scientific-scatac-signac

Analyze scATAC-seq data with Signac pipelines for regulatory programs.

3|1|Updated Feb 11, 2026

Search Tags:#multimodal #scatac-seq #signac #gene-activity #motif-enrichment #snapatac2 #episcanpy

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-scatac-signac

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-scatac-signac
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-scatac-signac
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-scatac-signac

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides an end-to-end pipeline for scATAC-seq analysis using Signac, SnapATAC2, and episcanpy to preprocess data, call peaks, analyze motif enrichment, compute Gene Activity scores, and integrate RNA data for multimodal interpretation.

Core Features & Use Cases

Preprocessing & QC: Normalize, filter, and prepare scATAC-seq data for downstream analysis.
Peak Calling & Motif Enrichment: Identify accessible regions and enrich transcription factor motifs.
Gene Activity & Multimodal Integration: Derive gene activity scores and combine with scRNA-seq for RNA+ATAC analysis; supports WNN-like integration.
Use Case: A researcher has scATAC-seq and a matching scRNA-seq dataset and wants to cluster cells, interpret regulatory programs, and map regulatory relationships to gene expression.

Quick Start

Run scatac_pipeline with a fragment_file and optional rna_h5ad to execute the full scATAC-seq integration pipeline.

Frequently Asked Questions about scientific-scatac-signac

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

FAQPage Schema

How do I run an end-to-end scATAC-seq analysis pipeline using Signac?▼

Run the scatac pipeline by providing a fragment file and an optional RNA h5ad file. The pipeline executes end-to-end scATAC-seq analysis using Signac, covering data preprocessing, peak calling, motif enrichment, and gene activity scoring.

Can I integrate scRNA-seq data with scATAC-seq for multimodal analysis?▼

Yes, you can integrate scRNA-seq data with scATAC-seq for multimodal analysis. Provide a matching scRNA-seq dataset to cluster cells, interpret regulatory programs, and map regulatory relationships to gene expression using WNN-like integration.

How do I calculate gene activity scores from chromatin accessibility data?▼

To calculate gene activity scores from chromatin accessibility data, use the Signac-based pipeline. It derives gene activity scores from accessible regions to enable multimodal interpretation and combine them with scRNA-seq data.

Does the scATAC-seq pipeline support peak calling and transcription factor motif enrichment?▼

Yes, the scATAC-seq pipeline supports peak calling and motif enrichment. It identifies accessible regions and enriches transcription factor motifs as core features of the Signac-based workflow.

Can I use SnapATAC2 and episcanpy alongside Signac for single-cell chromatin accessibility studies?▼

Yes, you can use SnapATAC2 and episcanpy alongside Signac. The pipeline uses these tools to preprocess data, call peaks, analyze motif enrichment, and compute gene activity scores in diverse organisms and genomes.

What preprocessing and quality control steps are needed for scATAC-seq data?▼

scATAC-seq data preprocessing and QC involve normalizing, filtering, and preparing data for downstream analysis. The pipeline handles these steps to ensure data quality before peak calling and motif analysis.