tutorials/retentiontime/mq-evidence-to-ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab8800d7",
   "metadata": {},
   "source": [
    "---\n",
    "title: \"Preparing a retention time data set for machine learning\"\n",
    "\n",
    "date: 2022-09-23\n",
    "\n",
    "# date-modified: last-modified\n",
    "\n",
    "author:\n",
    "\n",
    "- name: Robbin Bouwmeester\n",
    "\n",
    "  orcid: 0000-0001-6807-7029\n",
    "  \n",
    "  affiliations:\n",
    "    - VIB-UGent Center for Medical Biotechnology, VIB, Belgium\n",
    "    - Department of Biomolecular Medicine, Ghent University, Belgium\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d00980",
   "metadata": {},
   "source": [
    "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ProteomicsML/ProteomicsML/blob/main/tutorials/retentiontime/_mq-evidence-to-ml.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab65a51",
   "metadata": {},
   "source": [
    "# Preparing a retention time data set for machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad3a6c7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pygam in c:\\users\\robbin\\anaconda3\\envs\\py310\\lib\\site-packages (0.8.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\robbin\\anaconda3\\envs\\py310\\lib\\site-packages (from pygam) (1.23.3)\n",
      "Requirement already satisfied: progressbar2 in c:\\users\\robbin\\anaconda3\\envs\\py310\\lib\\site-packages (from pygam) (4.0.0)\n",
      "Requirement already satisfied: scipy in c:\\users\\robbin\\anaconda3\\envs\\py310\\lib\\site-packages (from pygam) (1.9.1)\n",
      "Requirement already satisfied: future in c:\\users\\robbin\\anaconda3\\envs\\py310\\lib\\site-packages (from pygam) (0.18.2)\n",
      "Requirement already satisfied: python-utils>=3.0.0 in c:\\users\\robbin\\anaconda3\\envs\\py310\\lib\\site-packages (from progressbar2->pygam) (3.3.3)\n",
      "Requirement already satisfied: tqdm in c:\\users\\robbin\\anaconda3\\envs\\py310\\lib\\site-packages (4.64.1)\n",
      "Requirement already satisfied: colorama in c:\\users\\robbin\\anaconda3\\envs\\py310\\lib\\site-packages (from tqdm) (0.4.5)\n"
     ]
    }
   ],
   "source": [
    "# Skip if pygam already installed\n",
    "! pip install pygam==0.8.0\n",
    "! pip install tqdm==4.64.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4152277",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import os\n",
    "\n",
    "from pygam import LinearGAM, s, f\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "from tqdm import tqdm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36d31c6",
   "metadata": {},
   "source": [
    "In this tutorial you will learn how to go from MaxQuant evidence files to a data set that is ready for training a retention time prediction model. Retention time is the time it takes for an analyte travels through a column. The travel time depends on the interaction with the stationary phase (usually C18 for proteomics) and mobile phase. Where the mobile phase consists of solvents and changes in physicochemical properties over time with a predefined gradient. The stationary phase remains the same over time. This allows for peptides to elude at different time points, e.g., when it prefers to interact with the mobile phase at a certain percentage of the hydrophobic solvent."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa62230",
   "metadata": {},
   "source": [
    "The retention time between different runs can differ significantly and depending on the abundance of the precusor calling the elution apex can be difficult. This means we need to preprocess the data before it is used for machine learning. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347341ba",
   "metadata": {},
   "source": [
    "## Reading and formatting input data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef7d219",
   "metadata": {},
   "source": [
    "We will not need all the columns, define those that might be useful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c8b4753",
   "metadata": {},
   "outputs": [],
   "source": [
    "sel_columns = ['Raw file', 'Sequence', 'Modifications', 'Modified sequence',\n",
    "               'Retention time','Calibrated retention time', 'PEP']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f9a0bb",
   "metadata": {},
   "source": [
    "Read the input files, here a csv. If you read the standard txt you need to modify the read_csv with:\n",
    "\n",
    "```pd.read_csv(\"evid_files/PXD028248_evidence_selected_columns.csv\",sep=\"\\t\",low_memory=False)```\n",
    "\n",
    "Fill all the NA values with 0.0 and filter on only the most confident identifications (PEP <= 0.001)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32dfc690",
   "metadata": {},
   "outputs": [],
   "source": [
    "evid_df = pd.read_csv(\"https://github.com/ProteomicsML/ProteomicsML/blob/main/datasets/retentiontime/PXD028248/PXD028248_evidence_selected_columns.zip?raw=true\",compression=\"zip\",low_memory=False)\n",
    "evid_df.fillna(0.0,inplace=True)\n",
    "evid_df = evid_df[evid_df[\"PEP\"] <= 0.001][sel_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63449961",
   "metadata": {},
   "source": [
    "The file in a pandas dataframe looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc7957e8",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raw file</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Length</th>\n",
       "      <th>Modifications</th>\n",
       "      <th>Modified sequence</th>\n",
       "      <th>Retention time</th>\n",
       "      <th>Retention length</th>\n",
       "      <th>Calibrated retention time</th>\n",
       "      <th>Calibrated retention time start</th>\n",
       "      <th>Calibrated retention time finish</th>\n",
       "      <th>Retention time calibration</th>\n",
       "      <th>Match time difference</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>PEP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20191028_SJ_QEx_LC1200_4_Sommerfeld_OC218_ADP_...</td>\n",
       "      <td>AAAAAAAAAAGAAGGR</td>\n",
       "      <td>16</td>\n",
       "      <td>Acetyl (Protein N-term)</td>\n",
       "      <td>_(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_</td>\n",
       "      <td>90.531</td>\n",
       "      <td>0.58251</td>\n",
       "      <td>90.531</td>\n",
       "      <td>90.202</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40715000.0</td>\n",
       "      <td>9.822100e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20191126_SJ_QEx_LC1200_4_Sommerfeld_OC218_Tumo...</td>\n",
       "      <td>AAAAAAAAAAGAAGGR</td>\n",
       "      <td>16</td>\n",
       "      <td>Acetyl (Protein N-term)</td>\n",
       "      <td>_(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_</td>\n",
       "      <td>97.132</td>\n",
       "      <td>0.51271</td>\n",
       "      <td>97.132</td>\n",
       "      <td>96.824</td>\n",
       "      <td>97.337</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19359000.0</td>\n",
       "      <td>4.269700e-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20191129_SJ_QEx_LC1200_4_Sommerfeld_OC193_Tumo...</td>\n",
       "      <td>AAAAAAAAAAGAAGGR</td>\n",
       "      <td>16</td>\n",
       "      <td>Acetyl (Protein N-term)</td>\n",
       "      <td>_(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_</td>\n",
       "      <td>97.495</td>\n",
       "      <td>0.82283</td>\n",
       "      <td>97.495</td>\n",
       "      <td>96.848</td>\n",
       "      <td>97.671</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173850000.0</td>\n",
       "      <td>1.198900e-42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20191129_SJ_QEx_LC1200_4_Sommerfeld_OC193_Tumo...</td>\n",
       "      <td>AAAAAAAAAAGAAGGR</td>\n",
       "      <td>16</td>\n",
       "      <td>Acetyl (Protein N-term)</td>\n",
       "      <td>_(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_</td>\n",
       "      <td>96.580</td>\n",
       "      <td>0.46060</td>\n",
       "      <td>96.580</td>\n",
       "      <td>96.321</td>\n",
       "      <td>96.781</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10126000.0</td>\n",
       "      <td>3.280800e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20191204_SJ_QEx_LC1200_4_Sommerfeld_OC217_Tumo...</td>\n",
       "      <td>AAAAAAAAAAGAAGGR</td>\n",
       "      <td>16</td>\n",
       "      <td>Acetyl (Protein N-term)</td>\n",
       "      <td>_(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_</td>\n",
       "      <td>91.611</td>\n",
       "      <td>0.58345</td>\n",
       "      <td>91.611</td>\n",
       "      <td>91.341</td>\n",
       "      <td>91.924</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16703000.0</td>\n",
       "      <td>4.950100e-17</td>\n",
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       "    <tr>\n",
       "      <th>1056802</th>\n",
       "      <td>20191204_SJ_QEx_LC1200_4_Sommerfeld_OC189_Tumo...</td>\n",
       "      <td>YYYVCQYCPAGNWANR</td>\n",
       "      <td>16</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_YYYVCQYCPAGNWANR_</td>\n",
       "      <td>93.503</td>\n",
       "      <td>0.45542</td>\n",
       "      <td>93.503</td>\n",
       "      <td>93.299</td>\n",
       "      <td>93.754</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4054800.0</td>\n",
