---
title: "Getting Started"
package: teal.modules.hermes
output:
  BiocStyle::html_document:
    toc_float: true
vignette: |
  %\VignetteIndexEntry{Getting Started}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options:
  chunk_output_type: console
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(teal.modules.hermes)
```

# Introduction

`teal.modules.hermes` is a package implementing a number of `teal` modules for the exploration of RNA-sequencing counts data. In addition to predefined modules, `teal.modules.hermes` enables quick and easy ad-hoc module creation.

# 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:

```{r}
ui <- function(id, mae_name) {
  ns <- NS(id)

  teal.widgets::standard_layout(
    encoding = uiOutput(ns("encoding_ui")),
    output = plotOutput(ns("awesome_plot"))
  )
}
```

## Server function

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

```{r}
srv <- function(input,
                output,
                session,
                data,
                filter_panel_api,
                mae_name) {
  output$encoding_ui <- renderUI({
    tags$div(
      experimentSpecInput(session$ns("experiment"), data, mae_name),
      assaySpecInput(session$ns("assay"))
    )
  })
  experiment <- experimentSpecServer(
    "experiment",
    data = data,
    filter_panel_api = filter_panel_api,
    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$data()
    assay <- assay()
    req(assay %in% SummarizedExperiment::assayNames(data))
    counts <- SummarizedExperiment::assay(data, assay)
    df <- data.frame(gene = rownames(counts), counts = rowSums(counts))
    df <- na.omit(df[order(df$counts, decreasing = TRUE), ])
    df$gene <- factor(df$gene, levels = df$gene)
    df <- df[1:10, ]
    ggplot(df, aes(x = gene, y = counts)) +
      geom_col() +
      theme(axis.text.x = element_text(angle = 90))
  })
}
```

## App function

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

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

## Testing it

To test this:

```{r, eval=interactive()}
awesome_app(hermes::multi_assay_experiment)
```