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scanpy

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

6|Updated Dec 30, 2025
Search Tags:#pipeline#trajectory#single-cell#clustering#scanpy#scrna
One-click install
npx skills add https://github.com/pur3v4d3r/pur3-pkb-codebase --skill scanpy-pur3v4d3r
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/pur3v4d3r/pur3-pkb-codebase/tree/main/.claude/skills/__scientific-skills/scanpy
Command: npx skills add https://github.com/pur3v4d3r/pur3-pkb-codebase --skill scanpy-pur3v4d3r

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Analyze and interpret single-cell RNA-seq data using a scalable, reusable Scanpy-based workflow that streamlines QC, normalization, dimensionality reduction, clustering, and annotation.

Core Features & Use Cases

  • Quality Control & Filtering: compute QC metrics and filter cells/genes to improve data quality.
  • Normalization, HVG & Dimensionality Reduction: normalize counts, identify highly variable genes, perform PCA and UMAP.
  • Clustering & Annotation: run Leiden clustering and assign cell-type labels based on marker genes.
  • Trajectory Inference: explore developmental trajectories and pseudotime.
  • Use Case: process a 10X or h5ad dataset to generate publication-ready figures and annotated cell types.

Quick Start

Run a basic Scanpy workflow on an h5ad or 10X dataset to generate QC metrics, PCA/UMAP plots, Leiden clusters, and marker gene visualizations.

Frequently Asked Questions about scanpy

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

FAQPage Schema
How do I run an end-to-end single-cell RNA-seq analysis pipeline?▼

Single-cell RNA-seq clustering groups cells by transcriptomic similarity using Leiden clustering on a neighbor graph built from PCA space. It identifies cell populations and marker genes to assign biological cell-type labels to each cluster.

Can I use Scanpy to analyze 10X and h5ad single-cell datasets?▼

Yes, Scanpy processes 10X, h5ad, and CSV single-cell RNA-seq formats. It loads these files into the AnnData framework to execute quality control, normalization, and clustering workflows.

How does trajectory inference work in single-cell RNA-seq data?▼

Trajectory inference orders single-cell RNA-seq cells along a developmental path to estimate pseudotime. It maps progressive transcriptional states to explore developmental trajectories within annotated cell populations.

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

To annotate cell types in scRNA-seq, identify differentially expressed marker genes for each Leiden cluster, then compare them against known references to assign biological cell-type labels to the populations.

Do I need matplotlib to generate UMAP plots from scRNA-seq data?▼

Yes, matplotlib is required to render UMAP plots and publication-ready figures from scRNA-seq data. Scanpy plotting modules depend on it to visualize clustering and quality control metrics.

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