- Fix: Fixed the bug of create taxa table and function table from the…

… OTF only,the option didn't work correctly. - Change: Changed the message of the result of creating Taxon-Function Table.
byemaxx · Sep 23, 2024 · 9e23875 · 9e23875
1 parent 3aae95b
commit 9e23875
Show file tree

Hide file tree

Showing 9 changed files with 135 additions and 72 deletions.
diff --git a/Docs/ChangeLog.md b/Docs/ChangeLog.md
@@ -1,3 +1,9 @@
+# Version: 1.114.4
+## Date: 2024-09-23
+### Changes:
+- Fix: Fixed the bug of create taxa table and function table from the OTF only,the option didn't work correctly.
+- Change: Changed the message of the result of creating Taxon-Function Table.
+
 # Version: 1.114.3
 ## Date: 2024-09-22
 ### Changes:

diff --git a/metax/gui/main_gui.py b/metax/gui/main_gui.py
@@ -1852,46 +1852,102 @@ def run_after_set_multi_tables(self):
 
 
         # Final message
-        outlier_detect_method = self.comboBox_outlier_detection.currentText()
-
-        if outlier_detect_method != 'None':
-            nan_stats_str = '\n\nLeft row after data preprocessing:\n'
-            for i, j in self.tfa.outlier_status.items():
-                if i not in ['peptide', 'custom']:
-                    continue
-                if j:
-                    nan_stats_str += f'{i}: [{j}]\n'
-            # print(nan_stats_str)
-        else:    
-            nan_stats_str = ''
-
         if self.tfa.any_df_mode:
             num_item = self.tfa.custom_df.shape[0]
-            msg = f'Custom data is ready! \
-            \n{nan_stats_str}\
-            \n\nNumber of item: [{num_item}]'
+            msg = f"""<html>
+            <body>
+            <p>Custom data is ready!</p>
+            <p>{nan_stats_str}</p>
+            <p>Number of items: [{num_item}]</p>
+            </body>
+            </html>
+            """
         else:
-            msg = f'Operational Taxa-Functions (OTF) data is ready! \
-            \n{nan_stats_str}\
-            \n\nFunction: [{self.tfa.func_name}]\
-            \nNumber of peptide: [{num_peptide} ({num_peptide/self.tfa.original_df.shape[0]*100:.2f}% of all peptides)]\
-            \nNumber of function: [{num_func}]\
-            \nNumber of taxa: [{num_taxa}]\
-            \nNumber of taxa-function: [{num_taxa_func}]\
-            \nNumber of protein: [{num_protein}]'
-
-        print(f'\n----Multi Table Result----\n{msg}\n---------------------------\n')
-        self.logger.write_log(msg.replace('\n', ''))
-        QMessageBox.information(self.MainWindow, 'Information', msg )
-
-        print("\n---------------------------------- Set Multi Table End ----------------------------------\n")
-        # go to basic analysis tab and the first tab
-        self.stackedWidget.setCurrentIndex(0) # go to page_analyzer
-        self.tabWidget_TaxaFuncAnalyzer.setCurrentIndex(3)
-        self.tabWidget_4.setCurrentIndex(0)
-        self.pushButton_set_multi_table.setEnabled(True)
-
-
+            original_num_peptide = self.tfa.original_df.shape[0]
+
+            msg = f"""<html>
+            <head>
+            <style>
+                table {{
+                    border-collapse: collapse;
+                    width: 100%;
+                }}
+                th, td {{
+                    border: 1px solid black;
+                    padding: 8px;
+                    text-align: left;
+                }}
+                h2 {{
+                    text-align: center;
+                }}
+            </style>
+            </head>
+            <body>
+                <h2>Operational Taxa-Functions (OTF) data is ready!</h2>
+                <p>Taxa Level: <b>{self.tfa.taxa_level}</b></p>
+                <p>Function Category: <b>{self.tfa.func_name}</b></p>
+                <hr>
+                <table>
+                    <tr>
+                        <th>Category</th>
+                        <th>Number</th>
+                        <th>Used Peptides</th>
+                        <th>% of All Peptides</th>
+                    </tr>
+                    <tr>
+                        <td>Taxa</td>
+                        <td>{num_taxa}</td>
+                        <td>{self.tfa.peptide_num_used["taxa"]}</td>
+                        <td>{self.tfa.peptide_num_used["taxa"] / original_num_peptide * 100:.2f}%</td>
+                    </tr>
+                    <tr>
+                        <td>Functions</td>
+                        <td>{num_func}</td>
+                        <td>{self.tfa.peptide_num_used["func"]}</td>
+                        <td>{self.tfa.peptide_num_used["func"] / original_num_peptide * 100:.2f}%</td>
+                    </tr>
+                    <tr>
+                        <td>OTFs</td>
+                        <td>{num_taxa_func}</td>
+                        <td>{self.tfa.peptide_num_used["taxa_func"]}</td>
+                        <td>{self.tfa.peptide_num_used["taxa_func"] / original_num_peptide * 100:.2f}%</td>
+                    </tr>
+                    <tr>
+                        <td>Clean Peptides</td>
+                        <td>{num_peptide}</td>
+                        <td>-</td>
+                        <td>{num_peptide / original_num_peptide * 100:.2f}%</td>
+                    </tr>"""
+
+            # add protein number if protein df is not None
+            if num_protein != 'NA':
+                msg += f"""
+                    <tr>
+                        <td>Proteins</td>
+                        <td>{num_protein}</td>
+                        <td>{self.tfa.peptide_num_used["protein"]}</td>
+                        <td>{self.tfa.peptide_num_used["protein"] / original_num_peptide * 100:.2f}%</td>
+                    </tr>"""
+
+            # close the HTML
+            msg += """
+                </table>
+            </body>
+            </html>"""
+
+        msg_for_print = f'''
+        Taxa Level: {self.tfa.taxa_level}
+        Function Category: {self.tfa.func_name}
+        Number of Taxa: {num_taxa} (Peptides Used: {self.tfa.peptide_num_used["taxa"]})
+        Number of Functions: {num_func} (Peptides Used: {self.tfa.peptide_num_used["func"]})
+        Number of OTFs: {num_taxa_func} (Peptides Used: {self.tfa.peptide_num_used["taxa_func"]})
+        Number of Peptides: {num_peptide} ({num_peptide / original_num_peptide * 100:.2f}%)
+        '''
+
+        print(f'\n----Multi Table Result----\n{msg_for_print}\n---------------------------\n')
+        self.logger.write_log(msg_for_print.replace('\n', ''))
+        QMessageBox.information(self.MainWindow, 'Result', msg)
+
 
