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scvi-tools

Analyze single-cell genomics data with scvi-tools deep generative models.

23.4k|2.8k|Updated Jan 23, 2026
Search Tags:#genomics#single-cell#batch correction#data integration#label transfer#spatial transcriptomics#scvi-tools
One-click install
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill scvi-tools-anthropics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/anthropics/knowledge-work-plugins/tree/main/bio-research/skills/scvi-tools
Command: npx skills add https://github.com/anthropics/knowledge-work-plugins --skill scvi-tools-anthropics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scvi-tools, scanpy, mudata, scvelo, squidpy, pdf2image, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the complexity of analyzing single-cell genomics data (like scRNA-seq, CITE-seq, ATAC-seq, Multiome, Spatial) by providing robust tools for batch correction, integration, label transfer, and differential analysis. It simplifies advanced deep learning methods, making them accessible for researchers.

Core Features & Use Cases

  • Data Integration: Seamlessly combine datasets from different batches, technologies, or studies using scVI or scANVI.
  • Label Transfer: Annotate cell types in new datasets by mapping them to a reference atlas using scANVI or scArches.
  • Multi-modal Analysis: Analyze CITE-seq (RNA+protein) with totalVI or Multiome (RNA+ATAC) with MultiVI.
  • Spatial Genomics: Perform deconvolution with DestVI or build spatial models with resolVI.
  • RNA Velocity: Infer cell differentiation dynamics with veloVI.
  • Use Case: A researcher has three scRNA-seq datasets from different experiments. They can use this Skill to integrate them, correct for batch effects, and then use scANVI to transfer cell type labels from a public reference atlas to their combined data.

Quick Start

Use the scvi-tools skill to integrate three scRNA-seq datasets named 'study1.h5ad', 'study2.h5ad', and 'study3.h5ad' using cell type labels from the 'cell_type' column.

Frequently Asked Questions about scvi-tools

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate multiple scRNA-seq datasets and correct for batch effects?▼

To integrate scRNA-seq datasets and correct for batch effects, this Skill uses deep generative models like scVI and scANVI. It seamlessly combines data from different experiments or technologies to enable unified downstream analysis.

Can I transfer cell type labels from a reference atlas to my new single-cell data?▼

Yes, you can transfer cell type labels using scANVI or scArches. This Skill maps your new query dataset to a reference atlas, automatically annotating cell types based on the learned latent representations.

Does this Skill support multi-modal data analysis like CITE-seq and Multiome?▼

Yes, it supports multi-modal analysis by using totalVI for CITE-seq RNA and protein data, and MultiVI for Multiome RNA and ATAC data, allowing comprehensive interpretation of complex genomic datasets.

What is the best way to perform spatial transcriptomics deconvolution?▼

For spatial transcriptomics deconvolution, this Skill utilizes DestVI to estimate cell type abundances within spatial spots. It also uses resolVI to build spatial models for advanced spatial genomics analysis.

Do I need scanpy and mudata installed to run scvi-tools analysis?▼

Yes, you need scanpy, mudata, and scvi-tools installed as core dependencies. Additional libraries like scvelo and squidpy are required for RNA velocity inference and spatial data analysis respectively.

Can I infer RNA velocity dynamics using single-cell genomics data?▼

Yes, you can infer RNA velocity and cell differentiation dynamics using the veloVI deep generative model. This requires the scvelo library to properly process and interpret the spliced and unspliced RNA data.

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