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This article describes how to customize certain visual aspects using the configuration file and how to extend the portal with custom modules.
There are two ways to customize the interface of a deployed portal: global settings and module-specific settings.
In the current version, you can add an acronym or logo, change the title of the tab in the browser, add an icon-based shortcut menu to the landing page and modify the Bootswatch theme.
The configuration file should look like the following:
name: PROJ
logo: logo.png
windowtitle: MyProject analysis portal
iconMenu:
- moduleName
- moduleName2
bootswatch:
theme: theme_name
version: 3, 4 or 5For the logo and the icon menu, you should create a folder named www in your project’s folder and place the corresponding images inside, so the resulting file tree looks like this:
project/
├─about.md
├─app.R
├─config.yaml
├─...
└─www/
├─logo.png
└─...The icon menu enables using a screenshot of a module to highlight results and provide a shortcut to it. A PNG file named for each module should be created and placed inside the www folder and the names of the modules should be listed under iconMenu in the configuration file, as shown above.
Regarding the themes, the current version of the package will work well with most light-based themes. Not every theme was tested so it’s recommended that you test different themes until you find one that works well for your project.
Every module included in the package supports title and
description fields. The title is the entry on the menu,
whereas the description field is the text that appears between the menu
and the content of the module. The description should be used to
instruct visitors about how to use the module and also describe some
aspects of that module if needed. For example, it could include a link
to an external resource or references.
Besides these two fields, most modules include some degree of customization of colors:
compareTrajGroups: a list of HTML-compatible colors for the points for each different value of trajectory_category (e.g. time points).
degDetails: a list of HTML-compatible colors for non-significant, logFC-only significant, p-value-only significant and logFC and p-value significant genes in the volcano plot
geneModulesHeatmap: colors (discrete or for interpolation) or RColorBrewer palettes for the variables that can be used in annotations for the heatmaps and a RColorBrewer palette for the heatmap colors.
geneModulesHeatmap:
custom_annotation_colors:
NumericVar1: ["red", "blue"]
NumericVar2: "RdBu"
DiscreteVar1: ["yellow", "green", "blue"]
custom_heatmap_palette: "BrBG"multiMeasureCorr: RColorBrewer palete for heatmap colors.
singleGeneCorr: list of colors for categorical variables in scatterplot.
The package also supports the inclusion of new external models,
without requiring any changes to the source code of the package. The
feature is primarily aimed at supporting features that may have a more
niche use case or experimenting with new ideas. Any new code should be
implemented based on the following template, for a new
moduleName:
moduleName_config <- function(config, ...) {
message("Checking moduleName configuration")
# Add dependency names here
requiredPackages <- c("")
stopIfNotInstalled(requiredPackages, "moduleName")
if (is.null(config$required_variable)) {
stop("moduleName:
'required_variable' is missing")
}
config
}
mod_moduleName_ui <- function(module_name, config, module_config) {
ns <- NS(module_name)
title <- module_config$title
description <- module_config$description
required_variable <- module_config$required_variables
tabPanel(
title = title %||% "Default title",
value = "moduleName",
tags$h5(description %||% "Default description"),
splitLayout(
verticalLayout(
wellPanel(
# Inputs that use the ns object above
)
),
verticalLayout(
# Outputs
),
cellWidths = c("20%", "80%"),
cellArgs = list(style = "white-space: normal;")
)
)
}
mod_moduleName_server <- function(module_name, config, module_config) {
moduleServer(module_name, function(input, output, session) {
ns <- session$ns
measures <- config$data$measures
expression_matrix <- config$data$expression_matrix
sample_lookup <- config$data$sample_lookup
subject_var <- config$subject_variable
sample_var <- config$sample_variable
required_variable <- module_config$required_variable
# Module code here
})
}
moduleName can now be included in config.yaml file along
with required or optional properties. Note that the config function
above must be defined otherwise the new module will not be used,
although the property validation step is not needed. Each new module can
be placed in the main app.R or in its own .R
file. If the second option is used, then each R file must be sourced in
the app.R, e.g. source("newCode.R").
Create the template in your code folder: the package includes a function that will create a file with the above themplate. After loading the package, run
create_module_template("moduleName")to create the file and edit it.
At deployment level, besides sourcing the moduleName file if
required, two other changes must be made in the app.R file: setting a
vector that contains the names of any new modules and passing that
variable as the extra_modules argument to
run_app:
library(shinyExprPortal)
source("newModule.R")
source("anotherModule.R")
extra_modules <- c("newModule", "anotherModule")
run_app("config.yaml", extra_modules = extra_modules)If you are unsure about how to implement a feature in a new module, please post an issue on the package GitHub or check the source code for the existing modules.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.