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BKZhao
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scvi-tools

Train probabilistic variational models for single-cell omics analysis with batch correction.

Updated Aug 23, 2026
Search Tags:#multimodal#single-cell#batch-correction#scvi-tools#differential-expression#probabilistic
One-click install
npx skills add https://github.com/BKZhao/MIMIC-Papers-Repro-Agent --skill scvi-tools-bkzhao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/BKZhao/MIMIC-Papers-Repro-Agent/tree/main/.codex/skills/scvi-tools
Command: npx skills add https://github.com/BKZhao/MIMIC-Papers-Repro-Agent --skill scvi-tools-bkzhao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

scvi-tools provides a probabilistic, scalable framework for single-cell omics analysis, enabling batch correction, multimodal integration, and uncertainty-aware differential analysis.

Core Features & Use Cases

  • Variational models for scRNA-seq, scATAC-seq, and multimodal data
  • Batch correction, data integration, and cell-type annotation transfer
  • Differential expression, protein/RNA imputation, and cross-modality analyses

Quick Start

Train a model on your AnnData and inspect the learned latent representations for quality control.

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 multimodal single-cell datasets?▼

Batch correction for multimodal single-cell datasets uses a probabilistic framework to integrate scRNA-seq and scATAC-seq data. It trains variational models on your data to learn corrected latent representations for downstream analysis.

Can I do differential expression analysis with uncertainty awareness?▼

Uncertainty-aware differential expression is achieved through probabilistic variational models. This framework scales to large single-cell datasets and provides reproducible workflows using standardized APIs.

Does scvi-tools work with Python and PyTorch for single-cell analysis?▼

Yes, this single-cell analysis framework requires Python and PyTorch. It leverages PyTorch to provide scalable variational models for processing scRNA-seq, scATAC-seq, and multimodal datasets.

What is the best way to integrate scRNA-seq and scATAC-seq data?▼

Integrating scRNA-seq and scATAC-seq data is best achieved using a probabilistic framework for multimodal integration. This enables cross-modality analyses like cell-type annotation transfer and protein or RNA imputation.

How do I transfer cell-type annotations across single-cell modalities?▼

Cell-type annotation transfer across modalities is performed by training variational models on integrated multimodal data. This probabilistic approach maps annotations between scRNA-seq and scATAC-seq datasets.

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