Cargando…

Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data

SUMMARY: 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Sturm, Gregor, Szabo, Tamas, Fotakis, Georgios, Haider, Marlene, Rieder, Dietmar, Trajanoski, Zlatko, Finotello, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751015/
https://www.ncbi.nlm.nih.gov/pubmed/32614448
http://dx.doi.org/10.1093/bioinformatics/btaa611
_version_ 1783625588644249600
author Sturm, Gregor
Szabo, Tamas
Fotakis, Georgios
Haider, Marlene
Rieder, Dietmar
Trajanoski, Zlatko
Finotello, Francesca
author_facet Sturm, Gregor
Szabo, Tamas
Fotakis, Georgios
Haider, Marlene
Rieder, Dietmar
Trajanoski, Zlatko
Finotello, Francesca
author_sort Sturm, Gregor
collection PubMed
description SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
format Online
Article
Text
id pubmed-7751015
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-77510152020-12-28 Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data Sturm, Gregor Szabo, Tamas Fotakis, Georgios Haider, Marlene Rieder, Dietmar Trajanoski, Zlatko Finotello, Francesca Bioinformatics Applications Notes SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-07-02 /pmc/articles/PMC7751015/ /pubmed/32614448 http://dx.doi.org/10.1093/bioinformatics/btaa611 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Sturm, Gregor
Szabo, Tamas
Fotakis, Georgios
Haider, Marlene
Rieder, Dietmar
Trajanoski, Zlatko
Finotello, Francesca
Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data
title Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data
title_full Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data
title_fullStr Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data
title_full_unstemmed Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data
title_short Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data
title_sort scirpy: a scanpy extension for analyzing single-cell t-cell receptor-sequencing data
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751015/
https://www.ncbi.nlm.nih.gov/pubmed/32614448
http://dx.doi.org/10.1093/bioinformatics/btaa611
work_keys_str_mv AT sturmgregor scirpyascanpyextensionforanalyzingsinglecelltcellreceptorsequencingdata
AT szabotamas scirpyascanpyextensionforanalyzingsinglecelltcellreceptorsequencingdata
AT fotakisgeorgios scirpyascanpyextensionforanalyzingsinglecelltcellreceptorsequencingdata
AT haidermarlene scirpyascanpyextensionforanalyzingsinglecelltcellreceptorsequencingdata
AT riederdietmar scirpyascanpyextensionforanalyzingsinglecelltcellreceptorsequencingdata
AT trajanoskizlatko scirpyascanpyextensionforanalyzingsinglecelltcellreceptorsequencingdata
AT finotellofrancesca scirpyascanpyextensionforanalyzingsinglecelltcellreceptorsequencingdata