scvi-tools

SKILL.md

This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.

| Data Type | Model | Primary Use Case |

| scRNA-seq | scVI | Unsupervised integration, DE, imputation | | scRNA-seq + labels | scANVI | Label transfer, semi-supervised integration | | CITE-seq (RNA+protein) | totalVI | Multi-modal integration, protein denoising | | scATAC-seq | PeakVI | Chromatin accessibility analysis | | Multiome (RNA+ATAC) | MultiVI | Joint modality analysis |

Deep 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. Source: anthropics/life-sciences.

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