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scanpy

Analyze single-cell RNA-seq data with Scanpy for QC, clustering, and visualization.

4|1|Updated Jun 18, 2025
Search Tags:#visualization#trajectory#single-cell#clustering#annotation#scanpy#scrna-seq
One-click install
npx skills add https://github.com/HolobiomicsLab/Toolomics --skill scanpy-holobiomicslab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/HolobiomicsLab/Toolomics/tree/main/mcp_host/skills/scientific-skills/scientific-skills/scanpy
Command: npx skills add https://github.com/HolobiomicsLab/Toolomics --skill scanpy-holobiomicslab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

What problem does it solve?

Streamlines end-to-end single-cell RNA-seq analysis by providing a cohesive workflow for quality control, normalization, dimensionality reduction, clustering, marker gene identification, trajectory inference, and visualization.

Core Features & Use Cases

  • QC, normalization, HVG selection, and scaling to prepare data for downstream analysis
  • Dimensionality reduction (PCA, UMAP, t-SNE) and neighborhood graph construction for clustering
  • Leiden clustering, marker gene analysis, cell type annotation, and trajectory inference
  • Publication-ready visualizations and results export for downstream reporting

Quick Start

Load your data (e.g., h5ad or 10X) into Scanpy and run the complete pipeline to generate QC metrics, normalized data, reduced dimensions, clusters, and visualizations.

Frequently Asked Questions about scanpy

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

FAQPage Schema
How do I run end-to-end single-cell RNA-seq clustering and trajectory inference?▼

Single-cell RNA-seq clustering and trajectory inference are performed by loading h5ad or 10X data, running preprocessing, dimensionality reduction, Leiden clustering, and trajectory inference workflows.

What is the best way to normalize and identify marker genes in single-cell data?▼

To normalize single-cell data and identify marker genes, run QC, normalization, HVG selection, scaling, and Leiden clustering before performing downstream marker gene analysis and cell type annotation.

Can I use h5ad and 10X formats for single-cell RNA-seq dimensionality reduction?▼

Yes, h5ad and 10X formats are supported for single-cell RNA-seq dimensionality reduction, enabling PCA, UMAP, and t-SNE visualizations alongside neighborhood graph construction for clustering.

Does this single-cell analysis workflow include cell type annotation?▼

Yes, the single-cell analysis workflow includes cell type annotation by combining Leiden clustering with marker gene analysis to identify and assign cell types across the dataset.

How do I generate publication-ready visualizations from scRNA-seq clustering results?▼

Publication-ready visualizations from scRNA-seq clustering results are generated using built-in plotting APIs to map UMAP and t-SNE embeddings, with results exported for downstream reporting.

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