Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
15 09 2020
15 09 2020
Historique:
received:
11
04
2020
revised:
08
06
2020
accepted:
24
06
2020
pubmed:
3
7
2020
medline:
4
3
2021
entrez:
3
7
2020
Statut:
ppublish
Résumé
Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 32614448
pii: 5866543
doi: 10.1093/bioinformatics/btaa611
pmc: PMC7751015
doi:
Substances chimiques
Receptors, Antigen, T-Cell
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4817-4818Subventions
Organisme : European Research Council
ID : 786295
Pays : International
Organisme : Austrian Science Fund FWF
ID : T 974
Pays : Austria
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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