NetworkCommons: Table of Contents

Main

• Bridging the gap between data, methods and knowledge in the network inference field
  • Mission statement
  • License
• Installation
  • Requirements
  • PIP
• API
  • Methods
  • SignalingProfiler
  • Prior Knowledge
  • Datasets
  • Evaluation and description
  • Visualization
  • Utilities

Details

• Data
  • Omics data
  • Prior Knowledge
• Methods
  • Topological methods
  • Random walk with restart (RWR) methods
  • Recursive enrichment methods
  • ILP-based methods
• Evaluation strategies
  • Offtarget recovery
  • Phosphorylation sensitivity to drug perturbation
  • Pathway enrichment analysis
  • Recovery of dysregulated kinases

Contribution guidelines

• General guidelines for contribution
  • General Instructions
  • Bug Reports and Feature Requests
• Contribution’s guideline: Data
  • 1. Data information
  • 2. API
• Contribution’s guideline: Methods
  • 1. Documentation
  • 2. API
• Contribution’s guideline: Evaluation strategies
  • 1. Documentation
  • 2. API

Vignettes

• Vignette 1: Introduction to NetworkCommons
  • 1. Loading preprocessed transcriptomics data
  • 2. Network inference
• Vignette 2: Using multiple methods to infer the networks
  • 1. Loading preprocessed transcriptomics data
  • 2. Network inference
• Vignette 3: Evaluating methods under two scenarios: offtarget recovery and pathway activity
  • 1. Loading preprocessed transcriptomics data
  • 2. Network inference
  • 3. Evaluation strategy using offtarget recovery
  • 4. Evaluation strategy using pathway enrichment analysis
• Vignette 4: Sensitive response to drug perturbation using phosphoproteomics
  • 1. Data processing
  • 2. Network inference
  • 3. Evaluation using sensitivity to perturbation
• Vignette 5: Multi-omics validation - Recovery of dysregulated kinases in response to cancer mutations
  • 1. Data processing
  • 2. Prior knowledge and inputs
  • 3. Network inference
  • 4. Network evaluation using level of dysregulation in the phosphoproteome.

Additional resources

• Vignette A: recursive propagation with MOON
  • 1. Input preparation
  • 2. Network compression
  • 3. MOON scoring
  • 4. Decompression and solution network
• Vignette B: Pertpy bridging to NetworkCommons
  • 1. Fetch metadata with pertpy
  • 2. TF activity estimation with decoupler-py
  • 3. Network inference with NetworkCommons