2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      brief-report

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Related collections

          Most cited references15

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              SCANPY : large-scale single-cell gene expression data analysis

              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).
                Bookmark

                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 September 2020
                02 July 2020
                02 July 2020
                : 36
                : 18
                : 4817-4818
                Affiliations
                [1 ] Biocenter, Institute of Bioinformatics, Medical University of Innsbruck , Innsbruck 6020, Austria
                [2 ] Biocenter, Institute of Developmental Immunology, Medical University of Innsbruck , Innsbruck 6020, Austria
                Author notes
                To whom correspondence should be addressed. E-mail: francesca.finotello@ 123456i-med.ac.at
                Author information
                http://orcid.org/0000-0001-9745-6255
                http://orcid.org/0000-0001-9659-4226
                http://orcid.org/0000-0003-1754-690X
                http://orcid.org/0000-0002-0636-7351
                http://orcid.org/0000-0003-0712-4658
                Article
                btaa611
                10.1093/bioinformatics/btaa611
                7751015
                32614448
                8c1555f2-abde-44c8-b90b-9e01295dad3d
                © The Author(s) 2020. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 April 2020
                : 08 June 2020
                : 24 June 2020
                Page count
                Pages: 2
                Funding
                Funded by: Austrian Science Fund (FWF);
                Award ID: T 974-B30
                Award ID: I3978
                Funded by: European Research Council (ERC);
                Award ID: 786295
                Funded by: German Research Foundation (DFG);
                Award ID: TRR 241(INF)
                Categories
                Applications Notes
                Sequence Analysis
                AcademicSubjects/SCI01060

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article