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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,
Artikelnummer: 15
Verlag
BioMed Central
Verlagsort
London
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)