       "      <td>8.095500e-04</td>\n",
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       "    <tr>\n",
       "      <th>1056803</th>\n",
       "      <td>20191204_SJ_QEx_LC1200_4_Sommerfeld_OC196_Tumo...</td>\n",
       "      <td>YYYVCQYCPAGNWANR</td>\n",
       "      <td>16</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_YYYVCQYCPAGNWANR_</td>\n",
       "      <td>93.772</td>\n",
       "      <td>0.57550</td>\n",
       "      <td>93.772</td>\n",
       "      <td>93.492</td>\n",
       "      <td>94.067</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13780000.0</td>\n",
       "      <td>2.618200e-04</td>\n",
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       "    <tr>\n",
       "      <th>1056804</th>\n",
       "      <td>20191204_SJ_QEx_LC1200_4_Sommerfeld_OC217_Tumo...</td>\n",
       "      <td>YYYVCQYCPAGNWANR</td>\n",
       "      <td>16</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_YYYVCQYCPAGNWANR_</td>\n",
       "      <td>93.183</td>\n",
       "      <td>0.60296</td>\n",
       "      <td>93.183</td>\n",
       "      <td>92.890</td>\n",
       "      <td>93.493</td>\n",
       "      <td>-1.421100e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9741300.0</td>\n",
       "      <td>1.367500e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056805</th>\n",
       "      <td>20191210_SJ_QEx_LC1200_4_Sommerfeld_OC221_Tumo...</td>\n",
       "      <td>YYYVCQYCPAGNWANR</td>\n",
       "      <td>16</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_YYYVCQYCPAGNWANR_</td>\n",
       "      <td>95.546</td>\n",
       "      <td>0.50088</td>\n",
       "      <td>95.546</td>\n",
       "      <td>95.292</td>\n",
       "      <td>95.793</td>\n",
       "      <td>-1.421100e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7791200.0</td>\n",
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       "      <th>1056806</th>\n",
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       "      <td>YYYVCQYCPAGNWANR</td>\n",
       "      <td>16</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_YYYVCQYCPAGNWANR_</td>\n",
       "      <td>69.370</td>\n",
       "      <td>0.21532</td>\n",
       "      <td>69.370</td>\n",
       "      <td>69.250</td>\n",
       "      <td>69.466</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6157000.0</td>\n",
       "      <td>7.497600e-04</td>\n",
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      "text/plain": [
       "                                                  Raw file          Sequence  \\\n",
       "0        20191028_SJ_QEx_LC1200_4_Sommerfeld_OC218_ADP_...  AAAAAAAAAAGAAGGR   \n",
       "1        20191126_SJ_QEx_LC1200_4_Sommerfeld_OC218_Tumo...  AAAAAAAAAAGAAGGR   \n",
       "2        20191129_SJ_QEx_LC1200_4_Sommerfeld_OC193_Tumo...  AAAAAAAAAAGAAGGR   \n",
       "3        20191129_SJ_QEx_LC1200_4_Sommerfeld_OC193_Tumo...  AAAAAAAAAAGAAGGR   \n",
       "4        20191204_SJ_QEx_LC1200_4_Sommerfeld_OC217_Tumo...  AAAAAAAAAAGAAGGR   \n",
       "...                                                    ...               ...   \n",
       "1056802  20191204_SJ_QEx_LC1200_4_Sommerfeld_OC189_Tumo...  YYYVCQYCPAGNWANR   \n",
       "1056803  20191204_SJ_QEx_LC1200_4_Sommerfeld_OC196_Tumo...  YYYVCQYCPAGNWANR   \n",
       "1056804  20191204_SJ_QEx_LC1200_4_Sommerfeld_OC217_Tumo...  YYYVCQYCPAGNWANR   \n",
       "1056805  20191210_SJ_QEx_LC1200_4_Sommerfeld_OC221_Tumo...  YYYVCQYCPAGNWANR   \n",
       "1056806  20200229_SJ_QEx_LC1200_4_Sommerfeld_OC221_CAF_...  YYYVCQYCPAGNWANR   \n",
       "\n",
       "         Length            Modifications  \\\n",
       "0            16  Acetyl (Protein N-term)   \n",
       "1            16  Acetyl (Protein N-term)   \n",
       "2            16  Acetyl (Protein N-term)   \n",
       "3            16  Acetyl (Protein N-term)   \n",
       "4            16  Acetyl (Protein N-term)   \n",
       "...         ...                      ...   \n",
       "1056802      16               Unmodified   \n",
       "1056803      16               Unmodified   \n",
       "1056804      16               Unmodified   \n",
       "1056805      16               Unmodified   \n",
       "1056806      16               Unmodified   \n",
       "\n",
       "                                   Modified sequence  Retention time  \\\n",
       "0        _(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_          90.531   \n",
       "1        _(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_          97.132   \n",
       "2        _(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_          97.495   \n",
       "3        _(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_          96.580   \n",
       "4        _(Acetyl (Protein N-term))AAAAAAAAAAGAAGGR_          91.611   \n",
       "...                                              ...             ...   \n",
       "1056802                           _YYYVCQYCPAGNWANR_          93.503   \n",
       "1056803                           _YYYVCQYCPAGNWANR_          93.772   \n",
       "1056804                           _YYYVCQYCPAGNWANR_          93.183   \n",
       "1056805                           _YYYVCQYCPAGNWANR_          95.546   \n",
       "1056806                           _YYYVCQYCPAGNWANR_          69.370   \n",
       "\n",
       "         Retention length  Calibrated retention time  \\\n",
       "0                 0.58251                     90.531   \n",
       "1                 0.51271                     97.132   \n",
       "2                 0.82283                     97.495   \n",
       "3                 0.46060                     96.580   \n",
       "4                 0.58345                     91.611   \n",
       "...                   ...                        ...   \n",
       "1056802           0.45542                     93.503   \n",
       "1056803           0.57550                     93.772   \n",
       "1056804           0.60296                     93.183   \n",
       "1056805           0.50088                     95.546   \n",
       "1056806           0.21532                     69.370   \n",
       "\n",
       "         Calibrated retention time start  Calibrated retention time finish  \\\n",
       "0                                 90.202                            90.784   \n",
       "1                                 96.824                            97.337   \n",
       "2                                 96.848                            97.671   \n",
       "3                                 96.321                            96.781   \n",
       "4                                 91.341                            91.924   \n",
       "...                                  ...                               ...   \n",
       "1056802                           93.299                            93.754   \n",
       "1056803                           93.492                            94.067   \n",
       "1056804                           92.890                            93.493   \n",
       "1056805                           95.292                            95.793   \n",
       "1056806                           69.250                            69.466   \n",
       "\n",
       "         Retention time calibration  Match time difference    Intensity  \\\n",
       "0                      0.000000e+00                    0.0   40715000.0   \n",
       "1                      0.000000e+00                    0.0   19359000.0   \n",
       "2                      0.000000e+00                    0.0  173850000.0   \n",
       "3                      0.000000e+00                    0.0   10126000.0   \n",
       "4                      0.000000e+00                    0.0   16703000.0   \n",
       "...                             ...                    ...          ...   \n",
       "1056802                0.000000e+00                    0.0    4054800.0   \n",
       "1056803                0.000000e+00                    0.0   13780000.0   \n",
       "1056804               -1.421100e-14                    0.0    9741300.0   \n",
       "1056805               -1.421100e-14                    0.0    7791200.0   \n",
       "1056806                0.000000e+00                    0.0    6157000.0   \n",
       "\n",
       "                  PEP  \n",
       "0        9.822100e-15  \n",
       "1        4.269700e-21  \n",
       "2        1.198900e-42  \n",
       "3        3.280800e-05  \n",
       "4        4.950100e-17  \n",
       "...               ...  \n",
       "1056802  8.095500e-04  \n",
       "1056803  2.618200e-04  \n",
       "1056804  1.367500e-06  \n",
       "1056805  1.803700e-11  \n",
       "1056806  7.497600e-04  \n",
       "\n",
       "[1056807 rows x 14 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evid_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e81784c",
   "metadata": {},
   "source": [
