Proteomics_Complete_Clean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proteomics Data Analysis Workflow\n",
    "\n",
    "This notebook combines several analysis steps for proteomics data. \n",
    "\n",
    "The notebook has three parts:\n",
    "1. Proteomics Statistics:  \n",
    "    1.1. Analysis of the share of detected locus tags  \n",
    "    1.2. Coverage per detected protein  \n",
    "    1.3. Clustering of undetected proteins (e.g are they primarily membrane / secondary metabolite related)  \n",
    "2. Whole Proteome Analysis:   \n",
    "    2.1. Identification of shared core-proteome across all samples  \n",
    "    2.2. Whole proteome correlation  \n",
    "    2.3. Clustermap of all samples  \n",
    "    2.4. T-Test of each control / sample combination  \n",
    "    2.5. Plotting of clustermap for p-values < 0.05 / 0.005  \n",
    "    2.6. COG Annotation of significantly changed locus tags  \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Load Modules and Specify Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chrwhi/Software/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from IPython.display import display, HTML\n",
    "import seaborn as sns\n",
    "from Bio import SeqIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "INSTALLED VERSIONS\n",
      "------------------\n",
      "commit           : None\n",
      "python           : 3.7.4.final.0\n",
      "python-bits      : 64\n",
      "OS               : Darwin\n",
      "OS-release       : 22.1.0\n",
      "machine          : x86_64\n",
      "processor        : i386\n",
      "byteorder        : little\n",
      "LC_ALL           : en_US.UTF-8\n",
      "LANG             : en_US.UTF-8\n",
      "LOCALE           : en_US.UTF-8\n",
      "\n",
      "pandas           : 1.0.3\n",
      "numpy            : 1.17.2\n",
      "pytz             : 2019.3\n",
      "dateutil         : 2.8.0\n",
      "pip              : 19.2.3\n",
      "setuptools       : 41.4.0\n",
      "Cython           : 0.29.13\n",
      "pytest           : 5.2.1\n",
      "hypothesis       : None\n",
      "sphinx           : 2.2.0\n",
      "blosc            : None\n",
      "feather          : None\n",
      "xlsxwriter       : 1.2.1\n",
      "lxml.etree       : 4.4.1\n",
      "html5lib         : 1.0.1\n",
      "pymysql          : None\n",
      "psycopg2         : None\n",
      "jinja2           : 2.10.3\n",
      "IPython          : 7.8.0\n",
      "pandas_datareader: None\n",
      "bs4              : 4.8.0\n",
      "bottleneck       : 1.2.1\n",
      "fastparquet      : None\n",
      "gcsfs            : None\n",
      "lxml.etree       : 4.4.1\n",
      "matplotlib       : 3.3.3\n",
      "numexpr          : 2.7.0\n",
      "odfpy            : None\n",
      "openpyxl         : 3.0.0\n",
      "pandas_gbq       : None\n",
      "pyarrow          : None\n",
      "pytables         : None\n",
      "pytest           : 5.2.1\n",
      "pyxlsb           : None\n",
      "s3fs             : None\n",
      "scipy            : 1.3.1\n",
      "sqlalchemy       : 1.3.9\n",
      "tables           : 3.5.2\n",
      "tabulate         : None\n",
      "xarray           : None\n",
      "xlrd             : 1.2.0\n",
      "xlwt             : 1.3.0\n",
      "xlsxwriter       : 1.2.1\n",
      "numba            : 0.45.1\n"
     ]
    }
   ],
   "source": [
    "pd.show_versions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Directory Specification\n",
    "\n",
    "#define directory that contains the proteomics results\n",
    "proteomics_dir = \" \" \n",
    "\n",
    "#define directory for results (optional, but useful when analyzing different data sets)\n",
    "sub_dir =\"Analysis_BE/\" \n",
    "\n",
    "#give your data set a name \n",
    "data_name = \"MCBE_BE_data\"\n",
    "\n",
    "#Specify reference proteome file in .fasta format\n",
    "reference = proteomics_dir + \"01_CFB_NBC001_proteome.fasta\"\n",
    "\n",
    "#Extract all locus tags from the reference file \n",
    "locus_tags_list = []\n",
    "for record in SeqIO.parse(reference, \"fasta\"):\n",
    "    locus_tags_list.append(record.id)\n",
    "\n",
    "locus_tags_complete = pd.DataFrame(locus_tags_list, columns=['Locus_Tag'])\n",
    "\n",
    "#Load the proteomics data\n",
    "proteome_data = pd.read_excel(\"\")\n",
    "\n",
    "\n",
    "# Drop columns not of interest\n",
    "columns_drop = [\"Checked\", \"Protein FDR Confidence: Combined\", \"Master\"]\n",
    "proteome_data.drop(columns=columns_drop, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Proteomics Statistics\n",
    "\n",
    "This script takes a given proteomics output file and generates general statistics.\n",
    "\n",
    "1.1 What percentage of locus tags were detected\n",
    "\n",
    "1.2 Grouping of undetected locus tags --> are there any specific gene classifications that stand out (e.g membrane proteins)\n",
    "\n",
    "1.3 Identification of the shared detected proteome across samples and plotting of Venn diagramms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 What percantage of locus tags was detected? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This data shows how many locus tags were detected across the entire data set. For sample specific data, check next cell. \n",
    "\n",
    "#import package for venn diagramm\n",
    "from matplotlib_venn import venn2\n",
    "\n",
    "#Extract all detected locus tags from proteome data frame\n",
    "locus_tags_all_detected = proteome_data['Accession'].tolist()\n",
    "\n",
    "#remove all entries that do not have a locus tag (peptides from the sample prep/contaminations)\n",
    "locus_tags_all_detected[:] = [x for x in locus_tags_all_detected if x.startswith('FQ762')]\n",
    "\n",
    "#turn list into data frame\n",
    "locus_tags_all_detected_df = pd.DataFrame(locus_tags_all_detected, columns=['Locus_Tag'])\n",
    "\n",
    "#Create Venn Diagramm\n",
    "venn2([set(locus_tags_all_detected_df['Locus_Tag']),\n",
    "           set(locus_tags_complete['Locus_Tag'])],\n",
    "           set_labels=(\"Detected Locus Tags \",\"Not Detected Locus Tags\"))\n",
    "\n",
    "#Save in result dir if wanted\n",
    "#plt.savefig(proteomics_dir + sub_dir + \"Venn_\" + data_name+\".svg\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abundances (Normalized): MC01_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC02_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC03_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC01_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC02_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC03_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC04_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC05_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC06_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC04_Proteo2 Sample</th>\n",
       "      <th>...</th>\n",
       "      <th>Abundances (Normalized): MC09_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC07_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC08_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC09_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC10_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC11_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC12_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC10_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC11_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC12_Proteo2 Sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Locus Tags Detected</th>\n",
       "      <td>2792</td>\n",
       "      <td>2763</td>\n",
       "      <td>2765</td>\n",
       "      <td>2815</td>\n",
       "      <td>2731</td>\n",
       "      <td>2757</td>\n",
       "      <td>2714</td>\n",
       "      <td>2750</td>\n",
       "      <td>2789</td>\n",
       "      <td>2790</td>\n",
       "      <td>...</td>\n",
       "      <td>2782</td>\n",
       "      <td>2803</td>\n",
       "      <td>2798</td>\n",
       "      <td>2804</td>\n",
       "      <td>2726</td>\n",
       "      <td>2682</td>\n",
       "      <td>2707</td>\n",
       "      <td>2767</td>\n",
       "      <td>2767</td>\n",
       "      <td>2768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing Locus Tags</th>\n",
       "      <td>5216</td>\n",
       "      <td>5245</td>\n",
       "      <td>5243</td>\n",
       "      <td>5193</td>\n",
       "      <td>5277</td>\n",
       "      <td>5251</td>\n",
       "      <td>5294</td>\n",
       "      <td>5258</td>\n",
       "      <td>5219</td>\n",
       "      <td>5218</td>\n",
       "      <td>...</td>\n",
       "      <td>5226</td>\n",
       "      <td>5205</td>\n",
       "      <td>5210</td>\n",
       "      <td>5204</td>\n",
       "      <td>5282</td>\n",
       "      <td>5326</td>\n",
       "      <td>5301</td>\n",
       "      <td>5241</td>\n",
       "      <td>5241</td>\n",
       "      <td>5240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentage Detected</th>\n",
       "      <td>0.348651</td>\n",
       "      <td>0.34503</td>\n",
       "      <td>0.34528</td>\n",
       "      <td>0.351523</td>\n",
       "      <td>0.341034</td>\n",
       "      <td>0.344281</td>\n",
       "      <td>0.338911</td>\n",
       "      <td>0.343407</td>\n",
       "      <td>0.348277</td>\n",
       "      <td>0.348402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.347403</td>\n",
       "      <td>0.350025</td>\n",
       "      <td>0.349401</td>\n",
       "      <td>0.35015</td>\n",
       "      <td>0.34041</td>\n",
       "      <td>0.334915</td>\n",
       "      <td>0.338037</td>\n",
       "      <td>0.345529</td>\n",
       "      <td>0.345529</td>\n",
       "      <td>0.345654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Abundances (Normalized): MC01_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2792   \n",
       "Missing Locus Tags                                          5216   \n",
       "Percentage Detected                                     0.348651   \n",
       "\n",
       "                    Abundances (Normalized): MC02_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2763   \n",
       "Missing Locus Tags                                          5245   \n",
       "Percentage Detected                                      0.34503   \n",
       "\n",
       "                    Abundances (Normalized): MC03_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2765   \n",
       "Missing Locus Tags                                          5243   \n",
       "Percentage Detected                                      0.34528   \n",
       "\n",
       "                    Abundances (Normalized): MC01_Proteo2 Sample  \\\n",
       "Locus Tags Detected                                         2815   \n",
       "Missing Locus Tags                                          5193   \n",
       "Percentage Detected                                     0.351523   \n",
       "\n",
       "                    Abundances (Normalized): MC02_Proteo2 Sample  \\\n",
       "Locus Tags Detected                                         2731   \n",
       "Missing Locus Tags                                          5277   \n",
       "Percentage Detected                                     0.341034   \n",
       "\n",
       "                    Abundances (Normalized): MC03_Proteo2 Sample  \\\n",
       "Locus Tags Detected                                         2757   \n",
       "Missing Locus Tags                                          5251   \n",
       "Percentage Detected                                     0.344281   \n",
       "\n",
       "                    Abundances (Normalized): MC04_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2714   \n",
       "Missing Locus Tags                                          5294   \n",
       "Percentage Detected                                     0.338911   \n",
       "\n",
       "                    Abundances (Normalized): MC05_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2750   \n",
       "Missing Locus Tags                                          5258   \n",
       "Percentage Detected                                     0.343407   \n",
       "\n",
       "                    Abundances (Normalized): MC06_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2789   \n",
       "Missing Locus Tags                                          5219   \n",
       "Percentage Detected                                     0.348277   \n",
       "\n",
       "                    Abundances (Normalized): MC04_Proteo2 Sample  ...  \\\n",
       "Locus Tags Detected                                         2790  ...   \n",
       "Missing Locus Tags                                          5218  ...   \n",
       "Percentage Detected                                     0.348402  ...   \n",
       "\n",
       "                    Abundances (Normalized): MC09_Proteo2 Sample  \\\n",
       "Locus Tags Detected                                         2782   \n",
       "Missing Locus Tags                                          5226   \n",
       "Percentage Detected                                     0.347403   \n",
       "\n",
       "                    Abundances (Normalized): MC07_Proteo3 Sample  \\\n",
       "Locus Tags Detected                                         2803   \n",
       "Missing Locus Tags                                          5205   \n",
       "Percentage Detected                                     0.350025   \n",
       "\n",
       "                    Abundances (Normalized): MC08_Proteo3 Sample  \\\n",
       "Locus Tags Detected                                         2798   \n",
       "Missing Locus Tags                                          5210   \n",
       "Percentage Detected                                     0.349401   \n",
       "\n",
       "                    Abundances (Normalized): MC09_Proteo3 Sample  \\\n",
       "Locus Tags Detected                                         2804   \n",
       "Missing Locus Tags                                          5204   \n",
       "Percentage Detected                                      0.35015   \n",
       "\n",
       "                    Abundances (Normalized): MC10_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2726   \n",
       "Missing Locus Tags                                          5282   \n",
       "Percentage Detected                                      0.34041   \n",
       "\n",
       "                    Abundances (Normalized): MC11_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2682   \n",
       "Missing Locus Tags                                          5326   \n",
       "Percentage Detected                                     0.334915   \n",
       "\n",
       "                    Abundances (Normalized): MC12_Proteo1 Sample  \\\n",
       "Locus Tags Detected                                         2707   \n",
       "Missing Locus Tags                                          5301   \n",
       "Percentage Detected                                     0.338037   \n",
       "\n",
       "                    Abundances (Normalized): MC10_Proteo2 Sample  \\\n",
       "Locus Tags Detected                                         2767   \n",
       "Missing Locus Tags                                          5241   \n",
       "Percentage Detected                                     0.345529   \n",
       "\n",
       "                    Abundances (Normalized): MC11_Proteo2 Sample  \\\n",
       "Locus Tags Detected                                         2767   \n",
       "Missing Locus Tags                                          5241   \n",
       "Percentage Detected                                     0.345529   \n",
       "\n",
       "                    Abundances (Normalized): MC12_Proteo2 Sample  \n",
       "Locus Tags Detected                                         2768  \n",
       "Missing Locus Tags                                          5240  \n",
       "Percentage Detected                                     0.345654  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysis of sample specific number of detected locus tags \n",
    "\n",
    "#Define sample IDs/names of interest\n",
    "sample_IDs = [\"MC01_Proteo1\", \"MC02_Proteo1\",\"MC03_Proteo1\",\"MC01_Proteo2\",\"MC02_Proteo2\",\"MC03_Proteo2\", \"MC04_Proteo1\",\"MC05_Proteo1\",\"MC06_Proteo1\",\"MC04_Proteo2\",\"MC05_Proteo2\",\"MC06_Proteo2\",\"MC07_Proteo2\",\"MC08_Proteo2\",\"MC09_Proteo2\",\"MC07_Proteo3\",\"MC08_Proteo3\",\"MC09_Proteo3\",\"MC10_Proteo1\",\"MC11_Proteo1\",\"MC12_Proteo1\",\"MC10_Proteo2\",\"MC11_Proteo2\",\"MC12_Proteo2\"]\n",
    "#sample_IDs = [\"MC13_Proteo1_di\",\"MC14_Proteo1_di\",\"MC15_Proteo1_di\",\"MC16_Proteo1_di\",\"MC17_Proteo1_di\",\"MC18_Proteo1_di\",\"MC19_Proteo2_di\",\"MC20_Proteo2_di\",\"MC21_Proteo2_di\",\"MC13_Proteo2_di\",\"MC14_Proteo2_di\",\"MC15_Proteo2_di\",\"MC16_Proteo2_di\",\"MC17_Proteo2_di\",\"MC18_Proteo2_di\",\"MC19_Proteo3_di\",\"MC20_Proteo3_di\",\"MC21_Proteo3_di\"]\n",
    "\n",
    "#Extract column names for downstream analysis based on the sample IDs\n",
    "proteome_col = list(proteome_data.columns)\n",
    "columns_samples = []\n",
    "columns_samples_numerical = ['Accession']\n",
    "for i in sample_IDs:\n",
    "    for l in proteome_col:\n",
    "        if l.startswith(\"Abundances\"):\n",
    "            if i in l:\n",
    "                columns_samples.append(l)\n",
    "                columns_samples_numerical.append(l)\n",
    "\n",
    "total_locus_tags = len(locus_tags_complete.index) #total number of locus tags\n",
    "\n",
    "sample_stats = pd.DataFrame(columns=columns_samples, index=[\"Locus Tags Detected\", \"Missing Locus Tags\", \"Percentage Detected\"])\n",
    "\n",
    "#Extract number of detected locus tags for each sample\n",
    "for i in columns_samples:\n",
    "    sample_stats[i][\"Locus Tags Detected\"] = len(proteome_data[i].dropna().index)\n",
    "    sample_stats[i][\"Missing Locus Tags\"] = int(total_locus_tags - len(proteome_data[i].dropna().index))\n",
    "    sample_stats[i][\"Percentage Detected\"] = len(proteome_data[i].dropna().index)/total_locus_tags\n",
    "    \n",
    "#Save dataframe as csv\n",
    "#sample_stats.to_csv(proteomics_dir+sub_dir+data_name+\"detection_stats\"+\".csv\")\n",
    "sample_stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Optional plotting of the detection stats\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "labels = sample_stats.columns\n",
    "\n",
    "ax.bar(labels, sample_stats.loc[\"Locus Tags Detected\"],  label='Detected')\n",
    "ax.bar(labels, sample_stats.loc[\"Missing Locus Tags\"], bottom=sample_stats.loc[\"Locus Tags Detected\"],label='Not Detected')\n",
    "\n",
    "ax.set_ylabel('Counts')\n",
    "ax.set_title('Detected / Not Detected Locus Tags')\n",
    "ax.legend()\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting peptide coverage per protein\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(proteome_data[\"Accession\"],proteome_data[\"# Peptides\"])\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "peptides = proteome_data.set_index(\"Accession\")[\"# Peptides\"].sort_values()\n",
    "\n",
    "#Optional: Export peptide counts\n",
    "peptides.to_csv(proteomics_dir+sub_dir+data_name+\"peptide_stats\"+\".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Grouping and Analysis of undetected locus tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abundances (Normalized): MC01_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC02_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC03_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC01_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC02_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC03_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC04_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC05_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC06_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC04_Proteo2 Sample</th>\n",
       "      <th>...</th>\n",
       "      <th>Abundances (Normalized): MC09_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC07_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC08_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC09_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC10_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC11_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC12_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC10_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC11_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC12_Proteo2 Sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>FQ762_00015</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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>FQ762_40445</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_40450</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_40455</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_40460</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_40465</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8008 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Abundances (Normalized): MC01_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC02_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC03_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC01_Proteo2 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC02_Proteo2 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC03_Proteo2 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC04_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC05_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC06_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC04_Proteo2 Sample  ...  \\\n",
       "FQ762_00005                                           0.0  ...   \n",
       "FQ762_00010                                           0.0  ...   \n",
       "FQ762_00015                                           0.0  ...   \n",
       "FQ762_00020                                           0.0  ...   \n",
       "FQ762_00025                                           0.0  ...   \n",
       "...                                                   ...  ...   \n",
       "FQ762_40445                                           0.0  ...   \n",
       "FQ762_40450                                           0.0  ...   \n",
       "FQ762_40455                                           0.0  ...   \n",
       "FQ762_40460                                           0.0  ...   \n",
       "FQ762_40465                                           0.0  ...   \n",
       "\n",
       "             Abundances (Normalized): MC09_Proteo2 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC07_Proteo3 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC08_Proteo3 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC09_Proteo3 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC10_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC11_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC12_Proteo1 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC10_Proteo2 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC11_Proteo2 Sample  \\\n",
