Turn single-cell measurements into model-based insights.
Fit single-cell omics models to produce integrated representations, annotations, multimodal estimates, and posterior differential expression.
When to use it
Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty rather than standard preprocessing and exploratory analysis.
Give it single-cell measurements and an analysis request; it registers and trains an appropriate model, evaluates its fit, and saves representations, model data, and reproducibility details in the project.
What you provide
Requires Python 3.12 or later; the installation example creates a dedicated Python 3.13 environment.
Requires scvi-tools and model-specific dependencies; the installation pins scvi-tools to 1.5.1.
This skill
arXiv
Fetches the paper's citation record
scvi-tools
Installs scvi-tools