    "As you can see in this example there are many of the same peptidoforms (minus charge) for the different runs. We will want to create a single value for each peptidoform per run in a matrix instead of a single peptidoform+run combo per row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69f7e41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_73</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_74</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_75</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_76</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_77</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_78</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_79</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_80</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_81</th>\n",
       "      <th>20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_HPMC_FCS_82</th>\n",
       "      <th>...</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC221_HPMC_50Asc_522</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_523</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_524</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_525</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_526</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_527</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_528</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_529</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_530</th>\n",
       "      <th>20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_531</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_</th>\n",
       "      <td>106.540</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>73.638</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.596</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation (M))DVNTALQEVLK_</th>\n",
       "      <td>138.075</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>121.063333</td>\n",
       "      <td>121.605</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>_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation (M))ADGTCQDAAIVGYK_</th>\n",
       "      <td>136.850</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>107.397250</td>\n",
       "      <td>107.130</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>_(Acetyl (Protein N-term))SDAAVDTSSEITTK_</th>\n",
       "      <td>62.757</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>34.013000</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>_AADDTWEPFASGK_</th>\n",
       "      <td>90.319</td>\n",
       "      <td>89.749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.24</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.282000</td>\n",
       "      <td>54.873</td>\n",
       "      <td>52.801</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>...</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>_TALLTWTEPPVR_</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>76.442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_TQFNNNEYSQDLDAYNTKDK_</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>45.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_VATGTDLLSGTR_</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>36.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_VNWMPPPSR_</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>40.657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_</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>32.140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49983 rows × 441 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_73  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                106.540   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                           138.075   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                           136.850   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                     62.757   \n",
       "_AADDTWEPFASGK_                                                                               90.319   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_74  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                    NaN   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                        NaN   \n",
       "_AADDTWEPFASGK_                                                                               89.749   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_75  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                    NaN   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                        NaN   \n",
       "_AADDTWEPFASGK_                                                                                  NaN   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_76  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                    NaN   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                        NaN   \n",
       "_AADDTWEPFASGK_                                                                               88.939   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_77  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                    NaN   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                        NaN   \n",
       "_AADDTWEPFASGK_                                                                                  NaN   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_78  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                    NaN   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                        NaN   \n",
       "_AADDTWEPFASGK_                                                                                  NaN   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_79  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                    NaN   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                        NaN   \n",
       "_AADDTWEPFASGK_                                                                                  NaN   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_80  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                    NaN   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                        NaN   \n",
       "_AADDTWEPFASGK_                                                                                  NaN   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_ADP_81  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                    NaN   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                               NaN   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                        NaN   \n",
       "_AADDTWEPFASGK_                                                                                  NaN   \n",
       "...                                                                                              ...   \n",
       "_TALLTWTEPPVR_                                                                                   NaN   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                           NaN   \n",
       "_VATGTDLLSGTR_                                                                                   NaN   \n",
       "_VNWMPPPSR_                                                                                      NaN   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                 NaN   \n",
       "\n",
       "                                                    20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_HPMC_FCS_82  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN       \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN       \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN       \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN       \n",
       "_AADDTWEPFASGK_                                                                                105.24       \n",
       "...                                                                                               ...       \n",
       "_TALLTWTEPPVR_                                                                                    NaN       \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN       \n",
       "_VATGTDLLSGTR_                                                                                    NaN       \n",
       "_VNWMPPPSR_                                                                                       NaN       \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN       \n",
       "\n",
       "                                                    ...  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_       ...   \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...  ...   \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...  ...   \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_           ...   \n",
       "_AADDTWEPFASGK_                                     ...   \n",
       "...                                                 ...   \n",
       "_TALLTWTEPPVR_                                      ...   \n",
       "_TQFNNNEYSQDLDAYNTKDK_                              ...   \n",
       "_VATGTDLLSGTR_                                      ...   \n",
       "_VNWMPPPSR_                                         ...   \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_    ...   \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC221_HPMC_50Asc_522  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN          \n",
