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scanpy

Analyze single-cell RNA-seq data to produce QC metrics, embeddings, clusters, and marker genes.

1|Updated Mar 4, 2026
Search Tags:#visualization#single-cell#clustering#anndata#scanpy#qc
One-click install
npx skills add https://github.com/Hung-3008/agusta --skill scanpy-hung-3008
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/scanpy
Command: npx skills add https://github.com/Hung-3008/agusta --skill scanpy-hung-3008

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

An end-to-end solution for analyzing single-cell RNA-seq data that consolidates quality control, normalization, feature selection, dimensionality reduction, clustering, and cell type annotation into a reproducible workflow.

Core Features & Use Cases

  • QC metrics calculation and filtering to remove low-quality cells and genes.
  • Normalization, log transformation, HVG selection, and scaling to prepare data for downstream analyses.
  • Dimensionality reduction (PCA, UMAP, t-SNE) and neighborhood graph construction for robust clustering.
  • Leiden/Louvain clustering, marker gene identification, and manual or automated cell type annotation.
  • Trajectory inference, differential expression analyses, and flexible plotting for publication-ready figures.
  • Outputs include processed AnnData, cell/gene metadata, and figures suitable for reports.

Quick Start

Load your scRNA-seq dataset and run the standard Scanpy workflow to obtain QC metrics, embeddings, clusters, and marker genes.

Frequently Asked Questions about scanpy

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

FAQPage Schema
How do I process single-cell RNA-seq data from a 10x CSV file to get UMAP embeddings and clusters?▼

Single-cell RNA-seq data from 10x, h5ad, or CSV formats can be processed end-to-end to produce reduced-dimension UMAP embeddings and Leiden or Louvain clusters through a parameterized workflow.

What does an end-to-end scRNA-seq analysis workflow include?▼

An end-to-end scRNA-seq workflow includes quality control, normalization, HVG selection, PCA and UMAP visualization, clustering, marker gene identification, and trajectory inference to yield a processed AnnData object.

Can I perform batch correction and cell type annotation within this single-cell analysis workflow?▼

Yes, the scRNA-seq workflow supports batch correction during processing and provides both manual and automated cell type annotation based on marker gene identification.

What is the best way to generate publication-ready plots from scRNA-seq clustering results?▼

Generating publication-ready plots from scRNA-seq clustering results is achieved through flexible plotting functions that visualize QC metrics, embeddings, and annotated cell types from the processed AnnData object.

Does the Scanpy workflow output cell and gene metadata alongside the processed dataset?▼

Yes, the workflow outputs a processed AnnData object along with extracted cell and gene metadata, ensuring reproducible scRNA-seq analysis results suitable for downstream reports.

Why do I need quality control filtering before dimensionality reduction in scRNA-seq analysis?▼

Quality control filtering is required before dimensionality reduction to remove low-quality cells and genes, ensuring that normalization, HVG selection, and clustering accurately reflect biological signals.

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