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single-popv-annotation

Annotate single-cell RNA sequencing data via multi-algorithm consensus voting.

32|5|Updated Mar 12, 2026
Search Tags:#bioinformatics#cell type#single-cell#omicverse#annotation#scRNA-seq#consensus voting
One-click install
npx skills add https://github.com/Starlitnightly/omicclaw --skill single-popv-annotation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-popv-annotation
Source: https://github.com/Starlitnightly/omicclaw/tree/main/src/omicverse_skills/skills/single-popv-annotation
Command: npx skills add https://github.com/Starlitnightly/omicclaw --skill single-popv-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires omicverse, scanpy, anndata, scvi-tools, torch, scikit-learn, xgboost, harmonypy, bbknn, scanorama, celltypist, OnClass, obonet, pronto, huggingface_hub, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex and time-consuming process of annotating cell types in single-cell RNA sequencing data by leveraging multiple algorithms and a consensus voting mechanism.

Core Features & Use Cases

  • Multi-Algorithm Annotation: Integrates up to 10 different cell type classification algorithms (e.g., SCVI, SCANVI, CellTypist) for robust predictions.
  • Consensus Voting: Aggregates predictions from multiple algorithms to produce a more reliable and robust cell type assignment.
  • Ontology-Aware Annotation: Supports hierarchical label resolution using the Cell Ontology (CL) for improved accuracy.
  • Pretrained Models: Allows the use of pre-trained models from a hub for faster annotation, especially with large references.
  • Use Case: Annotate a new single-cell dataset of immune cells by comparing it against a comprehensive reference atlas, ensuring accurate identification of T-cells, B-cells, and other immune populations.

Quick Start

Use the single-popv-annotation skill to annotate query data against a reference dataset using a majority vote of all available algorithms.

Frequently Asked Questions about single-popv-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 sequencing data using consensus voting?▼

Consensus voting improves single-cell annotation by aggregating predictions from up to 10 different classification algorithms, ensuring a more reliable cell type assignment than relying on a single method.

Can I use pretrained models for scRNA-seq cell type annotation?▼

Yes, you can use pretrained models from a hub for scRNA-seq cell type annotation, which accelerates the process when comparing query datasets against large reference atlases.

Does this single-cell annotation approach support ontology-aware refinement?▼

Yes, this single-cell annotation approach supports ontology-aware refinement by utilizing the Cell Ontology (CL) for hierarchical label resolution and improved annotation accuracy.

What dependencies are required to run population-level cell type annotation?▼

Population-level cell type annotation requires omicverse, scanpy, anndata, scvi-tools, and torch for core functionality, alongside scikit-learn and xgboost for algorithmic classification.

What is the best way to identify immune cell populations in a new scRNA-seq dataset?▼

The best way to identify immune cell populations in a new scRNA-seq dataset is comparing it against a comprehensive reference atlas using multi-algorithm consensus voting to accurately identify T-cells and B-cells.

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