       "_AADDTWEPFASGK_                                                                                   NaN          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_523  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                         121.063333          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                         107.397250          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                   34.013000          \n",
       "_AADDTWEPFASGK_                                                                             55.282000          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_524  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                  73.638          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                            121.605          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                            107.130          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN          \n",
       "_AADDTWEPFASGK_                                                                                54.873          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_525  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN          \n",
       "_AADDTWEPFASGK_                                                                                52.801          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_526  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN          \n",
       "_AADDTWEPFASGK_                                                                                   NaN          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_527  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN          \n",
       "_AADDTWEPFASGK_                                                                                   NaN          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_528  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                  72.596          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN          \n",
       "_AADDTWEPFASGK_                                                                                   NaN          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_529  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN          \n",
       "_AADDTWEPFASGK_                                                                                   NaN          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_530  \\\n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN          \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN          \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN          \n",
       "_AADDTWEPFASGK_                                                                                   NaN          \n",
       "...                                                                                               ...          \n",
       "_TALLTWTEPPVR_                                                                                    NaN          \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                            NaN          \n",
       "_VATGTDLLSGTR_                                                                                    NaN          \n",
       "_VNWMPPPSR_                                                                                       NaN          \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                                  NaN          \n",
       "\n",
       "                                                    20200319_SJ_QEx_LC1200_4_Sommerfeld_OC222_HPMC_50Asc_531  \n",
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                                                     NaN         \n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation...                                                NaN         \n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation...                                                NaN         \n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                                                         NaN         \n",
       "_AADDTWEPFASGK_                                                                                   NaN         \n",
       "...                                                                                               ...         \n",
       "_TALLTWTEPPVR_                                                                                 76.442         \n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                                         45.327         \n",
       "_VATGTDLLSGTR_                                                                                 36.155         \n",
       "_VNWMPPPSR_                                                                                    40.657         \n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                                               32.140         \n",
       "\n",
       "[49983 rows x 441 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention_dict = {}\n",
    "\n",
    "# Group by the raw file\n",
    "for gidx,g in evid_df.groupby(\"Raw file\"):\n",
    "    # Group by peptidoform and take the mean for each group\n",
    "    retention_dict[gidx]  = g.groupby(\"Modified sequence\").mean()[\"Calibrated retention time\"].to_dict()\n",
    "\n",
    "#Transform the dictionary in a df where each row is a peptidoform and each column a run\n",
    "retention_df = pd.DataFrame(retention_dict)\n",
    "\n",
    "retention_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4374f2bd",
   "metadata": {},
   "source": [
    "We can than have a look at the absence of each peptidoform in all runs (value = absence in that many runs):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3fb3bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                          385\n",
      "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation (M))DVNTALQEVLK_       408\n",
      "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation (M))ADGTCQDAAIVGYK_    397\n",
      "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                              430\n",
      "_AADDTWEPFASGK_                                                        359\n",
      "                                                                      ... \n",
      "_TALLTWTEPPVR_                                                         440\n",
      "_TQFNNNEYSQDLDAYNTKDK_                                                 440\n",
      "_VATGTDLLSGTR_                                                         440\n",
      "_VNWMPPPSR_                                                            440\n",
      "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                       440\n",
      "Length: 49983, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "prevelence_peptides = retention_df.isna().sum(axis=1)\n",
    "print(prevelence_peptides)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec139b38",
   "metadata": {},
   "source": [
    "We can penalize the absence the absence of highly abundant peptidoforms per run (lower = more abundant peptidoforms present) by taking the dot product of presence/absence in the matrix and the above absence scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d9d5681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20200319_SJ_QEx_LC1200_4_Sommerfeld_OC186_HPMC_50Asc_498     18417985\n",
       "20200319_SJ_QEx_LC1200_4_Sommerfeld_OC186_HPMC_50Asc_499     18633580\n",
       "20200319_SJ_QEx_LC1200_4_Sommerfeld_OC186_HPMC_50Asc_502     18802678\n",
       "20200319_SJ_QEx_LC1200_4_Sommerfeld_OC186_HPMC_50Asc_501     18807279\n",
       "20191129_SJ_QEx_LC1200_4_Sommerfeld_OC193_Tumor_50Asc_138    18877939\n",
       "                                                               ...   \n",
       "20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_HPMC_FCS_85        21261614\n",
       "20191121_SJ_QEx_LC1200_4_Sommerfeld_0Wert_CAF_FCS_100        21262661\n",
       "20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_HPMC_FCS_83        21263993\n",
       "20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_HPMC_FCS_82        21265683\n",
       "20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_HPMC_FCS_87        21268762\n",
       "Length: 441, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_per_run = retention_df.isna().astype(int).T.dot(prevelence_peptides)\n",
    "score_per_run.sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04a95bb",
   "metadata": {},
   "source": [
    "We will use a single run to align all the first experiments against, this is the one with the lowest penalty score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "188f5508",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_highest_overlap_score = score_per_run.sort_values(ascending=True).index[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c81acc4",
   "metadata": {},
   "source": [
    "## Retention time alignment between runs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "751e9b50",
   "metadata": {},
   "source": [
    "The first step after reading and loading the data is to align retention times between runs. Here we will use splines in a GAM for that. The algorithm below follows these steps:\n",
    "\n",
    "1. Iterate over all runs, sorted by the earlier defined penalty score\n",
