Mhealthannotator is a RShiny-as-a-service App for visualizing Files in Synapse Tables used @Sage Bionetworks.
This app's purpose is to help researchers/engineers/analyst be able to spin up Image Label Annotations App more seamlessly through using RShiny as its core UI/UX.
It will help interact with Synapse by providing:
a. Configuration files:
- Containing information to interact with synapse
- Containing information to the RShiny App UI/UX
b. Visuallization functions: Custom function you want to use to visualize your data
Current package is not distributed via CRAN but will be installable through Github as we are doing further testing and future use-cases.
To install the development from GitHub, run:
devtools::install_github("Sage-Bionetworks/mhealthannotator")Notes on Installation: All functionalities in mhealthannotator use reticulate and the Synapse Python client. You can set up your environment by having a anaconda environment or a virtual environment set up with Synapseclient. Because mhealthannotator uses reticulate, it is not compatible with the synapser package..
- Configure Python Environment: As we require to use synapse python client, python environment needed to be configured: Resources can be found here:
- Reticulate: https://rstudio.github.io/reticulate/
- Renv (Suggested): https://rstudio.github.io/renv/articles/python.html
- Build Configuration After your python environment is set up, you will be able to parse in your configuration and visualization function.
- Config Template
- Function Template
Notes: Example configuration can be found in this package inst/* and visualization on R/visualizer.R
- Run Shiny App
mhealthannotator::run_app(config = <PATH_TO_CONFIG_FILE>, funs = <PLOT_FUNCTION>)Resources to connect to Sage SciComp Shiny Server Instance
- Git Clone this Repo on your server directory
git clone https://github.com/Sage-Bionetworks/mhealthannotator.git- Repeat Step 1 and 2 of
How to Run - Modify app.R based on your parameter of Step 3 of
How to Run