       "FQ762_00005                                           0.0   \n",
       "FQ762_00010                                           0.0   \n",
       "FQ762_00015                                           0.0   \n",
       "FQ762_00020                                           0.0   \n",
       "FQ762_00025                                           0.0   \n",
       "...                                                   ...   \n",
       "FQ762_40445                                           0.0   \n",
       "FQ762_40450                                           0.0   \n",
       "FQ762_40455                                           0.0   \n",
       "FQ762_40460                                           0.0   \n",
       "FQ762_40465                                           0.0   \n",
       "\n",
       "             Abundances (Normalized): MC12_Proteo2 Sample  \n",
       "FQ762_00005                                           0.0  \n",
       "FQ762_00010                                           0.0  \n",
       "FQ762_00015                                           0.0  \n",
       "FQ762_00020                                           0.0  \n",
       "FQ762_00025                                           0.0  \n",
       "...                                                   ...  \n",
       "FQ762_40445                                           0.0  \n",
       "FQ762_40450                                           0.0  \n",
       "FQ762_40455                                           0.0  \n",
       "FQ762_40460                                           0.0  \n",
       "FQ762_40465                                           0.0  \n",
       "\n",
       "[8008 rows x 24 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#empty data frame for absence presence matrix \n",
    "locus_presence = pd.DataFrame()\n",
    "\n",
    "#Create boolean matrix of proteome data based on whether a cell is NaN (\"Not a Number\"), meaning no peptide detected for the given locus tag\n",
    "proteome_data_na = proteome_data.set_index('Accession').isna()\n",
    "proteome_data_na.index\n",
    "\n",
    "#go through boolean matrix and turn NaNs into 0, everything else into 1. 0 indicates absence, 1 presence\n",
    "for i in columns_samples:\n",
    "    for l in locus_tags_complete['Locus_Tag'].tolist():\n",
    "        if l in locus_tags_all_detected_df['Locus_Tag'].tolist():\n",
    "            if proteome_data_na[i][l] == True:\n",
    "                locus_presence.at[l, i] = 0\n",
    "            elif proteome_data_na[i][l] == False:\n",
    "                locus_presence.at[l, i] = 1\n",
    "        else:\n",
    "            locus_presence.at[l,i] = 0\n",
    "\n",
    "#Optional: Save absence/presence matrix\n",
    "locus_presence.sort_index().to_csv(proteomics_dir+sub_dir+data_name+\"absence_presence_matrix\"+\".csv\")\n",
    "display(locus_presence.sort_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abundances (Normalized): MC01_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC02_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC03_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC04_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC05_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC06_Proteo1 Sample</th>\n",
       "      <th>Abundances (Normalized): MC07_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC08_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC09_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC10_Proteo1 Sample</th>\n",
       "      <th>...</th>\n",
       "      <th>Abundances (Normalized): MC03_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC04_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC05_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC06_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC07_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC08_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC09_Proteo3 Sample</th>\n",
       "      <th>Abundances (Normalized): MC10_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC11_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC12_Proteo2 Sample</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accession</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>FQ762_24085</th>\n",
       "      <td>1.171586e+09</td>\n",
       "      <td>1.107951e+09</td>\n",
       "      <td>1.085799e+09</td>\n",
       "      <td>8.397252e+08</td>\n",
       "      <td>8.831024e+08</td>\n",
       "      <td>8.870422e+08</td>\n",
       "      <td>1.742838e+09</td>\n",
       "      <td>1.894844e+09</td>\n",
       "      <td>1.843363e+09</td>\n",
       "      <td>1.101435e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>8.332682e+08</td>\n",
       "      <td>8.532477e+08</td>\n",
       "      <td>8.696870e+08</td>\n",
       "      <td>9.037809e+08</td>\n",
       "      <td>1.539099e+09</td>\n",
       "      <td>1.565285e+09</td>\n",
       "      <td>1.542599e+09</td>\n",
       "      <td>1.017561e+09</td>\n",
       "      <td>1.117278e+09</td>\n",
       "      <td>1.060976e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_24080</th>\n",
       "      <td>9.569938e+08</td>\n",
       "      <td>9.242977e+08</td>\n",
       "      <td>8.896606e+08</td>\n",
       "      <td>7.269815e+08</td>\n",
       "      <td>7.292593e+08</td>\n",
       "      <td>7.393495e+08</td>\n",
       "      <td>1.538613e+09</td>\n",
       "      <td>1.613691e+09</td>\n",
       "      <td>1.574196e+09</td>\n",
       "      <td>9.288558e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>6.943140e+08</td>\n",
       "      <td>7.134240e+08</td>\n",
       "      <td>7.314466e+08</td>\n",
       "      <td>7.728698e+08</td>\n",
       "      <td>1.317006e+09</td>\n",
       "      <td>1.355001e+09</td>\n",
       "      <td>1.331623e+09</td>\n",
       "      <td>8.526796e+08</td>\n",
       "      <td>9.539871e+08</td>\n",
       "      <td>8.815348e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_27290</th>\n",
       "      <td>6.384761e+08</td>\n",
       "      <td>6.165301e+08</td>\n",
       "      <td>6.170672e+08</td>\n",
       "      <td>5.991847e+08</td>\n",
       "      <td>5.976254e+08</td>\n",
       "      <td>6.193623e+08</td>\n",
       "      <td>9.259152e+08</td>\n",
       "      <td>9.932674e+08</td>\n",
       "      <td>9.615943e+08</td>\n",
       "      <td>6.992379e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>6.147268e+08</td>\n",
       "      <td>6.472840e+08</td>\n",
       "      <td>6.723428e+08</td>\n",
       "      <td>6.844034e+08</td>\n",
       "      <td>9.999289e+08</td>\n",
       "      <td>1.027562e+09</td>\n",
       "      <td>1.025154e+09</td>\n",
       "      <td>7.108583e+08</td>\n",
       "      <td>7.997206e+08</td>\n",
       "      <td>7.721618e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_16075</th>\n",
       "      <td>6.479556e+08</td>\n",
       "      <td>6.373093e+08</td>\n",
       "      <td>6.237242e+08</td>\n",
       "      <td>6.317717e+08</td>\n",
       "      <td>6.506322e+08</td>\n",
       "      <td>6.349711e+08</td>\n",
       "      <td>5.915943e+08</td>\n",
       "      <td>6.033881e+08</td>\n",
       "      <td>5.977442e+08</td>\n",
       "      <td>9.246726e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>7.056409e+08</td>\n",
       "      <td>6.821330e+08</td>\n",
       "      <td>7.251131e+08</td>\n",
       "      <td>7.157364e+08</td>\n",
       "      <td>8.438576e+08</td>\n",
       "      <td>8.824270e+08</td>\n",
       "      <td>8.804646e+08</td>\n",
       "      <td>1.006729e+09</td>\n",
       "      <td>1.143494e+09</td>\n",
       "      <td>1.056634e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_11145</th>\n",
       "      <td>4.028639e+08</td>\n",
       "      <td>4.006740e+08</td>\n",
       "      <td>3.793118e+08</td>\n",
       "      <td>3.179372e+08</td>\n",
       "      <td>3.382410e+08</td>\n",
       "      <td>3.537483e+08</td>\n",
       "      <td>4.515330e+08</td>\n",
       "      <td>4.696944e+08</td>\n",
       "      <td>4.602387e+08</td>\n",
       "      <td>1.338808e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.402384e+08</td>\n",
       "      <td>3.383388e+08</td>\n",
       "      <td>3.583782e+08</td>\n",
       "      <td>3.494747e+08</td>\n",
       "      <td>4.628978e+08</td>\n",
       "      <td>4.564082e+08</td>\n",
       "      <td>4.707294e+08</td>\n",
       "      <td>1.219885e+09</td>\n",
       "      <td>1.392688e+09</td>\n",
       "      <td>1.332704e+09</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>FQ762_26965</th>\n",
       "      <td>1.672995e+06</td>\n",
       "      <td>1.565424e+06</td>\n",
       "      <td>1.542756e+06</td>\n",
       "      <td>1.396169e+06</td>\n",
       "      <td>1.378737e+06</td>\n",
       "      <td>1.508704e+06</td>\n",
       "      <td>2.156559e+06</td>\n",
       "      <td>2.256712e+06</td>\n",
       "      <td>2.143512e+06</td>\n",
       "      <td>1.610014e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.213512e+06</td>\n",
       "      <td>1.437787e+06</td>\n",
       "      <td>1.271606e+06</td>\n",
       "      <td>1.213706e+06</td>\n",
       "      <td>2.127784e+06</td>\n",
       "      <td>2.017837e+06</td>\n",
       "      <td>2.061343e+06</td>\n",
       "      <td>1.462316e+06</td>\n",
       "      <td>1.543806e+06</td>\n",
       "      <td>1.398290e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_22695</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_22970</th>\n",
       "      <td>3.616373e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_27120</th>\n",
       "      <td>2.366113e+05</td>\n",
       "      <td>2.578939e+05</td>\n",
       "      <td>1.600964e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.686838e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.566200e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.995243e+05</td>\n",
       "      <td>3.968429e+05</td>\n",
       "      <td>2.991957e+05</td>\n",
       "      <td>2.922106e+05</td>\n",
       "      <td>2.098819e+05</td>\n",
       "      <td>2.195769e+05</td>\n",
       "      <td>2.501133e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.298261e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_38295</th>\n",
       "      <td>8.196730e+07</td>\n",
       "      <td>8.031982e+07</td>\n",
       "      <td>8.074603e+07</td>\n",
       "      <td>7.937630e+07</td>\n",
       "      <td>8.267516e+07</td>\n",
       "      <td>8.073148e+07</td>\n",
       "      <td>8.698724e+07</td>\n",
       "      <td>9.613904e+07</td>\n",
       "      <td>9.123226e+07</td>\n",
       "      <td>8.995315e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>8.342011e+07</td>\n",
       "      <td>9.537679e+07</td>\n",
       "      <td>9.322721e+07</td>\n",
       "      <td>8.502474e+07</td>\n",
       "      <td>9.950975e+07</td>\n",
       "      <td>1.041660e+08</td>\n",
       "      <td>1.043989e+08</td>\n",
       "      <td>9.674822e+07</td>\n",
       "      <td>1.057416e+08</td>\n",
       "      <td>9.694637e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3115 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Abundances (Normalized): MC01_Proteo1 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.171586e+09   \n",
       "FQ762_24080                                  9.569938e+08   \n",
       "FQ762_27290                                  6.384761e+08   \n",
       "FQ762_16075                                  6.479556e+08   \n",
       "FQ762_11145                                  4.028639e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.672995e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                  3.616373e+06   \n",
       "FQ762_27120                                  2.366113e+05   \n",
       "FQ762_38295                                  8.196730e+07   \n",
       "\n",
       "             Abundances (Normalized): MC02_Proteo1 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.107951e+09   \n",
       "FQ762_24080                                  9.242977e+08   \n",
       "FQ762_27290                                  6.165301e+08   \n",
       "FQ762_16075                                  6.373093e+08   \n",
       "FQ762_11145                                  4.006740e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.565424e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  2.578939e+05   \n",
       "FQ762_38295                                  8.031982e+07   \n",
       "\n",
       "             Abundances (Normalized): MC03_Proteo1 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.085799e+09   \n",
       "FQ762_24080                                  8.896606e+08   \n",
       "FQ762_27290                                  6.170672e+08   \n",
       "FQ762_16075                                  6.237242e+08   \n",
       "FQ762_11145                                  3.793118e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.542756e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  1.600964e+05   \n",
       "FQ762_38295                                  8.074603e+07   \n",
       "\n",
       "             Abundances (Normalized): MC04_Proteo1 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.397252e+08   \n",
       "FQ762_24080                                  7.269815e+08   \n",
       "FQ762_27290                                  5.991847e+08   \n",
       "FQ762_16075                                  6.317717e+08   \n",
       "FQ762_11145                                  3.179372e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.396169e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                           NaN   \n",
       "FQ762_38295                                  7.937630e+07   \n",
       "\n",
       "             Abundances (Normalized): MC05_Proteo1 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.831024e+08   \n",
       "FQ762_24080                                  7.292593e+08   \n",
       "FQ762_27290                                  5.976254e+08   \n",
       "FQ762_16075                                  6.506322e+08   \n",
       "FQ762_11145                                  3.382410e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.378737e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                           NaN   \n",
       "FQ762_38295                                  8.267516e+07   \n",
       "\n",
       "             Abundances (Normalized): MC06_Proteo1 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.870422e+08   \n",
       "FQ762_24080                                  7.393495e+08   \n",
       "FQ762_27290                                  6.193623e+08   \n",
       "FQ762_16075                                  6.349711e+08   \n",
       "FQ762_11145                                  3.537483e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.508704e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  2.686838e+05   \n",
       "FQ762_38295                                  8.073148e+07   \n",
       "\n",
       "             Abundances (Normalized): MC07_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.742838e+09   \n",
       "FQ762_24080                                  1.538613e+09   \n",
       "FQ762_27290                                  9.259152e+08   \n",
       "FQ762_16075                                  5.915943e+08   \n",
       "FQ762_11145                                  4.515330e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  2.156559e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                           NaN   \n",
       "FQ762_38295                                  8.698724e+07   \n",
       "\n",
       "             Abundances (Normalized): MC08_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.894844e+09   \n",
       "FQ762_24080                                  1.613691e+09   \n",
       "FQ762_27290                                  9.932674e+08   \n",
       "FQ762_16075                                  6.033881e+08   \n",
       "FQ762_11145                                  4.696944e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  2.256712e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                           NaN   \n",
       "FQ762_38295                                  9.613904e+07   \n",
       "\n",
       "             Abundances (Normalized): MC09_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.843363e+09   \n",
       "FQ762_24080                                  1.574196e+09   \n",
       "FQ762_27290                                  9.615943e+08   \n",
       "FQ762_16075                                  5.977442e+08   \n",
       "FQ762_11145                                  4.602387e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  2.143512e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  1.566200e+05   \n",
       "FQ762_38295                                  9.123226e+07   \n",
       "\n",
       "             Abundances (Normalized): MC10_Proteo1 Sample  ...  \\\n",
       "Accession                                                  ...   \n",
       "FQ762_24085                                  1.101435e+09  ...   \n",
       "FQ762_24080                                  9.288558e+08  ...   \n",
       "FQ762_27290                                  6.992379e+08  ...   \n",
       "FQ762_16075                                  9.246726e+08  ...   \n",
       "FQ762_11145                                  1.338808e+09  ...   \n",
       "...                                                   ...  ...   \n",
       "FQ762_26965                                  1.610014e+06  ...   \n",
       "FQ762_22695                                           NaN  ...   \n",
       "FQ762_22970                                           NaN  ...   \n",
       "FQ762_27120                                           NaN  ...   \n",
       "FQ762_38295                                  8.995315e+07  ...   \n",
       "\n",
       "             Abundances (Normalized): MC03_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.332682e+08   \n",
       "FQ762_24080                                  6.943140e+08   \n",
       "FQ762_27290                                  6.147268e+08   \n",
       "FQ762_16075                                  7.056409e+08   \n",
       "FQ762_11145                                  3.402384e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.213512e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  1.995243e+05   \n",
       "FQ762_38295                                  8.342011e+07   \n",
       "\n",
       "             Abundances (Normalized): MC04_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.532477e+08   \n",
       "FQ762_24080                                  7.134240e+08   \n",
       "FQ762_27290                                  6.472840e+08   \n",
       "FQ762_16075                                  6.821330e+08   \n",
       "FQ762_11145                                  3.383388e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.437787e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  3.968429e+05   \n",
       "FQ762_38295                                  9.537679e+07   \n",
       "\n",
       "             Abundances (Normalized): MC05_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.696870e+08   \n",
       "FQ762_24080                                  7.314466e+08   \n",
       "FQ762_27290                                  6.723428e+08   \n",
       "FQ762_16075                                  7.251131e+08   \n",
       "FQ762_11145                                  3.583782e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.271606e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  2.991957e+05   \n",
       "FQ762_38295                                  9.322721e+07   \n",
       "\n",
       "             Abundances (Normalized): MC06_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  9.037809e+08   \n",
       "FQ762_24080                                  7.728698e+08   \n",
       "FQ762_27290                                  6.844034e+08   \n",
       "FQ762_16075                                  7.157364e+08   \n",
       "FQ762_11145                                  3.494747e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.213706e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  2.922106e+05   \n",
       "FQ762_38295                                  8.502474e+07   \n",
       "\n",
       "             Abundances (Normalized): MC07_Proteo3 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.539099e+09   \n",
       "FQ762_24080                                  1.317006e+09   \n",
       "FQ762_27290                                  9.999289e+08   \n",
       "FQ762_16075                                  8.438576e+08   \n",
       "FQ762_11145                                  4.628978e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  2.127784e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  2.098819e+05   \n",
       "FQ762_38295                                  9.950975e+07   \n",
       "\n",
       "             Abundances (Normalized): MC08_Proteo3 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.565285e+09   \n",
       "FQ762_24080                                  1.355001e+09   \n",
       "FQ762_27290                                  1.027562e+09   \n",
       "FQ762_16075                                  8.824270e+08   \n",
       "FQ762_11145                                  4.564082e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  2.017837e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  2.195769e+05   \n",
       "FQ762_38295                                  1.041660e+08   \n",
       "\n",
       "             Abundances (Normalized): MC09_Proteo3 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.542599e+09   \n",
       "FQ762_24080                                  1.331623e+09   \n",
       "FQ762_27290                                  1.025154e+09   \n",
       "FQ762_16075                                  8.804646e+08   \n",
       "FQ762_11145                                  4.707294e+08   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  2.061343e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                  2.501133e+05   \n",
       "FQ762_38295                                  1.043989e+08   \n",
       "\n",
       "             Abundances (Normalized): MC10_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.017561e+09   \n",
       "FQ762_24080                                  8.526796e+08   \n",
       "FQ762_27290                                  7.108583e+08   \n",
       "FQ762_16075                                  1.006729e+09   \n",
       "FQ762_11145                                  1.219885e+09   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.462316e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                           NaN   \n",
