Scvi Tools Deep Learning Skill
Skill ActiveDeep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
To enable users to perform advanced deep learning-based analyses on single-cell omics data using the scvi-tools library.
Features
- Data integration and batch correction
- ATAC-seq analysis with PeakVI
- CITE-seq multi-modal analysis with totalVI
- Multiome (RNA+ATAC) analysis with MultiVI
- RNA velocity analysis with veloVI
Use Cases
- When scvi-tools, scVI, scANVI, or related models are mentioned
- When deep learning-based batch correction or integration is needed
- When working with multi-modal data (CITE-seq, multiome)
- When reference mapping or label transfer is required
Non-Goals
- Performing wet-lab experiments or generating raw data
- Replacing specialized bioinformatics software for tasks outside scvi-tools
- Providing generic Python utilities not related to scvi-tools
Trust
- warning:Issues AttentionThere are 29 open issues and 4 closed issues in the last 90 days, indicating a low closure rate (approx. 12%) and potentially slow maintainer response.
Installation
First, add the marketplace
/plugin marketplace add anthropics/knowledge-work-plugins/plugin install bio-research@knowledge-work-pluginsQuality Score
Trust Signals
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