figure_generation_invitro_afermentans_reordered.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7998a5a1-e3de-42b7-980c-626c1ae24168",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ----------------------------------------------------------------------------\n",
    "# SkinCom Analysis - Perform t-test & boxplots for log-ratios\n",
    "# Author: Sherlyn Weng\n",
    "# Version: 1.0.0\n",
    "# Mmaintainer: Sherlyn Weng\n",
    "# Email: y1weng@ucsd.edu\n",
    "# This code is adapted from https://github.com/knightlab-analyses/reference-frames\n",
    "# ----------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "005db807-d8b9-4eb4-893b-ec07aba48ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from biom import load_table\n",
    "from scipy.stats import ttest_rel, wilcoxon, ttest_ind, pearsonr, spearmanr\n",
    "from skbio.stats.composition import multiplicative_replacement\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "from collections import namedtuple\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\"\"\"\n",
    "Here, we have implemented an extended version of scipy's paired t-test function.\n",
    "see https://github.com/scipy/scipy/blob/master/scipy/stats/mstats_basic.py#L1085\n",
    "\n",
    "This should really be pushed to scipy -- any volunteers?\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "from scipy.stats import t\n",
    "from scipy.special import betainc\n",
    "from collections import namedtuple\n",
    "\n",
    "\n",
    "Ttest_relResult = namedtuple('Ttest_relResult', \n",
    "                             ('statistic', 'pvalue', 'lower_CI', 'upper_CI', 'ddof'))\n",
    "\n",
    "\n",
    "def ttest_rel(x, y):\n",
    "    \n",
    "    D = x - y\n",
    "    n = len(D)\n",
    "    \n",
    "    sd = np.std(D, ddof=1) \n",
    "    se = sd / np.sqrt(n)\n",
    "    mu = np.mean(D)\n",
    "    T = mu / se\n",
    "    ddof = n-1\n",
    "    ci = se * t.ppf( 0.05 / 2, ddof) \n",
    "    lower = mu - np.abs(ci)\n",
    "    upper = mu + np.abs(ci)\n",
    "    \n",
    "    prob = betainc(0.5*ddof, 0.5, ddof/(ddof + T*T))\n",
    "    return Ttest_relResult(T, prob, lower, upper, int(ddof))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2e265a3f-520c-43bc-955b-20b179fe1cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def paired_t_test(category, md, df):\n",
    "    \"\"\" Performs paired t-test on a dataframe with complementing\n",
    "    sample metadata.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    category : str\n",
    "       Category to test in the dataframe df\n",
    "    md : pd.DataFrame\n",
    "       Sample metadata specifying groupings\n",
    "    df : pd.Dataframe\n",
    "       Counts of microbes across all samples\n",
    "              \n",
    "    Returns\n",
    "    -------\n",
    "    T : float\n",
    "       T-statistic\n",
    "    prob : float\n",
    "       p-value\n",
    "    lower : float\n",
    "       lower 95% CI bound\n",
    "    upper : float\n",
    "        upper 95% CI bound\n",
    "    ddof : int\n",
    "        degrees of freedom\n",
    "    \"\"\"\n",
    "    cats = list(md[category].value_counts().index)\n",
    "    src = md[md[category] == cats[0]].index\n",
    "    print('hellooo')\n",
    "    print(src)\n",
    "    dest = md[md[category] == cats[1]].index\n",
    "    return ttest_rel(df.loc[dest].values, df.loc[src].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5242c6a2-b6b2-497e-aa34-225f3a9c1cab",
   "metadata": {},
   "source": [
    "## Input Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0f656bb7-fbf6-4b0e-909b-d94835646df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dir = \"../20231128_run_songbird_skincom\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b78015-1a9a-4d8e-97d0-e7ff85a1e82c",
   "metadata": {},
   "source": [
    "## Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1164688b-1357-4d73-bedb-66dc5b71386a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## SLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d993c044-e503-4682-9279-dbde03602d26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "hellooo\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>24hControl_vs_24hChemical_tstat</th>\n",
       "      <th>24hControl__vs_24hChemical_pvalue</th>\n",
       "      <th>24hControl_vs_24hChemical_upperCI</th>\n",
       "      <th>24hControl_vs_24hChemical_lowerCI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>log(C.acnes/C.afermentans)</th>\n",
