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Plate IV.404 · Publications

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

Article in Genome Biology, 6 February 2018.

Open accessSingle Cell RNA, Gene Expression

  1. F. Alexander Wolf
  2. Philipp Angerer
  3. Fabian J. Theis

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).

Versions2

  1. PublishedGenome Biology10.1186/s13059-017-1382-0
  2. PreprintbioRxiv10.1101/174029

Cite

@article{wolf2018scanpy,
  title = {{SCANPY: large-scale single-cell gene expression data analysis}},
  author = {Wolf, F. Alexander and Angerer, Philipp and Theis, Fabian J.},
  journal = {Genome Biology},
  publisher = {Springer Science and Business Media LLC},
  year = {2018},
  month = feb,
  doi = {10.1186/s13059-017-1382-0},
  url = {https://doi.org/10.1186/s13059-017-1382-0},
  pmid = {29409532},
  pmcid = {PMC5802054}
}
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