Authors: Jianhong Ou¹
Last modified: 2023-07-06

Single-cell RNA sequencing (scRNA-seq) is a powerful technique to study gene expression, cellular heterogeneity, and cell states within samples in single-cell level. The development of scRNA-seq shed light to address the knowledge gap about the cell types, cell interactions, and key genes involved in biological process and their dynamics. To precisely meet the publishing requirement, reduce the time of communication the bioinformatician with researchers, and increase the re-usability and reproducibility of scientific findings, multiple interactive visualization tools were developed to provide the researchers access to the details of the data. Based on ShinyCell, the scRNAseqApp package is developed with multiple highly interactive visualizations of how cells and subsets of cells cluster behavior for scRNA-seq, scATAC-seq and sc-multiomics data. The end users can discover the expression of genes in multiple interactive manners with highly customized filter conditions by selecting metadata supplied with the publications and download the ready-to-use results for republishing.

• Basic knowledge of R syntax
• Basic knowledge of Docker
• Basic knowledge of shell commands
• Basic knowledge of Shiny
• A computer with internet connection

Attendance must be familiarity with scRNAseq analysis and know the data structure of Seurat object.

• scRNAseqApp

An example for a 45-minute workshop:

• Gain the knowledge of typical workflows for creating a shiny APP via scRNAseqApp package
• Understand the user management system of the APP.
• Learn the available visualization tools provided by the package

• Learn how to set up a shiny APP via scRNAseqApp package
• Learn how to add a new data to the APP
• Learn how to distribute the APP to a shiny server