PT  - JOURNAL ARTICLE
AU  - Gregor Sturm
AU  - Tamas Szabo
AU  - Georgios Fotakis
AU  - Marlene Haider
AU  - Dietmar Rieder
AU  - Zlatko Trajanoski
AU  - Francesca Finotello
TI  - Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data
AID  - 10.1101/2020.04.10.035865
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.04.10.035865
4099  - http://biorxiv.org/content/early/2020/04/13/2020.04.10.035865.short
4100  - http://biorxiv.org/content/early/2020/04/13/2020.04.10.035865.full
AB  - 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 in cancer, but also in infectious diseases like COVID-19. 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 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.Competing Interest StatementThe authors have declared no competing interest.