Analyzes single-cell RNA-seq data using Python for quality control, clustering, and cell type annotation.
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
01Marker gene identification and automated cell type annotation guidance
02Automated QC scripts and comprehensive AnnData manipulation support
030 GitHub stars
04Publication-quality visualization templates for UMAP, heatmaps, and dot plots
05High-performance dimensionality reduction and Leiden clustering
06Complete scRNA-seq analysis workflows from QC to trajectory inference
Use Cases
01Conducting trajectory inference and pseudotime analysis for developmental biology
02Identifying differentially expressed genes between experimental conditions
03Processing raw 10X Genomics data into annotated cell clusters
