TransProPyBook

TransProPy is a Python package that is part of the TransPro series under the SuperOmics framework.

Preface

TransProPy：A python package that integrate algorithms and various machine learning approaches to extract features (genes) effective for classification and attribute them accordingly.

To learn more about TransProPy visit https://github.com/SSSYDYSSS/TransProPy.

Table of Contents

UtilsFunction*

Note

These functions usually contain various utilities and helper functions, which can sometimes be considered as low-level functionalities.

• UtilsFunction1

• Auc.py
• AutoNorm.py
• FeatureRanking.py
• NewFeatureRanking.py
• LoadData.py
• PrintResults.py
• FilterSamples.py
• GeneNames.py
• GeneToFeatureMapping.py

• UtilsFunction2

• splitdata.py
• LogTransform.py

• UtilsFunction3

• LoadFilterTranspose.py
• LoadEncodeLabels.py
• ExtractCommonSamples.py
• LoadAndPreprocessData.py
• SetupLoggingAndProgressBar.py
• UpdateProgressBar.py
• LoggingCustomScorer.py
• TqdmCustomScorer.py
• TrainModel.py
• EnsembleForRFE.py
• SetupFeatureSelection.py
• PrintBoxedText.py
• ExtractAndSaveResults.py

*main Function

Tip

The main function constructed with the help of auxiliary functions.

• MACFCmain

• Parameters
• Returns
• Function Principle Explanation
• Usage Workflow
• Usage of MACFCmain (Significant correlation)
• Usage of MACFCmain (Insignificant correlation)
• References

• NewMACFCmain

• Parameters
• Returns
• Function Principle Explanation
• Usage Workflow
• Usage of New_MACFCmain (four_methods_degs_union)
• Usage of New_MACFCmain (all_count_exp)
• References

• AutogluonTimeLimit

• Parameters
• Returns
• Usage of Autogluon_TimeLimit
• Insignificant Correlation
• Significant Correlation
• References

• AutoGluonSelectML

• Parameters
• Returns
• Usage of Autogluon_SelectML
• Insignificant Correlation
• Significant Correlation
• References

• AutoFeatureSelection

• Parameters
• Please note
• Description
• Usage

Comparison of TransPropy with Other Tool Packages

• funkyheatmap

• Percentage Stacked Line Bar Chart

• Ridge Plot

• CFD FullGSEA

• ANKRD35 FullGSEA

• ALOXE3 FullGSEA

• GeneCount and GseaNES

• All gene line bar

• Seprate All GSEA

• VennNetwork core enrichment

• CircosBar core enrichment

Citation

Yu Dongyue (2023). TransProPy: A python package that integrate algorithms and various machine learning approaches to extract features (genes) effective for classification and attribute them accordingly. https://github.com/SSSYDYSSS/TransProPy, https://sssydysss.gitbook.io/transpropy-manual/.

License

This project is licensed under the BSD 3-Clause License - see the https://github.com/SSSYDYSSS/TransProPy/blob/main/LICENSE.txt file for details.