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

Wolf FA, Angerer P, Theis FJ (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Genome Biology

Book Volume: 19

Article Number: 15

Journal Issue: 1

DOI: 10.1186/s13059-017-1382-0

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 ).

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE)

How to cite

APA:

Wolf, F.A., Angerer, P., & Theis, F.J. (2018). SCANPY: Large-scale single-cell gene expression data analysis. Genome Biology, 19(1). https://doi.org/10.1186/s13059-017-1382-0

MLA:

Wolf, F. Alexander, Philipp Angerer, and Fabian J. Theis. "SCANPY: Large-scale single-cell gene expression data analysis." Genome Biology 19.1 (2018).

BibTeX: Download