teal.modules.hermes: Teal Modules for RNAseq Data Analysis

Welcome to teal.modules.hermes! Jump right into an ad-hoc module example or read a bit more about what is what, i.e. how the pieces fit together.

Ad-hoc module example

Let’s assume you have a function awesome_plot() which takes a count matrix and makes an awesome plot out of it. Now you would like to make a Shiny app where you can filter patients, samples, select the experiment out of your MultiAssayExperiment (MAE), select the count matrix from the experiment, etc. Nothing is easier than that with teal.modules.hermes! We show you below how to quickly spin up your UI, server and put them together into a nice little app.

UI function

In teal.modules.hermes we provide modules that make the experiment and assay selection super easy, see here for the UI part:

ui <- function(id,
               datasets,
               mae_name) {
  ns <- NS(id)

  teal.widgets::standard_layout(
    encoding = div(
      experimentSpecInput(ns("experiment"), datasets, mae_name),
      assaySpecInput(ns("assay"))
    ),
    output = plotOutput(ns("awesome_plot"))
  )
}

Server function

Similarly for the server we use the modules, and call then our awesome plotting function.

srv <- function(input,
                output,
                session,
                datasets,
                mae_name) {
  experiment <- experimentSpecServer(
    "experiment",
    datasets = datasets,
    mae_name = mae_name,
    name_annotation = NULL  # If you have a gene name column in your rowData, can specify here.
  )
  assay <- assaySpecServer("assay", experiment$assays)
  output$awesome_plot <- renderPlot({
    data <- experiment$data()
    assay <- assay()
    req(assay %in% SummarizedExperiment::assayNames(data))
    counts <- SummarizedExperiment::assay(data, assay)
    awesome_plot(counts)
  })
}

App function

Now let’s assume you want to spin up your app for an MAE.

library(teal.modules.hermes)
awesome_app <- function(mae, label = "My awesome app") {
  mae_name <- "MAE"
  mae <- hermes::lapply(mae, hermes::HermesData)
  mae_data <- dataset(mae_name, mae)
  data <- teal_data(mae_data)
  app <- init(
    data = data,
    modules = root_modules(
      module(
        label = label,
        server = srv,
        server_args = list(mae_name = mae_name),
        ui = ui,
        ui_args = list(mae_name = mae_name),
        filters = mae_name
      )
    )
  )
  shinyApp(app$ui, app$server)
}

Testing it

Let’s test this.

awesome_plot <- image
awesome_app(hermes::multi_assay_experiment)

What is what

What is teal?

teal is a shiny-based interactive exploration framework for analyzing clinical trials data. teal currently provides a dynamic filtering facility and diverse data viewers. teal shiny applications are built using standard shiny modules. See github for more details.

What is hermes?

hermes facilitates preprocessing, analyzing, and reporting of RNA-seq data. The core functionality is built on the BioConductor ecosystem, especially the SummarizedExperiment class from which the HermesData class inherits. See the vignette for more details.

So what is then teal.modules.hermes?

teal.modules.hermes provides teal modules (which can be used as part of any teal app), for interactive RNA-seq data analysis using hermes. Again it is heavily built on the BioConductor classes, in particular MultiAssayExperiment (MAE) which is expected to contain the HermesData experiments.

Installation

For releases from August 2022 it is recommended that you create and use a Github PAT to install the latest version of this package. Once you have the PAT, run the following:

Sys.setenv(GITHUB_PAT = "your_access_token_here")
if (!require("remotes")) install.packages("remotes")
remotes::install_github("insightsengineering/teal.modules.hermes@*release")

A stable release of all NEST packages from June 2022 is also available here.

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