alterlab-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 ### Does NOT Trigger | Scenario | Use Instead | |----------|-------------| | Standard QC -> normalize -> HVG -> PCA -> UMAP -> Leiden -> marker DE | `alterlab-scanpy` | | Reading, reshaping, or concatenating the `.h5ad` object itself | `alterlab-anndata` | | Zero-shot annotation or embeddings from a pretrained foundation model | `alterlab-scgpt` | | RNA velocity from spliced/unspliced counts (scVelo's EM/dynamical models) | `alterlab-scvelo` | | Spatial neighborhood statistics, Moran's I, ligand-receptor on a Visium/Xenium slide | `alterlab-squidpy-spatial` | | Bulk RNA-...