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.
View on GitHubJanuary 16, 2026
Select agents to install to:
npx add-skill https://github.com/davila7/claude-code-templates/blob/8d42f52a6a5e89e6f71f44f13b7f8fa86c6177f3/cli-tool/components/skills/scientific/scvi-tools/SKILL.md -a claude-code --skill scvi-toolsInstallation paths:
.claude/skills/scvi-tools/# 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 - Conducting cell type annotation or transfer learning tasks - Working with specialized single-cell modalities (methylation, cytometry, RNA velocity) - Building custom probabilistic models for single-cell analysis ## Core Capabilities scvi-tools provides models organized by data modality: ### 1. Single-Cell RNA-seq Analysis Core models for expression analysis, batch correction, and integration. See `references/models-scrna-seq.md` for: - **scVI**: Unsupervised dimensionality reduction and batch correction - **scANVI**: Semi-supervised cell type annotation and integration - **AUTOZI**: Zero-inflation detection and modeling - **VeloVI**: RNA velocity analysis - **contrastiveVI**: Perturbation effect isolation ### 2. Chromatin Accessibility (ATAC-seq) Models for analyzing single-cell chromatin data. See `references/models-atac-seq.md` for: - **PeakVI**: Peak-based ATAC-seq analysis and integration - **PoissonVI**: Quantitative fragment count modeling - **scBasset**: Deep learning approach with motif analysis ### 3. Multimodal & Multi-omics Integration Joint analysis of multiple data types. See `references/models-multimodal.md` for: - **totalVI**: CITE-seq protein and RNA joint modeling - **MultiVI**: Paired and unpaired multi-omic integration - **MrVI**: Multi-resolution
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