exports_Prevotella_flux_analysis_swegle-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a96ecd0-4bde-4081-b39c-bae3f90e61ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4a6f4bc-2125-4bd0-af59-19447df58aa8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "exchanges = pd.read_csv(\"/proj/gibbons/sswegle_micom/Res_Saves/exchanges_swegle.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73da13a0-b9fc-4fa0-a1fe-74af4937a702",
   "metadata": {},
   "source": [
    "select exchanges for only one taxa "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fcc5f118-4922-437d-b24b-ef2f383becc4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>taxon</th>\n",
       "      <th>sample_id</th>\n",
       "      <th>tolerance</th>\n",
       "      <th>reaction</th>\n",
       "      <th>flux</th>\n",
       "      <th>abundance</th>\n",
       "      <th>metabolite</th>\n",
       "      <th>direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6409</th>\n",
       "      <td>23085</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_h(e)</td>\n",
       "      <td>-97.331225</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>h[e]</td>\n",
       "      <td>import</td>\n",
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       "    <tr>\n",
       "      <th>6410</th>\n",
       "      <td>23092</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_malt(e)</td>\n",
       "      <td>-0.027801</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>malt[e]</td>\n",
       "      <td>import</td>\n",
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       "    <tr>\n",
       "      <th>6411</th>\n",
       "      <td>23093</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_pi(e)</td>\n",
       "      <td>-64.707708</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>pi[e]</td>\n",
       "      <td>import</td>\n",
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       "    <tr>\n",
       "      <th>6412</th>\n",
       "      <td>23094</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_acnam(e)</td>\n",
       "      <td>-5.268732</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>acnam[e]</td>\n",
       "      <td>import</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6413</th>\n",
       "      <td>23100</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_gly(e)</td>\n",
       "      <td>15.048312</td>\n",
       "      <td>0.215981</td>\n",
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       "      <th>490766</th>\n",
       "      <td>2504824</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_succ(e)</td>\n",
       "      <td>-53.906947</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>succ[e]</td>\n",
       "      <td>import</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490767</th>\n",
       "      <td>2504826</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_MGlcn171_rl(e)</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>MGlcn171_rl[e]</td>\n",
       "      <td>export</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490768</th>\n",
       "      <td>2504827</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_gncore1_rl(e)</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>gncore1_rl[e]</td>\n",
       "      <td>export</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490769</th>\n",
       "      <td>2504828</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_MGlcn14(e)</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>MGlcn14[e]</td>\n",
       "      <td>export</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490770</th>\n",
       "      <td>2504829</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_MGlcn40_rl(e)</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>MGlcn40_rl[e]</td>\n",
       "      <td>import</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12311 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0       taxon    sample_id  tolerance           reaction  \\\n",
       "6409         23085  Prevotella  SRR15032095   0.000001            EX_h(e)   \n",
       "6410         23092  Prevotella  SRR15032095   0.000001         EX_malt(e)   \n",
       "6411         23093  Prevotella  SRR15032095   0.000001           EX_pi(e)   \n",
       "6412         23094  Prevotella  SRR15032095   0.000001        EX_acnam(e)   \n",
       "6413         23100  Prevotella  SRR15032095   0.000001          EX_gly(e)   \n",
       "...            ...         ...          ...        ...                ...   \n",
       "490766     2504824  Prevotella  SRR15032510   0.000001         EX_succ(e)   \n",
       "490767     2504826  Prevotella  SRR15032510   0.000001  EX_MGlcn171_rl(e)   \n",
       "490768     2504827  Prevotella  SRR15032510   0.000001   EX_gncore1_rl(e)   \n",
       "490769     2504828  Prevotella  SRR15032510   0.000001      EX_MGlcn14(e)   \n",
       "490770     2504829  Prevotella  SRR15032510   0.000001   EX_MGlcn40_rl(e)   \n",
       "\n",
       "             flux  abundance      metabolite direction  \n",
       "6409   -97.331225   0.215981            h[e]    import  \n",
       "6410    -0.027801   0.215981         malt[e]    import  \n",
       "6411   -64.707708   0.215981           pi[e]    import  \n",
       "6412    -5.268732   0.215981        acnam[e]    import  \n",
       "6413    15.048312   0.215981          gly[e]    export  \n",
       "...           ...        ...             ...       ...  \n",
       "490766 -53.906947   0.022368         succ[e]    import  \n",
       "490767   0.000007   0.022368  MGlcn171_rl[e]    export  \n",
       "490768   0.000002   0.022368   gncore1_rl[e]    export  \n",
       "490769   0.000002   0.022368      MGlcn14[e]    export  \n",
       "490770  -0.000003   0.022368   MGlcn40_rl[e]    import  \n",
       "\n",
       "[12311 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prevotella = exchanges[exchanges.taxon == \"Prevotella\"]\n",
    "Prevotella"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e965266f-41bf-4eec-83dd-c3aa671f16df",
   "metadata": {},
   "outputs": [
    {
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       "      <td>27.0</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.855661</td>\n",
       "      <td>fermented</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>SRR15032081</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR15032081.pickle</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.898909</td>\n",
       "      <td>western</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>7</td>\n",
       "      <td>SRR15032082.pickle</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.696970</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>306</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR15032510.pickle</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.881483</td>\n",
       "      <td>western</td>\n",
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       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>307</td>\n",
       "      <td>SRR15032511</td>\n",
       "      <td>High fiber diet</td>\n",
       "      <td>6</td>\n",
       "      <td>SRR15032511.pickle</td>\n",
       "      <td>22.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.799526</td>\n",
       "      <td>high_fiber</td>\n",
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       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>308</td>\n",
       "      <td>SRR15032512</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>7</td>\n",
       "      <td>SRR15032512.pickle</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.621622</td>\n",
       "      <td>0.843232</td>\n",
       "      <td>fermented</td>\n",
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       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>309</td>\n",
       "      <td>SRR15032513</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>6</td>\n",