     ## Database builder by own Table
     def show_toolButton_db_own_anno_help(self):

diff --git a/metax/gui/metax_gui/main_window.ui b/metax/gui/metax_gui/main_window.ui
@@ -246,7 +246,7 @@
                  <x>0</x>
                  <y>0</y>
                  <width>528</width>
-                 <height>530</height>
+                 <height>534</height>
                 </rect>
                </property>
                <attribute name="label">
@@ -1385,7 +1385,7 @@
                  <string>Create Taxa and Functions only from OTFs</string>
                 </property>
                 <property name="checked">
-                 <bool>true</bool>
+                 <bool>false</bool>
                 </property>
                </widget>
               </item>
@@ -5644,7 +5644,7 @@
                            <x>0</x>
                            <y>0</y>
                            <width>996</width>
-                           <height>99</height>
+                           <height>103</height>
                           </rect>
                          </property>
                          <layout class="QGridLayout" name="gridLayout_68">
@@ -7425,7 +7425,7 @@
                         <x>0</x>
                         <y>0</y>
                         <width>1016</width>
-                        <height>101</height>
+                        <height>105</height>
                        </rect>
                       </property>
                       <layout class="QGridLayout" name="gridLayout_57">
@@ -10240,7 +10240,7 @@
      <x>0</x>
      <y>0</y>
      <width>1122</width>
-     <height>23</height>
+     <height>21</height>
     </rect>
    </property>
    <widget class="QMenu" name="menuTools">

