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bio-single-cell-cell-annotation

Annotate single-cell cell types using reference models and trained classifiers.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automated cell-type annotation across diverse datasets using reference-based methods to ensure consistent labeling and reproducibility.

Core Features & Use Cases

  • Automated, multi-tool cell-type annotation using reference datasets (CellTypist, SingleR, Azimuth, scPred) with a consensus label.
  • Confidence-aware predictions with filtering and provenance-ready outputs for Seurat/AnnData workflows.
  • Use Case: Apply to PBMC or tissue datasets to assign cell types and compare results across references.

Quick Start

Annotate my Seurat or AnnData object with cell-type labels using a reference model.

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 annotate cell types in single-cell RNA-seq data using reference models?▼

You can annotate scRNA-seq cell types by projecting transcriptomic profiles onto trained classifiers like CellTypist, SingleR, Azimuth, or scPred to assign labels based on existing references.

Can I use Azimuth and CellTypist to annotate both Seurat and AnnData objects?▼

Yes, this workflow supports input data in formats compatible with both Seurat and AnnData, allowing you to apply reference-based cell typing across these standard single-cell frameworks.

What is the best way to handle low-confidence cell type predictions in scRNA-seq?▼

Low-confidence predictions are managed through optional confidence filtering, ensuring that ambiguous cell type assignments are clearly flagged rather than forcefully assigned an inaccurate label.

Does automated cell typing work for single-cell datasets across different tissues and species?▼

Yes, reference-based cell typing is applicable to single-cell datasets across diverse tissues and species, ensuring consistent labeling and reproducibility beyond just standard PBMC samples.

How do multiple reference models like SingleR and scPred reach a consensus cell type label?▼

The workflow supports automated multi-tool annotation, comparing results across references like SingleR and scPred to generate a consensus label for more reliable cell type identification.

Are there limitations to using reference-based methods for scRNA-seq cell typing?▼

Reference-based cell typing depends on the availability of compatible reference models and requires input data to match the expected transcriptomic profile format, limiting its use for highly novel or unstudied cell populations.

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