R/splicejam-shiny.R

In jmw86069/jambio: Analysis and Visualization of Gene Splice Variants and Transcriptome Data

#' Launch Sashimi R-shiny application
#'
#' Launch Sashimi R-shiny application
#'
#' This function launches the Sashimi visualization
#' R-shiny app.
#'
#' The R objects required to prepare sashimi plots are
#' defined by the function `sashimiAppConstants()`, which
#' documents each R object required and how it is used.
#' The `sashimiAppConstants()` function returns an `environment`
#' in which the required sashimi plot data is stored and
#' used by the R-shiny app. The default environment is `globalenv()`
#' however any custom environment can be used, for example
#' with `myenv <- new.env()`.
#'
#' The most straightforward way to run a new Sashimi R-shiny
#' app is to define `filesDF` and `gtf` in the global environment,
#' or define `filesDF` and `gtf` inside a custom environment.
#' The required data will be derived from the GTF file `gtf`.
#' This step is somewhat slow the first time (10 minutes) and
#' saves intermediate files for rapid re-use.
#'
#' The data derived from the GTF file is listed below. Any data object
#' that already exists in the `environment` is used in subsequent steps:
#'
#' * `txdb` - TranscriptDb from which `exonsByGene` and `exonsByTx`
#'    are derived.
#' * `tx2geneDF` - `data.frame` with transcript-to-gene relationship,
#'    with colnames `"gene_name"` and `"transcript_id"`. See
#'    `makeTx2geneFromGtf()` for details.
#' * `detectedTx` - `character` vector of detected transcripts, used
#'    to match `tx2geneDF$transcript_id`. When `detectedTx` is NULL,
#'    all entries in `tx2geneDF` are used. Note that we found it is
#'    *much better to use only a subset of detected transcripts*,
#'    mainly because many GTF sources include a large number of potential
#'    alternative isoforms, many of which have no supported evidence in
#'    any one given cell type. See `defineDetectedTx()` for one method
#'    to define detected transcripts.
#' * `detectedGenes` - `character` vector of genes that match
#'    `tx2geneDF$gene_name`. When `detectedGenes` is NULL, it is
#'    inferred using `detectedTx` and `tx2geneDF$transcript_id`.
#' * `exonsByTx`, `cdsByTx` - derived from `txdb` and annotated to include
#'    values from `tx2geneDF$gene_name`.
#' * `flatExonsByGene`, `flatExonsByTx` - `GRangesList` objects derived
#'    from `exonsByGene` and `exonsByTx`, using `detectedTx`.
#'    They also use `cdsByTx` to indicate coding regions (CDS) of exons.
#'
#' Note that if `detectedTx` is not defined, it will use all transcripts
#' at this stage, which can be substantially slower than using only
#' the subset of "observed/detected" transcripts.
#'
#' An alternative is to supply one `detectedGenes` gene value, which will prepare
#' only one gene for `flatExonsByGene` in the R-shiny app. However, the
#' R-shiny app has the option to search all non-detected genes, which
#' are prepared one by one inside the R-shiny app. This process is slightly
#' slower when using the app by a few seconds, and will use all transcripts
#' for `detectedTx`.
#'
#' The `filesDF` object should be a `data.frame` with at least three colnames:
#'
#' * `"sample_id"`
#' * `"type"` (with values either `"bw"` or `"junction"`)
#' * `"url"` (a URL or file path to each file.)
#'
#' If coverage or junctions are available in multiple files, for example
#' sequencing replicates, use the same `sample_id` for each file, and
#' the coverage and junctions will be combined using the sum, after
#' multiplying an optional `"scale_factor"` to each file.
#'
#' For more direct control over the data preparation, including
#' `tx2geneDF`, `detectedTx`, `exonsByGene`, and `flatExonsByGene`,
#' see `sashimiAppConstants()` which calls `sashimiDataConstants()`,
#' both of these functions return an environment that contains the
#' required data.
#'
#' When the R-shiny app is created, the `ui` and `server` components
#' have their environments set to `envir` - so their context will
#' include the variables defined in that environment.
#'
#' @family splicejam R-shiny functions
#'
#' @return output from `shiny::shinyApp()` which is an object of
#'    class "shiny.appobj", whose default print method is to run
#'    the app.
#'
#' @param envir `environment` that contains data needed for sashimi plots.
#'    If `envir=NULL` by default it will use `globalenv()`. Otherwise,
#'    call `sashimiDataConstants()` or `sashimiAppConstants()`
#'    to prepare data inside a specific environment that can be
#'    used by this function.
#' @param options `list` of R-shiny app options, for example two
#'    common options are: `host` to indicate the host or IP address
#'    the R-shiny app will bind to respond to requests; and
#'    `port` for the port number. For example:
#'    `launchSashimiApp(options=list(host="0.0.0.0", port=8080))`
#'    where `port="0.0.0.0"` will listen to any request sent
#'    to the current machine, whether by host name, or any valid
#'    IP address; and `port=8080` will only listen to port 8080.
#' @param ... additional arguments are passed to `sashimiAppConstants()`.
#'
#' @examples
#' # Note: disabled for web page examples
#' # launchSashimiApp();
#'
#' @export
launchSashimiApp <- function
(...,
 envir=globalenv(),
 options=list(width=1200),
 verbose=TRUE)
{

   # retrieve an environment that contains the required data
   envir <- sashimiAppConstants(envir=envir,
      verbose=verbose,
      ...);

   # Define these functions specifically so we can set the environment.
   # - Now the environment will contain the data obtained above.
   ui <- sashimiAppUI;
   server <- sashimiAppServer;
   environment(ui) <- envir;
   environment(server) <- envir;

   ##
   shiny::shinyApp(ui=ui,
      server=server,
      options=options
   );
   #shiny::shinyApp(ui=ui,#sashimiAppUI,
   #   server=sashimiAppServer,
   #   onStart=sashimiAppConstants,
   #   options=options
   #);
}