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scanpy

Automate single-cell RNA-seq analysis with Scanpy from quality control to visualization.

Updated May 8, 2026
Search Tags:#data analysis#single-cell RNA-seq#genomics#single-cell analysis#Scanpy
One-click install
npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill scanpy-zeyuyang-0420
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/single-cell-sequencing/scanpy
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill scanpy-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates and accelerates single-cell RNA-seq analysis workflows, from quality control to visualization, using the Scanpy toolkit.

Core Features & Use Cases

  • Quality Control: Automate filtering, normalization, and visualization of single-cell data.
  • Dimensionality Reduction: Perform PCA, UMAP, and t-SNE for data visualization.
  • Clustering: Leiden clustering for cell grouping.
  • Marker Gene Identification: Automatically identify marker genes for each cluster.
  • Use Case: Efficiently analyze single-cell RNA-seq data for exploratory analysis or for identifying novel cell populations.

Quick Start

Run the following command to initiate the analysis: python analyze_data.py data.h5ad.

Frequently Asked Questions about scanpy

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

FAQPage Schema
How do I automate single-cell RNA-seq analysis from quality control to clustering?▼

Automate single-cell RNA-seq analysis by processing AnnData files through a Python script. The workflow executes quality control, dimensionality reduction, and clustering to identify novel cell populations.

Can I use Scanpy for dimensionality reduction and marker gene identification?▼

Yes, Scanpy performs dimensionality reduction using PCA, UMAP, and t-SNE for visualization. It also automatically identifies marker genes for each cell cluster generated during the single-cell analysis workflow.

What is the best way to process large single-cell RNA-seq datasets for exploratory analysis?▼

Process large single-cell RNA-seq datasets by leveraging Scanpy and AnnData structures. This approach handles data of varying sizes and complexity, executing filtering and Leiden clustering for efficient exploratory analysis.

Do I need AnnData and pandas to run single-cell analysis workflows?▼

Yes, single-cell analysis workflows require Scanpy, AnnData, pandas, and numpy as dependencies. These libraries provide the foundational data structures and matrix operations needed for single-cell RNA-seq processing.

Does this single-cell analysis workflow support Leiden clustering for cell grouping?▼

Yes, the single-cell analysis workflow supports Leiden clustering for cell grouping. It integrates this clustering method with automatic marker gene identification to characterize distinct cell populations.

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