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SCANPY: Large-scale single-cell gene expression data analysis.
Genome Biol. 19:15 (2018)
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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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Single-cell Transcriptomics ; Machine Learning ; Scalability ; Graph Analysis ; Clustering ; Pseudotemporal Ordering ; Trajectory Inference ; Differential Expression Testing ; Visualization ; Bioinformatics; Rna-sequencing Data; Diffusion Maps; Heterogeneity; Reconstruction; Visualization; Trajectories
ISSN (print) / ISBN
1474-760X
e-ISSN
1465-6906
Journal
Genome Biology
Quellenangaben
Volume: 19,
Issue: 1,
Article Number: 15
Publisher
BioMed Central
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)