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multimodal-single-cell

Orchestrates integration workflows for multimodal single-cell data using MuData, Muon, and scvi-tools.

29|3|Updated Jun 11, 2026
Search Tags:#multimodal#bioinformatics#single-cell#scvi-tools#multiome#cite-seq#mudata
One-click install
npx skills add https://github.com/inflexa-ai/inflexa --skill multimodal-single-cell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multimodal-single-cell
Source: https://github.com/inflexa-ai/inflexa/tree/main/skills/multimodal-single-cell
Command: npx skills add https://github.com/inflexa-ai/inflexa --skill multimodal-single-cell

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of integrating diverse single-cell data types like RNA, protein, and chromatin accessibility, ensuring reproducible and scientifically rigorous analysis.

Core Features & Use Cases

  • Method Selection: Provides expert guidance on choosing between joint embedding models like TOTALVI, MultiVI, or GLUE based on assay type.
  • Quality Control: Implements rigorous per-modality QC standards to prevent technical noise from biasing downstream integration.
  • Use Case: A researcher performing CITE-seq can use this skill to correctly normalize protein data using DSB or CLR and integrate it with RNA counts using TOTALVI to identify cell types with high confidence.

Quick Start

Use the multimodal-single-cell skill to guide the integration of my RNA and ATAC data using the MultiVI model.

Frequently Asked Questions about multimodal-single-cell

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

FAQPage Schema
How do I integrate CITE-seq protein and RNA data using TOTALVI?▼

CITE-seq integration uses TOTALVI to jointly analyze RNA counts and protein data. You normalize protein data using DSB or CLR, then apply the model to identify cell types with high confidence.

What's the best way to perform multi-modal single-cell quality control?▼

Multi-modal single-cell quality control requires per-modality QC standards. This approach prevents technical noise from individual assays, such as RNA or chromatin accessibility, from biasing downstream integration.

How do I choose between MultiVI, TOTALVI, and GLUE for single-cell integration?▼

Choosing between MultiVI, TOTALVI, and GLUE depends on your assay type. MultiVI integrates RNA and ATAC data, TOTALVI handles CITE-seq protein and RNA, while GLUE suits other multiome modalities.

Can I use MuData and Muon for multiome regulatory inference?▼

Yes, MuData and Muon support multiome regulatory inference. These established frameworks help orchestrate joint embedding workflows and manage multi-modal single-cell data structures.

Does scvi-tools support joint embedding for TEA-seq data?▼

Yes, scvi-tools supports joint embedding for TEA-seq data. The framework orchestrates integration workflows across multi-modal single-cell assays to generate reproducible and auditable bioinformatics pipelines.

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