skills.rest
HolobiomicsLab
HolobiomicsLabOFFICIAL
Explore all skills by this author →
WebsiteGitHub

scvi-tools

Train scvi-tools models on AnnData for batch-corrected latent spaces and differential expression.

4|1|Updated Jun 18, 2025
Search Tags:#multimodal#single-cell#batch-correction#scvi-tools#differential-expression#probabilistic
One-click install
npx skills add https://github.com/HolobiomicsLab/Toolomics --skill scvi-tools-holobiomicslab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/HolobiomicsLab/Toolomics/tree/main/mcp_host/skills/scientific-skills/scientific-skills/scvi-tools
Command: npx skills add https://github.com/HolobiomicsLab/Toolomics --skill scvi-tools-holobiomicslab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Single-cell data are noisy and high-dimensional; this skill provides probabilistic models to denoise, batch-correct, and interpret cells.

Core Features & Use Cases

  • Probabilistic modeling for batch correction, denoising, and integration of single-cell data across modalities
  • Supports differential expression with uncertainty and transfer learning
  • Works with RNA, ATAC, and multimodal datasets to learn joint latent spaces

Quick Start

Train a scvi-tools model on your AnnData to obtain a batch-corrected latent space and probabilistic differential expression results.

Frequently Asked Questions about 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?▼

Batch effects in single-cell RNA-seq data are corrected using probabilistic deep generative models that learn a batch-corrected latent space. The models capture uncertainty while integrating data across different batches and modalities.

Can I perform differential expression analysis with uncertainty on scRNA-seq data?▼

Differential expression analysis with uncertainty is supported on scRNA-seq data by leveraging posterior distributions from probabilistic models. This approach provides denoised expression estimates and posterior differential expression results.

Does this approach support multimodal datasets like RNA and ATAC integration?▼

Multimodal datasets including RNA+ATAC and RNA+protein are supported for joint integration. The models learn joint latent spaces across modalities to effectively handle noisy, high-dimensional single-cell data.

What input format is required for probabilistic single-cell modeling?▼

AnnData inputs are required for probabilistic single-cell modeling. The models use NB or ZINB likelihoods to handle covariates and output latent representations, denoised expressions, and posterior differential expression results.

Related Skills

Explore More Data & Analytics Skills→
swaruplabswaruplab
94

scvi-tools

Model single-cell omics data with probabilistic, batch-corrected latent representations.

Community
Advanced
qinyan-aiqinyan-ai
48

scvi-tools

Provides probabilistic modeling and latent representation for single-cell omics data.

Community
Advanced
zongtingweizongtingwei
25

trajectory-lineage

Infer pseudotime, lineage branches, and state transitions from single-cell data using Scanpy.

Community
Intermediate
crazymsncrazymsn
22

scvi-tools

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

Community
Advanced