If you are using the app intensively, or if you want to customize it, you can download the code from the Github repository along with the dataset (>600 MB) and run them on a local R Shiny instance. A version with reduced functionality is also available (see below).
Step by step (full App)
First, you need to install a recent version of R and RStudio (recommended). Launch RStudio, and select “File > New Project”. In the pop-up window, choose “Version Control”, then “Git”. Enter the repository address:
and select where on your computer to create the directory. Then click “Create Project”, you should arrive in a new RStudio session. In the R console, copy/paste this command and press enter:
This should download the dataset, it may take a few minutes.
Then you need to install all the dependencies (R packages that are required for this app to run). In the R console, copy/paste these two commands:
required_packages <- c("tidyverse", "BiocManager", "DT", "expss", "Seurat", "ggrepel", "ggridges", "cowplot", "plotly", "pheatmap", "shiny", "shinyjs", "shinythemes", "shinybusy", "htmltools", "jsonlite", "xlsx") install.packages(required_packages)
and make sure the process concludes without errors. Note: you may need to close any other open RStudio window.
Open the file “ui.R” (click on it in the “Files” panel), and delete or comment out the line 55:
And save the modified script.
Finally if all steps were successful, you should be able to start the App with this command:
On older versions of R (<4.0), the app runs, but some functionalities may not work as expected.
Older versions of the dataset are available upon request: 1 May 2021, 15 June 2021. Current: 6 July 2021.
A version with reduced functionality is also available.
- Enriched Genes by cell type
- Find Differential Expression between Cell Types
- Single cell plot
- Gene expression by cell type
- Find markers based on percentage of expression
- Heatmaps of gene expression
The dataset it uses has been reduced in size to improve loading speed and make it possible to run on computers with more limited memory. Install as above by downloading the code and the data (you will be prompted to download that data automatically if you have not done it already). The required packages list is also reduced:
required_packages <- c("tidyverse", "DT", "expss", "Matrix",
"shiny", "shinyjs", "shinythemes", "shinybusy")
On Windows, double-clicking “run.bat” should launch the App automatically (you may need to add R to the path).