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bio-workflows-multiome-pipeline

Analyze joint scRNA-seq and scATAC-seq data with Seurat/Signac WNN integration.

Updated Mar 30, 2026
Search Tags:#integration#single-cell#Seurat#multiome#scRNA#scATAC#Signac
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-workflows-multiome-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-workflows-multiome-pipeline
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-wf-multiome-pipeline
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-workflows-multiome-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

End-to-end multiome workflow for joint scRNA-seq and scATAC-seq analysis, enabling streamlined data loading, QC, modality processing, and integrated interpretation.

Core Features & Use Cases

  • Load 10X Multiome outputs and create a unified Seurat object with RNA and ATAC assays.
  • Independently QC and preprocess RNA and ATAC modalities, then integrate them via Weighted Nearest Neighbors (WNN) for joint clustering and visualization.
  • Generate linked features to explore regulatory relationships and multi-omics drivers across cell types.
  • Use cases include discovering cell types with combined RNA/ATAC signals and comparing modality contributions per cluster.

Quick Start

Load your 10X Multiome output and run the end-to-end Seurat/Signac pipeline to generate integrated RNA and ATAC analyses.

Frequently Asked Questions about bio-workflows-multiome-pipeline

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

FAQPage Schema
How do I perform scRNA-seq and scATAC-seq integration for 10X Multiome data?▼

Multiome integration for joint scRNA-seq and scATAC-seq data is achieved by loading 10X outputs into a unified Seurat object, applying independent QC, and integrating modalities via Weighted Nearest Neighbors (WNN) for joint clustering.

What is Weighted Nearest Neighbors (WNN) for single-cell multiome analysis?▼

Weighted Nearest Neighbors (WNN) is an integration method in Seurat used for single-cell multiome analysis. It computes modality weights for joint scRNA-seq and scATAC-seq data to enable combined clustering and visualization across cell types.

How do I run end-to-end QC and preprocessing for RNA and ATAC modalities in Seurat?▼

End-to-end QC and preprocessing for RNA and ATAC modalities in Seurat involves loading 10X Multiome outputs, independently filtering cells based on modality-specific metrics, and normalizing data before applying WNN integration.

Can I use Signac to explore regulatory relationships across cell types with scATAC-seq data?▼

Yes, Signac can explore regulatory relationships across cell types with scATAC-seq data. The workflow generates linked features within the integrated Seurat object to identify multi-omics drivers and peak-to-gene regulatory relationships.

Does this Seurat multiome pipeline compare modality contributions per cluster?▼

Yes, the Seurat multiome pipeline compares modality contributions per cluster. It uses Weighted Nearest Neighbors (WNN) to evaluate combined RNA and ATAC signals, allowing you to discover cell types and assess modality contributions across clusters.

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