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scvi-tools

combined

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. Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.

When to use

• 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

Skill page

Conflict

The three copies overlap on the core scvi-tools workflow (setup_anndata, train, get_latent_representation, Scanpy downstream, DE, save/load) and the modality-to-model mapping. However, they disagree on version pinning, Python/dependency requirements, and preprocessing details, and each contains material steps the others lack. K-Dense-AI pins scvi-tools 1.5.1, requires Python 3.12+, and includes a detailed count-validation and batch-aware HVG workflow plus release-boundary notes (JAX removal, spatial model maintenance). aipoch uses a looser Python 3.9+ requirement and includes a differential_expression example with mode/delta parameters. FreedomIntelligence provides a broader 'when to use' list and theoretical foundations but lacks the version pinning and count-validation rigor. Crowning one would hide the version-specific constraints and the alternative dependency assumptions.