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scanpy

Perform single-cell RNA-seq analysis with scanpy from loading to visualization.

Updated Dec 17, 2025
Search Tags:#data analysis#single-cell RNA-seq#clustering#scanpy#cell type annotation#trajectory inference
One-click install
npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill scanpy-robotlearning123
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/robotlearning123/claude-scientific-skills/tree/main/scientific-skills/scanpy
Command: npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill scanpy-robotlearning123

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, anndata, umap-learn, scipy, pandas, numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit provides a comprehensive toolset for single-cell RNA-seq analysis, streamlining the process from raw data loading to the generation of publication-quality plots.

Core Features & Use Cases

  • Data Loading: Supports loading various file formats (h5ad, 10X, CSV, etc.) for analysis.
  • Quality Control: Performs quality control measures such as mitochondrial gene removal, cell and gene filtering, and QC metrics visualization.
  • Normalization and Preprocessing: Implements normalization, log transformation, and scaling of data.
  • Feature Selection: Identifies highly variable genes for robust analysis.
  • Dimensionality Reduction: Offers PCA, UMAP, and t-SNE for dimensionality reduction and visualization.
  • Clustering: Provides Leiden clustering for cell clustering.
  • Marker Gene Identification: Identifies marker genes for each cluster.
  • Cell Type Annotation: Annotates cell types based on marker genes.
  • Trajectory Inference: Performs trajectory inference for single-cell data.
  • Differential Expression: Compares expression between conditions.
  • Gene Set Scoring: Scores cells for gene set expression.
  • Batch Correction: Implements ComBat for batch correction.
  • Use Case: Imagine you have a single-cell RNA-seq dataset. Use this Skill to perform quality control, normalization, dimensionality reduction, clustering, and analysis to identify cell types and marker genes.

Quick Start

Run the analysis template with the provided script to load your data and execute the full workflow.

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 clustering and cell type annotation?▼

Single-cell RNA-seq clustering and cell type annotation are achieved by running Leiden clustering on your data, identifying marker genes for each cluster, and mapping them to known cell types.

What is the best way to do quality control and normalization for single-cell RNA-seq data?▼

Quality control and normalization for single-cell RNA-seq data involve filtering cells and genes, removing mitochondrial genes, and applying log transformation and scaling to prepare the dataset.

Can I use scanpy for trajectory inference and batch correction in single-cell data analysis?▼

Yes, scanpy supports trajectory inference for single-cell data analysis and implements ComBat for batch correction to integrate multiple datasets and remove technical variations.

Does this single-cell RNA-seq analysis workflow support UMAP visualization and h5ad file formats?▼

Yes, this single-cell RNA-seq analysis workflow supports loading h5ad file formats and provides UMAP, t-SNE, and PCA for dimensionality reduction and publication-quality visualization.

Do I need pandas and scipy to run single-cell RNA-seq dimensionality reduction and differential expression analysis?▼

Yes, pandas, scipy, numpy, and umap-learn are required dependencies to execute dimensionality reduction, differential expression analysis, and data processing within this single-cell RNA-seq workflow.

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