.145376</td>\n",
       "      <td>0.221779</td>\n",
       "      <td>0.224000</td>\n",
       "      <td>0.321056</td>\n",
       "      <td>-0.649002</td>\n",
       "      <td>7.483036e-01</td>\n",
       "      <td>5.166387e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>K__Bacteria</td>\n",
       "      <td>0.728297</td>\n",
       "      <td>-0.852217</td>\n",
       "      <td>0.282818</td>\n",
       "      <td>0.284876</td>\n",
       "      <td>2.575145</td>\n",
       "      <td>-2.991543</td>\n",
       "      <td>1.066047e-02</td>\n",
       "      <td>3.084978e-03</td>\n",
       "      <td>4.477397e-01</td>\n",
       "      <td>1.326541e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>O__Bacteroidales</td>\n",
       "      <td>0.172533</td>\n",
       "      <td>-0.177738</td>\n",
       "      <td>0.285316</td>\n",
       "      <td>0.286868</td>\n",
       "      <td>0.604706</td>\n",
       "      <td>-0.619584</td>\n",
       "      <td>5.462000e-01</td>\n",
       "      <td>5.363814e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>O__Clostridiales</td>\n",
       "      <td>1.107677</td>\n",
       "      <td>-1.269890</td>\n",
       "      <td>0.299315</td>\n",
       "      <td>0.307385</td>\n",
       "      <td>3.700713</td>\n",
       "      <td>-4.131265</td>\n",
       "      <td>2.412024e-04</td>\n",
       "      <td>4.291052e-05</td>\n",
       "      <td>1.326613e-02</td>\n",
       "      <td>2.402989e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>P__Firmicutes</td>\n",
       "      <td>0.794569</td>\n",
       "      <td>-0.930675</td>\n",
       "      <td>0.275196</td>\n",
       "      <td>0.279294</td>\n",
       "      <td>2.887278</td>\n",
       "      <td>-3.332243</td>\n",
       "      <td>4.065425e-03</td>\n",
       "      <td>9.296097e-04</td>\n",
       "      <td>1.870095e-01</td>\n",
       "      <td>4.369166e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>P__Proteobacteria</td>\n",
       "      <td>0.581189</td>\n",
       "      <td>-0.724779</td>\n",
       "      <td>0.230860</td>\n",
       "      <td>0.231440</td>\n",
       "      <td>2.517498</td>\n",
       "      <td>-3.131609</td>\n",
       "      <td>1.263676e-02</td>\n",
       "      <td>2.010466e-03</td>\n",
       "      <td>5.054704e-01</td>\n",
       "      <td>8.846052e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  taxon  lfc_(Intercept)  lfc_GroupPSC  \\\n",
       "1                C__Alphaproteobacteria        -0.280237      0.228478   \n",
       "2   F__Clostridiales_Incertae_Sedis_XII         1.007926     -1.174295   \n",
       "3                  F__Coriobacteriaceae         0.366674     -0.480108   \n",
       "4                 F__Enterobacteriaceae        -0.354231      0.326151   \n",
       "5                F__Erysipelotrichaceae         0.071204     -0.145376   \n",
       "..                                  ...              ...           ...   \n",
       "58                          K__Bacteria         0.728297     -0.852217   \n",
       "59                     O__Bacteroidales         0.172533     -0.177738   \n",
       "60                     O__Clostridiales         1.107677     -1.269890   \n",
       "61                        P__Firmicutes         0.794569     -0.930675   \n",
       "62                    P__Proteobacteria         0.581189     -0.724779   \n",
       "\n",
       "    se_(Intercept)  se_GroupPSC  W_(Intercept)  W_GroupPSC  p_(Intercept)  \\\n",
       "1         0.180359     0.178853      -1.553773    1.277461   1.224186e-01   \n",
       "2         0.131312     0.122364       7.675824   -9.596738   8.913345e-12   \n",
       "3         0.189552     0.187581       1.934427   -2.559470   5.403327e-02   \n",
       "4         0.195322     0.198003      -1.813577    1.647206   7.098150e-02   \n",
       "5         0.221779     0.224000       0.321056   -0.649002   7.483036e-01   \n",
       "..             ...          ...            ...         ...            ...   \n",
       "58        0.282818     0.284876       2.575145   -2.991543   1.066047e-02   \n",
       "59        0.285316     0.286868       0.604706   -0.619584   5.462000e-01   \n",
       "60        0.299315     0.307385       3.700713   -4.131265   2.412024e-04   \n",
       "61        0.275196     0.279294       2.887278   -3.332243   4.065425e-03   \n",
       "62        0.230860     0.231440       2.517498   -3.131609   1.263676e-02   \n",
       "\n",
       "      p_GroupPSC  q_(Intercept)    q_GroupPSC  diff_(Intercept)  \\\n",
       "1   2.034810e-01   1.000000e+00  1.000000e+00             False   \n",
       "2   4.928430e-16   5.437140e-10  3.055627e-14              True   \n",
       "3   1.099049e-02   1.000000e+00  4.286290e-01             False   \n",
       "4   1.008136e-01   1.000000e+00  1.000000e+00             False   \n",
       "5   5.166387e-01   1.000000e+00  1.000000e+00             False   \n",
       "..           ...            ...           ...               ...   \n",
       "58  3.084978e-03   4.477397e-01  1.326541e-01             False   \n",
       "59  5.363814e-01   1.000000e+00  1.000000e+00             False   \n",
       "60  4.291052e-05   1.326613e-02  2.402989e-03              True   \n",
