Individual skill
scvi-tools
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.
skills-imported/scvi-tools/SKILL.md
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- Oct 10, 2026
- License
- MIT
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SKILL.mdcore · 7,234 bytesSKILL.mdcore · 7,234 bytesSKILL.mdcore · 7,234 bytesSKILL.mdcore · 7,234 bytesSKILL.mdcore · 7,234 bytesSKILL.mdcore · 7,234 bytesreferences/differential-expression.mddoc · 14,342 bytesreferences/models-atac-seq.mddoc · 9,481 bytesreferences/models-multimodal.mddoc · 10,570 bytesreferences/models-scrna-seq.mddoc · 9,837 bytesreferences/models-spatial.mddoc · 11,260 bytesreferences/models-specialized.mddoc · 10,404 bytesreferences/theoretical-foundations.mddoc · 11,723 bytesreferences/workflows.mddoc · 12,508 bytes
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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
- Integrating multimodal data (CITE-seq, multiome, paired/unpaired datasets)
- Analyzing spatial transcriptomics data (deconvolution, spatial mapping)
- Performing differential expression analysis on single-cell data