       "FQ762_38295                                  9.674822e+07   \n",
       "\n",
       "             Abundances (Normalized): MC11_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  1.117278e+09   \n",
       "FQ762_24080                                  9.539871e+08   \n",
       "FQ762_27290                                  7.997206e+08   \n",
       "FQ762_16075                                  1.143494e+09   \n",
       "FQ762_11145                                  1.392688e+09   \n",
       "...                                                   ...   \n",
       "FQ762_26965                                  1.543806e+06   \n",
       "FQ762_22695                                           NaN   \n",
       "FQ762_22970                                           NaN   \n",
       "FQ762_27120                                           NaN   \n",
       "FQ762_38295                                  1.057416e+08   \n",
       "\n",
       "             Abundances (Normalized): MC12_Proteo2 Sample  \n",
       "Accession                                                  \n",
       "FQ762_24085                                  1.060976e+09  \n",
       "FQ762_24080                                  8.815348e+08  \n",
       "FQ762_27290                                  7.721618e+08  \n",
       "FQ762_16075                                  1.056634e+09  \n",
       "FQ762_11145                                  1.332704e+09  \n",
       "...                                                   ...  \n",
       "FQ762_26965                                  1.398290e+06  \n",
       "FQ762_22695                                           NaN  \n",
       "FQ762_22970                                           NaN  \n",
       "FQ762_27120                                  1.298261e+05  \n",
       "FQ762_38295                                  9.694637e+07  \n",
       "\n",
       "[3115 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define sample sets --> Samples that should be compared/plotted together\n",
    "#Set 1\n",
    "#sample_IDs = [\"MC01_Proteo1\",\"MC02_Proteo1\",\"MC03_Proteo1\",\"MC04_Proteo1\",\"MC05_Proteo1\",\"MC06_Proteo1\",\"MC07_Proteo2\",\"MC08_Proteo2\",\"MC09_Proteo2\",\"MC10_Proteo1\",\"MC11_Proteo1\",\"MC12_Proteo1\"]\n",
    "#Set2\n",
    "#sample_IDs = [\"MC01_Proteo2\",\"MC02_Proteo2\",\"MC03_Proteo2\",\"MC04_Proteo2\",\"MC05_Proteo2\",\"MC06_Proteo2\",\"MC07_Proteo3\",\"MC08_Proteo3\",\"MC09_Proteo3\",\"MC10_Proteo2\",\"MC11_Proteo2\",\"MC12_Proteo2\"]\n",
    "#Set 3\n",
    "#sample_IDs = [\"MC13_Proteo1_di\",\"MC14_Proteo1_di\",\"MC15_Proteo1_di\",\"MC16_Proteo1_di\",\"MC17_Proteo1_di\",\"MC18_Proteo1_di\",\"MC19_Proteo2_di\",\"MC20_Proteo2_di\",\"MC21_Proteo2_di\"]\n",
    "#Set 4\n",
    "#sample_IDs = [\"MC13_Proteo2_di\",\"MC14_Proteo2_di\",\"MC15_Proteo2_di\",\"MC16_Proteo2_di\",\"MC17_Proteo2_di\",\"MC18_Proteo2_di\",\"MC19_Proteo3_di\",\"MC20_Proteo3_di\",\"MC21_Proteo3_di\"]\n",
    "#Set3+4\n",
    "#sample_IDs = [\"MC13_Proteo1_di\",\"MC14_Proteo1_di\",\"MC15_Proteo1_di\",\"MC16_Proteo1_di\",\"MC17_Proteo1_di\",\"MC18_Proteo1_di\",\"MC19_Proteo2_di\",\"MC20_Proteo2_di\",\"MC21_Proteo2_di\",\"MC13_Proteo2_di\",\"MC14_Proteo2_di\",\"MC15_Proteo2_di\",\"MC16_Proteo2_di\",\"MC17_Proteo2_di\",\"MC18_Proteo2_di\",\"MC19_Proteo3_di\",\"MC20_Proteo3_di\",\"MC21_Proteo3_di\"]\n",
    "#Set1+2\n",
    "sample_IDs = [\"MC01_Proteo1\",\"MC02_Proteo1\",\"MC03_Proteo1\",\"MC04_Proteo1\",\"MC05_Proteo1\",\"MC06_Proteo1\",\"MC07_Proteo2\",\"MC08_Proteo2\",\"MC09_Proteo2\",\"MC10_Proteo1\",\"MC11_Proteo1\",\"MC12_Proteo1\",\"MC01_Proteo2\",\"MC02_Proteo2\",\"MC03_Proteo2\",\"MC04_Proteo2\",\"MC05_Proteo2\",\"MC06_Proteo2\",\"MC07_Proteo3\",\"MC08_Proteo3\",\"MC09_Proteo3\",\"MC10_Proteo2\",\"MC11_Proteo2\",\"MC12_Proteo2\"]\n",
    "\n",
    "\n",
    "#Extract the columns of interest\n",
    "columns_samples_numerical = [] \n",
    "columns_samples_numerical_prot = [\"Accession\"] #add 'Accession' when working with proteome_samples df\n",
    "for i in sample_IDs:\n",
    "    for l in proteome_col:\n",
    "        if l.startswith(\"Abundances\"):\n",
    "            if i in l:\n",
    "                columns_samples_numerical.append(l)\n",
    "                columns_samples_numerical_prot.append(l)\n",
    "\n",
    "locus_presence_samples = locus_presence[columns_samples_numerical]\n",
    "proteome_data_samples = proteome_data[columns_samples_numerical_prot].set_index('Accession')\n",
    "\n",
    "locus_presence_samples\n",
    "proteome_data_samples.to_csv(proteomics_dir+sub_dir+data_name+\"_proteome_data_samples_t2\"+\".csv\")\n",
    "proteome_data_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COG Group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>RNA processing and modification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Chromatin Structure and dynamics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Energy production and conversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Cell cycle control and mitosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>Amino Acid metabolis and transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>Nucleotide metabolism and transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>Carbohydrate metabolism and transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>Coenzyme metabolis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>Lipid metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>Tranlsation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>Transcription</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>Replication and repair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Cell wall/membrane/envelop biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>Cell motility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>Post-translational modification, protein turno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>Secondary Structure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>Signal Transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>Intracellular trafficing and secretion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>Nuclear structure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z</th>\n",
       "      <td>Cytoskeleton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>General Functional Prediction only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>Function Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>Defense mechanisms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>Extracellular structures</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Description\n",
       "COG Group                                                   \n",
       "A                            RNA processing and modification\n",
       "B                           Chromatin Structure and dynamics\n",
       "C                           Energy production and conversion\n",
       "D                             Cell cycle control and mitosis\n",
       "E                         Amino Acid metabolis and transport\n",
       "F                        Nucleotide metabolism and transport\n",
       "G                      Carbohydrate metabolism and transport\n",
       "H                                         Coenzyme metabolis\n",
       "I                                           Lipid metabolism\n",
       "J                                                Tranlsation\n",
       "K                                              Transcription\n",
       "L                                     Replication and repair\n",
       "M                      Cell wall/membrane/envelop biogenesis\n",
       "N                                              Cell motility\n",
       "O          Post-translational modification, protein turno...\n",
       "P                     Inorganic ion transport and metabolism\n",
       "Q                                        Secondary Structure\n",
       "T                                        Signal Transduction\n",
       "U                     Intracellular trafficing and secretion\n",
       "Y                                          Nuclear structure\n",
       "Z                                               Cytoskeleton\n",
       "R                         General Functional Prediction only\n",
       "S                                           Function Unknown\n",
       "V                                         Defense mechanisms\n",
       "W                                   Extracellular structures"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To cluster undetected locus tags, COG categories are used. Requires both a COG annotation table with the single letter codes and categories, and a COG annotation of the reference genome\n",
    "\n",
    "#File path to COG annotation table \n",
    "COG_categories = pd.read_csv(\"COG_Categories.csv\", delimiter=';').set_index(\"COG Group\")\n",
    "\n",
    "#File path to COG annotation of the reference genome\n",
    "coelicolor_COG = pd.read_csv(\"Scoelicolor_COG_annotations.csv\",sep=\";\").set_index(\"query\")\n",
    "\n",
    "coelicolor_COG\n",
    "COG_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COG_category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not detected</th>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Description\n",
       "COG_category             \n",
       "-                     794\n",
       "A                       2\n",
       "ADL                     1\n",
       "B                       1\n",
       "C                     192\n",
       "...                   ...\n",
       "T                     236\n",
       "TV                      1\n",
       "U                      23\n",
       "V                     125\n",
       "not detected          344\n",
       "\n",
       "[63 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Annotate locus tags that were not detected across the entire data set\n",
    "\n",
    "#extract undetected locus tags\n",
    "undetected_locustags = []\n",
    "for i in locus_tags_complete[\"Locus_Tag\"].tolist():\n",
    "    if i in locus_tags_all_detected_df[\"Locus_Tag\"].tolist():\n",
    "        continue\n",
    "    else:\n",
    "        undetected_locustags.append(i)\n",
    "        \n",
    "#Create dataframe of undetected locustags\n",
    "undetected_locustags_df = pd.DataFrame(undetected_locustags, columns = [\"Locus_Tag\"])\n",
    "undetected_locustags_df[\"COG_category\"] = 1\n",
    "undetected_locustags_df = undetected_locustags_df.set_index(\"Locus_Tag\")\n",
    "\n",
    "#annotate undetected locus tags with COG categories and descriptions\n",
    "# 1) COG categories\n",
    "for i in undetected_locustags_df.index:\n",
    "    if i in coelicolor_COG.index:\n",
    "        undetected_locustags_df[\"COG_category\"].loc[i] =  coelicolor_COG[\"COG_category\"].loc[i]\n",
    "    else:\n",
    "        undetected_locustags_df[\"COG_category\"].loc[i] = \"not detected\" #not detected means the locus tag is not in the COG annotation\n",
    "\n",
    "# 2) COG descriptions\n",
    "undetected_locustags_df[\"Description\"] = \"-\"\n",
    "for i in undetected_locustags_df[\"COG_category\"].tolist():\n",
    "    if not i == '-':\n",
    "        if len(i) == 1: #if len = 1 only one COG category\n",
    "            undetected_locustags_df[\"Description\"][undetected_locustags_df[\"COG_category\"] == i] = COG_categories[\"Description\"].loc[i]\n",
    "        elif len(i) > 1 and i != \"not detected\": #if len > 1 more than one COG category\n",
    "            descript = []\n",
    "            for l in list(i): #extract all descriptions for the categories\n",
    "                descript.append(COG_categories[\"Description\"].loc[l])  \n",
    "            undetected_locustags_df[\"Description\"][undetected_locustags_df[\"COG_category\"] == i] = ','.join(descript)\n",
    "    else:\n",
    "        continue\n",
    "\n",
    "undetected_locustags_df.groupby(\"COG_category\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count occurence of each COG category through groupby and count and perform sorting \n",
    "undetected_locustags_df_counts = undetected_locustags_df.groupby(\"COG_category\").count().sort_values(by=['Description'])\n",
    "undetected_locustags_df_counts.to_csv(proteomics_dir+sub_dir+data_name+\"_undetected_locustags_COG_t2\"+\".csv\")\n",
    "\n",
    "#plotting of COG category counts of undetected locus tags\n",
    "fig, ax = plt.subplots(figsize=(20,7)) \n",
    "ax = sns.barplot(x=undetected_locustags_df_counts.index, y=\"Description\", data=undetected_locustags_df_counts)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Whole Proteome Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Identification of shared proteome data across all samples\n",
    "\n",
    "All analysis regarding whole proteome correlation is performed with the shared proteome across the entire sample space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abundances (Normalized): MC13_Proteo2_di Sample</th>\n",
       "      <th>Abundances (Normalized): MC14_Proteo2_di Sample</th>\n",
       "      <th>Abundances (Normalized): MC15_Proteo2_di Sample</th>\n",
       "      <th>Abundances (Normalized): MC16_Proteo2_di Sample</th>\n",
       "      <th>Abundances (Normalized): MC17_Proteo2_di Sample</th>\n",
       "      <th>Abundances (Normalized): MC18_Proteo2_di Sample</th>\n",
       "      <th>Abundances (Normalized): MC19_Proteo3_di Sample</th>\n",
       "      <th>Abundances (Normalized): MC20_Proteo3_di Sample</th>\n",
       "      <th>Abundances (Normalized): MC21_Proteo3_di Sample</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accession</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>FQ762_24085</th>\n",
       "      <td>1.565191e+09</td>\n",
       "      <td>1.539403e+09</td>\n",
       "      <td>1.558302e+09</td>\n",
       "      <td>1.621189e+09</td>\n",
       "      <td>1.706793e+09</td>\n",
       "      <td>1.587041e+09</td>\n",
       "      <td>1.425646e+09</td>\n",
       "      <td>1.424577e+09</td>\n",
       "      <td>1.316643e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_24080</th>\n",
       "      <td>1.333856e+09</td>\n",
       "      <td>1.372496e+09</td>\n",
       "      <td>1.362016e+09</td>\n",
       "      <td>1.390068e+09</td>\n",
       "      <td>1.390743e+09</td>\n",
       "      <td>1.366475e+09</td>\n",
       "      <td>1.233834e+09</td>\n",
       "      <td>1.222607e+09</td>\n",
       "      <td>1.140707e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_27290</th>\n",
       "      <td>1.073218e+09</td>\n",
       "      <td>1.043110e+09</td>\n",
       "      <td>1.075693e+09</td>\n",
       "      <td>1.206569e+09</td>\n",
       "      <td>1.219965e+09</td>\n",
       "      <td>1.154348e+09</td>\n",
       "      <td>1.121177e+09</td>\n",
       "      <td>1.125578e+09</td>\n",
       "      <td>1.078252e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_16075</th>\n",
       "      <td>1.010895e+09</td>\n",
       "      <td>8.888453e+08</td>\n",
       "      <td>9.998728e+08</td>\n",
       "      <td>1.349805e+09</td>\n",
       "      <td>1.304110e+09</td>\n",
       "      <td>1.258194e+09</td>\n",
       "      <td>1.160034e+09</td>\n",
       "      <td>1.110536e+09</td>\n",
       "      <td>1.081192e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_22215</th>\n",
       "      <td>9.433617e+09</td>\n",
       "      <td>9.549136e+09</td>\n",
       "      <td>9.445389e+09</td>\n",
       "      <td>1.053353e+10</td>\n",
       "      <td>1.070477e+10</td>\n",
       "      <td>1.068618e+10</td>\n",
       "      <td>1.031709e+10</td>\n",
       "      <td>1.011135e+10</td>\n",
       "      <td>1.038987e+10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_31105</th>\n",
       "      <td>2.141917e+06</td>\n",
       "      <td>1.791110e+06</td>\n",
       "      <td>2.476166e+06</td>\n",
       "      <td>1.857087e+06</td>\n",
       "      <td>1.854378e+06</td>\n",
       "      <td>1.986184e+06</td>\n",
       "      <td>1.787656e+06</td>\n",
       "      <td>2.149359e+06</td>\n",
       "      <td>2.210844e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_00415</th>\n",
       "      <td>1.380519e+06</td>\n",
       "      <td>9.060811e+05</td>\n",
       "      <td>1.139104e+06</td>\n",
       "      <td>1.103899e+06</td>\n",
       "      <td>8.750006e+05</td>\n",
       "      <td>1.031775e+06</td>\n",
       "      <td>1.266539e+06</td>\n",
       "      <td>9.820346e+05</td>\n",
       "      <td>9.487480e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_00920</th>\n",
       "      <td>2.095208e+07</td>\n",
       "      <td>2.354512e+07</td>\n",
       "      <td>2.087075e+07</td>\n",
       "      <td>1.358577e+07</td>\n",
       "      <td>1.375323e+07</td>\n",
       "      <td>1.560903e+07</td>\n",
       "      <td>1.665474e+07</td>\n",
       "      <td>1.722720e+07</td>\n",
       "      <td>1.591190e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_04490</th>\n",
       "      <td>4.370735e+05</td>\n",
       "      <td>6.616646e+05</td>\n",
       "      <td>6.009172e+05</td>\n",
       "      <td>5.341269e+05</td>\n",
       "      <td>4.881925e+05</td>\n",
       "      <td>6.426197e+05</td>\n",
       "      <td>7.565229e+05</td>\n",
       "      <td>4.784343e+05</td>\n",
       "      <td>6.038623e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_31620</th>\n",
       "      <td>5.520524e+06</td>\n",
       "      <td>4.993812e+06</td>\n",
       "      <td>5.377911e+06</td>\n",
       "      <td>5.607231e+06</td>\n",
       "      <td>5.860290e+06</td>\n",
       "      <td>6.038846e+06</td>\n",
       "      <td>5.885719e+06</td>\n",
       "      <td>5.576569e+06</td>\n",
       "      <td>5.166245e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2523 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Abundances (Normalized): MC13_Proteo2_di Sample  \\\n",
       "Accession                                                      \n",
       "FQ762_24085                                     1.565191e+09   \n",
       "FQ762_24080                                     1.333856e+09   \n",
       "FQ762_27290                                     1.073218e+09   \n",
       "FQ762_16075                                     1.010895e+09   \n",
       "FQ762_22215                                     9.433617e+09   \n",
       "...                                                      ...   \n",
       "FQ762_31105                                     2.141917e+06   \n",
       "FQ762_00415                                     1.380519e+06   \n",
       "FQ762_00920                                     2.095208e+07   \n",
       "FQ762_04490                                     4.370735e+05   \n",
       "FQ762_31620                                     5.520524e+06   \n",
       "\n",
       "             Abundances (Normalized): MC14_Proteo2_di Sample  \\\n",
       "Accession                                                      \n",
       "FQ762_24085                                     1.539403e+09   \n",
       "FQ762_24080                                     1.372496e+09   \n",
       "FQ762_27290                                     1.043110e+09   \n",
       "FQ762_16075                                     8.888453e+08   \n",
       "FQ762_22215                                     9.549136e+09   \n",
       "...                                                      ...   \n",
       "FQ762_31105                                     1.791110e+06   \n",
       "FQ762_00415                                     9.060811e+05   \n",
       "FQ762_00920                                     2.354512e+07   \n",
       "FQ762_04490                                     6.616646e+05   \n",
       "FQ762_31620                                     4.993812e+06   \n",
       "\n",
       "             Abundances (Normalized): MC15_Proteo2_di Sample  \\\n",
       "Accession                                                      \n",
       "FQ762_24085                                     1.558302e+09   \n",
       "FQ762_24080                                     1.362016e+09   \n",
       "FQ762_27290                                     1.075693e+09   \n",
       "FQ762_16075                                     9.998728e+08   \n",
       "FQ762_22215                                     9.445389e+09   \n",
       "...                                                      ...   \n",
       "FQ762_31105                                     2.476166e+06   \n",
       "FQ762_00415                                     1.139104e+06   \n",
       "FQ762_00920                                     2.087075e+07   \n",
       "FQ762_04490                                     6.009172e+05   \n",
       "FQ762_31620                                     5.377911e+06   \n",
       "\n",
       "             Abundances (Normalized): MC16_Proteo2_di Sample  \\\n",
       "Accession                                                      \n",
       "FQ762_24085                                     1.621189e+09   \n",
       "FQ762_24080                                     1.390068e+09   \n",
       "FQ762_27290                                     1.206569e+09   \n",
       "FQ762_16075                                     1.349805e+09   \n",
       "FQ762_22215                                     1.053353e+10   \n",
       "...                                                      ...   \n",
       "FQ762_31105                                     1.857087e+06   \n",
       "FQ762_00415                                     1.103899e+06   \n",
       "FQ762_00920                                     1.358577e+07   \n",
       "FQ762_04490                                     5.341269e+05   \n",
       "FQ762_31620                                     5.607231e+06   \n",
       "\n",
       "             Abundances (Normalized): MC17_Proteo2_di Sample  \\\n",
       "Accession                                                      \n",
       "FQ762_24085                                     1.706793e+09   \n",
       "FQ762_24080                                     1.390743e+09   \n",
       "FQ762_27290                                     1.219965e+09   \n",
       "FQ762_16075                                     1.304110e+09   \n",
       "FQ762_22215                                     1.070477e+10   \n",
       "...                                                      ...   \n",
       "FQ762_31105                                     1.854378e+06   \n",
       "FQ762_00415                                     8.750006e+05   \n",
       "FQ762_00920                                     1.375323e+07   \n",
       "FQ762_04490                                     4.881925e+05   \n",
       "FQ762_31620                                     5.860290e+06   \n",
       "\n",
       "             Abundances (Normalized): MC18_Proteo2_di Sample  \\\n",
       "Accession                                                      \n",
