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cell-type-annotation

Assign cell type labels to single-cell RNA-seq clusters using automated classifiers.

13|5|Updated May 4, 2026
Search Tags:#bioinformatics#single-cell#annotation#scanpy#scrna-seq#celltypist
One-click install
npx skills add https://github.com/awslabs/hcls-agent-skills --skill cell-type-annotation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cell-type-annotation
Source: https://github.com/awslabs/hcls-agent-skills/tree/main/skills/cell-type-annotation
Command: npx skills add https://github.com/awslabs/hcls-agent-skills --skill cell-type-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, celltypist, scvi-tools, SingleR, celldex, SingleCellExperiment.

What problem does it solve?

This skill addresses the bottleneck of manually assigning cell identities to clusters in single-cell RNA-seq data, which is often time-consuming and prone to subjective bias.

Core Features & Use Cases

  • Automated Annotation: Leverages CellTypist for high-throughput, model-based cell type classification.
  • Reference-based Transfer: Supports SingleR and scANVI/ingest for transferring labels from curated reference atlases.
  • Marker-based Validation: Provides workflows for manual assignment using canonical marker gene panels and dotplot visualization.
  • Use Case: A researcher with a clustered AnnData object can use this skill to automatically label immune cell populations using a tissue-matched CellTypist model, followed by hierarchical refinement for fine-grained subtypes.

Quick Start

Use the cell-type-annotation skill to label clusters in my AnnData object using the Immune_All_Low CellTypist model.

Frequently Asked Questions about cell-type-annotation

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

FAQPage Schema
How do I automate cell type annotation for single-cell RNA-seq clusters?▼

Automated cell type annotation assigns labels to single-cell RNA-seq clusters using automated classifiers like CellTypist, reference-based transfer via SingleR, and marker-based scoring to ensure reproducible cell identity identification across diverse tissues.

Can I transfer cell type labels from a reference atlas to my scanpy AnnData object?▼

Yes, you can transfer cell type labels to a scanpy AnnData object by leveraging SingleR for curated reference atlas transfers, or using scANVI and ingest workflows to map query datasets onto established reference atlases.

Does this approach support hierarchical refinement for fine-grained immune cell subtypes?▼

Yes, hierarchical refinement supports fine-grained immune cell subtypes by applying tissue-matched CellTypist models to broadly classify populations, followed by targeted marker-based scoring for canonical genes to validate subtypes.

What is the best way to validate automated scRNA-seq annotation against canonical marker genes?▼

The best way to validate automated scRNA-seq annotation against canonical marker genes is by using marker-based scoring workflows, which cross-reference automated classifier labels with canonical marker gene panels and dotplot visualizations.

Do I need Bioconductor packages to perform reference-based single-cell annotation?▼

Yes, you need Bioconductor packages such as SingleR, celldex, and SingleCellExperiment to perform robust reference-based label transfer, alongside scanpy and scvi-tools, ensuring standardized cell identity annotation across species.

Why does manual cluster assignment in single-cell RNA-seq data lead to subjective bias?▼

Manual cluster assignment in single-cell RNA-seq data leads to subjective bias because it relies on time-consuming, human interpretation of marker genes, whereas automated classification provides standardized, reproducible cell identity labels.

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