diff --git a/metax/gui/metax_gui/ui_main_window.py b/metax/gui/metax_gui/ui_main_window.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 
-# Form implementation generated from reading ui file 'c:\Users\max\OneDrive - University of Ottawa\code\TaxaFunc\MetaX\metax\gui\metax_gui\main_window.ui'
+# Form implementation generated from reading ui file 'c:\Users\Qing\OneDrive - University of Ottawa\code\TaxaFunc\MetaX\metax\gui\metax_gui\main_window.ui'
 #
 # Created by: PyQt5 UI code generator 5.15.9
 #
@@ -147,7 +147,7 @@ def setupUi(self, metaX_main):
         self.toolBox_2.setMaximumSize(QtCore.QSize(1677, 16777215))
         self.toolBox_2.setObjectName("toolBox_2")
         self.page_2 = QtWidgets.QWidget()
-        self.page_2.setGeometry(QtCore.QRect(0, 0, 528, 530))
+        self.page_2.setGeometry(QtCore.QRect(0, 0, 528, 534))
         self.page_2.setObjectName("page_2")
         self.gridLayout_27 = QtWidgets.QGridLayout(self.page_2)
         self.gridLayout_27.setObjectName("gridLayout_27")
@@ -682,7 +682,7 @@ def setupUi(self, metaX_main):
         self.gridLayout_17.addLayout(self.horizontalLayout, 1, 3, 1, 1)
         self.checkBox_set_otf_taxa_and_func_only_from_otf = QtWidgets.QCheckBox(self.tab_set_taxa_func)
         self.checkBox_set_otf_taxa_and_func_only_from_otf.setStatusTip("")
-        self.checkBox_set_otf_taxa_and_func_only_from_otf.setChecked(True)
+        self.checkBox_set_otf_taxa_and_func_only_from_otf.setChecked(False)
         self.checkBox_set_otf_taxa_and_func_only_from_otf.setObjectName("checkBox_set_otf_taxa_and_func_only_from_otf")
         self.gridLayout_17.addWidget(self.checkBox_set_otf_taxa_and_func_only_from_otf, 1, 4, 1, 1)
         self.gridLayout_25.addLayout(self.gridLayout_17, 1, 0, 1, 1)
@@ -2879,7 +2879,7 @@ def setupUi(self, metaX_main):
         self.scrollArea_3.setWidgetResizable(True)
         self.scrollArea_3.setObjectName("scrollArea_3")
         self.scrollAreaWidgetContents_4 = QtWidgets.QWidget()
-        self.scrollAreaWidgetContents_4.setGeometry(QtCore.QRect(0, 0, 996, 99))
+        self.scrollAreaWidgetContents_4.setGeometry(QtCore.QRect(0, 0, 996, 103))
         self.scrollAreaWidgetContents_4.setObjectName("scrollAreaWidgetContents_4")
         self.gridLayout_68 = QtWidgets.QGridLayout(self.scrollAreaWidgetContents_4)
         self.gridLayout_68.setObjectName("gridLayout_68")
@@ -3825,7 +3825,7 @@ def setupUi(self, metaX_main):
         self.scrollArea_5.setWidgetResizable(True)
         self.scrollArea_5.setObjectName("scrollArea_5")
         self.scrollAreaWidgetContents_6 = QtWidgets.QWidget()
-        self.scrollAreaWidgetContents_6.setGeometry(QtCore.QRect(0, 0, 1016, 101))
+        self.scrollAreaWidgetContents_6.setGeometry(QtCore.QRect(0, 0, 1016, 105))
         self.scrollAreaWidgetContents_6.setObjectName("scrollAreaWidgetContents_6")
         self.gridLayout_57 = QtWidgets.QGridLayout(self.scrollAreaWidgetContents_6)
         self.gridLayout_57.setObjectName("gridLayout_57")
@@ -5345,7 +5345,7 @@ def setupUi(self, metaX_main):
         self.statusbar.setObjectName("statusbar")
         metaX_main.setStatusBar(self.statusbar)
         self.menuBar = QtWidgets.QMenuBar(metaX_main)
-        self.menuBar.setGeometry(QtCore.QRect(0, 0, 1122, 23))
+        self.menuBar.setGeometry(QtCore.QRect(0, 0, 1122, 21))
         self.menuBar.setObjectName("menuBar")
         self.menuTools = QtWidgets.QMenu(self.menuBar)
         self.menuTools.setObjectName("menuTools")

