Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.
The scanpy skill is designed for analyzing single-cell RNA-sequencing (scRNA-seq) data, providing a comprehensive toolkit to handle various data formats including .h5ad, 10X, and CSV. This skill addresses the complexities associated with single-cell data analysis, including quality control, normalization, and visualization. By leveraging the AnnData structure, scanpy enables users to efficiently manage large datasets and perform extensive analyses, making it essential for researchers in genomics and bioinformatics.
Scanpy can analyze single-cell RNA-seq data in .h5ad, 10X, and CSV formats.
Yes, scanpy is built on top of AnnData and is compatible with various Python libraries for data manipulation and visualization.
Scanpy requires a Python environment with necessary libraries installed, such as numpy, pandas, and matplotlib.
Yes, scanpy includes functionalities for trajectory inference and pseudotime analysis.
While scanpy is powerful, users should have a basic understanding of Python and bioinformatics to effectively utilize its features.
Quick Setup:
.claude/skills/Repository
davila7/claude-code-templates