       "      <td>-4.546796</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>-3.477390</td>\n",
       "      <td>-1.097931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(M.luteus/C.afermentans)</th>\n",
       "      <td>1.786337</td>\n",
       "      <td>0.117204</td>\n",
       "      <td>-0.013707</td>\n",
       "      <td>0.098391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.aureus/C.afermentans)</th>\n",
       "      <td>-12.891296</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>-4.310889</td>\n",
       "      <td>-2.974538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.capitis/C.afermentans)</th>\n",
       "      <td>0.706425</td>\n",
       "      <td>0.502753</td>\n",
       "      <td>-0.041647</td>\n",
       "      <td>0.077132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.epidermidis/C.afermentans)</th>\n",
       "      <td>1.257191</td>\n",
       "      <td>0.249005</td>\n",
       "      <td>-0.074636</td>\n",
       "      <td>0.244093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.hominis/C.afermentans)</th>\n",
       "      <td>9.084779</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.452786</td>\n",
       "      <td>0.771429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.warneri/C.afermentans)</th>\n",
       "      <td>8.768258</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.333996</td>\n",
       "      <td>0.580661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.mitis/C.afermentans)</th>\n",
       "      <td>-7.348948</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>-0.141701</td>\n",
       "      <td>-0.072711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  24hControl_vs_24hChemical_tstat  \\\n",
       "log(C.acnes/C.afermentans)                              -4.546796   \n",
       "log(M.luteus/C.afermentans)                              1.786337   \n",
       "log(S.aureus/C.afermentans)                            -12.891296   \n",
       "log(S.capitis/C.afermentans)                             0.706425   \n",
       "log(S.epidermidis/C.afermentans)                         1.257191   \n",
       "log(S.hominis/C.afermentans)                             9.084779   \n",
       "log(S.warneri/C.afermentans)                             8.768258   \n",
       "log(S.mitis/C.afermentans)                              -7.348948   \n",
       "\n",
       "                                  24hControl__vs_24hChemical_pvalue  \\\n",
       "log(C.acnes/C.afermentans)                                 0.002646   \n",
       "log(M.luteus/C.afermentans)                                0.117204   \n",
       "log(S.aureus/C.afermentans)                                0.000004   \n",
       "log(S.capitis/C.afermentans)                               0.502753   \n",
       "log(S.epidermidis/C.afermentans)                           0.249005   \n",
       "log(S.hominis/C.afermentans)                               0.000040   \n",
       "log(S.warneri/C.afermentans)                               0.000051   \n",
       "log(S.mitis/C.afermentans)                                 0.000156   \n",
       "\n",
       "                                  24hControl_vs_24hChemical_upperCI  \\\n",
       "log(C.acnes/C.afermentans)                                -3.477390   \n",
       "log(M.luteus/C.afermentans)                               -0.013707   \n",
       "log(S.aureus/C.afermentans)                               -4.310889   \n",
       "log(S.capitis/C.afermentans)                              -0.041647   \n",
       "log(S.epidermidis/C.afermentans)                          -0.074636   \n",
       "log(S.hominis/C.afermentans)                               0.452786   \n",
       "log(S.warneri/C.afermentans)                               0.333996   \n",
       "log(S.mitis/C.afermentans)                                -0.141701   \n",
       "\n",
       "                                  24hControl_vs_24hChemical_lowerCI  \n",
       "log(C.acnes/C.afermentans)                                -1.097931  \n",
       "log(M.luteus/C.afermentans)                                0.098391  \n",
       "log(S.aureus/C.afermentans)                               -2.974538  \n",
       "log(S.capitis/C.afermentans)                               0.077132  \n",
       "log(S.epidermidis/C.afermentans)                           0.244093  \n",
       "log(S.hominis/C.afermentans)                               0.771429  \n",