       "      <td>SRR15032513.pickle</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.839063</td>\n",
       "      <td>fermented</td>\n",
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       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>310</td>\n",
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       "      <td>37.0</td>\n",
       "      <td>0.648649</td>\n",
       "      <td>0.740106</td>\n",
       "      <td>western</td>\n",
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      ],
      "text/plain": [
       "     Unnamed: 0    sample_id               description  time_point  \\\n",
       "0             0  SRR15032078           High fiber diet           8   \n",
       "1             1  SRR15032079           High fiber diet           4   \n",
       "2             2  SRR15032080  High fermented food diet           3   \n",
       "3             3  SRR15032081  High fermented food diet           2   \n",
       "4             4  SRR15032082           High fiber diet           7   \n",
       "..          ...          ...                       ...         ...   \n",
       "306         306  SRR15032510  High fermented food diet           2   \n",
       "307         307  SRR15032511           High fiber diet           6   \n",
       "308         308  SRR15032512  High fermented food diet           7   \n",
       "309         309  SRR15032513  High fermented food diet           6   \n",
       "310         310  SRR15032514           High fiber diet           9   \n",
       "\n",
       "                   file  found_taxa  total_taxa  found_fraction  \\\n",
       "0    SRR15032078.pickle        24.0        37.0        0.648649   \n",
       "1    SRR15032079.pickle        21.0        25.0        0.840000   \n",
       "2    SRR15032080.pickle        16.0        27.0        0.592593   \n",
       "3    SRR15032081.pickle        11.0        17.0        0.647059   \n",
       "4    SRR15032082.pickle        23.0        33.0        0.696970   \n",
       "..                  ...         ...         ...             ...   \n",
       "306  SRR15032510.pickle        21.0        28.0        0.750000   \n",
       "307  SRR15032511.pickle        22.0        32.0        0.687500   \n",
       "308  SRR15032512.pickle        23.0        37.0        0.621622   \n",
       "309  SRR15032513.pickle        19.0        30.0        0.633333   \n",
       "310  SRR15032514.pickle        24.0        37.0        0.648649   \n",
       "\n",
       "     found_abundance_fraction      medium  \n",
       "0                    0.889586  high_fiber  \n",
       "1                    0.942212  high_fiber  \n",
       "2                    0.855661   fermented  \n",
       "3                    0.898909     western  \n",
       "4                    0.908204  high_fiber  \n",
       "..                        ...         ...  \n",
       "306                  0.881483     western  \n",
       "307                  0.799526  high_fiber  \n",
       "308                  0.843232   fermented  \n",
       "309                  0.839063   fermented  \n",
       "310                  0.740106     western  \n",
       "\n",
       "[311 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = pd.read_csv(\"/proj/gibbons/sswegle_micom/All the Steps/metadata_swegle.csv\")\n",
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a09aa56a-6704-4254-8ddc-547c82b4b684",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_meta = metadata[metadata.time_point < 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fefd9968-bb75-4bdc-b0a9-124f4bfda729",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>taxon</th>\n",
       "      <th>sample_id</th>\n",
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       "      <th>file</th>\n",
       "      <th>found_taxa</th>\n",
       "      <th>total_taxa</th>\n",
       "      <th>found_fraction</th>\n",
       "      <th>found_abundance_fraction</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23085</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_h(e)</td>\n",
       "      <td>-97.331225</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>h[e]</td>\n",
       "      <td>import</td>\n",
       "      <td>17</td>\n",
       "      <td>High fiber diet</td>\n",
       "      <td>6</td>\n",
       "      <td>SRR15032095.pickle</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.931123</td>\n",
       "      <td>high_fiber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23092</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_malt(e)</td>\n",
       "      <td>-0.027801</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>malt[e]</td>\n",
       "      <td>import</td>\n",
       "      <td>17</td>\n",
       "      <td>High fiber diet</td>\n",
       "      <td>6</td>\n",
       "      <td>SRR15032095.pickle</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.931123</td>\n",
       "      <td>high_fiber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23093</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_pi(e)</td>\n",
       "      <td>-64.707708</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>pi[e]</td>\n",
       "      <td>import</td>\n",
       "      <td>17</td>\n",
       "      <td>High fiber diet</td>\n",
       "      <td>6</td>\n",
       "      <td>SRR15032095.pickle</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.931123</td>\n",
       "      <td>high_fiber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23094</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_acnam(e)</td>\n",
       "      <td>-5.268732</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>acnam[e]</td>\n",
       "      <td>import</td>\n",
       "      <td>17</td>\n",
       "      <td>High fiber diet</td>\n",
       "      <td>6</td>\n",
       "      <td>SRR15032095.pickle</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.931123</td>\n",
       "      <td>high_fiber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23100</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032095</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_gly(e)</td>\n",
       "      <td>15.048312</td>\n",
       "      <td>0.215981</td>\n",
       "      <td>gly[e]</td>\n",
       "      <td>export</td>\n",
       "      <td>17</td>\n",
       "      <td>High fiber diet</td>\n",
       "      <td>6</td>\n",
       "      <td>SRR15032095.pickle</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.931123</td>\n",
       "      <td>high_fiber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019</th>\n",
       "      <td>2504824</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_succ(e)</td>\n",
       "      <td>-53.906947</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>succ[e]</td>\n",
       "      <td>import</td>\n",
       "      <td>306</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR15032510.pickle</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.881483</td>\n",
       "      <td>western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9020</th>\n",
       "      <td>2504826</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_MGlcn171_rl(e)</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>MGlcn171_rl[e]</td>\n",
       "      <td>export</td>\n",
       "      <td>306</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR15032510.pickle</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.881483</td>\n",
       "      <td>western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9021</th>\n",
       "      <td>2504827</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_gncore1_rl(e)</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>gncore1_rl[e]</td>\n",
       "      <td>export</td>\n",
       "      <td>306</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR15032510.pickle</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.881483</td>\n",
