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Scanpy – Single-Cell Analysis in Python

Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.

News - CZI grant 2019-11-14

Scanpy has been selected an essential open source software for science by CZI among 32 projects, along with giants such as Scipy, Numpy, Pandas, Matplotlib, scikit-learn, scikit-image/plotly, pip, jupyterhub/binder, Bioconda, Seurat, Bioconductor, and others.

Latest additions

On master

Bug fixes

  • Bumped version requirement of scipy to scipy>1.4 to support rmatmat argument of LinearOperator issue 1246

1.5.1 2020-05-21

Bug fixes

  • Fixed a bug in pca(), where random_state did not have an effect for sparse input PR 1240 I Virshup

  • Fixed docstring in pca() which included an unused argument PR 1240 I Virshup

1.5.0 2020-05-15

The 1.5.0 release adds a lot of new functionality, much of which takes advantage of anndata updates 0.7.0 - 0.7.2. Highlights of this release include support for spatial data, dedicated handling of graphs in AnnData, sparse PCA, an interface with scvi, and others.

Spatial data support

New functionality

External tools

Performance

  • pca() now uses efficient implicit centering for sparse matrices. This can lead to signifigantly improved performance for large datasets PR 1066 A Tarashansky

  • score_genes() now has an efficient implementation for sparse matrices with missing values PR 1196 redst4r.

Warning

The new pca() implementation can result in slightly different results for sparse matrices. See the pr (PR 1066) and documentation for more info.

Code design

Bug fixes