Scanpy is a scalable toolkit for analysing single-cell gene expression data.
It provides facilities for preprocessing, visualisation, clustering, trajectory inference and differential expression testing.
Built jointly with AnnData, Scanpy can efficiently handle datasets containing more than one million cells. Experimental Dask support lets some functions process datasets too large to fit in memory.
This is free and open source software.
Key Features
- Calculates quality-control metrics and filters cells and genes.
- Normalises, transforms, regresses and scales expression data.
- Identifies highly variable genes.
- Offers dimensionality reduction with PCA, t-SNE, UMAP and diffusion maps.
- Computes neighbourhood graphs for single-cell datasets.
- Supports Leiden and hierarchical clustering.
- Provides trajectory inference using PAGA and diffusion pseudotime.
- Ranks marker genes and performs differential expression testing.
- Creates scatter plots, heatmaps, dot plots, violin plots and matrix plots.
- Integrates with BBKNN, Harmony, MNN and Scanorama.
- Uses AnnData for storing annotated data matrices.
- Offers experimental Dask support for larger-than-memory datasets.
Website: github.com/scverse/scanpy
Support:
Developer: scverse
License: BSD 3-Clause “New” or “Revised” License
Scanpy is written in Python. Learn Python with our recommended free books and free tutorials.
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