What models are supported by the scvi-tools skill?

It supports a wide range of models including scVI for RNA-seq, totalVI for CITE-seq, PeakVI for ATAC-seq, MultiVI for multiome, and DestVI for spatial data.

Can I use this skill for batch correction?

Yes, scvi-tools is specifically designed for unsupervised and semi-supervised batch correction and data integration using variational autoencoders (VAEs).

Is GPU support included in the documentation?

The skill includes an environment setup guide to help configure GPU acceleration for training deep learning models efficiently.

Can I map new data to a reference atlas?

Yes, it supports label transfer and reference mapping using models like scANVI and the scArches architecture for query-to-reference integration.

Does the skill handle data preparation?

Absolutely. It provides scripts and guidance for QC, filtering, and highly variable gene (HVG) selection using scvi-tools best practices.

scvi-tools Single-Cell Analysis

Name: scvi-tools Single-Cell Analysis
Author: anthropics

byanthropics

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147

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Data Science & ML

Accelerates single-cell genomics research using probabilistic deep learning models for data integration, multi-modal analysis, and reference mapping.

This skill equips Claude with specialized knowledge of the scvi-tools framework, the industry standard for applying deep learning to single-cell genomics. It provides comprehensive guidance for performing unsupervised and semi-supervised data integration, multi-modal CITE-seq and multiome analysis, ATAC-seq peak analysis, and spatial transcriptomics deconvolution. By leveraging pre-built CLI scripts and Python utilities, users can efficiently process raw counts into meaningful latent representations, perform batch correction, and conduct differential expression analysis within a robust, reproducible workflow.

Key Features

01Automated data preparation and validation for deep learning models

02Comprehensive multi-modal integration for RNA, ATAC, and protein data

03Modular CLI scripts for training, clustering, and differential expression

04Specialized workflows for spatial transcriptomics and RNA velocity

05Reference-based label transfer and query mapping using scArches

06147 GitHub stars

Use Cases

01Integrating large-scale scRNA-seq datasets across multiple batches or technologies

02Jointly analyzing multi-modal data such as CITE-seq or 10x Multiome using probabilistic models

03Mapping query datasets to established reference atlases for automated cell-type annotation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add anthropics/life-sciences scvi-tools

For use in Claude.ai and ChatGPT

Download Skill