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.
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
- Interpretable and disentangled latent representation
- Data integration
- In silico perturbation
- Identification of treatment-responsive populations
There are several alternative options to install scCausalVI:
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Install the latest version of scCausalVI via pip:
pip install scCausalVI
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Or install the development version via pip:
pip install git+https://github.com/ShaokunAn/scCausalVI.git
See examples at our documentation site.
In order to reproduce paper results visit here.
For a detailed explanation of our methods, please refer to our bioRxiv manuscript.
Feel free to contact us by mail. If you find a bug, please use the issue tracker.