SCANNER: A Web Resource for Annotation, Visualization and Sharing of Single Cell RNA-seq Data

MotivationIn recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality brought challenges in data modeling, analysis, visualization and interpretation. Available analysis tools require extensive knowledge and training of data properties, statistical modeling and computational skills. It is challenging for biologists to efficiently view, browse and interpret the data. ResultsHere we developed SCANNER, as a public webserver resource to equip the biologists and bioinformatician to share and analyze scRNA-seq data in a comprehensive and collaborative manner. It is effort-less and host-free without requirement on software setup or coding skills, and enables a user-friendly way to compare the activation status of gene sets on single cell basis. Also, it is equipped with multiple data interfaces for easy data sharing and currently provide a database for studying the smoking effect on single cell gene expression in lung. Using SCANNER, we have identified larger proportions of cancer-associated fibroblasts cells and activeness of fibroblast growth related genes in melanoma tissues in females compared to males. Moreover, we found ACE2 is mainly expressed in pneumocytes, secretory cells and ciliated cells with disparity in gene expression by smoking behavior. Availability and implementationSCANNER is available at https://www.thecailab.com/scanner/. Supplementary informationSupplementary data are available online. ContactGCAI@mailbox.sc.edu or XIAOF@mailbox.sc.ecu Key PointsO_LISCANNER provides a new web server resource for promoting scRNA-seq data analysis C_LIO_LISCANNER enables comprehensive and dynamic analysis and visualization, novel functional annotation and activeness inference, online databases and easy data sharing. C_LIO_LISCANNER bridges the data analysis and the biological experiment units. C_LI