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scanpy

Process single-cell RNA-seq datasets into quality-controlled AnnData outputs with clustering and marker discovery.

Updated Jul 1, 2026
Search Tags:#batch correction#anndata#scanpy#umap#scrna-seq#pseudobulk#leiden
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill scanpy-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scanpy
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/scanpy
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill scanpy-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, pandas, numpy, matplotlib, bbknn, harmonypy, scikit-image, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill removes the manual overhead of single-cell RNA-seq analysis by turning raw or partially processed datasets into QCed, normalized, clustered, and annotated results.

Core Features & Use Cases

  • Full Scanpy workflow: run quality control, normalization, highly variable gene selection, PCA, UMAP, Leiden clustering, marker discovery, and cell-type annotation.
  • Flexible input handling: work with h5ad, 10x HDF5, 10x mtx folders, CSV, TSV, TXT, loom, and mtx inputs, then carry results forward in AnnData format.
  • Practical outputs: generate marker tables, pseudobulk-ready counts, publication-style plots, and reusable templates for common scRNA-seq tasks across exploratory and reproducible analyses.

Quick Start

Ask me to run the Scanpy single-cell workflow on your dataset and I will guide the analysis from QC through clustering, markers, and plotting using the bundled scripts.

Frequently Asked Questions about scanpy

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

FAQPage Schema
How do I run a complete scRNA-seq analysis workflow from raw data to UMAP and clustering?▼

To run scRNA-seq analysis end to end, this workflow applies quality control, normalization, highly variable gene selection, PCA, UMAP, and Leiden clustering to generate annotated AnnData outputs and publication-style plots.

What single-cell file formats can I use to start scRNA-seq quality control and normalization?▼

For scRNA-seq quality control and normalization, supported input formats include h5ad, 10x HDF5, 10x mtx folders, CSV, TSV, TXT, loom, and mtx, which are processed into AnnData-compatible outputs.

Does this scRNA-seq workflow support batch correction for multiple samples?▼

Yes, this scRNA-seq workflow supports batch correction across multiple samples by integrating Harmony, ComBat, and BBKNN algorithms to remove technical variation during dimensionality reduction and clustering.

How do I prepare pseudobulk counts from single-cell RNA-seq data for downstream analysis?▼

To prepare pseudobulk counts from single-cell RNA-seq data, the workflow aggregates single-cell expression matrices into pseudobulk-ready counts and exports marker tables for downstream differential expression analysis.

Can I use this Scanpy workflow to discover marker genes and annotate cell types?▼

Yes, you can use this Scanpy workflow to discover marker genes and annotate cell types, as it performs Leiden clustering, exports marker tables, and generates UMAP visualizations for exploratory scRNA-seq analysis.

What is the best way to handle doublet detection in scRNA-seq datasets before clustering?▼

The best way to handle doublet detection in scRNA-seq datasets before clustering is using the integrated Scrublet algorithm, which identifies potential doublets during the initial quality control phase of the Scanpy workflow.

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