Example 1: Identification of context-specific EGs ================================================= This example shows how to use HELP for computing two-class and three-class labelling of EGs based on tissue or disease-related information. The workflow involves the following steps .. #. Install HELP from GitHub #. Download the input files #. Load the input files #. Filter the information to be exploited #. Apply two-class or three-class labelling .. toctree:: :maxdepth: 1 examples/labelling Steps 1.-3. are needed only once, while steps 4.-5. are differentiated and executed three times to compute: .. * Example 1.1 two-class labelling of EGs based on tissue information * Example 1.2 three-class labelling of EGs based on tissue information * Example 1.3 two-class labelling of EGs based on disease-related information .. image:: https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey :target: https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/labelling.ipynb .. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/giordamaug/HELP/blob/main/HELPpy/notebooks/labelling.ipynb .. image:: https://kaggle.com/static/images/open-in-kaggle.svg :target: https://www.kaggle.com/notebooks/welcome?src=https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/labelling.ipynb Example 2: Identification of uncommon context-specific EGs ========================================================== This example shows how to use HELP for identifying uncommon tissue-specific EGs. The workflow involves the following steps .. #. Install HELP from GitHub #. Download the input files #. Load the input files #. Filter the information to be exploited #. Compute EGs common to all tissues (pan-tissue EGs) #. Subtract pan-tissue EGs from those of the chosen tissue .. toctree:: :maxdepth: 1 examples/csegs Finally, the example shows various ways of visualizing the obtained results. .. image:: https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey :target: https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/csegs.ipynb .. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/giordamaug/HELP/blob/main/HELPpy/notebooks/csegs.ipynb .. image:: https://kaggle.com/static/images/open-in-kaggle.svg :target: https://www.kaggle.com/notebooks/welcome?src=https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/csegs.ipynb Example 3: Prediction of EGs for a tissue ========================================= This example shows how to use HELP to estimate the performance of EG prediction for a tissue. The workflow involves the following steps .. #. Install HELP from GitHub #. Download the input files #. Load the input files and process the tissue attributes #. Estimate the performance of EGs prediction .. toctree:: :maxdepth: 1 examples/prediction Step 5. shows how to compute the True Positive Rate (TPR) for ucsEGs and csEGs and show their bar plot. .. image:: https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey :target: https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/prediction.ipynb .. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/giordamaug/HELP/blob/main/HELPpy/notebooks/prediction.ipynb .. image:: https://kaggle.com/static/images/open-in-kaggle.svg :target: https://www.kaggle.com/notebooks/welcome?src=https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/prediction.ipynb Example 4: Extraction of network embedding from tissue PPI ========================================================== This example shows how to use in the HELP framework the graph embedding functions of the `Karateclub ` python package to accomplish node embedding on the Protein-Protein Interaction (PPI) network of a tissue. The results can be used as features for EG prediction with HELP. The workflow involves the following steps .. #. Install HELP from GitHub #. Download the input files #. Load the PPI network and apply embedding #. Save the embedding and print it .. toctree:: :maxdepth: 1 examples/embedding .. image:: https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey :target: https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/embedding.ipynb .. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/giordamaug/HELP/blob/main/HELPpy/notebooks/embedding.ipynb .. image:: https://kaggle.com/static/images/open-in-kaggle.svg :target: https://www.kaggle.com/notebooks/welcome?src=https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/embedding.ipynb Example 5: Reproduce the experiments reported in the HELP paper =============================================================== This example shows how to use HELP to reproduce the experiments reported in the HELP paper. The workflow involves the following steps .. #. Install HELP from GitHub #. Download the input files #. Download the script for the experiments and show its man page #. Run the E vs NE experiments #. Run the E vs sNE experiments .. toctree:: :maxdepth: 1 examples/experiment .. image:: https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey :target: https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/experiment.ipynb .. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/giordamaug/HELP/blob/main/HELPpy/notebooks/experiment.ipynb .. image:: https://kaggle.com/static/images/open-in-kaggle.svg :target: https://www.kaggle.com/notebooks/welcome?src=https://github.com/giordamaug/HELP/blob/main/HELPpy/notebooks/experiment.ipynb