Shiny-iAtlas is an interactive web portal that provides multiple analysis modules to visualize and explore immune response characterizations across cancer types. The app is hosted on shinyapps.io at https://isb-cgc.shinyapps.io/shiny-iatlas/ and can also be accessed via the main CRI iAtlas page at http:https://www.cri-iatlas.org/.

The portal is built entirely in R and Shiny using the RStudio development environment. Layout and interactivity within the portal are achieved by heavy use of the following packages:

• shinydashboard
• plotly
• crosstalk

• R: https://www.r-project.org/ - v3.6.2

• RStudio: https://rstudio.com/products/rstudio/download

• lib cairo: https://www.cairographics.org/ (only required for iAtlas client)

• gfortran (libgfortran): usually installed with gcc

• Download and install gfortran: https://github.com/fxcoudert/gfortran-for-macOS/releases or https://cran.r-project.org/bin/macosx/tools/

• Download and install R-3.6.2.pkg: https://cran.r-project.org/bin/macosx/

• Download and install X11 via XQuartz: https://www.xquartz.org/

• Install cairo via Homebrew (https://formulae.brew.sh/formula/cairo ; requires brew: https://brew.sh/ )

• Download and install RStudio: https://rstudio.com/products/rstudio/download

To run the app locally:

1. Clone this repository

2. Open shiny-iatlas.Rproj

3. Install packages. In the RStudio console, run:

   renv::restore()

   This may take some time to complete - walk away from your computer, rest your eyes, and catch up on those stretching exercises you are meant to be doing :)

4. Start the app by running:

   shiny::runApp()

Please consult the Contributing Guide for pointers on how to get started on a new module.

When adding any new dependencies to the application, they may be added using (where "useful_package" is the name of the package to add):

renv::install("useful_package")

see https://rstudio.github.io/renv/reference/install.html for more details.

Once a new package is added, run:

renv::snapshot()

This will ensure the new package is added to the renv.lock file.

To remove an installed package, run (where "no_longer_useful_package" is the name of the package to remove):

renv::remove("no_longer_useful_package")

For more on package management with renv, please see https://rstudio.github.io/renv/articles/renv.html

The first time you deploy, go through the Deployment Setup instructions below. Afterwards, you can just deploy as per sub-section Deploy.

You'll need to set up your credentials for shinyapps.io. You can get your codes from:

• https://www.shinyapps.io/admin/#/tokens

Paste and evaluate your tokens in the RStudio console. They look like this:

# shinyapps.io example credentials
rsconnect::setAccountInfo(
  name='shiny-iatlas',
  token='xxx',
  secret='yyy'
)

rsconnect::deployApp()

Input data for the Shiny-iAtlas portal are to a large extent obtained from the Immune Landscape of Cancer study. These data can be accessed via the Immunity journal publication page , the manuscript page at NCI's Genomic Data Commons and on iAtlas pages on Synapse. This includes the main feature matrix and various feature and group annotations. Additionally some input files of results were pre-computed specifically for use by this app.

Some of the key input data can be found as dataframe objects in feather files within the data folder:

• cell_image_id_annotations.feather
• driver_mutations.feather
• feature_df.feather
• feature_method_df.feather
• fmx_df.feather
• im_direct_relationships.feather
• im_expr_df.feather
• im_potential_factors.feather
• im_target_annotations.feather
• im_target_expr_df.feather
• sample_group_df.feather

Methods employed by the app are described in Immune Landscape of Cancer study and can also be found in Methods descriptions displayed in the app (for example as seen in Data Description module).

Concordance indexes for survival endpoints with respect to different immune readouts are computed using a custom package developed by Tai-Hsien Ou Yang. The concordanceIndex package includes a single eponymous function that can be used as follows:

concordanceIndex::concordanceIndex(predictions, observations)

where predictions and observations are numerical vectors of identical length.

The iAtlas Immune Subtype Classifier tool uses the ImmuneSubtypeClassifier R package, developed by David L. Gibbs, for classification of immune subtypes, in cancer, using gene expression data.