       "FQ762_24085                                     1.587041e+09   \n",
       "FQ762_24080                                     1.366475e+09   \n",
       "FQ762_27290                                     1.154348e+09   \n",
       "FQ762_16075                                     1.258194e+09   \n",
       "FQ762_22215                                     1.068618e+10   \n",
       "...                                                      ...   \n",
       "FQ762_31105                                     1.986184e+06   \n",
       "FQ762_00415                                     1.031775e+06   \n",
       "FQ762_00920                                     1.560903e+07   \n",
       "FQ762_04490                                     6.426197e+05   \n",
       "FQ762_31620                                     6.038846e+06   \n",
       "\n",
       "             Abundances (Normalized): MC19_Proteo3_di Sample  \\\n",
       "Accession                                                      \n",
       "FQ762_24085                                     1.425646e+09   \n",
       "FQ762_24080                                     1.233834e+09   \n",
       "FQ762_27290                                     1.121177e+09   \n",
       "FQ762_16075                                     1.160034e+09   \n",
       "FQ762_22215                                     1.031709e+10   \n",
       "...                                                      ...   \n",
       "FQ762_31105                                     1.787656e+06   \n",
       "FQ762_00415                                     1.266539e+06   \n",
       "FQ762_00920                                     1.665474e+07   \n",
       "FQ762_04490                                     7.565229e+05   \n",
       "FQ762_31620                                     5.885719e+06   \n",
       "\n",
       "             Abundances (Normalized): MC20_Proteo3_di Sample  \\\n",
       "Accession                                                      \n",
       "FQ762_24085                                     1.424577e+09   \n",
       "FQ762_24080                                     1.222607e+09   \n",
       "FQ762_27290                                     1.125578e+09   \n",
       "FQ762_16075                                     1.110536e+09   \n",
       "FQ762_22215                                     1.011135e+10   \n",
       "...                                                      ...   \n",
       "FQ762_31105                                     2.149359e+06   \n",
       "FQ762_00415                                     9.820346e+05   \n",
       "FQ762_00920                                     1.722720e+07   \n",
       "FQ762_04490                                     4.784343e+05   \n",
       "FQ762_31620                                     5.576569e+06   \n",
       "\n",
       "             Abundances (Normalized): MC21_Proteo3_di Sample  \n",
       "Accession                                                     \n",
       "FQ762_24085                                     1.316643e+09  \n",
       "FQ762_24080                                     1.140707e+09  \n",
       "FQ762_27290                                     1.078252e+09  \n",
       "FQ762_16075                                     1.081192e+09  \n",
       "FQ762_22215                                     1.038987e+10  \n",
       "...                                                      ...  \n",
       "FQ762_31105                                     2.210844e+06  \n",
       "FQ762_00415                                     9.487480e+05  \n",
       "FQ762_00920                                     1.591190e+07  \n",
       "FQ762_04490                                     6.038623e+05  \n",
       "FQ762_31620                                     5.166245e+06  \n",
       "\n",
       "[2523 rows x 9 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identification of shared proteome data\n",
    "\n",
    "shared_proteome_detected = locus_presence_samples.loc[(locus_presence_samples==1).all(axis=1)]\n",
    "core_data = proteome_data_samples[proteome_data_samples.index.isin(shared_proteome_detected.index)]\n",
    "\n",
    "#Optional export to csv\n",
    "core_data.to_csv(proteomics_dir+sub_dir+data_name+\"core_data_time_point2\"+\".csv\")\n",
    "\n",
    "core_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Abundances (Normalized): MC13_Proteo2_di Sample    2.860449e+07\n",
       "Abundances (Normalized): MC14_Proteo2_di Sample    2.879018e+07\n",
       "Abundances (Normalized): MC15_Proteo2_di Sample    2.807908e+07\n",
       "Abundances (Normalized): MC16_Proteo2_di Sample    2.643666e+07\n",
       "Abundances (Normalized): MC17_Proteo2_di Sample    2.452489e+07\n",
       "Abundances (Normalized): MC18_Proteo2_di Sample    2.534865e+07\n",
       "Abundances (Normalized): MC19_Proteo3_di Sample    2.850450e+07\n",
       "Abundances (Normalized): MC20_Proteo3_di Sample    2.860480e+07\n",
       "Abundances (Normalized): MC21_Proteo3_di Sample    2.937456e+07\n",
       "Name: FQ762_07815, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_data.loc[\"FQ762_07815\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Whole proteome correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UWknfkTTVrz9NUvG6OR1KV3v2giAI2qJbt25Vb/VCtTkdCTzq/zcjPwWyhs8r+MyL9uArB3cIZjbRzMp+tqEdXAycXGXcqaQBkQW+DkzPHI8G3gI2NrOtgcOBgpE1htYF9pZA0nrA7sA/qsjDDWY2mNTf/3+S1i66VjmP8GFAu40y4F5ggJkNJA3g/El7LmJmexZm8RTwT3icCnzJr78daVHCIAiCQMuw1QkVjTJJvYAvAUew5MsXYBVJdyp9K+qSwqwESQsy6UdIGuP7YyRdIOkxSbMynhlJutCvcx+wVib9zyQ95V6Cy+T+SvcynC1pgtI3pXbw8O6SzvX4LZKO9fCtJT3k3ppxkvp4+HHuYWmRNLZE+VcGBprZlEzwHUB/SZuWiD/SPRvTJJ2dCV8g6TxJU4BhfjxK0nRJ90naxss0S9K+nqavpEckPe3b9iX0Fnss3dsy2bd5kg71+hjlddgi6ZhKdQ48AuxawZgpcBu+qrGkfsA84I3M8bbAaZnVjGeb2Z2+/zDJYCvF+STD0KrIA36914EXgfW9rV0i6UngHEmDJT2R8Wit6u1vCGkQ5mRJK5ZpJ0uld817MgM6nyBNoy6JX3+s0vfPbgVWzJx7SUt7BNcC3iENDsXMFpjZbI9/lN/TKZJulvQZDx8j6WLP6yxvH1e65piM3gJJ53v7u1/SmiXyW7IugiAIugLq2NmXXYJqPGX7AXeb2fPAm5K2zpzbBjiWNMCtH60r3ZajD8nI25s0KwHSyrib+nW+DWSNjwvNbKiZDSC9xPbOnFvBzLYBjgd+7mFHA32Bwe5duE5SD+B3wAj31lwJnOnxTwG29LjfLZHfIUBx99oi4BySB20xSt1gZwO7kL6BNVTScD+9EvCkmQ0ys0f9+AEz60968f6S9M2t/YEzPM3rwG5mthVwIEt665bCvS2DSQb0yySD6QhgnpkNBYYCR0nagDJ17gbUC8AgL9doeTdbCeYDcyQNIBntN2TO9Qcmm9nCkinbQKmL7p9FhnA16TYENvS8QzKQtjezHwF/AH7s93kq8HMftDkRONjr7RPabidLpS+Rhe8Afy2Txe8B75nZFzz91mXiQlol+jVgtqSrJO2TOXeLPxeDgJmk+1xgVWAYcAJpSvf5pHuxhaTBHmcl0oyi/sBDxeWp8MxQFHfxStmzHrylVJQgCIIOpxGNsmo8ISOB3/r+WD+e5McTzGwWgKTrScbWUjMTirjNX/oz1NrN9GXgen95/0vSA5n4O0s6GfgMsBqpa+wvfq7wBphEMsQAdgUuKXgvzOwtNxgGAPf6zekOvOrxW0iG220kI6aYPqQVeov5I3CqGzgFhgIPmtlcAEnXedluI63ue3Mm7kfA3b4/FfjQzD6WNDVTlh6k1YMHe/pNSuRjCdzbcg3wDTObJ2l3YKBaxwn1BjamfJ1DMgjXASaZWdkpvKR28U3gq8BXSF2U7cI9Pj8ldV1Wy4GSvgR8CBzj9xzgRjNbKKk38Fkze8jjXw3cWOI6m1KinVSTXtKpJKPuujL5/DJuWJtZi6SyXZGe9z1I7eorwPmStjaz04EBkn4JfBboRZrGXeAvZmbell4zs6mex+mktjWZ9MOiYEBfS+uzVLYu2sjn4pWyvz7m6ao9m0EQBMtDPRlb1VLWKJO0Gsnrs4XSuhzdAZN0kkcp/gNsJcJ7FsX5MCtRQb8ncBEwxMzmSDq96HqFay2kfFkETDezYSXO7UV6We5DMrK2yHRHAbxfogyFNU3OA35crgwZPijyGH1sZoV6WoSXxb8iXyjLCSRPySCSV/ODcgJKY9XGkj5+WvDuCTjWzMYVxd2zQn57kspeDXcAo0iel/mZB2U6MEhS92XwlvUDNgCm+HXWBZ6WtI2Z/buNNDeY2Q9KhL9bpWaBku3EjbK2E6VPbuwNfCVzTzsEv94EYIKke4GrgNNJ4/GGm9kU198pk6zwXCxiyedtEW0/J8X5LvfMBEEQ5E4jGmWVui9HANeY2fpm1tfM1gNm0/rdpm2UVr/tRupee9TDX5P0BQ/fv4p8PEzydnT3cSuF1W8LxtAbSmPbqpkVdi9wTMGwccPyOWBNScM8rIek/p6/9cxsPMm46k3yOmSZCWxEacaQPHOF8TgTgB2VVv3tTvIqPtRG2mroDbzqnsVvkYzicpwFtJhZdmzcOOB73h2FpE0krUTbdV5gE5buti2Jmb1Hqr8zi8JfJHUP/kJaPBawr6Q212wxs6lmtpa3t76kSRVblTHIqsnfPOA/8nGHpLos3Jd3gJV9v2Q7KZfePVknA/t6PZTjYeAgTzeACp/bkLSOpK0yQYNJ3dJ4nl/1+3pwBd1SdKP1eTqI1me3QMm6aIdOEARBp6BuqnqrFyoZZSNJ32rKcjOtszCfAi4kGS6zM3FPIXlPHqONLo8ibgX+Dswgjd15HBYvunY5yTgY53qVGE2asdeiNKj+IDP7iPQCOtvDJpPGUHUHrvVunmeAC4pnwJnZs0BvpQH/FJ37iNQdtZYfv+plH08aDzTJzP5cRZ7b4iLgUM/zZlT2/JwI7K7Wwf77kupjBsnbNA24lOQtKVnnAN6t/H7BEKowpgwAMxtrZk+XOHUksDbwguuPIXWNFrq8Hwc2lfSKpCNKpO8oDgVGeZfhYFrH7Y0BLpE0mdQeSrWTcukvJBlI93qdX1ImDxcDvSTN9PSTysSF1H19rtKSG5NJP3x+6Of+B3gS+BvwbIXrlOJd0o+qaSRv+BnZk2WemSAIgi5BpXFkqsMxZerg3paGROnzCO+Y2ei881ILvLzzzeyKvPMSdA6SFphZsVd4ucljTNkdv72y1pIAfHrz7Wqu+e+rD6m5JsANk+fkorvnpvlM+L3iqZcrR+oE7p3+es01j91xg8qROonhA/9ruayltb7zp6r/3rx+5TfqwjKrnxXV8uVilhyb0+i8TRrMHgRBEARdkwZcpyw+s1QF/l2ra/LOR60ws6vyzkMxkg6nteuuwN/M7Pul4ueJpK+SlkbJMtvMqhlfWRM6w0sWBEFQS+qpW7JawigL6gI3FLucsVgKn+k6rmLEIAiCoN2EURYEQRAEQdAFqKdvWlZLGGVBEARBENQfjecoC6MsCIIgCIL6I7ovgyAIynDPuOk118xjaQqAD2c8UXPNc8bnU9b7pr6Wi+7dM97MRffhx2fnovv2G2/XXPOtt6r9cEvHM3zgfy1X+jDKgiAIgiAIugBhlAVBEARBEHQB6unzSdUSRlkQBEEQBHVHeMqCIAiCIAi6AGGUBUEQBEEQdAHCKAuCIAiCIOgKNJ5NFkZZEARBEAT1R3jKgiAIgiAIugDdYvZlEARBEARB/jSip6yqr3lKGi7JJG2WCdtJ0h2dkSlJYySN6IxrtwdJW0q6wvcPk7RI0sDM+WmS+tYwP30lTfP9IZIu6IBrLq5zSWMlbVxFmtO9XWyUCTvew4b4cS9Jl0p6UdIkSQ9K2tbPXSnp9UJZiq77T0mTfduzTB52kjTP482U9PNlLPdwSZsvS5qi9D+SNENSi6T7Ja2/DGmzdT66VD4k7S3pGUlTXOeY9ua1yjydLunEztQIgiDoCKTqt3qh2k+sjwQe9f+bkZ8CWcPnFeDU9l5MUvflzpFjZhPN7LiOup5zMXBylXGnAt/MHH8dyH5rZzTwFrCxmW0NHA6s4efGAHu0cd3zzWywb3dVyMMjZjYYGAIcImmr7ElJ5TzCw4F2G2XAM8AQMxsI3ASc056LmNmRZjYjGyapB3AZsI+ZDQK2BB5cjrwGQRA0DJKq3uqFikaZpF7Al4AjWPLlC7CKpDslPSfpEkndPM2CTPoRksb4/hhJF0h6TNKsjJdAki7069wHrJVJ/zNJT7k36jJ57brH5WxJEyQ9L2kHD+8u6VyP3yLpWA/fWtJD7q0ZJ6mPhx+X8XSMLVH+lYGBZjYlE3wH0F/SpiXij5Q01fXPzoQvkHSepCnAMD8eJWm6pPskbeNlmiVpX0/TV9Ijkp72bfsSeos9lpLuyniX5kk61OtjlNdhS8HTUq7OgUeAXSsYMwVuA/bza/YD5gFvZI63BU4zs0UAZjbbzO70/YdJBluHYGbvApOAjdzjc42kvwHXeF0+kPFofd7rc19glNdZP9/u9nbyiNw7XCq9a443s/c8C08A67aVvwrt/EG5dzHDyqQhBm+61odm9pzH30fSk0petPskre3hp0u62vP+sqQDJJ3jbfJuJUMPSS9lwico4+3M5KlkXZSId7SkiZImfvTC+Ap3KQiCoGNoVk/ZfsDdZvY88KakrTPntgGOJXka+gEHVHG9PiQjb2/gLA/bH9jUr/NtIGt8XGhmQ81sALCipyuwgpltAxwPFLqtjgb6AoPde3Gdv4h+B4xwb82VwJke/xRgS4/73RL5HQJMKwpbRPKI/DQbKGkd4GxgF2AwMFTScD+9EvCkmQ0ys0f9+AEz6w+8A/wS2M3r4gxP8zqwm5ltBRzIkt66pTCzPd1jdATwMslgOgKYZ2ZDgaHAUZI2oEyduwH1AjDIyzW6hMFQYD4wR9IAktF+Q+Zcf2CymS0sl+82+IEbQFdKWrWaBJJWB7aj1VO3ObCrmY0k3f+rC20CuMDMHgNuB05yj9yLJM/Usd5OTgQu8mstlb5EFo4A/lomi+Xa+VKY2Vuev5clXS/pYPkPH5Lnejsz2xIYy5KezX6kNrgvcC0w3sy2AN4H9srEm+fhFwK/KZGFtuqiOJ+XmdkQMxvyqY12LlekIAiCDqNbN1W91QvVGGUjSX/08f+zXZgTzGyWv3SvJxlblbjNzBZ5V83aHvZl4HozW2hm/wIeyMTf2T0CU0kvmv6Zc7f4/5NIhhjArsClZvYJLH6xbQoMAO6VNBk4jVaPRgvJcDsE+KREfvsAc0uE/xHYzg2cAkOBB81srutf52UDWAjcnIn7EXC3708FHjKzj32/UJYewOVe9hupoptN0hrANcBBZjYP2B34tpf7SWB1YGPK1zkkg3AdWNy1NrGM7FiSQTYcuLVSHqvgYpJhMRh4FTivQvwdJD0D3AOcZWYFo+x2M3vf94eR7hmk+lmqrSp5hbcHbvT6upR0/yum9/YzBBhVJp+V6nwpzOxI4CvABJJhdKWfWhcY523jJJZ8Lv6aaUvdWbKd9c3Euz7z/7Ci8pSriyAIgtxpRKOsbPeUpNVIhtAWkoz0B94kneRRrCiJlQjvWRTnw6xEBf2epF/nQ8xsjqTTi65XuNZCypdFwHQzG1bi3F6kl+U+wKmStigYdM77JcqAmX0i6Tzgx+XKkOGDIo/Rx2ZWqKdFeFnMbFGm2/AE4DWSx6ob8EE5AaWxamOBM8ys4N0Tydsxrihum4PnnZ6kslfDHSRjZKKZzVerr3g6MEhS92XxlpnZa5l8Xu7XL8cjZrZ3ifB3q9V0ugFvu7exaiTtShpjuKOZfVgp/rJiZlOBqZKuAWYDh5E8d782s9sl7QScnkmSbUvF7Sz7nFgb+9DOugiCIKgVHd0tKWkP4LckW2e0mZ1VdH590g/jNUlDbw4xs1f83Nm09kT8r5nd4OEbkN7Lq5McSN8ys4/aykMlT9kI4BozW9/M+prZeqSXwg5+fhtJG3iXyoGkLhWA1yR9wcP3r1QRwMPAgUrjn/oAhT6QgjH0hv9yr2ZG5r3AMQXDxg3L54A1JQ3zsB6S+nv+1jOz8STjqjfQq+h6M4Glxts4Y0ieuTX9eAKwo6Q13EAaCTxURZ7bojfwqncnfovUUMpxFtBiZtmxceOA72XGEm0iaSXarvMCm7B0t21JfEzVj2ntEi6EvwhMBH4hLR4L2FfSXktfpRXPT4H9q81HBR6jdUzkwaRxc5C6jlf2/M4HZkv6uudDkgaVSy9pS5IXaV8ze71CHirV+RIozVzdKRM0mNQtDalt/NP3D62g2xYHZv5/PHuiQl0EQRDkjjpwoL+/s38P/DepV2qklp4Rfy7wBx/GcgbwK0+7F7AV6W/0tsCJklbxNGeTJq5tBPyHNMylTSoZZSNZujvqZlq7MJ8ijUeZSTLWCnFPIXk3HiN1P1XiVuDvwAzgD/gLwszeBi4nvZTHuV4lRgP/AFqUBtUf5FbpCOBsD5tM6prpDlzrXUDPkMYZvZ29mJk9C/RWGvBP0bmPSGOL1vLjV73s44EpwCQz+3MVeW6Li4BDPc+bUdnzcyKwu1oH++9Lqo8ZwNNKS09cSvKWlKxzAKVB4++b2b/9uNyYMgDMbKyZPV3i1JGkbuoXXH8MqWsUSde77qaSXpFUaKyFAegtJMPlhArlroZjgcP9mt8CfujhY4GTlAbM9yMZXEd4nU/HJzGUST+KZMjf6HV+e5k8tFnnbSDgZKWJAZOBX5C8ZJA8YzdKmoRPrGgHq3p5fkjpOm6rLoIgCHKnI40y0hj5F3xI1kekd0Px37zNaR12Mj5zfnPgYTP7xCectQB7uDNiF9LMfICrScN82i5Ta89G0BaSTgDeMbPReeelFnh555vZFXnnJegcJL1EGhbQXoOuJL1HXlPzPyjKabzIhzOeqLnmT845vuaaAPdNfa1ypE7gc2uslIvuw4/PzkX37TferrnmJgOqXlqxw3nm57ss18M7+PT7q/57M+UXux5DmghY4DIzu6xwoLQaxB4+jhdJ3wK2NbMfZOL8kTRh77eSDiA5qdYAtiZNNtwN+Ayp1+z3JCPsCfeSIWk90pjfAW3lM1b0r46LSetvNQtvkwazB0EQBEGXZFkG8LsBdlnFiOU5EbhQ0mGk4Sj/BBaa2T2ShpJ6B+eSekHas+pAGGXVYGYf0ERGipldlXceipH0VVLffJbZZlbNmMWaImkLlm4vH5rZtnnkpxRm1jfvPARBECwPVXZLVss/gfUyx+vSOm4XAJ81f4Br9wK+VhjyZGZn4uOq3aP2PGmNyc9KWsEnEC51zWLCKAvqAp89Oq5ixC6Az5YcnHc+giAIGpkOnn35FLCxz5b8J2li10FL6mkN4C2ffPcTfIkinyTwWTN7U+kTjAOBe8zMJI0njWkfS5qUVXacebWfWQqCIAiCIOgydORAf/dk/YD0438m8Cczmy7pDJ80B7AT8Jyk50kT2AorDvQAHpE0g9RFekhmaa0fAz+S9AJpWYyyY7XDUxYEQRAEQd3R0euUWfrO8l1FYT/L7N9E60zKbJwPaGNxdzObRZrZWRVhlAVB0GHstMsXaq55w+FlV2vpNM4Zv13NNX918m9qrglA38G5yD614M1cdFfdcONcdNfpW/uPZtTTdyGL6eAxZV2CMMqCIAiCIKg76unzSdUSRlkQBEEQBHVHAzrKwigLgiAIgqD+iO7LIAiCIAiCLkAD2mRhlAVBEARBUH+EpywIgiAIgqALEEZZEARBEARBFyBmXwZBEARBEHQBGtBRFkZZEARBEAT1RyN2X1b17UtJwyWZpM0yYTtJuqMzMiVpjKQRnXHt9iBpS0lX+P5hkhb5R0cL56dJ6lvD/PSVNM33h0i6oAOuubjOJY2VVHFJa0mne7vYKBN2vIcN8eNeki6V9KKkSZIelLStn7tS0uuFshRd+1hJz0qaLumcMnnYSdI8SZMlzZT082Us93BJJT+PUWX6H0maIalF0v2S1l+GtNk6H10qH5L2lvSMpCmuc0x781plnk6XdGJnagRBEHQEUvVbvVDtB8lHAo/6/83IT4Gs4fMKcGp7L+ZflO8QzGyimR3XUddzLgZOrjLuVOCbmeOvA9Mzx6OBt4CNzWxr4HBgDT83Btij+IKSdgb2AwaZWX/g3Ap5eMTMBgNDgEMkbVV0vXIe4eG08c2yKnkGGGJmA0nfRGvTgCyHmR1pZjOyYZJ6kD5uu4+ZDQK2BB5cjrwGQRA0DN2kqrd6oaJRJqkX8CXgCJZ8+QKsIulOSc9JukRSN0+zIJN+hKQxvj9G0gWSHpM0K+MlkKQL/Tr3AWtl0v9M0lPujbpM7q90j8vZkiZIel7SDh7eXdK5Hr9F0rEevrWkh9xbM05SHw8/LuPpGFui/CsDA81sSib4DqC/pE1LxB8paarrn50JXyDpPElTgGF+PMo9QfdJ2sbLNEv+RXr3iD0i6Wnfti+ht9hjKeku9xhNdu/RoV4fo7wOWwqelnJ1DjwC7FrBmClwG8mAQlI/YB7wRuZ4W+A0M1sEYGazzexO33+YZLAV8z3gLDP70OO9XkU+MLN3gUnARu7xuUbS34BrvC4fyHi0Pu/1uS8wyuusn293ezt5RO4dLpXeNceb2XuehSeAddvKX4V2/qDcu5hhZdIQgzdd60Mze87j7yPpSSUv2n2S1vbw0yVd7Xl/WdIBks7xNnm3kqGHpJcy4ROU8XZm8lSyLkrEO1rSREkTX3r4lgp3KQiCoGPo1k1Vb/VCNZ6y/YC7zex54E1JW2fObQMcS/I09AMOqOJ6fUhG3t7AWR62P7CpX+fbQNb4uNDMhprZAGBFT1dgBTPbBjgeKHRbHQ30BQa79+I6fxH9Dhjh3porgTM9/inAlh73uyXyOwQo7l5bRPKI/DQbKGkd4GxgF2AwMFTScD+9EvCkmQ0ys0f9+AH3BL0D/BLYzeviDE/zOrCbmW0FHMiS3rqlMLM93WN0BPAyyWA6AphnZkOBocBRkjagTJ27AfUCMMjLNbqEwVBgPjBH0gCS0X5D5lx/YLKZLSyX7xJsAuzgRsdDkoZWk0jS6sB2tHrqNgd2NbORpPt/daFNABeY2WPA7cBJZjbYzF4keaaO9XZyInCRX2up9CWycATw1zJZLNfOl8LM3vL8vSzpekkHy3/4kDzX25nZlsBYlvRs9iO1wX2Ba4HxZrYF8D6wVybePA+/EPhNiSy0VRfF+bzMzIaY2ZC+X67mT0AQBMHy003Vb/VCNZ6QkcBvfX+sH0/y4wlmNgtA0vUkY+umCte7zV/6Mwq/7oEvA9f7y/tfkh7IxN9Z0snAZ4DVSC/cv/i5ws/ySSRDDGBX4BIz+wTSi80NhgHAvUqOtu7Aqx6/hWS43UYyYorpA8wtEf5H4FQ3cAoMBR40s7kAkq7zst0GLARuzsT9CLjb96cCH5rZx5KmZsrSA7hQ0mBPv0mJfCyBpDWAa4BvmNk8SbsDA9U6Rq83sDHl6xySQbgOMMnMjqwgO5ZkkH0V+Aqpi3J5WIF0r7cj1emfJG1oZtZG/B0kPUMyls8ys+mSvg7cbmbve5xhtP5ouIYS3YxKXuHtgRvV6u7+dDXpJR1CMuB3LFOuSnW+FGZ2pKQtSO36RJLhfhjJI3eDksf3U8DsTLK/ZtpSd5ZsZ30z8a7P/H9+UXnK1UUQBEHuqI66JaulrFEmaTXSL+4tJBnpD7xJOsmjFL8krUR4z6I4H2YlKuj3JP06H2JmcySdXnS9wrUWUr4sAqab2bAS5/YivSz3IRlZWxQMOuf9EmXAzD6RdB7w43JlyPBBkcfo44yRsQgvi5ktynQbngC8RvJYdQM+KCegNFZtLHCGmRW8eyJ5O8YVxd2zQn57kspeDXcAo4CJZjY/86BMBwZJ6r6M3rJXgFu8fiZIWkQah1bKOIY0pmzvEuHvLoMmpDp+272NVSNpV9IYwx0LXa4diZlNBaZKuoZkfB1G8tz92sxul7QTcHomSbYtFbez7HNibexDO+siCIKgVjSgTVax+3IEcI2ZrW9mfc1sPdJLYQc/v42kDbxL5UBSlwrAa5K+4OH7V5GPh4EDlcY/9QF29vCCMfSG/3KvZkbmvcAxBcPGDcvngDUlDfOwHpL6e/7WM7PxJOOqN9Cr6HozgaXG2zhjSB6MNf14ArCjpDXcQBoJPFRFntuiN/Cqexa/RTKKy3EW0GJm2bFx44DvZcYSbSJpJdqu8wKbsHS3bUl8TNWPae0SLoS/CEwEfiEtHgvYV9JeS19lCW4r5EfSJiRP0BvV5KUMj9E6JvJg0rg5SF3HK3t+5wOz3ctWGAM2qFx6SVsClwL7VjH2rVKdL4HSzNWdMkGDSd3SkNrGP33/0Aq6bXFg5v/Hsycq1EUQBEHuaBn+1QuVjLKRwK1FYTfTOgvzKdJ4lJkkY60Q9xSS9+QxWrsJy3Er8HdgBvAH/AVhZm8Dl5OMg3GuV4nRwD+AFqVB9QeZ2Uckg+5sD5tM6prpDlzr3TzPkMYZvZ29mJk9C/RWGvBP0bmPSGOL1vLjV73s44EppK6/P1eR57a4CDjU87wZlT0/JwK7q3Ww/76k+pgBPK209MSlJG9JyToH8G7l983s335cbkwZAGY21syeLnHqSGBt4AXXH0PqGi10eT8ObCrpFUlHeJorgQ09/ljg0DJdl9VyLHC4pBaSgftDDx8LnKQ0YL4fyeA6wut8Oj6JoUz6USRD/kav89vL5KHNOm8DAScrTQyYDPyC5CWD5Bm7UdIk2m+wrurl+SHJK1tMW3URBEGQO404pkzL/65rfCSdALxjZqPzzkst8PLON7Mr8s5L0DlIeok0LGB5PZBLsN/lE2v+B+WGw8v+Xug0zhn/Qs01f3Xyb2quCUDfwfnoLngzF9lVN6y4TGOnsNIqn6m55sorf6rmmgWe/tkuy2UuDR9d/d+b244cUhemWbXrlDU7F7PkWLhG523g6rwzEQRBEARt0YjrlMVnlqrAzD4gzbhrCszsqrzzUIykr5KWG8ky28yqGbNYU3y2ZHF7+dDMts0jP6Uws7555yEIgmB5qCNbq2rCKAvqAp89Oq5ixC6Az5YcnHc+giAIGpmmWxIjCIIgCIKgK9KANlkYZUEQBEEQ1B/dG9AqC6MsCIIOo1fP2v9JuWHynJprAtw39bXai+Y1C/KlybnI9uxfar3vzqfHp3vkortg3rKud738fP7zvWuu2VFE92UQBEEQBEEXoJ7WH6uWMMqCIAiCIKg7wlMWBEEQBEHQBWhAmyyMsiAIgiAI6o/wlAVBEARBEHQBujfgoLIwyoIgCIIgqDsazySLb18GQRAEQVCHdPS3LyXtIek5SS9IOqXE+fUl3S+pRdKDktbNnDtH0nRJMyVdoKK+VUm3S5pWsUxV5TQIgiAIgqALIVW/Vb6WugO/B/4b2BwYKWnzomjnAn8ws4HAGcCvPO32wBeBgcAAYCiwY+baBwALqilTGGVBEARBENQdkqreqmAb4AUzm2VmHwFjgf2K4mwOPOD74zPnDegJfAr4NNADeM3z2Av4EfDLajJRlVEmabgkk7RZJmwnSXdUk35ZkTRG0ojOuHZ7kLSlpCt8/zBJiyQNzJyfJqlvDfPTt+AGlTRE0gUdcM3FdS5prKSNq0hzureLjTJhx3vYED/uJelSSS9KmuQu32393JWSXi926Uq6QdJk316SNLlMHnaSNM/jzpT082Us9/ASv4aWJf2PJM1wd/b9ktZfhrTZOh9dKh+S9pb0jKQprnNMe/NaZZ5Ol3RiZ2oEQRB0BMviKZN0tKSJme3oost9Dsh+HuQVD8syBTjA9/cHVpa0upk9TjLSXvVtnJnN9Hj/C5wHvFdNmar1lI0EHvX/m5GfAlnD5xXg1PZezN2kHYKZTTSz4zrqes7FwMlVxp0KfDNz/HVgeuZ4NPAWsLGZbQ0cDqzh58YAexRf0MwONLPBZjYYuBm4pUIeHvG4Q4BDJG2VPSmp3ISW4aRfP+3lGWCIu7NvAs5pz0XM7Egzm5ENk9QDuAzYx8wGAVsCDy5HXoMgCBqG7t1U9WZml5nZkMx2WTskTwR2lPQMqXvyn8BCd0x8AViXZMjtImkHSYOBfmZ2a7UCFY0yd719CTiCJV++AKtIutMHxl0iqZunWZBJP0LSGN8f4wPgHpM0K+MlkKQL/Tr3AWtl0v9M0lPujbqsMHjOPS5nS5og6XlJO3h4d0nnevwWScd6+NaSHnJvzThJfTz8uIynY2yJ8q8MDDSzKZngO4D+kjYtEX+kpKmuf3YmfIGk8yRNAYb58SilgYH3SdrGyzRL0r6epq+kRyQ97dv2JfQWeywl3ZXxMM2TdKjXxyivw5aCp6VcnQOPALtWMGYK3Ia7cCX1A+YBb2SOtwVOM7NFAGY228zu9P2HSQZbSfxefwO4vop8YGbvApOAjdzjc42kvwHXeF0+kPFofd7rc19glNdZP9/u9nbyiNw7XCq9a443s8IvoCdID2Wb5SnTzh+UexczrEyaIf2ma31oZs95/H0kPankRbtP0toefrqkqz3vL0s6QGkA6lQvVw+P91ImfIIy3s5MnkrWRRAEQVdAHdt9+U9gvczxuh62GDP7l5kdYGZb4o4ZM3ub5DV7wswWmNkC4K/AMN+GSHqJ5NjaRNKD5TJRjadsP+BuM3seeFPS1plz2wDHkjwN/Wh165WjD8nI2xs4y8P2Bzb163wbyBofF5rZUDMbAKzo6QqsYGbbAMcDhW6ro4G+wGD3XlznL6LfASPcW3MlcKbHPwXY0uN+t0R+hwDFMyYWkTwiP80GSloHOBvYBRgMDJU03E+vBDxpZoPM7FE/fsDM+gPvkPqbd/O6OMPTvA7sZmZbAQeypLduKcxsT/cYHQG8TDKYjgDmmdlQ0uDDoyRtQJk6dwPqBWCQl2t0CYOhwHxgjqQBJKP9hsy5/sBkM1tYLt9l2AF4zcz+Xk1kSasD29Hqqdsc2NXMRpLu/9WFNgFcYGaPAbcDJ7ln7kWSZ+pYbycnAhf5tZZKXyILR5AexrYo186Xwsze8vy9LOl6SQfLf/iQHvDt/I/DWJb0bPYjtcF9gWuB8Wa2BfA+sFcm3jwPvxD4TYkstFUXS6BMt8AL428uV6QgCIIOo9sybFXwFLCxpA0kfYr0Prs9G0HSGpm/wT8h2RIA/yB50FZwe2NHYKaZXWxm65hZX5Ld87yZ7VQuE9V4QkYCv/X9sX48yY8nmNksz+z1LnpThevd5i/9GYVf98CXgev95f0vSQ9k4u8s6WTgM8BqpBfuX/xcoVtrEskQA9gVuMTMPoH0YnODYQBwr1vM3Un9vgAtJMPtNpIRU0wfYG6J8D8Cp7qBU2Ao8KCZzQWQdJ2X7TZgIakrrsBHwN2+PxX40Mw+ljQ1U5YewIVKLtCFwCYl8rEEktYArgG+YWbzJO0ODFTrGL3ewMaUr3NIBuE6wCQzO7KC7FhSA/4q8BVSF2VHMJLqvGQ7KLmTFwFnmdl0SV8Hbjez9z3OMFp/NFxDiW5GJa/w9sCNmV9Wn64mvaRDSAb8jrRNpTpfCjM7UtIWpHZ9IslwP4z0K+4GJY/vp4DZmWR/zbSl7izZzvpm4l2f+f/8ovKUq4viPF5GMuA4+JrJVqlMQRAEHUGVHrCqMLNPJP0AGEf6u3mlv0vOACaa2e3ATsCvJBnwMPB9T34T6YfwVNKg/7vN7C/FGtVQ1iiTtJoLbeGZ6A6YpJMK5SguV4nwnkVxPsxKVNDvSfp1PsTM5kg6veh6hWstpHxZBEw3s2Elzu1FelnuQzKytigYdM77JcpQuIHnAT8uV4YMHxR5jD42s0I9LcLLYmaL1NpteAJpBscgkrH/QTkBpbFqY4EzzKzg3RPJ2zGuKO6eFfLbk1T2argDGEVquPMzD8p0YJCk7svqLfM6OADYulJc0piyvUuEv7ssmqQ6ftu9jVUjaVeSK3tHM/uwUvxlxcymAlMlXUMyvg4jee5+bWa3S9oJOD2TJNuWittZ9jmxNvahnXURBEFQKzp6QX8zuwu4qyjsZ5n9myjhePL3W9lJWGb2Esk5VJZKXr0RwDVmtr6Z9TWz9UgvhR38/Dbu6utG6l571MNfk/QFD9+/UiZIFueBSuOf+gA7e3jBGHrDf7lXMyPzXuCYgmHjhuVzwJqShnlYD0n9PX/rmdl4knHVG+hVdL2ZwFLjbZwxJA/Gmn48geTCXMMNpJHAQ1XkuS16A6+6Z/FbJKO4HGcBLWaWHRs3DvheZizRJpJWou06L7AJS3fblsTHVP2Y1i7hQviLwETgF9LisYB9Je219FWWYlfgWTN7pZo8VMFjtI6JPJg0bg5S1/HKnt/5wGz3shXGgA0ql17SlsClwL5m9nqFPFSq8yVQmrm6UyZoMKlbGlLbKIx3OLSCblscmPn/8eyJCnURBEGQO8sy0L9eqGSUjQSKZw3cTOsszKdI41Fmkoy1QtxTSN6Tx2jtJizHrcDfgRnAH/AXhA+gu5xkHIxzvUqMJvXvtigNqj/I0pojI4CzPWwyqWumO3Ctd/M8Qxpn9Hb2Ymb2LNBbacA/Rec+Io0tWsuPX/WyjydNnZ1kZn+uIs9tcRFwqOd5Myp7fk4EdlfrYP99SfUxA3haaemJS0nekpJ1DuDdyu+b2b/9uNyYMgDMbKyZPV3i1JHA2sALrj+G1DVa6PJ+HNhU0iuSjsik+yZVDvCvkmOBwyW1kAzcH3r4WOAkpQHz/UgG1xFe59NpXYemrfSjSIb8jV7nS4xBKKLNOm8DAScrTQyYDPyC5CWD5Bm7UdIkfGJFO1jVy/NDkle2mLbqIgiCIHe6qfqtXlBrz0bQFpJOAN4xs9F556UWeHnnm9kVeecl6ByUZgMNMbP2GnQlyWNM2e5fWL3WkgBc8fA/aq751CMzK0fqDF6anItsz/6lRpx0Pqustkouuh998FHNNTffYp2aaxa4/9hhy2UunXznc1X/vTlnr03rwjSLFf2r42KWHAvX6LwNXJ13JoIgCIKgLTr625ddgWpmXzY9ZvYBacZdU2BmV+Wdh2IkfZW03EiW2WZWzZjFmuKzJYvby4dmtm0e+SmFT9EOgiCoWxrRqxRGWVAX+OzRcRUjdgF8tuTgvPMRBEHQyNSRA6xqwigLgiAIgqDuqKdZldUSRlkQBEEQBHVHA9pkYZQFQdBxzJm7rOv1Lj97Dq+4HmOncPeMN2uu+dSC2mtCfrMgP5headWYzuGTTYbmott79d4115wzZ17NNTuKehrAXy1hlAVBEARBUHc0oE0WRlkQBEEQBPVHdF8GQRAEQRB0AVT+89l1SRhlQRAEQRDUHSs04EJlYZQFQRAEQVB3qAEHlYVRFgRBEARB3RFjyoIgCIIgCLoADegoC6MsCIIgCIL6I9YpC4IgCIIg6AJ0b8CB/lUVSdJwSSZps0zYTpLu6IxMSRojaURnXLs9SNpS0hW+f5ikRZIGZs5Pk9S3hvnpK2ma7w+RdEEHXHNxnUsaK2njKtKc7u1io0zY8R42xI97SbpU0ouSJkl6UNK2fu5KSa8XypK5xmBJT0iaLGmipG3K5OEwSXM97gxJRy1juQ+TtM6ypClKP0rSs5JaJN0q6bPLkPbBTD3dVSqtpO9ImurXnyZpv/bmtco8dalnLwiCoC26oaq3eqFaO3Mk8Kj/34z8FMgaPq8Ap7b3YpK6L3eOHDObaGbHddT1nIuBk6uMOxX4Zub468D0zPFo4C1gYzPbGjgcWMPPjQH2KHHNc4BfmNlg4Gd+XI4bPO5OwP9JWjt7UlI5j/BhQLuNMuBeYICZDQSeB37SnouY2Z5m9nY2TNK6pHb2Jb/+dkDLcuQ1CIKgYZCq3+qFikaZpF7Al4AjWPLlC7CKpDslPSfpEkndPM2CTPoRksb4/hhJF0h6TNKsjGdGki7069wHrJVJ/zNJT7mX4DL5HFj3MpwtaYKk5yXt4OHdJZ3r8VskHevhW0t6yL014yT18fDj3MPSImlsifKvDAw0symZ4DuA/pI2LRF/pHs2pkk6OxO+QNJ5kqYAw/x4lKTpku6TtI2XaZakfT1NX0mPSHrat+1L6C32WLq3ZbJv8yQd6vUxyuuwRdIxleoceATYtYIxU+A2YD+/Zj9gHvBG5nhb4DQzWwRgZrPN7E7ff5hksBVjwCq+3xv4VxX5wMxeB14E1ve2domkJ4FzMt63gkdrVW9/Q4DrvM5WLNNOlkrvmveY2SeehSeAddvKn19/rKSZkm4FVsyce0nSGkVJ1gLeARa41gIzm+3xj/J7OkXSzZI+4+FjJF3seZ3l7eNK1xyT0Vsg6Xxvf/dLWrNEfkvWRRAEQVegm6rf6oVqPGX7AXeb2fPAm5K2zpzbBjgW2BzoBxxQxfX6kIy8vYGzPGx/YFO/zreBrPFxoZkNNbMBpJfY3plzK5jZNsDxwM897GigLzDYvQvXSeoB/A4Y4d6aK4EzPf4pwJYe97sl8jsEmFYUtojkvflpNlCpG+xsYBdgMDBU0nA/vRLwpJkNMrNH/fgBM+tPevH+EtjN6+IMT/M6sJuZbQUcyJLeuqVwb8tgkgH9MslgOgKYZ2ZDgaHAUZI2oEyduwH1AjDIyzVa3s1WgvnAHEkDSEb7DZlz/YHJZrawXL5LcDwwStIc4Fyq9D5J2hDY0PMOyUDa3sx+BPwB+LHf56nAz83sJmAicLDX2ye03U6WSl8iC98B/lomi98D3jOzL3j6rcvEBZgCvAbMlnSVpH0y527x52IQMJN0nwusCgwDTgBuB84n3YstJA32OCsBE739PVRcngrPDEVxj1bqZp746uN/rlCkIAiCjqGbVPVWL1RjlI0ECh6ksSzZhTnBzGb5S/d6krFVidvMbJGZzQAK3UxfBq43s4Vm9i/ggUz8nSU9KWkqydjpnzl3i/8/iWSIAewKXFrwXpjZWyTjYwBwr6TJwGm0ejRaSIbbIaSXcjF9gLklwv8IbOcGToGhwINmNtf1r/OyASwEbs7E/Qi42/enAg+Z2ce+XyhLD+ByL/uNJAOqLO5tuQY4yMzmAbsD3/ZyPwmsDmxM+TqHZBCuA2BmR5rZxDKyY0kG2XDg1kp5rILvASeY2Xokw+KKCvEP9PJdDxzj9xzgRjNbKKk38Fkze8jDr6b1vmQp2U6qSS/pVFL7ua5MPr8MXAtgZi1U6Ir052oPYASpa/R8Saf76QHuRZ0KHMySz8VfzMxIbek1M5vqhvZ0WtvWIloN6GtZ+tkt98wU5/MyMxtiZkP6DOvUIW9BEASLacTuy7LdU5JWIxlCW0gyoDtgkk7yKFaUxEqE9yyK82FWooJ+T+AiYIiZzfEXUvZ6hWstpHxZBEw3s2Elzu1FelnuA5wqaYtMdxTA+yXKgJl9Iuk84MflypDhgyKP0cf+4oT0gvzQr7so0214AslTMohkQH9QTkBprNpY4AwzK3j3BBxrZuOK4u5ZIb89SWWvhjuAUSTPy3y1PgHTgUGSui+jt+xQ4Ie+fyNpXFo5bjCzH5QIf3cZNKGNduJGWduJpMNIHtyvZO5ph+DXmwBMkHQvcBVwOmk83nAzm+L6O2WSFZ6LRSz5vC2i7eekON/lnpkgCILc6V5P/ZJVUslTNgK4xszWN7O+7rmYDezg57eRtIHSWLIDSZMBAF6T9AUP37+KfDxM8nZ093ErO3t4wRh6Q2lsWzWzwu4FjikYNm5YPgesKWmYh/WQ1N/zt56ZjScZV72BXkXXmwlsRGnGkDxzhfE4E4AdJa3hBtJIUtdQe+kNvOpejm+RjOJynAW0mFl2bNw44HveHYWkTSStRNt1XmATlu62LYmZvUeqvzOLwl8kdQ/+Qlo8FrCvpL0qXPJfwI6+vwvw92ryUSZ/84D/yMcdkuqycF/eAVb2/ZLtpFx6SXuQJkXs6/VQjoeBgzzdAGBguciS1pG0VSZoMKlbGs/zq35fD66gW4putD5PB9H67BYoWRft0AmCIOgUui3DVi9UGsg9kjRGKsvNHn4D8BRwIcloGU9r19UpJO/JXNJLudjQKeZW0st3BvAP4HEAM3tb0uUk4+DfrleJ0SSDokXSx8DlZnah0qDuC9zrsQLwG1KX0LUeJuCC4hlwZvaspN6SVjazd4rOfaS0HMVv/fhVSad4XQi408yWZ5DNRcDNkr5N6uqs5Pk5EZju3U2QZi6OJnVZPe2G0VxauxmXqnMApdmL75vZv/14NHBJuS7MIkMwy5HAecALkt4nTQI4ya97PcnDs4akV0jjvK4AjgJ+64b1B6RxgsvLocAlSgPiZ5FmgUIyrC/xvA0jGSrF7WR6mfQXAp8mdfMBPGFmpcYmQprVepWkmSRjf1KFPPcAzvWxih+Q7l3h2v9D6o6e6/+vXPIKbfMu6UfVaaSu6gOzJ71tt1UXQRAEuaN66pesEnVwb0tDIukE4B0zq9SN1hB4eee7gRQ0IJIWmFmlH0vLzJd//bea/0G5+ahtay0JwHG3VuVI7lBuu2Zc5UidQM+125xU3Kl8MP3xypE6gRU2GZqLbu/Vy46U6BQ+02up0Tk14/lz9lguq+oPE+dU/ffm20PWqwsLrp68enlyMUuOzWl03iYNZg+CIAiCLkkjzr6MzyxVgZl9QJrR2BSY2VV556EYSYfTOvi/wN/M7Pt55Kcckr7K0t3+s82smvGVNaEzvGRBEAS1pH5MreoJoyyoC9xQ7HLGYil8pms+/UxBEARNQrcmnH0ZBEEQBEHQ5ejo2ZeS9lD6ys0LPmmv+Pz6Sl9AaVH6As+6Hr6zWr+mM1nSB/KF4yV9RemLPJMlParMt6LbKlMQBEEQBEFdIanqrYprdQd+D/w3aaH2kZKKF2w/F/iDf9nlDOBXAGY23swG+5dhdgHeA+7xNBfT+tWYP5IW4m6T6L4MgqDDeOrRZ2uuecXg/6q5JsDDj8+uueZqG22CLar9jPken+5Rc02AT3KaBfnJ89WsvtTxvLnaOrXXXLSsX8HrSPZYrtQd3Hm5DfCCmc0CUPoW9n6kZaMKbA78yPfHkz5lWMwI4K+ZdSuX6VvOYZQFQRDUCXkYZEHQVengdco+B8zJHL8CFK+3M4X0je/fkhbGX1nS6mb2ZibON4FfZ46PBO7ytTDnA9uVy0R0XwZBEARBUHd0l6reJB0taWJma8+i5CeSvtrzDOmrM/8kfeYRAP86zhYsOdHrBGBPM1uXNFkta7AtRXjKgiAIgiCoO5bFT2ZmlwGXlYnyT2C9zPG6Hpa9xr9InjL8049fK/oK0DeAW83sY4+zJjDIzJ708zeQvs7TJuEpC4IgCIKg7pCq36rgKWBj/573p0jdkLcvqac1/JvZAD8Briy6xkjg+szxf4Dekjbx491In9hrk/CUBUEQBEFQd3TrwKH+ZvaJpB+Quh67A1ea2XRJZwATzex20reafyXJgIeBxYuXS+pL8rQ9VHTNo0jfsF5EMtK+Uy4fYZQFQRAEQVB3dPTXk8zsLuCuorCfZfZvAm5qI+1LpMkCxeG3ArdWm4cwyoIgCIIgqDvUgB9aCqMsCIIgCIK6o3sdfWi8WsIoC4IgCIKg7mhAm6y62ZeShksySZtlwnaSdEdnZErSGEkjOuPa7UHSlpKu8P3DJC2SNDBzfpoP8qtVfvpKmub7QyRd0AHXXFznksZK2riKNKd7u9goE3a8hw3x416SLpX0oqRJ/r2wbf3clZJeL5Qlc41Bkh6XNFXSXyStQht4O5zn3xWbKenny1ju4SU+pbEs6b/r+Sx816zqa3ldFOrpLkmfLRHnO379Fm9n+7U3r1XmqUs9e0EQBG3RwbMvuwTVLokxEnjU/29GfgpkDZ9XgFPbezH/xlaHYGYTzey4jrqeczFwcpVxp5KmDhf4OjA9czwaeAvY2My2Bg4H1vBzYyj9nY3RwClmtgVpgORJFfLwiH9XbAhwiKStsicllfMIDyd9OqO9/NHMtnD9c6iwMGBbmNmeRevd4B+7PRX4kn9rbTugZTnyGgRB0DBoGf7VCxWNMl8g7UvAESz58gVYRdKdSl9Vv6SwfoekBZn0IySN8f0xki6Q9JikWRnPjCRd6Ne5D1grk/5nkp5yL8Fl8u8quJfhbEkTJD0vaQcP7y7pXI/fIulYD99a0kPurRnnK+8i6ThJMzzu2BLlXxkYaGZTMsF3AP0lbVoi/kj3bEyTdHYmfIGk8yRNAYb58ShJ0yXdJ2kbL9MsSft6mr6SHlH6wvzTkrYvobfYY+nelsJX6udJOtTrY5TXYYukYyrVOfAIsGsFY6bAbaTvgyGpHzAPeCNzvC1wmpktAjCz2WZ2p+8/TDLYitmENN0Y4F7ga1XkAzN7F5gEbKTkxbtG0t+Aa7wuH/A6uF/S570+9wVGeZ318+1ubyePyL3DpdK75vxMFlYifeesJJJWVPJCzpR0K7Bi5txLktYoSrIW8A6wwLUWmNlsj3+U39Mpkm6W9BkPHyPpYklPeFvaSckjObPwHHq8BZLO9/Z3v9Iih8X5LfnMBEEQdAW6qfqtXqjGU7YfcLeZPQ+8KWnrzLltgGNJnoZ++Eq3FehDMvL2Bs7ysP2BTf063wayxseFZjbUzAaQXmJ7Z86tYGbbAMcDhW6ro4G+wGD3LlwnqQfwO2CEe2uuBM70+KcAW3rc75bI7xBgWlHYIpJX5KfZQEnrAGeTvhI/GBgqabifXgl40swGmdmjfvyAmfUnvXh/SVpYbn/S1+cBXgd2M7OtgANZ0lu3FO5tGUwyoF8mGUxHAPPMbCgwFDhK0gaUqXM3oF4ABnm5Rsu72UowH5gjaQDJaL8hc64/MNnMlvWLt9NxQ4/keVuvTNzFSFqd5E0qeOo2B3Y1s5Gk+391oU0AF5jZY6TFAU8ys8Fm9iJpxedjvZ2cCFzk11oqfUb3+5JeJLWJcl7L7wHvmdkXSO116zJxIX1n7TVgtqSrJO2TOXeLPxeDSIsRHpE5tyowjPR5j9uB80n3YgtJgz3OSqS1d/qT1tVZotu3wjNDUdzFny/5ZPZDpaIEQRB0ON2kqrd6oRqjbCRQ8CCNZckuzAlmNstfuteTjK1K3GZmi8xsBrC2h30ZuN7MFvpnDB7IxN9Z0pOSppKMnf6Zc7f4/5NIhhjArsClZvYJgJm9RTI+BgD3SpoMnEb6hAKk7qDrJB0CfFIiv32AuSXC/whs5wZOgaHAg2Y21/Wv87JB+j7WzZm4H9H6uYWpwEP+aYapmbL0AC73st9IFd1s7m25BjjIzOYBuwPf9nI/CawObEz5OodkEK4DYGZHmtnEMrJjSQbZcJZhPZYyfAf4/yRNAlYm1VU5dlD6Ftk9wFlmVjDKbjez931/GOmeQaqfpdqqkld4e+BGr69LSfe/bHoz+72Z9QN+TGpbbfFl4FpP00KFrkh/rvYARgDPA+dLOt1PD3BP3lTgYJZ8Lv5iZkZqS6+Z2VQ3tKfT2rYW0WpAX8vS9VHumSnO52VmNsTMhqywwY7lihQEQdBhNGL3ZdnuKUmrkQyhLZRWsO0OmKTCGJ/irhorEd6zKM6HWYkK+j1JnoohZjbHX0jZ6xWutZDyZREw3cyGlTi3F+lluQ9wqqQtCgad836JMhRW6j2P9CKuhg+KPEYf+4sT0gvyQ7/uoky34QkkT8kgkgH9QTkBpbFqY4EzzKzg3RPJ8zOuKO6eFfLbk1T2argDGEXyvMxX66+S6cAgSd2XxVtmZs+SjEmUPk+xV4Ukj5jZ3iXC361W0+kGvO3exvYwljQer8PwNjIBmCDpXtIHbU8njccbbmZTJB1GWmm6QOG5WMSSz9si2n5Oip/lcs9MEARB7tRTt2S1VPKUjQCuMbP1zayvma0HzAZ28PPbKH0nqhupe+1RD39N0hc8fP8q8vEwcKCPf+oD7OzhBWPoDfdiVDMr7F7gmIJh44blc8CakoZ5WA9J/T1/65nZeJJx1RvoVXS9mcBGlGYMyTNXGI8zgfQF+TXcQBpJ5pML7aA38Kp7Ob5FMorLcRbQYmbZsXHjgO95dxSSNpG0Em3XeYFNWLrbtiRm9h6p/s4sCn8RmAj8Qlo8FrCvpLJGlqS1/P9uJA/NJdXkowKP0Tom8mDSuDlIXccre37nk7oKv+76kjSoXHotOUt1L+DvZfLwMHCQpxsADCwTF0nraMlJC4NJ3dJ4nl/1+3pwueu0QTdan6eDaH12C5R8ZtqhEwRB0Ck0oqesklE2kqW7o26mtQvzKeBCkuEyOxP3FJL35DHg1SrycSvpZTYD+APwOIDPRrucZByMc71KjAb+AbQoDao/yMw+Ir2AzvawyaRuqu7Atd4F9AxpnNHb2Yu516a30oB/is59RBpbtJYfv+plH08aDzTJzP5cRZ7b4iLgUM/zZlT2/JwI7K7Wwf77kupjBvC00tITl5K8JSXrHEDS2sD7ZvZvPy43pgwAMxtrZk+XOHUkqZv6BdcfQ+oaRdL1rruppFckFcZFjZT0PPAs8C+Sd2h5ORY4XFILycD9oYePBU6S9IzSxISDgSO8zrNj29pK/wMfLD8Z+BFwaJk8XAz0kjSTNG5wUoU89wDOlfSsX//AjO7/kLqj/0aqp2XlXdKPqmkkb/gZ2ZNlnpkgCIIuQSMuiaHWHrSgLSSdALxjZqPzzkst8PLON7Mr8s5L0DlIWmBmxV7h5WbFA66o+R+Un/9gp1pLAvC7G2u/OoktyufvdY9P98hF963XSk3O7nw+eb6a3/+dwGrr1F5z0bLOw+o43h//P8tlLv3t7/+p+oH44sar1oVpVu06Zc3OxSw5NqfReRu4Ou9MBEEQBEFbdJeq3uqF+MxSFZjZB6QZd02BmXVEd2GHIumrpOVGssw2s2rGLNaUeshrZ3jJgiAIakr92FpVE0ZZUBf47NFxFSN2Aeopr0EQBPVKPQ3gr5YwyoIgCIIgqDvqqFeyasIoC4IgCIKg7mhAmyyMsiAIOo5u3Wo/d+je6a/XXBPg7TfezkV3nb61/wTpgnnLug5zx9B79d656L6ZxyxIgLf+VXvNz29Re82OogGtsjDKgiAI6oQ8DLIg6KrU0zctqyWMsiAIgiAI6o7GM8nCKAuCIAiCoB5pQKssjLIgCIIgCOqOWBIjCIIgCIKgC9CAQ8rCKAuCIAiCoP4IoywIgiAIgqALEN2XQRAEQRAEXYDwlAVBEARBEHQBGtAmo6rltyUNl2SSNsuE7STpjs7IlKQxkkZ0xrXbg6QtJV3h+4dJWiRpYOb8NEl9a5ifvpKm+f4QSRd0wDUX17mksZI2riLN6d4uNsqEHe9hQ/y4l6RLJb0oaZKkByVtK2k9SeMlzZA0XdIPM9dYTdK9kv7u/69aJg+HSZorabJf66hlLPdhktq9fLek/5XU4vr3LMu1JL0kaQ3ff6yNOKd6/RQ0tm1vXqvM04OFexcEQdCl0TJsdUK130QZCTzq/zcjPwWyhs8rwKntvZik7sudI8fMJprZcR11Pedi4OQq404Fvpk5/jowPXM8GngL2NjMtgYOB9YAPgH+n5ltDmwHfF/S5p7mFOB+M9sYuN+Py3GDmQ0GdgL+T9La2ZOSynmEDwOW55sqo8xsoOvfAfysPRcxs+2LwyQNA/YGtjKzgcCuwJzlyGsQBEHDoGX4Vy9UNMok9QK+BBzBki9fgFUk3SnpOUmXSOrmaRZk0o+QNMb3x0i6QNJjkmZlPDOSdKFf5z5grUz6n0l6yr1Rl0mpF9l/0Z8taYKk5yXt4OHdJZ3r8VskHevhW0t6yL014yT18fDj3MPSImlsifKvDAw0symZ4DuA/pI2LRF/pKSprn92JnyBpPMkTQGG+fEo94LcJ2kbL9MsSft6mr6SHpH0tG+lXtyLPZaS7nJvymRJ8yQd6vUxyuuwRdIxleoceATYtYIxU+A2YD+/Zj9gHvBG5nhb4DQzWwRgZrPN7E4ze9XMnvawd4CZwOf8mvsBV/v+1cDwKvKBmb0OvAis723tEklPAudIGizpCa+DWyWt6u1vCHCd19mKZdrJUuldc34mCysB1lb+JK2u5E2bLmk0md9v2WcmQx/gDTP70LXeMLN/efxyz8X5kiZKmilpqKRblLyOv/Q4fSU9K+k6j3OTpM+UyO/ukh73tnej0t+CIAiCLkE3Vb/VC9V4yvYD7jaz54E3JW2dObcNcCywOdAPOKCK6/UhGXl7A2d52P7Apn6dbwNZ4+NCMxtqZgOAFT1dgRXMbBvgeODnHnY00BcY7N6F6yT1AH4HjHBvzZXAmR7/FGBLj/vdEvkdAkwrClsEnEPyoC1GqevqbGAXYDAwVNJwP70S8KSZDTKzR/34ATPrD7wD/BLYzeviDE/zOrCbmW0FHMiS3rqlMLM93WNzBPAyyWA6AphnZkOBocBRkjagTJ27AfUCMMjLNVptd2nNB+ZIGkAy2m/InOsPTDazheXyrdT1uyXwpAetbWav+v6/gbVLpStxnQ2BDT3vAOsC25vZj4A/AD/2+zwV+LmZ3QRMBA72evuEttvJUukzumdKmgMcTHlP2c+BR/2e3wp8vkKR7gHW8x8dF0naMXOu3HPxkZkNAS4B/gx8HxgAHCZpdY+zKXCRmX2BdA//v6ywUrfqacCu3v4mAj8qlUlJR7sROPGTWQ9WKFIQBEEH0aTdlyOBggdpLEt2YU4ws1n+0r2eZGxV4jYzW2RmM2h92X4ZuN7MFron4IFM/J0lPSlpKsnY6Z85d4v/P4lkiEHq4rnUzD4BMLO3SC+gAcC9kiaTXjbrevwWkuF2COmlXEwfYG6J8D8C27mBU2Ao8KCZzXX967xsAAuBmzNxPwLu9v2pwENm9rHvF8rSA7jcy34jyYAqi79MrwEOMrN5wO7At73cTwKrAxtTvs4hGYTrAJjZkWY2sYzsWJJBNpxkbFSNe19uBo4v8jrh2kYZ75NzoJfveuAYv+cAN5rZQkm9gc+a2UMefjWt9yVLyXZSKb2ZnWpm65Hu9w/K5PPLwLWe5k7gP+UKZWYLgK1JPzTmAjdIOsxPl3subvf/pwLT3Sv5ITALWM/PzTGzv/n+tSz97G5Ham9/87o4FFi/jXxeZmZDzGzIChvuVK5IQRAEHUZHd19K2sN7j16QtNSwGUnrS7rfe0welLSuh++c6aWaLOmDgkPGeySe816NK91J1CZlu6ckrUb6g7+FJAO6AybpJI9S/LK0EuE9i+J8mJWooN8TuAgYYmZzJJ1edL3CtRZSviwivZyGlTi3F+lluQ9wqqQtCgad836JMmBmn0g6D/hxuTJk+KDIY/SxGxyQPG+FLqpFmW7DE4DXSB6rbsAH5QSUxqqNBc4ws4J3T8CxZjauKO6eFfLbk1T2argDGAVMNLP5ap2nPB0YJKl7KW+ZN86bgevM7JbMqdck9TGzV7378PUK+jeYWSlj6N0q8784S5RoJ26UVcN1wF1kvGjLi9fbg8CDboAdqtTNXs1zsYgln7dFtD4nbT27BQTca2bNOo40CIIuTkcuieHvz9+TeqxeAZ6SdLs7kAqcC/zBzK6WtAvwK+BbZjae1DtWsJteIPV0QHovHOL7fwSOJI3bLkklT9kI4BozW9/M+ro3YDawg5/fRtIGSmPJDiRNBoD0Uv2Ch+9fQQPgYZK3o7u/hHf28MKL5g33qFQzI/Ne4JiCYeMV9BywptLAaST1kNTf87eeV+iPgd5A8biZmcBGlGYMyTO3ph9PAHaUtIbf4JHAQ22krYbewKvenfgtklFcjrOAFjPLjo0bB3yvYJ1L2kTSSrRd5wU2Yelu25KY2Xuk+juzKPxFUrfXL6TFY576StrLj68AZprZr4sueTvJM4P//+dq8lEmf/OA/8jHHZLqsnBf3gFW9v2S7aRcei05S3U/4NkyWXkYOMjT/TfQ5qxSj7Np0fUHk7ql2/NcFPP5Qjk9T48WnX8C+KJ8Zq2klSRt0g6dIAiCTqGDey+3AV7w3r+PSA6O/YribE5rr9L4Euch/T3+q78XMbO7zCHZCOuWSLOYSkbZSJbujrqZ1i7Mp4ALSYbL7EzcU0jek8eAV6nMrcDfgRmksTuPe2HeBi4nGQfjXK8So4F/AC1Kg+oP8goeAZztYZNJY6i6A9e6B+IZ4ALXXIyZPQv0VhrwT9G5j0jjvNby41e97OOBKcAkM1seg+IikmdkCrAZlT0/JwK7Z1yo+5LqYwbwtNIyGpeSvCUl6xxAafbi+2b2bz8uN6YMADMbWxi4X8SRpG7qF1x/DMnz9UWScbNLJr8F791ZwG6S/k4yes9a+rLLzKHAKEktJOOmMG5vDHCJd9F1p3Q7KZf+LHdLt5C6ihcv7VGCXwBfljSdNP7yHxXy3Au4Wj4RhfQH4fR2PhfFPEea8TqTZBwu8cvNzOaSZqZe79qPk9pgEARB16BjrbLPseTs9ldonXxWYAqtY+f3B1ZW6zjdAt8kDaVZMqvJMfItWoctlUStPWhBW0g6AXjHzEbnnZda4OWdb2ZX5J2XoONRmlhxh08S6FBWGnFVzf+gbLdjPrbiEw+Xc4p2Duv07VNzTYAF85Z1JEDH0H2FDls9aJl487kZlSN1Bm/9q/aan9+i9prO+38+Zrk6IF94/f2q/95svPZnjiGNzy1wmZldVjhQmo2/h5kd6cffArbNDo1Rmsx3IbABqefja8CAgjPHe51agHV8jDiZtJcD75rZ8eXyGSv6V8fFpPW3moW3SZMFgiAIgqBLsiwWnRtgl5WJ8k9aJ0JB6mb8Z9E1/oV7ynzoyNeKete+AdxawiD7OWmY0zGV8hlGWRWY2Qc0kZFiZlflnYdiJB3O0l2DfzOz7+eRn3J09bya2UukWaZBEAT1S8cudfEUsLHSigr/JHVDHrSEXFrd4C0f5/0T0rJJWUZ6eDbNkcBXga94urKEURbUBW4odjljsRT1lNcgCIJ6pSNX6vcVFX5AGqfbHbjSzKZLOoO0ssDtpK/G/MpXo3iYtAZkyksaFrIeS0/uu4Q0Qetxn+92i5mdQRuEURYEQRAEQd3RkUtiQJopSVrWKBv2s8z+TcBNbaR9iaUnBmBmy2RnhVEWBEEQBEHd0dFGWVcgjLIgCIIgCOqOevrQeLWEURYEQYehHL78e+yOG1SO1Am89Va1H7zoOPLyDHz+89V+1KJjmTNnXi66by4q+7neziOP5Sn+MbX2mh1EeMqCIAiCIAi6AA1ok4VRFgRBEARB/RGesiAIgiAIgi5B41llYZQFQRAEQVB35DCEtdMJoywIgiAIgrojui+DIAiCIAi6ALEkRhAEQRAEQVeg8WyyMMqCIAiCIKg/GtAmo1s1kSQNl2SSNsuE7STpjs7IlKQxkkZ0xrXbg6QtJV3h+4dJWiRpYOb8NP8Yaa3y01fSNN8fIumCDrjm4jqXNFbSxlWkOd3bxUaZsOM9bIgf95J0qaQXJU2S9KCkbSWtJ2m8pBmSpkv6YeYaX/ewRYXrlMnDTpLmSZosaaakny9juYdL2nxZ0hSl/66kqa7/6LJcy+uiUE93SfpsiTjf8eu3eDvbr715rTJPXerZC4IgaAup+q1eqMooA0YCj/r/zchPgazh8wpwansvJqn7cufIMbOJZnZcR13PuRg4ucq4U4FvZo6/DkzPHI8G3gI2NrOtgcOBNYBPgP9nZpsD2wHfzxg004ADgIerzMMjZjYYGAIcImmr7ElJ5TzCw4F2G2XAH81sC9c/B/h1ey5iZnua2dvZMEnrktrZl8xsIKmeWpYjr0EQBA2DpKq3eqGiUSapF/Al4AiWfPkCrCLpTknPSbpEUjdPsyCTfoSkMb4/RtIFkh6TNCvjmZGkC/069wFrZdL/TNJT7iW4TF677mU4W9IESc9L2sHDu0s61+O3SDrWw7eW9JB7a8ZJ6uPhx7m3pkXS2BLlXxkYaGZTMsF3AP0lbVoi/kj3bEyTdHYmfIGk8yRNAYb58Sj3CN0naRsv0yxJ+3qavpIekfS0b9uX0FvssXRvy2Tf5kk61OtjlNdhi6RjKtU58AiwawVjpsBtwH5+zX7APOCNzPG2wGlmtgjAzGab2Z1m9qqZPe1h7wAzgc/58Uwze64K7SUws3eBScBGSl68ayT9DbjG6/IBr4P7JX3e63NfYJTXWT/f7vZ28ojcO1wqvWvOz2RhJcDayp+kFZW8kDMl3QqsmDn3kqQ1ipKsBbwDLHCtBWY22+Mf5fd0iqSbJX3Gw8dIuljSE96WdpJ0pWuOyegtkHS+t7/7Ja1ZIr8ln5kgCIKugJZhqxeq8ZTtB9xtZs8Db0raOnNuG+BYkqehH8m7UYk+JCNvb+AsD9sf2NSv820ga3xcaGZDzWwA6SW2d+bcCma2DXA8UOi2OhroCwx278J1knoAvwNGuLfmSuBMj38KsKXH/W6J/A4heW6yLCJ5RX6aDZS0DnA2sAswGBgqabifXgl40swGmdmjfvyAmfUnvXh/CezmdXGGp3kd2M3MtgIOZElv3VK4t2UwyYB+mWQwHQHMM7OhwFDgKEkbUKbO3YB6ARjk5RqttrsR5wNzJA0gGe03ZM71ByabWdkPySl1/W4JPFkuXiUkrU7yJhU8dZsDu5rZSNL9v7rQJoALzOwx4HbgJDMbbGYvApcBx3o7ORG4yK+1VPqM7vclvUhqE+W8lt8D3jOzL5Da69Zl4gJMAV4DZku6StI+mXO3+HMxiGTQHpE5tyowDDjBy3c+6V5sIWmwx1kJmOjt7yFan59Cmco9MxTFPVrSREkTP571YIUiBUEQdAzN2n05Eih4kMayZBfmBDOb5S/d60nGViVuM7NFZjYDWNvDvgxcb2YLzexfwAOZ+DtLelLSVJKx0z9z7hb/fxLJEAPYFbjUzD4BMLO3SMbHAOBeSZOB04B1PX4LyXA7hNSlVkwfYG6J8D8C27mBU2Ao8KCZzXX967xsAAuBmzNxPwLu9v2pwENm9rHvF8rSA7jcy34jVXSzubflGuAgM5sH7A5828v9JLA6sDHl6xySQbgOgJkdaWYTy8iOJRlkw4FbK+WxKL+9SPVyfJHXaVnYQdIzwD3AWWZWMMpuN7PCV6OHke4ZpPpZqq16XrYHbvT6upR0/8umN7Pfm1k/4MekttUWXwau9TQtVOiK9OdqD2AE8DxwvqTT/fQA9+RNBQ5myefiL2ZmpLb0mplNdUN7Oq1taxGtBvS1LF0f5Z6Z4nxeZmZDzGxIjw13KlekIAiCDkPL8K9eKNs9JWk1kiG0hSQDugMm6SSPUtxVYyXCexbF+TArUUG/J8lTMcTM5vgLKXu9wrUWUr4sAqab2bAS5/YivSz3AU6VtEXBoHPeL1EGzOwTSeeRXsTV8EGRx+hjf3FCekF+6NddlOk2PIHkKRlEMqA/KCegNFZtLHCGmRW8eyJ5fsYVxd2zQn57kspeDXcAo0iel/lq/VkyHRgkqXspb5l7Y24GrjOzW4rPLwOPmNneJcLfXcbrdAPedm9jexhLGo/XYXgbmQBMkHQvcBVwOjAGGG5mUyQdBuyUSVZ4Lhax5PO2iLafk+JnudwzEwRBkDv15AGrlkqeshHANWa2vpn1NbP1gNnADn5+G0kbKI0lO5A0GQDgNUlf8PD9q8jHw8CBPv6pD7CzhxeMoTfci1HNrLB7gWMKho0bls8Ba0oa5mE9JPX3/K1nZuNJxlVvoFfR9WYCG1GaMSTPXGE8zgRgR0lruIE0ktQ11F56A6+6l+NbJKO4HGcBLWaWHRs3DvieG0BI2kTSSrRd5wU2Yelu25KY2Xuk+juzKPxFYCLwC2nxWMC+kvby4yuAmWbWrsHxy8hjtI6JPJg0bg5S1/HKnt/5pK7Cr3teJWlQufRacpbqXsDfy+ThYeAgTzcAGFgmLpLW0ZKTFgaTuqXxPL/q9/Xgctdpg260Pk8H0frsFij5zLRDJwiCoFNoxu7LkSzdHXUzrV2YTwEXkgyX2Zm4p5C8J48Br1aRj1tJL7MZwB+AxwF8NtrlJONgnOtVYjTwD6BFaVD9QWb2EekFdLaHTSZ1U3UHrvUuoGdI44zezl7MzJ4FeisN+Kfo3EeksUVr+fGrXvbxpPFAk8zsz1XkuS0uAg71PG9GZc/PicDuah3svy+pPmYATysto3EpyVtSss4BJK0NvG9m//bjcmPKADCzsYWB+0UcSeqmfsH1x5C6Rr9IMjR3yeR3T9fbX9IrpC7DOyWNK3HdZeVY4HBJLa5bWIJjLHCSpGeUJiYcDBzhdT4dn8RQJv0PlAbLTwZ+BBxaJg8XA70kzSSNG5xUIc89gHMlPevXPzCj+z+k7ui/Ac9WKnwJ3iX9qJpG8oafkT1Z5pkJgiDoEjRi96Vae9CCtpB0AvCOmY3OOy+1wMs738yuyDsvQecgaYGZFXuFl5te3xhT8z8o1562R60lAfjFrTNqrpnXL/5VV12xcqROYM6cefnoPlXN7/9OYJW1KsfpaP4xtfaazvvPXLhcLXr+B4uq/nuzSs/6+Hx5teuUNTsXs+TYnEbnbeDqvDMRBEEQBG3RiEtixGeWqsDMPiDNuGsKzOyqvPNQjKSvkpYbyTLbzKoZs1hT6iGvneElC4IgqCn1ZG1VSRhlQV3gs0c7YmxZp1NPeQ2CIKhX6mmsWLWEURYEQRAEQd1RH6PElo0wyoIgCIIgqD/CKAuCIAiCIMifRuy+jCUxgiDoEkg62swua3TN0G1czdANlpdYEiMIgq7C0U2iGbqNqxm6wXIRRlkQBEEQBEEXIIyyIAiCIAiCLkAYZUEQdBXyGJeS11iY0G1MzdANlosY6B8EQRAEQdAFCE9ZEARBEARBFyCMsiAIgiAIgi5AGGVBEARBEARdgDDKgiDIBUlrS7pC0l/9eHNJRzSw7iaS7pc0zY8HSjqt0TRz1m2aNiXpM5L+R9LlfryxpL07U9N1cqnjZiGMsiAI8mIMMA5Yx4+fB45vYN3LgZ8AHwOYWQvwzQbUzFN3DM3Tpq4CPgSG+fE/gV92sibkV8dNQRhlQRDkxRpm9idgEYCZfQIsbGDdz5jZhKKwTxpQM0/dZmpT/czsHFoN3/eozSe686rjpiCMsiAI8uJdSasDBiBpO2BeA+u+IalfRncE8GoDauap20xt6iNJK2Y0+5E8Z51NXnXcFKyQdwaCIGhafgTcDvST9DdgTWBEA+t+n7TQ5maS/gnMBg5pQM08dZupTf0cuBtYT9J1wBeBwzpZE/Kr46YgFo8NgiA3JK0AbErqdnnOzD5uZF3XXgnoZmbvNLJmXrrN1KbcY7Wdaz5hZm90tqbr5vb8NDphlAVBUFMkHVDuvJnd0mC6P6qg++tG0MxZt2nalKStKmg+3dGarptLHTcb0X0ZBEGt2afMOQM66497Xrord9J1u5pmnrrN1KbOq6C5SydoQn513FSEpywIgiAIgqALELMvgyDIBUmrS7pA0tOSJkn6rY+RaVTdDSX9RdJcSa9L+rOkDRtNM2fdpmlTknpK+pGkWyTdLOl4ST07U9N1c6njZiGMsiAI8mIsMBf4Gmn21lzghgbW/SPwJ6APaeHNG4HrG1AzT91malN/APoDvwMu9P1rOlkT8qvjpiC6L4MgyAVJ08xsQFHYVDPbokF1W8xsYFHYFDMb1EiaOes2TZuSNMPMNq8U1gm6udRxsxCesiAI8uIeSd+U1M23b5A+39Koun+VdIqkvpLWl3QycJek1SSt1kCaeeo2U5t6WmnhVgAkbQtM7GRNyK+Om4LwlAVBkAuS3gFWwj/XQvqR+K7vm5mt0mC6s8ucNjPr8DFXeWjmrNs0bUrSTNJaYf/woM8Dz5E+Z2XFnsoO1M2ljpuFMMqCIAiCoM6QtH6582b2cq3yEnQcYZQFQZAbkgYCfcmsmViLRSjz0JXUHdirhG6nLKial2aeuq7dTG1qVWC9Is1OWTy2SDeXOm4GYvHYIAhyQdKVwEBgOq1dIZ2+CGVeusBfgA+AqRndziYPzdx0m6lNSfpf0rcuX3StgmZnLR5b0M3r+WkKwlMWBEEu1GKmWBfTXWpGYiNq5qzbNG1K0nPAFmb2UY11c6njZiFmXwZBkBePS8rjj3teun+VtHsTaOap20xtahrw2RprQn513BRE92UQBHnxB9If+H8DHwKiE2eNdQHdJ4BbJXUDPs7oduZstTw089Rtpjb1K+AZSdNcE5Lovp2oCfnVcVMQ3ZdBEOSCpBeAH1E07qizZ43lqDsb2A+YajX6w5uHZs66TdOmJE0HLi2h+VBnabpuLnXcLISnLAiCvJhrZrc3ke4cYFotjZScNPPUbaY29Z6ZXVBjTcivjpuC8JQFQZALki4ijYn5C0t2v3T2TLm8dMcAGwJ/LdLtzCUxaq6Zs27TtClJv3at24s0O3VJjLzquFkIT1kQBHmxIumPenZAeC2m1uelO9u3T/lWC/LQzFO3mdrUlv7/dpmwTl8Sg/zquCkIT1kQBEEQBEEXIDxlQRDkgqSewBFAf6BnIdzMvtOgumsCJ5fQ7TTPRh6aOes2W5vaq4TmGZ2smUtZm4VYpywIgry4Bvgv4KvAQ8C6wDsNrHsd8CywAfAL4CXgqQbUzFO3adqUpEuAA4FjSctSfB0o+z3MDiKvOm4KovsyCIJckPSMmW1ZWP1dUg/gETPbrmLi+tSdZGZbZ1e7l/SUmQ1tJM2cdZumTWW0Cv/3Av5qZjt0lqbr5lLHzUJ0XwZBkBcf+/9vSxoA/BtYqwl0X/Vup38BqzWgZlfQbYY29b7//56kdYA3gT6drAn51XFTEEZZEAR5cZmkVYH/IU3r7+X7jar7S0m9gf8H/A5YBTihATXz1G2mNnWHpM8Co4CnSTMgL+9kTcivjpuC6L4MgiAIgjpG0qeBnmY2L++8BMtHDPQPgqCmSNpH0vqZ459JmiLpdkkbNKDuUZI29n1JukrSPEktkraslL5eNHPWbZo2JWmopP/KHH8b+BPwv5I6rYs4rzpuOswstthii61mG9ACfMb39waeB7YGjgTGNaDuNKCH7x8ETAJWB3YlDZBuCM2cdZumTZG6Klfz/S+Txut9Dfhf4KZGKmszbuEpC4Kg1piZvef7BwBXmNkkMxsNrNmAup+YWWFw9N7AH8zsTTO7D1ipgTTz1G2mNtXdzN7y/QOBy8zsZjP7H2CjTtKE/Oq4qQijLAiCWiNJvSR1A74C3J8517ONNPWsu0hSH6VFN78C3Jc5t2IDaeap20xtqrukwiS9rwAPZM515uS9vOq4qYjZl0EQ1JrfAJOB+cBMM5sI4GOOXm1A3Z8BE4HuwO1mNt11dwRmNZBmnrq/oXna1PXAQ5LeIC2L8YhrbgR05kD/35BPHTcVMfsyCIKaI+lzpLWNppjZIg/rQxqP9I8G1F0BWNnM/pMJW4n0N3hBo2jmrNs0bUrSdqQ1ye4xs3c9bBOgl5k93RmarpFLHTcTYZQFQRAEQRB0AWJMWRAEQRAEQRcgjLIgCIIgCIIuQAz0D4Kgpkhaxczmt7XQZWa6f6PobmZmz0raqg3dDh8DlIdmzrpN1abyoJnKmicxpiwIgpoi6Q4z21vSbNL3+pQ5bWa2YYPpXm5mR0kaX+K0mdkujaCZs27TtClJj5rZlyS945qLT7nmKh2t6bq51HGzEUZZEARBEARBFyC6L4MgqCltdW0V6MQurrx0D6ige0sjaOas2zRtqtL3LTuxqzaXOm42wlMWBEFNyXRt9QSGAFNIXSEDgYlmNqzBdK/y3bWA7WldgX1n4DEz27sRNHPWbZo2VdR9+HngP77/WeAfZtZZH0LPpY6bjfCUBUFQU8xsZwBJtwBbmdlUPx4AnN6Auoe7zj3A5mb2qh/3AcY0imbOuk3TpgpGl6TLgVvN7C4//m9geGdoum4uddxsxJIYQRDkxaaFP+wAZjYN+EID665XMFKc10iejkbTzFO3mdrUdgWDzDX/SvJOdjZ51XFTEJ6yIAjyokXSaOBaPz4YaGlg3fsljSN9uxDgQJb8YHejaOap20xt6l+STivS/Fcna0J+ddwUxJiyIAhyQVJP4HvAlz3oYeBiM/ugEXVde/+srpnd2oiaeek2U5vyAf8/d01zzTM6e72wPJ+fZiCMsiAIuiSSbjazrzWR7uO1Hiydh2bOuk3TpiT9zsyOraWm6+ZSx41CjCkLgqCrktdilHnp9mwSzTx1m6lNfTEHTcivjhuCMMqCIOiq5OXGbybdZiprM+rmQTOVtcMJoywIgiAIgqALEEZZEARdFVWOErp1qBm6jauZp25DEEZZEARdlR83me63mkQzT91malO/zUET8qvjhiCMsiAIaoqkVST9StI1kg4qOndRYd/M7mkQ3fUkjZX0iKSfSuqROXdbRndaPWvmrJvXvd1M0l8l3Smpn6Qxkt6WNEHS4gVVO1JX0n9JuljS7yWtLul0SVMl/cm/nFDQHNNRmq5b87I2I2GUBUFQa64idXHcDHxT0s2SPu3ntmtA3SuBB4FjgT7AQ5JW93PrN5Bmnrp53dvLgItIC6k+ANwNrAr8L3BhJ2mOAWYAc4DxwPvAnsAjwCWdpAn5lLX5MLPYYosttpptwOSi41OBvwGrA083ge4hwHSgX2fp5qHZxXRrdW+fyey/UHSus+5tVvMf5eqh3svajFt8ZikIglrzaUndzGwRgJmdKemfpJXBezWgbg9JPc1XPDezayX9GxgHrNRAmnnq5nVvu2f2f1107lOdpJnt4fpDmXMdTR5lbTqi+zIIglrzF2CXbICl8S//D/ioAXVHA9sW6d4HfB3o0LFVOWvmqZvXvf29pF6ut3jsmqSN6Lxvff45o3lakebznaQJ+ZS16YjPLAVBEARBEHQBwlMWBEEuSOot6XxJE307T1LvBtZdV9KtkuZKet0Ho6/baJo56zbTvW2asjYTYZQFQZAXVwLzgW/4Np80i65Rda8CbifNSlyH1OXW2bp5aOap20z3tpnK2jRE92UQBLkgabKZDa4UFrr1pRm6tdFtprI2E+EpC4IgL96X9KXCgaQvktZcalTdNyUdIqm7b4cAbzagZp66zXRvm6msTUN4yoIgyAVJg4GrgcI4mP8Ah5nZlAbVXR/4HTAMMOAx4Dgz+0cjaeasO5jmubeDaZKyNhNhlAVBkCuSVgEws/nNoBt0Ps10b5uprM1AdF8GQZALktaWdAVwg5nNl7S5pCMaWHcTSfdLmubHAyWdVildvWnmrNtM97ZpytpMhFEWBEFejCGt9L6OHz8PHN/AupcDPwE+BjCzFuCbDaiZp+4YmufejqF5yto0hFEWBEFerGFmfwIKn8b5BFjYwLqfMbMJRWGfNKBmnrrNdG+bqaxNQxhlQRDkxbuSVicNFkbSdsC8BtZ9Q1K/jO4I4NUG1MxTt5nubTOVtWmIgf5BEOSCpK1Is7gGkL6LuCbw9RrMHstLd0PgMmB70ky52cDBZvZyI2nmrNtM97ZpytpMhFEWBEEuSPo0qbtlU0DAc0A3M/uwQXU3MLPZklZyvXcKYY2kmbNuM93bpilrMxHdl0EQ5MXjZvaJmU03s2lm9jHweAPr3gxgZu+a2TsedlMDauap20z3tpnK2jSskHcGgiBoLiT9F/A5YEVJW5J+5QOsAnymAXU3A/oDvSUdkDm1CtCzUTRz1m2me9s0ZW1GwigLgqDWfBU4DFgX+HUm/B3gpw2ouymwN/BZYJ8i3aMaSDNP3Wa6t81U1qYjxpQFQZALkr5mZjc3ke4wM6tF91KumjnrNtO9bZqyNhMxpiwIgry4X9KvJU307TxJvSsnq1vdOZJulfS6bzdLWrcBNfPUbaZ720xlbRrCKAuCIC+uIHV9fMO3+cBVDax7FXA7aQX2dYC/1EA3D808dZvp3jZTWZuG6L4MgiAXJE02s8GVwhpId4qZDaqlbh6aOes2071tmrI2E+EpC4IgL96X9KXCgaQvAu83sO4bkg6R1N23Q4A3G1AzT91murfNVNamITxlQRDkgqRBwB+AwjiY/wCH+geOG1F3fdIK7MNIn6h5DDjOzP7RSJo56zbTvW2asjYTsSRGEAQ1R1J34FtmNkjSKgBmNr/Bdf/PzPbtbK08NbuAbjPd26Yoa7MR3ZdBENQcM1sIfMn359fihdIFdNeX9Kla6OWl2QV0m+neNkVZm43wlAVBkBfPSLoduBF4txBoZrc0qO4s4G+undX9ddtJ6lIzT91murfNVNamIYyyIAjyoidpgPAumTADOvulkpfui751A1buZK08NfPUbaZ720xlbRpioH8QBDVH0prA+sALZvZ2E+gOBjYCppvZzEbVzFm3me5t05S12YgxZUEQ1BRJRwLTSTO4npVUk0HDOer+DPgT8DXgTkmd/p3APDRz1m2me9s0ZW1GwlMWBEFNkTQN2NnM5kraELjOzIY1sO50YKiZvSdpdeBuMxvaaJo56zbTvW2asjYj4SkLgqDWfGRmcwHMbBbw6QbX/dDM3nPdN6nN3908NPPUbaZ720xlbTrCUxYEQU2R9DowNhP0zeyxmR3XYLpvAw8XDoEdMsd0xppPeWjmrNtM97ZpytqMhFEWBEFNkXRoufNmdnWD6e5YQfehRtDMWbeZ7m3TlLUZCaMsCIIuiaTfmdmxTaR7s5l9rdE1c9ZtpnvbNGVtJKJPOAiCrsoXm0x3wybRzFO3me5tM5W1YQijLAiCoGuQR7dFXl0lzdZF00zlbaaydjhhlAVBEARBEHQBwigLgqCrotBtSM3QbVzNPHUbgjDKgiDoqvy2swUkrZWHbhv8uDMv7gt+1lSzDHnpNuS9bYNmKmvDEEZZEAQ1RdJ/SbpY0u8lrS7pdElTJf1JUp9CPDMb08G6qxVtqwMTJK0qabVO1N0js99b0hWSWiT9UdLaGd17OlDzLElr+P4QSbOAJyW9nF3aoCM1XetpSadJ6lcuXifoDpE0XtK1ktaTdK+keZKekrRlRndMB+v2knSGpOmuN1fSE5IOy8br4Hvb2+/vs5LekvSmpJke9tmM5piO0qwiT3/N6HbovW02wigLgqDWjAFmAHOA8cD7wJ7AI8Alnaj7BjAps00EPgc87fudxf9l9s8DXgX2AZ4CLu0kzb3M7A3fHwUcaGYbAbt5HjqLVYHPAuMlTZB0gqR1OlGvwEXAOcCdwGPApWbWGzjFz3UW1wGzgK8CvwAuAL4F7Czp/8olXA7+BPwH2MnMVjOz1YGdPexPnaSJpK3a2LYGBneWbrMR65QFQVBTJD1jZlv6/j/M7POZc5PNbHAn6f4/klFykplN9bDZZrZBZ+hldJ82s618f4nydVZ5Jc0EtjCzTyQ9YWbbZc5NNbMtOlrTr50t6w7ASOAAYCZwvZld1km65drU4nOdoDvFzAZljp8ys6GSugEzzGyzTtB8zsw2XdZzHaC7EHiI0mPGtjOzFTtDt9lYIe8MBEHQdGQ99H8oc65DMbPzJN0AnC9pDvBzajN9fy1JPyK9zFaRJGv9NdxZ5b0IuEvSWcDdkn4L3ALsAkzuJM0lMLNHgEckHUsyhg8EOsUoAz6QtDvQGzBJw83sNu+qXdhJmgDvSvqSmT0qaV/gLQAzWySpswa8vyzpZOBqM3sNwLvBDyN5nzuLmcAxZvb34hP+PAUdQBhlQRDUmj9L6mVmC8zstEKgpI2A5ztT2MxeAb7uL9B7gc90pp5zObCy718NrAHMlfRfdJKBZGa/kzQV+B6wCelv/cbAbcAvO0PTWer+mdlC4G7fOovvkrovF5G6Er8naQzwT+DoTtYdLWljYDrwHQBJawK/7yTNA0ndsg9lJqq8BtwOfKOTNAFOp+0fETX/ckCjEt2XQRA0JZJWBPqZ2bS88xIEQQAx0D8Igi6EpMNrpWVm7xcMslrqZslDt5nK2qi6kjaT9BVJKxWF79FWmg7W7VVL3WYiPGVBEHQZigdph25jaIZuh173OOD7pDFeg4Efmtmf/dziiRaNottsxJiyIAhqiqSWtk4Ba7dxLnS7uGbo1kz3KGBrM1sgqS9wk6S+ZvZbOnc1/bx0m4owyoIgqDVrkwZj/6coXKQ1pkK3PjVDtza63cxsAYCZvSRpJ5KBtD6daxzlpdtUhFEWBEGtuQPoZWaTi09IejB061YzdGuj+5qkwQVN91ztDVwJdMr6cznrNhUxpiwIgiAI6gRJ6wIfF9YoKzr3RTP7WyPpNhsx+zIIgpoiaaik/y4Rvqd/siV061AzdGum2wdYalC95+ODTtLMU7epCKMsCIJaczbp25fFTCd9pzF061MzdGuj25bmjE7UzFO3qQijLAiCWrOymb1cHOhha4Ru3WqGbm10m6msTUcYZUEQ1JpVy5zrzM8eNZNuM5W12XSbqaxNRxhlQRDUmvsknZn9YLMSZwAPhG7daoZubXSbqaxNR8y+DIKgpih9GmY0sA2tH+QeBEwEjiyshRS69aUZunFvO0O32QijLAiCXJC0IdDfD6eb2azQrX/N0I17G7SfMMqCIKgpksp+I8/Mng7d+tMM3droNlNZm5EwyoIgqCmSFgHTgDcKQZnTZma7hG79aYZubXSbqazNSHxmKQiCWvMjYATwPjAWuLVG41GaSbeZytpsus1U1qYjPGVBEOSCj035JrAf8DLwf6W+IRi69aUZunFvg/YTS2IEQZALPkD4z8A9pBldm4Ru/WuGbtzboP2EpywIgppS9Et7Dqkr5E4zez9061czdOPeBstPGGVBENQUHzDcQvq1PR9Y4o+Qmf06dOtPM3Tj3naGbrMRA/2DIKg1Z9D6B71X6DaMZug2rmaeuk1FeMqCIAiCIAi6ADHQPwiCIAiCoAsQRlkQBEEQBEEXIIyyIAiCIAiCLkAYZUEQ5Ebx9/QqfV8vdOtDM3Tj3gbtI4yyIAjy5HsVjkO3PjVDN+5t0A5i9mUQBEEQBEEXINYpC4IgFyR1AwYB65A+cjzNzF4P3frWDN24t0H7CU9ZEAQ1RVI/4MfArsDfgblAT9I39N4DLgWuNrNFoVs/mqEb97YzdJuNMMqCIKgpkq4HLgYesaI/QJLWAg4C/mNmV4du/WiGbtzbztBtNsIoC4IgCIIg6ALEmLIgCGqKpAPKnTezW0K3/jRDtza6zVTWZiSMsiAIas0+/v9awPbAA368M/AY0Fl/3JtJt5nK2my6zVTWpiOMsiAIaoqZHQ4g6R5gczN71Y/7AGNCtz41QzfubbD8xOKxQRDkxXqFP+zOa8DnQ7fuNUM37m3QTsJTFgRBXtwvaRxwvR8fCNwXunWvGbpxb4N2ErMvgyDIDUn7A1/2w4fN7NbQrX/N0I17G7SPMMqCIMgNSesDG5vZfZI+A3Q3s3dCt741QzfubdA+YkxZEAS5IOko4CbSSuAAnwNuC9361gzd2ug2U1mbiTDKgiDIi+8DXwTmA5jZ30nT7UO3vjVDN+5t0E7CKAuCIC8+NLOPCgeSVgBqMZ6imXSbqazNpttMZW0awigLgiAvHpL0U2BFSbsBNwJ/Cd261wzduLdBO4mB/kEQ5IKkbsARwO6AgHFmdnno1rdm6Ma9DdpPGGVBEOSCpG8Bt2VnbUna28zuCN361Qzd2ug2U1mbiTDKgiDIBUlvAy8BI81spoc9bWZbhW79aoZu3Nug/cSYsiAI8mI28B3gJklf9zCFbt1rhm5tdJuprE1DfGYpCIK8MDN7WtKOwPWStgW6h27da4Zu3NugnYSnLAiCvHgVwMzeAL5KmlY/IHTrXjN0494G7STGlAVBEARBEHQBovsyCIKaIuk3Zna8pL9QYtFJM9s3dOtPM3Tj3naGbrMRRlkQBLXmGv//3NBtKM3QbVzNPHWbiui+DIIgCIIg6AKEpywIgpoiaSplvpVnZgNDt/40Q7c2us1U1mYkPGVBENQUSeuXO29mL4du/WmGbm10m6mszUgYZUEQBEEQBF2AWKcsCIJckLSdpKckLZD0kaSFkuaHbn1rhm7c26D9hFEWBEFeXAiMBP4OrAgcCfw+dOteM3Tj3gbtJIyyIAhyw8xeALqb2UIzuwrYI3TrXzN0494G7SNmXwZBkBfvSfoUMFnSOaTPt9Tih2Iz6TZTWZtNt5nK2jRERQZBkBffIn3I+AfAu8B6wNdCt+41QzfubdBOYvZlEARBEARBFyA8ZUEQ5IKkvSU9I+ktSfMlvVOj2WNNo9tMZW023WYqazMRnrIgCHJB0gvAAcBUq+EfombSbaayNptuM5W1mQhPWRAEeTHn/2/v7kIuq+o4jn9/M1oDM01GaY2glmXh+IJCUpkJangT9jIlg0VEehEkRQVhLzdRVFIRRBCKVhKUGKRi1qRllmYXkk6Yppa9UJBKdTFjKYON/y7OeeAxRGgPs9fZe30/IDNnPxdfF384rNl7nfMA9zZ4Y++p29Nae+v2tNZueKdMUhNJTgM+A/wc2Ld2vaq+bHe6TbvOVsP5lRiSWvks8C9gE/Acu7Np2p1vs2W3C27KJLVyZFWdaHd2TbvzbbbsdsEzZZJa+WGSc+3Orml3vs2W3S54pkxSE0keAzazOJfyJBCgqmqr3ek27TpbDeemTNLokmwAXldVd9idT9PufJstuz1xUyapiSS7q+pUu/Nq2p1vs2W3F54pk9TKLUneniR2Z9W0O99my24XvFMmqYl1Z1P2A08w/pmY2Xd7Wmtv3Z7W2hM3ZZIkSSvA7ymT1EySNwNnLl/+rKputDv9pl1nq2G8UyapiSSXAqcB315eugD4VVV93O50m3bH6fa01p64KZPURJJ7gFOq6qnl643A7qo62e50m3adrYbz05eSWjps3d+fb3c2Tbvzbbbszp5nyiS18nlgd5JbWXyC60zgY3Yn37TrbDWQjy8lNZNkG4vzKQB3VtUjdqfftOtsNYybMkmjSnL0s/28qv5id3pNu+N0e1prj9yUSRpVkt8AxeLRx5oCDgeOqKqNdqfXtDtOt6e19sgzZZJGVVUnrX+d5KXAJcAbgc/ZnWbTrrPVgfPTl5KaSHJckquAXcBdwPaq+qrdaTftOlsN5+NLSaNKciLwSeAE4AvA1VW13+60m3adrQ6cmzJJo0qyH/gr8AMWv9T4aarqg3an17TrbA9GtzeeKZM0totYHBC2O6+m3fk2W3a74p0ySZKkFeBBf0mjSnLF8nzKM/1sc5ILk7zL7rSadsfp9rTWHnmnTNKokpwCfAI4CbgX+DuwCTgO2Ap8A7isqvbZnU7TrrM9GN3euCmT1ESSLcCrgW3AE8D9VfWg3Wk37TpbDeemTJIkaQV4pkySJGkFuCmTJElaAW7KJDWXZEOSrXbn1bQ732bL7py5KZPURJLvJNmaZDOLT3P9NslH7U67adfZajg3ZZJa2V5Ve4G3svjlxi8D3m138k27zlYDuSmT1MqhSQ5l8eZ+Q1U9yTi/xqWnbk9r7a3b01q74aZMUiuXA38GNgO3JTkG2Gt38k27zlYD+T1lklZGkkOq6j9259W0O99my+4ceadMUhNJXpzk60l2LV9vB95jd9pNu85Ww7kpk9TKVcBNwJHL178DPmR38k27zlYDuSmT1MqLquq7wFMAy8cf++1OvmnX2WogN2WSWvl3khey/ORWktcCe+xOvmnX2WqgQ1r/D0jq1keAG4CXJ7kDOBx4h93JN+06Ww3kpy8lNZPkEOBVQIAHl995ZHfiTbvOVsP4+FJSE0kuBrZU1X1VdS+wJcn77U67adfZajjvlElqIsmvq+qU/7m2u6pOtTvdpt1xuj2ttSfeKZPUysYkWXuRZCPwHLuTb9p1thrIg/6SWvkRcE2Sy5ev37e8ZnfaTbvOVgP5+FJSE0k2sHhDP2d56cfAlVV1UL/zqKduT2vtrdvTWnvipkySJGkF+PhSUhNJXg98CjiGxXtRgKqqY+1Ot2nX2Wo475RJaiLJA8CHgbtY92taquqfdqfbtOtsNZx3yiS1sqeqdtmdXdPufJstu13wTpmkJpJcCmwErgX2rV2vqrvtTrdp19lqODdlkppIcuszXK6qOtvudJt2x+n2tNaeuCmTJElaAZ4pk9RMkjcBJwCb1q5V1aftTrtpd5xuT2vthb9mSVITSS4DdgIfYPGx+vNZfMze7oSbdp2thvPxpaQmktxTVSev+3MLsKuq3mB3uk27zlbDeadMUitPLP98PMmRwJPANruTb9p1thrIM2WSWrkxyWHAF4G7gQKutDv5pl1nq4F8fCmpuSTPBTZV1R6782nanW+zZXfO3JRJGlWSHc/286q61u70mnbH6fa01h75+FLS2M5b/nkEcDrw0+Xrs4BfsvimcLvTa9p1tjpAbsokjaqq3guQ5GZge1U9vHy9DbjK7jSbdp2tDpyfvpTUylFrb+xLjwJH2518066z1UDeKZPUyi1JbgKuXr7eCfzE7uSbdp2tBvKgv6RmloeH17508raqus7u9Jt2na2GcVMmSZK0AjxTJqmJJDuS/D7JniR7kzyWZK/daTftOlsN550ySU0keQg4r6rutzufpt35Nlt2e+GdMkmtPNrojb2nbk9r7a3b01q74Z0ySU0k+QrwEuB6YN/a9YP9zeA9dXtaa2/dntbaE78SQ1IrW4HHgXPXXSsO/jeD99Ttaa29dXtaaze8UyZJkrQCvFMmqYkk32TxL+ynqaoL7U63aXecbk9r7YmbMkmt3Lju75uAtwF/szv5pl1nq4F8fClpJSTZAPyiqk63O5+m3fk2W3bnyq/EkLQqjgOOsDu7pt35Nlt2Z8nHl5KaSPIYi7MpWf75CHCJ3Wk37TpbDefjS0mSpBXgnTJJzSTZAZzB4l/ct1fV9Xan37TrbDWMd8okNZHka8ArgKuXl3YCf6iqi+1Ot2l3nG5Pa+2JmzJJTSR5ADi+lm9Cy09x3VdVx9udbtOus9VwfvpSUisPAUeve33U8prdaTftOlsN5JkySaNK8n0WZ1GeB9yf5M7l69cAd9qdZtOus9WBc1MmaWxfepafHczzFD11e1prb92e1todz5RJWglJzgAuGPvAcE/dntbaW7entc6Zd8okNZPkVOCdwPnAn4Dv2Z1+066z1TBuyiSNKskrgQuW//0DuIbFXfuz7E63adfZ6sD5+FLSqJI8BdwOXFRVDy2v/bGqjrU73aZdZ6sD51diSBrbDuBh4NYkVyQ5h8Xv0bM77aZdZ6sD5J0ySU0k2Qy8hcXjkLOBbwHXVdXNdqfbtOtsNZybMknNJXkBi0PDO6vqHLvzaNp1tvr/uCmTJElaAZ4pkyRJWgFuyiRJklaAmzJJkqQV4KZMkiRpBbgpkyRJWgH/BZONROQ++bXQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting of Pearson correlation matrix for the entire data\n",
