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omicverse-single-cell-scenic

Convert single-cell SCENIC analysis into a reusable regulon workflow for AnnData datasets.

13|2|Updated Apr 5, 2026
Search Tags:#bioinformatics#single-cell#rss#anndata#aucell#scenic#regulon
One-click install
npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-scenic-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omicverse-single-cell-scenic
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/single-cell-scenic
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-scenic-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anndata, numpy, scipy, omicverse, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill turns fragmented single-cell SCENIC notebook steps into a reusable workflow for regulon inference, scoring, and interpretation, so you can move from raw AnnData input to validated biological insight without manually reassembling the pipeline.

Core Features & Use Cases

  • GRN Inference: Choose between regdiffusion, grnboost2, and genie3 to infer TF-target relationships from raw-count single-cell data.
  • Regulon Construction: Validate cisTarget ranking databases and motif annotation resources, then prune modules and compute AUCell regulon activity scores.
  • Downstream Interpretation: Run RSS scoring, regulon binarization, marker ranking, and TF-centered network exploration on finished SCENIC outputs.
  • Use Case: Use this Skill when a single-cell analysis stalls because you need to know which backend to run, which layer contains counts, and how to inspect the resulting regulons.

Quick Start

Ask the agent to run OmicVerse SCENIC on your AnnData object, verify the required motif and ranking resources, select the GRN backend, and return regulon, AUCell, and downstream interpretation outputs.

Frequently Asked Questions about omicverse-single-cell-scenic

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

FAQPage Schema
How do I run SCENIC regulon analysis on an AnnData object?▼

SCENIC regulon analysis on an AnnData object requires raw-count input layers to infer GRNs, prune modules via cisTarget ranking databases, and compute AUCell activity scores for downstream interpretation.

What is the difference between grnboost2, genie3, and regdiffusion for single-cell GRN inference?▼

GRN inference backends grnboost2, genie3, and regdiffusion offer different algorithms for predicting TF-target relationships from raw counts. This workflow provides branch control to select the optimal backend for your single-cell dataset.

Do I need external ranking databases and motif annotation tables for cisTarget pruning?▼

Yes, external ranking databases and motif annotation tables are required for cisTarget pruning. The SCENIC pipeline validates these resources to accurately prune inferred modules and construct reliable regulons.

How does RSS ranking and regulon binarization help interpret single-cell SCENIC outputs?▼

RSS ranking and regulon binarization help interpret single-cell SCENIC outputs by scoring regulon specificity across cell clusters and converting continuous AUCell scores into binary active or inactive states for clearer downstream exploration.

Can I use this SCENIC workflow for TF-centered network exploration after AUCell scoring?▼

Yes, you can use this SCENIC workflow for TF-centered network exploration after AUCell scoring. It applies marker ranking and network visualization to identify key transcription factors driving cell states.

Why does my single-cell SCENIC analysis stall when selecting a GRN backend and count layers?▼

Single-cell SCENIC analysis stalls when the correct raw-count input layer is missing or the GRN backend is unconfigured. This workflow resolves the stall by verifying count layers and automating backend selection.

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