scrna-embedding

Generate scVI/scANVI latent embeddings and batch integration for scRNA-seq data.

Updated May 10, 2026

Search Tags:#single-cell RNA-seq #scRNA-seq #scVI #batch integration #scanvi #latent embedding

Ready to run

npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill scrna-embedding-mubashermohammed

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scrna-embedding
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/scrna-embedding
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill scrna-embedding-mubashermohammed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, anndata, torch, scvi-tools, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a streamlined approach to single-cell RNA-seq latent embedding and batch integration, allowing users to efficiently analyze complex single-cell datasets.

Core Features & Use Cases

Latent Embedding: Perform scVI/scANVI-based latent embedding for single-cell data.
Batch Integration: Achieve batch-aware integration and correction for single-cell data.
Use Case: For a user with single-cell RNA-seq data, this Skill can be used to generate a latent embedding that accounts for batch effects, enabling more accurate downstream analysis.

Quick Start

Run the scrna-embedding skill on your .h5ad file to generate a latent embedding and batch-aware integration.

Frequently Asked Questions about scrna-embedding

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

FAQPage Schema

How do I perform batch integration and latent embedding for single-cell RNA-seq data?▼

You can perform single-cell RNA-seq latent embedding and batch integration using scVI and scANVI models. This approach generates a batch-aware latent representation to correct for technical variations across datasets.

Can I use my existing .h5ad files with scVI for batch correction?▼

Yes, you can run batch integration directly on .h5ad files. The workflow processes anndata structures to compute scVI latent embeddings that account for batch effects in your single-cell data.

Do I need PyTorch and scvi-tools installed for scRNA-seq batch integration?▼

Yes, torch and scvi-tools are required dependencies for model training. You also need scanpy and anndata installed to manage the single-cell RNA-seq data structures and execute the latent embedding workflow.

What is the difference between scVI and scANVI for single-cell RNA-seq analysis?▼

scVI provides an unsupervised latent embedding for batch integration, while scANVI extends this architecture for semi-supervised cell type annotation. Both generate batch-aware latent representations for scRNA-seq data.

How does scVI handle batch effects in single-cell RNA-seq datasets?▼

scVI uses deep generative neural networks to learn a latent embedding that conditions on batch covariates. This batch-aware integration removes technical variation while preserving true biological signals in scRNA-seq data.