       "log(S.warneri/C.afermentans)                               0.580661  \n",
       "log(S.mitis/C.afermentans)                                -0.072711  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load md\n",
    "metadata = pd.read_table(f'{input_dir}/data/syncom_sles_md.tsv', index_col = 0)\n",
    "\n",
    "#load samples\n",
    "table = load_table(f'{input_dir}/data/syncom_sles.biom').to_dataframe().T\n",
    "\n",
    "# load multinomial ranks (differentials)\n",
    "beta = pd.read_csv(f'{input_dir}/out_sles/differentials.tsv', sep='\\t', index_col = 0)\n",
    "beta = beta.iloc[1:]\n",
    "\n",
    "# table normalization by column\n",
    "table = table.loc[:, table.sum(axis=0)>0]\n",
    "table = table.apply(lambda x: x / x.sum(), axis=1)\n",
    "\n",
    "reference_species = 'Corynebacterium afermentans'\n",
    "\n",
    "def balance_f(x):\n",
    "    lr = [np.log(x['Cutibacterium acnes']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Micrococcus luteus']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus aureus']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus capitis']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus epidermidis']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus hominis']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus warneri']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Streptococcus mitis']+0.001) - np.log(x[reference_species]+0.001)\n",
    "         ]\n",
    "    cols = [\n",
    "        'log(C.acnes/C.afermentans)',\n",
    "        'log(M.luteus/C.afermentans)',\n",
    "        'log(S.aureus/C.afermentans)',\n",
    "        'log(S.capitis/C.afermentans)',\n",
    "        'log(S.epidermidis/C.afermentans)',\n",
    "        'log(S.hominis/C.afermentans)',\n",
    "        'log(S.warneri/C.afermentans)',\n",
    "        'log(S.mitis/C.afermentans)'\n",
    "    ]\n",
    "    return pd.Series(lr, index=cols)\n",
    "\n",
    "balances = table.apply(balance_f, axis = 1)\n",
    "\n",
    "f = lambda x: paired_t_test(category='treatment', md=metadata, df=x)[0]\n",
    "tstats = balances.apply(f, axis=0)\n",
    "\n",
    "f = lambda x: paired_t_test(category='treatment', md=metadata, df=x)[1]\n",
    "tpvals = balances.apply(f, axis=0)\n",
    "\n",
    "f = lambda x: paired_t_test(category='treatment', md=metadata, df=x)[2]\n",
    "tlower = balances.apply(f, axis=0)\n",
    "\n",
    "f = lambda x: paired_t_test(category='treatment', md=metadata, df=x)[3]\n",
    "tupper = balances.apply(f, axis=0)\n",
    "\n",
    "stats = pd.DataFrame(\n",
    "    {\n",
    "        '24hControl_vs_24hChemical_tstat': tstats,\n",
    "        '24hControl__vs_24hChemical_pvalue': tpvals,\n",
    "        '24hControl_vs_24hChemical_upperCI': tlower,\n",
    "        '24hControl_vs_24hChemical_lowerCI': tupper\n",
    "    }\n",
    ")\n",
    "\n",
    "data = pd.merge(balances, metadata, left_index=True, right_index=True)\n",
    "\n",
    "subdata = data[[\n",
    "    'log(C.acnes/C.afermentans)',\n",
    "    'log(M.luteus/C.afermentans)',\n",
    "    'log(S.aureus/C.afermentans)',\n",
    "    'log(S.capitis/C.afermentans)',\n",
    "    'log(S.epidermidis/C.afermentans)',\n",
    "    'log(S.hominis/C.afermentans)',\n",
    "    'log(S.warneri/C.afermentans)',\n",
    "    'log(S.mitis/C.afermentans)',\n",
    "    'treatment']]\n",
    "\n",
    "df = pd.melt(subdata, id_vars = 'treatment', var_name = 'balance')\n",
    "\n",
    "# get the palette color name\n",
    "deep_palette = sns.color_palette(\"Set1\")\n",
    "\n",
    "# Extract the first two colors\n",
    "color1 = deep_palette[0]\n",
    "color2 = deep_palette[1]\n",
    "\n",
    "#, notch=True\n",
    "sns.set(style=\"white\", font_scale=1.5, font=\"sans-serif\", palette=\"Set1\")\n",
    "\n",
    "# get a label for outlier,figure out why there is error bar \n",
    "# customize which counts as outlier\n",
    "g = sns.catplot(x='value', y='balance', data=df, hue='treatment', kind='box',\n",
    "                hue_order=['reference', 'SLES'], n_boot = 1000,\n",
    "                height = 10, aspect = 1.8, legend = False, whis = [0,100])\n",
    "g.set_xlabels('log fold change', fontsize=14)\n",
    "g.set_ylabels('', fontsize=14)\n",
    "plt.legend(fontsize = 20, frameon=False)\n",
    "# plt.legend(loc='lower right', labels=['Control', 'Treatment'])\n",
    "plt.legend(fontsize=20, frameon=False, loc='lower right')\n",
