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Probabilistic models for single-cell omics data

scvi-tools accelerates data analysis and model development, powered by PyTorch and AnnData.

pip install scvi-tools

End-to-end analysis

Dimensionality reduction, dataset integration, differential expression, automated annotation. scvi-tools contains models that perform a wide variety of tasks across many omics, all while accounting for the statistical properties of the data.

Easy-to-use implementations

Each model (e.g., scVI, scANVI, Stereoscope, totalVI) follows the same user interface that couples nicely with Scanpy, Seurat, or Bioconductor workflows. No more searching through source code.

Rapid development of new models

Building novel probabilistic models with scvi-tools is simplified by its object-oriented design and base components powered by PyTorch, PyTorch Lightning, Pyro, and AnnData. No need to write a dataloader or trainer ever again.

Stochastic, GPU-accelerated inference

scvi-tools models are trained efficiently through minibatching and can naturally be used with a GPU. These models are prepared to scale with growing dataset sizes.