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bio-single-cell-cell-annotation

Automate cell-type labeling for single-cell gene expression data using reference-based methods.

Updated Mar 30, 2026
Search Tags:#single-cell#scRNA-seq#cell-type-annotation#celltypist#SingleR#reference-datasets#Azimuth
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-cell-annotation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-single-cell-cell-annotation
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-single-cell-cell-annotation
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-cell-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automated cell-type labeling across single-cell datasets using reference-based methods to achieve consistent, reproducible annotations.

Core Features & Use Cases

  • Automated annotation using multiple reference tools (CellTypist, scPred, SingleR, Azimuth) to assign cell-type labels.
  • Confidence scoring, filtering, and consensus labeling for robust results.
  • Compatibility with common single-cell workflows and integration with AnnData/Seurat objects, plus validation against canonical markers.

Quick Start

Provide an annotated single-cell data matrix and run automated cell-type annotation using reference-based methods such as CellTypist, SingleR, Azimuth, or scPred.

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

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

FAQPage Schema
How do I automate cell-type annotation for scRNA-seq data?▼

Automate cell-type annotation for scRNA-seq data by providing a single-cell gene expression matrix to reference-based methods like CellTypist, SingleR, Azimuth, or scPred for automated cluster- and cell-level labeling.

What is the best way to assign cell types across diverse single-cell datasets?▼

The best way to assign cell types across diverse single-cell datasets is using automated reference-based labeling with confidence scoring and optional consensus labeling to ensure consistent, reproducible annotations.

Can I use CellTypist and SingleR with AnnData and Seurat objects?▼

Yes, automated cell-type labeling is compatible with common single-cell workflows and integrates directly with AnnData and Seurat objects to assign labels using tools like CellTypist and SingleR.

How does consensus labeling improve single-cell annotation results?▼

Consensus labeling improves single-cell annotation results by combining predictions from multiple reference tools, applying confidence scoring and filtering to generate robust cell-type assignments across diverse datasets.

Do I need pre-installed reference datasets to run scRNA-seq cell-type annotation?▼

Yes, you need pre-installed reference data or models and appropriate Python and R packages to run scRNA-seq cell-type annotation, plus validation against canonical markers to ensure reliable results.

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