SCANPY: large-scale single-cell gene expression data analysis

被引：0
|
作者：
F. Alexander Wolf
Philipp Angerer
Fabian J. Theis
机构：
[1] Institute of Computational Biology,Helmholtz Zentrum München – German Research Center for Environmental Health
[2] Technische Universität München,Department of Mathematics
来源：
Genome Biology | / 19卷
关键词：
Single-cell transcriptomics; Machine learning; Scalability; Graph analysis; Clustering; Pseudotemporal ordering; Trajectory inference; Differential expression testing; Visualization; Bioinformatics;
D O I：
暂无
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学科分类号：
摘要：
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).
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