Merge branch 'develop'

Author: andrewelamb

.Rbuildignore

@@ -1,2 +1,4 @@
+^renv$
+^renv\.lock$
 ^.*\.Rproj$
 ^\.Rproj\.user$

.Rprofile

@@ -0,0 +1,15 @@
+if (file.exists("renv/activate.R")) {
+  source("renv/activate.R")
+  if (!"startup" %in% installed.packages()) {renv::install("startup")}
+  if (!"crayon" %in% installed.packages()) {renv::install("crayon")}
+  startup::startup()
+  
+  cat(
+    crayon::green("================================================================="),
+    crayon::green("NOTE: run 'renv::restore()' to install packages"),
+    crayon::green("      run 'shiny::runApp()' to run the app locally"),
+    crayon::green("      run 'rsconnect::deployApp()' to deploy to shinyapps.io"),
+    crayon::green("See README.md for more.\n"),
+    fill = TRUE
+  )
+}

.gitignore

@@ -1,7 +1,7 @@
+.DS_Store
+.httr-oauth
 .Rhistory
-*.Rproj
 .Rproj.user
-.httr-oauth
 *~
-.DS_Store
-rsconnect
+data/io*
+rsconnect/

DEPENDENCIES

@@ -1,64 +1,152 @@
 # Shiny-iAtlas
 
-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://www.cri-iatlas.org/.
+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/](https://isb-cgc.shinyapps.io/shiny-iatlas/) and can also be accessed via the main CRI iAtlas page at [http://www.cri-iatlas.org/](http://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`**
+- [shinydashboard](https://rstudio.github.io/shinydashboard/)
+- [plotly](https://plot.ly/r/)
+- [crosstalk](https://rstudio.github.io/crosstalk/)
 
-Violin plots are not available in the current version of plotly in CRAN; need to use development version from GitHub (e.g., 4.7.1.9000). To install the development version:
+## Install
 
-```R
-devtools::install_github("ropensci/plotly")
-```
+### Requirements
 
-Other data transformation and formatting operations, as well as many other general application tasks, are supported by a variety of packages in the **`tidyverse`**. For a full list of dependencies (as generated by `rsconnect::appDependencies()`), see the [`DEPENDENCIES`](https://github.com/CRI-iAtlas/shiny-iatlas/blob/master/DEPENDENCIES) file.
+- R: https://www.r-project.org/ - v3.6.2
 
-## Data
+- RStudio: https://rstudio.com/products/rstudio/download
 
-Input data for the Shiny-iAtlas portal were accessed from multiple remote sources, including **Synapse**, the **ISB Cancer Genomics Cloud**, and **Google Drive**. For convenience, we have created locally cached versions of dataframe objects as **`feather`** files:
+- lib cairo: https://www.cairographics.org/ (only required for iAtlas client)
 
-+ `fmx_df.feather`
-+ `feature_df.feather`
-+ `feature_method_df.feather`
-+ `im_direct_relationships.feather`
-+ `im_potential_factors.feather`
-+ `im_expr_df.feather`
-+ `sample_group_df.feather`
+- gfortran (libgfortran): usually installed with gcc
 
-## Methods
+### MacOS Install instructions
 
-While many of the results presented in tables and plots are taken directly from IRWG data (including the main **feature matrix** and various feature and group annotations), we compute some values internally. Unless otherwise noted, the following methods/tools were used to compute summary statistics:
+Install brew: https://brew.sh/
+
+Then:
+
+- brew install R
+- brew install cairo
+- download and install RStudio: https://rstudio.com/products/rstudio/download
+
+### Initialize R Packages, Database and run App
+
+To run the app locally:
+
+1. Clone this repository
+
+1. Open `shiny-iatlas.Rproj`
+
+1. Install packages. In the RStudio console, run:
+
+   ```R
+   renv::restore()
+   ```
+
+   This may take some time to complete - get something nice to drink :)
 
