nbs/getting_started.ipynb

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  {
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   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"openpyxl\") # remove warning from mac"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "63ea0375-2937-4689-b1c9-f2186776b329",
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   "source": [
    "# Workflow Example\n",
    "## MaxQuant\n",
    "\n",
    "To perform a downstream analysis on MaxQuant output data we requirer the `proteinGroups.txt` file generated by MaxQuant. To gain biological insight between various phenotypes an additional metadata file should be provided. Hereby the sample names match with the sample names in the proteinGroups.txt file. \n",
    "At first take a look at the files:\n",
    "The `proteinGroups.txt` contains all standard column headers from MaxQuant. Later, for our analysis we will use \n",
    "the Protein Intensity described in `\"LFQ intensity [sample]\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a1480b1-1a76-4888-b7e9-6583687625ea",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Protein IDs</th>\n",
       "      <th>Majority protein IDs</th>\n",
       "      <th>Peptide counts (all)</th>\n",
       "      <th>Peptide counts (razor+unique)</th>\n",
       "      <th>Peptide counts (unique)</th>\n",
       "      <th>Protein names</th>\n",
       "      <th>Gene names</th>\n",
       "      <th>Fasta headers</th>\n",
       "      <th>Number of proteins</th>\n",
       "      <th>Peptides</th>\n",
       "      <th>...</th>\n",
       "      <th>Potential contaminant</th>\n",
       "      <th>id</th>\n",
       "      <th>Peptide IDs</th>\n",
       "      <th>Peptide is razor</th>\n",
       "      <th>Mod. peptide IDs</th>\n",
       "      <th>Evidence IDs</th>\n",
       "      <th>MS/MS IDs</th>\n",
       "      <th>Best MS/MS</th>\n",
       "      <th>Oxidation (M) site IDs</th>\n",
       "      <th>Oxidation (M) site positions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P01911;Q29830;Q9MXZ4;Q3LTJ8;Q3LTJ4;Q3LRY0;Q8HW...</td>\n",
       "      <td>P01911;Q29830;Q9MXZ4;Q3LTJ8;Q3LTJ4;Q3LRY0;Q8HW...</td>\n",
       "      <td>4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;...</td>\n",
       "      <td>4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;...</td>\n",
       "      <td>0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;...</td>\n",
       "      <td>HLA class II histocompatibility antigen, DRB1-...</td>\n",
       "      <td>HLA-DRB1;HLA-DR15;HLA-DRB1*;HLA-DRB1*1327;MHC ...</td>\n",
       "      <td>;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;...</td>\n",
       "      <td>1834.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1287;1288;3971;15222</td>\n",
       "      <td>True;True;True;True</td>\n",
       "      <td>1415;1416;4387;16993</td>\n",
       "      <td>117340;117341;117342;117343;117344;117345;1173...</td>\n",
       "      <td>56384;56385;56386;159948;159949;159950;602426;...</td>\n",
       "      <td>56384;56385;159949;602427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P05121;A0A024QYT5;B7ZAB0;B7Z4X6;B7Z1D9</td>\n",
       "      <td>P05121;A0A024QYT5;B7ZAB0;B7Z4X6;B7Z1D9</td>\n",
       "      <td>10;10;9;8;5</td>\n",
       "      <td>10;10;9;8;5</td>\n",
       "      <td>10;10;9;8;5</td>\n",
       "      <td>Plasminogen activator inhibitor 1</td>\n",
       "      <td>SERPINE1</td>\n",
       "      <td>;;;;</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1592;3771;4396;4628;6470;7404;9193;11222;13191...</td>\n",
       "      <td>True;True;True;True;True;True;True;True;True;True</td>\n",
       "      <td>1746;4166;4871;5128;7155;8188;10148;10149;1257...</td>\n",
       "      <td>136637;136638;136639;136640;136641;318672;3186...</td>\n",
       "      <td>64917;149749;149750;178775;178776;184126;25946...</td>\n",
       "      <td>64917;149749;178775;184126;259465;290355;36587...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P55083;A0A024QZ34;K7ES70</td>\n",
       "      <td>P55083;A0A024QZ34;K7ES70</td>\n",
       "      <td>2;2;2</td>\n",
       "      <td>2;2;2</td>\n",
       "      <td>2;2;2</td>\n",
       "      <td>Microfibril-associated glycoprotein 4</td>\n",
       "      <td>MFAP4</td>\n",
       "      <td>;;</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140;15067</td>\n",
       "      <td>True;True</td>\n",
       "      <td>155;156;16823</td>\n",
       "      <td>13174;13175;13176;13177;13178;13179;13180;1318...</td>\n",
