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Genome Biol. 2018 Feb 6;19(1):15. doi: 10.1186/s13059-017-1382-0.

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

Author information

1
Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Neuherberg, Germany. alex.wolf@helmholtz-muenchen.de.
2
Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Neuherberg, Germany.
3
Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Neuherberg, Germany. fabian.theis@helmholtz-muenchen.de.
4
Department of Mathematics, Technische Universität München, Munich, Germany. fabian.theis@helmholtz-muenchen.de.

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 ).

KEYWORDS:

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

PMID:
29409532
PMCID:
PMC5802054
DOI:
10.1186/s13059-017-1382-0
[Indexed for MEDLINE]
Free PMC Article

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