    "plt.xticks(fontsize=12)\n",
    "\n",
    "ax = plt.gca()\n",
    "leg = ax.legend(fontsize=20, frameon=False, loc='lower right')\n",
    "leg.legendHandles[0].set_linewidth(8)\n",
    "leg.legendHandles[0].set_color(color1)\n",
    "leg.legendHandles[1].set_linewidth(8)\n",
    "leg.legendHandles[1].set_color(color2)\n",
    "leg.get_frame().set_linewidth(0)\n",
    "#plt.xticks(rotation=60)\n",
    "\n",
    "# g.savefig('./out_sles/fig_sles.svg', format='svg')\n",
    "stats.to_csv('./out_c_afermentans/stats_invitro_sles.csv')\n",
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef86565d-5578-45fd-8423-4ad797746748",
   "metadata": {},
   "source": [
    "def paired_t_test(category, md, df):\n",
    "    \"\"\" Performs paired t-test on a dataframe with complementing\n",
    "    sample metadata.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    category : str\n",
    "       Category to test in the dataframe df\n",
    "    md : pd.DataFrame\n",
    "       Sample metadata specifying groupings\n",
    "    df : pd.Dataframe\n",
    "       Counts of microbes across all samples\n",
    "              \n",
    "    Returns\n",
    "    -------\n",
    "    T : float\n",
    "       T-statistic\n",
    "    prob : float\n",
    "       p-value\n",
    "    lower : float\n",
    "       lower 95% CI bound\n",
    "    upper : float\n",
    "        upper 95% CI bound\n",
    "    ddof : int\n",
    "        degrees of freedom\n",
    "    \"\"\"\n",
    "    cats = list(md[category].value_counts().index)\n",
    "    src = md[md[category] == cats[0]].index\n",
    "    print(src)\n",
    "    dest = md[md[category] == cats[1]].index\n",
    "    return ttest_rel(df.loc[dest].values, df.loc[src].values)# RL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ac689ee4-cdd1-4996-a62a-f96b95fefbbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def paired_t_test(category, md, df):\n",
    "    \"\"\" Performs paired t-test on a dataframe with complementing\n",
    "    sample metadata.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    category : str\n",
    "       Category to test in the dataframe df\n",
    "    md : pd.DataFrame\n",
    "       Sample metadata specifying groupings\n",
    "    df : pd.Dataframe\n",
    "       Counts of microbes across all samples\n",
    "              \n",
    "    Returns\n",
    "    -------\n",
    "    T : float\n",
    "       T-statistic\n",
    "    prob : float\n",
    "       p-value\n",
    "    lower : float\n",
    "       lower 95% CI bound\n",
    "    upper : float\n",
    "        upper 95% CI bound\n",
    "    ddof : int\n",
    "        degrees of freedom\n",
    "    \"\"\"\n",
    "    cats = list(md[category].value_counts().index)\n",
    "    src = md[md[category] == cats[0]].index\n",
    "    print('***')\n",
    "    print(src)\n",
    "    dest = md[md[category] == cats[1]].index\n",
    "    return ttest_rel(df.loc[dest].values, df.loc[src].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "43dcf082-3703-40f0-b5af-ca7175ace397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n",
      "***\n",
      "Index(['Comm-1_S4_L001', 'Comm-1_S4_L002', 'Comm-2_S14_L001',\n",
      "       'Comm-2_S14_L002', 'Comm-3_S23_L001', 'Comm-3_S23_L002',\n",
      "       'Comm-4_S32_L001', 'Comm-4_S32_L002'],\n",
      "      dtype='object', name='sampleid')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>24hControl_vs_24hChemical_tstat</th>\n",
       "      <th>24hControl__vs_24hChemical_pvalue</th>\n",
       "      <th>24hControl_vs_24hChemical_upperCI</th>\n",
       "      <th>24hControl_vs_24hChemical_lowerCI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>log(C.acnes/C.afermentans)</th>\n",
       "      <td>-5.159104</td>\n",
       "      <td>0.001311</td>\n",
       "      <td>-4.112857</td>\n",
       "      <td>-1.527606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(M.luteus/C.afermentans)</th>\n",
       "      <td>0.827653</td>\n",
       "      <td>0.435172</td>\n",
       "      <td>-0.027550</td>\n",
       "      <td>0.057221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.aureus/C.afermentans)</th>\n",
       "      <td>-9.432206</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>-4.040212</td>\n",
       "      <td>-2.420525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.capitis/C.afermentans)</th>\n",
       "      <td>-3.635776</td>\n",
       "      <td>0.008334</td>\n",
       "      <td>-0.396925</td>\n",
