scanpy

Automate single-cell RNA-seq analysis with Scanpy from QC to marker genes.

Updated Apr 19, 2026
One-click install
npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill scanpy-chenyiru3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/CHENyiru3/AI-Skills-Collections/tree/main/skills-market/compbio/single-cell/analysis/scanpy
Command: npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill scanpy-chenyiru3

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

QC, normalization, dimensionality reduction, clustering, and interpretation of single-cell RNA-seq data via a scalable, reproducible Scanpy-based pipeline that turns raw counts into insights and publication-ready visuals.

Core Features & Use Cases

  • End-to-end Scanpy pipeline covering QC metrics, normalization, highly variable gene selection, PCA, UMAP/t-SNE, Leiden clustering, marker gene identification, and basic trajectory analysis.
  • Flexible input formats: supports h5ad, 10X, CSV, and other common single-cell data formats.
  • Reproducible research with bundled references (standard workflow, API reference, plotting guide) and templates (assets) to accelerate analyses.

Quick Start

Load your AnnData object and run the end-to-end Scanpy workflow to generate QC metrics, normalization, HVG selection, dimensionality reduction, clustering, and cell-type annotations.

Frequently Asked Questions about scanpy

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

FAQPage Schema
How do I run an end-to-end single-cell RNA-seq analysis workflow from raw counts to clustering?▼

Single-cell RNA-seq analysis can be automated end-to-end using Scanpy to deliver QC metrics, normalization, HVG selection, PCA, UMAP, Leiden clustering, and marker gene identification from raw counts.

What input formats are supported for loading scRNA-seq data into a Scanpy pipeline?▼

The Scanpy pipeline supports loading diverse scRNA-seq data formats including h5ad, 10X, and CSV files to generate a processed AnnData object for downstream analysis.

Does the Scanpy workflow support trajectory inference and cell type annotation?▼

Yes, the Scanpy workflow supports trajectory inference and cell type annotation, extending beyond standard dimensionality reduction and clustering to interpret cellular development and identify specific cell populations.

How do I generate publication-ready visualizations from an AnnData object after clustering?▼

You can generate publication-ready visualizations directly from a processed AnnData object using the bundled plotting templates and Scanpy's matplotlib-based plotting guide to visualize UMAP, t-SNE, and marker genes.

What is the best way to ensure reproducible scRNA-seq clustering and dimensionality reduction?▼

To ensure reproducible scRNA-seq clustering and dimensionality reduction, use a standardized Scanpy pipeline that bundles references, templates, and metadata exports to consistently reproduce QC, normalization, and UMAP results.

Do I need matplotlib to visualize highly variable genes and UMAP plots in Scanpy?▼

Yes, matplotlib is required as a dependency to render highly variable gene plots, UMAP embeddings, and other publication-ready visualizations within the Scanpy single-cell RNA-seq analysis pipeline.