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single-cell-annotation-skills-with-omicverse

Assign consensus cell type labels to single-cell RNA-seq clusters in AnnData.

32|5|Updated Mar 12, 2026
Search Tags:#ontology#single-cell#gpt#annotation#anndata#label-transfer#celltype
One-click install
npx skills add https://github.com/omicverse/omicclaw --skill single-cell-annotation-skills-with-omicverse-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-annotation-skills-with-omicverse
Source: https://github.com/omicverse/omicclaw/tree/main/src/omicverse_skills/skills/single-annotation
Command: npx skills add https://github.com/omicverse/omicclaw --skill single-cell-annotation-skills-with-omicverse-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a unified, reproducible way to assign and validate cell type labels for single-cell datasets by combining automated annotators, ontology mapping, consensus arbitration, LLM-assisted interpretation, and label transfer methods to reduce manual curation time and increase annotation consistency.

Core Features & Use Cases

  • Multi-method annotation: Run SCSA, MetaTiME, CellVote consensus, CellMatch ontology mapping, GPTAnno, and weighted KNN transfer in coordinated workflows.
  • Interoperability with AnnData: Designed to operate on preprocessed AnnData objects with clustering and marker gene results, compatible with scVI/GLUE embeddings and multiomic alignments.
  • Practical use cases: Rapidly annotate PBMC and tumour microenvironment datasets, map custom labels to Cell Ontology terms, benchmark automated outputs against manual markers, and propagate RNA labels to ATAC or other modalities.

Quick Start

Annotate a preprocessed AnnData by running SCSA for automated labels, generate cluster markers, run CellVote for consensus, and apply GPTAnno or CellOntologyMapper as needed to produce final cell type columns.

Frequently Asked Questions about single-cell-annotation-skills-with-omicverse

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

FAQPage Schema
How do I assign consensus cell type labels to single-cell RNA-seq clusters?▼

To assign consensus single-cell annotation, you can use CellVote to combine automated annotators like SCSA and MetaTiME on preprocessed AnnData objects, generating reliable cluster labels through consensus arbitration.

Can I transfer cell type labels from scRNA-seq to ATAC or other modalities?▼

Yes, you can propagate RNA labels to ATAC or other modalities using weighted KNN label transfer, leveraging scVI/GLUE embeddings and multiomic alignments within your AnnData workflow.

How does GPT-based annotation work for single-cell datasets?▼

GPT-based annotation uses LLM credentials to interpret cluster marker genes and assign cell type labels to AnnData objects, providing an automated, model-driven approach to single-cell annotation.

Do I need preprocessed AnnData with marker genes to run automated cell type annotation?▼

Yes, automated cell type annotation operates on preprocessed AnnData objects that already contain clustering and marker gene results, serving as the foundational input for SCSA, CellMatch, and consensus workflows.

What is the best way to map custom cell labels to Cell Ontology terms?▼

To map custom labels to Cell Ontology terms, you can apply the CellOntologyMapper on your annotated AnnData object, using external Cell Ontology files to standardize single-cell annotations.

Can I benchmark automated cell type outputs against manual markers?▼

Yes, you can benchmark automated single-cell annotation outputs against manual markers by comparing the consensus labels generated from SCSA, MetaTiME, and GPTAnno with your known marker genes.

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