scanpy

Perform single-cell RNA-seq analysis with scanpy, including QC, clustering, and annotation.

1|Updated Jan 14, 2026

Search Tags:#data analysis #bioinformatics #genomics #single-cell #scanpy #scrna-seq

Ready to run

npx skills add https://github.com/Sologa/codex-pipeline --skill scanpy-sologa

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/scanpy
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill scanpy-sologa

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 and computationally intensive process of analyzing single-cell RNA-seq data, enabling researchers to gain insights into cellular heterogeneity and function.

Core Features & Use Cases

End-to-End Analysis: Guides users through the entire scRNA-seq workflow, from QC to cell type annotation.
Standardized Workflows: Provides reproducible analysis pipelines for common scRNA-seq tasks.
Use Case: A biologist has generated scRNA-seq data and needs to identify cell populations, find marker genes, and visualize results. They can use this Skill to perform a complete analysis, generating publication-quality figures and annotated data.

Quick Start

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

Frequently Asked Questions about scanpy

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

FAQPage Schema

How do I perform single-cell RNA-seq analysis from raw data to cell type annotation?▼

Single-cell RNA-seq analysis involves quality control, normalization, dimensionality reduction via PCA and UMAP, Leiden clustering, and marker gene identification to annotate cell types.

What is the standard workflow for scRNA-seq clustering and dimensionality reduction?▼

The standard scRNA-seq workflow uses PCA for initial dimensionality reduction, followed by UMAP for visualization, and applies Leiden clustering to identify distinct cellular populations.

Can I use scanpy to process h5ad files for identifying marker genes?▼

Yes, you can process h5ad files to perform comprehensive single-cell RNA-seq analysis, which includes identifying marker genes and generating publication-quality visualizations.

Does single-cell RNA-seq analysis require specific input formats for quality control?▼

Single-cell RNA-seq analysis supports various input formats, with h5ad being standard, and applies quality control and normalization steps to prepare data for downstream clustering.

What is the best way to visualize cellular heterogeneity in scRNA-seq data?▼

To visualize cellular heterogeneity in scRNA-seq data, apply UMAP for dimensionality reduction and generate publication-quality figures that highlight distinct cell populations and marker genes.