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- New: Added Mini peptides threshold for the Protein Suming part.
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- Fix: Fixed the index of peptide table lost after data preprossing.
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byemaxx committed Sep 22, 2024
1 parent 1b8b3e8 commit e1ba6c4
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6 changes: 6 additions & 0 deletions Docs/ChangeLog.md
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@@ -1,3 +1,9 @@
# Version: 1.114.2
## Date: 2024-09-22
### Changes:
- New: Added Mini peptides threshold for the Protein Suming part.
- Fix: Fixed the index of peptide table lost after data preprossing.


# Version: 1.114.1
## Date: 2024-09-19
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6 changes: 4 additions & 2 deletions metax/gui/main_gui.py
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Expand Up @@ -980,8 +980,10 @@ def change_event_checkBox_create_protein_table(self):
# self.checkBox_infrence_protein_by_sample.setEnabled(True)
# self.comboBox_protein_ranking_method.setEnabled(True)
self.comboBox_method_of_protein_inference.setEnabled(True)
self.spinBox_peptide_num_threshold_protein.setEnabled(True)
else:
self.comboBox_method_of_protein_inference.setEnabled(False)
self.spinBox_peptide_num_threshold_protein.setEnabled(False)
self.checkBox_infrence_protein_by_sample.setEnabled(False)
self.comboBox_protein_ranking_method.setEnabled(False)

Expand Down Expand Up @@ -2146,7 +2148,6 @@ def show_pushButton_preprocessing_help(self):
\n\nMultiple: Outliers will be imputed by using IterativeImputer with multiple imputations method. It uses the IterativeImputer with a specified number (K=5) of nearest features.\
\n\n\nData Normalization:\
\n\nIf you use [Z-Score, Mean centering and Pareto Scaling] data normalization, the data will be given a minimum offset again to avoid negative values.\
\n\n\nBatch Effect Correction: only apply to peptide data.
'''
msg_box.setText(help_text)
msg_box.exec_()
Expand Down Expand Up @@ -2589,7 +2590,8 @@ def set_multi_table(self, restore_taxafunc=False, saved_obj=None):
'method': self.comboBox_method_of_protein_inference.currentText(),
'by_sample': self.checkBox_infrence_protein_by_sample.isChecked(),
'rank_method' :self.comboBox_protein_ranking_method.currentText(),
'greedy_method': self.settings.value('protein_infer_greedy_mode', 'heap')
'greedy_method': self.settings.value('protein_infer_greedy_mode', 'heap'),
'peptide_mun_threshold': self.spinBox_peptide_num_threshold_protein.value(),
}


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