This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.
El comando exacto puede variar según el repositorio. Consulta el README en GitHub.
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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.]
