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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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter 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
Zeitschrift Genome Biology
Quellenangaben Band: 19, Heft: 1, Seiten: , Artikelnummer: 15 Supplement: ,
Verlag BioMed Central; Genome Biology Ltd
Verlagsort London
Begutachtungsstatus peer-reviewed