scanpy - Analyze single-cell RNA-seq data with Scanpy
Runs standard single-cell RNA-seq workflows including QC, normalization, dimensionality reduction, clustering, differential expression, visualization, and R-to-h5ad conversion.
Tags
Updated: 2026-09-29Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Perform quality control
- Normalize expression data
- Compute PCA embeddings
- Compute UMAP embeddings
- Compute t-SNE embeddings
- Cluster cells with Leiden
- Identify cluster marker genes
- Annotate cell types
- Infer trajectories and pseudotime
- Generate single-cell plots
- Export processed datasets
Inputs
- h5ad files
- 10X Genomics data
- CSV expression matrices
- R single-cell objects
- Cell metadata
- Gene metadata
- Marker gene lists
- Analysis parameters
Outputs
- Processed h5ad files
- Cell metadata CSV files
- Gene metadata CSV files
- Analysis figures
- Cluster assignments
- Marker gene results
- Cell type annotations
- Trajectory results
Requirements
- Python 3.12 or newer
- anndata 0.10 or newer
- Scanpy package
- Leiden dependencies for Leiden clustering
- R tooling for R-native input conversion