       "61  9.296097e-04   1.870095e-01  4.369166e-02             False   \n",
       "62  2.010466e-03   5.054704e-01  8.846052e-02             False   \n",
       "\n",
       "    diff_GroupPSC  passed_ss_(Intercept)  passed_ss_GroupPSC  \n",
       "1           False                   True                True  \n",
       "2            True                  False               False  \n",
       "3           False                  False               False  \n",
       "4           False                   True                True  \n",
       "5           False                   True                True  \n",
       "..            ...                    ...                 ...  \n",
       "58          False                  False               False  \n",
       "59          False                  False               False  \n",
       "60           True                   True                True  \n",
       "61           True                  False                True  \n",
       "62          False                  False               False  \n",
       "\n",
       "[62 rows x 15 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_prim_stool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7937989f",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_filt = res_prim_stool[(res_prim_stool['passed_ss_GroupPSC'] == True) & (res_prim_stool['q_GroupPSC'] < 0.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5c83571a",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_filt = res_prim_filt.sort_values(by='lfc_GroupPSC', ascending=False)\n",
    "# Create 'direct' column based on lfc_age\n",
    "res_prim_filt['direct'] = ['Positive LFC' if lfc > 0 else 'Negative LFC' for lfc in res_prim_filt['lfc_GroupPSC']]\n",
    "# Convert 'taxon' and 'direct' to categorical variables\n",
    "res_prim_filt['taxon'] = pd.Categorical(res_prim_filt['taxon'], categories=res_prim_filt['taxon'].unique())\n",
    "res_prim_filt['direct'] = pd.Categorical(res_prim_filt['direct'], categories=['Positive LFC', 'Negative LFC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ddd30a10",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive_color = 'red'\n",
    "negative_color = 'blue'\n",
    "# Plot\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.bar(x=res_prim_filt['taxon'], height=res_prim_filt['lfc_GroupPSC'], width=0.7, color=[positive_color if direct == 'Positive LFC' else negative_color for direct in res_prim_filt['direct']])\n",
    "plt.errorbar(x=res_prim_filt['taxon'], y=res_prim_filt['lfc_GroupPSC'], yerr=res_prim_filt['se_GroupPSC'], fmt='o', color='black')\n",
    "plt.xlabel(None)\n",
    "plt.ylabel('Log fold change')\n",
    "plt.xticks(rotation=60, ha='right')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
    "# Set y-axis limits\n",
    "plt.ylim(-5, 5)\n",
    "\n",
    "# Customize legend\n",
    "from matplotlib.patches import Patch\n",
    "legend_elements = [Patch(facecolor=positive_color, label='Positive LFC'),\n",
    "                   Patch(facecolor=negative_color, label='Negative LFC')]\n",
    "plt.legend(handles=legend_elements)\n",
    "plt.savefig('./results/diff_abund_primary_stool.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b8c03d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_filt = res_prim_seren[(res_prim_seren['passed_ss_GroupPSC'] == True) & (res_prim_seren['q_GroupPSC'] < 0.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "33512c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_prim_filt = res_prim_filt.sort_values(by='lfc_GroupPSC', ascending=False)\n",
    "# Create 'direct' column based on lfc_age\n",
    "res_prim_filt['direct'] = ['Positive LFC' if lfc > 0 else 'Negative LFC' for lfc in res_prim_filt['lfc_GroupPSC']]\n",
    "# Convert 'taxon' and 'direct' to categorical variables\n",
    "res_prim_filt['taxon'] = pd.Categorical(res_prim_filt['taxon'], categories=res_prim_filt['taxon'].unique())\n",
    "res_prim_filt['direct'] = pd.Categorical(res_prim_filt['direct'], categories=['Positive LFC', 'Negative LFC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "90f6470b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive_color = 'red'\n",
    "negative_color = 'blue'\n",
    "# Plot\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.bar(x=res_prim_filt['taxon'], height=res_prim_filt['lfc_GroupPSC'], width=0.7, color=[positive_color if direct == 'Positive LFC' else negative_color for direct in res_prim_filt['direct']])\n",
    "plt.errorbar(x=res_prim_filt['taxon'], y=res_prim_filt['lfc_GroupPSC'], yerr=res_prim_filt['se_GroupPSC'], fmt='o', color='black')\n",
    "plt.xlabel(None)\n",
    "plt.ylabel('Log fold change')\n",
    "plt.xticks(rotation=60, ha='right')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
    "# Set y-axis limits\n",
    "plt.ylim(-5, 5)\n",
    "\n",
    "# Customize legend\n",
    "from matplotlib.patches import Patch\n",
    "legend_elements = [Patch(facecolor=positive_color, label='Positive LFC'),\n",
    "                   Patch(facecolor=negative_color, label='Negative LFC')]\n",
    "plt.legend(handles=legend_elements)\n",
    "plt.savefig('./results/diff_abund_primary_seren.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36d2fe0b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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