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
Python (programming language)PreprocessorInferenceVisualizationComputational biologyBiologyCluster analysisComputer scienceScalabilityData visualizationData miningGene expression profilingGene expressionGeneGeneticsArtificial intelligenceProgramming languageDatabase
MeSH Terms
Gene Expression ProfilingGene Regulatory NetworksSingle-Cell AnalysisSoftware
Affiliated Institutions
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Publication Info
- Year
- 2018
- Type
- article
- Volume
- 19
- Issue
- 1
- Pages
- 15-15
- Citations
- 8088
- Access
- Closed
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Cite This
F. Alexander Wolf,
Philipp Angerer,
Fabian J. Theis
(2018).
SCANPY: large-scale single-cell gene expression data analysis.
Genome biology
, 19
(1)
, 15-15.
https://doi.org/10.1186/s13059-017-1382-0
Identifiers
- DOI
- 10.1186/s13059-017-1382-0
- PMID
- 29409532
- PMCID
- PMC5802054