How does this skill assist with cell type annotation?

It provides workflows for visualizing known marker genes via dot plots and heatmaps, enabling manual or dictionary-based cluster annotation.

Can this skill help with batch effect correction?

Yes, it provides implementation patterns for ComBat and integration advice for the scverse ecosystem to handle batch correction.

Does it include automated quality control?

Yes, the skill includes a bundled script (qc_analysis.py) for automated metric calculation, visualization, and filtering.

What data formats does the Scanpy skill support?

It supports standard single-cell formats including .h5ad (AnnData), 10X Genomics (mtx and h5), and CSV files.

Scanpy Single-Cell Analysis

Name: Scanpy Single-Cell Analysis
Author: K-Dense-AI

byK-Dense-AI

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3,719

Data Science & ML

Performs comprehensive single-cell RNA-seq analysis including quality control, clustering, and trajectory inference using the Scanpy framework.

About

Scanpy is a powerful Python-based toolkit designed for the analysis of single-cell gene expression data. This skill enables Claude to guide researchers through complex bioinformatic workflows, including data ingestion from 10X or H5AD formats, sophisticated quality control, dimensionality reduction with UMAP and t-SNE, and advanced clustering using the Leiden algorithm. Whether you are identifying novel cell types, finding marker genes, or performing trajectory analysis, this skill provides the domain-specific best practices and implementation patterns needed for publication-quality results.

Key Features

3,719 GitHub stars
Advanced dimensionality reduction using PCA, UMAP, and t-SNE
Cell clustering and automated marker gene identification
Generation of publication-ready visualizations and heatmaps
Trajectory inference and pseudotime analysis via PAGA
End-to-end single-cell RNA-seq preprocessing and quality control (QC)

Use Cases

Identifying differentially expressed genes across various experimental conditions
Processing raw 10X Genomics data into annotated cell clusters
Mapping developmental trajectories and lineage relationships in single-cell datasets

About

Key Features

3,719 GitHub stars
Advanced dimensionality reduction using PCA, UMAP, and t-SNE
Cell clustering and automated marker gene identification
Generation of publication-ready visualizations and heatmaps
Trajectory inference and pseudotime analysis via PAGA
End-to-end single-cell RNA-seq preprocessing and quality control (QC)

Use Cases

Identifying differentially expressed genes across various experimental conditions
Processing raw 10X Genomics data into annotated cell clusters
Mapping developmental trajectories and lineage relationships in single-cell datasets