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scrna-celltype-annotation

Annotate single-cell RNA-seq clusters with literature-backed evidence and R code.

6|1|Updated Jul 1, 2026
Search Tags:#bioinformatics#literature-review#cell-annotation#seurat#scrna-seq#ai4s
One-click install
npx skills add https://github.com/PancrePal-xiaoyibao/VitaForge --skill scrna-celltype-annotation-pancrepal-xiaoyibao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scrna-celltype-annotation
Source: https://github.com/PancrePal-xiaoyibao/VitaForge/tree/main/.gemini/skills/scrna-celltype-annotation
Command: npx skills add https://github.com/PancrePal-xiaoyibao/VitaForge --skill scrna-celltype-annotation-pancrepal-xiaoyibao

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the bottleneck of manually annotating single-cell clusters by providing a standardized, evidence-based workflow that integrates marker gene analysis with real-time literature retrieval.

Core Features & Use Cases

  • Automated Annotation: Performs two-level (major/minor) cell type classification based on Seurat FindAllMarkers results.
  • Evidence-Based Reporting: Automatically generates a Markdown report containing R recode code and specific literature citations (PMID/DOI) for each cluster.
  • Use Case: A researcher has completed Seurat clustering but lacks the time to manually verify cell identities; this skill automates the literature search and generates the final annotation code and documentation.

Quick Start

Use the scrna-celltype-annotation skill to process the current Seurat object and generate a comprehensive cell type annotation report with literature evidence.

Frequently Asked Questions about scrna-celltype-annotation

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

FAQPage Schema
How do I automate scRNA-seq cell type annotation with literature evidence?▼

Automate scRNA-seq cell type annotation by integrating Seurat FindAllMarkers differential expression results with biomedical literature retrieval to generate two-level classifications and verifiable citations. This standardizes cluster identification and produces reproducible Markdown documentation with R recode code.

What is the best way to annotate single-cell RNA-seq clusters after Seurat clustering?▼

The best way to annotate single-cell RNA-seq clusters is using a standardized workflow that matches differentially expressed marker genes against external literature databases. This approach ensures high-confidence cell type identification and generates R recode scripts for immediate pipeline integration.

How does literature-backed cell annotation work for scRNA-seq major and minor cell types?▼

Literature-backed cell annotation works by analyzing differentially expressed genes from scRNA-seq clusters and querying biomedical databases for matching evidence. It performs two-level major and minor classification, automatically assigning specific cell types while attaching verifiable PMID or DOI citations.

Can I generate R recode code for Seurat cluster annotations automatically?▼

Yes, you can generate R recode code automatically for Seurat cluster annotations. The workflow processes FindAllMarkers output, determines two-level cell type identities using literature evidence, and outputs a comprehensive Markdown report containing the executable R recode script.

Does automated scRNA-seq cell annotation require manual literature search for marker genes?▼

No, automated scRNA-seq cell annotation eliminates manual literature search by performing real-time literature retrieval. It directly queries biomedical databases using differentially expressed marker genes to find supporting evidence, generating verifiable PMID and DOI citations for each annotated cluster.

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