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scientific-scvi-integration

Integrates multi-batch scRNA-seq data using scVI-tools to produce unified latent representations and annotations.

3|1|Updated Feb 11, 2026
Search Tags:#integration#single-cell#annotation#batch-correction#scANVI#scvi#totalVI
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-scvi-integration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-scvi-integration
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-scvi-integration
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-scvi-integration

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This pipeline enables end-to-end integration and probabilistic modeling for multi-batch single-cell data using scVI-tools, SCVI/SCANVI/totalVI and SOLO to unify datasets and extract meaningful latent representations.

Core Features & Use Cases

  • Batch-aware integration: batch-corrected latent spaces for scRNA-seq across studies.
  • Semi-supervised annotation: transfer cell type labels with high confidence using scANVI.
  • Multi-modality support: combine RNA with protein (ADT) data via totalVI and detect doublets with SOLO.
  • End-to-end pipeline: from data preparation to latent space visualization and clustering.

Quick Start

Run the scVI integration pipeline on your AnnData to generate a unified latent space and annotated cell types.

Frequently Asked Questions about scientific-scvi-integration

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

FAQPage Schema
How do I perform batch correction on multi-batch scRNA-seq datasets?▼

Batch correction for multi-batch scRNA-seq data is achieved by integrating datasets with scVI to generate a unified, batch-corrected latent space. This latent representation can then be used directly for clustering and visualization across different studies.

Can I transfer cell type labels to an unannotated single-cell dataset?▼

Yes, you can transfer cell type labels to an unannotated single-cell dataset using the scANVI workflow. This semi-supervised approach leverages existing labels from reference data to confidently annotate target datasets within the integrated latent space.

Does this scVI integration pipeline support multi-modality CITE-seq data?▼

Yes, this scVI integration pipeline supports multi-modality CITE-seq data through the totalVI workflow. It combines RNA and protein (ADT) data to generate a joint latent space for integrated multi-modal analysis.

What is the best way to detect doublets in single-cell RNA-seq data?▼

Detecting doublets in single-cell RNA-seq data is handled using the SOLO workflow. It operates within the scVI-tools environment to identify doublets probabilistically from your raw count data.

What input format do I need to run an scVI integration pipeline?▼

You need to provide your data as an AnnData object to run the scVI integration pipeline. The pipeline processes this input to extract latent representations and generate clustering-ready outputs.

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