scCausalVI 0.0.7

A deep causality-aware model for disentangling treatment effects at single-cell resolution for perturbational scRNA-seq data

scCausalVI

scCausalVI is a causality-aware generative model designed to disentangle inherent cellular heterogeneity from treatment effects in single-cell RNA sequencing data, particularly in case-control studies.

Introduction

scCausalVI addresses a major analytical challenge in single-cell RNA sequencing: distinguishing inherent cellular variation from extrinsic cell-state-specific effects induced by external stimuli. The model:

• Decouples intrinsic cellular states from treatment effects through a deep structural causal network
• Explicitly models causal mechanisms governing cell-state-specific responses
• Enables cross-condition in silico prediction
• Accounts for technical variations in multi-source data integration
• Identifies treatment-responsive populations and molecular signatures

Key Features of scCausalVI

• Interpretable and disentangled latent representation
• Data integration
• In silico perturbation
• Identification of treatment-responsive populations

Installation

There are several alternative options to install scCausalVI:

1. Install the latest version of scCausalVI via pip:

   pip install scCausalVI
   

2. Or install the development version via pip:

   pip install git+https://github.com/ShaokunAn/scCausalVI.git
   

Examples

See examples at our documentation site.

Reproducing Results

In order to reproduce paper results visit here.

References

For a detailed explanation of our methods, please refer to our bioRxiv manuscript.

Contact

Feel free to contact us by mail. If you find a bug, please use the issue tracker.