Agent skill · research science · aiskillstore
scvi-tools
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
Why this skill is useful
Adds advanced probabilistic modeling capabilities for single-cell genomics that the AI cannot generate on its own.
What it needs
Requires python, pytorch, scanpy installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 Core Capabilities scvi-tools provides models organized by data modality: 1. Single-Cell RNA-seq Analysis Core models for expression analysis, batch correction, and integration. See references/models-scrna-seq.md for: scVI: Unsupervised dimensionality reduction and batch correction scANVI: Semi-supervised cell type annotation and integration AUTOZI: Zero-inflation detection and modeling VeloVI: RNA velocity analysis contrastiveVI: Perturbation effect isolation 2. Chromatin Accessibility (ATAC-seq) Models for analyzing single-cell chromatin data. See references/models-atac-seq.md for: PeakVI: Peak-based ATAC-seq analysis and integration PoissonVI: Quantitative fragment count modeling scBasset: Deep learning approach with motif analysis 3. Multimodal & Multi-omics Integration Joint analysis of multiple data types. See references/models-multimodal.md for: totalVI: CITE-seq protein and RNA joint modeling MultiVI: Paired and unpaired multi-omic integration MrVI: Multi-resolution cross-sample analysis 4. Spatial Transcriptomics Spatially-resolved transcriptomics analysis. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/scvi-tools--ee04c8