BACKGROUND Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information. RESULTS We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians. CONCLUSIONS VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview.

中文翻译：

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使用VDJView探索和分析单细胞多组学数据。

背景技术单细胞RNA测序提供了前所未有的机会来同时探索T细胞和B细胞的转录组和免疫受体多样性。但是，有限的工具可用于同时分析与元数据（例如患者和临床信息）集成的大型多组学数据集。结果我们开发了VDJView，它可以同时或独立地分析和可视化T细胞和B细胞的基因表达，免疫受体和临床元数据。该工具被实现为易于使用的R Shiny Web应用程序，该应用程序集成了众多基因表达和TCR分析工具，并接受来自基于板的分类或高通量单细胞平台的数据。我们利用VDJView分析了多个10X scRNA-seq数据集，包括最近的150个数据集，000个CD8 + T细胞，具有可用的基因表达，TCR序列，15种表面蛋白的定量和44种抗原特异性（跨病毒，癌症和自身抗原）。我们进行了质量控制，对四聚体非特异性细胞的过滤，聚类，随机抽样和假设测试，以发现与免疫细胞分化状态和病原体特异性T细胞的克隆扩增相关的抗原特异性基因标志。我们还分析了从11名受试者获得的563个单细胞（基于板的分类），揭示了在原发癌组织和转移性淋巴结中克隆扩增的T和B细胞。这些免疫细胞根据乳腺癌分子亚型聚集有独特的基因特征。VDJView已在实验室会议和点对点讨论中进行了测试，显示有效的数据生成和讨论，而无需咨询生物信息学家。结论VDJView使没有深厚的生物信息学技能的研究人员能够分析免疫scRNA-seq数据，并将其与克隆性和元数据配置文件集成并可视化，从而加快了假设检验，数据解释和细胞异质性发现的过程。VDJView可从https://bitbucket.org/kirbyvisp/vdjview免费获得。