How does it handle reproducibility in bioinformatics?

The skill enforces critical rules such as setting fixed random states and using the Leiden algorithm over Louvain to ensure consistent results across analysis runs.

Does this skill help with data normalization?

Yes, it includes optimized patterns for total count normalization and log transformation while ensuring raw data is preserved in the layers attribute.

Can I use this skill for UMAP and clustering?

Absolutely. It provides implementation-ready code for PCA, neighbor graph construction, UMAP embedding, and Leiden clustering.

Is it compatible with standard H5AD files?

Yes, the skill is designed around the AnnData object structure, which is the industry standard for H5AD file interactions in Python.

What is the scverse-compact skill?

It is a specialized capability for Claude Code that provides standardized, compact patterns for single-cell transcriptomics analysis using the Scanpy and AnnData libraries.

scverse & Scanpy Essentials

Name: scverse & Scanpy Essentials
Author: CodingKaiser

byCodingKaiser

Data Science & ML

Streamlines single-cell transcriptomics workflows using standardized scverse, AnnData, and Scanpy implementation patterns.

About

The scverse-compact skill provides an optimized toolkit for bioinformatics and computational biology, specifically focusing on the scverse ecosystem. It simplifies complex single-cell RNA-sequencing (scRNA-seq) tasks by offering high-efficiency patterns for AnnData manipulation, quality control, normalization, dimensionality reduction, and clustering. This skill is designed to help researchers and data scientists implement best practices—such as maintaining raw count layers, ensuring object independence via deep copies, and utilizing Leiden clustering—to produce reproducible and publication-ready genomic analysis.

Key Features

Complete preprocessing workflows including QC metrics and filtering
Robust clustering and marker gene identification via Leiden and Wilcoxon methods
0 GitHub stars
Standardized AnnData structure management for cells, genes, and embeddings
Optimized dimensionality reduction patterns using PCA and UMAP
High-quality visualization templates for dot plots, violins, and cluster maps

Use Cases

Generating publication-quality visualizations of gene expression and cell embeddings
Conducting end-to-end quality control and preprocessing on raw scRNA-seq datasets
Identifying distinct cell populations and cluster-specific marker genes

About

Key Features

Complete preprocessing workflows including QC metrics and filtering
Robust clustering and marker gene identification via Leiden and Wilcoxon methods
0 GitHub stars
Standardized AnnData structure management for cells, genes, and embeddings
Optimized dimensionality reduction patterns using PCA and UMAP
High-quality visualization templates for dot plots, violins, and cluster maps

Use Cases

Generating publication-quality visualizations of gene expression and cell embeddings
Conducting end-to-end quality control and preprocessing on raw scRNA-seq datasets
Identifying distinct cell populations and cluster-specific marker genes