prepareApp: Make UI and server functions for Shiny apps based on data...

In pinin4fjords/shinyngs: Shiny apps for NGS data

Make UI and server functions for Shiny apps based on data supplied as modfied SummarizedExperiments

Description

Draws on various components (heatmaps, tables etc) to produce the UI and server components of a variety of shiny apps, based on the type and data specified, using modularised Shiny components.

Usage

prepareApp(type, eselist, ui_only = FALSE, ...)

Arguments

type	A string specifying the type of shiny app required. Currently, 'rnaseq' or 'chipseq' produce large multi-panel applications designed to facilitate analysis of those data types. For any other 'type', the call is passed to simpleApp(), which will attempt to build an application using Input and Output functions of the same module.
eselist	An ExploratorySummarizedExperimentList object containing assay data (expression, counts...), sample data and annotation data for the rows.
ui_only	Don't add server components (for UI testing)
...	Additional argments passed to simpleApp()

Value

output A list of length 2 containing: the UI and server components

Examples

require(airway)
library(shinyngs)

# 1: BASIC RNA-SEQ APP

# Get an example RNA-seq dataset from the `airway` package

data(airway, package = "airway")

# Get some information about these data from the package description

expinfo <- packageDescription("airway")

# Convert to an ExploratorySummarizedExperiment (with extra slots)

ese <- as(airway, "ExploratorySummarizedExperiment")

# Make the ExploratorySummarizedExperimentList that represents the study as a
# whole.

eselist <- ExploratorySummarizedExperimentList(
  ese,
  title = expinfo$Title,
  author = expinfo$Author,
  description = expinfo$Description
)

# Make the app

app <- prepareApp("rnaseq", eselist)

# Run the app

shiny::shinyApp(ui = app$ui, server = app$server)

# 2: AUGMENT WITH ANNOTATION INFO FOR MORE INFORMATIVE APP

# Use Biomart to retrieve some annotation, and add it to the object

library(biomaRt)
attributes <- c(
  "ensembl_gene_id", # The sort of ID your results are keyed by
  "entrezgene", # Could be used for gene sets keyed by Entrez ID (must set \code{gene_set_id_type} correctly on the containing \code{ExploratorySummarizedExperimentList})
  "external_gene_name" # Used to annotate gene names on the plot
)

mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = "hsapiens_gene_ensembl", host = "www.ensembl.org")
annotation <- getBM(attributes = attributes, mart = mart)
annotation <- annotation[order(annotation$entrezgene), ]

mcols(ese) <- annotation[match(rownames(ese), annotation$ensembl_gene_id), ]
ese@labelfield <- "external_gene_name"

# Re-do app creation etc, choose to use a different grouping variable by
# default

eselist <- ExploratorySummarizedExperimentList(
  ese,
  title = expinfo$Title,
  author = expinfo$Author,
  description = expinfo$Description,
  default_groupvar <- "dex"
)
app <- prepareApp("rnaseq", eselist)
shiny::shinyApp(ui = app$ui, server = app$server)

# 3. MORE COMPLEX DATA FOR DIFFERENTIAL EXPRESSION ETC

# See vignette for more info. However, the included sample
# ExploratorySummarizedExperimentList has the appopriate slots populated
# to demonstrate contrasts, gene set annotations etc. The app produced in
# this way will have more panels for differential analyses.

data("zhangneurons")
app <- prepareApp("rnaseq", zhangneurons)
shiny::shinyApp(ui = app$ui, server = app$server)

pinin4fjords/shinyngs documentation built on Jan. 18, 2025, 7:09 p.m.