期刊论文详细信息
Genome Biology | |
SCANPY: large-scale single-cell gene expression data analysis | |
F. Alexander Wolf1  Philipp Angerer1  Fabian J. Theis1  | |
[1] Helmholtz Zentrum München – German Research Center for Environmental Health, Institute of Computational Biology; | |
关键词: Single-cell transcriptomics; Machine learning; Scalability; Graph analysis; Clustering; Pseudotemporal ordering; | |
DOI : 10.1186/s13059-017-1382-0 | |
来源: DOAJ |
【 摘 要 】
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).
【 授权许可】
Unknown