       "      <td>-0.084086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.epidermidis/C.afermentans)</th>\n",
       "      <td>2.843834</td>\n",
       "      <td>0.024907</td>\n",
       "      <td>0.082282</td>\n",
       "      <td>0.894311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.hominis/C.afermentans)</th>\n",
       "      <td>3.933570</td>\n",
       "      <td>0.005650</td>\n",
       "      <td>0.197734</td>\n",
       "      <td>0.793762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.warneri/C.afermentans)</th>\n",
       "      <td>2.485341</td>\n",
       "      <td>0.041881</td>\n",
       "      <td>0.017821</td>\n",
       "      <td>0.715997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(S.mitis/C.afermentans)</th>\n",
       "      <td>1.308089</td>\n",
       "      <td>0.232158</td>\n",
       "      <td>-0.234270</td>\n",
       "      <td>0.814365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  24hControl_vs_24hChemical_tstat  \\\n",
       "log(C.acnes/C.afermentans)                              -5.159104   \n",
       "log(M.luteus/C.afermentans)                              0.827653   \n",
       "log(S.aureus/C.afermentans)                             -9.432206   \n",
       "log(S.capitis/C.afermentans)                            -3.635776   \n",
       "log(S.epidermidis/C.afermentans)                         2.843834   \n",
       "log(S.hominis/C.afermentans)                             3.933570   \n",
       "log(S.warneri/C.afermentans)                             2.485341   \n",
       "log(S.mitis/C.afermentans)                               1.308089   \n",
       "\n",
       "                                  24hControl__vs_24hChemical_pvalue  \\\n",
       "log(C.acnes/C.afermentans)                                 0.001311   \n",
       "log(M.luteus/C.afermentans)                                0.435172   \n",
       "log(S.aureus/C.afermentans)                                0.000031   \n",
       "log(S.capitis/C.afermentans)                               0.008334   \n",
       "log(S.epidermidis/C.afermentans)                           0.024907   \n",
       "log(S.hominis/C.afermentans)                               0.005650   \n",
       "log(S.warneri/C.afermentans)                               0.041881   \n",
       "log(S.mitis/C.afermentans)                                 0.232158   \n",
       "\n",
       "                                  24hControl_vs_24hChemical_upperCI  \\\n",
       "log(C.acnes/C.afermentans)                                -4.112857   \n",
       "log(M.luteus/C.afermentans)                               -0.027550   \n",
       "log(S.aureus/C.afermentans)                               -4.040212   \n",
       "log(S.capitis/C.afermentans)                              -0.396925   \n",
       "log(S.epidermidis/C.afermentans)                           0.082282   \n",
       "log(S.hominis/C.afermentans)                               0.197734   \n",
       "log(S.warneri/C.afermentans)                               0.017821   \n",
       "log(S.mitis/C.afermentans)                                -0.234270   \n",
       "\n",
       "                                  24hControl_vs_24hChemical_lowerCI  \n",
       "log(C.acnes/C.afermentans)                                -1.527606  \n",
       "log(M.luteus/C.afermentans)                                0.057221  \n",
       "log(S.aureus/C.afermentans)                               -2.420525  \n",
       "log(S.capitis/C.afermentans)                              -0.084086  \n",
       "log(S.epidermidis/C.afermentans)                           0.894311  \n",
       "log(S.hominis/C.afermentans)                               0.793762  \n",
       "log(S.warneri/C.afermentans)                               0.715997  \n",
       "log(S.mitis/C.afermentans)                                 0.814365  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load md\n",
    "metadata = pd.read_table(f'{input_dir}/data/syncom_rl_md.tsv', index_col = 0)\n",
    "\n",
    "#load samples\n",
    "table = load_table(f'{input_dir}/data/syncom_rl.biom').to_dataframe().T\n",
    "\n",
    "# load multinomial ranks (differentials)\n",
    "beta = pd.read_csv(f'{input_dir}/out_rl/differentials.tsv', sep='\\t', index_col = 0)\n",
    "beta = beta.iloc[1:]\n",
    "\n",
    "# table normalization by column\n",
    "table = table.loc[:, table.sum(axis=0)>0]\n",
