What data format is required for this skill?

This skill works with AnnData (.h5ad) files and requires raw integer counts in a specific layer to satisfy the requirements of scvi-tools probabilistic models.

Does this skill support reference mapping?

Yes, it provides detailed workflows for using scArches and scANVI to map query datasets to existing reference models for rapid label transfer.

Can it handle spatial transcriptomics?

Absolutely. It includes specific guidance for using DestVI to perform cell type deconvolution when you have a matching scRNA-seq reference.

Which model should I use for CITE-seq data?

For multi-modal data involving both RNA and protein (CITE-seq), the skill recommends using the totalVI model for joint integration and protein denoising.

Single-Cell Deep Learning (scvi-tools)

Name: Single-Cell Deep Learning (scvi-tools)
Author: anthropics

byanthropics

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147

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Data Science & ML

Streamlines single-cell genomics workflows using scvi-tools for probabilistic data integration and deep learning analysis.

This skill integrates the scvi-tools framework into Claude Code, providing a comprehensive toolkit for deep learning-based single-cell analysis. It assists researchers and bioinformaticians in selecting and implementing the correct probabilistic models—such as scVI for integration, totalVI for CITE-seq, or PeakVI for ATAC-seq—and provides standardized scripts for data preparation, model training, and downstream visualization. By following domain-specific best practices, it ensures proper handling of raw counts, batch effects, and latent space representations within the scverse ecosystem.

Key Features

01147 GitHub stars

02Support for spatial transcriptomics deconvolution and RNA velocity

03Standardized scripts for data QC, HVG selection, and AnnData validation

04Reference mapping and label transfer using scArches and scANVI

05Advanced batch correction and multi-dataset integration workflows

06Guided model selection for scRNA-seq, scATAC-seq, and multi-modal data

Use Cases

01Mapping new query samples to a curated reference atlas for automated cell-type annotation

02Jointly analyzing gene expression and protein abundance in CITE-seq experiments

03Integrating heterogeneous scRNA-seq datasets to remove technical batch effects

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