       "      <td>7599;7600;7601;7602;7603;7604;7605;7606;598026...</td>\n",
       "      <td>7604;598026</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P09972;A0A024QZ64;A8MVZ9;B7Z3K9;B7Z1N6;B7Z3K7;...</td>\n",
       "      <td>P09972;A0A024QZ64;A8MVZ9;B7Z3K9;B7Z1N6;B7Z3K7;...</td>\n",
       "      <td>16;16;15;15;13;12;10;10;9;8;7;7;7;5</td>\n",
       "      <td>13;13;13;12;10;10;9;9;8;5;7;7;6;4</td>\n",
       "      <td>2;2;2;2;0;2;2;2;2;0;2;2;0;0</td>\n",
       "      <td>Fructose-bisphosphate aldolase C;Fructose-bisp...</td>\n",
       "      <td>ALDOC</td>\n",
       "      <td>;;;;;;;;;</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>312;749;1545;1675;2085;3265;5046;5708;8847;884...</td>\n",
       "      <td>True;True;True;False;True;True;True;True;True;...</td>\n",
       "      <td>346;827;1698;1838;2304;3606;5586;5587;6314;976...</td>\n",
       "      <td>29006;29007;29008;69091;69092;69093;69094;6909...</td>\n",
       "      <td>15520;15521;34013;34014;34015;34016;34017;6325...</td>\n",
       "      <td>15520;34015;63252;67942;84108;131214;198759;21...</td>\n",
       "      <td>1;2</td>\n",
       "      <td>40;251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q96C19;H0Y4Y4;A0A024QZ77</td>\n",
       "      <td>Q96C19;H0Y4Y4;A0A024QZ77</td>\n",
       "      <td>1;1;1</td>\n",
       "      <td>1;1;1</td>\n",
       "      <td>1;1;1</td>\n",
       "      <td>EF-hand domain-containing protein D2</td>\n",
       "      <td>EFHD2</td>\n",
       "      <td>;;</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8772</td>\n",
       "      <td>True</td>\n",
       "      <td>9685</td>\n",
       "      <td>734937;734938;734939;734940;734941;734942;734943</td>\n",
       "      <td>342134;342135</td>\n",
       "      <td>342134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2530 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Protein IDs  \\\n",
       "0  P01911;Q29830;Q9MXZ4;Q3LTJ8;Q3LTJ4;Q3LRY0;Q8HW...   \n",
       "1             P05121;A0A024QYT5;B7ZAB0;B7Z4X6;B7Z1D9   \n",
       "2                           P55083;A0A024QZ34;K7ES70   \n",
       "3  P09972;A0A024QZ64;A8MVZ9;B7Z3K9;B7Z1N6;B7Z3K7;...   \n",
       "4                           Q96C19;H0Y4Y4;A0A024QZ77   \n",
       "\n",
       "                                Majority protein IDs  \\\n",
       "0  P01911;Q29830;Q9MXZ4;Q3LTJ8;Q3LTJ4;Q3LRY0;Q8HW...   \n",
       "1             P05121;A0A024QYT5;B7ZAB0;B7Z4X6;B7Z1D9   \n",
       "2                           P55083;A0A024QZ34;K7ES70   \n",
       "3  P09972;A0A024QZ64;A8MVZ9;B7Z3K9;B7Z1N6;B7Z3K7;...   \n",
       "4                           Q96C19;H0Y4Y4;A0A024QZ77   \n",
       "\n",
       "                                Peptide counts (all)  \\\n",
       "0  4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;...   \n",
       "1                                        10;10;9;8;5   \n",
       "2                                              2;2;2   \n",
       "3                16;16;15;15;13;12;10;10;9;8;7;7;7;5   \n",
       "4                                              1;1;1   \n",
       "\n",
       "                       Peptide counts (razor+unique)  \\\n",
       "0  4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;4;...   \n",
       "1                                        10;10;9;8;5   \n",
       "2                                              2;2;2   \n",
       "3                  13;13;13;12;10;10;9;9;8;5;7;7;6;4   \n",
       "4                                              1;1;1   \n",
       "\n",
       "                             Peptide counts (unique)  \\\n",
       "0  0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;...   \n",
       "1                                        10;10;9;8;5   \n",
       "2                                              2;2;2   \n",
       "3                        2;2;2;2;0;2;2;2;2;0;2;2;0;0   \n",
       "4                                              1;1;1   \n",
       "\n",
       "                                       Protein names  \\\n",
       "0  HLA class II histocompatibility antigen, DRB1-...   \n",
       "1                  Plasminogen activator inhibitor 1   \n",
       "2              Microfibril-associated glycoprotein 4   \n",
       "3  Fructose-bisphosphate aldolase C;Fructose-bisp...   \n",
       "4               EF-hand domain-containing protein D2   \n",
       "\n",
       "                                          Gene names  \\\n",
       "0  HLA-DRB1;HLA-DR15;HLA-DRB1*;HLA-DRB1*1327;MHC ...   \n",
       "1                                           SERPINE1   \n",
