Welcome to HELPpy documentation!

Essential genes are commonly defined as those required for the growth and survival of any organism or cell. HELP stands for “Human Gene Essentiality Labelling & Prediction”, a computational framework for labelling and predicting essential genes, and HELPpy is a Python implementation of theHELP framework. Its double scope allows achieving the identification of genes through the dependency or not from experimental data. HELPpy is a library of tools useful for the identification of domain-specific context genes that can highlight multiple levels of essentiality, and thus significance in the functional role of genes.

This is the Big Picture to understand the HELPpy approach.

NOTES:

• Installation
  • Requirements
  • Pip
• Cite

PACKAGE REFERENCE:

• HELPpy package
  • Subpackages
    • HELPpy.models package
      • Submodules
      • HELPpy.models.labelling module
      • HELPpy.models.prediction module
      • Module contents
    • HELPpy.preprocess package
      • Submodules
      • HELPpy.preprocess.embedding module
      • HELPpy.preprocess.imputer module
      • HELPpy.preprocess.loaders module
      • Module contents
    • HELPpy.utility package
      • Submodules
      • HELPpy.utility.selection module
      • Module contents
    • HELPpy.visualization package
      • Submodules
      • HELPpy.visualization.plot module
      • HELPpy.visualization.ui module
      • Module contents
  • Module contents

EXAMPLES:

• Example 1: Identification of context-specific EGs
  • 1. Install HELP from GitHub
  • 2. Download the input files
  • 3. Load the input files
  • Example 1.1 two-class labeling of EGs based on tissue information
    • Filter the information to be exploited
    • Apply two-class HELP labelling
  • Example 1.2 three-class labeling of EGs based on tissue information
    • Apply three-class HELP labelling
  • Example 1.3 two-class labeling of EGs based on disease-related information
    • Filter the information to be exploited
    • Apply two-class HELP labelling
• Example 2: Identification of uncommon context-specific EGs
  • 1. Install HELP from GitHub
  • 2. Download the input files
  • 3. Load the input file
  • 4. Filter the information to be exploited
  • 5. Compute EGs common to all tissues (pan-tissue labeling)
  • 6. Subtract pan-tissue EGs from those of the chosen tissue
    • Visualizing the obtained results
• Example 3: Prediction of EGs for a tissue
  • 1. Install HELP from GitHub
  • 2. Download the input files
  • 3. Load the input files and process the tissue attributes
  • 4. Estimate the performance of EGs prediction
  • 5. Compute TPR for ucsEGs and csEGs
• Example 4: Extraction of network embedding from tissue PPI
  • 1. Install HELP from GitHub (and Karateclub)
  • 2. Download the PPI input file
  • 3. Load the PPI input file
  • 4. Compute the embedding
  • 5. Save and show the embedding
• Example 5: Reproduce the experiments reported in the HELP paper
  • 1. Install HELP from GitHub
  • 2. Download the input files
  • 3. Download the script for the experiments and show the man page
  • 4. Run the E vs NE experiments
  • 5. Run the E vs sNE experiments

Indices and tables

• Index

• Module Index

• Search Page