scvi-tools

Analyze single-cell omics data with scvi-tools probabilistic models.

1|Updated Mar 12, 2026

Search Tags:#machine learning #data analysis #bioinformatics #single-cell #omics #generative models

Ready to run

npx skills add https://github.com/yf8578/clawomics --skill scvi-tools-yf8578

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/yf8578/clawomics/tree/main/skills/scvi-tools
Command: npx skills add https://github.com/yf8578/clawomics --skill scvi-tools-yf8578

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scvi-tools, scanpy, pytorch, numpy, pandas, matplotlib, seaborn, optuna, shap, squidpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a suite of powerful probabilistic models for analyzing complex single-cell omics data, enabling deep insights into biological systems.

Core Features & Use Cases

Dimensionality Reduction & Batch Correction: Uncover underlying biological variation and remove technical noise from scRNA-seq, ATAC-seq, and other omics data.
Cell Type Annotation & Integration: Accurately annotate cell types and integrate datasets across different batches or modalities.
Differential Expression & Accessibility: Perform robust statistical tests to identify genes or regions that differ between conditions.
Use Case: Analyze a large scRNA-seq dataset with multiple batches, identify distinct cell populations, and perform differential gene expression analysis between disease and control groups.

Quick Start

Use the scvi-tools skill to analyze the provided single-cell RNA sequencing data.

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 with multiple batches?▼

Cell type annotation identifies distinct cell populations within single-cell omics data. This Skill uses probabilistic generative models to accurately annotate cell types and integrate datasets across different batches or modalities.

Can I analyze ATAC-seq data for differential accessibility testing?▼

Differential accessibility testing for ATAC-seq data performs robust statistical tests to identify regions differing between conditions. This Skill supports ATAC-seq analysis alongside scRNA-seq and multimodal data integration within an AnnData framework.

What is the best way to integrate multimodal single-cell omics datasets?▼

Integrating multimodal single-cell omics datasets aligns data across different modalities. This Skill provides advanced probabilistic models to integrate scRNA-seq, ATAC-seq, and multimodal data while performing dimensionality reduction.

Do I need AnnData objects to run single-cell dimensionality reduction?▼

AnnData objects are required to run single-cell dimensionality reduction with this Skill. You must have a Python environment with scvi-tools and scanpy installed to process scRNA-seq and other omics data.

How does probabilistic modeling help with differential gene expression analysis?▼

Probabilistic modeling helps with differential gene expression analysis by performing robust statistical tests to identify genes differing between disease and control groups. This approach accurately models technical noise in single-cell omics data.