       "2                                              MFAP4   \n",
       "3                                              ALDOC   \n",
       "4                                              EFHD2   \n",
       "\n",
       "                                       Fasta headers  Number of proteins  \\\n",
       "0  ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;...              1834.0   \n",
       "1                                               ;;;;                 5.0   \n",
       "2                                                 ;;                 3.0   \n",
       "3                                          ;;;;;;;;;                14.0   \n",
       "4                                                 ;;                 3.0   \n",
       "\n",
       "   Peptides  ...  Potential contaminant   id  \\\n",
       "0       4.0  ...                    NaN  0.0   \n",
       "1      10.0  ...                    NaN  1.0   \n",
       "2       2.0  ...                    NaN  2.0   \n",
       "3      16.0  ...                    NaN  3.0   \n",
       "4       1.0  ...                    NaN  4.0   \n",
       "\n",
       "                                         Peptide IDs  \\\n",
       "0                               1287;1288;3971;15222   \n",
       "1  1592;3771;4396;4628;6470;7404;9193;11222;13191...   \n",
       "2                                          140;15067   \n",
       "3  312;749;1545;1675;2085;3265;5046;5708;8847;884...   \n",
       "4                                               8772   \n",
       "\n",
       "                                    Peptide is razor  \\\n",
       "0                                True;True;True;True   \n",
       "1  True;True;True;True;True;True;True;True;True;True   \n",
       "2                                          True;True   \n",
       "3  True;True;True;False;True;True;True;True;True;...   \n",
       "4                                               True   \n",
       "\n",
       "                                    Mod. peptide IDs  \\\n",
       "0                               1415;1416;4387;16993   \n",
       "1  1746;4166;4871;5128;7155;8188;10148;10149;1257...   \n",
       "2                                      155;156;16823   \n",
       "3  346;827;1698;1838;2304;3606;5586;5587;6314;976...   \n",
       "4                                               9685   \n",
       "\n",
       "                                        Evidence IDs  \\\n",
       "0  117340;117341;117342;117343;117344;117345;1173...   \n",
       "1  136637;136638;136639;136640;136641;318672;3186...   \n",
       "2  13174;13175;13176;13177;13178;13179;13180;1318...   \n",
       "3  29006;29007;29008;69091;69092;69093;69094;6909...   \n",
       "4   734937;734938;734939;734940;734941;734942;734943   \n",
       "\n",
       "                                           MS/MS IDs  \\\n",
       "0  56384;56385;56386;159948;159949;159950;602426;...   \n",
       "1  64917;149749;149750;178775;178776;184126;25946...   \n",
       "2  7599;7600;7601;7602;7603;7604;7605;7606;598026...   \n",
       "3  15520;15521;34013;34014;34015;34016;34017;6325...   \n",
       "4                                      342134;342135   \n",
       "\n",
       "                                          Best MS/MS  Oxidation (M) site IDs  \\\n",
       "0                          56384;56385;159949;602427                     NaN   \n",
       "1  64917;149749;178775;184126;259465;290355;36587...                     NaN   \n",
       "2                                        7604;598026                       0   \n",
       "3  15520;34015;63252;67942;84108;131214;198759;21...                     1;2   \n",
       "4                                             342134                     NaN   \n",
       "\n",
       "   Oxidation (M) site positions  \n",
       "0                           NaN  \n",
       "1                           NaN  \n",
       "2                           117  \n",
       "3                        40;251  \n",
       "4                           NaN  \n",
       "\n",
       "[5 rows x 2530 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein_groups = pd.read_csv(\"../testfiles/maxquant/proteinGroups.txt\", sep = \"\\t\", low_memory=False)\n",
    "protein_groups.head(5)"
   ]
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   "source": [
    "Further, we created a excel-file with the corresponding metadata to our proteinGroups.txt-file. The sample names in the column \"sample\" match the names in `proteinGroups.txt` file."