       "      <td>western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9022</th>\n",
       "      <td>2504828</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_MGlcn14(e)</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>MGlcn14[e]</td>\n",
       "      <td>export</td>\n",
       "      <td>306</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR15032510.pickle</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.881483</td>\n",
       "      <td>western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9023</th>\n",
       "      <td>2504829</td>\n",
       "      <td>Prevotella</td>\n",
       "      <td>SRR15032510</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>EX_MGlcn40_rl(e)</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.022368</td>\n",
       "      <td>MGlcn40_rl[e]</td>\n",
       "      <td>import</td>\n",
       "      <td>306</td>\n",
       "      <td>High fermented food diet</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR15032510.pickle</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.881483</td>\n",
       "      <td>western</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9024 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0_x       taxon    sample_id  tolerance           reaction  \\\n",
       "0            23085  Prevotella  SRR15032095   0.000001            EX_h(e)   \n",
       "1            23092  Prevotella  SRR15032095   0.000001         EX_malt(e)   \n",
       "2            23093  Prevotella  SRR15032095   0.000001           EX_pi(e)   \n",
       "3            23094  Prevotella  SRR15032095   0.000001        EX_acnam(e)   \n",
       "4            23100  Prevotella  SRR15032095   0.000001          EX_gly(e)   \n",
       "...            ...         ...          ...        ...                ...   \n",
       "9019       2504824  Prevotella  SRR15032510   0.000001         EX_succ(e)   \n",
       "9020       2504826  Prevotella  SRR15032510   0.000001  EX_MGlcn171_rl(e)   \n",
       "9021       2504827  Prevotella  SRR15032510   0.000001   EX_gncore1_rl(e)   \n",
       "9022       2504828  Prevotella  SRR15032510   0.000001      EX_MGlcn14(e)   \n",
       "9023       2504829  Prevotella  SRR15032510   0.000001   EX_MGlcn40_rl(e)   \n",
       "\n",
       "           flux  abundance      metabolite direction  Unnamed: 0_y  \\\n",
       "0    -97.331225   0.215981            h[e]    import            17   \n",
       "1     -0.027801   0.215981         malt[e]    import            17   \n",
       "2    -64.707708   0.215981           pi[e]    import            17   \n",
       "3     -5.268732   0.215981        acnam[e]    import            17   \n",
       "4     15.048312   0.215981          gly[e]    export            17   \n",
       "...         ...        ...             ...       ...           ...   \n",
       "9019 -53.906947   0.022368         succ[e]    import           306   \n",
       "9020   0.000007   0.022368  MGlcn171_rl[e]    export           306   \n",
       "9021   0.000002   0.022368   gncore1_rl[e]    export           306   \n",
       "9022   0.000002   0.022368      MGlcn14[e]    export           306   \n",
       "9023  -0.000003   0.022368   MGlcn40_rl[e]    import           306   \n",
       "\n",
       "                   description  time_point                file  found_taxa  \\\n",
       "0              High fiber diet           6  SRR15032095.pickle        17.0   \n",
       "1              High fiber diet           6  SRR15032095.pickle        17.0   \n",
       "2              High fiber diet           6  SRR15032095.pickle        17.0   \n",
       "3              High fiber diet           6  SRR15032095.pickle        17.0   \n",
       "4              High fiber diet           6  SRR15032095.pickle        17.0   \n",
       "...                        ...         ...                 ...         ...   \n",
       "9019  High fermented food diet           2  SRR15032510.pickle        21.0   \n",
       "9020  High fermented food diet           2  SRR15032510.pickle        21.0   \n",
       "9021  High fermented food diet           2  SRR15032510.pickle        21.0   \n",
       "9022  High fermented food diet           2  SRR15032510.pickle        21.0   \n",
       "9023  High fermented food diet           2  SRR15032510.pickle        21.0   \n",
       "\n",
       "      total_taxa  found_fraction  found_abundance_fraction      medium  \n",
       "0           21.0        0.809524                  0.931123  high_fiber  \n",
       "1           21.0        0.809524                  0.931123  high_fiber  \n",
       "2           21.0        0.809524                  0.931123  high_fiber  \n",
       "3           21.0        0.809524                  0.931123  high_fiber  \n",
       "4           21.0        0.809524                  0.931123  high_fiber  \n",
       "...          ...             ...                       ...         ...  \n",
       "9019        28.0        0.750000                  0.881483     western  \n",
       "9020        28.0        0.750000                  0.881483     western  \n",
       "9021        28.0        0.750000                  0.881483     western  \n",
       "9022        28.0        0.750000                  0.881483     western  \n",
       "9023        28.0        0.750000                  0.881483     western  \n",
       "\n",
       "[9024 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = Prevotella.merge(filtered_meta, on=\"sample_id\")\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9923009b-41ba-4dfb-a4d9-062a33e0f8c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotations = pd.read_csv(\"/proj/gibbons/sswegle_micom/Res_Saves/annotations_swegle.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bac53d07-8b7a-459e-af11-182a5a68e9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_with_annotations = merged.merge(annotations, on=\"metabolite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7686ff76-2a4b-4b15-a459-a7b24de2d2df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0_x', 'taxon', 'sample_id', 'tolerance', 'reaction_x', 'flux',\n",
       "       'abundance', 'metabolite', 'direction', 'Unnamed: 0_y', 'description',\n",
       "       'time_point', 'file', 'found_taxa', 'total_taxa', 'found_fraction',\n",
       "       'found_abundance_fraction', 'medium', 'reaction_y', 'name',\n",
       "       'molecular_weight', 'C_number', 'N_number', 'hmdb', 'inchi',\n",
       "       'kegg.compound', 'pubchem.compound', 'reaction.1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_with_annotations.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e9b5c62-5b42-4f37-885d-da7ffb76d4c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sample_id</th>\n",
       "      <th>SRR15032079</th>\n",
       "      <th>SRR15032083</th>\n",
       "      <th>SRR15032087</th>\n",
       "      <th>SRR15032092</th>\n",
       "      <th>SRR15032094</th>\n",
       "      <th>SRR15032095</th>\n",
       "      <th>SRR15032096</th>\n",
       "      <th>SRR15032103</th>\n",
       "      <th>SRR15032108</th>\n",
       "      <th>SRR15032109</th>\n",
       "      <th>...</th>\n",
       "      <th>SRR15032449</th>\n",
       "      <th>SRR15032450</th>\n",
       "      <th>SRR15032453</th>\n",
       "      <th>SRR15032485</th>\n",
       "      <th>SRR15032487</th>\n",
       "      <th>SRR15032493</th>\n",
       "      <th>SRR15032495</th>\n",
       "      <th>SRR15032502</th>\n",
       "      <th>SRR15032505</th>\n",
       "      <th>SRR15032510</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(R)-lactate</th>\n",
       "      <td>21.247123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.698467</td>\n",
       "      <td>64.679196</td>\n",
       "      <td>33.822245</td>\n",
       "      <td>23.570252</td>\n",
