Does this skill handle batch correction?

Yes, it includes guidance for ComBat batch correction and provides patterns for integrating with other ecosystem tools like Harmony or scVI.

Can I generate publication-ready plots with this skill?

Absolutely. It includes specific settings for high-resolution, customized UMAPs, heatmaps, and dot plots with export options for PDF and PNG.

How does this skill assist with cell type annotation?

It provides methods for visualizing known marker genes across clusters using dot plots and heatmaps, followed by manual or dictionary-based mapping of clusters to biological identities.

What data formats does the Scanpy skill support?

It supports standard single-cell formats including .h5ad (AnnData), 10X Genomics (.mtx or .h5), CSV, and other text-based expression matrices.

What clustering method does this skill recommend?

The skill prioritizes Leiden clustering over Louvain for its improved community detection and better handling of single-cell connectivity graphs.

Scanpy Single-Cell Analysis

Name: Scanpy Single-Cell Analysis
Author: Activer007

byActiver007

Data Science & ML

Analyzes single-cell RNA-seq data using Python for quality control, clustering, and cell type annotation.

About

This skill equips Claude with specialized expertise to perform comprehensive single-cell RNA-seq workflows using the Scanpy toolkit and AnnData structures. It provides robust guidance for loading diverse data formats like 10X and .h5ad, performing rigorous quality control, executing dimensionality reduction via UMAP or t-SNE, identifying marker genes, and generating publication-ready visualizations. Whether you are conducting trajectory inference or differential expression analysis, this skill ensures best practices and efficient implementation patterns for complex transcriptomics research.

Key Features

Marker gene identification and automated cell type annotation guidance
Automated QC scripts and comprehensive AnnData manipulation support
0 GitHub stars
Publication-quality visualization templates for UMAP, heatmaps, and dot plots
High-performance dimensionality reduction and Leiden clustering
Complete scRNA-seq analysis workflows from QC to trajectory inference

Use Cases

Conducting trajectory inference and pseudotime analysis for developmental biology
Identifying differentially expressed genes between experimental conditions
Processing raw 10X Genomics data into annotated cell clusters

About

Key Features

Marker gene identification and automated cell type annotation guidance
Automated QC scripts and comprehensive AnnData manipulation support
0 GitHub stars
Publication-quality visualization templates for UMAP, heatmaps, and dot plots
High-performance dimensionality reduction and Leiden clustering
Complete scRNA-seq analysis workflows from QC to trajectory inference

Use Cases

Conducting trajectory inference and pseudotime analysis for developmental biology
Identifying differentially expressed genes between experimental conditions
Processing raw 10X Genomics data into annotated cell clusters