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scanpy

Automates end-to-end RNA-seq workflow to extract QC metrics clusters and cell-type annotations using Scanpy.

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#single-cell#clustering#anndata#scanpy#rna-seq#qc
One-click install
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill scanpy-k-dense-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/scanpy
Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill scanpy-k-dense-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Streamlines end-to-end single-cell RNA-seq analysis by providing a repeatable workflow that converts raw counts into quality metrics, interpretable clusters, and cell-type insights.

Core Features & Use Cases

  • End-to-end scRNA-seq workflow: Load data, QC, normalization, HVG selection, dimensionality reduction, clustering, and marker identification.
  • Cell-type annotation and visualization: Map clusters to cell types and generate publication-ready plots.
  • Use Case: A researcher analyzes a new scRNA-seq dataset to identify cell-type composition and differential expression across conditions.

Quick Start

Interpret and analyze single-cell RNA-seq data end-to-end using Scanpy to obtain QC metrics, clusters, and cell-type annotations.

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 scRNA-seq analysis workflow from raw counts to cell-type annotation?▼

End-to-end scRNA-seq analysis is automated by loading raw counts, extracting QC metrics, normalizing data, selecting highly variable genes, performing dimensionality reduction, identifying clusters, and mapping them to cell types for annotation.

Can I load 10X data and CSV files for single-cell RNA-seq clustering in Python?▼

Yes, single-cell RNA-seq clustering supports loading common data formats including 10X outputs, CSV files, and h5ad formats. This allows you to directly ingest raw gene expression matrices for downstream clustering and quality control.

What's the best way to identify cell types and generate publication-ready plots from scRNA-seq data?▼

The best way to identify cell types is by clustering scRNA-seq data and finding marker genes. This workflow then maps clusters to specific cell types and generates publication-ready plots and metadata for downstream interpretation.

Do I need the scanpy package and Python environment to process AnnData objects for QC metrics?▼

Yes, you need the scanpy package and a Python environment to process AnnData objects. The workflow relies on the Scanpy ecosystem to compute QC metrics, normalize counts, and output compatible Anndata-based workflows.

Why use Scanpy for single-cell RNA-seq quality control over other data and analytics tools?▼

Using Scanpy for single-cell RNA-seq quality control provides a repeatable workflow that converts raw counts into interpretable clusters and cell-type insights. It natively handles AnnData objects to extract QC metrics for publication-ready results.

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