diff --git a/metax/taxafunc_analyzer/analyzer.py b/metax/taxafunc_analyzer/analyzer.py
@@ -74,8 +74,7 @@ def __init__(
         self.protein_df: Optional[pd.DataFrame] = None
         self.any_df_mode = any_df_mode  # if True, the consider the TaxaFunc df as other_df
         self.custom_df: Optional[pd.DataFrame] = None # other df, any df that user want to add
-        self.outlier_status = {'peptide': None, 'taxa': None, 'func': None,
-                               'taxa_func': None, 'protein': None, 'custom': None}
+        self.peptide_num_used = {'taxa': 0, 'func': 0, 'taxa_func': 0, 'protein': 0}
 
         self.split_func_status:bool = False
         self.split_func_sep:str = ''
@@ -498,7 +497,6 @@ def set_any_df_table(self,
                          data_preprocess_params: dict = {'normalize_method': None, 'transform_method': None,
                                                             'batch_meta': None, 'processing_order': None}):
         df = self.original_df.copy()
-        self.outlier_status['custom'] = None # reset outlier_status
         df =self.detect_and_handle_outliers(df=df,  **outlier_params)
         df = self.data_preprocess(df=df,df_name = 'custom', **data_preprocess_params)
         # set index as first column
@@ -699,22 +697,22 @@ def set_multi_tables(self, level: str = 's', func_threshold:float = 1.00,
             return
 
         #! fllowing code is for the normal mode
-
         # add 'peptide_num_threshold' to 'data_preprocess_params
         data_preprocess_params['peptide_num_threshold'] = peptide_num_threshold
 
         #2. sum the protein intensity
         if sum_protein:
             # data preprocess for peptide table
             print("---Starting to create protein table---")
+            self.peptide_num_used['protein'] = 0
             df_peptide_for_protein = self.detect_and_handle_outliers(df=self.original_df, **outlier_params)
             self.protein_df = SumProteinIntensity(taxa_func_analyzer=self, df=df_peptide_for_protein).sum_protein_intensity( **sum_protein_params)
             self.protein_df = self.data_preprocess(df=self.protein_df,df_name = 'protein', 
                                                    **data_preprocess_params)
 
-
-        # reset outlier_status
-        self.outlier_status = {'peptide': None, 'taxa': None, 'func': None, 'taxa_func': None}
+        for df_name in ['taxa', 'func', 'taxa_func']:
+            self.peptide_num_used[df_name] = 0  # reset the peptide_num_used
+            
         # reset split_func status
         self.split_func_status = split_func
         self.split_func_sep = split_func_params['split_by']
@@ -767,27 +765,28 @@ def strip_taxa(x, level):
             raise ValueError("Please input the correct taxa level (m, s, g, f, o, c, p, d, l)")
 
 
-        # extract 'taxa', sample intensity #! and 'peptide_num' fto avoid the duplicated items when handling outlier
-        df_taxa_pep = df_filtered_peptides[[self.peptide_col_name,'Taxon'] + self.sample_list]
-        # add column 'peptide_num' to df_taxa as 1
-        df_taxa_pep['peptide_num'] = 1
-
-        # if taxa_and_func_only_from_otf:
-        if True: # for testing
+        if not taxa_and_func_only_from_otf:
+            # extract 'taxa', sample intensity #! and 'peptide_col' to avoid the duplicated items when handling outlier
+            df_taxa_pep = df_filtered_peptides[[self.peptide_col_name,'Taxon'] + self.sample_list]
+            # add column 'peptide_num' to df_taxa as 1
+            df_taxa_pep['peptide_num'] = 1
             # groupby 'Taxon' and sum the sample intensity
             print("\n-----Starting to perform outlier detection and handling for [Peptide-Taxon] table...-----")
             df_taxa_pep = self.detect_and_handle_outliers(df=df_taxa_pep, **outlier_params)
+            self.peptide_num_used['taxa'] = len(df_taxa_pep)
             df_taxa = df_taxa_pep.groupby('Taxon').sum(numeric_only=True)
             print("\n-----Starting to perform data pre-processing for Taxa table...-----")
             df_taxa = self.data_preprocess(df=df_taxa,df_name = 'taxa', **data_preprocess_params)
             self.taxa_df = df_taxa
             #-----Taxa Table End-----
+
             # create func table
             df_func_pep = self.filter_peptides_by_taxa_func(df= self.original_df, func_threshold=func_threshold,
                                        keep_unknow_func=keep_unknow_func, filter_taxa=False)
             df_func_pep = df_func_pep[[self.peptide_col_name, self.func_name] + self.sample_list]
             print("\n-----Starting to perform outlier detection and handling for [Peptide-Function] table...-----")
             df_func_pep = self.detect_and_handle_outliers(df=df_func_pep, **outlier_params)
+            self.peptide_num_used['func'] = len(df_func_pep)
             df_func_pep['peptide_num'] = 1
             df_func = df_func_pep.groupby(self.func_name).sum(numeric_only=True)
 
