Abstract
Motivation In recent years, efficient single cell RNA sequencing (scRNA-seq) methods have been developed, enabling the transcriptome profiling of each single cell massively in parallel. Meanwhile, its high dimensionality brought in challenges in data modeling, analysis, visualization and interpretation. Various tools have been developed for fast browsing the transcriptome gene expression in single cells. However, their applications require that users have extensive knowledge of data properties, statistical modeling, data dimension reduction techniques and substantial training of computational skills. This brings obstacles for biologists to efficiently view, browse and interpret the data. Also, currently available tools are either missing the scalability for accommodating multiple datasets, or not offering easy data sharing, or ignoring group information for comparison or providing limited annotation capacity on gene functions and involved pathways.
Results Here we published a user-friendly interactive web application, Single Cell Transcriptomics Annotated Viewer (SCANNER), as a public resource to equip the biologists and bioinformatician to share, analyze, visualize and interpret scRNA-seq data in a comprehensive, flexible and collaborative manner. It is effort-less without requirement on software setup or coding skills and enables an easy way to annotate, visualize and compare ontologies, pathways and functions in experimental groups on single cell basis. Also, it provides a user-friendly layout with side-by-side group-split view to compare experimental groups and equipped with multiple data interfaces for easy data sharing. In summary, SCANNER provides a useful way to share, visualize scRNA-seq data, as well as to annotate and interpret the analysis results.
Availability and implementation SCANNER is available at https://www.thecailab.com/scanner/.
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