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scanpy

Analyze single-cell RNA-seq data with scanpy from loading to visualization.

8|Updated Jan 13, 2026
Search Tags:#data analysis#bioinformatics#visualization#genomics#single-cell#rna-seq
One-click install
npx skills add https://github.com/hxk622/TokenDance --skill scanpy-hxk622
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/bioinformatics/scanpy
Command: npx skills add https://github.com/hxk622/TokenDance --skill scanpy-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the complex process of analyzing single-cell RNA-sequencing data, enabling researchers to derive meaningful biological insights from high-dimensional datasets.

Core Features & Use Cases

  • Comprehensive scRNA-seq Analysis: From quality control and normalization to dimensionality reduction, clustering, and marker gene identification.
  • Publication-Quality Visualizations: Generate publication-ready plots for UMAP, PCA, heatmaps, and more.
  • Use Case: A biologist has a new scRNA-seq dataset and needs to identify distinct cell populations, find marker genes for each population, and visualize the results. This Skill can perform the entire analysis pipeline, from raw data to annotated cell types and figures.

Quick Start

Use the scanpy skill to perform a standard single-cell RNA-seq analysis on the file 'my_data.h5ad'.

Frequently Asked Questions about scanpy

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

FAQPage Schema
How do I analyze single-cell RNA-seq data from raw files to cell clusters?▼

Yes, you can generate publication-quality plots for scRNA-seq analysis, including UMAP embeddings, PCA scatter plots, and marker gene heatmaps, to visually explore and present identified cell populations.

What is the standard pipeline for identifying marker genes in scRNA-seq data?▼

Supported input formats for single-cell RNA-seq analysis primarily include h5ad files, allowing you to seamlessly load high-dimensional gene expression matrices and metadata to initiate the full analysis pipeline.

What is the standard pipeline for identifying marker genes in scRNA-seq data?▼

The standard pipeline for identifying marker genes in scRNA-seq data involves loading data, performing quality control and normalization, reducing dimensions via PCA, clustering cells using the Leiden algorithm, and then statistically identifying genes specific to each cluster.

Can I generate publication-quality UMAP and PCA visualizations for genomics data?▼

Yes, you can generate publication-quality plots for scRNA-seq analysis, including UMAP embeddings, PCA scatter plots, and marker gene heatmaps, to visually explore and present identified cell populations.

What input formats are supported for loading single-cell RNA-seq datasets?▼

Supported input formats for single-cell RNA-seq analysis primarily include h5ad files, allowing you to seamlessly load high-dimensional gene expression matrices and metadata to initiate the full analysis pipeline.

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