-#### Correlation — Spearman's rank-order correlation:
+1. Start the app by running:
+
+   ```R
+   shiny::runApp()
+   ```
+
+## Development
+
+When adding any new dependencies to the application, they may be added using (where "useful_package" is the name of the package to add):
 
 ```R
-stats::cor(x, y, method = "spearman", use = "pairwise.complete.obs")
+renv::install("useful_package")
 ```
 
-#### Concordance Index (CI):
+see [https://rstudio.github.io/renv/reference/install.html](https://rstudio.github.io/renv/reference/install.html) for more details.
 
-Concordance indexes for survival endpoints with respect to different immune readouts were computed using a custom package developed by Tai-Hsien Ou Yang at Columbia University. The **concordanceIndex** package includes a single synonymous function that can be used as follows:
+Once a new package is added, run:
 
 ```R
-concordanceIndex::concordanceIndex(predictions, observations)
+renv::snapshot()
 ```
 
-... where `predictions` and `observations` are numerical vectors of the same length.
+This will ensure the new package is added to the renv.lock file.
 
-The **concordanceIndex** package can be installed from GitHub using *devtools*:
+To remove an installed package, run (where "useful_package" is the name of the package to remove):
 
 ```R
-devtools::install_github("th86/concordanceIndex")
+renv::remove("no_longer_useful_package")
 ```
 
+For more on package management with renv, please see [https://rstudio.github.io/renv/articles/renv.html](https://rstudio.github.io/renv/articles/renv.html)
 
-## Local Shiny-iAtlas Session
+## Deployment
 
-To run the app locally, clone this repository and use the following command in the `shiny-iatlas` directory:
+The first time you deploy, go through the Deployment-Setup instructions below. Afterwards, you can just deploy as needed.
 
+### Deployment Setup (First-Time-Only)
+
+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:
+
+```R
+# shinyapps.io example credentials
+rsconnect::setAccountInfo(
+  name='shiny-iatlas',
+  token='xxx',
+  secret='yyy'
+)
 ```
-shiny::runApp()
+
+
+### Deploy
+
+```R
+rsconnect::deployApp()
+```
+
+
+## Data
+
+Input data for the Shiny-iAtlas portal were accessed from multiple remote sources, including **Synapse**, the **ISB Cancer Genomics Cloud**, and **Google Drive**. For convenience, we have created locally cached versions of dataframe objects as **`feather`** files in the `data2` folder:
+
+- `driver_mutations1.feather`
+- `driver_mutations2.feather`
+- `driver_mutations3.feather`
+- `driver_mutations4.feather`
+- `driver_mutations5.feather`
+- `driver_results1.feather`
+- `driver_results2.feather`
+- `feature_values_long.feather`
+- `features.feather`
+- `groups.feather`
+- `immunomodulator_expr.feather`
+- `immunomodulators.feather`
+- `io_target_expr1.feather`
+- `io_target_expr2.feather`
+- `io_target_expr3.feather`
+- `io_target_expr4.feather`
+- `io_targets.feather`
+- `til_image_links.feather`
+
+## Methods
+
+While many of the results presented in tables and plots are taken directly from IRWG data (including the main **feature matrix** and various feature and group annotations), we compute some values internally. Unless otherwise noted, the following methods/tools were used to compute summary statistics:
+
+### Correlation — Spearman's rank-order correlation
+
+```R
+stats::cor(x, y, method = "spearman", use = "pairwise.complete.obs")
 ```
+
+### Concordance Index (CI)
+
+Concordance indexes for survival endpoints with respect to different immune readouts were computed using a custom package developed by Tai-Hsien Ou Yang at Columbia University. The **concordanceIndex** package includes a single synonymous function that can be used as follows:
+
+```R
+concordanceIndex::concordanceIndex(predictions, observations)
+```
+
+... where `predictions` and `observations` are numerical vectors of the same length.
+