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bio-single-cell-markers-annotation

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

Updated Aug 27, 2026
Search Tags:#scanpy#differential-expression#cell-annotation#seurat#scrna-seq#marker-genes
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-single-cell-markers-annotation-stellaromics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-single-cell-markers-annotation
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-single-cell-markers-annotation
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-single-cell-markers-annotation-stellaromics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Annotate cell types from marker genes in single-cell RNA-seq data, enabling researchers to interpret clustered populations.

Core Features & Use Cases

  • Identify cluster-specific marker genes using Seurat (R) or Scanpy (Python).
  • Score gene sets and assign cell type labels to clusters; export results for downstream analysis.
  • Use Case: given a multi-cluster scRNA-seq dataset, determine top markers per cluster and map them to known cell types.

Quick Start

Annotate clusters based on marker gene signatures in a Seurat or Scanpy workflow to obtain cell-type labels.

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 identify marker genes for cluster annotation in scRNA-seq data?▼

To identify marker genes for cluster annotation, you run differential expression analysis across clusters in scRNA-seq data. This Skill scores gene sets and assigns cell-type labels to clusters using Seurat or Scanpy workflows.

What is the best way to annotate cell types from single-cell RNA-seq marker genes?▼

Annotating cell types from marker genes involves identifying cluster-specific markers and mapping them to known cell types. This Skill guides marker validation and assigns labels to multi-cluster scRNA-seq datasets.

Can I use Scanpy and Seurat objects for differential expression and cell labeling?▼

Yes, you can use both Scanpy and Seurat objects for differential expression and cell labeling. The Skill supports annotated AnnData and Seurat objects to score gene sets and assign cell type labels across multiple clusters.

How do I export annotated scRNA-seq clusters for downstream analysis?▼

You export annotated scRNA-seq clusters after mapping marker genes to known cell types. The Skill processes Seurat or Scanpy objects, assigns cell-type labels, and exports the labeled results for downstream analysis.

Do I need pre-clustered scRNA-seq data to map marker genes to known cell types?▼

Yes, you need pre-clustered scRNA-seq data to map marker genes to known cell types. The Skill requires input datasets with annotated Seurat or AnnData objects to identify cluster-specific markers and assign cell labels.

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