       "      <td>26.910178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.350006</td>\n",
       "      <td>89.051015</td>\n",
       "      <td>...</td>\n",
       "      <td>31.461503</td>\n",
       "      <td>78.866144</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.817763</td>\n",
       "      <td>11.599154</td>\n",
       "      <td>37.468555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.532124</td>\n",
       "      <td>7.868149</td>\n",
       "      <td>73.844567</td>\n",
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       "    <tr>\n",
       "      <th>(S)-lactate</th>\n",
       "      <td>59.254264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.495828</td>\n",
       "      <td>98.115052</td>\n",
       "      <td>83.331424</td>\n",
       "      <td>25.394204</td>\n",
       "      <td>32.773299</td>\n",
       "      <td>99.067986</td>\n",
       "      <td>17.578354</td>\n",
       "      <td>87.071358</td>\n",
       "      <td>...</td>\n",
       "      <td>92.220399</td>\n",
       "      <td>66.680934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.150795</td>\n",
       "      <td>42.704926</td>\n",
       "      <td>52.940377</td>\n",
       "      <td>55.100151</td>\n",
       "      <td>156.378324</td>\n",
       "      <td>23.192683</td>\n",
       "      <td>91.618891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-malate(2-)</th>\n",
       "      <td>1.961657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.965876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.250784</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-propane-1,2-diol</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Oxoglutarate</th>\n",
       "      <td>8.172445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.106238</td>\n",
       "      <td>61.049738</td>\n",
       "      <td>4.220463</td>\n",
       "      <td>6.570709</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.564802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.897258</td>\n",
       "      <td>...</td>\n",
       "      <td>5.089532</td>\n",
       "      <td>95.575429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.997822</td>\n",
       "      <td>54.017004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.269466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_id             SRR15032079  SRR15032083  SRR15032087  SRR15032092  \\\n",
       "name                                                                       \n",
       "(R)-lactate             21.247123          0.0    25.698467    64.679196   \n",
       "(S)-lactate             59.254264          0.0    22.495828    98.115052   \n",
       "(S)-malate(2-)           1.961657          0.0     0.000000     0.000000   \n",
       "(S)-propane-1,2-diol     0.000000          0.0     0.000000     0.000000   \n",
       "2-Oxoglutarate           8.172445          0.0    83.106238    61.049738   \n",
       "\n",
       "sample_id             SRR15032094  SRR15032095  SRR15032096  SRR15032103  \\\n",
       "name                                                                       \n",
       "(R)-lactate             33.822245    23.570252    26.910178     0.000000   \n",
       "(S)-lactate             83.331424    25.394204    32.773299    99.067986   \n",
       "(S)-malate(2-)           0.000000     0.000000     0.000000     0.000000   \n",
       "(S)-propane-1,2-diol     0.000000     0.000000     0.000000     0.000000   \n",
       "2-Oxoglutarate           4.220463     6.570709     0.000000     8.564802   \n",
       "\n",
       "sample_id             SRR15032108  SRR15032109  ...  SRR15032449  SRR15032450  \\\n",
       "name                                            ...                             \n",
       "(R)-lactate             13.350006    89.051015  ...    31.461503    78.866144   \n",
       "(S)-lactate             17.578354    87.071358  ...    92.220399    66.680934   \n",
       "(S)-malate(2-)           0.000000     0.000000  ...     0.000000     0.000000   \n",
       "(S)-propane-1,2-diol     0.000000     0.000000  ...     0.000000     0.000000   \n",
       "2-Oxoglutarate           0.000000    19.897258  ...     5.089532    95.575429   \n",
       "\n",
       "sample_id             SRR15032453  SRR15032485  SRR15032487  SRR15032493  \\\n",
       "name                                                                       \n",
       "(R)-lactate                   0.0     7.817763    11.599154    37.468555   \n",
       "(S)-lactate                   0.0    12.150795    42.704926    52.940377   \n",
       "(S)-malate(2-)                0.0     0.000000     2.965876     0.000000   \n",
       "(S)-propane-1,2-diol          0.0     0.000000     0.000000     0.000000   \n",
       "2-Oxoglutarate                0.0     0.000000     8.997822    54.017004   \n",
       "\n",
       "sample_id             SRR15032495  SRR15032502  SRR15032505  SRR15032510  \n",
       "name                                                                      \n",
       "(R)-lactate              0.000000    19.532124     7.868149    73.844567  \n",
       "(S)-lactate             55.100151   156.378324    23.192683    91.618891  \n",
       "(S)-malate(2-)           0.000000     0.000000    15.250784     0.000000  \n",
       "(S)-propane-1,2-diol     0.000000     0.000000     0.000000     0.026879  \n",
       "2-Oxoglutarate           0.000000     0.000000     0.000000   156.269466  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "exports = pd.pivot_table(\n",
    "    merged_with_annotations[merged_with_annotations.direction == \"export\"], \n",
    "    index=\"name\", \n",
    "    columns=\"sample_id\", \n",
    "    values=\"flux\",\n",
    "    fill_value=0  # if the reactions is absent in a sample there is no flux\n",
    ")\n",
    "\n",
    "exports.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4df6593e-c8ff-4d0f-b953-5aa7ffde03da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/sswegle/miniconda3/envs/micom/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1728 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "label_map = pd.Series({\"high_fiber\": \"tomato\", \"fermented\": \"pink\", \"western\": \"seagreen\"})\n",
    "labels = merged_with_annotations[[\"sample_id\", \"medium\"]].drop_duplicates().set_index(\"sample_id\")\n",
    "labels[\"status\"] = label_map[labels.medium].values\n",
    "\n",
    "test_heat_map = sns.clustermap(\n",
    "    np.log10(exports + 1e-6), # Convert to positive fluxes and log-transform\n",
    "    figsize=(12, 24),           # we have way more reactions than samples here\n",
    "    col_colors=labels.status   # must be a series with one color for each sample ID \n",
    ")\n",
    "\n",
    "#test_heat_map.savefig(\"testing_saving_heatmaps.png\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0590d1f-4286-4ac1-91d4-5f2f4850b960",
   "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>sample_id</th>\n",
       "      <th>SRR15032079</th>\n",
       "      <th>SRR15032083</th>\n",
       "      <th>SRR15032087</th>\n",
       "      <th>SRR15032092</th>\n",
       "      <th>SRR15032094</th>\n",
       "      <th>SRR15032095</th>\n",
       "      <th>SRR15032096</th>\n",
       "      <th>SRR15032103</th>\n",
       "      <th>SRR15032108</th>\n",
       "      <th>SRR15032109</th>\n",
       "      <th>...</th>\n",
       "      <th>SRR15032449</th>\n",
       "      <th>SRR15032450</th>\n",
       "      <th>SRR15032453</th>\n",
       "      <th>SRR15032485</th>\n",
       "      <th>SRR15032487</th>\n",
       "      <th>SRR15032493</th>\n",
       "      <th>SRR15032495</th>\n",
       "      <th>SRR15032502</th>\n",
       "      <th>SRR15032505</th>\n",
       "      <th>SRR15032510</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(R)-lactate</th>\n",
