bio-single-cell-markers-annotation

Identify cluster-specific marker genes and annotate cell types in single-cell RNA-seq data.

Updated Mar 30, 2026

Search Tags:#scanpy #differential-expression #seurat #scrna-seq #marker-genes #cell-type-annotation #gene-set-scoring

Ready to run

npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-markers-annotation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bio-single-cell-markers-annotation
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-single-cell-markers-annotation
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-markers-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Find marker genes and annotate cell types in single-cell RNA-seq data analyzed with Seurat (R) or Scanpy (Python). This skill accelerates differential expression analysis between clusters, identifies cluster-specific markers, and supports scoring gene sets to infer cell identities.

Core Features & Use Cases

Marker discovery across clusters using statistical tests (e.g., Wilcoxon, MAST) to identify robust marker genes.
Manual or automated cell-type annotation based on curated marker sets and expression patterns.
Gene-set scoring and visualization readiness (score_genes, DotPlot, Heatmap) to interpret cluster identities.
Export of marker lists and integration with both Seurat and Scanpy workflows for downstream reporting.

Quick Start

Run marker discovery for each cluster and annotate cell types using your Seurat or Scanpy workflow.

Frequently Asked Questions about bio-single-cell-markers-annotation

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

FAQPage Schema

How do I find marker genes for clusters in scRNA-seq data?▼

Marker discovery in scRNA-seq data is performed by running differential expression analysis between clusters using statistical tests like Wilcoxon or MAST, then ranking and exporting the identified cluster-specific marker genes.

Can I use this skill for cell-type annotation in both Seurat and Scanpy?▼

Yes, cell-type annotation is supported in both Seurat and Scanpy workflows. The skill uses curated marker sets and expression patterns to infer cell identities, allowing for manual or automated annotation of single-cell RNA-seq clusters.

How do I score gene sets to interpret cluster identities in single-cell RNA-seq?▼

To score gene sets and interpret cluster identities, the skill calculates expression scores for curated marker sets and prepares the data for visualization using tools like DotPlot or Heatmap within your Seurat or Scanpy pipeline.

Does this skill support automated cell-type annotation based on curated marker sets?▼

Yes, automated cell-type annotation is supported. The skill infers cell identities by leveraging curated marker sets and their expression patterns across clusters, alongside manual annotation capabilities in both Seurat and Scanpy.

What is the best way to visualize marker genes across clusters in Scanpy or Seurat?▼

The best way to visualize marker genes is by using built-in plotting functions like DotPlot and Heatmap. This skill prepares the differential expression results and gene-set scores for direct visualization within your Seurat or Scanpy workflow.