scvi-tools

Model single-cell omics data with variational autoencoders for batch correction.

22|4|Updated May 25, 2026

Search Tags:#multimodal #single-cell #batch-correction #scvi-tools #scanpy #differential-expression #probabilistic

Ready to run

npx skills add https://github.com/crazymsn/academic-skills --skill scvi-tools-crazymsn

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

scvi-tools enables principled probabilistic modeling of single-cell data to separate biology from technical noise, integrate multiple modalities, and quantify uncertainty in downstream analyses.

Core Features & Use Cases

Unsupervised modeling and batch correction for scRNA-seq data and other modalities using scVI
Semi-supervised cell typing and annotation transfer with SCANVI
Differential expression with uncertainty and Bayesian testing
Multimodal integration and imputation (TOTALVI, MultiVI)
Seamless integration with Scanpy and GPU-accelerated training

Quick Start

Install scvi-tools, register your AnnData with batch/covariates, and train an scVI-based model to obtain a batch-corrected 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 data using a variational autoencoder?▼

Batch correction for scRNA-seq data uses a variational autoencoder (scVI) to learn a shared latent space that separates biological signal from technical noise across batches. Register your AnnData with batch covariates and train the model to obtain a corrected latent representation.

What is probabilistic differential expression in single-cell analysis and how does it handle uncertainty?▼

Probabilistic differential expression in single-cell analysis uses Bayesian testing to quantify uncertainty in gene expression differences between cell populations. It yields posterior distributions rather than static values, allowing you to assess the statistical confidence of observed expression changes.

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

Multimodal integration of scRNA-seq and scATAC-seq datasets is supported for cross-modality analysis. Models like TOTALVI and MultiVI map these modalities into a shared latent space, enabling joint analysis and imputation across modalities even when profiles are incomplete.

Does scvi-tools work with Scanpy and require GPU acceleration for training?▼

scvi-tools integrates seamlessly with Scanpy for downstream analysis and supports GPU-accelerated training to handle large-scale single-cell omics datasets efficiently. GPU acceleration speeds up training but is not strictly required to run the variational autoencoder models.

How do I transfer cell type annotations to unlabelled single-cell data semi-supervised?▼

Cell type annotation transfer to unlabelled single-cell data uses a semi-supervised approach (SCANVI) built on the scVI latent space. It leverages known labels in a subset of cells to predict and annotate cell types in the remaining unlabelled population.

When should I use probabilistic modeling over deterministic methods for single-cell omics?▼

Probabilistic modeling is preferred over deterministic methods when you need to quantify uncertainty in downstream analyses like differential expression or batch correction. It separates biological variation from technical noise, providing confidence estimates that deterministic methods cannot capture.