    "2. Obtain the overlapping peptidoforms between runs\n",
    "3. If there are less than 20 peptidoforms skip that run\n",
    "4. Divide the overlapping peptides into equidistant bins and enforce a percentage of the bins to be filled with a least one peptidoform (now 200 bins and 75 % occupancy). If requirements are not met skip that run.\n",
    "5. Fit the GAM with splines between the reference set and the selected run\n",
    "6. Calculate the error between aligned and values in the reference set. If selected it will run a second stage of the GAM filtering out any data points that were selected to have an error that is too high\n",
    "7. Assign aligned values to a new matrix\n",
    "8. Change the reference dataset to be the median of all aligned runs and the initial reference run\n",
    "\n",
    "In the next code block we will define two kinds of plots, first a performance scatter plot. Here we plot the retention time of the selected set against the reference set; before and after alignment. Next is the residual plot that subtracts the diagonal from the performance scatter plot and essentially shows the errors before and after alignment. The residual plot is generated for both the first and second stage GAM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08b7ea34",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_performance(retention_df,run_highest_overlap_score,align_name,non_na_sel):\n",
    "        plt.scatter(\n",
    "                    retention_df[run_highest_overlap_score][non_na_sel],\n",
    "                    retention_df[align_name][non_na_sel],\n",
    "                    alpha=0.05,\n",
    "                    s=10,\n",
    "                    label=\"Reference+selected set unaligned\"\n",
    "                )\n",
    "\n",
    "        plt.scatter(\n",
    "                    retention_df[run_highest_overlap_score][non_na_sel],\n",
    "                    gam_model_cv.predict(retention_df[align_name][non_na_sel]),\n",
    "                    alpha=0.05,\n",
    "                    s=10,\n",
    "                    label=\"Reference+selected set aligned\"\n",
    "                )\n",
    "        plt.plot(\n",
    "            [\n",
    "            min(retention_df[run_highest_overlap_score][non_na_sel]),\n",
    "            max(retention_df[run_highest_overlap_score][non_na_sel])\n",
    "\n",
    "            ],\n",
    "            [\n",
    "                min(retention_df[run_highest_overlap_score][non_na_sel]),\n",
    "                max(retention_df[run_highest_overlap_score][non_na_sel])\n",
    "\n",
    "            ],\n",
    "            c=\"black\",\n",
    "            linestyle=\"--\",\n",
    "            linewidth=1.0\n",
    "        )\n",
    "        plt.xlabel(\"Retention time reference set\")\n",
    "        plt.ylabel(\"Retention time selected set\")\n",
    "        leg = plt.legend()\n",
    "        for lh in leg.legendHandles:\n",
    "            lh.set_alpha(1)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "def plot_residual(run_highest_overlap_score,align_name,non_na_sel,title=\"Residual plot\"):\n",
    "        plt.scatter(\n",
    "                    retention_df[run_highest_overlap_score][non_na_sel],\n",
    "                    retention_df[align_name][non_na_sel]-retention_df[run_highest_overlap_score][non_na_sel],\n",
    "                    alpha=0.05,\n",
    "                    s=10\n",
    "                )\n",
    "\n",
    "        plt.scatter(\n",
    "                    retention_df[run_highest_overlap_score][non_na_sel],\n",
    "                    gam_model_cv.predict(retention_df[align_name][non_na_sel])-retention_df[run_highest_overlap_score][non_na_sel],\n",
    "                    alpha=0.05,\n",
    "                    s=10\n",
    "                )\n",
    "\n",
    "        plt.title(title)\n",
    "\n",
    "        plt.axhline(\n",
    "            y = 0.0,\n",
    "            color = \"black\",\n",
    "            linewidth=1.0,\n",
    "            linestyle = \"--\"\n",
    "        )\n",
    "\n",
    "        plt.ylabel(\"Residual\")\n",
    "        plt.xlabel(\"Retention time reference\")\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ebb5fc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 20200319_SJ_QEx_LC1200_4_Sommerfeld_OC186_HPMC_50Asc_499: : 0it [00:00, ?it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 20200109_SJ_QEx_LC1200_4_Sommerfeld_OC221_Tumor_5FCS_340: : 100it [01:23,  1.29it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 20200109_SJ_QEx_LC1200_4_Sommerfeld_OC217_Tumor_5FCS_318: : 200it [02:42,  1.42it/s] "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 20191210_SJ_QEx_LC1200_4_Sommerfeld_OC195_Tumor_50Asc_280: : 300it [03:54,  1.37it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZiZgbL2+4PgKihUHrWbejU1rFOHjMOBxTNirzRiemnTiF7nuyqth2q1W0qvgK90e3neH5MOZtfetsNDaDGJWdMpEG9Z/33EhpnpNkGZ0k5XKjQ+gFRGFKOfQQFdppRpoplchjrl7e3e1TyNYr39FNMsLAKfKeayfLlTTLmaq6VN/lTkI3zWh3c2olH08EVXWfOavLuSpLrRhUKEVKFR9F6SQZZdYMZ7ObFEYEOmnCg1eanJ6o0MkykiQj9H1aScr8UpeJekCrndJMEjw8vBBWWglxnFMKfeaXO8R5TjkISLwcp/QhTnLiNEcEFtsxY5WQet91Loc+9VJApko9CCiH/mpplUY3BRXqlczcTccIMw7HlH53TZJndOOcNFeCHMqhV/idc1rdlCAQfPE2FZ3rGY4kT4g8jyxV5x1S2fKcW/byr6HUtcsWUpI8g5RtS2L0XCXzK20C8VHJSRK3bzuPKWceSepRDp2ijtOUPFegw1S1RODJjq6n3jm6acaFxTaVko8qnK6XyEXoJM7gXG3FjJVDZ8x8j8U0xvOUXEHEI05T8JQ4zSmHzt0WJ0op9Ek6KXGSIeIR+EK9FDBdc4q2VgppxRndJKPZTVBgqZWgCnGaoSqMlSNKkVDyfKQiNLoZy52YJBM0CvE8Dz9XqiUf7UC9HODi3UrgOaOUZjmBL/i+D0XMZfV+KiZD9hv+TpKullZBWC2tYsbheGDG4QA4ilkc/f7+VjejHWd4vkAGqQdpmnN5pct8s0voeVRLAWOltduhP4um0c7wvJRcYbpWQnVrJdC/TyfJ6CRu8pyTI1t1QfR6xVvRPwpY7iS0uimdJGVurEyt5I5Dkq66MHrnjJOEi1e75JLTaqdUSh7VcsgXLzWZqASMV0NOjZXpJhndVJmslmjHOUiXViejVvKJAn9VIffLkmY5Wa5o7uRud1NqpYhWmhWjlf7Rj7LS7bLQiAnElR6ZHYtABBXFU49SNWes5MqAXFrpEgYel1c6dAQ8z2OiEpGprrvG1dBnuR0TeIIiZFmO5wlnxisstGLKoUc1Cog8Ie7mzIxHlEMhCnzC0AN1o59SEFCdDPDUw/PXUnvn6mXABeC3m6S3cYTXK63SztzIIar4VhLkGGHG4Ro56q+PXGm7SVmtJKMc+JyeKDNWCljpprSzjLDIgKlGfs+N7CjcJlmuIEq9HLLcSml0EnxPqIRrwch+hZ5kGY1uSpopnTij0XUZLs00pRtDlmdM10qcnaxu6R5aaHbJVOl2M1bimJW265UvtxNqpRDPFwLxKUcxc3XnF0+yhKVWQiOO8URopilX2zkztQzPU+rlgEYnoxwk5LkShS7jJ4oET4VWMektzlKqUUAZ1/aVdgoepElOI87oxBmPLKwwVSuhQLVUZqwcstJNivC388kvNGKWmglhKEzUQ4LAtbMShKvuscAXymGJTKEdp9RKIXHqrl2c5dSi+moAfNVNFbs5E1HoUQl9SqFHFAbM1MWV4/A8WknmjGaSM1EpEQSei3mUnDLvub42dmbKUcDZierestGGWFrFOHzMOFwjR+EdyNuNXNI8J84yaqXQZb9kGeMlp6yC1KMcBHSSLnnuFE5PGfWCqU6JpIgqzU6ymmAT+G6yGBRKtJNRjnx8T8g1p9GOyXJnEK4sdWl1E9ppRhQEnB4v0eimq8os1ZyS74MIzU7MUiclCnwWWl2uNp1seMLjVzuUQhirVJiqRWR5AHQYL4WkmhNFHnmuXE0SFpdiVHLnxhGfZkdZasfkWU458pnyBT+ESDyS3LV3rKxI0bteznKWWzGPLbaZqoW0k4x2NyFOXJxgodHF94WVdupGW0V8Ns1yFjoJzW5GkivNRkopaDNXc0Zso3utp1w7UYDndekmAfVSRCkUxirBmusmV5qdFESohM7NNFmNnN8/zxnz1rZ1BQp9skxRUSpFTKWXAbU6M3yAYPwgBL63LjZhHB/MOFwjG337YSak3vUzEBtHLhvz3T2KlMbAZ6wcUisFTmZVggCmyhGTNVc3CVgXTJ2uR3RSWGjFhJ5PJ0mYHitTiWC5FTOfZsSpkmrOWBKSasrVRsLlZperjS5h4LPS6bLSyVhYjlFRsrTOzESFx6626WZrLpvpsYgkgaV2l1oppBmnXFrs0E5TFptdcjzmxitcaSyh+RgTp8doxzl5HpPlzud+00yN9NIK7VAoRSXm6mXiJCEIUubqrhe91HYGJ1MolcqIwlSlTJLmTFR9Al9oJzmtOONqs0ujndDopig5Ueiz0k2ZqpZAoVLyWWwlKC6O0ezGhL7HSjvG8wTfFypBQJor5Wib8h2Fcg084WorXk0Y6Ll1egYbBN/zXOzAW0s33ejm6d2Lng+obFus0DB2Y0fjICL/ZKfvVfWnD1ac0aPft59kkGR6TTN990onyVjqxPgqiA/dJKMcBquGwg+E6WoIeNwwUV7Nv0/z3PUugUxdWmSmLvMm9DyuxB3SpYzFdpeVdkqS5sSas9DoUop8AhEQj9AXPE+41G3TijNaXTeS6KbK5ZUGnQSCQIiCAMjwfJ8sh3ac0EoUX2ClGzNWCl3vO1Eux2088Ygin0SVTqqEoeu1B35AGHoEIuQ+1EphUSoipZvlzI5XWe6kZHnOlWaXm6ZrjJdD/ACWWwlT1RK1cshUrUSe54Sex+x4SJoq5dD9Xp04I1elXgppxym+B6XAZXdNViImqxEz9QqiUIp8mp2M++eXybOcsUqpML4Z4CGeRytJt1Tm/ZSjgLk+d4/7bdNVVxDE1Mo+KKuuoY30pwX3ssmOWizMGB12GzmM7fK9QTEczz1Cn+vqXkqznEvNNvdfaOAX9YlunKkRBT65Ks2u6/UGfkA3T+mmOUGc0uymXFpp0+5kPLHUYWYsIs1zbp4dI45z4jyj0U1ot1OuNDsstVLiXBGFW2bqdJoJU/UycZpxudnFy4WpeomZeplmZ4WlTpdy4DNRKTFRUcT3aHgx9WqFm2dq1EsBceby87UI2nazBE8CqjWfTgeSxFU0FdyxRcGTnNvmJrj1VI1q6IyM73nUSwG+B75fZbmZEgWTpGmG+HB2sko1DIgCgUlY7iZ0YmcMVSEPhaydUAncfADFxw+gHPhUSx6++EyPhaA+Iq7s9uxEiUA8xkoBca4s57EbiXkhC80OY6WQW+bGSFJluhYRev5A90N/2uzGONbcWHkgRb9pNGFGwdgnOxoHVf2J6yXIqHMtKZuDsjrjtpjF2ujEXFnsup40HlEAS40YcnF1fUJ4YrFNTk43AV88HrnaoNPN+eTjy8RZwmMLHWbrEdVSQJbmzIxVaccxVxsp840Oi80uSa6MVUIEBRT1lCcWlsFzLhPfz8maMb7vEwbCRClgohY5P3cUoeQoQhgIZ8arbmauB+OVhE7q4iBTtYjFZoxKSFZSFltdxEvwPI/Tk2VEoBL63DJXoxaFq/M0OkWef+B5Lg3U8xAPqmGJhWaC5kIrzpiquuqotVLIfKNDnAgZTnmnqRIG4IlPGHikuU8pUk4FFZbbCVO1CBTGyiHlDTOu0ywnSVMmq2U3skkipuoR1Sig6ymeJ3tP4d0ijrVdsPcoZsoZx4OBYg4iUgZeATwDV34bAFX97iHJNXLsNtN3N3Z6yHtB4uVuTLOd00kSF2jMlVyg2XZZOLWyz3QtYLnVIQwC5pc6fOaRRVpJTCWI8CXnaium3U54YrFLNfJodDq0uymzEy5oGnhCEIYstTukWc5EtcTllQ6T5cgFOslZabm01MV2k5l6mSj08RACyTk3VWOsEtLqptTLZaolYbJSZbJWWi0NUQr8InsqWJ08Vo4C6qWQ+ZUOzTwj8H2masJUXZioRpQCj6lK5ILpfYo5LmIWaeaCw9VqxFQlcrWNooDId6Oo1UwsgXopQsouqyjJ3OSwXkXWJHVxkHIYUCmMSU/mreZCBL7HTL1CN8+5upxQGiszXgkYL0e7zp/Y9l4asKNx1DPljNFm0ID0LwOfAb4R+JfAy1n/XmiD/WV7wNZB5f6YQCtNaXdS5le6NJOUJMnwxKNeCYk8D9WcRidBKfGpxy8xUw9ZbCdorHzhSqNIR+2yFHcZr5QQhHaaUi9FTNer4OWcHqsQBeD5PqEv3DBV59GrTeJEmahG1Ms+t56qE8cZy6WMIBC+cHGZNMsIfTeqqEQhY5WIHKhHkVOuFX/L0hDbpVNO1SLyPKY85moCZVnOZCVkYouZt2meF3WCXLorpMxF5dXjZ8rqvIuegu0pXjej16caBuviAWmeUwmjVUPhe2vGbDslHPgeN4zXmK6sjequRUkP2tE4Cply14KNeo42gxqHL1HVl4rIi1X1rSLya8B7hinYSWJjPaD5Rod2nNPoJqi6HP80zfns40tcbnQYq0RUygGnxyqAstLuEoYRy80mzW7K6bESK62U5WabbhwTBiGhZuQZtBOlGgpnxirMjYc8qVQiy5RKCOO1MrdMu4DumQmfqWrISrtLtVxy6aKez1g9YiVukqRu7sNkJSLwhRtmqtSCEBGlGoWEgYAKgRQB1Q096