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scvi-tools

Provides Python probabilistic models for single-cell genomics data analysis.

Updated Mar 10, 2026
Search Tags:#bioinformatics#single-cell#data integration#omics#probabilistic modeling#scvi-tools
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill scvi-tools-felixboehm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/scvi-tools
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill scvi-tools-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scvi-tools, scanpy, pytorch, and includes references (resource) components.

What problem does it solve?

This Skill addresses the complexities of single-cell omics data analysis, enabling researchers to perform advanced tasks like batch correction, integration, and differential expression with probabilistic models.

Core Features & Use Cases

  • Probabilistic Modeling: Utilizes deep generative models for robust analysis.
  • Multi-modal Integration: Handles diverse omics data types (RNA, ATAC, protein).
  • Batch Correction & Integration: Seamlessly integrates datasets across batches and studies.
  • Differential Expression: Performs statistically sound DE analysis with uncertainty quantification.
  • Use Case: A researcher has multiple scRNA-seq datasets from different experiments and wants to integrate them, correct for batch effects, and identify cell types. This Skill provides the tools to achieve this with advanced probabilistic methods.

Quick Start

Use the scvi-tools skill to set up AnnData for scVI, train a model, and get the latent representation.

Frequently Asked Questions about scvi-tools

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

FAQPage Schema
How do I perform batch correction on scRNA-seq datasets from different experiments?▼

Batch correction for scRNA-seq datasets is achieved using probabilistic models like scVI. By training on AnnData objects, these models learn a shared latent representation that removes technical variation across different experimental batches.

Can I integrate multi-modal single-cell data combining RNA and ATAC measurements?▼

Multi-modal integration of RNA and ATAC data is supported using models like MultiVI and totalVI. These frameworks jointly analyze diverse omics data types within a single probabilistic model to identify cellular states across modalities.

What is the best way to quantify uncertainty in single-cell differential expression analysis?▼

Uncertainty quantification in differential expression analysis is provided by probabilistic models like scVI. Instead of point estimates, these frameworks use deep generative models to compute statistically sound posterior distributions for gene expression differences.

Do I need AnnData objects to run probabilistic models for single-cell genomics?▼

AnnData objects are required as the primary input format to run these probabilistic models. You must load your single-cell data into an AnnData structure to set up the environment for scVI, scANVI, or totalVI before model training.

Does scvi-tools support GPU acceleration for large-scale single-cell omics datasets?▼

GPU acceleration is supported for processing large-scale single-cell omics datasets. Built on PyTorch, the framework leverages GPU hardware to efficiently train deep generative models on massive datasets, significantly reducing computation time.

How does scANVI perform cell type annotation on new single-cell datasets?▼

Cell type annotation on new single-cell datasets is performed by scANVI using a semi-supervised probabilistic approach. It leverages pre-trained scVI latent representations and known cell labels to predict cell types for unannotated populations.

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