@@ -826,6 +825,11 @@ def strip_taxa(x, level):
         # ----- create taxa_func table -----
         df_taxa_func = df_half_processed_peptides[[self.peptide_col_name, 'Taxon', self.func_name] + self.sample_list]
         df_taxa_func['peptide_num'] = 1
+
+        for key in ['taxa_func', 'taxa', 'func']:
+            self.peptide_num_used[key] = len(df_taxa_func) if self.peptide_num_used[key] == 0 else self.peptide_num_used[key]
+
+
         df_taxa_func = df_taxa_func.groupby(['Taxon', self.func_name], as_index=True).sum(numeric_only=True)
 
         # split the function before data preprocess

diff --git a/metax/taxafunc_analyzer/analyzer_utils/data_preprocessing.py b/metax/taxafunc_analyzer/analyzer_utils/data_preprocessing.py
@@ -693,10 +693,6 @@ def data_preprocess(self, df: pd.DataFrame, normalize_method: str|None = None,
                 raise ValueError('processing_order must be in [outlier, batch, transform, normalize]')
         print(f'\n{self._get_current_time()} -----Data preprocessing of {df_name.upper()} finished.-----\n')
 
-        if df_name in {'peptide', 'taxa', 'func', 'taxa_func', 'protein', 'custom'}:
-            left_row_num = len(df)
-            # self.tfa.outlier_status[df_name] = f'{left_row_num}/{original_row_num} ({left_row_num/original_row_num*100:.2f}%)'
-            self.tfa.outlier_status[df_name] = f'{left_row_num} ({left_row_num/len(self.tfa.original_df)*100:.2f}%)'
-
+
         return df
 
diff --git a/metax/taxafunc_analyzer/analyzer_utils/sum_protein_intensity.py b/metax/taxafunc_analyzer/analyzer_utils/sum_protein_intensity.py
@@ -79,9 +79,11 @@ def sum_protein_intensity(self, method='razor', by_sample=False, rank_method='un
         self.rank_method = rank_method
         self.check_protein_col()
 
+        self.df = razor_integrator.remove_protein_less_than_threshold()
+        self.tfa.peptide_num_used['protein'] = len(self.df)
+
         if method == 'rank':
             print(f"\n-------------Start to sum protein intensity using method: [{method}]  by_sample: [{by_sample}] rank_method: [{rank_method}]-------------")   
-            self.df = razor_integrator.remove_protein_less_than_threshold()
             # make a dict to count the intensity of each protein, intensity sahred by peptides will be divided by the number of peptides
             if by_sample:
                 for sample in self.tfa.sample_list:
@@ -100,13 +102,12 @@ def sum_protein_intensity(self, method='razor', by_sample=False, rank_method='un
                     self._sum_protein_rank(sample, by_sample)
         elif method == 'razor':
             print('start to sum protein intensity using method: [razor]')
-                 
+            razor_integrator.peptide_mun_threshold = 1 # set the threshold to 1, to avoid run filter again
             res_df = razor_integrator.sum_protein_intensity(greedy_method=greedy_method)
             return res_df       
 
         elif method == 'anti-razor':
             print(f"\n-------------Start to sum protein intensity using method: [{method}]  by_sample: [True] rank_method: [Shared]-------------")
-            self.df = razor_integrator.remove_protein_less_than_threshold()
             for sample in self.tfa.sample_list:
                 self._sum_protein_anti_razor(sample)
 

diff --git a/metax/utils/version.py b/metax/utils/version.py
@@ -1,2 +1,2 @@
-__version__ = '1.114.3'
+__version__ = '1.114.4'
 API_version = '2'
diff --git a/pyproject.toml b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "MetaXTools"
-version = "1.114.3"
+version = "1.114.4"
 description = "MetaXTools is a novel tool for linking peptide sequences with taxonomic and functional information in Metaproteomics."
 readme = "README_PyPi.md"
 license = { text = "NorthOmics" }