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject external_id</th>\n",
       "      <th>biological_sample external_id</th>\n",
       "      <th>sample</th>\n",
       "      <th>tissue id</th>\n",
       "      <th>disease id</th>\n",
       "      <th>intervention id</th>\n",
       "      <th>tissue</th>\n",
       "      <th>disease</th>\n",
       "      <th>biological_sample quantity</th>\n",
       "      <th>biological_sample quantity_units</th>\n",
       "      <th>...</th>\n",
       "      <th>Alanine aminotransferase measurement (34608000)</th>\n",
       "      <th>Aspartate aminotransferase measurement (45896001)</th>\n",
       "      <th>Alkaline phosphatase measurement (88810008)</th>\n",
       "      <th>Gamma glutamyl transferase measurement (69480007)</th>\n",
       "      <th>Hemoglobin A1c measurement (43396009)</th>\n",
       "      <th>Total cholesterol:HDL ratio measurement (166842003)</th>\n",
       "      <th>High density lipoprotein measurement (17888004)</th>\n",
       "      <th>Low density lipoprotein cholesterol measurement (113079009)</th>\n",
       "      <th>VLDL cholesterol measurement (104585005)</th>\n",
       "      <th>Triglycerides measurement (14740000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>1_31_C6</td>\n",
       "      <td>BTO:0000131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blood plasma</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30</td>\n",
       "      <td>54</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.26</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>1_32_C7</td>\n",
       "      <td>BTO:0000131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blood plasma</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>38.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>1_33_C8</td>\n",
       "      <td>BTO:0000131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blood plasma</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21</td>\n",
       "      <td>69</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.12</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>1_34_C9</td>\n",
       "      <td>BTO:0000131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blood plasma</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26</td>\n",
       "      <td>101</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.91</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>1_35_C10</td>\n",
       "      <td>BTO:0000131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blood plasma</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25</td>\n",
       "      <td>61</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.21</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject external_id  biological_sample external_id    sample    tissue id  \\\n",
       "0                   31                             31   1_31_C6  BTO:0000131   \n",
       "1                   32                             32   1_32_C7  BTO:0000131   \n",
       "2                   33                             33   1_33_C8  BTO:0000131   \n",
       "3                   34                             34   1_34_C9  BTO:0000131   \n",
       "4                   35                             35  1_35_C10  BTO:0000131   \n",
       "\n",
       "  disease id  intervention id        tissue  disease  \\\n",
       "0        NaN              NaN  blood plasma  healthy   \n",
       "1        NaN              NaN  blood plasma  healthy   \n",
       "2        NaN              NaN  blood plasma  healthy   \n",
       "3        NaN              NaN  blood plasma  healthy   \n",
       "4        NaN              NaN  blood plasma  healthy   \n",
       "\n",
       "   biological_sample quantity  biological_sample quantity_units  ...  \\\n",
       "0                         NaN                               NaN  ...   \n",
       "1                         NaN                               NaN  ...   \n",
       "2                         NaN                               NaN  ...   \n",
       "3                         NaN                               NaN  ...   \n",
       "4                         NaN                               NaN  ...   \n",
       "\n",
       "   Alanine aminotransferase measurement (34608000)  \\\n",
       "0                                             24.0   \n",
       "1                                             27.0   \n",
       "2                                             18.0   \n",
       "3                                             22.0   \n",
       "4                                             18.0   \n",
       "\n",
       "   Aspartate aminotransferase measurement (45896001)  \\\n",
       "0                                                 30   \n",
       "1                                                 28   \n",
       "2                                                 21   \n",
       "3                                                 26   \n",
       "4                                                 25   \n",
       "\n",
       "   Alkaline phosphatase measurement (88810008)  \\\n",
       "0                                           54   \n",