    "corr = core_data.corr(method='pearson', min_periods=1)\n",
    "corr.to_csv(proteomics_dir+sub_dir+data_name+\"_whole_proteome_correlation_timpoint2\"+\".csv\")\n",
    "core_data_corr = sns.heatmap(corr, cmap=\"Blues\")\n",
    "\n",
    "#Optional: save figure\n",
    "#row_norm.savefig(proteomics_dir+sub_dir+data_name+\"core_Data_correlation_timepoint2\"+\".png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Clustermap of all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting of clustermap of core_data with normalization across rows / for each locus tag across all samples \n",
    "row_norm = sns.clustermap(core_data,metric='correlation',robust='True',method='average',z_score = 0, cmap='Blues')\n",
    "\n",
    "#Optional: save figure\n",
    "row_norm.savefig(proteomics_dir+sub_dir+data_name+\"clustermap_row_norm_timepoint2\"+\".svg\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'FQ762_32170'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/Software/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'FQ762_32170'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-a7c9c5a518b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#WT_CRISPRi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mproteome_CRISPRi_WT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcore_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproteome_data_WT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"outer\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"_left\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mproteome_CRISPRi_WT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FQ762_32170\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#[\"FQ762_16595\",\"FQ762_26280\",\"FQ762_26985\",\"FQ762_27465\",\"FQ762_29990\",\"FQ762_30470\",\"FQ762_31255\",\"FQ762_31390\",\"FQ762_32170\"]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Software/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1767\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1768\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1770\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1964\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1965\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m    623\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no slices here, handle elsewhere\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3535\u001b[0m             \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3536\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3537\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3539\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'FQ762_32170'"
     ]
    }
   ],
   "source": [
    "#CRISPR interference specifc: \n",
    "\n",
    "proteome_data_WT = pd.read_csv(\"/Analysis_BE/MCBE_BE_data_proteome_data_samples.csv\").set_index(\"Accession\").dropna(axis = 0, how = 'all')\n",
    "proteome_data_WT = proteome_data_WT[[\"Abundances (Normalized): MC01_Proteo2 Sample\",\"Abundances (Normalized): MC02_Proteo2 Sample\", \"Abundances (Normalized): MC03_Proteo2 Sample\" ]]\n",
    "\n",
    "proteome_CRISPRi_WT = core_data.join(proteome_data_WT, how=\"outer\", lsuffix=\"_left\")\n",
    "proteome_CRISPRi_WT.loc[\"FQ762_32170\"] #[\"FQ762_16595\",\"FQ762_26280\",\"FQ762_26985\",\"FQ762_27465\",\"FQ762_29990\",\"FQ762_30470\",\"FQ762_31255\",\"FQ762_31390\",\"FQ762_32170\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4. T-Tests for each control/sample combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data structure requires that the controls and sample triplicates are in seperate dataframes. Replace column names with your own sample names\n",
    "core_data_WT = core_data[['Abundances (Normalized): MC01_Proteo2 Sample','Abundances (Normalized): MC02_Proteo2 Sample','Abundances (Normalized): MC03_Proteo2 Sample']]\n",
    "core_data_BE9b = core_data[['Abundances (Normalized): MC04_Proteo2 Sample','Abundances (Normalized): MC05_Proteo2 Sample','Abundances (Normalized): MC06_Proteo2 Sample']]\n",
    "core_data_BE18 = core_data[['Abundances (Normalized): MC07_Proteo3 Sample','Abundances (Normalized): MC08_Proteo3 Sample','Abundances (Normalized): MC09_Proteo3 Sample']]\n",
    "core_data_BE28 = core_data[['Abundances (Normalized): MC10_Proteo2 Sample','Abundances (Normalized): MC11_Proteo2 Sample','Abundances (Normalized): MC12_Proteo2 Sample']]\n",
    "\n",
    "core_data_WT = core_data[['Abundances (Normalized): MC01_Proteo2 Sample','Abundances (Normalized): MC02_Proteo2 Sample','Abundances (Normalized): MC03_Proteo2 Sample']]\n",
    "core_data_BE9b = core_data[['Abundances (Normalized): MC04_Proteo2 Sample','Abundances (Normalized): MC05_Proteo2 Sample','Abundances (Normalized): MC06_Proteo2 Sample']]\n",
    "core_data_BE18 = core_data[['Abundances (Normalized): MC07_Proteo3 Sample','Abundances (Normalized): MC08_Proteo3 Sample','Abundances (Normalized): MC09_Proteo3 Sample']]\n",
    "core_data_BE28 = core_data[['Abundances (Normalized): MC10_Proteo2 Sample','Abundances (Normalized): MC11_Proteo2 Sample','Abundances (Normalized): MC12_Proteo2 Sample']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import scipy for statistics \n",
    "import scipy.stats as stats\n",
    "\n",
    "#set pvalue to use\n",
    "pval = 0.005\n",
    "\n",
    "#create empty dataframes for results\n",
    "result_ttest_BE9b = pd.DataFrame(columns=[\"tvalue\", \"pvalue\"], index=core_data.index)\n",
    "result_ttest_BE18 = pd.DataFrame(columns=[\"tvalue\", \"pvalue\"], index=core_data.index)\n",
    "result_ttest_BE28 = pd.DataFrame(columns=[\"tvalue\", \"pvalue\"], index=core_data.index)\n",
    "\n",
    "#Perform t-tests for each locus tag\n",
    "for locus_tag in core_data.index:\n",
    "    tvalue, pvalue = stats.ttest_ind(\n",
    "        core_data_WT.loc[[locus_tag]].T, #dataframe with control\n",
    "        core_data_BE9b.loc[[locus_tag]].T) #dataframe with sample 1\n",
    "    result_ttest_BE9b[\"tvalue\"].loc[[locus_tag]] = tvalue\n",
    "    result_ttest_BE9b[\"pvalue\"].loc[[locus_tag]] = pvalue\n",
    "    \n",
    "for locus_tag in core_data.index:\n",
    "    tvalue, pvalue = stats.ttest_ind(\n",
    "        core_data_WT.loc[[locus_tag]].T, #dataframe with control\n",
    "        core_data_BE18.loc[[locus_tag]].T) #dataframe with sample 2\n",
    "    result_ttest_BE18[\"tvalue\"].loc[[locus_tag]] = tvalue\n",
    "    result_ttest_BE18[\"pvalue\"].loc[[locus_tag]] = pvalue\n",
    "\n",
    "for locus_tag in core_data.index:\n",
    "    tvalue, pvalue = stats.ttest_ind(\n",
    "        core_data_WT.loc[[locus_tag]].T, #dataframe with control\n",
    "        core_data_BE28.loc[[locus_tag]].T) #dataframe with sample 3\n",
    "    result_ttest_BE28[\"tvalue\"].loc[[locus_tag]] = tvalue\n",
    "    result_ttest_BE28[\"pvalue\"].loc[[locus_tag]] = pvalue\n",
    "\n",
    "#Optional: Save test results\n",
    "#result_ttest_BE9b.to_csv(proteomics_dir+sub_dir+data_name+\"core_data_BE9b_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE18.to_csv(proteomics_dir+sub_dir+data_name+\"core_data_BE18_time_point2_t-test_\"+str(pval)+\".csv\")\n",
    "#result_ttest_BE28.to_csv(proteomics_dir+sub_dir+data_name+\"core_data_time_BE28_point2_t-test_\"+str(pval)+\".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tvalue</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accession</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FQ762_24085</th>\n",
       "      <td>-5.5925</td>\n",
       "      <td>0.00501641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_24080</th>\n",
       "      <td>-5.05322</td>\n",
       "      <td>0.00721478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_27290</th>\n",
       "      <td>-4.48122</td>\n",
       "      <td>0.0109795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_16075</th>\n",
       "      <td>-8.10166</td>\n",
       "      <td>0.00126178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_11145</th>\n",
       "      <td>-18.937</td>\n",
       "      <td>4.58007e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_07600</th>\n",
       "      <td>1.37666</td>\n",
       "      <td>0.240649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_09630</th>\n",
       "      <td>-2.32935</td>\n",
       "      <td>0.0803096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_06395</th>\n",
       "      <td>-0.81996</td>\n",
       "      <td>0.458287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_26965</th>\n",
       "      <td>-5.08337</td>\n",
       "      <td>0.00706408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_38295</th>\n",
       "      <td>-4.43649</td>\n",
       "      <td>0.0113648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              tvalue       pvalue\n",
       "Accession                        \n",
       "FQ762_24085  -5.5925   0.00501641\n",
       "FQ762_24080 -5.05322   0.00721478\n",
       "FQ762_27290 -4.48122    0.0109795\n",
       "FQ762_16075 -8.10166   0.00126178\n",
       "FQ762_11145  -18.937  4.58007e-05\n",
       "...              ...          ...\n",
       "FQ762_07600  1.37666     0.240649\n",
       "FQ762_09630 -2.32935    0.0803096\n",
       "FQ762_06395 -0.81996     0.458287\n",
       "FQ762_26965 -5.08337   0.00706408\n",
       "FQ762_38295 -4.43649    0.0113648\n",
       "\n",
       "[2401 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_ttest_BE28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sample groups dataframes for clustermap plotting\n",
    "core_data_WT_BE9b = pd.concat([core_data_WT, core_data_BE9b],axis=1)\n",
    "core_data_WT_BE18 = pd.concat([core_data_WT, core_data_BE18],axis=1)\n",
    "core_data_WT_BE28 = pd.concat([core_data_WT, core_data_BE28],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7fce37757a90>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Clustermap of data where expression is significantly changed\n",
    "#custom_cmap =sns.light_palette(\"#A80606\",as_cmap=True)\n",
    "#custom_cmap = sns.diverging_palette(240,0,as_cmap=True, s=99, l=40)\n",
    "BE9b_ttest = sns.clustermap(core_data_WT_BE9b[core_data_WT_BE9b.index.isin(result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval].index)],metric='correlation',robust='True',method='average',z_score = 0, cmap=\"Blues\")\n",
    "BE9b_ttest.savefig(proteomics_dir+sub_dir+data_name+\"clustermap_row_norm_T-test_\" +str(pval)+\"_BE9b_timepoint2\"+\".svg\")\n",
    "BE9b_ttest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7fce2e8ad050>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BE18_ttest = sns.clustermap(core_data_WT_BE18[core_data_WT_BE18.index.isin(result_ttest_BE18[result_ttest_BE18[\"pvalue\"] < pval].index)],metric='correlation',robust='True',method='average',z_score = 0, cmap='Blues')\n",
    "BE18_ttest.savefig(proteomics_dir+sub_dir+data_name+\"clustermap_row_norm_T-test_\" +str(pval)+\"_BE18_timepoint2\"+\".svg\")\n",
    "BE18_ttest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7fce45b6db90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BE28_ttest = sns.clustermap(core_data_WT_BE28[core_data_WT_BE28.index.isin(result_ttest_BE28[result_ttest_BE28[\"pvalue\"] < pval].index)],metric='correlation',robust='True',method='average',z_score = 0, cmap='Blues')\n",
    "#BE28_ttest.savefig(proteomics_dir+sub_dir+data_name+\"clustermap_row_norm_T-test_\" + str(pval)+\"_BE28_timepoint2\"+\".svg\")\n",
    "BE28_ttest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6. COG Annotation of significantly changed locus tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotate locus tags that are significantly changed with COG Categories\n",
    "\n",
    "#annotate undetected locus tags with COG categories and descriptions\n",
    "#list of dataframes with ttest results\n",
    "df_list=[result_ttest_BE9b,result_ttest_BE18,result_ttest_BE28]\n",
    "\n",
    "for df in df_list:\n",
    "    # 1) COG categories\n",
    "    df[\"COG_category\"] = \"-\"\n",
    "    for i in df.index:\n",
    "        if i in coelicolor_COG.index:\n",
    "            df[\"COG_category\"].loc[i] =  coelicolor_COG[\"COG_category\"].loc[i]\n",
    "        else:\n",
    "            df[\"COG_category\"].loc[i] = \"not detected\" #not detected means the locus tag is not in the COG annotation\n",
    "\n",
    "    # 2) COG descriptions\n",
    "    df[\"Description\"] = \"-\"\n",
    "    for i in df[\"COG_category\"].tolist():\n",
    "        if not i == '-':\n",
    "            if len(i) == 1: #if len = 1 only one COG category\n",
    "                df[\"Description\"][df[\"COG_category\"] == i] = COG_categories[\"Description\"].loc[i]\n",
    "            elif len(i) > 1 and i != \"not detected\": #if len > 1 more than one COG category\n",
    "                descript = []\n",
    "                for l in list(i): #extract all descriptions for the categories\n",
    "                    descript.append(COG_categories[\"Description\"].loc[l])  \n",
    "                df[\"Description\"][df[\"COG_category\"] == i] = ','.join(descript)\n",
    "        else:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tvalue</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>COG_category</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accession</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FQ762_24085</th>\n",
       "      <td>-1.27417</td>\n",
       "      <td>0.271598</td>\n",
       "      <td>K</td>\n",
       "      <td>Transcription</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_24080</th>\n",
       "      <td>-1.29668</td>\n",
       "      <td>0.264484</td>\n",
       "      <td>K</td>\n",
       "      <td>Transcription</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_27290</th>\n",
       "      <td>-2.37905</td>\n",
       "      <td>0.0760682</td>\n",
       "      <td>C</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_16075</th>\n",
       "      <td>-0.64939</td>\n",
       "      <td>0.551491</td>\n",
       "      <td>C</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_11145</th>\n",
       "      <td>-0.805551</td>\n",
       "      <td>0.465662</td>\n",
       "      <td>C</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_07600</th>\n",
       "      <td>1.15124</td>\n",
       "      <td>0.313765</td>\n",
       "      <td>K</td>\n",
       "      <td>Transcription</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_09630</th>\n",
       "      <td>-3.17377</td>\n",
       "      <td>0.0337356</td>\n",
       "      <td>K</td>\n",
       "      <td>Transcription</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_06395</th>\n",
       "      <td>0.429344</td>\n",
       "      <td>0.689788</td>\n",
       "      <td>I</td>\n",
       "      <td>Lipid metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_26965</th>\n",
       "      <td>-0.984576</td>\n",
       "      <td>0.380574</td>\n",
       "      <td>T</td>\n",
       "      <td>Signal Transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_38295</th>\n",
       "      <td>-1.63906</td>\n",
       "      <td>0.176544</td>\n",
       "      <td>EGP</td>\n",
       "      <td>Amino Acid metabolis and transport,Carbohydrat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               tvalue     pvalue COG_category  \\\n",
       "Accession                                       \n",
       "FQ762_24085  -1.27417   0.271598            K   \n",
       "FQ762_24080  -1.29668   0.264484            K   \n",
       "FQ762_27290  -2.37905  0.0760682            C   \n",
       "FQ762_16075  -0.64939   0.551491            C   \n",
       "FQ762_11145 -0.805551   0.465662            C   \n",
       "...               ...        ...          ...   \n",
       "FQ762_07600   1.15124   0.313765            K   \n",
       "FQ762_09630  -3.17377  0.0337356            K   \n",
       "FQ762_06395  0.429344   0.689788            I   \n",
       "FQ762_26965 -0.984576   0.380574            T   \n",
       "FQ762_38295  -1.63906   0.176544          EGP   \n",
       "\n",
       "                                                   Description  \n",
       "Accession                                                       \n",
       "FQ762_24085                                      Transcription  \n",
       "FQ762_24080                                      Transcription  \n",
       "FQ762_27290                   Energy production and conversion  \n",
       "FQ762_16075                   Energy production and conversion  \n",
       "FQ762_11145                   Energy production and conversion  \n",
       "...                                                        ...  \n",
       "FQ762_07600                                      Transcription  \n",
       "FQ762_09630                                      Transcription  \n",
       "FQ762_06395                                   Lipid metabolism  \n",
       "FQ762_26965                                Signal Transduction  \n",
       "FQ762_38295  Amino Acid metabolis and transport,Carbohydrat...  \n",
       "\n",
       "[2401 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_ttest_BE9b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1296 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_ttest_BE9b_counts = result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "result_ttest_BE18_counts = result_ttest_BE18[result_ttest_BE18[\"pvalue\"] < pval].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "result_ttest_BE28_counts = result_ttest_BE28[result_ttest_BE28[\"pvalue\"] < pval].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "\n",
    "#locus tags that are higher expressed in sample than in control\n",
    "result_ttest_BE9b_counts_up = result_ttest_BE9b[(result_ttest_BE9b[\"pvalue\"] < pval) & (result_ttest_BE9b[\"tvalue\"] >0)].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "result_ttest_BE18_counts_up = result_ttest_BE18[(result_ttest_BE18[\"pvalue\"] < pval) & (result_ttest_BE18[\"tvalue\"] >0)].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "result_ttest_BE28_counts_up = result_ttest_BE28[(result_ttest_BE28[\"pvalue\"] < pval) & (result_ttest_BE28[\"tvalue\"] >0)].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "\n",
    "#locus tags that are lower expressed in sample than in control\n",
    "result_ttest_BE9b_counts_down = result_ttest_BE9b[(result_ttest_BE9b[\"pvalue\"] < pval) & (result_ttest_BE9b[\"tvalue\"] < 0)].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "result_ttest_BE18_counts_down = result_ttest_BE18[(result_ttest_BE18[\"pvalue\"] < pval) & (result_ttest_BE18[\"tvalue\"] < 0)].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "result_ttest_BE28_counts_down = result_ttest_BE28[(result_ttest_BE28[\"pvalue\"] < pval) & (result_ttest_BE28[\"tvalue\"] < 0)].groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n",
    "\n",
    "#result_ttest_BE9b_counts.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE9b_counts_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE18_counts.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE18_counts_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE28_counts.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE28_counts_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE9b_counts_up.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE9b_counts_up_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE18_counts_up.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE18_counts_up_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE28_counts_up.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE28_counts_up_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE9b_counts_down.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE9b_counts_down_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE18_counts_down.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE18_counts_down_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "#result_ttest_BE28_counts_down.to_csv(proteomics_dir+sub_dir+data_name+\"result_ttest_BE28_counts_down_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "\n",
    "#plotting of COG category counts of undetected locus tags\n",
    "fig, axes = plt.subplots(3,1,figsize=(17,18)) \n",
    "axes[0].set_title(\"WT vs. BE9b\")\n",
    "axes[1].set_title(\"WT vs. BE18\")\n",
    "axes[2].set_title(\"WT vs. BE28\")\n",
    "sns.barplot(ax=axes[0],x=result_ttest_BE9b_counts_down.index, y=\"Counts\", data=result_ttest_BE9b_counts_down, color=\"#457B9D\")\n",
    "sns.barplot(ax=axes[1],x=result_ttest_BE18_counts_down.index, y=\"Counts\", data=result_ttest_BE18_counts_down,color=\"#A80606\")\n",
    "sns.barplot(ax=axes[2],x=result_ttest_BE28_counts_down.index, y=\"Counts\", data=result_ttest_BE28_counts_down,color=\"#A8DADC\")\n",
    "#plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tvalue</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COG_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EP</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KLT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GK</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GM</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JM</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EH</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IQ</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tvalue  pvalue  Counts\n",
       "COG_category                        \n",
       "EP                 1       1       1\n",
       "KLT                1       1       1\n",
       "CE                 1       1       1\n",
