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bio-scvi-tools

Train SCVI models on single-cell RNA-seq data with AnnData.

Updated Mar 13, 2026
Search Tags:#single-cell#batch correction#data integration#omics#scvi-tools#generative models
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-scvi-tools
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-scvi-tools
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/bio-scvi-tools
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-scvi-tools

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the complexity of analyzing single-cell omics data by providing advanced probabilistic models for tasks like batch correction, integration, and differential expression analysis.

Core Features & Use Cases

  • Probabilistic Modeling: Utilizes deep generative models (VAEs) for robust analysis.
  • Data Modalities: Supports RNA-seq, ATAC-seq, CITE-seq, and spatial transcriptomics.
  • Use Case: You have multiple batches of single-cell RNA-seq data and need to integrate them, correct for batch effects, and perform differential gene expression analysis between cell types. This Skill provides the tools to achieve this with uncertainty quantification.

Quick Start

Use the bio-scvi-tools skill to analyze single-cell RNA-seq data by setting up AnnData, training an SCVI model, and extracting the latent representation.

Frequently Asked Questions about bio-scvi-tools

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

FAQPage Schema
How do I correct batch effects in single-cell RNA-seq data?▼

To correct batch effects in single-cell RNA-seq data, this framework uses deep generative models to integrate multiple batches and produce a unified latent representation with uncertainty quantification.

Can I integrate multimodal single-cell omics like CITE-seq and ATAC-seq?▼

Yes, you can integrate multimodal single-cell omics data including CITE-seq, ATAC-seq, and spatial transcriptomics using probabilistic models designed for diverse data integration.

How does deep generative modeling work for single-cell dimensionality reduction?▼

Deep generative modeling for single-cell dimensionality reduction works by training variational autoencoders to learn a latent representation of the data while providing uncertainty quantification for downstream analysis.

What is the best way to perform differential gene expression analysis with uncertainty?▼

The best way to perform differential gene expression analysis with uncertainty is using a probabilistic framework that quantifies confidence intervals during the comparison of cell types across integrated batches.

Do I need AnnData to analyze spatial transcriptomics with deep generative models?▼

You need AnnData to set up your spatial transcriptomics data before training the generative model and extracting the latent representation for downstream analysis.

When should I use deep generative models instead of standard dimensionality reduction for single-cell data?▼

You should use deep generative models instead of standard dimensionality reduction when your single-cell analysis requires batch correction, multimodal integration, or differential expression with uncertainty quantification.

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