https://github.com/luka-kovacevic/causalregnet

Python package for SCM-based simulation of gene perturbation data and benchmarking of causal structure learning algorithms.
https://github.com/luka-kovacevic/causalregnet

benchmarking causal-discovery causal-machine-learning simulation

Last synced: about 1 month ago
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Python package for SCM-based simulation of gene perturbation data and benchmarking of causal structure learning algorithms.

• Host: GitHub
• URL: https://github.com/luka-kovacevic/causalregnet
• Owner: luka-kovacevic
• License: mit
• Created: 2024-01-23T17:09:52.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-03T20:41:29.000Z (about 2 months ago)
• Last Synced: 2024-12-03T21:29:55.401Z (about 2 months ago)
• Topics: benchmarking, causal-discovery, causal-machine-learning, simulation
• Language: Python
• Homepage:
• Size: 4.67 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# causalregnet

This is a library for simulating realistic single-cell RNA sequencing data based on a given causal structure, scalable to the dimension of genome-wide gene perturbation screens.

Implementation of method developed in [*"Simulation-based Benchmarking of Causal Structure Learning in Gene Perturbation Experiments"*](https://arxiv.org/abs/2407.06015).

## Installation guide 

1. Download the package from GitHub

```

git clone https://github.com/luka-kovacevic/causalregnet

```

2. Navigate to the package 

```

cd causalregnet/

```

3. Check your environment (ensure it's the same one you're using to run code)

4. Install the package

```

pip install .

```