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scvi-tools

Train scVI models on AnnData for batch-corrected latent representations.

Updated Apr 19, 2026
Search Tags:#multimodal#single-cell#batch-correction#scvi-tools#differential-expression#transfer-learning#probabilistic
One-click install
npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill scvi-tools-chenyiru3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/CHENyiru3/AI-Skills-Collections/tree/main/skills-market/compbio/single-cell/analysis/scvi-tools
Command: npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill scvi-tools-chenyiru3

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provide probabilistic, batch-aware modeling for single-cell omics to disentangle biological signal from technical variation and to quantify uncertainty in downstream analyses.

Core Features & Use Cases

  • Advanced variational models for RNA, protein, and multimodal data (scVI, TOTALVI, MultiVI) that learn a shared latent space.
  • Batch correction and data integration across experiments, enabling transfer learning and cross-study comparisons.
  • Differential expression with posterior uncertainty analysis and flexible hypothesis testing.
  • Multimodal integration and protein/RNA imputation to leverage paired measurements.
  • Real-world scenario: unify scRNA-seq and CITE-seq datasets to compare cell-type programs across batches.

Quick Start

Train an scVI model on your AnnData with raw counts to obtain batch-corrected latent representations.

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 data across different experiments?▼

Batch correction on scRNA-seq data disentangles biological signal from technical variation using probabilistic models like scVI. This approach learns a shared, batch-aware latent space to enable robust cross-study comparisons and downstream integration.

Can I integrate scRNA-seq and scATAC-seq multimodal data for cross-modality analysis?▼

Multimodal data integration across scRNA-seq and scATAC-seq leverages the MultiVI model to learn a shared latent space. This probabilistic approach aligns paired measurements, enabling cross-modality integration and supporting robust multimodal analysis.

How does differential expression analysis handle uncertainty in single-cell omics?▼

Differential expression analysis in single-cell omics quantifies posterior uncertainty using scalable variational inference. This probabilistic method enables flexible hypothesis testing while explicitly accounting for technical variation and batch effects.

Do I need PyTorch to run scvi-tools for single-cell probabilistic modeling?▼

PyTorch and PyTorch Lightning are required to run scvi-tools for single-cell probabilistic modeling. These frameworks provide the scalable variational inference and amortized encoder infrastructure necessary to train scVI, TOTALVI, and MultiVI models.

What is the best way to unify scRNA-seq and CITE-seq datasets for cell-type comparison?▼

Unifying scRNA-seq and CITE-seq datasets is best achieved using the TOTALVI model to learn a shared latent space. This probabilistic method performs multimodal integration and protein imputation, enabling accurate cell-type program comparisons across batches.

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