[HTML][HTML] SCANPY: large-scale single-cell gene expression data analysis
Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes
methods for preprocessing, visualization, clustering, pseudotime and trajectory inference,
differential expression testing, and simulation of gene regulatory networks. Its Python-based
implementation efficiently deals with data sets of more than one million cells (https://github.
com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling
annotated data matrices (https://github. com/theislab/anndata).
methods for preprocessing, visualization, clustering, pseudotime and trajectory inference,
differential expression testing, and simulation of gene regulatory networks. Its Python-based
implementation efficiently deals with data sets of more than one million cells (https://github.
com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling
annotated data matrices (https://github. com/theislab/anndata).
Abstract
Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).
Springer