       "      <td>21.247123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.698467</td>\n",
       "      <td>64.679196</td>\n",
       "      <td>33.822245</td>\n",
       "      <td>23.570252</td>\n",
       "      <td>26.910178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.350006</td>\n",
       "      <td>89.051015</td>\n",
       "      <td>...</td>\n",
       "      <td>31.461503</td>\n",
       "      <td>78.866144</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.817763</td>\n",
       "      <td>11.599154</td>\n",
       "      <td>37.468555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.532124</td>\n",
       "      <td>7.868149</td>\n",
       "      <td>73.844567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-lactate</th>\n",
       "      <td>59.254264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.495828</td>\n",
       "      <td>98.115052</td>\n",
       "      <td>83.331424</td>\n",
       "      <td>25.394204</td>\n",
       "      <td>32.773299</td>\n",
       "      <td>99.067986</td>\n",
       "      <td>17.578354</td>\n",
       "      <td>87.071358</td>\n",
       "      <td>...</td>\n",
       "      <td>92.220399</td>\n",
       "      <td>66.680934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.150795</td>\n",
       "      <td>42.704926</td>\n",
       "      <td>52.940377</td>\n",
       "      <td>55.100151</td>\n",
       "      <td>156.378324</td>\n",
       "      <td>23.192683</td>\n",
       "      <td>91.618891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-malate(2-)</th>\n",
       "      <td>1.961657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.965876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.250784</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-propane-1,2-diol</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Oxoglutarate</th>\n",
       "      <td>8.172445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.106238</td>\n",
       "      <td>61.049738</td>\n",
       "      <td>4.220463</td>\n",
       "      <td>6.570709</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.564802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.897258</td>\n",
       "      <td>...</td>\n",
       "      <td>5.089532</td>\n",
       "      <td>95.575429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.997822</td>\n",
       "      <td>54.017004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.269466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>released sialyl-Tn antigen</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sialyl-T antigen</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sialyl-Tn antigen</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tetradecanoate (n-C14:0)</th>\n",
       "      <td>1.097101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.132545</td>\n",
       "      <td>5.438303</td>\n",
       "      <td>1.533780</td>\n",
       "      <td>1.430764</td>\n",
       "      <td>1.427182</td>\n",
       "      <td>1.778062</td>\n",
       "      <td>4.870582</td>\n",
       "      <td>3.728376</td>\n",
       "      <td>...</td>\n",
       "      <td>0.929327</td>\n",
       "      <td>7.200076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.668781</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.917064</td>\n",
       "      <td>2.970975</td>\n",
       "      <td>0.387554</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_id                                          SRR15032079  SRR15032083  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                          21.247123          0.0   \n",
       "(S)-lactate                                          59.254264          0.0   \n",
       "(S)-malate(2-)                                        1.961657          0.0   \n",
       "(S)-propane-1,2-diol                                  0.000000          0.0   \n",
       "2-Oxoglutarate                                        8.172445          0.0   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000          0.0   \n",
       "sialyl-T antigen                                      0.000000          0.0   \n",
       "sialyl-Tn antigen                                     0.000000          0.0   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000          0.0   \n",
       "tetradecanoate (n-C14:0)                              1.097101          0.0   \n",
       "\n",
       "sample_id                                          SRR15032087  SRR15032092  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                          25.698467    64.679196   \n",
       "(S)-lactate                                          22.495828    98.115052   \n",
       "(S)-malate(2-)                                        0.000000     0.000000   \n",
       "(S)-propane-1,2-diol                                  0.000000     0.000000   \n",
       "2-Oxoglutarate                                       83.106238    61.049738   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000     0.000000   \n",
       "sialyl-T antigen                                      0.000000     0.000000   \n",
       "sialyl-Tn antigen                                     0.000000     0.000000   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000     0.000000   \n",
       "tetradecanoate (n-C14:0)                              5.132545     5.438303   \n",
       "\n",
       "sample_id                                          SRR15032094  SRR15032095  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                          33.822245    23.570252   \n",
       "(S)-lactate                                          83.331424    25.394204   \n",
       "(S)-malate(2-)                                        0.000000     0.000000   \n",
       "(S)-propane-1,2-diol                                  0.000000     0.000000   \n",
       "2-Oxoglutarate                                        4.220463     6.570709   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000     0.000000   \n",
       "sialyl-T antigen                                      0.000000     0.000000   \n",
       "sialyl-Tn antigen                                     0.000000     0.000000   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000     0.000000   \n",
       "tetradecanoate (n-C14:0)                              1.533780     1.430764   \n",
       "\n",
       "sample_id                                          SRR15032096  SRR15032103  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                          26.910178     0.000000   \n",
       "(S)-lactate                                          32.773299    99.067986   \n",
       "(S)-malate(2-)                                        0.000000     0.000000   \n",
       "(S)-propane-1,2-diol                                  0.000000     0.000000   \n",
       "2-Oxoglutarate                                        0.000000     8.564802   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000     0.000000   \n",
       "sialyl-T antigen                                      0.000000     0.000000   \n",
       "sialyl-Tn antigen                                     0.000000     0.000000   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000     0.000000   \n",
       "tetradecanoate (n-C14:0)                              1.427182     1.778062   \n",
       "\n",
       "sample_id                                          SRR15032108  SRR15032109  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                          13.350006    89.051015   \n",
       "(S)-lactate                                          17.578354    87.071358   \n",
       "(S)-malate(2-)                                        0.000000     0.000000   \n",
       "(S)-propane-1,2-diol                                  0.000000     0.000000   \n",
