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popv-cell-annotation

Annotate scRNA-seq cell types via consensus voting across ten algorithms.

298|27|Updated Feb 18, 2026
Search Tags:#bioinformatics#single-cell#data integration#scRNA-seq#cell annotation#ensemble learning
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill popv-cell-annotation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: popv-cell-annotation
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/genomics-bioinformatics/popv-cell-annotation
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill popv-cell-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires popv, scanpy, anndata, scvi-tools, harmonypy, bbknn, celltypist, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex task of annotating cell types in single-cell RNA sequencing (scRNA-seq) data by leveraging the consensus of multiple algorithms, providing more robust and reliable results than single-method approaches.

Core Features & Use Cases

  • Ensemble Annotation: Integrates 10+ algorithms (e.g., KNN-Harmony, scVI, CellTypist, Random Forest) for a consensus cell type prediction.
  • Uncertainty Quantification: Provides an agreement score to identify novel or ambiguous cell states where methods disagree.
  • Batch Effect Robustness: Designed to handle substantial batch effects between reference and query datasets.
  • Use Case: Annotate a new single-cell dataset from a patient's tumor sample by transferring cell type labels from a large, well-curated reference atlas, while also identifying rare cell populations that might be missed by a single annotation tool.

Quick Start

Use the popv-cell-annotation skill to annotate your query dataset using a labeled reference atlas.

Frequently Asked Questions about popv-cell-annotation

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

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

Consensus cell type annotation for scRNA-seq data integrates predictions from over ten independent algorithms against a labeled reference atlas, generating per-method labels and a majority voting consensus prediction.

How do I quantify uncertainty and identify novel cell states in single-cell RNA sequencing?▼

To identify novel cell states in single-cell RNA sequencing, this approach generates a confidence score quantifying agreement across multiple annotation methods, highlighting ambiguous populations where algorithms disagree.

Can I use scVI and CellTypist together to annotate scRNA-seq query datasets?▼

Yes, you can integrate scVI and CellTypist together alongside KNN-Harmony and Random Forest, using ensemble learning to transfer cell type labels from a reference atlas to scRNA-seq query datasets.

What is the best way to annotate scRNA-seq data with substantial batch effects?▼

The best way to annotate scRNA-seq data with substantial batch effects is using an ensemble approach designed for batch effect robustness, transferring labels from a curated reference atlas while handling variability.

Do I need anndata and scanpy to run ensemble cell annotation?▼

Yes, you need anndata and scanpy as foundational dependencies, along with harmonypy and bbknn, to process the scRNA-seq query datasets and execute the ensemble annotation workflow effectively.

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