scViewer Application- A single-cell data viewer shiny application

Windows users

All the dependencies such as R packages etc are pre-compiled and loaded into the Application bundle

1. Download the scViewer GitHub zip repo and
2. Unzip the scViewer folder
3. Double click the scViewer.bat file and the application will be launched

Linux or Mac users

1. Download the scViewer GitHub zip repo and
2. Unzip the scViewer folder
3. Option1- Install the following packages in your R studio/ R

The package dependencies can also be found here- scViewer -> app -> packages.txt

install.packages("shiny") install.packages("shinythemes") install.packages("Seurat") install.packages("data.table") install.packages("DT") install.packages("stringr") install.packages("ggpubr") install.packages("tibble") install.packages("nebula") install.packages("scater") install.packages("SingleCellExperiment") install.packages("gridExtra") install.packages("cowplot")

Option2- Linux/ Mac users can also redirect by loading the pre-compiled libraries from scViewer -> app -> library folder instead of installing them.

For ex, library(devtools) then load package w/o installing using the PATH of pre-installed packages using- load_all('/scViewer/app/library/packagename') R v 4.1.2 was used to develop the shiny app. All the packages version are mentioned in the manuscript.

4. Go to scViewer -> app -> shiny folder where you can locate the data, www folder, and app.R script to run the shiny app.
5. Run the shiny app using the app.R script

Data

The demo dataset can be found in scViewer -> app -> data

Users can store the processed single-cell RNA_seq data (Seurat .RDS object) in the data folder. The uploaded object will be available in the drop-down menu of Load data tab

Supplementary Code documentation for processing raw data

The scRNA-seq data processing code can be found in the vignette here scViewer -> Supplementary_File.html

We provide the step-by-step example code documentation for processing the scRNA-seq data. We also provide the metadata format for the scRNA-seq data that is compatible with the app.

Publication to cite

Patil, A.R.; Kumar, G.; Zhou, H.; Warren, L. scViewer: An Interactive Single-Cell Gene Expression Visualization Tool. Cells 2023, 12, 1489. https://doi.org/10.3390/cells12111489