       "2-Oxoglutarate                                        0.000000    19.897258   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000     0.000000   \n",
       "sialyl-T antigen                                      0.000000     0.000000   \n",
       "sialyl-Tn antigen                                     0.000000     0.000000   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000     0.000000   \n",
       "tetradecanoate (n-C14:0)                              4.870582     3.728376   \n",
       "\n",
       "sample_id                                          ...  SRR15032449  \\\n",
       "name                                               ...                \n",
       "(R)-lactate                                        ...    31.461503   \n",
       "(S)-lactate                                        ...    92.220399   \n",
       "(S)-malate(2-)                                     ...     0.000000   \n",
       "(S)-propane-1,2-diol                               ...     0.000000   \n",
       "2-Oxoglutarate                                     ...     5.089532   \n",
       "...                                                ...          ...   \n",
       "released sialyl-Tn antigen                         ...     0.000000   \n",
       "sialyl-T antigen                                   ...     0.000000   \n",
       "sialyl-Tn antigen                                  ...     0.000000   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})  ...     0.000000   \n",
       "tetradecanoate (n-C14:0)                           ...     0.929327   \n",
       "\n",
       "sample_id                                          SRR15032450  SRR15032453  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                          78.866144          0.0   \n",
       "(S)-lactate                                          66.680934          0.0   \n",
       "(S)-malate(2-)                                        0.000000          0.0   \n",
       "(S)-propane-1,2-diol                                  0.000000          0.0   \n",
       "2-Oxoglutarate                                       95.575429          0.0   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000          0.0   \n",
       "sialyl-T antigen                                      0.000000          0.0   \n",
       "sialyl-Tn antigen                                     0.000000          0.0   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000          0.0   \n",
       "tetradecanoate (n-C14:0)                              7.200076          0.0   \n",
       "\n",
       "sample_id                                          SRR15032485  SRR15032487  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                           7.817763    11.599154   \n",
       "(S)-lactate                                          12.150795    42.704926   \n",
       "(S)-malate(2-)                                        0.000000     2.965876   \n",
       "(S)-propane-1,2-diol                                  0.000000     0.000000   \n",
       "2-Oxoglutarate                                        0.000000     8.997822   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000     0.000000   \n",
       "sialyl-T antigen                                      0.000000     0.000000   \n",
       "sialyl-Tn antigen                                     0.000000     0.000000   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000     0.000000   \n",
       "tetradecanoate (n-C14:0)                              0.000000     0.668781   \n",
       "\n",
       "sample_id                                          SRR15032493  SRR15032495  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                          37.468555     0.000000   \n",
       "(S)-lactate                                          52.940377    55.100151   \n",
       "(S)-malate(2-)                                        0.000000     0.000000   \n",
       "(S)-propane-1,2-diol                                  0.000000     0.000000   \n",
       "2-Oxoglutarate                                       54.017004     0.000000   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000     0.000000   \n",
       "sialyl-T antigen                                      0.000000     0.000000   \n",
       "sialyl-Tn antigen                                     0.000000     0.000000   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000     0.000000   \n",
       "tetradecanoate (n-C14:0)                              0.000000    18.917064   \n",
       "\n",
       "sample_id                                          SRR15032502  SRR15032505  \\\n",
       "name                                                                          \n",
       "(R)-lactate                                          19.532124     7.868149   \n",
       "(S)-lactate                                         156.378324    23.192683   \n",
       "(S)-malate(2-)                                        0.000000    15.250784   \n",
       "(S)-propane-1,2-diol                                  0.000000     0.000000   \n",
       "2-Oxoglutarate                                        0.000000     0.000000   \n",
       "...                                                        ...          ...   \n",
       "released sialyl-Tn antigen                            0.000000     0.000000   \n",
       "sialyl-T antigen                                      0.000000     0.000000   \n",
       "sialyl-Tn antigen                                     0.000000     0.000000   \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000     0.000000   \n",
       "tetradecanoate (n-C14:0)                              2.970975     0.387554   \n",
       "\n",
       "sample_id                                          SRR15032510  \n",
       "name                                                            \n",
       "(R)-lactate                                          73.844567  \n",
       "(S)-lactate                                          91.618891  \n",
       "(S)-malate(2-)                                        0.000000  \n",
       "(S)-propane-1,2-diol                                  0.026879  \n",
       "2-Oxoglutarate                                      156.269466  \n",
       "...                                                        ...  \n",
       "released sialyl-Tn antigen                            0.000000  \n",
       "sialyl-T antigen                                      0.000000  \n",
       "sialyl-Tn antigen                                     0.000000  \n",
       "starch, structure 2 (1,6-{2[1,4-Glc], [1,4-Glc]})     0.000000  \n",
       "tetradecanoate (n-C14:0)                              0.000000  \n",
       "\n",
       "[440 rows x 81 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "204049a6-048f-4900-b2db-a14674cffd7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>sample_id</th>\n",
       "      <th>SRR15032079</th>\n",
       "      <th>SRR15032083</th>\n",
       "      <th>SRR15032087</th>\n",
       "      <th>SRR15032092</th>\n",
       "      <th>SRR15032094</th>\n",
       "      <th>SRR15032095</th>\n",
       "      <th>SRR15032096</th>\n",
       "      <th>SRR15032103</th>\n",
       "      <th>SRR15032108</th>\n",
       "      <th>SRR15032109</th>\n",
       "      <th>...</th>\n",
       "      <th>SRR15032449</th>\n",
       "      <th>SRR15032450</th>\n",
       "      <th>SRR15032453</th>\n",
       "      <th>SRR15032485</th>\n",
       "      <th>SRR15032487</th>\n",
       "      <th>SRR15032493</th>\n",
       "      <th>SRR15032495</th>\n",
       "      <th>SRR15032502</th>\n",
       "      <th>SRR15032505</th>\n",
       "      <th>SRR15032510</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(R)-lactate</th>\n",
       "      <td>21.247123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.698467</td>\n",
       "      <td>64.679196</td>\n",
       "      <td>33.822245</td>\n",