O0MaTkMqJQzFpsJjW5CvRSSbdrKsVoIL9P1Bsff/jWfG4O2G5V8/3a9fQctAXKQhegG6WhcD1fmsLAqr0efQY1DUnwuisgzgSeAW4ci0QhwLT2ejft24pRm7HLo09xjqRXTTd1EqHY3o9GJubDQ4lIj5rHFFu0kZSLJGM9CqoFHGHhkOTx6aYXxUsDVVsyVVkCzm+AXgeiVZpubp8eYqJQYq/qMlSNOTVaZqYRM1MJVJZsBlTAkDNykuNlaiYevtojTnJLn4ghjpYDxSsByO+a203WqYUCcZ0xVSpRDn06SEYUu377fZTQoge8xXS3hC1Qjn3o53HZGduA7w9OfAtqvILdTsAMp3g3bHEUlfK2uzMPEqrwefQZ9at8kIlPAP8e9ya0O/IuhSXWEuRY/b39vKdaUNM64uBxTK4UoEAq0s4yHrzS4uNyl0UkIPVdHZ36lSzOOyRNFKiHVMEQ1J8t9auWQmVyZrUZMj0UEklM7M049CrhxpUO3m3LLqTrjlYg8z5iouZm9k9Voy3IR/T3qm4HLRfpqKfAphwH1csREtbR6HUK8VVdK3fdWJ2ftV2EFvsdEpYSIqx+0k0LemAI6DAV5lJXwfl2Zh8/WVV6No8NAxkFV31z8+0fAbcMT5+izHz9vL6C81OlwteHeEPapR5e40mgT58q5qRqh71MLYL6T8MR8g0utLt12RhZAu52yFKfEaUaW59Q7IeOViCefHkc8LUpCeMzVK1Qin7FywNV2l0YrpRqNgaecGS/RSnLOTNYJRBgvhdS28J1vbEu94nr+O7lntgrSXqvC2otCvh4KcnSV8NFkqyqvxtFi0GylLUcJqvovD1aco89uft5NJauznEsrba40XDG2Vjfl8YUmn3piyR1DhEazS61aZrLs8+BCk/mVDlkOmuUE6pHlKRFKqspcvcptszWeNF3lhqkytXJIN8546tkxOrGr8FkKfW6crNLspiDuVZVx6txGY+WIJM0pRYMru2txz1wLppCPL73SIUdxNGY4BnUrNfv+LwN3c0KzlXbq0W4MslUDn4Vml8cWWzS7GU9cbbGw3ObBy8ssdlLSTMgUxiKPsqaIFzBRDvGAxVZC7AmBCLEXoF5KrVyiWvWp1UqM10LOjFdAhLFSsFpuoV+uemV9cbzlTnLk/ObGycWM/9FmULfSf+xfFpGfwsUeTiT9M1l7JQ4AllpdlpopYSAsLHZpxa7k9ENXWswvd3ngygpLrRYrnZRquUSWxoxXK8yMV4k8odVN8cWnErlKoqUgZLzs02wltDTHU6iVQ26brfOU0+OUo2DV798r2bFVqeWezEfVb24Y/ViK69Fgv4X3qpz02ENfeem0mDiVpDmXWh3IPR68tEwzTqhXSyRJRpYlRCJMlMt4XsZkKaQUlJgbj5iuhYh6TFR9Qj9gpdvlltk63cTV+0lSWOnElEOfsUrAU06PM1krr1bhHDT+YT0146hjE/uODoPGHD7O2quhfGAONxlup33KwB8DpeI871DVe0VkGvhNXCrsg8C3qOrV/Qh/mDQ6CReX24gKj15tkKgSiedem9iKubjSJlaP8Tij4vnUo5BqNcNLfERSZsZKnJooc3qiTMkPGKsFePhEvlApha6wm8BEOaQc+CR5juAKzfVSQ0c5z90wtmKrd4BbRdnDYdCRw919/6fARVVNd9mnC/wtVW2ISAj8qYj8AfB3gfer6utF5DXAa4B/ulfBD5M0y7mw1OILF5e5upLw+GKTJFNqFZ9mOwNRWnGKIpyulbj97Bg3zlR57HKTTpbjATdMVahHEaXIZ6WTUI582t2cSskjTpVaFBAF3rpU042Yq8g4bvQ6PO04dS8pivyiNtbe5ssY185uJbuni39XNnw1Lu4l6Qvb7auqCjSKxbD4U+DFwF3F+rcCH2DEjMNSs8sjVxo8fqXNA5eXWWlndFWZ65RIcQq+HAS04gzfF06NV3jy7DhPnh13NX8CNyegN3yuRMHqy29W36gzYE0ecxUZx4leh6fZdYYhzZVuktNN2pyZqJiBuI7sdqU/xKqq4mbgavH/JPAw8KSddhYRvzjGlwA/r6p/JSKnVfUCgKpeEJFT2+z7KuBVADfffPOg7Rk6aZbzxEqbx652uLjcotFJqUU+aZxRq3pU/RIEUI8CfE/4snOTzI6VQVxpiP6be7syDoZxkgl897a7VpzRTXLaSUY59LjaipmzztB1Y7eS3U8CEJH/Btynqu8ull8AfN1uB1f3xpPniMgk8DtF6Y2BUNU3AW8CuPPOO3WXza8bnSTj8lKXhxdWuLDcZLGZMXF6nHO1EjdNVrnl1DilEPIMAl+YHa8QFZVEN2K9fsPYml5plG7Sphx6BL6sFlm015JeHwYdo/01Vf2e3oKq/oGI/KtBT6KqiyLyAeD5wEUROVuMGs4Cl/Yk8SHRq4H0+GKLjz58hSuNhDhzZaVP10KedfMct52qMF0vUw7djOJeQbhrrdJpGCeRchRwZqLC/EqHdpKRoCR5hyzLN83uNw6eQY3DZRH5MeBXcG6mbweu7LSDiMwBSWEYKriRxr/HzY+4B3h98fnOfcp+3Wi0Yz57cYlHrrT5xKOXubTSZaGRUg5gol7hzHSVc7MVbp4dtxvWMA6QXhl3abkXQy01E5qdhIlKibmxksUghsigV/ZbgXuB3ymW/7hYtxNngbcWcQcPeJuqvktE/gJ4m4i8Ahe3eOnexb5+pFnOQ1cafPiBK3z+4jIXGzElzyPwlfFyyO2zdZ40N865yaoZBsMYAuUwwPdS0lTBgzSHRpwQtMRiEENk0BnSC8Cr93JgVf0Y8Nwt1l8BnreXYx0mzU7CFy+v8NknlrjSiml2U2Ym68xFEc+6aYpnnJvmlpnaulIVhmEcHL34Q5rnpK2cRHPKQbDu/R3GwbNbKuvPqOoPisjvsTYJbhVVfdHQJDtEetP30zTnc5eW+PhDCzS7OR4+0xXhzGTE08/NcOetU8zUK3ZzGsaQKUcBZyeqjJViVjop5SIb0CZ+Do/dRg6/XHz+1LAFOSr0pu9nufLwlQafe3SRVg7iQd0XnjQ3ydc94wZuma3baMEwriOB7zFZK1MvW+2l68FuqawfKj7/qLeueOnPTYXb6NiwOlrI3PT9XHMev9riwkqX5ZWYsu8xXYt4zi3T3DI3Rr0cHrbIhnEisRTw68NAV1hEPiAi48WM6Y8CvyQiPz1c0a4fvdFCO3afjW7M44stPvnYMo8uNFlsdwl8n2feNMNM8d5mwzCM48yg5ndCVZdxdZF+SVX/HwaYBDcKpFlOs5vQTTKWO12eWGyx3OrywMUlWt2EKAyZKAfMjZeYqYdM16Nty2IbhmEcFwbVckExYe1bgNcNUZ7rSm/E0E0zHrzc4PJKh8VWTL0UkuaQZRmox0StxK2zNW6bHePUmAWgDcM4/gxqHP4l8B7gz1T1gyJyG/D54Yl1fVgtD+x7NLsJD1xaIU5zHstb1EPh3GSdTp5waqzKM89NccrmMhjGkcZeFHRwDDrP4e3A2/uWvwj8vWEJdb0IPI920mV+qctHHrrKowsNluOcsUg4N1Xj1ukSN0xOMlMvc+ts3W42wzjC2IuCDpZBA9JPEZH3i8gniuVnFeU0RpY0y7my0ub+i8t84pF5FppdwiCAPCf0PCZrJcaqIacnqtw6W7dp+oZxxOl/UZAgpHl+2CKNNIOa1f8OvBZIYHX288uGJdSwSbOcC1db/N8HF/j4o4s8ttil2U4IA4/JSsSZiSrnpmqcHa8wV7f6LYYxCrgXBenqmxFR6CQpaWZGYj8MqvWqqvp/RKR/3W5vgjuydJKMS40Oy3HM5UZMyfeYHCtxZjzi1PgUpycqbjZmJaRm8xkMYyTofzMiCq0kI8uVNMvtTXL7YC9VWZ9MkeAvIt8MXBiaVEMmzTIWWjGPXmmx0GwzXSlz29w4T7/BFdGrl8KB38RmGMbRoTdBrpOkxfsf3MRWe1HQ3hnUOHwv7sU7TxORx4AHgJcPTaohs9SJeeCJZdqdjJrvc9NsjaecqXP76Qkma+XDFu/6kKWQp+AF4FuPyjheBJ7nMpeynMAXK9K3DwbNVvoi8HUiUsPFKdrA3wceGqJsQ6ETp3zu8WUuLnfJgHK5zETZ58xklXr5hNRKylLoLIIIqEJ50gyEcazoVXK92ooJfM+K9O2DHa9WUTLjtSLycyLy9UAL94KeL+AmxI0cl5ZaPL7Uxg88RIR6Sbh5Zoyz4ydoclueOsPgR+4zH9nwkWFsSzkKmBsrM1YOLK11HwxSlfUq8BfAK4EfBSLgJar6keGKdvB04pRHrrRoxRkRQqXi88wbJ3na2YmTFazyAjdiyGL36Z2gthsnCivSt3920wq3qeqXAYjIm4HLwM2qujJ0yYbAcitmJc6IfJ8VSTg3XuW5t0yfvNLbfuBcSRZzMAxjG3bTCknvH1XNROSBUTUMAO085cpKh1Y3ZiwKuGG6dnKL6PlmFAzD2J7dtMOzRWS5+F+ASrEsgKrq+FClO0A6ccrFqzGPXm1xpdFhpl4lSXOyzMpvG4ZhbGS3l/3410uQYdOMExaabaq+sBJ4VCMhy4qZlIZhGMY6hhapEZGbROR/i8inReSTIvLqYv20iLxPRD5ffE4NS4Z+0jTnkSstPne5weVmlzhTJmoRgX9s7J9hGMaBMcwwfgr8kKp+KfAVwPeKyNOB1wDvV9XbgfcXy0OnGacsdxJmqhVqUcCNkyVm6iXKoRkHwzCMjQwtIqmqFyhKbKjqioh8GrgReDFwV7HZW4EPAP90WHL06KYZE+USY+WcVjdishZx5iTNbTAMw9gD10UzisitwHOBvwJOF4ajZ0BObbPPq0TkvIicn5+fv6bzp1lOOQypV0JEldMTJZ5zElNYDcPYljTLrYprH0M3DiJSB34L+MHiPdQDoapvUtU7VfXOubm5a5IhzXPwoF7yGCt73DRVZbJyQmooGYaxK70XBbVj92kGYsjGQURCnGH4VVX97WL1xeJ91BSfl4YpA7hg9Gcevsr98w1WYmW5m7LUjod9WsMwRoT+FwVludLsmoEYZraSAL8AfFpVf7rvq/tw9ZkoPt85LBl6tLopy52UVpyx1E653GiTZtmwT2sYxojQe1FQO05ZbifEmbLQ6tI4waOIYU6R/SrgO4CPi8hHinX/DHg98DYReQXwMPDSIcoAQEJGJ1Mkz2llGROVMWolizcYhuHovSio2U0Yr4SEvsdCIybLlTjLT2ThvmFmK/0pbib1VjxvWOfdijR1w0T1Icw8xiuBpbAahrGOwPeolUKyTkInzkCUShigyol8F8SxL66TZjnNbspkNWSs5BP6PrNjpe3N1nHFXu5jGLvSG0F0/BTPc0WLFT2R74I49loizXOqUUCtFLHSifE9oR6FJ+vHtpf7GMbABL5H3Y8oh7kbMZzQ1wUfew0ReB6+53NuukSalpiqRsyNl0/Wj93/cp8sdstmHAxjR076uyBOhIZQLyfwAqo1j8la6eSV6baX+xiGsUeOvZboJClpIkzUItqdjKh42fiJwl7uYxjGHjkBWkJAlEB8SpGe3Cqs9nIfwzD2wLHvQpdDn1CEVjcmFLEUVsMwjAE49sYhzXLaaU6uQjvNT+xsR8MwjL1w7I1DN80IfY+peonQ9+imVjbDMIyD47hWcz32TuhS4LPUbnDhaoNqOeTshFVjNQzjYOhVcxUEJTtWZTaORyt2oNGNeXShxUKry6WlDp0kPWyRDMM4JvRXcxXEvR7gmHCsjUOa5Tw036TdTaiXyoSBx0o7OWyxDMM4JvSquSZpfuzKbBxrt1KjE9NIM5Y6GVebVzk9WWWsEh62WMPD6icZxnWlV4vpOJbZONYaJMuhFgU86VSdhZU2N0+XqZePaaluq59kGIfCcS2zcfxa1EetFJAkKSuNNlO1EnNj1WPlE1xHf/0kEbdsGMahcBwymI5117KTpFxpJlxpJFxppUzWImbHjmm2ktVPMowjQS+DKcuVNMuZqkaUo9F7Ho/1yGGlnaA5TNQiQk9otNPjO3Lo1U8KawfvUspSSDru0zCMHUnznCxXOklGJ8m52opHcgQxeuZsD4ShcGmlxVInw/fgqWWfY/2Wn2HUT7JYhmHsicDzSLOcNFMAUs3pJBn1EYtLHOunPPQCbjszxspKQu7BZBRZbaW9Yu+CMIw9EfgeU9WI+ZUOnTRHE49WkFIO/ZEKXB/rp9z3oB5F1GciuknGzHg0Uj/OkcBiGYaxZ8pRwFQtotHJKEf+SL6HemiSisgvisglEflE37ppEXmfiHy++Jwa1vkBymFAvRQS+R4z9TL10jFNYx0mw4xlGMYxphwGRKE3su+hHqa0bwGev2Hda4D3q+rtwPuL5eEhMDtW4sbpKrNjpWMdbhgqfgBh2QyDYeyB3gS5SuSt1lwapRTXoRkHVf1jYGHD6hcDby3+fyvwkmGdH9amtqepjqTlNgxjtAl8j3IYrBqG5U5CO3afR91AXG9teVpVLwAUn6e221BEXiUi50Xk/Pz8/P7PqKCioPs/hGEYxrUyakX6jmxXWlXfpKp3quqdc3Nz+zpGmueEvs9YOSL0/SP/YxiGcXwZtSJ911u6iyJyFqD4vDTMk43aj2EYxvFlqxjEUeZ6S3cfcE/x/z3AO4d5slH7MYwdsFnaxjGgPwZx1Bla+omI/DpwFzArIo8C9wKvB94mIq8AHgZeOqzz9ziuFRNPFDZL2zCuO0N7wlT1W7f56nnDOudGXNpYBujIWOvrwqi998FmaRvGdefYPmFplrPQ7HKl0aWbZkzXI26YqJmBGMVeuM3SNozrzrF9ytI8pxWnzDc6aAbNbsp4KWKyVjps0Q6XUeyF92Zp72W0M2qjI8M4YhzbpybwPJZaXR5fbDFWKVHNfbpJCpxw4zCqvfC9VJwdxdGRYfSRZvmhv3r02D4xaZaTZBDgk8Qp4bhPKTy2zR2c/fTCR41RHB0ZRkFvJrUgKNmhZVoeWwd8N82YrJZ4ytlxZuol5mpl6uXwsMU6Ghz3WkmjOjoyDNbPpM5ypdk9nFIbx9Y4lAKfXBVfhMlqiRunqhaMPilYJVljhOlN3m3HKcvthDjTQ6nFdGy1ZeB7aK4st9pEvjeS73A1roHjPjoyji29ybuRL4xXQipRcCi1mI7tk3O50ebPvzBPq9ulWmoyUQs4M1E7bLGMa8WykIwTQOB71EohWSc5tPI/x/bpevDSMh9++CrVyKcVt/jSszUzDqOOZSEZJ4jeCOKwspaOrVupk2RonhMEHpr3ZkoPiZNQ9+ew25il0G1AnrksJBE3gjioY2/VtsNus3HiOcxaTMe223XDdJ3pWkSeZ0zXIm6Yrg/nRCehNzvsNu7mKuqdXzPoLLl1nr/3LKStzrNd27ZaD+bSMk4Mx/YOv2GyyvOffSNLzRYTtSo3TFaHc6KTkFOfdiDtQlQF8oNtY9KB5hV3PM/f2vD0rnFQ/IZeCUr1vcmwnRHY7vfbuD7tuL/j3AkwjD6O7d1dDgOefHqMPBtzOmdYE+D6c+qzBLzU/R0XxZGl0G1C0nJ/pTEo7VEpb9fbzlJoXnZKVwMIKlsbnv5rLP7eDQNsbwS2mxOxcb1y/DsBhtHHsb27A99juloafjCnl1OfdiBNIe9Cp3N8epZ5CkEAwSwkbQj3oJh3c0flKQSh+8xSkHRrV9FBzOrezghsd+yN68H9rtc6sc6yrQbHrtWhcqyv+HV7l4MfQB44JXqUepaDPFy7bdNTqpJDELm5A4Oym8vNC9xIIKpAmkBtZns591Jbabv992pgNp7zWg3UIC40w3ESYnlHHLvaB8VRK9kwyMM1yDbX0mvf7Zr0H7tyQL3DnYzdVgZmL0roWgzUoC406y07TkIs74hjV3s/bPUAH7WCdlsFVBNAgKC8czC2R387w/Jaaudeet67XZPeul5a6rUo37Tj4iNBMHhv83opoUFcaCett7yTITxqna0TiF3xvbLTA3ytPcv9Gpat9u09XEkL4g7QgDx235XGoDq78wPY384sAb8MWQf8cG+KazflfxAKsXeMtOvaG8w6N9ggin43JXRQPflBXGgnqbe82+9+1DpbJxC74ntlGA/wtSjIjftGxXwOL3D/N684ORuXoTru1uXZ2kMnxQhiYwZQr53iQXcFaLlge3kSPG/wdg8SlL7W69k7RlR1xqG74uQOB5gRv1XguTc6goOb6zCIC20rQ9VvnDaee7dMsKOsWAf53fs7Fr3OxVFtz0FzBH6/E3CVB2TQH2MYPc2ND0rSgbQ4Vi/dsn9bZa2oXNKBzjKoB744t0ov4BnW3cSxlXmn5NoLMH0z1ObccVYuQtoq2pSCN7t2jjyFuA1aFPuKqrB0GfLcjR5KE/tr21ZB6Wt1H/SOQQ5hBbLMuXDixmC/g9+nbPuNQVDe31yHpOP+wvL6AP52I8v+e2ZThlQhT5qC6NrILaq79m0ly6CdjcNUQIP87v2TH9tLUJ44GYH8I+JePMZXeBeyvt5Inrqbb7X3XXM/Rs83309/6mrSgfai63V7haLuLgLibuLq7Ob9e4qjp/Tzwo+fdsEv/pcVlzbaWYGg5B6g0oRz64i445enoDIBSw/B5ftdJpEXQW0KKtOuB+0FsPAQrDwB5TGozEBpysnfvAxx07maSiU3mugpv7yYiRzVIFfnDmlddZPPyhPOQGQpDPJ6jJ4SiFvFPht68zv13Ad9IHrHSDrgK3hFiY2kXayrD3acjYZM2X2uQ0/eXiwnT2HxYTdy0Rwmb96c4dV/72UpJI317rre9kln7XxJd+3e7HUgNnYo8r57arde+WErIL8Y2SYdiLapnttrh3rueopfGMq+tu4m82EawP2e+4i4Fw/FOIjI84E3AD7wZlV9/dBO1m1AaxGSJjTmizkIlbW0zPZK4Qduu+XFx6E6DWNz7oH1yq4CVdyFPIHKpFMEFz4FS0/A+CxUpqBUcz3W9mWnQFULAQK3f89907zkbvTli65HtHQRJs+4G8Evud5ht+kUvSbu+7TjjM3KFSfr5FloL0NrDJrzkHUBcSOHJIFKcerWEiw+4lxLK5fghhpUJ3s/gvtM2oULqrqm/MR3MvqRG1FcveC+ay+4UUptCsJobfSyW4ZQVHfGKOvC5Qec/JXJ9dv1B5Q1c4p17MyaotzNhZJ03LXvtqC7DNGYc38l7a3dZluxsTfb6/n3G65m0ylq8Z1xbF6BeNl9Vxpzv7N4TonHzbURRL+sncU1A+wFzpDXZpzRTfuM2UZ58tQZWc93CjVuuOWkA37bjZp6o4qkDY0F8AT6C072rmO2DwV0kIo2S9dGPtuN8PpHhJq7+yJTN4oaJOngMA1g0nH3fBC6e2Uv5z4iwfjrflYR8YGfB74eeBT