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

F. Alexander Wolf; Philipp Angerer; Fabian J. Theis · Genome biology · 2018

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Author / responsible party
F. Alexander Wolf; Philipp Angerer; Fabian J. Theis
Publisher
Genome biology
Year
2018
Language
English

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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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APA 7

Wolf, F. A, Angerer, P, & Theis, F. J. (2018). SCANPY: large-scale single-cell gene expression data analysis. https://doi.org/10.1186/s13059-017-1382-0

MLA

Wolf, F. Alexander, et al. "SCANPY: large-scale single-cell gene expression data analysis." 2018. https://doi.org/10.1186/s13059-017-1382-0.

Chicago

Wolf, F. Alexander, Philipp Angerer, and Fabian J. Theis. 2018. "SCANPY: large-scale single-cell gene expression data analysis.". https://doi.org/10.1186/s13059-017-1382-0.

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Wolf, F. A, Angerer, P. and Theis, F. J. 2018, SCANPY: large-scale single-cell gene expression data analysis, Genome biology, available at: https://doi.org/10.1186/s13059-017-1382-0 [Accessed 8 Aug. 2026].

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Title
SCANPY: large-scale single-cell gene expression data analysis
Author / contributors
F. Alexander Wolf; Philipp Angerer; Fabian J. Theis
Publisher
Genome biology
Publication year
2018
Language
English

Subjects

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Python (programming language); Preprocessor; Inference; Visualization; Computational biology; Biology; Cluster analysis; Computer science; Scalability; Data visualization; Data mining; Gene expression profiling
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