       "1                                           27   \n",
       "2                                           69   \n",
       "3                                          101   \n",
       "4                                           61   \n",
       "\n",
       "   Gamma glutamyl transferase measurement (69480007)  \\\n",
       "0                                               21.0   \n",
       "1                                               38.0   \n",
       "2                                               18.0   \n",
       "3                                               20.0   \n",
       "4                                               13.0   \n",
       "\n",
       "   Hemoglobin A1c measurement (43396009)  \\\n",
       "0                                    6.3   \n",
       "1                                    5.8   \n",
       "2                                    6.2   \n",
       "3                                    6.2   \n",
       "4                                    5.4   \n",
       "\n",
       "   Total cholesterol:HDL ratio measurement (166842003)  \\\n",
       "0                                                3.6     \n",
       "1                                                6.6     \n",
       "2                                                5.7     \n",
       "3                                                6.7     \n",
       "4                                                5.5     \n",
       "\n",
       "  High density lipoprotein measurement (17888004)  \\\n",
       "0                                            1.26   \n",
       "1                                            1.70   \n",
       "2                                            1.12   \n",
       "3                                            0.91   \n",
       "4                                            1.21   \n",
       "\n",
       "   Low density lipoprotein cholesterol measurement (113079009)  \\\n",
       "0                                                2.1             \n",
       "1                                                4.3             \n",
       "2                                                4.1             \n",
       "3                                                4.8             \n",
       "4                                                3.9             \n",
       "\n",
       "   VLDL cholesterol measurement (104585005)  \\\n",
       "0                                       0.3   \n",
       "1                                       0.6   \n",
       "2                                       0.5   \n",
       "3                                       1.0   \n",
       "4                                       0.4   \n",
       "\n",
       "   Triglycerides measurement (14740000)  \n",
       "0                                  0.58  \n",
       "1                                  1.24  \n",
       "2                                  1.12  \n",
       "3                                  2.20  \n",
       "4                                  0.90  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = pd.read_excel(\"../testfiles/maxquant/metadata.xlsx\")\n",
    "metadata.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1b2da0df-dd79-429f-815e-cea5228790dd",
   "metadata": {},
   "source": [
    "## Start Downstream Analysis\n",
    "\n",
    "## 0. Import AlphaStats\n",
    "\n",
    "Import library alphastats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc23624d-edf6-4b8f-90f9-e30f67d01cb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import alphastats"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "71235990-e4bc-491f-8828-8f9445dcac3b",
   "metadata": {},
   "source": [
    "## 1. Import Data\n",
    "Load the MaxQuant proteinGroups.txt file and specify the columns indicating the intensity as well as the column that is used for indexing, like here the \"Protein IDs\" or the gene names. As the column is used for indexing, the values of this column must be unqiue. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8fafbdca-c13f-4a2d-99c5-704aa299bea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/drq441/opt/anaconda3/lib/python3.9/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.1, the latest is 0.2.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n"
     ]
    }
   ],
   "source": [
    "maxquant_data = alphastats.MaxQuantLoader(\n",
    "    file=\"../testfiles/maxquant/proteinGroups.txt\",\n",
    "    intensity_column=\"LFQ intensity [sample]\",\n",
    "    index_column=\"Protein IDs\"\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aefc5fe7-0f97-42d5-acb6-d9a151320627",
   "metadata": {},
   "source": [
    "## 2. Create a DataSet\n",
    "Combine the imported MaxQuant data with the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d418173-699f-4e2e-9e21-1937218630af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSet has been created.\n",
      "Attributes of the DataSet can be accessed using: \n",
      "DataSet.rawinput:\t Raw Protein data.\n",
      "DataSet.mat:\tProcessed data matrix with ProteinIDs/ProteinGroups as columns and samples as rows. All computations are performed on this matrix.\n",
