Tutorial

This section contains various tutorials showcasing use cases in the context of a typical perturbation analysis workflow.

The notebooks demonstrate how to prepare data, train PerturbGen models, generate in silico perturbations, and perform post-perturbation analyses. They are intended as practical example references, and assume familiarity with Python-based scientific computing and single-cell or high-dimensional biological data.

• 1. Data preprocessing and curation
  • 1.1. Public LPS data
  • 1.2. Private LPS data
  • 1.3. Concatenation of public and preprocessed prived data
• 2. Tokenize transcriptomes and data pairing
  • 2.1. Configure paths and parameters
  • 2.2. Build the tokenization command
  • 2.3. Run tokenization (CPU-only)
  • 2.4. Outputs
• 3. Train Masking Model and Count Decoder
  • 3.1. Train Masking Model (GPU required)
  • 3.2. Train count decoder (GPU required)
• 4. Gene Embedding Extraction
  • 4.1. Parameters (GPU required)
  • 4.2. Build the command
• 5. Gene Embedding Analysis Tutorial
• 6. Perturbation (in silico)
  • 6.1. Config
  • 6.2. Perturbation (GPU required)
• 07. Post Perturbation Analysis Tutorial