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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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 |
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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 |
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