       "CH                 1       1       1\n",
       "CO                 1       1       1\n",
       "KO                 1       1       1\n",
       "EG                 1       1       1\n",
       "GK                 1       1       1\n",
       "FG                 1       1       1\n",
       "GM                 2       2       2\n",
       "JM                 2       2       2\n",
       "EH                 3       3       3\n",
       "D                  4       4       4\n",
       "IQ                 6       6       6\n",
       "V                  7       7       7\n",
       "Q                 13      13      13\n",
       "M                 14      14      14\n",
       "L                 15      15      15\n",
       "O                 15      15      15\n",
       "H                 15      15      15\n",
       "F                 15      15      15\n",
       "P                 18      18      18\n",
       "T                 21      21      21\n",
       "K                 29      29      29\n",
       "-                 30      30      30\n",
       "I                 31      31      31\n",
       "G                 32      32      32\n",
       "J                 32      32      32\n",
       "C                 41      41      41\n",
       "E                 64      64      64\n",
       "S                 64      64      64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_ttest_BE9b_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "482 900 457\n",
      "292 225 315\n",
      "['FQ762_30015', 'FQ762_27005', 'FQ762_34365', 'FQ762_18790', 'FQ762_26415', 'FQ762_20450', 'FQ762_28290', 'FQ762_34370', 'FQ762_11130', 'FQ762_18850', 'FQ762_09860', 'FQ762_34375', 'FQ762_09920', 'FQ762_26065', 'FQ762_16125', 'FQ762_13500', 'FQ762_10620', 'FQ762_22900', 'FQ762_03370', 'FQ762_30190', 'FQ762_10210', 'FQ762_08435', 'FQ762_22220', 'FQ762_19515', 'FQ762_29340', 'FQ762_09910', 'FQ762_11135', 'FQ762_38755', 'FQ762_13040', 'FQ762_36310', 'FQ762_34350', 'FQ762_09800', 'FQ762_09790', 'FQ762_20975', 'FQ762_12635', 'FQ762_28505', 'FQ762_34360', 'FQ762_20130', 'FQ762_24945', 'FQ762_19985', 'FQ762_28265', 'FQ762_11370', 'FQ762_17190', 'FQ762_11280', 'FQ762_12935', 'FQ762_20320', 'FQ762_14820', 'FQ762_19635', 'FQ762_26055', 'FQ762_14915', 'FQ762_22000', 'FQ762_20050', 'FQ762_28255', 'FQ762_39465', 'FQ762_24620', 'FQ762_08360', 'FQ762_17030', 'FQ762_20035', 'FQ762_11385', 'FQ762_09785', 'FQ762_09695', 'FQ762_38730', 'FQ762_19645', 'FQ762_19670', 'FQ762_21095', 'FQ762_25250', 'FQ762_28515', 'FQ762_08110', 'FQ762_27970', 'FQ762_22120', 'FQ762_15315', 'FQ762_15445', 'FQ762_22390', 'FQ762_07915', 'FQ762_39370', 'FQ762_17610', 'FQ762_20485', 'FQ762_21540', 'FQ762_15880', 'FQ762_21905', 'FQ762_25255', 'FQ762_01095', 'FQ762_19385', 'FQ762_28300', 'FQ762_08995', 'FQ762_08430', 'FQ762_19560', 'FQ762_26530', 'FQ762_19795', 'FQ762_10395', 'FQ762_10900', 'FQ762_21965', 'FQ762_19680', 'FQ762_18250', 'FQ762_39120', 'FQ762_23230', 'FQ762_20145', 'FQ762_25275', 'FQ762_15665', 'FQ762_15805', 'FQ762_21645', 'FQ762_16200', 'FQ762_05460', 'FQ762_14295', 'FQ762_21235', 'FQ762_23555', 'FQ762_24320', 'FQ762_31155', 'FQ762_07970', 'FQ762_06205', 'FQ762_22225', 'FQ762_24395', 'FQ762_07510', 'FQ762_05455', 'FQ762_38725', 'FQ762_24485', 'FQ762_25395', 'FQ762_14205', 'FQ762_27815', 'FQ762_13465', 'FQ762_24065', 'FQ762_31525', 'FQ762_25460', 'FQ762_10795', 'FQ762_17555', 'FQ762_28905', 'FQ762_22025', 'FQ762_24340', 'FQ762_34275', 'FQ762_20355', 'FQ762_25570', 'FQ762_07175', 'FQ762_20970', 'FQ762_24450', 'FQ762_06995', 'FQ762_29495', 'FQ762_17545', 'FQ762_14235', 'FQ762_21470', 'FQ762_06150', 'FQ762_26080', 'FQ762_12230', 'FQ762_33410', 'FQ762_25385', 'FQ762_19815', 'FQ762_07885', 'FQ762_26040', 'FQ762_06215', 'FQ762_25360', 'FQ762_38580', 'FQ762_07550', 'FQ762_21945', 'FQ762_24345', 'FQ762_05870', 'FQ762_10390', 'FQ762_33465', 'FQ762_17680', 'FQ762_09880', 'FQ762_06195', 'FQ762_14590', 'FQ762_24490', 'FQ762_24330', 'FQ762_26700', 'FQ762_28200', 'FQ762_14140', 'FQ762_06085', 'FQ762_09335', 'FQ762_18420', 'FQ762_20025', 'FQ762_24820', 'FQ762_27210', 'FQ762_07515', 'FQ762_28700', 'FQ762_36655', 'FQ762_08420', 'FQ762_31530', 'FQ762_16120', 'FQ762_18890', 'FQ762_21870', 'FQ762_24315', 'FQ762_06210', 'FQ762_11355', 'FQ762_14855', 'FQ762_18765', 'FQ762_23225', 'FQ762_26875', 'FQ762_11140', 'FQ762_19785', 'FQ762_23315', 'FQ762_18835', 'FQ762_07920', 'FQ762_16750', 'FQ762_07525', 'FQ762_22680', 'FQ762_30110', 'FQ762_29575', 'FQ762_29580', 'FQ762_26570', 'FQ762_13035', 'FQ762_24370', 'FQ762_03190', 'FQ762_26140', 'FQ762_12725', 'FQ762_16105', 'FQ762_06775', 'FQ762_19710', 'FQ762_11740', 'FQ762_16130', 'FQ762_24045', 'FQ762_26860', 'FQ762_38435', 'FQ762_37005', 'FQ762_30900', 'FQ762_16100', 'FQ762_25705', 'FQ762_21920', 'FQ762_13495', 'FQ762_22155', 'FQ762_19615', 'FQ762_29845', 'FQ762_15615', 'FQ762_03650', 'FQ762_30230', 'FQ762_24060', 'FQ762_02000', 'FQ762_34750', 'FQ762_06290', 'FQ762_11680', 'FQ762_01600', 'FQ762_37665', 'FQ762_07590', 'FQ762_10305', 'FQ762_16195', 'FQ762_10950', 'FQ762_14920', 'FQ762_34535', 'FQ762_14970', 'FQ762_21105', 'FQ762_28000', 'FQ762_22840', 'FQ762_09545', 'FQ762_07665', 'FQ762_25330', 'FQ762_28975', 'FQ762_22780', 'FQ762_26045', 'FQ762_21975', 'FQ762_12825', 'FQ762_17175', 'FQ762_22210', 'FQ762_25480', 'FQ762_31490', 'FQ762_13145', 'FQ762_24965', 'FQ762_29110', 'FQ762_37970', 'FQ762_19900', 'FQ762_13455', 'FQ762_32275', 'FQ762_28815', 'FQ762_28570', 'FQ762_10820', 'FQ762_09515', 'FQ762_16375', 'FQ762_36490', 'FQ762_12640', 'FQ762_19650', 'FQ762_05445', 'FQ762_14445', 'FQ762_02455', 'FQ762_14480', 'FQ762_38305', 'FQ762_35395', 'FQ762_14095', 'FQ762_30055', 'FQ762_12185', 'FQ762_10315', 'FQ762_03445', 'FQ762_07035', 'FQ762_18725', 'FQ762_37250', 'FQ762_15810', 'FQ762_31535', 'FQ762_09395', 'FQ762_22315', 'FQ762_32135', 'FQ762_26165', 'FQ762_05390', 'FQ762_31110', 'FQ762_38925', 'FQ762_05815', 'FQ762_20515']\n",
      "166 166\n"
     ]
    }
   ],
   "source": [
    "#check for overlaps between significant locus tags\n",
    "BE9b_BE18_ttest_shared = []\n",
    "BE9b_BE28_ttest_shared = []\n",
    "BE18_BE28_ttest_shared = []\n",
    "BE9b_BE18_BE28_ttest_shared = []\n",
    "\n",
    "for i in result_ttest_BE28[result_ttest_BE28[\"pvalue\"] < pval].index:\n",
    "    if any(i in s for s in result_ttest_BE18[result_ttest_BE18[\"pvalue\"] < pval].index):\n",
    "        BE18_BE28_ttest_shared.append(i)\n",
    "    if any(i in s for s in result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval].index):\n",
    "        BE9b_BE28_ttest_shared.append(i)\n",
    "for i in result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval].index:\n",
    "    if any(i in s for s in result_ttest_BE18[result_ttest_BE18[\"pvalue\"] < pval].index):\n",
    "        BE9b_BE18_ttest_shared.append(i)\n",
    "for i in BE9b_BE18_ttest_shared:\n",
    "    if any(i in s for s in BE9b_BE28_ttest_shared) and any(i in l for l in BE18_BE28_ttest_shared):\n",
    "        BE9b_BE18_BE28_ttest_shared.append(i)\n",
    "\n",
    "print(len(result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval].index),len(result_ttest_BE18[result_ttest_BE18[\"pvalue\"] < pval].index),len(result_ttest_BE28[result_ttest_BE28[\"pvalue\"] < pval].index))\n",
    "print(len(BE9b_BE18_ttest_shared), len(BE9b_BE28_ttest_shared), len(BE18_BE28_ttest_shared))\n",
    "\n",
    "\n",
    "#venn2([result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval && ].index[,set(BE9b_BE28_ttest_shared),set(BE18_BE28_ttest_shared)], set_labels=(\"BE9b \",\"BE18\",\"BE28\"))\n",
    "\n",
    "#print(set(result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval].index) & set(result_ttest_BE18[result_ttest_BE18[\"pvalue\"] < pval].index) & set(result_ttest_BE28[result_ttest_BE28[\"pvalue\"] < pval].index))\n",
    "print(BE9b_BE18_ttest_shared)\n",
    "print(len(BE9b_BE18_BE28_ttest_shared), len(set(BE9b_BE18_BE28_ttest_shared)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>tvalue</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>COG_category</th>\n",
       "      <th>Description</th>\n",
       "      <th>tvalue</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>COG_category</th>\n",
       "      <th>Description</th>\n",
       "      <th>tvalue</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>COG_category</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accession</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>FQ762_30015</th>\n",
       "      <td>-12.5495</td>\n",
       "      <td>0.000231994</td>\n",
       "      <td>F</td>\n",
       "      <td>Nucleotide metabolism and transport</td>\n",
       "      <td>-10.9538</td>\n",
       "      <td>0.000394577</td>\n",
       "      <td>F</td>\n",
       "      <td>Nucleotide metabolism and transport</td>\n",
       "      <td>-11.1673</td>\n",
       "      <td>0.000366014</td>\n",
       "      <td>F</td>\n",
       "      <td>Nucleotide metabolism and transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_27005</th>\n",
       "      <td>-16.5966</td>\n",
       "      <td>7.72031e-05</td>\n",
       "      <td>F</td>\n",
       "      <td>Nucleotide metabolism and transport</td>\n",
       "      <td>-25.7974</td>\n",
       "      <td>1.34124e-05</td>\n",
       "      <td>F</td>\n",
       "      <td>Nucleotide metabolism and transport</td>\n",
       "      <td>-14.6407</td>\n",
       "      <td>0.000126622</td>\n",
       "      <td>F</td>\n",
       "      <td>Nucleotide metabolism and transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_34365</th>\n",
       "      <td>-20.4325</td>\n",
       "      <td>3.38813e-05</td>\n",
       "      <td>G</td>\n",
       "      <td>Carbohydrate metabolism and transport</td>\n",
       "      <td>-28.1072</td>\n",
       "      <td>9.53295e-06</td>\n",
       "      <td>G</td>\n",
       "      <td>Carbohydrate metabolism and transport</td>\n",
       "      <td>-34.8695</td>\n",
       "      <td>4.03637e-06</td>\n",
       "      <td>G</td>\n",
       "      <td>Carbohydrate metabolism and transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_20450</th>\n",
       "      <td>-9.05532</td>\n",
       "      <td>0.000824191</td>\n",
       "      <td>E</td>\n",
       "      <td>Amino Acid metabolis and transport</td>\n",
       "      <td>-38.6562</td>\n",
       "      <td>2.6751e-06</td>\n",
       "      <td>E</td>\n",
       "      <td>Amino Acid metabolis and transport</td>\n",
       "      <td>-15.1211</td>\n",
       "      <td>0.000111497</td>\n",
       "      <td>E</td>\n",
       "      <td>Amino Acid metabolis and transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_34370</th>\n",
       "      <td>-7.23445</td>\n",
       "      <td>0.00193704</td>\n",
       "      <td>G</td>\n",
       "      <td>Carbohydrate metabolism and transport</td>\n",
       "      <td>-13.4986</td>\n",
       "      <td>0.000174288</td>\n",
       "      <td>G</td>\n",
       "      <td>Carbohydrate metabolism and transport</td>\n",
       "      <td>-15.7507</td>\n",
       "      <td>9.4923e-05</td>\n",
       "      <td>G</td>\n",
       "      <td>Carbohydrate metabolism and transport</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_09395</th>\n",
       "      <td>-10.1879</td>\n",
       "      <td>0.000522898</td>\n",
       "      <td>S</td>\n",
       "      <td>Function Unknown</td>\n",
       "      <td>-8.30954</td>\n",
       "      <td>0.0011456</td>\n",
       "      <td>S</td>\n",
       "      <td>Function Unknown</td>\n",
       "      <td>-8.42003</td>\n",
       "      <td>0.00108924</td>\n",
       "      <td>S</td>\n",
       "      <td>Function Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_22315</th>\n",
       "      <td>-18.7021</td>\n",
       "      <td>4.81233e-05</td>\n",
       "      <td>L</td>\n",
       "      <td>Replication and repair</td>\n",
       "      <td>-24.3138</td>\n",
       "      <td>1.6977e-05</td>\n",
       "      <td>L</td>\n",
       "      <td>Replication and repair</td>\n",
       "      <td>-12.3188</td>\n",
       "      <td>0.000249481</td>\n",
       "      <td>L</td>\n",
       "      <td>Replication and repair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_32135</th>\n",
       "      <td>6.99303</td>\n",
       "      <td>0.00220033</td>\n",
       "      <td>E</td>\n",
       "      <td>Amino Acid metabolis and transport</td>\n",
       "      <td>6.11202</td>\n",
       "      <td>0.00362767</td>\n",
       "      <td>E</td>\n",
       "      <td>Amino Acid metabolis and transport</td>\n",
       "      <td>6.11375</td>\n",
       "      <td>0.00362388</td>\n",
       "      <td>E</td>\n",
       "      <td>Amino Acid metabolis and transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_05390</th>\n",
       "      <td>-5.92724</td>\n",
       "      <td>0.00405992</td>\n",
       "      <td>C</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "      <td>-13.3379</td>\n",
       "      <td>0.000182685</td>\n",
       "      <td>C</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "      <td>-6.84198</td>\n",
       "      <td>0.00238767</td>\n",
       "      <td>C</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_20515</th>\n",
       "      <td>35.0592</td>\n",
       "      <td>3.94996e-06</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-7.97579</td>\n",
       "      <td>0.00133924</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>28.5139</td>\n",
       "      <td>9.00274e-06</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              tvalue       pvalue COG_category  \\\n",
       "Accession                                        \n",
       "FQ762_30015 -12.5495  0.000231994            F   \n",
       "FQ762_27005 -16.5966  7.72031e-05            F   \n",
       "FQ762_34365 -20.4325  3.38813e-05            G   \n",
       "FQ762_20450 -9.05532  0.000824191            E   \n",
       "FQ762_34370 -7.23445   0.00193704            G   \n",
       "...              ...          ...          ...   \n",
       "FQ762_09395 -10.1879  0.000522898            S   \n",
       "FQ762_22315 -18.7021  4.81233e-05            L   \n",
       "FQ762_32135  6.99303   0.00220033            E   \n",
       "FQ762_05390 -5.92724   0.00405992            C   \n",
       "FQ762_20515  35.0592  3.94996e-06            -   \n",
       "\n",
       "                                       Description   tvalue       pvalue  \\\n",
       "Accession                                                                  \n",
       "FQ762_30015    Nucleotide metabolism and transport -10.9538  0.000394577   \n",
       "FQ762_27005    Nucleotide metabolism and transport -25.7974  1.34124e-05   \n",
       "FQ762_34365  Carbohydrate metabolism and transport -28.1072  9.53295e-06   \n",
       "FQ762_20450     Amino Acid metabolis and transport -38.6562   2.6751e-06   \n",
       "FQ762_34370  Carbohydrate metabolism and transport -13.4986  0.000174288   \n",
       "...                                            ...      ...          ...   \n",
       "FQ762_09395                       Function Unknown -8.30954    0.0011456   \n",
       "FQ762_22315                 Replication and repair -24.3138   1.6977e-05   \n",
       "FQ762_32135     Amino Acid metabolis and transport  6.11202   0.00362767   \n",
       "FQ762_05390       Energy production and conversion -13.3379  0.000182685   \n",
       "FQ762_20515                                      - -7.97579   0.00133924   \n",
       "\n",
       "            COG_category                            Description   tvalue  \\\n",
       "Accession                                                                  \n",
       "FQ762_30015            F    Nucleotide metabolism and transport -11.1673   \n",
       "FQ762_27005            F    Nucleotide metabolism and transport -14.6407   \n",
       "FQ762_34365            G  Carbohydrate metabolism and transport -34.8695   \n",
       "FQ762_20450            E     Amino Acid metabolis and transport -15.1211   \n",
       "FQ762_34370            G  Carbohydrate metabolism and transport -15.7507   \n",
       "...                  ...                                    ...      ...   \n",
       "FQ762_09395            S                       Function Unknown -8.42003   \n",
       "FQ762_22315            L                 Replication and repair -12.3188   \n",
       "FQ762_32135            E     Amino Acid metabolis and transport  6.11375   \n",
       "FQ762_05390            C       Energy production and conversion -6.84198   \n",
       "FQ762_20515            -                                      -  28.5139   \n",
       "\n",
       "                  pvalue COG_category                            Description  \n",
       "Accession                                                                     \n",
       "FQ762_30015  0.000366014            F    Nucleotide metabolism and transport  \n",
       "FQ762_27005  0.000126622            F    Nucleotide metabolism and transport  \n",
       "FQ762_34365  4.03637e-06            G  Carbohydrate metabolism and transport  \n",
       "FQ762_20450  0.000111497            E     Amino Acid metabolis and transport  \n",
       "FQ762_34370   9.4923e-05            G  Carbohydrate metabolism and transport  \n",
       "...                  ...          ...                                    ...  \n",
       "FQ762_09395   0.00108924            S                       Function Unknown  \n",
       "FQ762_22315  0.000249481            L                 Replication and repair  \n",
       "FQ762_32135   0.00362388            E     Amino Acid metabolis and transport  \n",
       "FQ762_05390   0.00238767            C       Energy production and conversion  \n",
       "FQ762_20515  9.00274e-06            -                                      -  \n",
       "\n",
       "[166 rows x 12 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttests_shared = pd.concat([result_ttest_BE9b, result_ttest_BE18, result_ttest_BE28],axis=1)\n",
    "ttests_shared[ttests_shared.index.isin(BE9b_BE18_BE28_ttest_shared)]\n",
    "#ttests_shared[ttests_shared.index.isin(BE9b_BE18_BE28_ttest_shared)].to_csv(proteomics_dir+sub_dir+data_name+\"ttests_shared_significant_change_t2_t-test_\"+ str(pval)+\".csv\")\n",
    "#sns.barplot(x=result_ttest_BE9b_counts_down.index, y=\"Counts\", data=result_ttest_BE9b_counts_down)\n",
    "ttests_shared[ttests_shared.index.isin(BE9b_BE18_BE28_ttest_shared)] #.groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BE9b:Out of  482 significantly changed locus tags,  3 have SNPs in or around the CDS.\n",
      "BE18:Out of  900 significantly changed locus tags,  1 have SNPs in or around the CDS.\n",
      "BE28:Out of  457 significantly changed locus tags,  2 have SNPs in or around the CDS.\n"
     ]
    }
   ],
   "source": [
    "##Extra: Check if significantly changed locus tags were edited\n",
    "BE28_SNPs = pd.read_csv(\"cwi100_strict_results.csv\",sep=\";\")\n",
    "BE28_SNPs[\"gene\"].replace('→','', regex=True, inplace=True)\n",
    "BE28_SNPs[\"gene\"].replace('←','', regex=True, inplace=True)\n",
    "\n",
    "BE18_SNPs = pd.read_csv(\"cwi99_strict_results.csv\",sep=\";\")\n",
    "BE18_SNPs[\"gene\"].replace('→','', regex=True, inplace=True)\n",
    "BE18_SNPs[\"gene\"].replace('←','', regex=True, inplace=True)\n",
    "\n",
    "BE9b_SNPs = pd.read_csv(\"cwi98_strict_results.csv\",sep=\";\")\n",
    "BE9b_SNPs[\"gene\"].replace('→','', regex=True, inplace=True)\n",
    "BE9b_SNPs[\"gene\"].replace('←','', regex=True, inplace=True)\n",
    "\n",
    "proteome_BE28_edited = []\n",
    "proteome_BE18_edited = []\n",
    "proteome_BE9b_edited = []\n",
    "\n",
    "for i in result_ttest_BE28[result_ttest_BE28[\"pvalue\"] < pval].index:\n",
    "    if any(i in s for s in BE28_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE28_edited.append(i)\n",
    "\n",
    "for i in result_ttest_BE18[result_ttest_BE18[\"pvalue\"] < pval].index:\n",
    "    if any(i in s for s in BE18_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE18_edited.append(i)\n",
    "        \n",
    "for i in result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval].index:\n",
    "    if any(i in s for s in BE9b_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE9b_edited.append(i)\n",
    "\n",
    "print(\"BE9b:Out of \" , len(result_ttest_BE9b[result_ttest_BE9b[\"pvalue\"] < pval].index), \"significantly changed locus tags, \" , len(proteome_BE9b_edited), \"have SNPs in or around the CDS.\")\n",
    "print(\"BE18:Out of \" , len(result_ttest_BE18[result_ttest_BE18[\"pvalue\"] < pval].index), \"significantly changed locus tags, \" , len(proteome_BE18_edited), \"have SNPs in or around the CDS.\")\n",
    "print(\"BE28:Out of \" , len(result_ttest_BE28[result_ttest_BE28[\"pvalue\"] < pval].index), \"significantly changed locus tags, \" , len(proteome_BE28_edited), \"have SNPs in or around the CDS.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abundances (Normalized): MC01_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC02_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC03_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC04_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC05_Proteo2 Sample</th>\n",
       "      <th>Abundances (Normalized): MC06_Proteo2 Sample</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accession</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>FQ762_24085</th>\n",
       "      <td>8.846772e+08</td>\n",
       "      <td>7.813055e+08</td>\n",
       "      <td>8.332682e+08</td>\n",
       "      <td>8.532477e+08</td>\n",
       "      <td>8.696870e+08</td>\n",
       "      <td>9.037809e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_24080</th>\n",
       "      <td>7.451347e+08</td>\n",
       "      <td>6.614162e+08</td>\n",
       "      <td>6.943140e+08</td>\n",
       "      <td>7.134240e+08</td>\n",
       "      <td>7.314466e+08</td>\n",
       "      <td>7.728698e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_27290</th>\n",
       "      <td>6.497865e+08</td>\n",
       "      <td>5.853108e+08</td>\n",
       "      <td>6.147268e+08</td>\n",
       "      <td>6.472840e+08</td>\n",
       "      <td>6.723428e+08</td>\n",
       "      <td>6.844034e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_16075</th>\n",
       "      <td>7.216484e+08</td>\n",
       "      <td>6.418126e+08</td>\n",
       "      <td>7.056409e+08</td>\n",
       "      <td>6.821330e+08</td>\n",
       "      <td>7.251131e+08</td>\n",
       "      <td>7.157364e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_11145</th>\n",
       "      <td>3.558141e+08</td>\n",
       "      <td>3.237328e+08</td>\n",
       "      <td>3.402384e+08</td>\n",
       "      <td>3.383388e+08</td>\n",
       "      <td>3.583782e+08</td>\n",
       "      <td>3.494747e+08</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_07600</th>\n",
       "      <td>3.521366e+06</td>\n",
       "      <td>9.081316e+06</td>\n",
       "      <td>6.395035e+06</td>\n",
       "      <td>4.218836e+06</td>\n",
       "      <td>4.506184e+06</td>\n",
