Agent skill · research science · aiskillstore
scvi-tools
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.
Why this skill is useful
Adds specialized probabilistic modeling scripts and domain-specific workflows for analyzing single-cell omics data that the AI wouldn't generate on its own.
What it needs
Requires python, scanpy, scikit-learn, torch, torchvision installed locally. About 3k 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