Andreas Hoek1, Katharina Maibach2,3, Ebru Özmen2, Ana Ivonne Vazquez-Armendariz4, Jan Philipp Mengel5, Torsten Hain5,6, Susanne Herold4, Alexander Goesmann2,6. 1. Bioinformatics and Systems Biology, Justus Liebig University Giessen, 35392, Giessen, Germany. andreas.hoek@computational.bio.uni-giessen.de. 2. Bioinformatics and Systems Biology, Justus Liebig University Giessen, 35392, Giessen, Germany. 3. Algorithmic Bioinformatics, Justus Liebig University Giessen, 35392, Giessen, Germany. 4. Department of Internal Medicine II, and Cardio-Pulmonary Institute (CPI), Universities of Giessen and Marburg Lung Center (UGMLC), Member of the German Center for Lung Research (DZL) and The Institute of Lung Health (ILH), 35392, Giessen, Germany. 5. Institute of Medical Microbiology, Justus Liebig University Giessen, 35392, Giessen, Germany. 6. Center for Infection Research (DZIF), Justus-Liebig-University Giessen, Partner Site Giessen-Marburg-Langen, 35392, Giessen, Germany.
Abstract
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
Entities:
Keywords:
10x; Barcode; R shiny; RNA-Seq; Single cell; Snakemake; UMI; ddSEQ
Authors: Xun Zhu; Thomas K Wolfgruber; Austin Tasato; Cédric Arisdakessian; David G Garmire; Lana X Garmire Journal: Genome Med Date: 2017-12-05 Impact factor: 11.117
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