       "      <td>4.706812e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_09630</th>\n",
       "      <td>5.075202e+06</td>\n",
       "      <td>5.741530e+06</td>\n",
       "      <td>4.722125e+06</td>\n",
       "      <td>6.664806e+06</td>\n",
       "      <td>8.936869e+06</td>\n",
       "      <td>7.126178e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_06395</th>\n",
       "      <td>8.580000e+05</td>\n",
       "      <td>5.779918e+05</td>\n",
       "      <td>6.084867e+05</td>\n",
       "      <td>4.915512e+05</td>\n",
       "      <td>6.014825e+05</td>\n",
       "      <td>7.910082e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_26965</th>\n",
       "      <td>1.267609e+06</td>\n",
       "      <td>1.238311e+06</td>\n",
       "      <td>1.213512e+06</td>\n",
       "      <td>1.437787e+06</td>\n",
       "      <td>1.271606e+06</td>\n",
       "      <td>1.213706e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FQ762_38295</th>\n",
       "      <td>8.682455e+07</td>\n",
       "      <td>8.688471e+07</td>\n",
       "      <td>8.342011e+07</td>\n",
       "      <td>9.537679e+07</td>\n",
       "      <td>9.322721e+07</td>\n",
       "      <td>8.502474e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Abundances (Normalized): MC01_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.846772e+08   \n",
       "FQ762_24080                                  7.451347e+08   \n",
       "FQ762_27290                                  6.497865e+08   \n",
       "FQ762_16075                                  7.216484e+08   \n",
       "FQ762_11145                                  3.558141e+08   \n",
       "...                                                   ...   \n",
       "FQ762_07600                                  3.521366e+06   \n",
       "FQ762_09630                                  5.075202e+06   \n",
       "FQ762_06395                                  8.580000e+05   \n",
       "FQ762_26965                                  1.267609e+06   \n",
       "FQ762_38295                                  8.682455e+07   \n",
       "\n",
       "             Abundances (Normalized): MC02_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  7.813055e+08   \n",
       "FQ762_24080                                  6.614162e+08   \n",
       "FQ762_27290                                  5.853108e+08   \n",
       "FQ762_16075                                  6.418126e+08   \n",
       "FQ762_11145                                  3.237328e+08   \n",
       "...                                                   ...   \n",
       "FQ762_07600                                  9.081316e+06   \n",
       "FQ762_09630                                  5.741530e+06   \n",
       "FQ762_06395                                  5.779918e+05   \n",
       "FQ762_26965                                  1.238311e+06   \n",
       "FQ762_38295                                  8.688471e+07   \n",
       "\n",
       "             Abundances (Normalized): MC03_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.332682e+08   \n",
       "FQ762_24080                                  6.943140e+08   \n",
       "FQ762_27290                                  6.147268e+08   \n",
       "FQ762_16075                                  7.056409e+08   \n",
       "FQ762_11145                                  3.402384e+08   \n",
       "...                                                   ...   \n",
       "FQ762_07600                                  6.395035e+06   \n",
       "FQ762_09630                                  4.722125e+06   \n",
       "FQ762_06395                                  6.084867e+05   \n",
       "FQ762_26965                                  1.213512e+06   \n",
       "FQ762_38295                                  8.342011e+07   \n",
       "\n",
       "             Abundances (Normalized): MC04_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.532477e+08   \n",
       "FQ762_24080                                  7.134240e+08   \n",
       "FQ762_27290                                  6.472840e+08   \n",
       "FQ762_16075                                  6.821330e+08   \n",
       "FQ762_11145                                  3.383388e+08   \n",
       "...                                                   ...   \n",
       "FQ762_07600                                  4.218836e+06   \n",
       "FQ762_09630                                  6.664806e+06   \n",
       "FQ762_06395                                  4.915512e+05   \n",
       "FQ762_26965                                  1.437787e+06   \n",
       "FQ762_38295                                  9.537679e+07   \n",
       "\n",
       "             Abundances (Normalized): MC05_Proteo2 Sample  \\\n",
       "Accession                                                   \n",
       "FQ762_24085                                  8.696870e+08   \n",
       "FQ762_24080                                  7.314466e+08   \n",
       "FQ762_27290                                  6.723428e+08   \n",
       "FQ762_16075                                  7.251131e+08   \n",
       "FQ762_11145                                  3.583782e+08   \n",
       "...                                                   ...   \n",
       "FQ762_07600                                  4.506184e+06   \n",
       "FQ762_09630                                  8.936869e+06   \n",
       "FQ762_06395                                  6.014825e+05   \n",
       "FQ762_26965                                  1.271606e+06   \n",
       "FQ762_38295                                  9.322721e+07   \n",
       "\n",
       "             Abundances (Normalized): MC06_Proteo2 Sample  \n",
       "Accession                                                  \n",
       "FQ762_24085                                  9.037809e+08  \n",
       "FQ762_24080                                  7.728698e+08  \n",
       "FQ762_27290                                  6.844034e+08  \n",
       "FQ762_16075                                  7.157364e+08  \n",
       "FQ762_11145                                  3.494747e+08  \n",
       "...                                                   ...  \n",
       "FQ762_07600                                  4.706812e+06  \n",
       "FQ762_09630                                  7.126178e+06  \n",
       "FQ762_06395                                  7.910082e+05  \n",
       "FQ762_26965                                  1.213706e+06  \n",
       "FQ762_38295                                  8.502474e+07  \n",
       "\n",
       "[2401 rows x 6 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_data_WT_BE9b.iloc[: , :6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try to plot volcano plot, Log2(Avg(cases)/Avg(controls)) on x axis,  -log10 of the P-value.\n",
    "volcano_BE9b = result_ttest_BE9b\n",
    "volcano_BE9b[\"-log10 Pvalue\"] = np.log10(volcano_BE9b[\"pvalue\"].astype('float64'))\n",
    "volcano_BE9b[\"-log10 Pvalue\"] *= -1\n",
    "volcano_BE9b[\"fold change\"] = np.log2(core_data_WT_BE9b[core_data_WT_BE9b.index.isin(volcano_BE9b.index)].iloc[: , 3:6].mean(axis=1)/core_data_WT_BE9b[core_data_WT_BE9b.index.isin(volcano_BE9b.index)].iloc[: , :3].mean(axis=1))\n",
    "\n",
    "volcano_BE18 = result_ttest_BE18\n",
    "volcano_BE18[\"-log10 Pvalue\"] = np.log10(volcano_BE18[\"pvalue\"].astype('float64'))\n",
    "volcano_BE18[\"fold change\"] = np.log2(core_data_WT_BE18[core_data_WT_BE18.index.isin(volcano_BE18.index)].iloc[: , 3:6].mean(axis=1)/core_data_WT_BE18[core_data_WT_BE18.index.isin(volcano_BE18.index)].iloc[: , :3].mean(axis=1))\n",
    "volcano_BE18[\"-log10 Pvalue\"] *= -1\n",
    "\n",
    "volcano_BE28 = result_ttest_BE28\n",
    "volcano_BE28[\"-log10 Pvalue\"] = np.log10(volcano_BE28[\"pvalue\"].astype('float64'))\n",
    "volcano_BE28[\"fold change\"] = np.log2(core_data_WT_BE28[core_data_WT_BE28.index.isin(volcano_BE28.index)].iloc[: , 3:6].mean(axis=1)/core_data_WT_BE28[core_data_WT_BE28.index.isin(volcano_BE28.index)].iloc[: , :3].mean(axis=1))\n",
    "volcano_BE28[\"-log10 Pvalue\"] *= -1\n",
    "\n",
    "volcano_BE9b.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE9b_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE18.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE18_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE28.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE28_time_point2_t-test_\"+ str(pval)+\".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3, figsize=(20,4))\n",
    "\n",
    "#sns.scatterplot(ax=axes[0],x=volcano_BE9b[\"fold change\"][(volcano_BE9b[\"fold change\"]<(-np.log2(2))) |(volcano_BE9b[\"fold change\"]>(np.log2(2)))] , y=volcano_BE9b[\"-log10 Pvalue\"][(volcano_BE9b[\"-log10 Pvalue\"]>(-np.log10(pval)))], data=volcano_BE9b, color=\"red\")\n",
    "sns.scatterplot(ax=axes[0],x=\"fold change\", y=\"-log10 Pvalue\", data=volcano_BE28)\n",
    "axes[0].axhline((-np.log10(pval)), color='blue', ls='--', lw=2)\n",
    "axes[0].axvline((-np.log2(2)), color='blue', ls='--', lw=2)\n",
    "axes[0].axvline((np.log2(2)), color='blue', ls='--', lw=2)\n",
    "\n",
    "sns.scatterplot(ax=axes[1],x=\"fold change\", y=\"-log10 Pvalue\", data=volcano_BE18)\n",
    "axes[1].axhline((-np.log10(pval)), color='blue', ls='--', lw=2)\n",
    "axes[1].axvline((-np.log2(2)), color='blue', ls='--', lw=2)\n",
    "axes[1].axvline((np.log2(2)), color='blue', ls='--', lw=2)\n",
    "\n",
    "sns.scatterplot(ax=axes[2],x=\"fold change\", y=\"-log10 Pvalue\", data=volcano_BE28)\n",
    "axes[2].axhline((-np.log10(pval)), color='blue', ls='--', lw=2)\n",
    "axes[2].axvline((-np.log2(2)), color='blue', ls='--', lw=2)\n",
    "axes[2].axvline((np.log2(2)), color='blue', ls='--', lw=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "volcano_BE9b_up = volcano_BE9b[((volcano_BE9b[\"fold change\"]>1))&(volcano_BE9b[\"-log10 Pvalue\"]>(-np.log10(pval)))]\n",
    "volcano_BE9b_down = volcano_BE9b[((volcano_BE9b[\"fold change\"]<-1))&(volcano_BE9b[\"-log10 Pvalue\"]>(-np.log10(pval)))]\n",
    "\n",
    "volcano_BE9b_up.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE9b_fold_up_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE9b_down.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE9b_fold_down_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE9b_up.groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"}).to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE9b_fold_up_counts_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE9b_down.groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"}).to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE9b_fold_down_counts_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "\n",
    "volcano_BE18_up = volcano_BE18[((volcano_BE18[\"fold change\"]>1))&(volcano_BE18[\"-log10 Pvalue\"]>(-np.log10(pval)))]\n",
    "volcano_BE18_down = volcano_BE18[((volcano_BE18[\"fold change\"]<-1))&(volcano_BE18[\"-log10 Pvalue\"]>(-np.log10(pval)))]\n",
    "\n",
    "volcano_BE18_up.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE18_fold_up_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE18_down.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE18_fold_down_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE18_up.groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"}).to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE18_fold_up_counts_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE18_down.groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"}).to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE18_fold_down_counts_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "\n",
    "volcano_BE28_up = volcano_BE28[((volcano_BE28[\"fold change\"]>1))&(volcano_BE28[\"-log10 Pvalue\"]>(-np.log10(pval)))]\n",
    "volcano_BE28_down = volcano_BE28[((volcano_BE28[\"fold change\"]<-1))&(volcano_BE28[\"-log10 Pvalue\"]>(-np.log10(pval)))]\n",
    "\n",
    "volcano_BE28_up.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE28_fold_up_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE28_down.to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE28_fold_down_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE28_up.groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"}).to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE28_fold_up_counts_time_point2_t-test_\"+ str(pval)+\".csv\")\n",
    "volcano_BE28_down.groupby(\"COG_category\").count().sort_values(by=['Description']).rename(columns={\"Description\": \"Counts\"}).to_csv(proteomics_dir+sub_dir+data_name+\"volcano_data_BE28_fold_down_counts_time_point2_t-test_\"+ str(pval)+\".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "##NEW SNP Matching based on transcriptomics approach\n",
    "BE9b_SNPs = pd.read_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/NGS_Data/cwi98-strict/cwi98_strict_results_no_intergenic_no_genenames.csv\")\n",
    "BE18_SNPs = pd.read_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/NGS_Data/cwi99-strict/cwi99_strict_results_no_intergenic_no_genenames.csv\")\n",
    "BE28_SNPs = pd.read_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/NGS_Data/cwi100-strict/cwi100_strict_results_no_intergenic_no_genenames.csv\")\n",
    "\n",
    "volcano_BE9b = pd.read_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/OMICS_Data/Proteomics/Analysis_BE/MCBE_BE_datavolcano_data_BE9b_time_point2_t-test_0.005.csv\").set_index(\"Accession\")\n",
    "volcano_BE18 = pd.read_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/OMICS_Data/Proteomics/Analysis_BE/MCBE_BE_datavolcano_data_BE18_time_point2_t-test_0.005.csv\").set_index(\"Accession\")\n",
    "volcano_BE28 = pd.read_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/OMICS_Data/Proteomics/Analysis_BE/MCBE_BE_datavolcano_data_BE28_time_point2_t-test_0.005.csv\").set_index(\"Accession\")\n",
    "\n",
    "##matching on locustags\n",
    "#data for all targeted locustags\n",
    "\n",
    "locus_tags = (\"FQ762_00575\",\"FQ762_00875\",\"FQ762_01290\",\"FQ762_02415\",\"FQ762_03740\",\"FQ762_06065\",\"FQ762_09505\",\"FQ762_13840\",\"FQ762_14260\",\"FQ762_16595\",\"FQ762_26280\",\"FQ762_26985\",\"FQ762_27465\",\"FQ762_29990\",\"FQ762_30470\",\"FQ762_31255\",\"FQ762_31390\",\"FQ762_32170\",\"FQ762_32415\",\"FQ762_33215\",\"FQ762_34460\",\"FQ762_34870\",\"FQ762_35220\",\"FQ762_35750\",\"FQ762_37095\",\"FQ762_38505\",\"FQ762_39600\",\"FQ762_37265\")\n",
    "\n",
    "BE9b_WT_locustags = volcano_BE9b[volcano_BE9b.index.isin(locus_tags)]\n",
    "BE18_WT_locustags = volcano_BE18[volcano_BE18.index.isin(locus_tags)]\n",
    "BE28_WT_locustags = volcano_BE28[volcano_BE28.index.isin(locus_tags)]\n",
    "\n",
    "\n",
    "BE9b_SNPs[\"gene\"] = BE9b_SNPs[\"gene\"].str.strip()\n",
    "BE9b_SNPs_reindex = BE9b_SNPs.set_index(\"gene\")\n",
    "BE9b_WT_locustags_SNPs = BE9b_WT_locustags.merge(BE9b_SNPs_reindex[\"annotation\"], how='left', left_index=True, right_index=True)\n",
    "\n",
    "BE18_SNPs[\"gene\"] = BE18_SNPs[\"gene\"].str.strip()\n",
    "BE18_SNPs_reindex = BE18_SNPs.set_index(\"gene\")\n",
    "BE18_WT_locustags_SNPs = BE18_WT_locustags.merge(BE18_SNPs_reindex[\"annotation\"], how='left', left_index=True, right_index=True)\n",
    "\n",
    "BE28_SNPs[\"gene\"] = BE28_SNPs[\"gene\"].str.strip()\n",
    "BE28_SNPs_reindex = BE28_SNPs.set_index(\"gene\")\n",
    "BE28_WT_locustags_SNPs = BE28_WT_locustags.merge(BE28_SNPs_reindex[\"annotation\"], how='left', left_index=True, right_index=True)\n",
    "\n",
    "#BE9b_WT_locustags_SNPs.to_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/OMICS_Data/Proteomics/Analysis_BE/20220902_BE9b_Proteomics_targeted_genes.csv\")\n",
    "#BE18_WT_locustags_SNPs.to_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/OMICS_Data/Proteomics/Analysis_BE/20220902_BE18_Proteomics_targeted_genes.csv\")\n",
    "#BE28_WT_locustags_SNPs.to_csv(\"/Users/chrwhi/Documents/PhD/Lab/MCBE/OMICS_Data/Proteomics/Analysis_BE/20220902_BE28_Proteomics_targeted_genes.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.901915673504474"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BE9b_WT_counts_up_new[\"-log10 Pvalue\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Up:\n",
      "BE9b:Out of  122 significantly changed locus tags,  2 have SNPs in or around the CDS.\n",
      "BE18:Out of  136 significantly changed locus tags,  1 have SNPs in or around the CDS.\n",
      "BE28:Out of  83 significantly changed locus tags,  3 have SNPs in or around the CDS.\n",
      "Down:\n",
      "BE9b:Out of  29 significantly changed locus tags,  0 have SNPs in or around the CDS.\n",
      "BE18:Out of  105 significantly changed locus tags,  3 have SNPs in or around the CDS.\n",
      "BE28:Out of  44 significantly changed locus tags,  4 have SNPs in or around the CDS.\n"
     ]
    }
   ],
   "source": [
    "#Map to SNPs\n",
    "#Here, fold change is already the log2, so use 1 instead of 2! \n",
    "BE9b_WT_counts_up_new = volcano_BE9b[(volcano_BE9b[\"pvalue\"] < 0.005) & (volcano_BE9b[\"fold change\"] >1)]\n",
    "BE9b_WT_counts_down_new = volcano_BE9b[(volcano_BE9b[\"pvalue\"] < 0.005) & (volcano_BE9b[\"fold change\"] <(-1))]\n",
    "\n",
    "BE18_WT_counts_up_new = volcano_BE18[(volcano_BE18[\"pvalue\"] < 0.005) & (volcano_BE18[\"fold change\"] >1)]\n",
    "BE18_WT_counts_down_new = volcano_BE18[(volcano_BE18[\"pvalue\"] < 0.005) & (volcano_BE18[\"fold change\"] <(-1))]\n",
    "\n",
    "BE28_WT_counts_up_new = volcano_BE28[(volcano_BE28[\"pvalue\"] < 0.005) & (volcano_BE28[\"fold change\"] >1)]\n",
    "BE28_WT_counts_down_new = volcano_BE28[(volcano_BE28[\"pvalue\"] < 0.005) & (volcano_BE28[\"fold change\"] <(-1))]\n",
    "\n",
    "proteome_BE28_edited_up = []\n",
    "proteome_BE18_edited_up = []\n",
    "proteome_BE9b_edited_up = []\n",
    "\n",
    "proteome_BE28_edited_down = []\n",
    "proteome_BE18_edited_down = []\n",
    "proteome_BE9b_edited_down = []\n",
    "\n",
    "for i in BE28_WT_counts_up_new.index:\n",
    "    if any(i in s for s in BE28_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE28_edited_up.append(i)\n",
    "\n",
    "for i in BE18_WT_counts_up_new.index:\n",
    "    if any(i in s for s in BE18_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE18_edited_up.append(i)\n",
    "        \n",
    "for i in BE9b_WT_counts_up_new.index:\n",
    "    if any(i in s for s in BE9b_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE9b_edited_up.append(i)\n",
    "        \n",
    "for i in BE28_WT_counts_down_new.index:\n",
    "    if any(i in s for s in BE28_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE28_edited_down.append(i)\n",
    "\n",
    "for i in BE18_WT_counts_down_new.index:\n",
    "    if any(i in s for s in BE18_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE18_edited_down.append(i)\n",
    "        \n",
    "for i in BE9b_WT_counts_down_new.index:\n",
    "    if any(i in s for s in BE9b_SNPs[\"gene\"].to_list()):\n",
    "        proteome_BE9b_edited_down.append(i)\n",
    "        \n",
    "\n",
    "print(\"Up:\")\n",
    "print(\"BE9b:Out of \" , len(BE9b_WT_counts_up_new.index), \"significantly changed locus tags, \" , len(proteome_BE9b_edited_up), \"have SNPs in or around the CDS.\")\n",
    "print(\"BE18:Out of \" , len(BE18_WT_counts_up_new.index), \"significantly changed locus tags, \" , len(proteome_BE18_edited_up), \"have SNPs in or around the CDS.\")\n",
    "print(\"BE28:Out of \" , len(BE28_WT_counts_up_new.index), \"significantly changed locus tags, \" , len(proteome_BE28_edited_up), \"have SNPs in or around the CDS.\")\n",
    "\n",
    "print(\"Down:\")\n",
    "print(\"BE9b:Out of \" , len(BE9b_WT_counts_down_new.index), \"significantly changed locus tags, \" , len(proteome_BE9b_edited_down), \"have SNPs in or around the CDS.\")\n",
    "print(\"BE18:Out of \" , len(BE18_WT_counts_down_new.index), \"significantly changed locus tags, \" , len(proteome_BE18_edited_down), \"have SNPs in or around the CDS.\")\n",
    "print(\"BE28:Out of \" , len(BE28_WT_counts_down_new.index), \"significantly changed locus tags, \" , len(proteome_BE28_edited_down), \"have SNPs in or around the CDS.\")\n",
    "\n",
    "#save matched locustags \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FQ762_00335  /  FQ762_00340\n",
      "FQ762_00475 \n",
      "FQ762_00555 \n",
      "narH \n",
      "FQ762_01390 \n",
      "FQ762_01470 \n",
      "FQ762_01520 \n",
      "FQ762_03140 \n",
      "FQ762_03150 \n",
      "FQ762_03450 \n",
      "FQ762_03940 \n",
      "FQ762_05270  /  FQ762_05275\n",
      "FQ762_06290 \n",
      "FQ762_06325 \n",
      "FQ762_06325 \n",
      "FQ762_06520 \n",
      "FQ762_07020 \n",
      "FQ762_07630 \n",
      "FQ762_07775 \n",
      "FQ762_08090 \n",
      "FQ762_08595 \n",
      "FQ762_08880 \n",
      "FQ762_10065 \n",
      "FQ762_10355 \n",
      "FQ762_10875 \n",
      "efeB \n",
      "FQ762_12310 \n",
      "FQ762_12615  /  FQ762_12620\n",
      "FQ762_12720 \n",
      "nirB \n",
      "FQ762_12780 \n",
      "FQ762_12910 \n",
      "FQ762_13455 \n",
      "pepN \n",
      "FQ762_13715 \n",
      "FQ762_13755 \n",
      "FQ762_13995 \n",
      "FQ762_14055  /  FQ762_14060\n",
      "FQ762_14265 \n",
      "FQ762_15500 \n",
      "FQ762_15505 \n",
      "FQ762_16365 \n",
      "FQ762_16600 \n",
      "FQ762_17350 \n",
      "FQ762_18605  /  FQ762_18610\n",
      "FQ762_19795 \n",
      "FQ762_19925  /  FQ762_19930\n",
      "FQ762_20285 \n",
      "FQ762_21580 \n",
      "FQ762_21915 \n",
      "ugpC  /  FQ762_21935\n",
      "FQ762_23150  /  FQ762_23155\n",
      "nusG  /  rplK\n",
      "FQ762_24070  /  FQ762_24075\n",
      "rpoB \n",
      "FQ762_24750 \n",
      "FQ762_24775 \n",
      "FQ762_25170 \n",
      "FQ762_26045 \n",
      "FQ762_26370 \n",
      "FQ762_26565  /  FQ762_26570\n",
      "FQ762_27010  /  FQ762_27015\n",
      "FQ762_27060  /  FQ762_27065\n",
      "FQ762_28580 \n",
      "FQ762_28605 \n",
      "FQ762_28795 \n",
      "FQ762_28895 \n",
      "FQ762_29075 \n",
      "FQ762_29100–[FQ762_29105]\n",
      "infB \n",
      "FQ762_29810 \n",
      "FQ762_30215 \n",
      "FQ762_30650  /  FQ762_30655\n",
      "FQ762_30920 \n",
      "FQ762_31210 \n",
      "FQ762_31530 \n",
      "FQ762_31770 \n",
      "FQ762_32020 \n",
      "FQ762_32655 \n",
      "FQ762_32655 \n",
      "FQ762_32845 \n",
      "FQ762_32850 \n",
      "FQ762_33785 \n",
      "FQ762_34570 \n",
      "FQ762_34670 \n",
      "FQ762_34720 \n",
      "pstC \n",
      "FQ762_35220 \n",
      "FQ762_36030 \n",
      "FQ762_36720 \n",
      "FQ762_36745 \n",
      "FQ762_36815 \n",
      "FQ762_36890 \n",
      "FQ762_37035 \n",
      "FQ762_37240 \n",
      "proP \n",
      "FQ762_39270 \n",
      "FQ762_39680 \n",
      "FQ762_40045 \n",
      "FQ762_40090  /  FQ762_40095\n"
     ]
    }
   ],
   "source": [
    "for i in BE18_SNPs[\"gene\"].to_list():\n",
    "    if not i in BE9b_SNPs[\"gene\"].to_list():\n",
    "        if not i in BE28_SNPs[\"gene\"].to_list():\n",
    "            print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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