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

被引：3647

作者：

Wolf, F. Alexander ^{[1
]}

Angerer, Philipp ^{[1
]}

Theis, Fabian J. ^{[1
,2
]}

机构：

[1] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Computat Biol, Munich, Germany

[2] Tech Univ Munich, Dept Math, Munich, Germany

来源：

GENOME BIOLOGY | 2018年 / 19卷

关键词：

Single-cell transcriptomics; Machine learning; Scalability; Graph analysis; Clustering; Pseudotemporal ordering; Trajectory inference; Differential expression testing; Visualization; Bioinformatics; HETEROGENEITY; RECONSTRUCTION; VISUALIZATION;

D O I：

10.1186/s13059-017-1382-0

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

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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