scanpy - Single-cell RNA-seq analysis with Scanpy
Runs standard single-cell RNA-seq workflows for quality control, preprocessing, dimensionality reduction, clustering, differential expression, visualization, and format conversion.
Tags
Updated: 2026-10-01Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Perform quality control
- Normalize expression data
- Select variable genes
- Reduce dimensions
- Cluster cells
- Identify marker genes
- Annotate cell types
- Correct batch effects
- Infer trajectories
- Generate visualizations
- Convert files to h5ad
- Aggregate pseudobulk counts
Inputs
- Single-cell expression datasets
- h5ad files
- 10X files
- CSV files
- R single-cell objects
- Analysis parameters
- Batch labels
- Gene signatures
- Cell-type mappings
Outputs
- Processed h5ad files
- Quality-control plots
- Dimensionality-reduction plots
- Cluster assignments
- Marker-gene CSV files
- Cell-type annotations
- Pseudobulk matrices
- Analysis figures
Requirements
- Python 3.12 or newer
- scanpy 1.12.x
- anndata 0.10 or newer
- python-igraph and leidenalg for Leiden clustering
- R tooling for R-native conversion