       "      <td>23.570252</td>\n",
       "      <td>26.910178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.350006</td>\n",
       "      <td>89.051015</td>\n",
       "      <td>...</td>\n",
       "      <td>31.461503</td>\n",
       "      <td>78.866144</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.817763</td>\n",
       "      <td>11.599154</td>\n",
       "      <td>37.468555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.532124</td>\n",
       "      <td>7.868149</td>\n",
       "      <td>73.844567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-lactate</th>\n",
       "      <td>59.254264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.495828</td>\n",
       "      <td>98.115052</td>\n",
       "      <td>83.331424</td>\n",
       "      <td>25.394204</td>\n",
       "      <td>32.773299</td>\n",
       "      <td>99.067986</td>\n",
       "      <td>17.578354</td>\n",
       "      <td>87.071358</td>\n",
       "      <td>...</td>\n",
       "      <td>92.220399</td>\n",
       "      <td>66.680934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.150795</td>\n",
       "      <td>42.704926</td>\n",
       "      <td>52.940377</td>\n",
       "      <td>55.100151</td>\n",
       "      <td>156.378324</td>\n",
       "      <td>23.192683</td>\n",
       "      <td>91.618891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-malate(2-)</th>\n",
       "      <td>1.961657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.965876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.250784</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-propane-1,2-diol</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Oxoglutarate</th>\n",
       "      <td>8.172445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.106238</td>\n",
       "      <td>61.049738</td>\n",
       "      <td>4.220463</td>\n",
       "      <td>6.570709</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.564802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.897258</td>\n",
       "      <td>...</td>\n",
       "      <td>5.089532</td>\n",
       "      <td>95.575429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.997822</td>\n",
       "      <td>54.017004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.269466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_id             SRR15032079  SRR15032083  SRR15032087  SRR15032092  \\\n",
       "name                                                                       \n",
       "(R)-lactate             21.247123          0.0    25.698467    64.679196   \n",
       "(S)-lactate             59.254264          0.0    22.495828    98.115052   \n",
       "(S)-malate(2-)           1.961657          0.0     0.000000     0.000000   \n",
       "(S)-propane-1,2-diol     0.000000          0.0     0.000000     0.000000   \n",
       "2-Oxoglutarate           8.172445          0.0    83.106238    61.049738   \n",
       "\n",
       "sample_id             SRR15032094  SRR15032095  SRR15032096  SRR15032103  \\\n",
       "name                                                                       \n",
       "(R)-lactate             33.822245    23.570252    26.910178     0.000000   \n",
       "(S)-lactate             83.331424    25.394204    32.773299    99.067986   \n",
       "(S)-malate(2-)           0.000000     0.000000     0.000000     0.000000   \n",
       "(S)-propane-1,2-diol     0.000000     0.000000     0.000000     0.000000   \n",
       "2-Oxoglutarate           4.220463     6.570709     0.000000     8.564802   \n",
       "\n",
       "sample_id             SRR15032108  SRR15032109  ...  SRR15032449  SRR15032450  \\\n",
       "name                                            ...                             \n",
       "(R)-lactate             13.350006    89.051015  ...    31.461503    78.866144   \n",
       "(S)-lactate             17.578354    87.071358  ...    92.220399    66.680934   \n",
       "(S)-malate(2-)           0.000000     0.000000  ...     0.000000     0.000000   \n",
       "(S)-propane-1,2-diol     0.000000     0.000000  ...     0.000000     0.000000   \n",
       "2-Oxoglutarate           0.000000    19.897258  ...     5.089532    95.575429   \n",
       "\n",
       "sample_id             SRR15032453  SRR15032485  SRR15032487  SRR15032493  \\\n",
       "name                                                                       \n",
       "(R)-lactate                   0.0     7.817763    11.599154    37.468555   \n",
       "(S)-lactate                   0.0    12.150795    42.704926    52.940377   \n",
       "(S)-malate(2-)                0.0     0.000000     2.965876     0.000000   \n",
       "(S)-propane-1,2-diol          0.0     0.000000     0.000000     0.000000   \n",
       "2-Oxoglutarate                0.0     0.000000     8.997822    54.017004   \n",
       "\n",
       "sample_id             SRR15032495  SRR15032502  SRR15032505  SRR15032510  \n",
       "name                                                                      \n",
       "(R)-lactate              0.000000    19.532124     7.868149    73.844567  \n",
       "(S)-lactate             55.100151   156.378324    23.192683    91.618891  \n",
       "(S)-malate(2-)           0.000000     0.000000    15.250784     0.000000  \n",
       "(S)-propane-1,2-diol     0.000000     0.000000     0.000000     0.026879  \n",
       "2-Oxoglutarate           0.000000     0.000000     0.000000   156.269466  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "exports = pd.pivot_table(\n",
    "    merged_with_annotations[(merged_with_annotations.direction == \"export\") & (merged_with_annotations.flux > 1e-4)], \n",
    "    index=\"name\", \n",
    "    columns=\"sample_id\", \n",
    "    values=\"flux\",\n",
    "    fill_value=0  # if the reactions is absent in a sample there is no flux\n",
    ")\n",
    "\n",
    "exports.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f22f91d0-5599-47bc-8a57-1d6ea915e3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#top_imports = imports.nlargest(40, \"flux\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01a64dc9-72b5-41e1-b17e-3b7257d90b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1728 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_map = pd.Series({\"high_fiber\": \"tomato\", \"fermented\": \"pink\", \"western\": \"seagreen\"})\n",
    "labels = merged_with_annotations[[\"sample_id\", \"medium\"]].drop_duplicates().set_index(\"sample_id\")\n",
    "labels[\"status\"] = label_map[labels.medium].values\n",
    "\n",
    "exports_Prevotella_filtered_heatmap = sns.clustermap(\n",
    "    np.log10(exports + 1e-5), # Convert to positive fluxes and log-transform\n",
    "    figsize=(12, 24),           # we have way more reactions than samples here\n",
    "    col_colors=labels.status,   # must be a seried with one color for each sample ID\n",
    "    yticklabels = True      \n",
    ")\n",
    "\n",
    "exports_Prevotella_filtered_heatmap\n",
    "\n",
    "exports_Prevotella_filtered_heatmap.savefig(\"exports_Prevotella_filtered_heatmap.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6060b1-d071-444d-810a-9a40e8882dc1",
   "metadata": {},
   "source": [
    "# statistical tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "042e2a14-bc03-4d9f-9fed-dc10a2b1ca46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from micom.stats import compare_groups\n",
    "\n",
    "long_exports = merged[merged.direction == \"export\"]\n",
    "long_exports[\"flux\"] = long_exports[\"flux\"].abs()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e6e064-9410-48aa-ac6a-621454444eaf",
   "metadata": {},
   "source": [
