Is there a template to start a new analysis?

Yes, the skill includes an analysis_template.py script that provides a complete end-to-end workflow from data loading to annotation.

Which clustering algorithms are recommended?

The skill primarily utilizes the Leiden algorithm for clustering due to its efficiency and biological relevance, though it also supports Louvain.

What file formats does the Scanpy skill support?

It supports standard single-cell formats including .h5ad (AnnData), 10X Genomics (MTX and H5), and CSV files.

Does it support publication-quality plotting?

Absolutely. It includes a comprehensive plotting guide for creating high-DPI PDFs, heatmaps, and customized UMAP visualizations.

Can this skill help with batch effect correction?

Yes, it provides guidance and code for ComBat batch correction and integration with other tools like Harmony or scVI.

Scanpy Single-Cell Analysis

Name: Scanpy Single-Cell Analysis
Author: Microck

byMicrock

•

Data Science & ML

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

This skill equips Claude with specialized knowledge and workflows for Scanpy, the industry-standard Python toolkit for single-cell genomics. It enables automated quality control, normalization, dimensionality reduction (PCA/UMAP), and sophisticated clustering using the Leiden algorithm. Users can leverage this skill to identify marker genes, annotate cell types, and perform trajectory inference, while generating publication-quality visualizations for complex biological datasets. It includes bundled templates and scripts to accelerate bioinformatics research from raw data to biological insight.

Key Features

01Marker gene identification and automated cell type annotation workflows

02Generation of publication-ready figures, heatmaps, and dot plots

03Trajectory inference and pseudotime analysis using PAGA and DPT

0481 GitHub stars

05Advanced dimensionality reduction including UMAP, t-SNE, and PCA visualization

06Automated quality control and mitochondrial gene filtering for scRNA-seq data

Use Cases

01Investigating cellular differentiation paths and lineages through trajectory analysis

02Analyzing 10X Genomics or .h5ad single-cell transcriptomics datasets

03Identifying distinct cell populations and their biological markers in complex tissues

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills scanpy

For use in Claude.ai and ChatGPT

Download Skill