Hoek Andreas, Maibach Katharina, Özmen Ebru, Vazquez-Armendariz Ana Ivonne, Mengel Jan Philipp, Hain Torsten, Herold Susanne, Goesmann Alexander
Bioinformatics and Systems Biology, Justus Liebig University Giessen, 35392, Giessen, Germany.
Algorithmic Bioinformatics, Justus Liebig University Giessen, 35392, Giessen, Germany.
BMC Genomics. 2021 Mar 18;22(1):195. doi: 10.1186/s12864-021-07469-6.
The technology of single cell RNA sequencing (scRNA-seq) has gained massively in popularity as it allows unprecedented insights into cellular heterogeneity as well as identification and characterization of (sub-)cellular populations. Furthermore, scRNA-seq is almost ubiquitously applicable in medical and biological research. However, these new opportunities are accompanied by additional challenges for researchers regarding data analysis, as advanced technical expertise is required in using bioinformatic software.
Here we present WASP, a software for the processing of Drop-Seq-based scRNA-Seq data. Our software facilitates the initial processing of raw reads generated with the ddSEQ or 10x protocol and generates demultiplexed gene expression matrices including quality metrics. The processing pipeline is realized as a Snakemake workflow, while an R Shiny application is provided for interactive result visualization. WASP supports comprehensive analysis of gene expression matrices, including detection of differentially expressed genes, clustering of cellular populations and interactive graphical visualization of the results. The R Shiny application can be used with gene expression matrices generated by the WASP pipeline, as well as with externally provided data from other sources.
With WASP we provide an intuitive and easy-to-use tool to process and explore scRNA-seq data. To the best of our knowledge, it is currently the only freely available software package that combines pre- and post-processing of ddSEQ- and 10x-based data. Due to its modular design, it is possible to use any gene expression matrix with WASP's post-processing R Shiny application. To simplify usage, WASP is provided as a Docker container. Alternatively, pre-processing can be accomplished via Conda, and a standalone version for Windows is available for post-processing, requiring only a web browser.
单细胞RNA测序（scRNA-seq）技术已广受欢迎，因为它能以前所未有的方式洞察细胞异质性，以及识别和表征（亚）细胞群体。此外，scRNA-seq几乎普遍适用于医学和生物学研究。然而，这些新机遇给研究人员带来了数据分析方面的额外挑战，因为使用生物信息软件需要先进的技术专长。
我们在此展示WASP，一种用于处理基于Drop-Seq的scRNA-Seq数据的软件。我们的软件便于对使用ddSEQ或10x协议生成的原始读数进行初始处理，并生成包括质量指标的解复用基因表达矩阵。处理流程通过Snakemake工作流实现，同时提供了一个R Shiny应用程序用于交互式结果可视化。WASP支持对基因表达矩阵进行全面分析，包括检测差异表达基因、细胞群体聚类以及结果的交互式图形可视化。R Shiny应用程序可与WASP流程生成的基因表达矩阵一起使用，也可与来自其他来源的外部提供的数据一起使用。
通过WASP，我们提供了一个直观且易于使用的工具来处理和探索scRNA-seq数据。据我们所知，它是目前唯一免费提供的软件包，结合了基于ddSEQ和10x数据的预处理和后处理。由于其模块化设计，可以将任何基因表达矩阵与WASP的后处理R Shiny应用程序一起使用。为简化使用，WASP以Docker容器的形式提供。或者，预处理可以通过Conda完成，并且有一个适用于Windows的独立版本用于后处理，仅需一个网络浏览器。