    "long_imports comes from merged, which comes from Prevotella, meaning it contains the exchange fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88380311-ca1c-477b-a5e9-4ced5d9d9ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_exports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f41e6ec7-8996-49de-ad33-098690b75dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "tests = compare_groups(long_exports, \"medium\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f300e88f-c724-4883-be16-d2c4a0246520",
   "metadata": {},
   "outputs": [],
   "source": [
    "tests.sort_values(by=\"q\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9e0d284-9ffe-4a7d-b5f6-6ec1576e1e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "theme_set(theme_minimal())\n",
    "\n",
    "selected_mets = tests.sort_values(by=\"q\").metabolite[0:4] \n",
    "\n",
    "pl = (\n",
    "    ggplot(long_exports[long_exports.metabolite.isin(selected_mets)])\n",
    "    + aes(x=\"medium\", y=\"flux\")\n",
    "    + geom_boxplot() \n",
    "    + geom_jitter(width=.2)\n",
    "    + scale_y_log10()\n",
    "    + facet_wrap(\"~ metabolite\")\n",
    ")\n",
    "pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d8e61fa-953c-426d-9b6a-9d01c029729e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "theme_set(theme_minimal())\n",
    "\n",
    "selected_mets = tests[tests.q < 0.05].metabolite\n",
    "\n",
    "#sort_values(by=\"q\").metabolite[0:4] \n",
    "\n",
    "pl = (\n",
    "    ggplot(long_exports[long_exports.metabolite.isin(selected_mets)])\n",
    "    + aes(x=\"medium\", y=\"flux\")\n",
    "    + theme(figure_size = (12,4))\n",
    "    + geom_boxplot() \n",
    "    + geom_jitter(width=.2)\n",
    "    + scale_y_log10()\n",
    "    + facet_wrap(\"~ metabolite\")\n",
    ")\n",
    "pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d74a40ea-6b30-426c-877a-7b3ca18699de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merged_with_host_id = pd.read_csv(\"/proj/gibbons/sswegle_micom/All the Steps/merged_with_host_id.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a4ac9e5-2e66-4d87-af98-ed60551d03ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pandas as pd\n",
    "#from micom.viz import plot_exchanges_per_taxon\n",
    "#from plotnine import *\n",
    "#theme_set(theme_minimal())\n",
    "#import warnings\n",
    "#warnings.simplefilter(\"ignore\")  # don't show warnings\n",
    "#import numpy as np\n",
    "\n",
    "#meta = merged_with_host_id.sort_values(by=[\"host_subject_id\", \"time_point\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b26914e-cd4c-42c8-81f3-1b8502d99239",
   "metadata": {},
   "source": [
    "checks if all the data comes from the same distribution \n",
    "- they dont look like theyre coming from the same distribution\n",
    "\n",
    "- maltose consumtion is lower in western "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6269c1-00a8-430d-a0ef-cb1119748cd3",
   "metadata": {},
   "source": [
    "can add timepoints !!\n",
    "- higher or lower imports over time? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54e04220-637e-4ce6-8b0e-a8e270358f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "theme_set(theme_minimal())\n",
    "\n",
    "selected_mets = tests[tests.q < 0.05].metabolite\n",
    "\n",
    "#sort_values(by=\"q\").metabolite[0:4] \n",
    "\n",
    "pl = (\n",
    "    ggplot(long_exports[long_exports.metabolite.isin(selected_mets)])\n",
    "    + aes(x=\"medium\", y=\"flux\")\n",
    "    + theme(figure_size = (12,4))\n",
    "    + geom_boxplot() \n",
    "    + geom_jitter(width=.2)\n",
    "    + scale_y_log10()\n",
    "    + facet_wrap(\"~ metabolite\")\n",
    ")\n",
    "pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f311a247-c232-47ce-9133-bf40054b86c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba45c062-2d3f-42bc-87b4-1e955118227a",
   "metadata": {},
   "source": [
    "need to first sort by q value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09211262-6790-4235-9c19-60b38afdb8d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "theme_set(theme_minimal())\n",
    "\n",
    "selected_with_q_and_log = tests[tests.q < 0.05].nlargest(10, \"log_mean_std\")\n",
    "\n",
    "pl = (\n",
    "    ggplot(long_exports[long_exports.metabolite.isin(selected_with_q_and_log.metabolite)])\n",
    "    + aes(x=\"medium\", y=\"flux\")\n",
    "    + theme(figure_size = (20,10))\n",
    "    + geom_boxplot() \n",
    "    + geom_jitter(width=.2)\n",
    "    + scale_y_log10()\n",
    "    + facet_wrap(\"~ metabolite\")\n",
    ")\n",
    "pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a4c1ad6-19f3-44a2-ab69-7aab6b49221b",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_with_q_and_log"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6a7887-ed0f-4a53-b546-4345d1afb76e",
   "metadata": {},
   "source": [
    "merging with annotations so the real names of the metabolites show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b7c40e1-5d34-4aa6-a11e-41a3b7575161",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotations = pd.read_csv(\"/proj/gibbons/sswegle_micom/Res_Saves/annotations_swegle.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a97baa3-ee8c-4dd6-887b-0b30374b9a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "tests_merged_with_annotations = tests.merge(annotations, on=\"metabolite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5de61f2d-380c-49ea-a32c-e201480c363f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Prevotella_merged_with_annotations = long_exports.merge(annotations, on=\"metabolite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f95f7bdd-a348-46fc-ab4b-f61b297d1c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "tests_merged_with_annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c40d80ab-7cba-4137-92fa-9f5efc430da4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Prevotella_merged_with_annotations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84beb4ed-fdea-4589-a18a-bcd6fbfb8662",
   "metadata": {},
   "source": [
    "re making the graphs, with the actual metabolite names now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaf6a6c8-53b9-4fd3-85e2-6279e2e015f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "theme_set(theme_minimal())\n",
    "\n",
    "Prevotella_with_name = tests_merged_with_annotations[tests_merged_with_annotations.q < 0.05].nlargest(6, \"log_mean_std\")\n",
    "\n",
    "exports_Prevotella_filtered_metabolites = (\n",
    "    ggplot(Prevotella_merged_with_annotations[Prevotella_merged_with_annotations.name.isin(Prevotella_with_name.name)])\n",
    "    + aes(x=\"medium\", y=\"flux\")\n",
    "    + theme(figure_size = (12,4))\n",
    "    + geom_boxplot() \n",
    "    + geom_jitter(width=.2)\n",
    "    + scale_y_log10()\n",
    "    + facet_wrap(\"~ name\")\n",
    ")\n",
    "\n",
    "exports_Prevotella_filtered_metabolites\n",
    "\n",
    "exports_Prevotella_filtered_metabolites.save(\"exports_Prevotella_filtered_metabolites.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76473614-1dca-4ff0-bab5-e2bd687fb833",
   "metadata": {},
   "outputs": [],
   "source": [
    "exports_Prevotella_filtered_metabolites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44aae4ae-c0e9-4dba-81d9-c71ac19aa986",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "theme_set(theme_minimal())\n",
    "\n",
    "Prevotella_with_name = tests_merged_with_annotations[tests_merged_with_annotations.q < 0.05].nlargest(4, \"log_mean_std\")\n",
    "\n",
    "Prevotella_metabolites_with_time = (\n",
    "    ggplot(Prevotella_merged_with_annotations[Prevotella_merged_with_annotations.name.isin(Prevotella_with_name.name)])\n",
    "    + aes(x=\"medium\", y=\"flux\", color = \"time_point\")\n",
    "    + theme(figure_size = (12,4))\n",
    "    + geom_boxplot() \n",
    "    + geom_jitter(width=.2)\n",
    "    + scale_y_log10()\n",
    "    + facet_wrap(\"~ name\")\n",
    ")\n",
    "\n",
    "Prevotella_metabolites_with_time"
   ]
  }
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