scRepertoire 2: Enhanced and efficient toolkit for single-cell immune profiling.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2025-06-27 eCollection Date: 2025-06-01 DOI:10.1371/journal.pcbi.1012760

Qile Yang, Ksenia R Safina, Kieu Diem Quynh Nguyen, Zewen Kelvin Tuong, Nicholas Borcherding

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引用次数: 0

Abstract

Single-cell adaptive immune receptor repertoire sequencing (scAIRR-seq) and single-cell RNA sequencing (scRNA-seq) provide a transformative approach to profiling immune responses at unprecedented resolution across diverse pathophysiologic contexts. This work presents scRepertoire 2, a substantial update to our R package for analyzing and visualizing single-cell immune receptor data. This new version introduces an array of features designed to enhance both the depth and breadth of immune receptor analysis, including improved workflows for clonotype tracking, repertoire diversity metrics, and novel visualization modules that facilitate longitudinal and comparative studies. Additionally, scRepertoire 2 offers seamless integration with contemporary single-cell analysis frameworks like Seurat and SingleCellExperiment, allowing users to conduct end-to-end single-cell immune profiling with transcriptomic data. Performance optimizations in scRepertoire 2 resulted in a 85.1% increase in speed and a 91.9% reduction in memory usage from the first version over the range repertoire size tested in benchmarking, addressing the demands of the ever-increasing size and scale of single-cell studies. This release marks an advancement in single cell immunogenomics, equipping researchers with a robust toolset to uncover immune dynamics in health and disease.

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scRepertoire 2：增强和高效的单细胞免疫分析工具包。

单细胞适应性免疫受体库测序（scAIRR-seq）和单细胞RNA测序（scRNA-seq）提供了一种变革性的方法，以前所未有的分辨率在不同的病理生理背景下分析免疫反应。这项工作提出了scRepertoire 2，这是我们的R包的实质性更新，用于分析和可视化单细胞免疫受体数据。这个新版本引入了一系列功能，旨在增强免疫受体分析的深度和广度，包括改进的克隆型跟踪工作流程，曲目多样性指标，以及促进纵向和比较研究的新型可视化模块。此外，scRepertoire 2提供与现代单细胞分析框架（如Seurat和singlecellexexperiment）的无缝集成，允许用户使用转录组学数据进行端到端的单细胞免疫分析。在基准测试中测试的曲目大小范围内，scRepertoire 2的性能优化使速度比第一个版本提高了85.1%，内存使用减少了91.9%，解决了单细胞研究中不断增加的大小和规模的需求。此次发布标志着单细胞免疫基因组学的进步，为研究人员提供了一个强大的工具集，以揭示健康和疾病中的免疫动力学。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.