4oIjcp6qfOvCTffoP4E9/0SnVrLWm5MvjMD4H4zfA+DSUpotef8M92BOn4NTTYOlRWLwErSfcjRbV3F+3CY3H3U1aHof6WeeuGZt1PeygBMuXnHGZOuOMhgTuQQwCt/6BP4PGFciaMHGLU/qVaUiWYWXBGSIpRiBZFxafgNYVd4ypczD3NJg+B+o745enzqh5PlxNoVSGq0/A/OeLB7ANc1/qFEYUOTk1Bzx345Yn3U3Ym+jVbThltXwRmhdd+xvzUJ0qer2+a1NYdqOunXL3k47b9+pDbpvmRbjxOW5/Td31TFtOWbaXnHLzPKdA66fd8XZzoXSWYf5z7kFsLzk5q1OwfAEWHoWZm1wvfqcHdKtRzEYfv2gx70PdshajFHDyhmVnnBrzEJZg4tza8fuLB4rvrn+3Ae2rTpGMnXbXpDdiXTciStz922nA+Ok1g9O8XLhdilGmeO5aLV9cy9wKy1A/47bvuWniYnQWsdaR2WnElqXQuuxk325UvBcGjTn0Ruleae03SJogEexWyiXpQBo7ozlogkI/gxrDjdtlqes0pB23PupLWx7kmP33Ye9a9dZfRw7DJH058AVV/SKAiPwG8GLgYI3D/R+Ad78OVu7f/F0XWKpC7ZRT9lM3w/K86415HixNw2OfgOYTEC+50QQ+VMZcpkkQuQcw9yBvQfU0zNwKk7e4HzCPoX3F9eJXbnFKwi85BdZZgXYDOpfduTQDQidHEkPjojMC/UHj7oq72Uggxz0cShF/KHqb+M4ttfhRp1CbV1wP9/LninhCBPOfci4MT5ziD4vebVC4lWozxfmuAClcfhiaF6Bz1clGBjoJiw9CcwHmbnEGsjReKKbCN16qr40q2ovwxKdg/gvQvrimLK884B7auAUoVOruQe4sObnLhUtLc5ctFZbdeTRb/5D3lAze+h72xc9DWIX0KozfBBcbbv/67M73Te+4aTFhLc9c22ozTtlQBL7jlruu4kOy5LbrjSrby24E4wcw3VlLA+4vHthzqaQd8IoU13DMKfP+9vmBc6GkxahQE1h6HMZOFR2OsMjSarsYUmUSOm23LigMTVLES7LUdTTiwo0ZlJ1r0iuMb9aFbscZn9IGV1zacfehH7mRTlR3I5z9jiZ2y5TrN9C9OE/ccR2tdIBSLj13XdJ0f9H43su+DBq32bhd7/fTwhikiUtC2OucGjhc15+qXtc/4JtxrqTe8ncAP7fFdq8CzgPnJyYmlMIDDOj58+f1/Pnz69bde++9qqp69uzZ1XV33FBWvXdWX3lHuG7bx/5JXe972fS6dW/89i9T/YW/u27d3V82q/qfv1bvflp93Xp9w9fqG//ODevW3fc9z9LH3vB31q175V1PVv3179E7bju1uu7sREn1p/6a3nv3k9e36T+8TM//zHevb9OLn6r6iy/VsxPR+jb9/DfqK5/3pevb9JZ/qPf9+Leub9M/fL7qr33X+jZ9+e2q73u93n3nrevb9Gdv1jf+u9eub9N/+Ef62K+/Zn2bXvQ1qn/xJr3jqWv7n52bVv3in+i933/P+jb9+R/r+d97y/o2veJFqh9+u549NbvWpmfcrvrgX+krX/ZN69v0P39R73vD+vO/8d++RnXpwvo2veAbVBcf07vv+or1bXrPv9M3fv/d69v02+/Qxx57bH2bXvlKVVW944471tp0+pTqJ9+l9/7gP1jfpve8Tc+/5+3r2/QjP6h6+X49e3rtd77j6V+i+uHf0Fd+2/p74rEvflbv+83/sb5N//EnVB/8s/VtuvtuVVW9++718usDf6Jv/Nc/sr5N/+O/6GMfX7//K7/1JaqffZ/e8cynrLXpzGnVRz6s9756/X12/i//TM9/4N3r2/Rjr9v8PD3rGaoLD+orv+Nb1rfpsx/R+37tl9a36Y1vVHUP8u5tShN94xvfuL5Nb/8VfezzH1/fpnu+TXXxMb3jOc9ea9PZs6qqeu+9925u05/+z/Vt2kpH3HGHqqq+8pWvXN+mB+/X+97+K7u36YUvVG1e1rtf+Pz1bWrM6xvf8JOb770vbGjTVvder00/9rrNbRpE761v0/n96mrRVd/49UFEXgp8o6r+g2L5O4AvV9Xv326fO++8U8+fP7+3E93/AbjvR2DpAdzsrx41oAzVCahPgleBudugNQ9SgrwNtdNuBuvCF92Qv7PsemmlurPitWnnNsgE/Bz8ivMtnv0y17PqLEO84nrl47e4EUdnCRYfBU2dq8OvuZHDxBk4/SzXm88yF/u4+ClcLz13vd2VedcLai+73tSpp8D4WRg/B+UqpFkRdJ6AxQfciKhxybk+Gpdcb37y5mIY70MusPK48+nHi0DJ9dSnngQ3PgtQ1/POihFK87Jrx8QsxIkbTTSvuhHO5FlQH6afBDM3u15ep+nOUa452VsXCtdXBjNPgYk5UIGVC67XXBmHyVsL90Ls9l963H3fXXEjgFK9GN3MupjH+E0wecNarzwvYhYXPwsLDwMd9/vMfw7qM65Np54B9TmY+5LNPeMeScf9VkuPw8IDLpDfc29N3+ruh6TrRiCeV/jAW2vuoTSBxYeKgHQK9RvdqLIy4Y5bmVjzQUORatx1v1PccNcjqsLs7WsyZqkbgV25340CJHMjhzx3lWqTprsn/dJaFlPWheYylCqQ5c6t4gfF6GzFudw0g7GzbgSx8JAbIUYVJ1/9zFp8qidD84rbR/wiRpK6NqVt931QdvfUtfRuk45rT8/d5JfWRg5Z4u6bYcQbtkoJ386VOch59lIeflCZ9jFyEJEPqeqde9qp4DDcSo8CN/UtnwMeP/CzPPkueNFPwofe6RT1xA3uZs898BL3kKYJjN0AlRpQ+OWzrrspoxKcuwNWrrpgc9JwSsYPoDztArKNBee7z1LoLjg/rKYuXtFpOuU8fUsRg7jolP3KRaeg6qfdsHjirFMC5Ql306fqhvV+2fnmJYS5Ih6SdJ3bYuIGmP0SpyDai87d4FFkMNXBrzpjEFWc/7294M7XXnJGJAycDH7JGZbIKwLw4pT5+Gn38C+3nOLzAwhDF9OoiDtXNO4e5LgLvucC3qUKzN8Py487Ob3IKa/quDNyUzfDqacWcyeK64Pn3Eu1woe9fKkoMZEWcYiGU5xe5BSgeM7911mCZsn56Xv+eS+AidPut1p6AjR2v4UfOtdeEDp3xMql9Zlo/Q9yb25BELrfPW4491ZjHloL7ljlCSdTVHfGtrXgDFIYuayysTMunuQF7l5aLpIcyhNsKjdem+n7bYtMnLhPRnAKIi6yz8qRy1qL286IVKcgq7nfsmdM/LLbthY4ZZ4uueuXpW678SqrGXW96zB+2in5XjLCxgwrP1gzCBsVXlas680Av9bJgv3upqC8lgXWcwvt5MbaLi14N5k2xkBgsP23m6y3VdryXif2HfJEwMMwDh8EbheRJwGPAS8Dvm0oZ3ryXe6vR+/G6WVr+IHrGffSEMEpg8ZCodz8NT98b5/+3PVeloxQ+KiXXFqoXyivtOOOX5l0AdgzT4NL9+NiB+oMVGXS9Ygqk84whSXXyy6PQ/dWd6ys8DPniVNIU+dcTz0oF8ahAzc+w40swrJ7qNpX3V9Yhumb3NyGpFkElouJcZ4PyZe6kUBUdw/G5FmnAM89GxZnnbGoF0q5NFnEXdpOEc7jDEVl0in/ZtO1CwX1IG06ZeRVYGLSGbOwUlwTz123PHf792IU9Wnw1BmFxUcKA3cFJmcKJZG49paKdON+ZdTz3595ukvbzROnQNtXi5Fe17Wzl4HU22djyY1aMdLwItdbDgJYeNBdX9+DiZtc8oAXQjRRBPMfdgatWQTtJ0quUzJ22q3vLLsR0sasKb8YkcZNF+NKuu6+6Jcxz5yhIy967afdsbLMrfP8NcPQ62nGbfc7BhX3u/dmaNdm1+7nfoVTqrsOx1bzM/pl3Sh7bWZ9Vs61ZtbspGwHfSlUf0970EKR/UZptfR+MNj+281fudZt97P9AXLdz6qqqYh8H/AewAd+UVU/eV1OPsiFrkwWs4gHsNZ+wGraYanuHrz+4/SMQ0/xnXkGzD11bY5E3CyUOU7Z9R6siT5D1Js/MXObkykorymYLHUKtlSkMM4U5/cCmLjRGRkpZFNcj1rEfReUnRxB6LZLc6hUndJrXXajnIkilTRtA+rOU52FG+suA6wy5XrMUlS+nbkJtAsL94MmxYijBGNT7tr0DFqvneXJ9dcH3PdRzY2OlnpupdCNQOqn3TknzhaBVN/tk3TcZ++9E3kRJPeLvHi/4lxCnZYbdWTJWtaQps7NUplk9dWjYeEeSTtOloWHnNEbm3Ptbi2uBd3TxLmXqlNum7Tr3GDTp51BCSLnQqxOr1f4G5Xs2Gk3wmwtFO5Lf+0ezNLCoE45A16ddttsVJb9cx+C1LkT/aAIxtbWj5a2uq+3Mwo70btWB9m73eo5HcTFci3zA/ozo45j6f19cCitVtV3A+8+jHMPxEFY637DsdX6Uh2yyQGNUB3Y4lhbDYV7D7cfOOXeT085VWbW98Rqp933nUUgX69MqrOuFx8ExeiikD8oO3dc/RS0lt1IwvOcG03V9Zr9EKozLu22VHH7rJsxvM31KU+6bc+lRZwmc+612gzM3b7mZoC+XPmOk7E86Vw+AUWWSs3FZaqzUE1dOm3ryvpRQrzsztFzGfXwip6x5xdzCpqunbXpNQVemylcK3ExkdF3RkK8whiW1iZJ7lZLqD7rjFTScYY86zplVZmAtq71zoO+37j/GP29X/GhVrSlNOTe5/Xo3Q6i+K91fkAvM+yold4/JE5mq48K1/pQ7fVh2Mot0L/cG873KxN/m6F1T4lHdRi/0a1LO1Cdc8ozbgLFhDpPXA+7NGC7egZo+jaXbquZU7b9k+I29pT91J1DxPXWoyrEuevF92pBxQ24+rBz8eQpVFpu39LYWs0o2NxLrUzC6adAc9H5+8fPrhmGsOwmw/nn1t7VgLrSJf0joo0zmrdTOj0FlYdr23rB9r3zjaOH41qsbpB7/SDaf0QmoB0FTm7LjwMHrQyu1R/aGwmE5TVF2VNee6l11CMsOxfYdu3bWJqjN+taAudO0xw3u7aYDd5YcEars+gUb2+Ckh+wWr5iq/ITSaeoZDtTlCXZ4h3WYdm5hnaTdRCls9W2e3G1HCej0GPQe/1a23/Qz9QRKKC3X0ZLWmMzR1EZ9CvKrPDf7neYvlP7/GCtNEcQupFLzyWVp2suop7CL9edgu9l+ozPgl9263vb9c7Vr5x7pSp2e4f1brIOqnQG3faI1OC5blyve/2gznPY9auukdGR1Bgteg+Ylx7Mu5d3IgiBYrZ5L6C81UMYRC4m4pdcyq8KkDnFv/G9HBuV81YZPnvloLNazAVytBlx4z06khqjyfXwg7eXXEwii53LZ7sCgL0YSf2UcxVlXTeC6K+w2i/nTvGZo8BxjjEcB0bceI+WtMZoMizF2ptnEtWKgnuLRd2pdPv3YffWeQF0UhdDSBM3mhhkRuxR4ygaLcMx4sbbO2wBDGNf9Py5WdcFmbPYpZFG1bX5CjvRi1ekqQtGt5dYrbA6yP6GMQi9bL8RMwxgIwdjVOn5c6NqscJ3MQXN9zaEj8rOIMTFCMIbTReAYRw09gQYo0m/P7f3HgnY2xB+0zH6sptGsKdnGAeJPQHGaLKdP3evabIj7BM2jGFiT4MxuhxYmRN7DAxjIxaQNgzDMDZhxsEwDMPYhBkHwzAMYxNmHAzDMIxNmHEwDMMwNmHGwTAMw9iEqOphy7ArIjIPNIHLhy3LNTDL6Mo/yrKDyX+YjLLsMNryzwI1VZ3bz84jYRwAROS8qt552HLsl1GWf5RlB5P/MBll2WG05b9W2c2tZBiGYWzCjINhGIaxiVEyDm86bAGukVGWf5RlB5P/MBll2WG05b8m2Ucm5mAYhmFcP0Zp5GAYhmFcJ8w4GIZhGJsYCeMgIs8Xkc+KyBdE5DWHLc9OiMhNIvK/ReTTIvJJEXl1sX5aRN4nIp8vPqcOW9adEBFfRD4sIu8qlkdCfhGZFJF3iMhnit/gr4+K7AAi8o+L++YTIvLrIlI+yvKLyC+KyCUR+UTfum3lFZHXFs/xZ0XkGw9H6lVZtpL9J4t752Mi8jsiMtn33ZGRvZBnk/x93/2wiKiIzPat25P8R944iIgP/DzwAuDpwLeKyNMPV6odSYEfUtUvBb4C+N5C3tcA71fV24H3F8tHmVcDn+5bHhX53wD8oao+DXg2rg0jIbuI3Aj8AHCnqj4T8IGXcbTlfwvw/A3rtpS3eA5eBjyj2Oe/FM/3YfEWNsv+PuCZqvos4HPAa+FIyg5by4+I3AR8PfBw37o9y3/kjQPw5cAXVPWLqhoDvwG8+JBl2hZVvaCq/7f4fwWnnG7EyfzWYrO3Ai85FAEHQETOAX8beHPf6iMvv4iMA38D+AUAVY1VdZERkL2PAKiISABUgcc5wvKr6h8DCxtWbyfvi4HfUNWuqj4AfAH3fB8KW8muqu9V1bRY/EvgXPH/kZIdtr32AP8J+FGgP9toz/KPgnG4EXikb/nRYt2RR0RuBZ4L/BVwWlUvgDMgwKlDFG03fgZ3c+V960ZB/tuAeeCXCpfYm0WkxmjIjqo+BvwUrsd3AVhS1fcyIvL3sZ28o/YsfzfwB8X/IyG7iLwIeExVP7rhqz3LPwrGQbZYd+Tzb0WkDvwW8IOqunzY8gyKiNwNXFLVDx22LPsgAO4A/quqPhdXj+souWB2pPDNvxh4EnADUBORbz9cqQ6UkXmWReR1OBfxr/ZWbbHZkZJdRKrA64B/sdXXW6zbUf5RMA6PAjf1LZ/DDbWPLCIS4gzDr6rqbxerL4rI2eL7s8Clw5JvF74KeJGIPIhz4f0tEfkVRkP+R4FHVfWviuV34IzFKMgO8HXAA6o6r6oJ8NvAVzI68vfYTt6ReJZF5B7gbuDlujYRbBRkfzKuY/HR4vk9B/xfETnDPuQfBePwQeB2EXmSiES4oMp9hyzTtoiI4Hzen1bVn+776j7gnuL/e4B3Xm/ZBkFVX6uq51T1Vty1/l+q+u2MgPyq+gTwiIg8tVj1POBTjIDsBQ8DXyEi1eI+eh4uZjUq8vfYTt77gJeJSElEngTcDvyfQ5BvW0Tk+cA/BV6kqq2+r4687Kr6cVU9paq3Fs/vo8AdxXOxd/lV9cj/AS/EZQ7cD7zusOXZRdavxg3XPgZ8pPh7ITCDy9z4fPE5fdiyDtCWu4B3Ff+PhPzAc4DzxfX/XWBqVGQv5P8J4DPAJ4BfBkpHWX7g13HxkaRQRq/YSV6c2+N+4LPAC46g7F/A+eZ7z+5/O4qybyf/hu8fBGb3K7+VzzAMwzA2MQpuJcMwDOM6Y8bBMAzD2IQZB8MwDGMTZhwMwzCMTZhxMAzDMDZhxsE4MEQkE5GPFBVFf6+/ouU227/kWoooisitIvJtfct3isjP7vd4G479zzYs//lBHHe/FBVaPyYi//gw5TBODpbKahwYItJQ1Xrx/1uBz6nqv9lh+7fg5lG8Y5/nuwv4YVW9ez/773Ls1bZcD0Qk0LWCbxu/OwP8larechDHM4xBsJGDMSz+gqKwl4g8WUT+UEQ+JCJ/IiJPE5GvBF4E/GQx2njyVtsV+79FRH5WRP5cRL4oIt9cnOP1wNcU+/9jEblL1r9/4neL3vZfisizivU/XtTB/0BxrB/YKLiIvB5XGfUjIvKrxbpG8XmXiPyRiLxNRD4nIq8XkZeLyP8RkY+LyJOL7eZE5LdE5IPF31dtcZ7vFJG3i8jvAe8VkVoh2weLwoG96sPvBU4V8nzNLtfpp0XkfwP/fh/XExH50aIdHy2uw5a/3/5vC2NkOOxZfvZ3fP6ARvHpA28Hnl8svx+4vfj//8WV5ABXj/6b+/bfabu34zozT8eVcIe+Gdwbl4H/DNxb/P+3gI8U//848Oe4mcezwBUg3K4tW7TtLmAROFsc4zHgJ4rvXg38TPH/rwFfXfx/M66cysZzfCduZut0sfxvgW8v/p/EVQWoAbcCnxjwOr0L8Pd5PV9QXJtqsTy903Hs73j/BRjGwVERkY/glNmHgPeJq077lcDbXbkgwCnVdQyw3e+qag58SkRODyDLVwN/D0BV/5eIzIjIRPHd76tqF+iKyCXgNE5JD8oHtShJLSL343r2AB8H/mbx/9cBT+9ry7iIjKl7x0c/71PVXk3+b8AVPfzhYrmMMyzt3sYDXKe3q2q2z+v5dcAvaVFTSFUXBv39jOOHGQfjIGmr6nMKJfwu4HtxvdRFVX3OLvt6u2zX7ft/q/LDG9mpRHH/sTL2/hz075/3Led9x/KAv66qbXam2fe/AH9PVT/bv4G494L02O06NQfcbqvrKWwu47zbcYxjisUcjANHVZdwr7v8YVyv9wEReSm4qrUi8uxi0xVgrNhneYfttmN1/y34Y+DlxbHuAi7r3t6rkYgrvb5f3gt8X29BRJ4zwD7vAb5fii66iDx34waDXqd9Xs/3At8t7r0AiMj0Po9jHAPMOBhDQVU/DHwUV/b75cArROSjwCdZe83rbwA/UgRfn7zDdtvxMSAtgqcbUzx/HLhTRD6GC1zfs3HnXXgT8LFeQHof/EDv/CLyKeB7BtjnXwFhcd5PFMtbMeh12tP1VNU/xJV2Pl+4B3vurb3+LsYxwFJZDcMwjE3YyMEwDMPYhBkHwzAMYxNmHAzDMIxNmHEwDMMwNmHGwTAMw9iEGQfDMAxjE2YcDMMwjE38/5b/g7pTT/PXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 20191028_SJ_QEx_LC1200_4_Sommerfeld_0Wert_HPMC_FCS_87: : 440it [04:40,  1.57it/s]    \n"
     ]
    }
   ],
   "source": [
    "#constraints = \"monotonic_inc\"\n",
    "constraints = \"none\"\n",
    "\n",
    "# Align parameters\n",
    "perform_second_stage_robust = True\n",
    "error_filter_perc = 0.005\n",
    "num_splines = 150\n",
    "min_coverage = 0.75\n",
    "coverage_div = 200\n",
    "plot_res_every_n = 100\n",
    "min_overlap = 20\n",
    "\n",
    "run_highest_overlap_score = score_per_run.sort_values(ascending=True).index[0]\n",
    "\n",