    "table = table.apply(lambda x: x / x.sum(), axis=1)\n",
    "\n",
    "reference_species = 'Corynebacterium afermentans'\n",
    "\n",
    "def balance_f(x):\n",
    "    lr = [np.log(x['Cutibacterium acnes']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Micrococcus luteus']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus aureus']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus capitis']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus epidermidis']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus hominis']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Staphylococcus warneri']+0.001) - np.log(x[reference_species]+0.001),\n",
    "          np.log(x['Streptococcus mitis']+0.001) - np.log(x[reference_species]+0.001)\n",
    "         ]\n",
    "    cols = [\n",
    "        'log(C.acnes/C.afermentans)',\n",
    "        'log(M.luteus/C.afermentans)',\n",
    "        'log(S.aureus/C.afermentans)',\n",
    "        'log(S.capitis/C.afermentans)',\n",
    "        'log(S.epidermidis/C.afermentans)',\n",
    "        'log(S.hominis/C.afermentans)',\n",
    "        'log(S.warneri/C.afermentans)',\n",
    "        'log(S.mitis/C.afermentans)'\n",
    "    ]\n",
    "    return pd.Series(lr, index=cols)\n",
    "\n",
    "balances = table.apply(balance_f, axis = 1)\n",
    "\n",
    "f = lambda x: paired_t_test(category='treatment', md=metadata, df=x)[0]\n",
    "tstats = balances.apply(f, axis=0)\n",
    "\n",
    "f = lambda x: paired_t_test(category='treatment', md=metadata, df=x)[1]\n",
    "tpvals = balances.apply(f, axis=0)\n",
    "\n",
    "f = lambda x: paired_t_test(category='treatment', md=metadata, df=x)[2]\n",
    "tlower = balances.apply(f, axis=0)\n",
    "\n",
    "f = lambda x: paired_t_test(category='treatment', md=metadata, df=x)[3]\n",
    "tupper = balances.apply(f, axis=0)\n",
    "\n",
    "stats = pd.DataFrame(\n",
    "    {\n",
    "        '24hControl_vs_24hChemical_tstat': tstats,\n",
    "        '24hControl__vs_24hChemical_pvalue': tpvals,\n",
    "        '24hControl_vs_24hChemical_upperCI': tlower,\n",
    "        '24hControl_vs_24hChemical_lowerCI': tupper\n",
    "    }\n",
    ")\n",
    "\n",
    "data = pd.merge(balances, metadata, left_index=True, right_index=True)\n",
    "\n",
    "subdata = data[[\n",
    "    'log(C.acnes/C.afermentans)',\n",
    "    'log(M.luteus/C.afermentans)',\n",
    "    'log(S.aureus/C.afermentans)',\n",
    "    'log(S.capitis/C.afermentans)',\n",
    "    'log(S.epidermidis/C.afermentans)',\n",
    "    'log(S.hominis/C.afermentans)',\n",
    "    'log(S.warneri/C.afermentans)',\n",
    "    'log(S.mitis/C.afermentans)',\n",
    "    'treatment']]\n",
    "\n",
    "df = pd.melt(subdata, id_vars = 'treatment', var_name = 'balance')\n",
    "\n",
    "# get the palette color name\n",
    "deep_palette = sns.color_palette(\"Set1\")\n",
    "\n",
    "# Extract the first two colors\n",
    "color1 = deep_palette[0]\n",
    "color2 = deep_palette[1]\n",
    "\n",
    "#, notch=True\n",
    "sns.set(style=\"white\", font_scale=1.5, font=\"sans-serif\", palette=\"Set1\")\n",
    "\n",
    "# get a label for outlier,figure out why there is error bar \n",
    "# customize which counts as outlier\n",
    "g = sns.catplot(x='value', y='balance', data=df, hue='treatment', kind='box',\n",
    "                hue_order=['reference', 'RL'], n_boot = 1000,\n",
    "                height = 10, aspect = 1.8, legend = False, whis = [0,100])\n",
    "g.set_xlabels('log fold change', fontsize=14)\n",
    "g.set_ylabels('', fontsize=14)\n",
    "plt.legend(fontsize = 20, frameon=False)\n",
    "# plt.legend(loc='lower right', labels=['Control', 'Treatment'])\n",
    "plt.legend(fontsize=20, frameon=False, loc='lower right')\n",
    "plt.xticks(fontsize=12)\n",
    "\n",
    "ax = plt.gca()\n",
    "leg = ax.legend(fontsize=20, frameon=False, loc='lower right')\n",
    "leg.legendHandles[0].set_linewidth(8)\n",
    "leg.legendHandles[0].set_color(color1)\n",
    "leg.legendHandles[1].set_linewidth(8)\n",
    "leg.legendHandles[1].set_color(color2)\n",
    "leg.get_frame().set_linewidth(0)\n",
    "#plt.xticks(rotation=60)\n",
    "\n",
    "# g.savefig('./out_rl/fig_rl.svg', format='svg')\n",
    "stats.to_csv('./out_c_afermentans/stats_invitro_rl.csv')\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "474a775a-0c73-4754-b887-2529decd1843",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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