      "DataSet.metadata:\tMetadata for the samples in the matrix. Metadata will be matched with DataSet.mat when needed (for instance Volcano Plot).\n"
     ]
    }
   ],
   "source": [
    "ds = alphastats.DataSet(\n",
    "    loader = maxquant_data, \n",
    "    metadata_path = \"../testfiles/maxquant/metadata.xlsx\",\n",
    "    sample_column = \"sample\" # specify the column that corresponds to the sample names in proteinGroups\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e0efd5a2-22cf-4504-9ce8-0490a7967e92",
   "metadata": {},
   "source": [
    "AlphaStats will create a matrix of the Protein Intensities, which will be accessable using `ds.mat` and will save the metadata as a dataframe `ds.metadata`. \n",
    "Our original MaxQuant ProteinGroup file contains much more samples, than we have metadata for"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b8244053-e920-447d-a405-7049507733a9",
   "metadata": {},
   "source": [
    "## 3. Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d5a8aad-fda7-4e64-a7d0-fa76dc5cc5d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples in the matrix: 312, number of samples in metadata: 48.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of samples in the matrix: {ds.mat.shape[0]}, number of samples in metadata: {ds.metadata.shape[0]}.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1cd24307-cd84-411b-8c3c-dccc3be08dbc",
   "metadata": {},
   "source": [
    "Firstly, we will subset the matrix it will only contains samples, that are also described in the metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "421c9313-d989-4494-ba13-7dcc1f2377a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.preprocess(subset=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f2bf67f-fd1e-4bd6-934a-473d3eed5d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples in the matrix: 48, number of samples in metadata: 48.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of samples in the matrix: {ds.mat.shape[0]}, number of samples in metadata: {ds.metadata.shape[0]}.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bc027a63-318a-475d-a04e-7e27002e2ee2",
   "metadata": {},
   "source": [
    "#### Unnormalized data, Sample Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d52a08b7-c7a3-4b37-85d3-4507da25b4d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.plot_sampledistribution(color = \"disease\").show(renderer = \"png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "51c7655f-9bb7-4473-be47-563e8ced0e98",
   "metadata": {},
   "source": [
    "- Contaminations get removed indicated in following columns Only identified by site, Reverse, Potential contaminant (MaxQuant specific) and contamination_library (added by AlphaStats)\n",
    "- Normalized using quantile normalization\n",
    "- Missing Values get imputed using K-nearest neighbour imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50c33b0c-8361-4567-9b5c-3a4974cc8d78",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/drq441/opt/anaconda3/lib/python3.9/site-packages/alphastats/DataSet_Preprocess.py:41: FutureWarning:\n",
      "\n",
      "In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ds.preprocess(\n",
    "    remove_contaminations=True,\n",
    "    normalization = \"quantile\",\n",
    "    imputation = \"knn\"\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d4d74bd7-3937-4155-965c-302c7a7795c4",
   "metadata": {},
   "source": [
    "#### After quantile normalization, Sample Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee3d3367-9060-44b8-bc7b-effb96332f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.plot_sampledistribution(method = \"box\", color = \"disease\").show(renderer = \"png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4357b5ee-e208-4992-9f52-60ec5eb73758",
   "metadata": {},
   "source": [
    "The preprocessing steps can be accessed using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79caa1e0-ce55-439f-99de-d8ecc99f9165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Raw data number of Protein Groups': 2596,\n",
       " 'Matrix: Number of ProteinIDs/ProteinGroups': 1318,\n",
       " 'Matrix: Number of samples': 48,\n",
       " 'Intensity used for analysis': 'LFQ intensity [sample]',\n",
       " 'Normalization': 'quantile',\n",
       " 'Imputation': 'k-Nearest Neighbor',\n",
       " 'Contaminations have been removed': True,\n",
       " 'Contamination columns': ['Only identified by site',\n",
       "  'Reverse',\n",
       "  'Potential contaminant',\n",
       "  'contamination_library'],\n",
       " 'Number of removed ProteinGroups due to contaminaton': 46}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.preprocessing_info"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7bdf8679-0451-4f2f-b041-637d134c7ea2",
   "metadata": {},
   "source": [
    "## 4. Visualization\n",
    "\n",
    "### Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42cc84ae-aba1-4c90-b6c7-ba560428855f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.plot_pca(group = \"disease\", circle = True).show(renderer = \"png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}