    "unique_peptides = []\n",
    "unique_peptides.extend(list(retention_df[retention_df[run_highest_overlap_score].notna()].index))\n",
    "\n",
    "retention_df_aligned = retention_df.copy()\n",
    "\n",
    "keep_cols = [run_highest_overlap_score]\n",
    "\n",
    "error_filter_perc_threshold = max(retention_df[run_highest_overlap_score])*error_filter_perc\n",
    "\n",
    "# Iterate over runs sorted by a penalty score\n",
    "# For version 3.8 or later uncomment the for loop below and comment the other for loop; also uncomment the line after update progressbar\n",
    "#for idx,align_name in (pbar := tqdm(enumerate(score_per_run.sort_values(ascending=True)[1:].index))):\n",
    "for idx,align_name in tqdm(enumerate(score_per_run.sort_values(ascending=True)[1:].index)):\n",
    "    # Update progressbar\n",
    "    #pbar.set_description(f\"Processing {align_name}\")\n",
    "\n",
    "    # Check overlap between peptidoforms\n",
    "    non_na_sel = (retention_df[align_name].notna()) & (retention_df[run_highest_overlap_score].notna())\n",
    "\n",
    "    # Continue if insufficient overlapping peptides\n",
    "    if len(retention_df[run_highest_overlap_score][non_na_sel].index) < min_overlap:\n",
    "        continue\n",
    "\n",
    "    # Check spread of overlapping peptidoforms, continue if not sufficient\n",
    "    if (len(set(pd.cut(retention_df[align_name][non_na_sel], coverage_div, include_lowest = True))) / coverage_div) < min_coverage:\n",
    "        continue\n",
    "\n",
    "    # Fit the GAM\n",
    "    gam_model_cv = LinearGAM(s(0, n_splines=num_splines), constraints=constraints, verbose=True).fit(\n",
    "                                                            retention_df[align_name][non_na_sel],\n",
    "                                                            retention_df[run_highest_overlap_score][non_na_sel])\n",
    "\n",
    "\n",
    "    # Plot results alignment\n",
    "    if idx % plot_res_every_n == 0 or idx == 0:\n",
    "        plot_performance(\n",
    "            retention_df,\n",
    "            run_highest_overlap_score,\n",
    "            align_name,\n",
    "            non_na_sel\n",
    "        )\n",
    "        plot_residual(\n",
    "            run_highest_overlap_score,\n",
    "            align_name,\n",
    "            non_na_sel\n",
    "        )\n",
    "\n",
    "\n",
    "    # Calculate errors and create filter that can be used in the second stage\n",
    "    errors = abs(gam_model_cv.predict(retention_df[align_name][non_na_sel])-retention_df[run_highest_overlap_score][non_na_sel])\n",
    "    error_filter = errors < error_filter_perc_threshold\n",
    "\n",
    "    # Perform a second stage GAM removing high error from previous fit\n",
    "    if perform_second_stage_robust:\n",
    "        gam_model_cv = LinearGAM(s(0, n_splines=num_splines), constraints=constraints, verbose=True).fit(\n",
    "                                                                retention_df[align_name][non_na_sel][error_filter],\n",
    "                                                                retention_df[run_highest_overlap_score][non_na_sel][error_filter])\n",
    "\n",
    "        if idx % plot_res_every_n == 0  or idx == 0:\n",
    "            plot_residual(\n",
    "            run_highest_overlap_score,\n",
    "            align_name,\n",
    "            non_na_sel,\n",
    "            title=\"Residual plot second stage GAM\"\n",
    "        )\n",
    "\n",
    "\n",
    "    # Write alignment to new matrix\n",
    "    retention_df_aligned.loc[retention_df[align_name].notna(),align_name] = gam_model_cv.predict(retention_df.loc[retention_df[align_name].notna(),align_name])\n",
    "\n",
    "    unique_peptides.extend(list(retention_df[retention_df[align_name].notna()].index))\n",
    "\n",
    "    keep_cols.append(align_name)\n",
    "\n",
    "    # Create reference set based on aligned retention times\n",
    "    retention_df[\"median_aligned\"] = retention_df_aligned[keep_cols].median(axis=1)\n",
    "    run_highest_overlap_score = \"median_aligned\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462e2252",
   "metadata": {},
   "source": [
    "The data points acquired looks as the following vector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a1aeb9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_(Acetyl (Protein N-term))ACGLVASNLNLKPGECLR_                           70.639004\n",
       "_(Acetyl (Protein N-term))AEEGIAAGGVM(Oxidation (M))DVNTALQEVLK_       118.580954\n",
       "_(Acetyl (Protein N-term))AGWNAYIDNLM(Oxidation (M))ADGTCQDAAIVGYK_    106.010118\n",
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_                               28.451475\n",
       "_AADDTWEPFASGK_                                                         54.339311\n",
       "                                                                          ...    \n",
       "_TALLTWTEPPVR_                                                          70.065188\n",
       "_TQFNNNEYSQDLDAYNTKDK_                                                  39.432309\n",
       "_VATGTDLLSGTR_                                                          30.655512\n",
       "_VNWMPPPSR_                                                             35.224075\n",
       "_VTDIDSDDHQVM(Oxidation (M))YIM(Oxidation (M))K_                        27.263047\n",
       "Length: 49983, dtype: float64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention_df_aligned[keep_cols].median(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d3a693",
   "metadata": {},
   "source": [
    "If we look at the standard deviation we can see that this is still relatively large for some peptidoforms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25bde631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(retention_df_aligned[keep_cols].std(axis=1),bins=500)\n",
    "plt.xlabel(\"Standard deviation retention time\")\n",
    "plt.show()\n",
    "\n",
    "plt.hist(retention_df_aligned[keep_cols].std(axis=1),bins=500)\n",
    "plt.xlim(0,7.5)\n",
    "plt.xlabel(\"Standard deviation retention time (zoomed)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15c5b81a",
   "metadata": {},
   "source": [
    "In addition to the std there is another factor that can play a big role, the amount of times a peptidoform was observed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56b92129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(retention_df_aligned[keep_cols].notna().sum(axis=1),bins=100)\n",
    "plt.xlabel(\"Count peptidoforms across runs\")\n",
    "plt.show()\n",
    "\n",
    "plt.hist(retention_df_aligned[keep_cols].notna().sum(axis=1),bins=100)\n",
    "plt.xlabel(\"Count peptidoforms across runs (zoomed)\")\n",
    "plt.xlim(0,20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e37f74d",
   "metadata": {},
   "source": [
    "If we plot both values against each other we get the following plot (the lines indicate possible thresholds):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4807dcaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    retention_df_aligned[keep_cols].notna().sum(axis=1),\n",
    "    retention_df_aligned[keep_cols].std(axis=1),\n",
    "    s=5,\n",
    "    alpha=0.1\n",
    ")\n",
    "\n",
    "plt.ylabel(\"Standard deviation retention time\")\n",
    "plt.xlabel(\"Count peptidoforms across runs\")\n",
    "\n",
    "plt.axhline(\n",
    "    y = 2.0,\n",
    "    color = \"black\",\n",
    "    linewidth=1.0,\n",
    "    linestyle = \"--\"\n",
    ")\n",
    "\n",
    "plt.axvline(\n",
    "    x = 5.0,\n",
    "    color = \"black\",\n",
    "    linewidth=1.0,\n",
    "    linestyle = \"--\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53cc8518",
   "metadata": {},
   "source": [
    "If we set a threshold for a minimum of 5 observations and a maximum standard deviation of 2 we get the following final data set. Here we take the median for each peptidoform across all runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "757a5174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_(Acetyl (Protein N-term))SDAAVDTSSEITTK_     28.451475\n",
       "_AADDTWEPFASGK_                               54.339311\n",
       "_AAGVNVEPFWPGLFAK_                           104.666562\n",
       "_AAPSVTLFPPSSEELQANK_                         64.668819\n",
       "_ADDKETCFAEEGKK_                               6.963496\n",
       "                                                ...    \n",
       "_AEPYCSVLPGFTFIQHLPLSER_                     106.951930\n",
       "_AFMTADLPNELIELLEK_                          131.490540\n",
       "_IAQLRPEDLAGLAALQELDVSNLSLQALPGDLSGLFPR_     132.956933\n",
       "_VETNMAFSPFSIASLLTQVLLGAGENTK_               136.516511\n",
       "_VNTFSALANIDLALEQGDALALFR_                   133.205437\n",
       "Length: 21588, dtype: float64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_observations = 5\n",
    "max_std = 2.0\n",
    "\n",
    "observation_filter = retention_df_aligned[keep_cols].notna().sum(axis=1) > min_observations\n",
    "std_filter = retention_df_aligned[keep_cols].std(axis=1) < max_std\n",
    "\n",
    "retention_df_aligned[keep_cols][(observation_filter) & (std_filter)].median(axis=1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}