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scvi-tools

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Deep generative models for single-cell omics; use when you need probabilistic batch correction (scVI), transfer learning, uncertainty-aware differential expression, or multimodal integration (totalVI/MultiVI).

scientific-skills/Data Analysis/scvi-tools/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## When to Use

Use **scvi-tools** when you need probabilistic, model-based single-cell analysis beyond standard pipelines (e.g., beyond typical Scanpy workflows), such as:

1. **Batch correction and dataset integration** for scRNA-seq using a probabilistic latent space (e.g., scVI).
2. **Transfer learning / semi-supervised annotation** when you have partial labels or want to map new data onto a reference (e.g., scANVI).
3. **Uncertainty-aware differential expression** where effect sizes and posterior uncertainty matter (Bayesian DE).
4. **Multimodal integration** across RNA+protein (CITE-seq) or RNA+ATAC (multiome), including paired/unpaired settings (e.g., totalVI, MultiVI).
5. **Specialized modalities** such as ATAC-seq, spatial transcriptomics deconvolution/mapping, doublet detection, methylation, or RNA velocity.

## Key Features

- **Unified model API**: `setup_anndata(...) → Model(adata) → train() → get_*()` across model families.
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