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scanpy-scrna-seq

Analyze single-cell RNA-seq data with Scanpy from QC to marker identification.

298|27|Updated Feb 18, 2026
Search Tags:#bioinformatics#single-cell#dimensionality reduction#clustering#scanpy#gene expression#scrna-seq
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill scanpy-scrna-seq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy-scrna-seq
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/genomics-bioinformatics/scanpy-scrna-seq
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill scanpy-scrna-seq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, leidenalg, igraph, anndata, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of analyzing single-cell RNA-seq data, from raw counts to annotated cell types, enabling rapid biological insights.

Core Features & Use Cases

  • End-to-End Workflow: Handles QC, normalization, dimensionality reduction (PCA, UMAP), clustering (Leiden), and marker gene identification.
  • Publication-Ready Output: Generates annotated datasets and visualizations suitable for scientific publications.
  • Use Case: Analyze a new single-cell dataset to identify distinct cell populations, their marker genes, and potential cell states, producing figures for a research paper.

Quick Start

Load your 10X Genomics data, perform quality control, normalize, identify highly variable genes, run PCA and UMAP, cluster cells using Leiden, and identify marker genes.

Frequently Asked Questions about scanpy-scrna-seq

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 counts to cell type annotation?▼

You can perform single-cell RNA-seq analysis end-to-end by loading data, applying quality control filters, normalizing, selecting highly variable genes, and running dimensionality reduction and Leiden clustering to identify cell populations.

How does Scanpy dimensionality reduction and Leiden clustering work for gene expression data?▼

Scanpy dimensionality reduction uses PCA and UMAP to project high-dimensional gene expression data, while Leiden clustering groups cells into distinct populations based on their shared transcriptional profiles for downstream marker identification.

Can I use this workflow to generate publication-quality plots for single-cell data?▼

Yes, this single-cell RNA-seq workflow generates publication-ready visualizations and annotated AnnData objects, providing standardized plots of UMAP embeddings and marker gene expression suitable for scientific research papers.

Does the single-cell RNA-seq pipeline support loading data from various formats?▼

Yes, the single-cell RNA-seq pipeline supports loading data from various formats, including 10X Genomics outputs, and processes them into annotated AnnData objects for downstream gene expression analysis.

What is the best way to identify marker genes and distinct cell populations in scRNA-seq?▼

The best way to identify marker genes is by running differential gene expression analysis after Leiden clustering, which groups cells into distinct populations based on highly variable gene expression profiles in your single-cell dataset.

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