Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
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scvi-tools
Overview
scvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities.
When to Use This Skill
Use this skill when:
Analyzing single-cell RNA-seq data (dimensionality reduction, batch correction, integration)
Working with single-cell ATAC-seq or chromatin accessibility data
Integrat
[Description truncada. Veja o README completo no GitHub.]
